esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation and Results
### Number of Collisions:  
Collision events, such as Reader-to-Reader Interference (RRI) and Reader-to-Tag Interference (RTI), significantly impact the performance and reliability of Radio Frequency Identification (RFID) systems. In our study, we propose a comprehensive formula to quantify the total number of collision occurrences within an RFID network across multiple simulation periods.  
The formula, denoted as  
\[\begin{array}{c}C=\sum_{i=1}^{P}\sum_{j=1}^{R}RRI(i,j,f)+RTI(i,j,f,ts)\\ RRI,RTI=\left\{\begin{array}{l}0\ \mathit{No}\ \mathit{Collision}\\ 1\ \mathit{Collision}\end{array}\right.\\ \left\{\begin{array}{l}RRI\ \mathit{Minimise}\\ RTI\ \mathit{Minimise}\end{array}\right.\end{array} \tag{2}\]  
captures the cumulative impact of RRI and RTI events over \(P\) simulation periods. Here, \(R\) represents the total number of readers in the system, and \(P\) signifies the number of simulation periods.  
The component \(RRI(i\_jf)\) indicates whether the jth reader at the ith period enters into an RRI-type collision using frequency \(f\). Similarly, \(RTI(i\_jf,ts)\) indicates the case of reader interference entering an RTI-type collision using frequency \(f\) and time slot \(ts\).  
The binary representation of collision events (0 for no collision, 1 for collision) facilitates the minimization of collision occurrences for both RRI and RTI scenarios. By minimizing collision events, the formula aids in optimizing system throughput and reliability, enhancing the overall performance of RFID deployments.
# Realizing Efficient Security and Privacy in IoT Networks
## 1 Introduction
* Using a mathematical model, an analysis of a novel transmission system where the eavesdroppers attack is tackled by injecting artificial noise which is transmitted by the receiver who has full-duplex aptitude with the same frequency in the channel, which reduces [PERSON]'s reception quality. * Proposal of a new jamming mitigation technique and developed a sequential convex estimation and optimization (SCEO) algorithm for an optimized and enhanced privacy to solve the optimization problem in a three-node network where network users do not have knowledge of [PERSON]'s channel state information (CSI). * The vulnerability of the proposed privacy enhancing scheme to [PERSON]'s increasing number of antennas is characterized and explored, while the performance of the proposed algorithm in a three-node network is established. * Finally, because privacy capacity in a three-node network have been over-studied by several authors, this research is expanded to cover a MIMOME scenario, justifying its applicability in secured IoT network transmission. The remainder of this paper is organized as follows. The privacy capacity model in wireless networks is described in Section 2. Review of recent works related to the research is presented in Section 3. System model is described in Section 4. Numerical analysis and results are provided in Section 5, and finally conclusions are outlined in Section 6.
# Dynamic Reconfiguration of Active Distribution Network Based on Improved Equilibrium Optimizer
## 5 Case Study and Result
### Basic Parameter  
The topology of the IEEE 33-bus distribution system [14] is shown in Figure 2. The system has a standard load value of 3175 kW, a rated voltage of 12.66 kV, and a reference power of 10 MW. The line length is calculated based on branch impedance conversion. DG units are connected to nodes 9, 18, 19, 21, and 32. Wind turbines with a rated power of 500 kW are connected to nodes 9, 18, and 21. These turbines have a cut-in wind speed of 3 m/s, a rated wind speed of 15 m/s, and a cut-off wind speed of 20 m/s. Nodes 19 and 32 are connected to photovoltaic units with a total array area of 7000 m\({}^{2}\) and a conversion efficiency of 19.8%. The wind speed \(v\) and solar intensity \(r\) for each time period are taken from reference [12], with a load forecasting error of \(\pm\)10%. The fluctuation range caused by forecasting errors in wind speed and light intensity is [0.9, 1.1] p.u. The total number of switch actions during the reconstruction period is limited to no more than 20, with no individual switch action occurring more than 4 times.  
