esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Best Model, Variable Importance, and Testing  
All the models generated during tuning (trained and validated) are scouted for the model with the best performance, which is evaluated based on the prediction of each model on the validation set. After the best-performing model is detected, the final model is built, which is the ultimate output of the training procedure. The final training is carried out on a compound dataset including both training and validation data (joined into a single training set), and the parameters are assigned the values that those same parameters have in the best-performing model of the tuning phase. When the final model is obtained, its ability to generalise its predictive power is evaluated on the test set.  
Here, the best-performing among the 98 models of the tuning phase was chosen based on multiple factors. First, the best model is the one whose prediction on the validation set returned the lowest log loss; this ensures that the final model is capable of attributing sex with the highest per-class probability. Second, we wanted to maximise the area under the receiver operating characteristic (ROC) curve, a quantity also known as area under curve (AUC). The ROC is a probability curve comparing the false positive rate (FPR or 1-specificity) and true positive rate (TPR or sensitivity) of a binary outcome when the cut-off probability for deciding whether to assign an observation to a certain class is lowered sequentially from one to zero. Therefore, AUC measures how well the model distinguishes both classes of a binary outcome in a way that is independent of the cut-off chosen for the class attribution [53]. Finally, when different models have similar log loss and AUC, the models with lower complexity are chosen; in our case, we gave preference to models with only one layer and a low number of nodes to reduce the chances of overfitting.  
When the best-performing model was chosen, the final model was trained on the whole [PERSON]'s dataset using the values of the parameters shown in Table 3, as we did during the tuning phase, with the exception of the tuned parameters, whose values were those found via parameter tuning. The relative importance of each feature in the model was computed in the \"h2o\" R package following the method of Gedeon [57]. The final model was then used to predict the known sex of the observations in the test set, to assess its performance on data not included in the training phase and, therefore, unseen by the model. The model and code to use it are made publicly available on GitHub (github.com/[PERSON]/Papers/tree/main/DelBove_%26_Veneziano_2022, accessed on 9 September 2022).
## 2 Materials and Methods
### Physical Properties of Testing Core Samples by P-Wave Velocity and Resonance Vibration Methods  
The elastic modulus of a material is an essential physical property that characterizes the deformation behavior of the material. This parameter can be determined by static or dynamic techniques. The first group of methods includes the direct loading of material and the acquisition of stress-strain responses where static modulus can be computed from the slope of the stress-strain curve in the elastic deformation range. In contrast, the dynamic modulus of elasticity can be received non-destructively which gives advantages in faster execution and without disruption of the material structural integrity. In this research, the physical properties of concrete core samples were investigated by the NDT methods. The ultrasonic pulse velocity (UPV) and resonant frequency tests were employed to investigate the internal concrete structure and calculate the dynamic modulus of elasticity. Both these techniques are most commonly used in the determination of materials' physical properties [30]. According to ASTM C 597-02, P-wave velocity was measured by the through-transmission technique using a UT device (Pundit Lab system manufactured by PROCEQ, Zurich, Switzerland) [31]. The input wave voltage and frequency were set to 50 V and 150 kHz, respectively; the receiver was set to double. Polyether polyol was used as a coupling agent between the transducers and the specimen's surfaces.  
In accordance with JIS A 1127:2001, the fundamental longitudinal resonant frequency of the concrete was measured by the generation of vibration by the sweep excitation shaker at one end of the specimen and monitoring its reaction at the other [32]. The input voltage and sweeping time were set at 100 mV and 30 s, respectively. The sweep vibration condition is 500-20,000 Hz. The dynamic modulus of elasticity \(E_{D}\) calculated from resonant frequency can be calculated by following Equation (1) [33]:  
\[E_{D}=4.00\cdot 10^{-3}\frac{1}{\text{A}}\textit{m}\textit{f}\ ^{2}, \tag{1}\]  
where \(\textit{f}\) is the resonant frequency and \(m,\textit{f}\) and A are the mass, length and area of the testing sample, respectively.
