esa-sceva/satcom-chunk-collection huggingface dataset:
# Building Wireless Control Applications with XBee and LabVIEW
## 4 Experimental Tests
### Comparing Different ADCs for XBee Modules  
This test was aimed at designing the interface between sensors and XBee modules. In particular, this subsection analyzed the influence of the ADC in the process of digitalizing the sensor measures. For this purpose, three different ADCs were compared--(1) the ADC included at the XBee module; (2) an Arduino Uno connected in series to an XBee module; and (3) a specific acquisition board, namely the NI myDAQ USB-6008, wired to the computer by means of a USB link. With regards to the resolution, the XBee ADC represented the measured analog voltage into a 10-bit value (same as that of Arduino Uno), and finally, the NI myDAQ USB-6008, which had a higher resolution was taken as the reference value. The values measured from the three were compared and transmitted to a computer using LabVIEW. These tests were done at a distance of 0.5 m, using a sample period of 100 milliseconds.  
The comparison between the XBee ADC and the NI myDAQ proved that the behavior of the XBee ADC was quite limited at low voltages. In particular, it was shown that the XBee ADC introduced an offset of around 0.4 Volts when 0 volts were measured. The higher the voltage, the lower the difference between the values obtained from the XBee ADC and the NI myDAQ. Due to these poor results, the  
Figure 2: Example of a typical XBee API transmission frame.  
authors introduced an Arduino Uno in order to digitalize the signal of the sensor. These values were compared with those acquired by the myDAQ board. In Figure 3 (left panel), the wireless signal obtained from the ADC at the XBee is compared with the wired signal, measured by the myDAQ. The difference could reach up to 0.4 volts. Figure 3 (right panel) compared the wireless signal (measured by the Arduino Uno connected to the XBee module) with the wired signal received from the myDAQ. It could be noticed that, in this case, the difference between both signals was considerably smaller. For this reason, the authors connected the sensor using an Arduino Uno and an XBee module.
# An Adaptive Active Learning Method for Multiclass Imbalanced Data Streams with Concept Drift
## 4 Experimental Evaluation and Analysis
### Algorithm Comparison Experiments  
In this section, AdaAL-MID is compared with other data stream classification algorithms, including the following nine comparative algorithms: LB, MOOB, ARFre, SRP, CSARF, ROSE, ROALE-DI, CALMID, and MicFoal. Among the supervised learning algorithms, LB and SRP are general ensembles with concept drift handling mechanisms, MOOB is a specific ensemble for imbalanced data streams, and ARFre, CSARF, and ROSE are advanced algorithms addressing the joint challenge of class imbalance and concept drift. ROALE-DI, CALMID, and MicFoal are the latest active learning algorithms addressing joint problems, with MicFoal-E being an ensemble variant of MicFoal for comparison with other ensemble classification algorithms. In this section, comparisons and analyses were conducted on 18 synthetic data streams with fixed or variable class ratios and 6 real data streams, followed by an overall performance evaluation. To ensure the fairness of the experiments, the number of base classifiers for all ensemble algorithms was set to 10, the label budgets for the AdaAL-MID and the other three active learning algorithms were set to 0.2, the random selection ratios were set to 0.1, and the window sizes for the algorithms using sliding windows were set to 500, with other parameters following the default settings in their original papers. The algorithms were evaluated using the following five metrics: recall, accuracy, G-mean, F1-score, and kappa. Tables 2-6 present the detailed results and rankings of each algorithm for each metric, with the best values highlighted in bold (\"/\" indicates no valid value was obtained). The average ranks (Avg. Rank) of each algorithm for each type of data stream are provided at the end of the corresponding data stream type, and the overall average rank (Total Rank) for all data streams is provided at the end. Figure 3: Effect of parameter variation on the AdaAL-MID’s performance: **(a)** changes in \(N\) on Fixed_MIX_SIG; **(b)** changes in _sizeW_ on Fixed_MIX_SIG; **(c)** changes in \(\theta_{1}\) on Fixed_MIX_SIG; **(d)** changes in \(\theta_{2}\) on Fixed_MIX_SIG; **(e)** changes in \(N\) on Var_MIX_SIG; **(f)** changes in _sizeW_ on Var_MIX_SIG; **(g)** changes in \(\theta_{1}\) on Var_MIX_SIG; **(h)** changes in \(\theta_{2}\) on Var_MIX_SIG.
