esa-sceva/satcom-chunk-collection huggingface dataset:
# On-the-Fly Sleep Scoring Algorithm with Heart Rate, RR Intervals and Accelerometer as Input
## 7 Conclusions  
This work shows that an on-the-fly sleep scoring algorithm is possible also with HR and RR intervals. For better results, if the situation allows, it is possible to consider also the acceleration for sleep staging. This study demonstrates that an on-the-fly sleep scoring algorithm can be effectively implemented using Heart Rate (HR) and RR intervals data from consumer wearable devices, with acceptable accuracy and reliability for practical applications. The algorithm was tested with and without accelerometer input to assess the influence of motion data on sleep stage identification.  
The results suggest that while accelerometer data improves the identification of certain stages, particularly wakefulness and REM sleep, a reliable level of performance is still achievable with HR and RR intervals alone.  
The findings underscore the potential of such algorithms in applications where on-the-fly monitoring is essential, such as in safety-critical environments like autonomous driving. In these cases, the ability to exclude the accelerometer makes the algorithm less sensitive to external motion interference, offering a versatile solution. Future work could focus on further refining this model to enhance accuracy across all sleep stages, potentially incorporating additional physiological markers as wearable technology evolves.  
Conceptualization, M.V.; methodology, M.V., S.G. and M.G.; software, M.G., S.G. and L.P.; validation, M.G., S.G., L.P. and M.V.; formal analysis, M.G. and L.P.; resources, M.V.; writing--original draft preparation, M.G.; writing--review and editing, M.G., S.G., L.P. and M.V., supervision. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
* Restrictions apply to the availability of the first set of data. The second set of data is available in The DREAMS Databases and Assessment Algorithm at [[https://zenodo.org/records/2650142#.XvUU5](https://zenodo.org/records/2650142#.XvUU5) NkjJ8]([https://zenodo.org/records/2650142#.XvUU5](https://zenodo.org/records/2650142#.XvUU5) NkjJ8).  
**Acknowledgments:** The authors would like to thank [PERSON] for his technical feedback and suggestions about sleep scoring.  
**Conflicts of Interest:** The authors declare no conflicts of interest. Author [PERSON] was employed by the company Sleep Advice Technologies s.r.l. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 4 Experiments and Results
### The Model Training Process  
In this study, we began by performing the necessary data preprocessing, including text cleaning, tokenization, and data splitting, ultimately dividing the dataset into training and testing sets. For the model input, we used the RoBERTa model, which converts the text into high-quality word embeddings. After the word embeddings are obtained from RoBERTa, they are passed into the multi-head self-attention mechanism, which captures different semantic information in the text through parallel computations.  
After processing through the multi-head attention, the output is passed through a residual connection module to ensure that information is not lost during the transfer. Additionally, layer normalization is applied to further stabilize the training process and accelerate convergence. The processed information is then passed through a linear layer, mapping it to the target class space, and the final classification result is obtained via the Softmax activation function.  
To enhance the model's performance, we extracted the query, key, and value vectors from the multi-head attention process and calculated the inconsistency loss based on their differences. This loss was combined with the traditional cross-entropy loss to form a composite loss function, which improved the model's classification capability and generalization ability.  
The overall model flow is both efficient and effective, incorporating RoBERTa word embeddings, multi-head attention, residual connections, and inconsistency loss to ensure strong performance in text classification tasks.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Number** \\ \hline Batch size & 8 \\ Epoch & 5 \\ Learning rate & \(1\times 10^{-5}\) \\ Dropout & 0.3 \\ Attention headcount & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Table of parameter settings.
## 2 Experimental Program  
### Specim Design  
This experiment's research object was the frame structure's beam-column middle connections. According to the Chinese \"Code for design of concrete structures (GB 50010-2010)\" [26], five specimens with a 1:2 scale ratio were designed. They consisted of four assembled MRPC beam-column connections using different steel fiber typesin critical cast-in-place regions and a monolithic concrete beam-column connection. Micro-copper-plated straight, end-bent, and wavy steel fibers were used as MRPC steel fibers, as shown in Figure 1. Table 1 shows these steel fibers' characteristics, and Table 2 shows the number of each of the five specimens. The axial load ratio was 0.3. The sectional dimensions of beams and columns were 200 mm \(\times\) 300 mm and 300 mm \(\times\) 300 mm, respectively. The use of grout sleeves to connect the longitudinal reinforcements is widely used in engineering. In the previous investigation of the research group [27], the stiffness and strength of the structure can be stably improved. Therefore, this study uses grout sleeves to connect reinforcements. According to the Chinese code, \"The grouting sleeve for rebars splicing (JG/T 398-2019)\" [28], the connection area at one end of the grout sleeve is greater than 8 d (steel diameter). Therefore, the length of the grout sleeve selected is selected as 280 mm. Figure 2 shows the MRPC's assembled connections' dimensions and reinforcement configurations.  