# Mapping Server Collaboration Architecture Design with OpenVSLAM for Mobile Devices
## 3 Visual-SLAM-Based Mobile AR System
### OpenVSLAM-Based Mobile AR System Architecture  
The mobile AR system proposed in this paper consists of sensors, networks, a Unity engine, and a FastAPI server. The image and IMU data collected by the camera of the mobile device are serialized through ProtoBuffer and transmitted to the SLAM system through Socket.IO. At this time, the SLAM system can be a SLAM system or a FastAPI server built into the Unity Android app in the form of a plugin. In the former case, SLAM is performed in the mobile device app, whereas in the latter case, SLAM can be performed through the multi-process environment considering multiple clients.  
The SLAM system running on the Android app performs a function corresponding to Google ARCore and is used as an algorithm to track the camera movement and demonstrate AR technology. The SLAM system running on the FastAPI server is designed to merge the maps of multiple clients and perform localization to provide the same AR experience to multiple users by integrating data received from multiple clients. Figure 3 shows an integrated mobile SLAM architecture of the stand-alone type and mapping server type.  
Figure 2: FastAPI-based single-process and multi-process models; (**a**) Single Process Model; (**b**) Multi-process Model.
## 5 Applications and Discussions
### Identification of Electrical Water Heater  
#### 5.2.1 Description of the System  
The schematic diagram of the water heater system is shown in Figure 2. The flow rate of cold water, \(F\), which comes from the pipeline is controlled by the valve CV. Next, the cold water flows through a pair of metal pipes containing a cartridge heater. The outlet temperature, \(T_{out}\), of the water can be varied by adjusting the heating signal, \(u\), of the cartridge heater [30]. To physical modeling, there are four parts in the circle: the cartridge-heater (subscript \(h\)), the streaming water (subscript \(w\)), the pipe wall (subscript \(p\)) and the environment (subscript \(e\)). The following three heat balances in the form of partial differential equations can be established:  
\[\left\{\begin{array}{l}V_{h}\rho_{h}C_{ph}\frac{\partial T_{h}}{\partial t}( t,z)=Q(u)-\alpha_{1}A_{1}(T_{h}-T_{w})\\ V_{w}\rho_{w}C_{pw}\frac{\partial T_{w}}{\partial t}(t,z)+(F_{\rho}C_{p})_{w} \frac{\partial T_{w}}{\partial z}(t,z)=\alpha_{1}A_{1}(T_{h}-T_{w})-\alpha_{2 }A_{2}(T_{w}-T_{p})\\ V_{p}\rho_{C}p\frac{\partial T_{p}}{\partial t}(t,z)=\alpha_{2}A_{2}(T_{w}-T_{ p})-\alpha_{e}A_{e}(T_{p}-T_{e})\end{array}\right. \tag{24}\]  
where, \(V\) is volume, \(\rho\) is density, \(C\) is specific heat, \(Q\) is the heat power, \(\alpha\) is heat transfer coefficient, \(A\) is area, \(T\) is temperature, and \(F\) is flow rate. Here, \(z\in[1,L]\) with \(L\) being the length of the pipe. Additional, \(Q\) is nonlinear with the manipulated heating signal, while it can be approximated with a generalized linear dynamics function described as:  
\[G(z)=\frac{b_{1}z^{-1}+b_{2}z^{-2}}{1-a_{1}z^{-1}-a_{2}z^{-2}}+\
u_{0} \tag{25}\]  
where \(a\) and \(b\) are the coefficients of the model, \(\
u_{0}\) is the initial value. Two different conditions mentioned in [30] are listed in Table 4, and the parameters will change with the process.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Data** & **RMSE** \\ \hline ABFT-SMM & 600 & 0.0037 \\ Cluster Estimation-Based [23] & 500 & 0.014 \\ ANFIS [26] & 500 & 0.007 \\ eTS [28] & 300 & 0.0925 \\ IT2 FNN [29] & 500 & 0.0053 \\ Linear Predictive Method [27] & 2000 & 0.55 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of results from different existed methods.