## 7 Conclusions  
In this paper, we propose a MIMU/ODO/MC integrated navigation method to solve the problem of the large positioning errors when depending solely on INS composed of MEMS in tunnels. Our approach makes full use of the train motion constraints and sensors that already exist on trains (odometer). In order to better exploit the potential of motion constraints, we consider the installation angles of MIMU relative to the train body in the motion constraint model. The installation angles are estimated online when both the GNSS and the motion constraints are available. In a tunnel, the train motion constraints provide the lateral and upward velocity observations, and the odometer provides the forward velocity observation. The tests carried out in a real tunnel environment and with an artificially disconnected GNSS signal both showed that the accuracy of the MIMU/ODO/MC integrated method was significantly higher than any of P-INS, TMC-INS, and CIAMC-INS, and achieved seamless navigation inside and outside the tunnel. However, there are still shortcomings in our method. For example, the navigation accuracy of the algorithm will decrease in long, curved tunnels. In future work, we will consider the lateral and upward velocity caused by the train turning in the motion constraint model in order to improve the adaptability of the algorithm to various patterns of train movement. In addition, we will study how to apply this method in other contexts to improve navigation accuracy when satellite signals are disturbed.  
[PERSON] and [PERSON] discussed and designed the methodology. [PERSON] provided the dataset for experiment. [PERSON] and [PERSON] completed the simulation program. [PERSON] and [PERSON] verified the rationality of the experiment. [PERSON] polished the manuscript. [PERSON] and [PERSON] revised the paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under grants 61973312 and young teacher innovation research project of the College of Intelligent Science, National University of Defense Technology under grants ZN2019-16.  
This paper does not involve human or animal research.  
The study did not report any data.  
**Acknowledgments:** The author would like to thank all the teachers and colleagues who provided inspiration and equipment in the experiment. The author would like to thank all the anonymous reviewers for their meticulous comments and helpful suggestions.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Evolutionary Algorithms for Optimization Sequence of Cut in the Laser Cutting Path Problem
## 2 The Laser Cutting Path Problem (LCPP)
### An Integer Programming Formulation  
A mathematical model serves several purposes. Firstly, it provides a common language that enables effective communication between researchers, practitioners, and stakeholders. Mathematical notation can precisely describe the problem and its requirements, facilitating a shared understanding among different parties.  
The model is crucial for formalizing an LCPP: We have two sets of binary variables. The first set of variables are variables \(x_{uv,i}\) that represent wherever the cutting edge \(uv\) is the \(i\)-th edge to be cut or not. These variables represent the LCPP solution since this represents the order in which one must cut the pieces. As we know the cut order for the cutting edges, to compute the total cost of this solution, one only needs to determine the edges used as slight edges. Such information is encoded by the second set of binary variables used to represent whether or not we require edge \(uv\) to be used as a sliding edge in the time \(i\). This variable is defined as \(y_{uv,i}\) for all \(uv\in E\) and \(i\in\{1,\ldots,q\}\). Under such variables, we can define the following model.  
min \[\sum\limits_{i=1}^{q-1}\sum\limits_{uv\in E}V_{m}w_{u,v}y_{uv,i}+ \sum\limits_{i=1}^{q}\sum\limits_{uv\in R}V_{c}w_{u,v}x_{uv,i}\] (2a) s.t. \[\sum\limits_{i=1}^{q}x_{uv,i} =1 \forall uv\in R,\] (2b) \[\sum\limits_{uv\in E}(x_{uv,i}+x_{vu,i}) =1 \forall i\in\{1,\ldots,q\},\] (2c) \[\sum\limits_{u\in V-\{v\}}x_{uv,i}+\sum\limits_{p\in V-\{w\}}x_{uv,i+1}-1 \leq y_{vw,i}\ \forall uv\in E\setminus R,\forall i\in\{1,\ldots,q-1\},\] (2d) \[x_{uv,i} \in\{0,1\}\forall uv\in R,i\in\{1,\ldots,q\},\] (2e) \[y_{uv,i} \in\{0,1\}\forall uv\in E\subseteq R,i\in\{1,\ldots,q\}\] (2f)The objective function (2a) computes the cost of a given solution under the form of an ordering of the required edges; constraints (2b) and constraints (2c) enforce a total ordering of the edges in \(R\), (2c) enforces that each position is an edge, and (2b) ensures that every edge is listed; constraints (2d) compute the movements outside the required edges; and finally, constraints (2e) and constraints (2f) define the bounds on the variables.  