Considering the design requirements for strong columns and weak beams, precast columns required segmented and integrated MRPC and C35 concrete pouring, while MRPC at the beam ends was poured after connections assembly and maintenance were completed [29]. The MRPC assembled connection's production process included three main steps: production of the precast beam and column, assembling and sleeve grouting, and MRPC casting at the beam end, as shown in Figure 3. When tying reinforcements, using the limit plate ensured accurate spacing between each steel bar.  
The interface between the precast beam and the MRPC was the structure's weak region. To increase interface bonding, the assembled beams' ends were irregularly shaped, and an air bubble film was pasted on the formwork to increase its roughness [30], as shown in Figure 4. Assembled beam-column connections using MRPC in critical cast-in-place regions are shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Specimens** & **XJ** & **SFZP-1** & **SFZP-2** & **SFZP-3** & **SFZP-4** \\ \hline Concrete in connection regions & C35 & MRPC & MRPC & MRPC & MRPC \\ Type of steel fiber & \(\backslash\) & SF-1 & SF-2 & SF-3 & SF-4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The conditions of the five specimens.  
Figure 3: Construction sequence of an assembled beam–column connection. (**a**) Production of precast components. (**b**) Assembling and sleeve grouting. (**c**) Casting of MRPC in situ.  
Figure 2: Dimension and details of specimens.
## 3 Model Problems and Numerical Results
### Model Problem 1
#### 3.1.4 Modeling the Spatial Variability  
We select a correlation length \(\lambda=0.3\), a standard deviation \(\sigma=1.0\) and a smoothness factor \(\
u=0.6\) for the covariance kernel in Equation (21) and generation of the Gaussian random field according to Equation (22). We will perform simulations with two different stochastic dimensions. A high stochastic dimension \(s=586\) and a moderate stochastic dimension \(s=78\). The value \(s=586\) accounts for 98% of the variance in the random field, while \(s=78\) only accounts for 92%, see Figure 12.  
Once the Gaussian field has been generated, a memoryless transformation is applied pointwise, i.e.,  
\[g(y)=F^{-1}\left[\Phi(y)\right], \tag{33}\]  
in order to obtain the gamma random field, see [38] for details.  
Here, \(F\) denotes the marginal cumulative density function (CDF) of the target distribution, \(\Phi\) denotes the marginal CDF of the standard normal distribution, and \(y\) is a discrete value of a realization of the random field. The memoryless transformation is depicted in Figure 13, with the solid line representing \(F\), and the dashed line representing \(\Phi\). The characteristics of the Gamma distribution used to represent the uncertainty in concrete are as follows: a mean of 30 GPa and a standard deviation  
Figure 12: Magnitude of the eigenvalues and their cumulative sum for model problem 1.  
of 7.74 GPa, see [39]. It is known that such a memoryless transformation distorts the underlying covariance function. In the scope of this paper, we will however not address the correction for this distortion. Instead, we will focus on the multilevel methodology. We refer the reader to [40; 41; 42; 43; 44] where iterative methods are described which correct this distortion.
## 2 Primer Knowledge
### IEEE 802.11 Standard  
IEEE 802.11 is a standard for wireless network communication defined by the Institute of Electrical and Electronics Engineers (IEEE). With the development of technology and hardware equipment, IEEE has successively introduced a series of protocols. Each protocol specifies its communication rates, applicable to different frequency bands, modulation modes, etc. Table 1 lists several typical 802.11 protocols [11; 12; 13]. Different modulation modes have their own advantages. Direct sequence spread spectrum (DSSS) has a strong anti-interference ability. It uses a high-speed spread-spectrum sequence to expand the spectrum of the signal at the transmitter, and de-expands and restores the original signal at the receiver. Orthogonal frequency division multiplexing (OFDM) realizes the parallel transmission of high-speed serial data through frequency division multiplexing. It can resist multipath fading and achieve high data rate transmission in multipath channels. Among the modulation modes, OFDM is widely used in 802.11 protocols.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**802.11 Version** & **Modulation Mode** & **Frequency Band (GHz)** \\ \hline
802.11a & OFDM & 5 \\ \hline
802.11b & DSSS & 2.4 \\ \hline
802.11g & OFDM & 2.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: A list of several typical 802.11 versions.  