## 3 System Architecture and Methodology
### Multi-Criteria Decision Making (MCDM)
\[\widetilde{A}_{1}\times\widetilde{A}_{2}=(l_{1},\ m_{1},\ u_{1})\times(l_{2}, \ m_{2},\ u_{2})=(l_{1}\times l_{2},\ m_{1}\times m_{2},\ u_{1}\times u_{2}) \tag{3}\]  
Here is the equation for adding two fuzzy values to get the summation:  
\[\widetilde{A}_{1}\times\widetilde{A}_{2}=(I_{1},\ m_{1},\ u_{1})\times(I_{2}, \ m_{2},\ u_{2})=(I_{1}\times I_{2},\ m_{1}\times m_{2},\ u_{1}\times u_{2}) \tag{4}\]  
Hence, fuzzy weights can be calculated. \[\widetilde{w}_{1}=\widetilde{r}_{1}\times(\widetilde{r}_{1}+\widetilde{r}_{2 }+\cdots+\widetilde{r}_{n})^{-1} \tag{5}\]  
In order to get the weights \(w_{i}\), the center of area equation should be adopted: \(W_{i}=(\frac{l+m+u}{3})\). The weights could be used as the requirement for further calculation. The total of the criteria weight is 1.058, which is not acceptable. Only when the normalized weights are equal to one can the normalized weights be applied for further calculation. Table 8 shows the fuzzuified pairwise comparison matrix and Table 9 shows the eleven-point spherical fuzzy linguistic term scale. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Price or Cost** & **Storage Space** & **Camera** & **Looks** & **The Fuzzy Geometric Mean Value \(\widetilde{\mathbf{r}}\)** & **Fuzzy Weights \(\widetilde{\mathbf{w}}\)** \\ \hline Cost & (1,1,1) & (4,5,6) & (3,4,5) & (6,7,8) & (2.91, 3.44, 3.94) & (0.428, 0.610, 0.859) \\ \hline Comfortability & (\(\frac{1}{6}\), \(\frac{1}{5}\), \(\frac{1}{4}\)) & (1,1,1) & (\(\frac{1}{3}\), \(\frac{1}{2}\), \(\frac{1}{1}\)) & (2,3,4) & (0.58, 0.74, 1) & (0.085, 0.131, 0.218) \\ \hline Processability & (\(\frac{1}{5}\), \(\frac{1}{4}\), \(\frac{1}{3}\)) & (1,2,3) & (1,1,1) & (2,3,4) & (0.80, 1.11, 1.41) & (0.117, 0.196, 0.309) \\ \hline Flexibility & (\(\frac{1}{8}\), \(\frac{1}{7}\), \(\frac{1}{6}\)) & (\(\frac{1}{4}\), \(\frac{1}{3}\), \(\frac{1}{2}\)) & (\(\frac{1}{4}\), \(\frac{1}{3}\), \(\frac{1}{2}\)) & (1,1,1) & (0.30, 0.35, 0.45) & (0.044, 0.063, 0.099) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Fuzzified pairwise comparison matrix. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Fuzzy Weights \(\widetilde{\mathbf{w}}\)** & **Weights \(\mathbf{w}_{1}\)** \\ \hline Cost & (0.428, 0.610, 0.859) & 0.633 \\ \hline Comfortability & (0.085, 0.131, 0.218) & 0.145 \\ \hline Processability & (0.117, 0.196, 0.309) & 0.207 \\ \hline Flexibility & (0.044, 0.063, 0.099) & 0.068 \\ \hline  & Weights \(w_{i}\) & Normalized weights \\ \hline Cost & 0.633 & 0.633 \\  & & 1.058 \\ \hline Comfortability & 0.145 & 0.145 \\  & & 1.058 \\ \hline Processability & 0.207 & 0.207 \\  & & 1.058 \\ \hline Flexibility & 0.068 & 0.068 \\ \hline Total & 0.633 + 0.145 + 0.207 + 0.068 = 1.058 & 0.601 + 0.138 + 0.197 + 0.065 = 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Fuzzified pairwise comparison matrix.