The simulation environment is as follows: an Intel(r) CoreTM i5-7400T processor (Intel Corporation, Santa Clara, CA, USA) with a clock speed of 2.40 GHz and 8 GB of RAM. The relevant algorithms were implemented and validated using MATLAB R2021a. The specific algorithm parameters are as follows: the maximum number of iterations, _Max_Iter_, is set to 100, and the probability parameter \(GP\) is 0.5. To balance global and local search capabilities in the reconstruction process, the weight constant for global search, \(a_{1}\), is set to 2, and the weight constant for local search, \(a2\), is set to 1. Each algorithm was independently run 100 times in this study.
# Towards Automatic Depression Detection:
## 6 Results
### Results of Experiment 1  
In the first experiment, the performance of our method was compared with other methods on the DAIC-WoZ dataset. Because different methods adopt different types of features, we compared the proposed 1D CNN model with six other methods that only accept audio features. Similarly, we compared the proposed BiLSTM model with five methods that only accept text features. In addition, we compared our method with two traditional classifiers, namely SVM and decision tree. In the end, the performance of our proposed multi-model fusion network was compared with two methods that also accept both audio and text features. It should be noted that the data sampling method was only used to increase the size of the training samples in the minority class. Therefore, the different data sampling methods did not directly affect the performance of the methods to be compared. Besides, the data sampling step was only performed on the training set, not on the development set. During the evaluation, none of the methods performed any data sampling.  
From Table 3, it can be seen that, for the methods utilizing only one type of features, the methods based on the text features perform better than those based on the audio features in the both depression classification task and depression severity assessment task. Compared with the methods adopting only audio features, the proposed 1D CNN model yields the highest performance with an F1 score of 0.81 and an MAE of 4.25. Compared with the proposed 1D CNN model, the proposed BiLSTM model with an attention layer based on text features performs even better, with its F1 score equal to 0.83 and the MAE value equal to 3.88. Compared with other methods adopting only text features, the proposed BiLSTM model achieves the second best performance in the classification task, which is merely 0.01 worse than the best method in the F1 score and 0.58/0.98 worse in the MAE/RMSE values.  
Compared with the two proposed models that accept single modalities, the proposed multi-modal fusion method produces better results in both the classification and regression task (with an F1 score equal to 0.85 and an MAE equal to 3.75), which indicates that more information leads to better prediction performance. Compared with the other two methods, our method achieves the second best performance with merely 0.02 worse on the F1 score. However, our method has the best score for the recall metric, which is 0.92 and is much higher than other methods, for which the score of the recall metric is 0.83. This indicates that our method can find much more depressed patients than its counterparts with a comparative accuracy.
## 2 Materials and Methods
### Experiment Data  
#### 2.2.1 Canopy Hyperspectral Data Acquisition  
The hyper-spectrometer ASD FieldSpec 4 (ASD Inc., Boulder, Colorado, USA) was used to collect the canopy spectral data, with the spectrum ranging from 350-2500 nm. The measurements were conducted on sunny days from 10:00 am to 2:00 pm. A white reference panel (Chemical composition: BaSO4, Size: 25.4 \(\times\) 25.4 cm) was used to make relative radiometric correction before each measurement. The hyper-spectrometer was positioned above the WOR canopy at a height of 1 m with a 25\({}^{\circ}\) field-of-view of 0.1544 m\({}^{2}\) area. Five spectral measurements were averaged to reduce the uncertainty error of spectral measurement in every plot. In this study, the spectral waveband was set in the range of visible light and near-infrared shortwaves (400-1300 nm), with a high signal to noise ratio.
# Factors of the Technology Acceptance Model for Construction IT
## 2 Theoretical Background: Technology Acceptance  
### Technology Acceptance Model  
The most important aspect of information systems study and execution is IT acceptance and actual usage. Although companies have made significant investment in the progressive growth of hardware, software, and IT, a productivity paradox is occurring because of the limited understanding of IT usage, which requires urgent improvement [3]. The study of IT acceptance has gradually developed over the past 20 years.  
Some researchers have studied the relationship between the usefulness and ease of use of IT [4; 5; 6]. In particular, [PERSON] proposed TAM--a major contribution to the theory and study of IT usage and acceptance [5]. [PERSON] studied and analyzed why people do or do not accept information systems.  
