esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Research Sample  
The research motivation was to assess the performance of advanced technologies and whether they can assist a human operator with difficult detectable defects that are often missed. Thus, the research sample was intended to cover a variety of different defect types and severities, with specific focus on the challenging threshold defects. High-pressure compressor (HPC) blades of V2500 gas turbines with airfoil dents, bends, dents, nicks, tears, tip curls and tip rubs were tested in the study. Non-defective blades were also included. The sample size varied between the three comparisons and included 118 blade images, 20 borescope images and 26 physical parts for piece-part, borescope, and visual-tactile inspection, respectively. For each comparison, the exact same set of blades was presented to both the human operator and technological inspection agent. For more details about the image acquisition process please see [88].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Comparison 1:** & **Comparison 2:** & **Comparison 3:** \\  & **Piece-Part Inspection** & **Borescope Inspection** & **Visual–Tactile Inspection** \\ \hline  & Screen-based & Screen-based & Part-based \\ Inspection mode & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\ \hline Research & 50 industry & 50 industry & 6 industry \\ population & practitioners & practitioners & practitioners \\ \hline  & 47 male, 3 female & 47 male, 3 female & 5 male, 1 female \\  & Mean age: 44.5 years & Mean age: 44.5 years & Mean age: 44.5 years \\ Demographics & (SD = 10.3 years) & (SD = 10.3 years) & (SD = 12.1 years) \\  & Mean work experience: 17.7 years & Mean work experience: 17.7 years & Mean work experience: 15.5 years \\  & (SD = 9.4 years) & (SD = 9.4 years) & (SD = 9.9 years) \\ \hline  & Self-developed defect detection & Commercially available AI &
\begin{tabular}{c} GOM Atos Q [89] \\ software [88] \\ system \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of research design. Icons adapted from [87].
# Multi-Patch Time Series Transformer for Robust Bearing Fault Detection with Varying Noise
## 5 Experiments
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Noise Type** & **Model** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline \multirow{8}{*}{No noise} & LSTM & 0.9844 & 0.9854 & 0.9851 & 0.9853 \\ \cline{2-6}  & GRU & 0.9865 & 0.987 & 0.9869 & 0.986 \\ \cline{2-6}  & FCN & 0.9854 & 0.9868 & 0.9862 & 0.9865 \\ \cline{2-6}  & ResNet & 0.9861 & 0.9871 & 0.9868 & 0.9869 \\ \cline{2-6}  & InceptionTime & 0.9842 & 0.9853 & 0.9847 & 0.9849 \\ \cline{2-6}  & XceptionTime & 0.9849 & 0.9855 & 0.9859 & 0.9857 \\ \cline{2-6}  & XCM & 0.9855 & 0.9864 & 0.9864 & 0.9864 \\ \cline{2-6}  & LSTMAttention & 0.9855 & 0.9863 & 0.9864 & 0.9864 \\ \cline{2-6}  & GRUAttention & 0.9852 & 0.9867 & 0.986 & 0.9863 \\ \cline{2-6}  & TSSequencer & 0.9859 & 0.9867 & 0.987 & 0.9868 \\ \cline{2-6}  & PatchTST & 0.9868 & 0.9873 & 0.9874 & 0.9874 \\ \cline{2-6}  & **MultiPatchTST** & **0.9881** & **0.9887** & **0.9887** & **0.9887** \\ \hline \multirow{8}{*}{Random shock noise} & LSTM & 0.9717 & 0.9738 & 0.9723 & 0.9723 \\ \cline{2-6}  & GRU & 0.9681 & 0.9695 & 0.969 & 0.9687 \\ \cline{2-6}  & FCN & 0.9691 & 0.9711 & 0.9694 & 0.9696 \\ \cline{2-6}  & ResNet & 0.9758 & 0.9768 & 0.9764 & 0.9763 \\ \cline{2-6}  & InceptionTime & 0.9727 & 0.9739 & 0.9733 & 0.9731 \\ \cline{2-6}  & XceptionTime & 0.9782 & 0.9791 & 0.9784 & 0.9784 \\ \cline{2-6}  & XCM & 0.978 & 0.9789 & 0.9782 & 0.9783 \\ \cline{2-6}  & LSTMAttention & 0.9801 & 0.98 & 0.9802 & 0.9802 \\ \cline{2-6}  & GRUAttention & 0.9767 & 0.9778 & 0.9769 & 0.9769 \\ \cline{2-6}  & TSSequencer & 0.9784 & 0.9794 & 0.9788 & 0.9788 \\ \cline{2-6}  & PatchTST & 0.9814 & 0.982 & 0.9819 & 0.9817 \\ \cline{2-6}  & **MultiPatchTST** & **0.9865** & **0.987** & **0.9869** & **0.9869** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental results on PU dataset with mixed noise. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Noise Type** & **Model** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline \multirow{8}{*}{