Figure 2: WiFi receiver processing.
## 3 Motivation and Proposed Method Design
### Main Program of the Proposed Method  
In the CDG scheme, the key problem is how these measurements will be formed and sent to the sink node in an energy-efficiently manner. As we mentioned above, in actual network deployment, network sensed data is in fluctuation, and thus, the sparsity also changes with time. Given that the required number of measurements (\(M\)) cannot be decided at the beginning (it is up to the sparsity of the network sensed data \(X\)), it is inappropriate to use a specific upper bound in previous researches. In order to make up this defect, we propose a feedback control strategy without the requirement of having the specific sparsity knowledge of the network sensed data. The procedure is as follows (as shown in Figure 4 and Algorithm 1). During each epoch, the sink node evaluates the reconstruction quality of measurements which have been received; if the reconstruction quality indicators have not been obtained, a feedback packet will be sent to inform the nodes that additional \(\Delta M\) measurements are required. This measurement-reconstruction process will repeat until the reconstruction quality is satisfied. Therefore, unlike existing schemes, a typical feature of our scheme is that the measurement  
Figure 3: Histogram of the minimum times distribution of Gaussian (dense) (**top**) and Bernoulli (sparse) measurement for a 10-sparse signal (**bottom**).  
procedure during each epoch is separated into several rounds. Since the network sensed data is adaptively and progressively reconstructed in our scheme, the sink node can also satisfy the varying levels of quality requirement of network sensed data by different users.  
```
Input: Reconstruction quality requirement, time of each data gathering period \(T\) Output: Network sensed data \(X\) in epoch \(T_{i}\)
1 Initiate the network settings, flag = 1
2 If clock interrupt of \(T_{i}\) is received do
3 generate feed-back packet and propagate it to the network
4 \(\Delta M\) measurements are propagated to the sink node through designed routing architecture
5 the sink node receives and adds these projections into projection pool and gets reconstruction result \(\hat{X}\)
6 evaluate \(\hat{X}\)
7 If stopping rule is achieved then
8 flag = 0
9 Ifend
10 While flag = 0
11 Ifend
12 Return \(\hat{X}\)
```  
**Algorithm 1**Main Procedure of the Proposed Scheme
## 5 Experiments and Analysis
### Evaluation Metrics  
To further validate the classification performance of the model proposed in this paper, experiments were conducted using different models, and the accuracy (\(A_{c}\)), recall (\(R_{e}\)), and similarity coefficient (\(F_{1}\)) were used as metrics to measure the classification effect. Each metric is defined as follows:  
\[A_{c}=\frac{T_{p}+T_{N}}{T_{p}+T_{N}+F_{p}+F_{N}}\times 100\%, \tag{7}\]  
\[R_{e}=\frac{T_{p}}{T_{p}+F_{N}}\times 100\%, \tag{8}\]  
\[F_{1}=2\frac{T_{e}\cap P_{e}}{T_{e}\cup P_{e}}\times 100\%=2\frac{T_{p}}{F_{p}+2 T_{p}+F_{N}}\times 100\%, \tag{9}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Class** & **Number** \\ \hline Sphere & 1224 \\ Cylinder & 2438 \\ Truncated cone & 2447 \\ Line & 1220 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental dataset.  
Figure 9: Simulated Images of experimental dataset. (**a**) Simulated sphere object. (**b**) Simulated cylinder object. (**c**) Simulated truncated cone object. (**d**) Simulated line object.  
where \(T_{e}\) is the true class of object \(e\), \(P_{e}\) is the prediction class of object \(e\), \(T_{P}\) is the true positive example, \(F_{N}\) is the false negative example, \(F_{P}\) is the false positive example, and \(T_{N}\) is the true negative example. Here, \(\cap\) is the case where the true class is the same as the prediction, and \(\cup\) is the total probability of all cases [39].  
