esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Prediction Models  
Since belt failure records can be considered as a multivariate time series and the current state of a particular failure is expected to be related to previous states, two models were considered for this study.  
Initially, the ARIMA (AutoRegressive Integrated Moving Average) model was considered due to its widespread application in time series forecasting [10; 11]. ARIMA is particularly effective for modeling univariate time series that exhibit non-stationary behavior, which can be addressed through differencing techniques [33]. The model comprises the following three key components: an autoregressive (AR) part that captures the influence  
Figure 5: Schematic view of the motor current spectrum with belt failure.  
Figure 6: Transformation of electrical current signals into time-series belt fault frequency features via spectral decomposition.  
of past values, an integrated (I) part that accounts for the level of differencing required to achieve stationarity, and a moving average (MA) part that models the impact of past forecast errors. These are denoted by the parameters \(p\), \(d\), and \(q\), respectively. When non-stationarity is detected in the data, differencing is applied to stabilize the mean and remove trends, thus transforming the series into a stationary form suitable for further analysis [34].  
Subsequently, a deep learning model was considered, particularly an LSTM model [12, 13, 35]. A sequence of two years of failure component records was used to predict its future evolution.  
The values of the hyperparameters were experimentally optimized. Tables 4 and 5 present the parameters used for each model.  
For both the LSTM and ARIMA models, they utilized a time series of input data representing the historical performance of the belt system. Each data point in the time series represents a specific measurement of the belt failure frequency over time. The goal of both models was to predict the future performance of the electric motor based on the input data.  
For ARIMA, a series of iterations was conducted to discover the optimal values of \(p\), \(d\), and \(q\), and the autocorrelation plots and model diagnostic tests were performed to ensure that the model was well-constructed. Similarly, for LSTM, the same process was carried out, starting with its parameterization and various tests on the correct percentage division into training and testing data until reaching the parameterization described above.  
In summary, both the LSTM and ARIMA models underwent rigorous testing and parameter optimization to ensure accurate predictions of the electric motor's performance. The details of these experiments and the results obtained are discussed in the following sections.
# Comparison of Thermal Response for RF Exposure in Human and Rat Models
## 2 Models and Methods
### Computational Models
#### 2.2.1 SAR Computation  
The FDTD method was used to compute the RF power absorbed in the computational human head and rat models [30]. Convolutional perfectly matched layers (CPML) were used as the absorbing boundary conditions for absorbing outgoing scattered waves to simulate an infinite space [31]. The dielectric properties of each type of tissue were determined with a Cole-Cole dispersion model in both rat and human models [32]. The variability caused by different sets of dielectric properties has been shown to be marginal [33]. The SAR was defined for sinusoidal waves as follows:  
\[SAR(\mathbf{r})=\frac{\sigma(\mathbf{r})}{2\rho(\mathbf{r})}\ |\mathbf{E}( \mathbf{r})|^{2}, \tag{1}\]  
where \(|\mathbf{E}|\) denotes the temporal peak value of the electric field at position \(\mathbf{r}\), and the parameters \(\sigma\) and \(\rho\) denote the conductivity and mass density of the tissue, respectively.  
The SAR was averaged over 10 g of tissue in a cubic shape following the IEEE standard [34]. Even though the SAR is not used at frequencies above 6 GHz by the IEEE standard [2], the same metric was used at 10 GHz for a proper comparison. The 10-g SAR averaged in a single contiguous tissue following the ICNIRP guidelines [1] was difficult to use in the rat model, because the rat skin weight over the whole body was 32 g.