\begin{tabular}{c} Harmonic noise \\ \end{tabular} } & LSTM & 0.9857 & 0.9867 & 0.986 & 0.9863 \\ \cline{2-6}  & GRU & 0.9877 & 0.988 & 0.9881 & 0.9881 \\ \cline{2-6}  & FCN & 0.9857 & 0.9861 & 0.9863 & 0.9862 \\ \cline{2-6}  & ResNet & 0.985 & 0.9854 & 0.9857 & 0.9855 \\ \cline{2-6}  & InceptionTime & 0.9858 & 0.9864 & 0.9866 & 0.9865 \\ \cline{2-6}  & XceptionTime & 0.9853 & 0.986 & 0.9853 & 0.9857 \\ \cline{2-6}  & XCM & 0.9872 & 0.9876 & 0.9877 & 0.9876 \\ \cline{2-6}  & LSTMAttention & 0.9859 & 0.9864 & 0.9861 & 0.9862 \\ \cline{2-6}  & GRUAttention & 0.9846 & 0.9852 & 0.9849 & 0.9851 \\ \cline{2-6}  & TSSequencer & 0.986 & 0.987 & 0.9867 & 0.9868 \\ \cline{2-6}  & PatchTST & 0.9862 & 0.9872 & 0.9869 & 0.987 \\ \cline{2-6}  & **MultiPatchTST** & **0.9881** & **0.9898** & **0.9883** & **0.989** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._
## 3 Results
As shown in the results, the proposed model has the best performance with an average correlation of 0.7540, prediction accuracy of 65.34%, and playoff accuracy of 89.06%. In most seasons, the TeamRank model outperforms other models. Although the prediction accuracy of the Elo model is close to that of TeamRank, the correlation and playoff accuracy of TeamRank exceed the Elo model greatly.
# ILRS Reference Point Determination Using Close Range Photogrammetry
## 3 Satellite Observing System Wettzell
### Analysis and Results
#### 3.2.2 Reference Point Determination  
The coordinates of the observed markers as well as the obtained fully populated dispersion matrix are treated as incompings for the reference point determination described in Section 2.2. Figure 7 depicts the variations of the estimated coordinate components of the reference point and the derived axis offset. Error-bars indicate the \(3\sigma\) confidence level of the estimates. Corresponding numerical values are summarized in Table 3.  
Figure 7: Variations of the coordinate components of the reference point \(\mathbf{P}_{\text{IRP}}\) and the axis offset \(e_{\text{AO}}\). Error bars indicate the \(3\sigma\) confidence level.  
The coordinate components of the reference point \(\mathbf{P}_{\text{IRP}}\) and the axis offset \(e_{\text{AO}}\) vary in a range of about \(\pm 0.1\,\text{mm}\) and \(\pm 0.02\,\text{mm}\), respectively. Moreover, the axis offset is close to zero and is insignificant. This result confirms prior investigations using a mobile laser tracker [26]. The derived standard deviations of the reference point coordinate components are comparable to the standard deviations of marker positions at the telescope. Values larger than \(\pm 0.03\,\text{mm}\) are exceptions. Largest values can be found for DOY 259, which confirms the observed peculiarity during the bundle adjustment. The larger standard deviations of DOY 259 result from the uncertainty propagation, and are a direct consequence of the derived dispersion of the bundle adjustment, cf. Table 2. However, GGOS aims for an accuracy of \(1\,\text{mm}\) for the reference points and the local ties. This requirement contains the uncertainty budgeting of the reference point determination procedure, the local tie preparation, and the uncertainties of the transformation to the GGRF. As shown in Figure 7, close range photogrammetry allows for a reference point determination with superior accuracy. For comparison, the reported repeatability of a reference point determination using a total station is about ten times larger [49; 54; 55]. Therfore, the use of close range photogrammetry reduces the overall uncertainties significantly. The benefit of a dense point cloud, as it is realized by configuration i, does not significantly improve the estimates in comparison to the reduced configurations ii and iii but increases the measurement effort.