The actual behavior of an individual is decided by behavioral intention, which is determined by the attitude and subjective norms, which in turn are determined by beliefs or other factors.  
The theory of reasoned action (TRA) and the theory of planned behavior (TPB) were predecessors of the TAM. [PERSON] suggested the TPB to extend the TRA to apply to situations in which behavior is not under the individual's control [7]. [PERSON] said that behavior is determined by three faiths: behavioral faith, subjective faith, and control faith [8].  
In this model, behavior is affected by some factors out of the individual's control. Therefore, we must consider how much the individual's will controls his or her behavior [9]. Thus, the value of how much behavior is under the individual's control--Called perceived behavioral control--is included as an additional factor influencing the behavioral intention in [PERSON]'s TPB.  
[PERSON] developed the TAM to explain IT users' behaviors and anticipate them based on the TRA, which came from the social psychology field [5]. Perceived usefulness and ease of use are specified as the main factors impacting attitude in the TAM, which utilizes only the relations among belief, attitude, intention, and behavior. All other external factors are supposed to influence intention and attitude indirectly through perceived usefulness and ease of use. Thus, the variables called perceived usefulness and perceived ease of use are the keys to TAM, as shown in Figure 1.  
The perceived usefulness of IT indicates how much users believe that using some kind of system improves their task achievement ability, and perceived ease of use indicates how much they believe that a specific system is easy to use.
# Identification of Industrial Heat Source Production Areas Based on SDGSAT-1 Thermal Infrared Imager
## 4 Discussion
### Comparison with Existing IHS Data

Table 7: Comparison of our results with [PERSON]’s and [PERSON]’s inventories. A total of 371 IHSs were identified from the IHSPA results by a manual identification method using high-resolution remote sensing images. A total of 371 IHS objects were compared with [PERSON]’s inventory based on active fire point data [21] and [PERSON]’s inventory based on night fires [3]. The accuracy and some area information of the IHSs were calculated. Figure 4: Comparison of our results with IHSs detected by [PERSON] et al. [3] and [PERSON] et al. [21]. The blue, green, and red polygon-labeled areas represent IHS objects detected by [PERSON], [PERSON], and our results, respectively. There are many more of our IHS objects (red polygons) and they cover a broader spatial layout, while the IHS objects in the other two datasets have high spatial overlap.
# A landslide Warning Method Based on K-Means-ResNet Fast Classification Model
## 4 Case Study: Shangmao Landslide  
### Landslide Overview  
The Shangmao landslide locates in Chihu Village, Hunan Province, China (Figure 13), which has an armchair shape. The front edge is about 45 m (vertical length) \(\times\) 62 m (horizontal length) \(\times\) 45 m (thickness), and the back edge is about 45 m (vertical length) \(\times\) 35 m (horizontal length) \(\times\) 60 m (thickness). This is a small soil landslide. The main sliding direction is 225\({}^{\circ}\). The slope started sliding in June 2017 after a strong rainfall. The landslide experienced the second failure in the 2019 flood season, the original crack formed a scarp, and the front edge of the retaining wall was displaced. The landslide body is mainly muddy siltstone, gravel and clay containing gravel.  
Similar to the Zhongma landslide, the upper soil layer of the landslide is affected by the infiltration of rainfall, which causes the landslide to start deformation acceleration. Rainfall is the main precipitating factor of landslide, so we must consider the rainfall factor when conducting early warning.
# Real-Time Detection Method for Center and Attitude Precise Positioning of Cross Laser-Pattern
## 4 Experiments and Results  
### Principle Verification Experiment on Cross Center Positioning Accuracy of Cross Laser-Pattern  
Since the center coordinates in theory of the cross laser-pattern cannot be obtained directly, the horizontal displacement measurement experiment was conducted to verify the positioning accuracy of the cross center indirectly. The image acquisition system was placed on a PI micro translation platform, and the accuracy of the translation platform is  
Figure 13: Schematic diagram of central line detection.  
\(\pm 1\) um. The experimental schematic diagram and the experimental process diagram are shown in Figure 14.  
The experiment data are shown in Table 1.  