In addition, as two important indicators, the parameters and calculate consumption of the model are usually used to evaluate the complexity of the network model. The parameters and calculate consumption mainly come from the convolution layer and full connection layer in the network. The calculation process can be expressed as  
\[\left\{\begin{array}{l}P_{cm}=\sum\limits_{l=1}^{D_{1}}K_{l}^{2}\cdot C_{l-1 }\cdot C_{l}\\ F_{cm}=\sum\limits_{l=1}^{D_{1}}M_{l}^{2}\cdot K_{l}^{2}\cdot C_{l-1}\cdot C_ {l}\end{array}\right., \tag{10}\]  
\[P_{dense}=F_{dense}=\sum\limits_{l=1}^{D_{2}}C_{l-1}\cdot C_{l}, \tag{11}\]  
where \(P\) and \(F\) represent the parameters and calculate consumption of the model, respectively; \(M_{l}\) and \(K_{l}\) represent the size of the input image and the size of the convolution kernel used in the network, respectively; \(C_{l-1}\) and \(C_{l}\) are
# Multi-Stage Dynamic Transmission Network Expansion Planning Using LSHADE-SPACMA
## 3 Load Forecasting Using ANFIS  
The ANFIS was used to obtain long-term load forecasting of WDN up to year 2040. Artificial intelligence techniques such as the fuzzy logic controller (FLC) and the artificial neural network (ANN) are commonly used in long-term load forecasting [24; 25; 26; 27; 28; 29].  
Recognizing the non-linear relationship between the selected input and the given output is the FLC's key benefit in many applications in diverse fields. ANNs can \"learn\" because of the versatility of neuron-linking weights; thus, the solution can be adapted to boost efficiency. ANFIS combines ANN and FLC to enable this hybrid approach to adapt to various problems through machine learning using an iterative mechanism to detect non-linear relationships. ANFIS rapidly overcomes the complexities and robustness of the system by modeling its non-linear function with reasonable precision. The basic steps of ANFIS for long-term load forecasting are summarized as follows:  
1. Define the input and output of the model. Input is the historical year and output is the actual peak load data.
2. Collect all data from previous periods and normalize scales,  
Figure 1: Proposed strategy to solve transmission network expansion planning (TNEP) problem.  
* Divide data into two sets--training data and test data. Training data are about 70-90% of the data available.
* Run and estimate all reasonable ANFIS and determine the type of membership function and number of linguistic variables.
* Select the best ANFIS model.
* Project input variables using an autoregressive model in years (defined for the future).
* Predict total peak loads using selected ANFIS.
# Conductive Textiles for Signal Sensing and Technical Applications
## 4 Testing for Conductive Textiles
### Electromagnetic Shielding (EMS) Testing  
The EMS effectiveness test is a measurement that determines how a textile substrate performs against electromagnetic interference by weakening it. There is no standardized method to test the EMS of conductive textiles to this day. The following properties or parameters of the textile shielding materials used to manufacture textile assemblies or products for EMI shields are very important to determine [278]:  
* Reflection coefficient, which depicts the amount of reflected EM wave under an impedance discontinuity in the transmission medium;
* Absorption coefficient, which signifies a parameter on how much energy from an EMR (electromagnetic radiation) wave a material can absorb;
* Transmission coefficient, which indicates the quantity of EMR penetration through a shield.  
Determining the effectiveness of EMI shielding or, more distinctly, determining the attenuation of an EM shield can be very complex as the methods of determining shielding vary with the particularity of application. To figure out the performance of the planer shielding structure, a wide range of standards have been adopted [279]. Generally used techniques for testing shielding strength for plane materials incorporate open-field, coaxial transmission lines, and shielded room tests [280; 281].
## 2 System Model
 \tag{13}\]  
The users need to perform SIC to obtain the desired signal. The instantaneous signal-to-interference-plus-noise ratio (SINR) of the \(i\)th user needed to decode the \(j\)th user's signal (\(j\leq i\)) can be expressed as  