## 4 Direct Predictive Torque Control Algorithm for SRM  
### Purpose of the Algorithm  
Torque ripple is a non-negligible problem for torque control of the SRM, but a higher copper loss is also undesirable [9]. It can be seen from the previous section that the PWM-DITC algorithm maintains the reachability of the phase reference torque, and the sum of the phase reference torque is always equal to the total reference torque. However, the phase current during commutation is not optimized, and undesirable results, such as a higher copper loss in the phase winding, may occur. Therefore, a novel DPTC algorithm is proposed in this section for optimizing the commutation operation of the SRM.  
The DPTC algorithm aims to improve the torque control performance and minimize the copper loss during commutation. This algorithm solves the problem of uncontrollable current in the PWM-DITC algorithm, and the principle of commutation is shown in Figure 4. In the PWM-DITC algorithm, once the incoming phase is excited, the phase commutation is completed as soon as possible. The available positive torque area is underutilized. The proposed method eliminates this shortcoming. In the DPTC method, the reference torque is directly divided within the reachable range of the phase torque, and the effective commutation at each operating point is achieved by a cost function.  
It is well known that the higher the motor speed, the less commutation time is available. In this case, the commutation should be completed as soon as possible to avoid negative torque. Thus, the optimization of commutation by the DPTC is not included in high-speed operation. Nevertheless, this method is still very advantageous at low speeds, especially in the operating area where the copper loss is dominant. Furthermore, please note that the torque ripple is suppressed in all the torque allocation schemes of the DPTC method, which is different from the conventional MPTC. Therefore, the cost function is only used to select the torque allocation scheme with the minimum copper loss.
## 2 Background and Related Work
### Machine Learning-Based Human Activity Anomaly Detection  
Identifying human activities is very important to automate the monitoring of the health of elderly people. A significant number of research works and studies have been conducted to identify and classify human activities by analyzing the motion from video captured through closed-circuit television (CCTV) or other types of camera systems. Machine learning and deep learning models were widely used in many works to identify anomalies in activities [31; 32] along with classification [33; 34; 35]. Since placing surveillance cameras to observe the residents presents data privacy issues/concerns, sensor-based observation has become popular. [PERSON] et al. proposed a method for identifying human activities using the Hidden Markov Model (HMM) and a wearable motion suit with a sensor-attached glove [5]. [PERSON], [PERSON] et al. conducted a similar study by placing sensors on seven body parts and using the Convolutional Neural Network (CNN) model over the collected frequency images [24]. While machine learning and deep learning classifiers work well over sensor data, placing sensors on residents for a long time is inconvenient. Researchers suggest deploying sensors throughout the home to detect human activity and anomalies in order to create a smart home. [PERSON] et al. utilized SVM, one-vs-one (OSVM), and K-means classifiers to identify human activities and anomalies in the ARAS and the Center for Advanced Studies in Adaptive Systems (CASAS) smart home datasets [36]. Similarly, [PERSON] et al. established a sensor-based test bed to collect data and used HMM to detect anomalies in user behavior [15]. All these discussed works assumed a single resident at home, but it is more realistic to consider multiple residents as the activities of one resident are directly affected by the others. Several studies were conducted later to detect anomalies in user behavior by considering multiple residents. In their research, [PERSON] et al. employed the power of machine learning to identify and differentiate between multiple residents' activities accurately while also flagging any unusual activities [9]. [PERSON] et al. utilized an innovative technique based on entropy to detect anomalies in the presence of visitors, ensuring maximum safety and security for residents [6]. [PERSON] et al. introduced a novel algorithm that leverages temporal pattern discovery to identify any irregularities in user activities [37].