Notice that the model encodes the solution as a sequence of edges to be crossed as it is building the path of the solution. Moreover, notice that the formulation has a large number of variables, \(O(|V|^{3})\), and a large number of constraints \(O(|V|^{2})\). The computational results show that this large number of variables take their tool in the formulation and serve as a motivation for the proposition of heuristic methods.
# A Survey on Secure WiFi Sensing Technology: Attacks and Defenses
## 2 Background
### WiFi Sensing Basics  
In this section, the basic metric enabling WiFi sensing is WiFi CSI. CSI refers to the detailed data that describe how a WiFi signal propagates from the transmitter to the receiver, including how the signal is affected by obstacles, reflections, and scattering along the path [44]. Essentially, CSI provides a fine-grained representation of the wireless channel at the level of individual subcarriers, which are components of the overall WiFi signal. WiFi networks, particularly those using the IEEE 802.11n/ac/ax standards, employ Orthogonal Frequency-Division Multiplexing (OFDM). OFDM divides the WiFi channel into multiple subcarriers, each carrying a portion of the data. CSI captures the amplitude and phase information for each subcarrier, providing a snapshot of the wireless channel at a given moment.  
The CSI for a WiFi signal is often represented as a complex matrix \(H\), where each element corresponds to the channel response of a specific subcarrier between a Tx-Rx antenna pair. The matrix \(H\) can be expressed as follows:  
\[H=\begin{bmatrix}h_{11}&\cdots&h_{1N}\\ \vdots&\ddots&\vdots\\ h_{M1}&\cdots&h_{MN}\end{bmatrix}\]  
where \(M\) is the number of transmit antennas, \(N\) is the number of receive antennas, and \(h_{MN}\) represents the channel response of the \(m\)th transmit antenna to the \(n\)th receive antenna for a specific subcarrier.  
Specifically, for a subcarrier with center frequency \(f\), the corresponding channel response is as follows:  
\[h=\sum a(t)e^{-j\psi(t)}\]  
where \(a(t)\) is the amplitude attenuation factor and phase, and \(\phi(t)\) is the phase shift, respectively.  
The CSI amplitude and phase are impacted by the displacements and movements of the transmitter, receiver, and surrounding objects and humans. In other words, CSI captures the wireless characteristics of the nearby environment. These characteristics, assisted by mathematical modeling or machine learning algorithms, can be used for different sensing applications. This is the rationale for why CSI can be used for WiFi sensing.
# LoRa Communication Using TVWS Frequencies: Range and Data Rate
## 3 LoRa Radio Propagation Model
### Proposed LoRa over TVWS--Path-Loss Model (LoRaT-PLM)  
Several path-loss models are studied in the literature for LoRa and TV transmissions distinctly. The authors in [24] have investigated the coverage and performance of LoRa for smart lighting and smart building application using the one slope path-loss model for a real urban scenario. An automated approach to estimate LoRa coverage focusing on the Okumura-Hata model is proposed using the available multispectral images from remote sensing [25]. A channel-attenuation model based on LoRa measurements is proposed to derive the expected path loss and estimate the coverage [26]. Most of these models have been used in the context of 868 MHz LoRa transmissions. A path-loss model for LoRa signals in the framework of TVWS needs to be explored.  
Okumura-Hata model is a path-loss model commonly used to assess TV transmissions. When a transmitter sends any signal to a receiver, the signal strength changes due to obstructions such as mountains, construction structures, vegetation, and so on, and is termed as a shadowing effect. The shadowing effect, which is more prominent in outdoor signal propagation, impacts signal transmissions in terrestrial and wireless networks and needs to be addressed. In this paper, to consider and address this shadowing effect, especially for terrestrial TVWS-based LoRa transmissions, a hybrid path-loss model, based on the Okumura-Hata model, for TVWS-based LoRa is proposed with the inclusion of a correction factor \(C_{s}\) as given.  