## 3 Results and Discussion
### Evaluation of Colour Parameters of Bread Crumb
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Sample** & \(L^{*}\) & \(a^{*}\) & \(b^{*}\) & \(\Delta E^{*}\) & _WI_ & **BI** & **YI** \\ \hline CON & \(63.7\pm 2.9\) ab & \(0.7\pm 0.2\) ab & \(14.9\pm 0.6\) c & - & \(60.8\pm 2.7\) ab & \(26.9\pm 2.1\) c & \(33.4\pm 2.2\) d \\ \hline BO25 & \(60.1\pm 2.4\) cde & \(0.6\pm 0.2\) ab & \(15.2\pm 0.7\)c & \(3.9\pm 2.1\) abcd & \(57.2\pm 2.2\) cde & \(29.3\pm 2.1\) b & \(36.2\pm 2\) bcd \\ BO50 & \(60.1\pm 2.8\)bc & \(0.6\pm 0.2\) ab & \(15.3\pm 1.4\)abc & \(4.1\pm 2.6\)abcd & \(57.2\pm 2.3\) bde & \(29.5\pm 2.4\) abc & \(36.4\pm 2.4\) abcd \\ BO75 & \(58.1\pm 2.7\)e & \(0.5\pm 0.1\) ab & \(15.4\pm 0.3\)abc & \(5.6\pm 2.7\)ab & \(55.4\pm 2.6\)e & \(30.9\pm 2.5\)abc & \(38\pm 2.6\)ab \\ BO100 & \(57.8\pm 2.1\) e & \(0.6\pm 0.1\) ab & \(15.4\pm 0.5\)abc & \(6.2\pm 2.1\) a & \(55\pm 1.9\) e & \(31.1\pm 1.5\) ab & \(38.2\pm 1.5\)ab \\ \hline B825 & \(61.5\pm 1.9\)abc & \(0.6\pm 0.2\) ab & \(15.0\pm 0.7\)c & \(2.4\pm 1.8\) bcd & \(58.7\pm 1.8\)abc & \(28.2\pm 2.2\) bc & \(34.9\pm 1.9\) bcd \\ B850 & \(61.4\pm 2.2\)abc & \(0.7\pm 0.3\) ab & \(15.5\pm 1.2\)abc & \(2.7\pm 2.1\) abcd & \(58.3\pm 2.2\) abc & \(29.4\pm 3.6\)abc & \(36.2\pm 3.4\) abcd \\ B875 & \(61.1\pm 1.4\)abc & \(0.4\pm 0.2\) b & \(15.4\pm 0.5\)abc & \(2.7\pm 0.8\) bcd & \(58.2\pm 0.9\)abc & \(28.8\pm 1.3\) bc & \(35.9\pm 1.3\) bcd \\ B8100 & \(59.5\pm 3.6\)e & \(0.9\pm 0.2\) a & \(16.8\pm 0.8\) a & \(4.8\pm 2.6\)abc & \(56.2\pm 2.6\) de & \(33.5\pm 2.2\) a & \(40.3\pm 1.8\) a \\ \hline BM25 & \(63.6\pm 1.5\) ab & \(0.6\pm 0.2\) ab & \(15.0\pm 0.8\) c & \(1.5\pm 0.7\) d & \(60.6\pm 1.5\) ab & \(27.1\pm 2.6\) c & \(33.8\pm 2.6\) c \\ BM50 & \(62.7\pm 2.4\) abcd & \(0.6\pm 0.4\) ab & \(15.4\pm 1.1\) bc & \(2.5\pm 1.0\) bcd & \(59.6\pm 2.3\) abcd & \(28.3\pm 2.3\) bc & \(35.1\pm 3.1\) bcd \\ BM75 & \(64.3\pm 1.7\) a & \(0.7\pm 0.2\) ab & \(15.8\pm 0.8\) abc & \(1.9\pm 0.9\) d & \(60.9\pm 1.5\) a & \(28.4\pm 1.9\) bc & \(35.2\pm 1.8\) bcd \\ BM100 & \(63.5\pm 1.9\)abc & \(0.8\pm 0.2\) ab & \(16.5\pm 0.7\) ab & \(2.4\pm 0.9\) cd & \(59.9\pm 1.6\) abc & \(30.3\pm 1.7\) abc & \(37.1\pm 1.7\) abc \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Crumb colour of bread with increasing levels of water substitution using cereal-based plant beverages (0–100%).