From the table, the conversion coefficient is 0.0359, that is, one pixel is equivalent to 0.0359 mm in the world coordinate system. Then the horizontal displacement measurement experiment was conducted. Before the measurement, the image coordinate system X axis was adjusted to be parallel to the shifting axis of PI micro translation platform by rotating the image coordinate system, that is, when the image acquisition system moved horizontally with the PI micro translation platform, the vertical coordinates of the cross laser-pattern in the image coordinate system remained constant.  
After the calibration of conversion coefficient, the image acquisition system was ready to conduct displacement measurement experiment to verify cross center positioning accuracy. During the experiment, the cross laser was fixed as the measurement datum, and the PI micro translation stage provided displacement reference value to the image acquisition system, and with the measurement algorithm and conversion coefficient, the related displacement can be measured. The relationship between measurement error and displacement value is shown in Figure 15:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference Displacement/mm** & **X Value/Pixel** & **\(\Delta\)x Value/Pixel** \\ \hline
0 & 357.469 & — \\
5 & 497.567 & 140.098 \\
5 & 637.480 & 139.913 \\
5 & 777.376 & 139.896 \\
5  
After compensating the measured data according to the error relation curve shown in the figure, the results are shown in Figure 16:  
According to the experiment result, the accuracy of horizontal displacement measurement is better than \(\pm 3\)\(\upmu\)m, relative to the conversion coefficient of 0.0359, the center positioning accuracy is better than 0.2 pixels.
## 4 Conclusions  
The results of this study show that the adsorption of chlorinated organic compounds by silica gel and activated carbon can reach adsorption equilibrium in a short time. With increasing pollutant concentration, the equilibrium adsorption capacity of the adsorbent also increases, and the PCE removal efficiencies of silica gel and activated carbon are better than those of the TCE. According to the isotherm adsorption constant, the R\({}_{\rm L}\) value of the Langmuir constant is between 0 and 1 in the adsorption experiments of the two adsorbents on TCE and PCE. The values of n of the Freundlich constant are all greater than 1. Both results show that both adsorbents are favorable for the adsorption of TCE and PCE. In addition, the K\({}_{\rm F}\) value of the Freundlich constant of the TCE and PCE adsorbed by activated carbon is higher than that of silica gel. This result may be related to the fact that activated carbon has a higher specific surface area than silica gel, so its adsorption capacity is better.  
The data obtained from the actual adsorption experiments have less than 10% error between the removal efficiency and the adsorbent mass predicted by the Langmuir and Freundlich models. This means that both modes can be effectively used for the prediction of pollutant removal during the adsorption process. This result was substituted into the actual contaminated site for evaluation and then used to predict the required adsorbent quality and adsorption cost. Among them, the price of activated carbon is approximately 5-8 times higher than that of silica gel, and silica gel is inexpensive and easy to obtain. Therefore, in this study, silica gel was used to reduce the concentration of pollutants, and then activated carbon was added for adsorption to reduce the adsorption cost.  
It is estimated that the masses of silica gel and activated carbon required for the on-site remediation of the TCE in the RCA pollution incident in Taiwan are 6427 \(\pm\) 172 tons and 343 \(\pm\) 21 tons, respectively, and the total cost of the adsorbent is approximately 4,390,000 USD. The PCE requires 7553 \(\pm\) 57 tons of silica gel and 350 \(\pm\) 68 tons of activated carbon, resulting in a total cost of the adsorbents of approximately 5,030,000 USD.  
Conceptualization, H.-Y.C.; methodology, H.-Y.C. and I.-T.L.; software, I.-T.L.; validation, H.-Y.C.; formal analysis, I.-T.L.; investigation, I.-T.L.; resources, H.-Y.C.; data curation, I.-T.L.; writing--original draft preparation, I.-T.L.; writing--review and editing, H.-Y.C.; visualization, I.-T.L.; supervision, H.-Y.C.; funding acquisition, H.-Y.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by The National Science and Technology Council of the Republic of China (grant MOST111-2611-M-019-010).  
Not applicable.  