For urban settings  
\[L_{u}=69.5+26.16~{}log_{10}~{}f-13.82~{}log_{10}h_{B}-C_{H}+[44.9-6.55~{}log_{1 0}h_{B}]log_{10}~{}d+C_{s} \tag{13}\]  
For rural settings  
\[L_{o}=L_{u}-4.78(log_{10}f)^{2}+18.33(log_{10}f)-40.94+C_{s} \tag{14}\]  
This newly designed \(C_{s}\) is a correction factor that accounts for shadow fading. As shadow fading occurs when the line of sight is obstructed, it reflects conditions that may differ with corners and buildings that are more common for urban than rural scenarios. Hence \(C_{s}\) for urban would be higher than that for rural environments, and it is considered as 7.2 dB and 6.4 dB for urban and rural scenarios, respectively [19]. Having computed the path loss \(L_{u}\) or \(L_{o}\) from (3), the maximum range can be calculated for urban and rural scenarios from (6) and (9) as per the Okumura-Hata model and from (13) and (14) as per the proposed LoRaT-PLM model.
# Abnormal Activity Detection Using Pyroelectric Infrared Sensors
## 1 Introduction
We employ the reference structure tomography (RST) paradigm [9] to segment the Field of View (FOV) of each PIR sensor into sampling cells. Thus, different human activities will generate discriminative spatio-temporal signals under the monitor region. Next, we use the Hidden Markov Models (HMMs) [10], one kind of generative models, to profile normal activities. Each training sample is modeled by an HMM, and their dissimilarity is calculated based on the Kullback-Leibler (KL) divergence [11]. A self-tuning spectral clustering algorithm is used to cluster similar training samples, without the need to specify the number of cluster and the distance kernel width manually [12]. Finally, One-Class Support Vector Machines (OSVM) [13] are setup to profile the normal activities, and any unexpected activities will be classified as anomaly. It is worth pointing out that our system is trained in an unsupervised manner, which aims to avoid the laborious and inconsistent manual data labeling process. The rest of the paper is structured as follows: Section 2 introduces the related work. Section 3 provides the design and implement of the PIR sensors. Section 4 offers the overview of our proposed method. Section 5 presents the framework of human activity representation, dissimilarity calculation and clustering. Section 6 depicts the usage of the OSVM algorithm for abnormal activity detection. The experimental results are provided in Section 7. Conclusions and future work are given in Section 8.
# Residual Stress in Laser Welding of TC4 Titanium Alloy Based on Ultrasonic laser Technology
## 4 Results and Discussion
### Effects of Heat Input on Residual Stress  
Figure 8 presents the longitudinal residual stress measurements for a series of laser welding, grouped by increasing heat input. Heat input considerably influenced residual stress. For welds 1 and 7 with the same heat input of 28 kJ/m, there was little change in the distribution and the maximum value of residual stress. The difference in the maximum tensile stress was less than 15 MPa. For the welds with different heat inputs, the maximum value of residual stress increased with increasing heat input. The width of the tensile longitudinal residual stress zone within the half plate was 7 mm with a heat input of 28 kJ/m, and 13 mm with a heat input of 60 kJ/m, which means that the stress gradient within the fusion zone of full laser penetration welding was steep. When the heat input increased, the energy absorbed by the unit length increased, which expanded the high temperature zone generation plastic deformation and the corresponding plastic deformation zone moved outside. Therefore, the tensile residual stress region increased.  
Figure 7: Diagram of sensitive grid arrangement \(\theta\) is the angle between the principal stress \(\sigma_{x}\) and the strain gauge \(R_{1}\), and the angle between the strain gauge \(R_{2}\) and \(R_{3}\) is 135\({}^{\circ}\).  
As shown in Figure 9, with increasing heat input, the maximum longitudinal residual stress gradually increased. The whole change process can be divided into three stages. During the first stage (heat input 28-35 kJ/m) and the third stage (heat input 56-70 kJ/m), the maximum residual stress changed markedly, which was mainly due to the large temperature gradient at high heat input and the small plastic deformation region at low heat input. When the heat input changed from 35 kJ/m to 56 kJ/m, the maximum longitudinal residual stress changed slowly. The deduction from these data is that the second stage may be the preferred heat input range for laser welding of 2-mm-thick titanium alloy plates when residual stress and other factors affecting welding quality are considered.
