further work on background section

main.bib

@@ -1840,3 +1840,19 @@ Conclusion: {SWS} is a useful tool for confirming the fertile window and absenc
 	langid = {english},
 	file = {PDF:/home/alex/Zotero/storage/GHT5UMNX/Wu et al. - Deep Transformer Models for Time Series ForecastingThe Influenza Prevalence Case.pdf:application/pdf},
 }
+
+@online{noauthor_ovularing_nodate,
+	title = {{OvulaRing} Startseite},
+	url = {https://ovularing.com/},
+	abstract = {Erfahre hier mehr zu {OvulaRing} Startseite},
+	titleaddon = {{OvulaRing}},
+	urldate = {2025-06-25},
+	langid = {german},
+	file = {Snapshot:/home/alex/Zotero/storage/PD5DBIS4/ovularing.com.html:text/html},
+}
+
+@online{noauthor_ringpng_nodate,
+	title = {ring.png (800×800)},
+	url = {https://ovularing.com/wp-content/uploads/2021/07/ring.png},
+	urldate = {2025-06-25},
+}

thesis/sections/background.tex

@@ -53,7 +53,7 @@ Anovulation can have various causes, including hormonal imbalances, stress, or u
 \begin{figure}[htbp]
     \centering
     \includegraphics[width=0.9\textwidth]{background_anovulatory_cycle}
-    \caption{Example of a cycle without an ovulation and the resulting temperature rise}
+    \caption{Example of a cycle without an ovulation and the resulting absence of a temperature rise}
     \label{fig:background_anovulation}
 \end{figure}
 
@@ -100,7 +100,7 @@ rather than relying on population-wide assumptions.
     \label{fig:background_irregular_cycles}
 \end{figure}
 
-\begin{figure}[htbp]]
+\begin{figure}[htbp]
     \centering
     \includegraphics[width=0.9\textwidth]{background_regular_cycle_example}
     \caption{Example of a woman with regular menstrual rhythm}
@@ -117,7 +117,7 @@ the fertile window is typically defined as the five days before ovulation until
 Research by~\citeauthor{dunson_day-specific_1999} has shown that the highest chance of fertilization is around one day before ovulation,
 as illustrated in Figure~\ref{fig:background_pregnancy_chance}.
 
-\begin{figure}
+\begin{figure}[htbp]
     \centering
     \includegraphics[width=0.7\textwidth]{background_pregnancy_chance_over_time}
     \caption{Chance of fertilization depending on the day of the menstrual cycle.
@@ -147,7 +147,52 @@ detecting the slight temperature rise that follows ovulation.
 Advances in wearable technology have further enabled continuous and automated temperature monitoring,
 improving accessibility and usability~\cite{alexander_fertilitatsmonitoring_2014, luo_detection_2020, yu_tracking_2022}.
 