## 2 Materials
### Data
The multi-temporal Sentinel-2 images and the virtual Sentinel-2 image were also used as inputs for crop classification. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Date** & **Availability** & **Date** & **Availability** \\ \hline
3 June & \(\times\) & 3 July & \(\times\) \\
5 June & \(\Circarrow\) & 5 July & \(\times\) \\
8 June & \(\times\) & 8 July & \(\times\) \\
10 June & \(\blacklozenge\) & 10 July & \(\times\) \\
13 June & \(\times\) & 13 July & \(\blacklozenge\) \\
15 June & \(\times\) & 15 July & \(\times\) \\
18 June & \(\times\) & 18 July & \(\times\) \\
20 June & \(\times\) & 20 July & \(\times\) \\
23 June & \(\times\) & 23 July & \(\Circarrow\) \\
25 June & \(\blacklozenge\) & 25 July & \(\times\) \\
28 June & \(\Circarrow\) & 28 July & \(\times\) \\
30 June & \(\times\) & 30 July & \(\blacklozenge\) * \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of the availability of cloud-free Sentinel-2 images in the study area from June to July 2019 (\(\times\): cloud-free image unavailable; \(\Circarrow\): cloud-free image available; \(\blacklozenge\): cloud-free image used for the experiment; *: target image for virtual image generation). Figure 2: Temporal profiles of NDVI values for corn and soybean in 2019. C-band (5.4 GHz) Sentinel-1 SAR images with dual-polarization modes (VV and VH) were also collected for the virtual Sentinel-2 image generation. The Sentinel-1A image in an ascending mode on 29 July was finally selected to be input into the CGAN model in consideration of the target date established for virtual image generation (i.e., 30 July). The level-1 Ground Range Detected (GRD) high-resolution product with a pixel size of 10 m in the interferometric wide (IW) swath mode was downloaded from the Copernicus Open Access Hub [59]. Several preprocessing procedures, including the update of the orbit state vectors, thermal noise removal, border noise removal, radiometric calibration, speckle filtering, range Doppler terrain correction, and projection, were employed using the Sentinel Application Platform (SNAP) software [60]. After preprocessing, the calibrated backscattering coefficients in the dB unit for both VV and VH polarizations were finally prepared. As the geometric errors between the Sentinel-1 and -2 images affect the predictive performance of virtual image generation, the geometric correction of the Sentinel-1 image was implemented using ground control points manually extracted from the Sentinel-2 image. The CDL in 2019 in Figure 1 was used as the ground truth data for the extraction of crop parcels and crop classification. The original 30 m CDL data were resampled to 10 m using nearest neighbor resampling to match the spatial resolution of the Sentinel images. All of the land-cover types in the study area were reclassified into six classes: corn, soybean, wheat, alfalfa, double cropping of winter wheat and soybean, and non-crop. As crop classification is the main objective, water, built-up, and forest classes were merged as the non-crop class. The CDL data were used to extract training and test samples for crop classification.
# Implementation of a Lossless Moving Target Defense Mechanism
## 7 Performance Evaluation
### Numerical Experiment
\(T^{modify}(R)\) can be determined using  
\[T^{modify}(R)=R\cdot T^{mod}, \tag{8}\]  
Taking into account the modification times presented in [61], Formula (8) can be simplified to the form  
\[T^{modify}(R)=90\left\lceil\frac{R}{25}\right\rceil, \tag{9}\]  
Meanwhile, the time required to modify tables (denoted as \(T^{prepare}(k,b,c)\)) in all the switches of the considered network, which is composed of \(h\) hosts connected to each border switch, \(b\) border switches, and \(c\) core switches, is  
\[T^{prepare}(k,b,c)=b\cdot T^{modify}(k)+b\cdot T^{modify}(k(b-1))+c\cdot T^{ modify}((b\cdot k)^{2}) \tag{10}\]  
In Figure 11, the changes in the time necessary for updating the flow tables in all network switches are presented. It was assumed that each border switch was connected to 24 hosts. Meanwhile, the ratio of the number of core hosts to the number of border switches was 1:5 (similar to the comparison presented in Figure 10). As can be observed, the time required to prepare the switches for the change in IP address generation increases exponentially with the increase in the number of switches (and consequently with the increase in the number of hosts) from a few seconds to 3 h. It should be noted that this solution was not optimized in this respect. Figure 11: Comparison of switch preparation time for different number of border and core switches.