We should like to acknowledge the financial support of The National Science and Technology Council of the Republic of China (grant MOST111-2611-M-019-010). The authors are grateful to the reviewers for some important suggestions and providing some valuable references.  
The authors declare no conflict of interest.
###### Abstract  
In this paper, a comparison of signal analysis techniques for the diagnostics of rolling element bearings is carried out. Specifically, the comparison is performed in terms of fault detection, diagnosis and prognosis techniques with regards to the first rolling element bearing dataset released by NASA IMS Center in 2014. As for fault detection, it is obtained that RMS value, Kurtosis and Detectivity, as statistical parameters, are able to properly detect the arising of the fault on the defective bearings. Then, several signal processing techniques, such as deterministic/random signal separation, time-frequency and cyclostationary analyses are applied to perform fault diagnosis. Among these techniques, it is found that the combination of Cepstrum Pre-Whitening and Squared Envelope Spectrum, and Improved Envelope Spectrum, allow the faults to be correctly identified on specific bearing components. Finally, the Correlation, Monotonicity and Robustness of the previous statistical parameters are computed to identify the most accurate tools for bearing fault prognosis.  
vibrations; condition monitoring; rolling element bearings; signals; fault diagnosis +
Footnote †: journal: Applied  
0023
## 3 Development of the EMS and Obtained Results for a Specific Case Study
### Comparison of EMSs  
A varying scenario has been created to know which EMS is the best for this case study. Some EMSs have been optimized for specific conditions, but this does not mean that they are the best ones overall, considering the 4 different price profiles and the 30 power load profiles. The 120 possible combinations have been simulated for 5 days to compare the best three EMSs: the optimized GA-FL (EMS5), the adaptive Mamdani-type FL (Ad_Mandani_FL), and the adaptive Sugeno-type FL (Ad_Sugeno_FL).  
Figure 17 shows in what percentage of the 120 cases each EMS is the best option.  
The optimized FL is the best option in 43% of the cases, but this does not mean it is the best option overall. Moreover, from Figure 17, a comparison has been performed, but only between the adaptive Sugeno FL and EMS5 EMSs. The adaptive Sugeno FL EMS is the best in 57% of the cases, while the GA-FL EMS is the best in 43% of the cases.  
In addition, the distributions of the results have been analyzed seasonally. The results can be seen in Figure 18.  
Figure 17: Percentage of optimized GA-FL (EMS5), the adaptive Mamdani-type FL (Ad_Mandani_FL), and the adaptive Sugeno-type FL (Ad_Sugeno_FL) for the simulated scenarios.  
In these boxplots, the results of the best EMSs for the 30 different power load cases are shown seasonally separated. Each box shows the distribution of the 30 cases of each season's operational cost when that specific EMS is used.  
Regarding the results between the adaptative Mandani and Sugeno FLs, the distributions of spring and autumn are almost identical, but the adaptive Sugeno FL is cheaper in summer and winter overall. Thus, the adaptive Sugeno FL is the better EMS of the two.  
Regarding the results between the adaptive Sugeno FL and the EMS5, the conclusions are not so clear. The adaptive Sugeno FL performs better in autumn and winter, while the EMS5 is better in summer. In spring, the adaptive Sugeno FL results are distributed in a bigger range, while the EMS5 results are closer. The median of the results is slightly lower when the EMS5 is used, but the cheapest results of the adaptative Sugeno FL are cheaper than the ones of the EMS5.  
The analysis presented in this section demonstrate that the optimized FL can be improved using an adaptive approach.
# PUF and Chaotic Map-Based Authentication Protocol for Underwater Acoustic Networks
## 5 Informal Security Analysis
### Anonymity and Non-Traceability  
Our scheme provides user anonymity, as an adversary cannot obtain or eavesdrop on the user identity, \(id_{i}\), in the login and authentication phase because the identity, \(id_{i}\), is transferred in encrypted form by an encryption key \(K_{U-G}\) and \(GWN_{k}\) is a trusted entity. Meanwhile, the encryption key is generated randomly for every new session, so the message is dynamic for each session, and it is unable to distinguish between different users. Therefore, our scheme achieves user anonymity and cannot be traced.