\[ \begin{split}\text{SINR}_{i,j}=&\alpha^{2}|\hat{h }_{i}|^{2}A_{\text{R}}^{2}P_{\text{R}}|\hat{h}_{\text{SR}}|^{2}P_{\text{S}}a_{ j}\\ &\times\bigg{(}\alpha(|\hat{h}_{i}|^{2}+\sigma_{\text{RU}}^{2}) A_{\text{R}}^{2}P_{\text{R}}(|\hat{h}_{\text{SR}}|^{2}+\sigma_{\text{SR}}^{2})P_{ \text{S}}\\ &-\alpha^{2}|\hat{h}_{i}|^{2}A_{\text{R}}^{2}P_{\text{R}}|\hat{h} _{\text{SR}}|^{2}P_{\text{S}}\sum_{u=1}^{j}a_{u}\\ &+\alpha(|\hat{h}_{i}|^{2}+\sigma_{\text{RU}}^{2})A_{\text{R}}^{ 2}P_{\text{R}}(\ell_{\text{R}}P_{\text{R}}+\sigma_{\text{R}}^{2})+\alpha \sigma^{2}\bigg{)}^{-1}\end{split}  \tag{14}\]  
Here, for user \(j\), the signals to user \(\{j+1,\cdots,N\}\) are treated as multiple-access interference signals. We subtract only the power of the signals to user \(\{1,\cdots,j\}\) from the total power in the denominator. The SINR of the \(i\)th user necessary to decode the \(j\)th user's signal can be denoted by  
\[\text{SINR}_{i,j}=\frac{\alpha a_{j}\Gamma_{1}|\hat{h}_{i}|^{2}|\hat{h}_{ \text{SR}}|^{2}}{b_{j}\Gamma_{1}|\hat{h}_{i}|^{2}|\hat{h}_{\text{SR}}|^{2}+ \Gamma_{2}|\hat{h}_{i}|^{2}+\Gamma_{3}|\hat{h}_{\text{SR}}|^{2}+\Gamma_{4}}. \tag{15}\]  
where  
\[ \begin{split}\gamma_{1}&=P_{\text{S}}/\sigma_{ \text{R}}^{2}\\ \gamma_{2}&=P_{\text{R}}/\sigma_{\text{U}}^{2}\\ \gamma_{3}&=\beta_{\text{R}}P_{\text{R}}/\sigma_{\text{ R}}^{2}\\ \Gamma_{1}&=\gamma_{1}\gamma_{2}\\ \Gamma_{2}&=\gamma_{2}(\sigma_{\text{SR}}^{2}\gamma_{ 1}+\gamma_{3}+1)\\ \Gamma_{3}&=\gamma_{1}(\gamma_{2}\sigma_{\text{RU}}^{2} +1)\\ \Gamma_{4}&=(\sigma_{\text{RU}}^{2}\gamma_{2}+1)( \sigma_{\text{SR}}^{2}\gamma_{1}+\gamma_{3}+1)\\ b_{j}&=(1-\alpha\sum_{u=1}^{j}a_{u})\end{split}  \tag{16}\]
## 4 Experiment Results  
### SHAP Value Analyses  
In this experiment, to utilize the effective SHAP analysis technique, for each scenario, the dataset consists of 13 bytes that make up the payload section of messages exchanged on the CAN bus. Each byte stores information such as message ID, CAN ID, data, and CRC details. The patterns within the payload are analyzed through SHAP to distinguish between attack data and normal data. Finally, SHAP value analyses with Tree Explainer (XG Boost) were performed on the datasets for each scenario to measure the SHAP interaction value between feature importance and the important features.
# LaRoAwan and Urban Waste Management--A Trial
## 5 Evaluation
### Deployment Conditions Impact  
In order to deepen the knowledge about the limitations imposed by the material used in the containers, tests were carried out that allowed to determine the impact of different materials on the transmission. These tests were then carried out for Iglos (Empty and Full) and for underground containers (Full). For the tests, a probe was developed, visible in Figure 14, which sends five messages of two different dimensions (4 and 8 bytes), using each of the different parameters of the LoRa modulation (SF7 to SF12), which totals 60 messages sent in each of the tests. The sizes depict the actual size of the data sent by the Dingtek (4 Bytes) and IoSens (8 Bytes) sensors. Algorithm 1 depicts the implementation of the network assessment procedure.  
Figure 13: Measurements received from the underground container at Spacio Shoping.  
Since the ADR mechanism was disabled during the tests, and the spreading factor is changed cyclically, it is difficult to infer the relationship between the message SNR (Signal-to-Noise Ratio) and the SF used for transmission. However, it is possible to observe in Figure 15 the minimum SNR values for each SF, taking into account the limitations of LoRa modulation. In the figure, it is possible to observe, for each spreading factor, the measured SNR of each message. Thus, in addition to the minimum value, which varies between \(-9\) and \(-22\) dB, we can verify that the maximum reported value is \(15\) dB.  
Measuring received message SNR and RSSI, we can check the dependency of these two radio performance indicators. The scatter plot presented in Figure 16 confirms the  
Figure 14: LoRa probe installed on top of an Iglo.  
Figure 15: Received message SNR versus spreading factor.  
linearity of RSSI and SNR ratio between \(-130\) and \(-100\) dBm, where the maximum value and SNR is proportional to RSSI. For RSSI higher than \(-100\) dBm, the SNR will lead to saturation on 15 dB. It is also possible to observe the influence of interfering signals in the communication, causing high dispersion of SNR in some cases. This situation is justified because LoRaWAN uses the ISM (Industrial, Scientific and Medical) band of 868 MHz, so, depending on the use case, the radio channel potentially will present a high occupancy, and consequently, higher interference level.