# Cross-Regional Pavement Temperature Prediction Using Transfer Learning and Random Forest
## 2 Methodology
### Random Forest Models  
Random forest (RF) is a regression method that enhances prediction accuracy and model robustness by integrating multiple decision trees [32]. The bootstrap sampling method with replacement is used to generate multiple subsets from the original training data. Each decision tree is trained on a different subset to enhance the generalization ability. During the construction of each tree, the Classification and Regression Trees (CART) algorithm is adopted. At each node, a random subset of meteorological features is selected for optimal splitting. This helps reduce the impact of feature correlation on model performance. Finally, the predictions from all decision trees are averaged to produce the final temperature prediction. In this study, RF was applied to develop a pavement temperature prediction model. The model development and application were carried out on the MATLAB R2019a platform. Meteorological factors were used as input variables, and pavement temperatures at different structural depths were set as the target outputs. An integrated regression model was built using the _fitrenemble_ function, with the bagging strategy applied. Regression trees were selected as base learners. The number of learning rounds and tree structure parameters were set to train a stable prediction model. Figure 1a presents the workflow of the random forest model.
# A Survey on Unmanned Surface Vehicles for Disaster Robotics: Main Challenges and Directions
## 4 Use of USVs in Environmental Disasters with Contaminants
### Chemical Hazards
#### 4.1.2 Oil Spill Tracking  
Traditionally, oil slick location and trajectory are tracked using remote sensors on satellites and aircraft, such as ultraviolet, infrared, visible band, radar or passive microwave sensors [137]. Monitoring and tracking such pollutants is crucial to creating trajectory models to predict their expansion. Such understanding is essential when creating an action plan to minimize the damage caused by the oil spill over wider areas and over time.  
[PERSON] et al. [109; 110] describe a project (www.naoe.eng.osaka-u.ac.jp/~kato/project/en/) regarding detection of gas and oil spills in the period of 2011 to 2015 involving a SOTAB-I UUV, an underwater buoy robot, and SOTAB-II USVs, floating buoy robots. In this project, they perform autonomous tracking and monitoring of spilled plumes of oil and gas using the SOTAB-I connected to multiple SOTAB-II USVs. While the SOTAB-I collects rough data on physical and chemical characteristics of plumes, consisting of spilled out oil and gas, SOTAB-II USVs track the spilled oil on the sea surface. Also using SOTAB-I and SOTAB-II, [PERSON] et al. [111; 112] perform experiments in Biwa Lake and Osaka Bay, Japan, to verify the effectiveness of sail and sail control for tracking oil plumes. Experiments indicate that once the buoy is dropped into the oil slick drifting on the sea surface, it sets out to drift with the oil slick using the wind force by effectively controlling the sail size and direction. In 2015, [PERSON] et al. [113; 114; 138] changed SOTAB-II to a yacht-shaped USV that can track spilled oil on the sea surface, using data supplied by onboard sensors to control rudder angle and sail area for navigation. The new USV takes advantage of the wind to move on the oil spill surface.  
[PERSON] et al. [117] perform plume tracking using fluorometers sensors. The authors performed experiments in Oahu, Hawaii, using an in-house twin-hull catamaran USV, in a site diverse enough to capture most environmental conditions that typically affect the development of a plume. Data concerning the plume dispersion were collected to improve tests in simulators, such as the one previously tested by [PERSON] et al. [115].