# An Effective Scheduling Algorithm for Coverage Control in Underwater Acoustic Sensor Network
## 5 Conclusions  
A UASN is different from a wireless sensor network on land. It must account for the particularity of the underwater acoustic environment, and it requires greater energy efficiency. Therefore, this paper proposes an energy-saving coverage-scheduling algorithm called ESACC. A memetic algorithm is used to implement the sleep scheduling of redundant nodes, which simplifies the network topology and reduces the network energy consumption. A wake-up scheme is also established. The use of some dormant redundant nodes in the network to replace nodes that are about to die due to energy exhaustion maintains high network coverage. The algorithm uses a sleep-wake strategy to reduce network energy consumption and extend the network life cycle while maintaining high coverage. Simulation results show that, compared with the CMSS and LEACH-Coverage-U algorithms, ESACC can close redundant nodes and maintain UASN operation with the minimum number of active nodes. When a node's energy is close to critical, it can wake up a sleeping node to maintain high coverage. Next, applying ESACC to energy-saving deployment in UASNs is an important aim of future work.  
[PERSON] and [PERSON] conceived and designed the whole procedure of this paper. [PERSON] contributed to the Introduction and System Model Sections. [PERSON] performed the computer simulation and analyzed the results.  
**Funding:** This research was supported by the National Natural Science Foundation of China (61571250), Zhejiang Natural Science Foundation (LY18F010010), Key Laboratory of Mobile Network Application Technology of Zhejiang Province, and K. C. Wong Magna Fund of Ningbo University. Youth Project of Ningde Normal University (2017Q105,Q2018012), School Innovation Team of the Ningde Normal University (2015T011) and project of the Fujian Provincial Natural Science Fund (2017I0016, 2017J01775 ).  
Figure 11: Average residual energy of network nodes.  
**Acknowledgments:** The authors would like to express their gratitude to [PERSON] and [PERSON] from Ningbo University for their experimental help. At the same time, they would like to thank [PERSON] and [PERSON] from Taiwan University for their creative ideas. Their professionalism is critical to completing the research.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 ICT Fluency and the Digital Divide
### Measuring Digital Inequality
\begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline \(N=407\) & Gender & Age & Location & ClassYear & Major & PC,Owners & Educ,Att & Family\_Size & IncomePerCap & EmplStatus \\ \hline Gender & 1 & 0.031 & 0.201 \({}^{\star}\) & \(-\)0.016 & \(-\)0.058 & 0.135 & \(-\)0.112 & \(-\)0.139 & \(-\)0.194 \({}^{\star}\) & 0.008 \\ Age & 0.031 & 1 & \(-\)0.017 & 0.013 & \(-\)0.020 & 0.150 & 0.045 & 0.050 & 0.149 & \(-\)0.081 \\  & (0.734) & (0.085) & (0.088) & (0.882) & (0.097) & (0.620) & (0.579) & (0.100) & (0.371) \\ Location & 0.201\({}^{\star}\) & \(-\)0.017 & 1 & 0.116 & \(-\)0.103 & 0.010 & \(-\)0.062 & 0.107 & 0.056 & \(-\)0.045 \\  & (0.026) & (0.855) & (0.203) & (0.205) & (0.910) & (0.497) & (0.299) & (0.539) & (0.619) \\ ClassYear & \(-\)0.016 & 0.013 & 0.116 & 1 & \(-\)0.112 & 0.107 & 0.044 & 0.168 & \(-\)0.117 & \(-\)0.098 \\  & (0.857) & (0.830) & (0.203) & (0.203) & (0.216) & (0.240) & (0.628) & (0.063) & (0.199) & (0.283) \\ Major & \(-\)0.058 & (-0.020) & \(-\)0.103 & \(-\)0.112 & 1 & \(-\)0.055 & \(-\)0.121 & 0.065 & 0.106 & \(-\)0.092 \\  & (0.527) & (0.825) & (0.255) & (0.216) & (0.544) & (0.181) & (0.473) & (0.860) & (0.312) \\ PC,Owners & 0.135 & 0.150 & 0.010 & 0.107 & \(-\)0.055 & 1 & \(-\)0.014 & \(-\)0.048 & 0.145 & 0.009 \\  & (0.137) & (0.097) & (0.910) & (0.240) & (0.544) & (0.881) & (0.597) & (0.110) & (0.926) \\ Educ\_Att & \(-\)0.112 & 0.045 & \(-\)0.062 & 0.044 & \(-\)0.121 & \(-\)0.014 & 1 & \(-\)0.005 & 0.119 & 0.152 \\  & (0.217) & (0.620) & (0.497) & (0.628) & (0.131) & (0.881) & (0.476) & (0.190) & (0.094) \\ Family\_Size & \(-\)0.139 & 0.050 & 0.107 & 0.168 & 0.065 & 0.048 & 0.065 & 1 & 0.117 & \(-\)0.225 \({}^{\star}\) \\  & (0.129) & (0.579) & (0.239) & (0.063) & 0.479) & (0.597) & (0.476) & (0.199) & (0.012) \\ IncomePerCap & \(-\)0.194 \({}^{\star}\) & 0.149 & 0.056 & \(-\)0.117 & 0.016 & 0.145 & 0.119 & 0.117 & 1 & 0.047 \\  & (0.002) & (0.100) & (0.539) & (0.199) & (0.860) & (0.110) & (0.190) & (0.199) & & (0.604) \\ EmplStatus & 0.008 & \(-\)0.001 & \(-\)0.045 & \(-\)0.098 & \(-\)0.092 & 0.009 & 0.152 & \(-\)0.225 \({}^{\star}\) & 0.047 & 1 \\  & (0.931) & (0.371) & (0.619) & (0.283) & (0.312) & (0.926) & (0.094) & (0.012) & (0.604) & \\ \hline \hline \end{tabular} \({}^{\star}\) Correlation is significant at the 0.05 level (2-tailed).  
\end{table} 
Table 4: Correlation between variables characteristics of Matrix M and W for the 2 nd year.
## 6 Results  
### Reducing EIT Measurements  
In this section, the results of data reduction techniques applied to EIT measurements are analysed to evaluate their impact on classification (see Table 1). Four methods including PCA, Chi-square, mRMR, and Laplacian Score are evaluated for reducing the number of measurements. Then, the classification accuracies were compared across 10 subjects. As presented in Figure 5, Chi-square proved to be the most robust method, with accuracy reductions ranging from 1.4% to 6.4% across subjects. Notably, 70% of subjects experienced minimum accuracy reductions of less than 5%, while the maximum accuracy achieved after reduction was 95.6% (subject 9). This demonstrates that the Chi-square test effectively preserves critical statistical features, making it highly reliable for dimensionality reduction in this application. In contrast, PCA showed a significant loss of accuracy, with reductions  
Figure 4: Methodology for compensating the effect of reduced input dimensionality.  
ranging from 3.3% to 22.53%. For 90% of the subjects, the loss of accuracy exceeded 10%, indicating that PCA struggles to retain features essential for maintaining classification performance. mRMR performed moderately well, with accuracy losses ranging from 9.13% to 21.2%. The Laplacian Score method showed high variability with losses ranging from 5.8% to 28.57%. In summary, the Chi-square test proved to be the most effective feature reduction method, achieving a high classification accuracy with minimal degradation. However, mRMR, PCA, and the Laplacian Score showed significant limitations for this application. By preserving core statistical features during data reduction, the Chi-square test provides a useful first step towards developing more computationally efficient CNN model compression.