# Application of Polymers as a Tool in Crystallization--A Review
## 2 Basic Research
### Discussion and Conclusions
#### 3.2.3 Antibiotics  
[PERSON] et al. studied the isotactic and atactic poly(\(N\)-isopropyl acrylamide) (PNIPAM) as an additive in evaporative crystallization of nitrofurantoin (drug in the urinary tract infections treatment) [76]. They found that slower nucleation and growth rates of the crystals were observed regardless of the molecular weight or stereoconfiguration of the PNIPAM. [PERSON] et al. in their experiments used a polymer additive such as HPMC or PVP to successfully reject impurity of sulfonamide based drug which cannot be removed via conventional crystallization [77]. [PERSON] et al. using pyrazimamide (an antibiotic used to treat tuberculosis) and hydrochlorothiazzide (diuretic used to treat blood hypertension and edema) as model pharmaceutical, demonstrated that the rate of crystallization can be controlled by polymer solubility present in the solution [78]. The authors presented results for insoluble polymers which accelerated crystallization--functionalized Merrifield resin (MFR), and a cross-linked poly(styrene-_co_-chloromethylstyrene) (P(S-_co_-CIMS))--as well as for soluble polymers to inhibit crystallization-functionalized poly(hydroxyethyl acrylamide-_co_-chloromethylstyrene) (PHEAM). In addition to the previously mentioned work from [PERSON] et al., the crystallization of sulfamethaxazole was also investigated by [PERSON] et al., who observed that organogels prepared from carboxylated nanocellulose (CNC) in the presence of cationic surfactant can be used as novel crystallization media for pharmaceutical solid form control [75]. [PERSON] et al. showed that organogel based on f 2_H_-benzo[_d_]1,2,3-triazole derivative can be used in the crystallization of sulfathiazole, inducing a change in the polymorphic form of the drug [79].
## 4 Discussion
In addition, OOCA's effectiveness is sensitive to key parameters such as window size, directional sampling density, and dip constraints. These settings require tuning based on data characteristics and structural context. To date, validation has focused on strike-slip carbonate settings; its applicability to elastic formations, salt-influenced regions, or anisotropic media remains to be tested. These limitations should be acknowledged when extending the method to broader geological settings or integrating it into real-time workflows. Methodological Innovation and Broader Implications  
The OOCA method introduces three key innovations. First, it replaces fixed or symmetric analysis windows with a data-driven orientation strategy, selecting the optimal analysis direction by identifying orthogonal trace pairs with minimal correlation. Second, its distance-weighted model trace construction increases sensitivity to structural variations near the analysis center. Third, Gabor-based multi-frequency fusion enables balanced imaging of both large-scale continuity and small-scale discontinuities. These developments provide practical and theoretical value. OOCA improves the clarity and reliability of fault and fracture detection, especially in complex carbonate reservoirs. Its outputs may support early identification of high-risk zones, such as densely fractured corridors, that are relevant to drilling risk mitigation. Moreover, its orientation-aware, scale-adaptive design offers a reference framework for future seismic attribute development, promoting more intelligent, geology-informed, and computationally efficient approaches to structural interpretation.
## 2 Preliminaries  
### Notations  
Let \(\Sigma\) be a finite _alphabet_ of symbols. We assume that \(\Sigma\) is fixed and \(|\Sigma|\) is constant. An element of \(\Sigma^{*}\) is called a _string_. Strings \(x\), \(y\), and \(z\) are said to be a _prefix_, _factor_, and _suffix_ of string \(w=xyz\), respectively.  
The length of a string \(w\) is denoted by \(|w|\). The empty string is denoted by \(\varepsilon\), that is, \(|\varepsilon|=0\). Also, we assume that all strings end with a unique symbol \(\$\in\Sigma\) that does not occur anywhere else in the strings. Let \(\Sigma^{+}=\Sigma^{*}\backslash\{\varepsilon\}\). The \(i\)-th symbol of a string \(w\) is denoted by \(w[i]\) for \(1\leq i\leq|w|\), and the factor of a string \(w\) that begins at position \(i\) and ends at position \(j\) is denoted by \(w[i:j]\) for \(1\leq i\leq j\leq|w|\). For convenience, let \(w[i:j]=\varepsilon\) for \(j<i\), and \(w[i:]=w[i:|w|]\) for \(1\leq i\leq|w|\). For any strings \(x,w\), let \(\mathit{BP}_{w}(x)\) denote the set of the beginning positions of all the occurrences of \(x\) in \(w\). That is, \(\mathit{BP}_{w}(x)=\{i\mid x=w[i:i+|x|-1]\}\).  