# Techno-Economic Analysis of a Solar Thermal Plant for Large-Scale Water Pasteurization
## 2 Experimental Pasteurization Tests
The steep initial temperature increase corresponds to the rapid pre-heating in the thermostatic bath, followed by the pasteurization in the oven at nearly constant temperature and by the fast cooling to ambient conditions. Notice that, in the experiments, the average pasteurization temperature is kept for at least 582 s at \(\approx\)71 \({}^{\circ}\)C, 185 s at \(\approx\)76 \({}^{\circ}\)C, 60 s at \(\approx\)81 \({}^{\circ}\)C and 23 s at \(\approx\)85 \({}^{\circ}\)C, therefore achieving the target treatment time with some tolerance. The bacterial load (in terms of total coliforms and Escherichia coli) is provided according to the Most Probable Number (MPN [29]) method using a Colliert Quanti-Tray 2000 assay (IDEXX). Figure 1: **(Left)** Overview of the equipment for the experimental tests: (1) aluminum test tubes and tailor-made 3D-printed holder (detail of the CAD drawing in the inset); (2) thermostatic water bath (CORIO CD-300F, Julabo); (3) industrial furnace for thermostatic applications (TypeM120-VF, MPM Instruments); (4) laptop for data acquisition; (5) digital hot plate stirrer (AREX Digital, VELP Scientific) for sanitation of the test tubes after each test. **(Right)** Temperature of the samples during the disinfection tests for the four considered pasteurization protocols, namely 540 s at 70 \({}^{\circ}\)C, 114 s at 75 \({}^{\circ}\)C, 30 s at 80 \({}^{\circ}\)C and 8 s at 85 \({}^{\circ}\)C. The solid lines show the mean temperature obtained for each pasteurization protocol (four repetitions each), while the related transparent bands the range between the minimum and maximum values. Starting from an initial bacterial load in the raw water samples (taken from the Po river in Torino) of 2419.6 and 143.9 MPN of total coliforms and Escherichia coli per 100 mL, respectively, the results of the microbiological analysis on the treated samples show no residual bacteria in all cases (up to the detection limit of the tests, i.e., \(<\)1 MPN/100 mL). Therefore, each pasteurization protocol confirms its effectiveness in eliminating water pathogens. In the following, we adopt the 114 s at 75 \({}^{\circ}\)C protocol for the model design of the plant, since it represents a good compromise between the required temperature and resident time to be realized in a solar thermal plant.
## 4 Simulation Results and Discussion  
In this section, two sets of simulation results are given to evaluate the effectiveness of the proposed MTDC method, the target peaks resolution method and the target peaks range inversion method based on the B3I signal of four BDS satellites and the MIMO system. The system parameters and target parameters are listed in Tables 1 and 2. The geometry schematic diagram of the simulation is plotted in Figure 6, where the receiver was set as airborne-based to better reflect the Doppler frequency difference of each target during GNSS-R detection. During the 10 s pulse accumulation time in simulation 2, we set the receiver plane to move horizontally and at a constant speed according to the given direction and speed, and the ship does not change its track. The influence of the Doppler and distance values instigated by the aircraft movement can be compensated for by the known aircraft movement parameters combined with the geometric model. Additionally, we set the linear delay compensation to be once per pulse period to remove the delay effect caused by ship motion.
# Challenges in Physical Layer Security for Visible Light Communication Systems
## 5 Conclusions  
This article argued that existing physical layer security (PLS) techniques for visible light communication (VLC) systems are still susceptible to various eavesdropping attacks. We showed that, in contrast to radio frequency (RF) systems, the design features of VLC systems and the inherent characteristics of the VLC transceiver components enable the eavesdropper to improve its wiretapping capability and overcome several existing PLS strategies. Furthermore, the presence of active (or registered) and passive eavesdroppers was shown to be a security threat that a VLC system designer must seek to mitigate. To provide secure transmission for the legitimate user, it is necessary to clearly understand and utilize VLC and RF systems' intrinsic characteristics and improve/develop PLS techniques that can cope with any intelligent attempts that eavesdroppers may make.  
Moreover, VLC is viewed as a complementary technology to other RF wireless communication technologies, such as Wi-Fi, mobile networks, mmWave communications, etc., rather than a replacement of others. Hence, both technologies are likely to be synergistically combined in a single device to meet the demand for future wireless applications. In this sense, combining VLC and RF and simultaneously exploiting the inherently different characteristics of RF and VLC channels may significantly enhance secrecy performances.  
Conceptualization, S.C., G.C. and J.P.C.; methodology, S.C., G.C. and J.P.C.; software, S.C., G.C. and J.P.C.; validation, S.C., G.C., J.P.C. and P.X.; formal analysis, S.C. and G.C.; investigation, S.C., G.C. and J.P.C.; writing--original draft preparation, S.C.; writing--review & editing, G.C., J.P.C. and P.X.; supervision, J.P.C. and P.X.; funding acquisition, P.X. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# A Hierarchical Blockchain-Based Trust Measurement Method for Drone Cluster Nodes
## 3 Simulation and Results  
### Construction of the UAV Simulation Experiment Environment Based on the NS3 Platform  
The network topology of the UAV cluster built on the NS3 platform is shown in Figure 6, where different numbers are used to identify different drone nodes. Based on this experiment, the network performance data of the UAV cluster on a secondary blockchain are obtained, and the specific simulation parameters are shown in Table 2.  