## 4 Results and Discussion
### Experiment
#### 4.2.2 Statistical Test  
To further verify the overall performance of the WELAD, this study conducts a statistical analysis in which we follow the procedure proposed in [PERSON] [58] and use nonparametric statistical tests for multiple comparisons by combining all the datasets. First, we apply the Friedman test to rank the performances of the seven ensemble learning methods and evaluate whether these ensemble learning methods show statistically significant differences on the 30 datasets. Table 4 shows the [PERSON] rank comparison table, according to which the ensemble learning algorithms, ranked from best to worst, are WELAD (5.70), RF (4.60), ERT (4.38), GBDT (4.22), AdaBoost (3.47), DECORATE (3.15), and Bagging (2.48). The WELAD method is the best, as expected. RF, ERT, and GBDT are recently developed, well-known approaches that achieve the second-best results. The traditional ensemble learning methods AdaBoost, Bagging, and DECORATE with diverse factors perform relatively poorly.  
Figure 4: Boxplots of the performances of the compared algorithms on 9 selected datasets.  
Table 5 reflects the results of the Friedman test and shows whether an overall statistically significant difference exists between the mean ranks of related groups. Based on the Friedman test results in Table 5, a \(p\)-value from 0.000 to 0.05 indicates that the differences between the seven ensemble learning algorithms in this study are statistically significant. Then, we apply the nonparametric Wilcoxon signed-rank test to analyze the differences between paired ensemble learning algorithms.  
Table 5 shows whether statistically significant differences exist between the WELAD using the Wilcoxon signed-rank test and other ensemble learning methods. As shown in Table 6, the WELAD and the comparison group algorithms are statistically significantly different.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **WELAD-** & **WELAD-** & **WELAD-** & **WELAD-** & **WELAD-** & **WELAD-** \\  & **AdaBoost** & **Bigging** & **DECORATE** & **BERT** & **BERT** & **GBDT** & **RF** \\ \hline \(Z\) & \(-3.733\) & \(-4.350\) & \(-4.371\) & \(-2.376\) & \(-3.304\) & \(-3.219\) \\ Significance (two-tailed) & 0.000 & 0.000 & 0.000 & 0.018 & 0.002 & 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Wilcoxon signed-rank test.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset ID** & **AdaBoost** & **Bigging** & **DECORATE** & **ERT** & **GBDT** & **RF** & **WELAD** \\ \hline  
Mean Rank & 3.47 & 2.48 & 3.15 & 4.38 & 4.22 & 4.60 & **5.70** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ranks of the Friedman test.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number** & **Chi-Square** & **Degrees of Freedom (df)** & **Asymp. Sig. (\(p\)-Value)** \\
**(N)** & & & & \\ \hline
30 & 43.452 & 6 & 0.000 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Statistics of the Friedman test.  
In summary, the experimental results on these 30 sets of data show that the WELAD ensemble method proposed in this study has the highest accuracy and the best overall performance.
# Information-Centric Network-Based Vehicular Communications: Overview and Research Opportunities
## 5 ICN-Based VANET Research Opportunities
### ICN-Based VANET Security  
Numerous Internet security issues are mainly caused by data disruption at the application layer, which is primarily happened because of the IP--a best effort delivery but unreliable protocol. On the other hand, as ICN is a content-driven paradigm, content is published when it is requested. This approach curtails the transmission of unsolicited content objects and thereby automatically provides content security, which is achieved by using content names. In addition, ICN also affords malicious content filtering through in-network security policies [39]. Furthermore, ICN adds a point of indirection among the subscribers of requested contents, i.e., the content requests are assessed by in-network nodes before reaching the publisher [11]. As discussed above, coupling of ICN is possible with the Internet of vehicles, and this functionality offers opportunities for researchers to design an adaptable mechanism for their association.
## 4 Results and Discussion
### Verification of the Optimization Model  
The validity of the optimization model is an important process for the model performance measures. The percentage error (PE), as shown in Equation (19), was used to verify the result of the comparison between the profit of optimal solution and actual data [22,23].  
\[\text{Percentage Error.}=\frac{(\text{Predicted}-\text{Actual})}{\text{ Actual}}\times 100\% \tag{19}\]  
Figure 7 represents the number of houses for each type of actual data and the optimal solution or results of the optimization model. Then, as presented in Table 5, the profit/type data from the actual data (Table 4) were calculated with the number of houses for each type of the results from the optimization model, which is 211 for type one, 100 for type two, and 116 for type three. Therefore, the profit of the project was 967.36 MB, and that was very close to the project's profit of the actual data (963.97 MB). When verifying the model, it was found that the percentage error accounted for 0.35%, as shown in Table 5.  
Figure 6: Optimal solutions.  
Table 5 shows that the percentage error of the optimization model was 0.35%. The number is acceptable since it is within +/\(-\)10% [24].