We say that strings \(x,y\)_overlap_ in \(w\) if there exist integers \(i,j\) such that \(x=w[i:i+|x|-1]\), \(y=w[j:j+|y|-1]\), and \(i\leq j\leq i+|x|-1\) or \(j\leq i\leq j+|y|-1\).  
Let \(\#\mathit{occ}_{w}(x)\) denote the possible maximum number of _non-overlapping_ occurrences of \(x\) in \(w\). If \(\#\mathit{occ}_{w}(x)\geq 2\), then \(x\) is said to be _repeating_ in \(w\). We abbreviate a _longest_ repeating factor of \(w\) to an _LRF_ of \(w\). Remark that there can exist more than one LRF for \(w\).  
Let \(\Sigma\) and \(\Pi\) be the set of terminal and non-terminal symbols, respectively, such that \(\Sigma\cap\Pi=\emptyset\). A context-free grammar \(\mathcal{G}\) is a formal grammar in which every production rule is of the form \(A\to u\), where \(A\in\Pi\) and \(u\in(\Sigma\cup\Pi)^{*}\). Let \(u=xBy\) and \(v=x\beta y\) with \(x,y,\beta\in(\Sigma\cup\Pi)^{*}\) and \(B\in\Pi\). If there exists a production rule \(B\to\beta\) in \(\mathcal{G}\), then \(v=x\beta y\) is said to be directly derived from \(u=xBy\) by \(\mathcal{G}\), and it is denoted by \(u\Rightarrow_{\mathcal{G}}v\). If there exists a sequence \(w_{0},w_{1},\ldots,w_{n}\) such that \(w_{i}\in(\Sigma\cup\Pi)^{*}\) and  
\[u=w_{0}\Rightarrow_{\mathcal{G}}w_{1}\Rightarrow_{\mathcal{G}}\cdots\Rightarrow_ {\mathcal{G}}w_{n}=v,\]  
then we say that \(v\) is derived from \(u\). The _length_ of a non-terminal symbol \(A\), denoted \(|A|\), is the length of the string \(z\in\Sigma^{*}\) that is derived from the production rule \(A\to v\). For convenience, we assume that any non-terminal symbol \(A\) in \(\mathcal{G}\) has \(|A|\) positions. The _size_ of the production rule is the number of terminal and non-terminal symbols \(v\) contains.
# A Watermarking Protocol Based on Blockchain
## 4 Basics of the Protocol  
The proposed watermarking protocol is based on a limited set of well-known security facilities: public key infrastructure (PKI), homomorphic cryptosystem [49], encrypted and signed tokens [4; 5; 22], and blind and readable watermarking scheme [1]. Furthermore, it exploits the public key and secure communication support implemented by the SSL/TLS protocol for all the messages exchanged among the web entities involved in the protocol [46].  
In more detail, if a piece of content and a watermark can be described according to a block-wise representation in the form of \(X=\{x_{1},x_{2},\ldots x_{l}\}\) and \(W=\{w_{1},w_{2},\ldots w_{l}\}\) respectively, the watermark insertion adopted by the proposed protocol, denoted as \(\oplus\), results in the following expression:  
\[X\oplus W=\{x_{1}\oplus w_{1},x_{2}\oplus w_{2},\ldots x_{l}\oplus w_{l}\}=\tilde {X}\]  
since such an insertion is assumed to be based on linear watermarks [1; 10; 17; 50]. Furthermore, if \(X=\{x_{1},x_{2}\ldots x_{l}\}\) is a digital content, its encryption by means of the function \(\mathbb{E}\) results in the following expression:  
\[\mathbb{E}_{pk}(X)=\mathbb{E}_{pk}(x_{1},x_{2}\ldots x_{l})=(\mathbb{E}_{pk}(x _{1}),\mathbb{E}_{pk}(x_{2})\ldots\mathbb{E}_{pk}(x_{l}))\]  
since \(\mathbb{E}\) is assumed to be a block-wise function [10; 50].  