-\subsection{Data and Measurement}\label{subsec:data_background}
+\subsection{Data Source and Characteristics}\label{subsec:data_background}
+This study is based on a dataset collected from users of the \emph{OvulaRing}~\cite{noauthor_ovularing_nodate},
+an intravaginal wearable sensor developed by VivoSensMedical GmbH, located in Leipzig, Germany~\cite{noauthor_vivosens_nodate}.
+The device continuously records intravaginal core body temperature at 5-minute intervals.
+The sensor itself measures approximately 1\,cm $\times$ 1\,cm $\times$ 2\,cm and is embedded in a silicone ring with a diameter of 5\,cm for ease of use.
+It pairs with a smartphone via Bluetooth to synchronize and upload recorded data to a secure database.
+
+The product has been on the market for over a decade, resulting in an extensive longitudinal dataset of menstrual cycles.
+Cycle boundaries are defined by self-reported menstruation, which users manually log in the accompanying app to mark the beginning of each cycle.
+
+Thanks to a battery life of at least six months, the device supports continuous monitoring of long and irregular cycles, enabling the capture of highly variable menstrual patterns.
+
+A known limitation of manual cycle annotations is the potential for misalignment.
+Intermediate bleeding events unrelated to menstruation (e.g., ovulatory spotting or irregular shedding) or missing menstruation entries can lead to ambiguous cycle definitions.
+Therefore, all user-entered cycle starts undergo manual review to reduce annotation errors.
+
+\begin{figure}[htbp]
+    \centering
+    \includegraphics[width=0.3\textwidth]{ovularing}
+    \caption{The OvulaRing sensor attached to its silicone ring~\cite{noauthor_ringpng_nodate}.}
+    \label{fig:background_ovularing}
+\end{figure}
+
+In addition to temperature measurements, the database includes contextual metadata such as age, height, weight, and optional user-entered markers.
+These markers provide further physiological context and may include information about intermediate bleeding, sexual intercourse, or positive pregnancy tests.
+
+At the time of writing, the dataset contains approximately 65{,}000 annotated cycles, comprising more than 350 million individual temperature measurements.
+
+\subsubsection{Dataset Summary}
+
+\subsubsection{Irregularities and Confounding Factors}
+
+
+\begin{figure}[htbp]
+    \centering
+    \includegraphics[width=0.9\textwidth]{background_fever_cycle}
+    \caption{An example of a cycle with fever significantly altering the temperature data}
+    \label{fig:background_fever_cycle}
+\end{figure}
+Figure~\ref{fig:background_fever_cycle} shows a cycle, where the user had an illness that concluded in a rise of body core temperature.
+These anomalies can make analysis hard, as the resulting rise in temperature is hard to distinguish from the rise in temperature triggered by an ovulation.
+
+\subsubsection{Privacy}
+The dataset used in this study contains sensitive personal health information and is handled with strict privacy safeguards.
+All data is pseudonymized and processed exclusively on encrypted devices, ensuring that no identifiable information can be traced back to individual users.
+VivoSensMedical does not share user data with third parties; the data is used solely for internal research and product improvement efforts that directly benefit users at no additional cost.
 
 \subsection{Technical Background}\label{subsec:technological_background}
 
@@ -194,7 +239,7 @@ Figure~\ref{fig:rnn_unfolded} illustrates the unfolded structure of an RNN acros
 This technique, known as \emph{unfolding}, clarifies how sequential inputs update the hidden state and generate
 outputs at each time step.
 
-\begin{figure}
+\begin{figure}[htbp]
     \centering
     \includegraphics[width=0.8\textwidth]{recurrent_neural_network_unfold}
     \caption{Schematic diagram of the unfolded structure of a recurrent neural network~\cite{fdeloche_english_2017}}
@@ -216,7 +261,7 @@ Each gate employs a sigmoid activation function to regulate the flow of informat
 allowing LSTMs to preserve and update memory over long sequences.
 
 
-\begin{figure}
+\begin{figure}[htbp]
     \centering
     \includegraphics[width=0.6\textwidth]{lstm_cell_diagram}
     \caption{Diagram of a LSTM cell showing the flow of information \cite{chevalier_english_2018}}
@@ -263,7 +308,7 @@ context-aware representations by weighing the importance of different input posi
 This is achieved through \emph{scaled dot-product attention}, where queries, keys, and values are
 linearly projected from the input and used to compute attention scores.
 
-\begin{figure}
+\begin{figure}[htbp]
     \centering
     \includegraphics[width=0.4\textwidth]{background_transformer_architecture}
     \caption{The Transformer - architecture for an encoder-decoder model~\cite{vaswani_attention_2017}}
@@ -354,7 +399,7 @@ These techniques reduce the computational load while maintaining salient informa
 Figure~\ref{fig:background_convolution_example} illustrates a simple one-dimensional convolution applied to a sequence using a filter of size 3.
 The stride determines how far the filter moves at each step, affecting both the resolution and length of the resulting feature map.
 
-\begin{figure}
+\begin{figure}[htbp]
     \centering
     \includegraphics[width=0.6\textwidth]{background_convolution_example}
     \caption{Example of a simple 1-D convolution on an input sequence.}