1. Number of drones in each cluster: A UAV cluster in three simulated airspaces is selected, with 10 UAVs in each airspace, and the communication requirements among UAVs in the cluster are considered.
2. Data transmission rate: To simulate the rate of data transmission, we set the appropriate data transmission rate. According to the communication technology and transmission requirements used in the UAV cluster, we set the data rate of 11 Mbps transmission per second to ensure the reliability of the data transmission performance of the UAV cluster.
3. Routing protocol: To transmit data between drones, the ad hoc on-demand distance vector (AODV) routing protocol is selected as the communication protocol. AODV  
Figure 6: UAV cluster topology.  
is a commonly used wireless ad hoc network routing protocol, which is suitable for the dynamic network environment in UAV clusters. At the same time, the wireless device is set to AdhocWifiMac, and the networking mode of the drone cluster is set to ad-hoc, which is more in line with the data networking behavior of real drones.
4. Wireless scenario: Using the appropriate wireless WIFI scene in the simulation model, to simulate the real environment of the wireless communication environment, the GroupMobilityModel mobile model and FriisPropagationLossModel path loss model are chosen to accurately describe the wireless transmission characteristics between the unmanned aerial vehicles (UAVs). GroupMobilityModel is a group movement model that is used to simulate simulation nodes to form groups and carry out cooperative movement according to certain rules, and can simulate the cooperative task movement of UAV clusters. The FriisPropagationLossModel fries propagation loss model, also called the free space propagation loss model, is a kind based on free space transmission theory, suitable for no obstacle of the simple path loss model of the open space environment, which will simulate UAV cluster signal propagation loss in a three-dimensional space.
5. Communication distance: In the simulation, we set the communication distance between drones. This is determined according to the communication technology and scenario requirements of the UAV. We set the communication range of each UAV to 50 m to simulate the communication distance in the real environment; when the communication distance is too large, the communication efficiency will rapidly decline.
# Provably Secure ECC-Based Anonymous Authentication and Key Agreement for IoT
## 3 Security Analysis of [PERSON] et al.'s Scheme [24]
### No Resistance to ESL Attacks  
An AKA protocol is designed to resist an ESL attack, meaning that even if all the session-specific information of the entities in a session is compromised, the secrecy of the session key would remain uncompromised. During the authentication process, once the ephemeral secrets \(r_{i}\) and \(r_{j}\) are compromised, \(\mathcal{A}\) can compromise the session key \(SK_{ij}\) or \(SK_{ji}\) by the following steps:  
A1: \(\mathcal{A}\) obtains the messages \(MSG_{1}=\{R_{i},TS_{i}\}\), \(MSG_{2}=\{R_{j},V_{j},T_{SP_{j}},TS_{j}\}\) and \(MSG_{3}=\{B_{i},C_{i},TS_{i}^{\prime}\}\) by eavesdropping via the open channels;  
A2: \(\mathcal{A}\) extracts \(TS_{i}\), \(T_{SP_{j}}\), \(TS_{j}\), \(B_{i}\) and \(TS_{i}^{\prime}\) from the messages, then \(\mathcal{A}\) calculates \(S_{i}=h(r_{i}\|TS_{i})\cdot(T_{SP_{j}}+h(T_{SP_{j}}\|ID_{SP_{j}})\cdot T_{pub})\);  
A3: For \(S_{i}=S_{j}\), \(\mathcal{A}\) gets \((ID_{SM_{i}}\|T_{SM_{i}})=B_{i}\oplus h(S_{i}\|TS_{i}^{\prime})\) then calculates \(U_{j}=h(r_{j}\|TS_{j})\cdot(T_{SM_{i}}+h(T_{SM_{i}}\|ID_{SM_{i}})\cdot T_{pub})\).  
A4: For \(A_{i}=U_{j}\), \(\mathcal{A}\) calculates \(SK_{ij}=h(A_{i}\|S_{i}\|ID_{SM_{i}}\|ID_{SP_{j}})\).