Finally, the encryption function \(\mathbb{E}\) is assumed to be \"homomorphic\" with respect to the watermark insertion. This means that any linear watermark can be embedded directly into the encrypted domain according to the following expression [10; 50]:  
\[\mathbb{E}_{pk}(X\oplus W)=\mathbb{E}_{pk}(X)\oplus\mathbb{E}_{pk}(W)=\mathbb{ E}_{pk}(\tilde{X})\]  
In fact, a cryptosystem \(\mathbb{E}\) is homomorphic with respect to an operation \(\odot\) if  
\[\mathbb{E}_{pk}(m_{1}\odot m_{2})=\mathbb{E}_{pk}(m_{1})\odot\mathbb{E}_{pk}(m _{2})\]  
for any two plain messages \(m_{1}\) and \(m_{2}\)[49]. As a consequence, homomorphic encryption makes it possible to perform operations by directly working on encrypted data.
## 4 Our Prototype
### System Architecture
Once the data packets have been received, the server processes and stores them. Such data are accessible using a web interface that is similar to the one available on the main server of the campus. Even if this backup communication channel is designed to enhance the resilience of the support tool during emergencies, it is important to recognize the inherent trade-offs in this design. The system's architecture is carefully balanced to maintain a low-cost solution while providing practical support for emergency management. In this context, LoRa serves as a targeted solution rather than a comprehensive network replacement. The architecture is not designed to ensure that all nodes maintain communication with LoRa-connected nodes in every possible scenario. Instead, it focuses on providing critical connectivity where it is most needed, while accepting certain limitations to preserve the system's affordability and practicality. In the event of a severe disaster that results in widespread network failure, including the potential isolation of LoRa-connected nodes from the rest of the system, our design acknowledges these limitations as part of a deliberate strategy. Ensuring full communication coverage in all extreme scenarios would require additional infrastructure and more sophisticated technologies, significantly increasing the system's complexity and cost. This would fundamentally alter the nature of our system, moving it away from its intended purpose as a cost-effective and accessible support tool for emergency management. Finally, we want to remark that the focus of this work is to develop a support tool for emergency management that operates with minimal and well-defined requirements. Specifically, the tool is designed to gather and utilize occupancy data, detailing the number of people present in various classrooms and laboratories within a smart campus. While our case study employs cameras and Raspberry Pi devices to collect this information, the system is intentionally designed to be flexible. The web UI we have developed, which will be detailed in the following subsection, relies solely on an API to retrieve occupancy data, making it adaptable to a variety of sensor technologies. The decision to limit the scope of required sensors was driven by practical considerations. Although integrating additional IoT devices, such as fire detection sensors, could enhance the system's functionality, we recognized that many buildings, including those on the campus of our case study, lack the necessary APIs or interfaces to retrieve data from these existing emergency sensors. This limitation could significantly restrict the applicability of a system reliant on such integrations. By focusing on a solution that can operate independently of these additional sensors, we ensure that our tool remains versatile and can be implemented in a wide range of environments. This approach allows the system to be deployed in buildings that may not have advanced IoT infrastructure while still providing essential support for emergency management.
## 2 Materials and Methods  
### Experimental Approach to the Problem  
This study adopted a quantitative experimental approach to assess the accuracy of three heart rate (HR) monitoring devices during an intermittent indoor cycling protocol. To compare the performance of different types of devices (two chest straps and one armband),a structured protocol included alternating effort and recovery intervals, simulating HR fluctuations typical of intermittent physical activities. The protocol structure (7 intervals of 2 min of effort followed by 2 min of recovery) was specifically chosen to induce controlled, repetitive intensity changes, thus allowing for the assessment of each device's ability to accurately capture HR variations during both exertion and rest phases.  
Each participant wore the three HR devices simultaneously, ensuring identical conditions for each comparison and minimising individual variability. To synchronise and acquire data, all devices were connected via ANT+ technology to WIMU PROTIM inertial sensors (Realtrack Systems, Almeria, Spain), enabling real-time HR data recording and synchronised processing to ensure temporal consistency across device measurements.  
HR values under each experimental condition (device type and protocol stage) were compared using repeated measures ANOVA and Tukey's post-hoc tests to identify specific differences between devices. This statistical approach was selected to address within-subject variability, providing a robust analysis of differences in each device's measurement accuracy across effort and recovery stages.