esa-sceva/satcom-chunk-collection huggingface dataset:
# A Tool for Pre-Operational Daily Mapping of Floods and Permanent Water Using Sentinel-1 Data
## 3 Methods  
In the following, the main functionalities of AUTOWADE, schematically shown in Figure 3, are described in detail.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Instrument** & \multicolumn{2}{c}{**Data**} & \multicolumn{1}{c}{**Location**} \\ \hline Sentinel-1 & S1B\_IW\_GRDH\_1 SDV\_202009271T12230\_20200927T172255\_023564\_02 CC5D\_1886 & pre-flood & \\  & S1A\_IW\_GRDH\_1 SDV\_20201003T172311\_20201003T172336\_034635\_040884\_7989 & flood image & \\ \hline Sentinel-2 & S2A\_MSII2A\_20200928T102031\_N0214\_R065\_T32 TMR\_20200928T131819 & pre-flood & \\  & S2B\_MSII2A\_20201003T101759\_N0214\_R065\_T32 TMR\_20201003T145325 & flood image & \\ \hline Sentinel-1 & S1A\_IW\_GRDH\_1 SDV\_20210115T051146\_20210115T051211\_036144\_03 CD9\_7753 & pre-flood & \\  & SIA\_IW\_GRDH\_1 SDV\_20210127T051146\_20210127T051211\_036319\_0442F1\_EF15 & flood image & \\ \hline Sentinel-2 & S2A\_MSII2A\_20201221T100431\_N0214\_R122\_T33 TUH\_20201221T115632 & pre-flood & \\ \hline \hline \end{tabular}
\end{table}
Table 4: S1-GRD slices and S2-L2A tiles used in this study for flooded vegetation mapping.  
Figure 3: Block diagram of the AUTOWADE’s main functionalities.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**CEMS Activation Code** & \multicolumn{2}{c}{**Sentinel-1 Data**} \\ \hline EMSR496 & \begin{tabular}{c} S1A\_IW\_GRDH\_1 SDV\_20210115T051146\_20210115T051211\_036144\_043 CD9\_7753 \\ SIA\_IW\_GRDH\_1 SDV\_20210127T051146\_20210127T051211\_036319\_0442F1\_EF15 \\ \hline EMSR501 & \
\begin{tabular}{c} S1B\_IW\_GRDH\_1 SDV\_20201214T163224\_2021214T163249\_024701\_02F011\_8A13 \\ S1B\_IW\_GRDH\_1 SDV\_20210212T163221\_20210212T163246\_025576\_030 CD\_87F3 \\ \hline EMSR502 &
\begin{tabular}{c} S1B\_IW\_GRDH\_1 SDV\_20210217T182208\_20210217T182233\_025650\_030 EBA\_Fb05 \\ SIA\_IW\_GRDH\_1 SDV\_20210223T182257\_20210223T182322\_036721\_0450 DC\_EC43 \\ \end{tabular} & pre-flood \\ \hline \hline \end{tabular}
\end{table}
Table 3: \(Cont.\)
# Electrically Tunable Hydrogen-Bonded Liquid Crystal Phase Control Device
## 3 Results and Discussions
In addition, an exact circular polarized output was obtained at \(R=\lambda/4\) in the case of LC1. However, the polarization of the output terahertz wave became ellipsoidal in the case of E44 because of its dichroism at 2.5 THz. These results indicate that a phase control device using LC1 was almost operating ideally at \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Liquid Crystal** & \(f\) **(THz)** & \(\Delta n\) & \(n_{e}^{*}\) & \(\alpha_{e}\) **(cm\({}^{-1}\))** & \(n_{o}^{*}\) & \(\alpha_{o}\) **(cm\({}^{-1}\))** & \(\Delta\epsilon^{6}\) \\ \hline LC1 & 2.5 & 0.17 & 0.035 & 37 & 0.035 & 37 & 0.6 (1 kHz) \\ E44 1 & 2.5 & 0.2 & 0.022 & 23.5 & 0.06 & 64.1 & 16.8 (1 kHz) \\ K15 2 & 2.5 & 0.165 & 0.0159 & 16.9 & 0.0379 & 40.4 & 20.07 (1 kHz) \\ Er 3 & 1 & 0.14 & 0.015 & 3 & 0.035 & 7 & 13.8 (1 kHz) \\ NJU-LDn-4 4 & 1.6 & 0.314 & 0.0526 & 12 & 0.0352 & 18 & 6.01 (1 kHz) \\
1825 5 & 2.5 & 0.371 & 0.0363 & 19 & 0.0262 & 13.7 & 17.0 (1.5 kHz) \\ \hline \hline \end{tabular} \({}^{1}\) Results from Reference [33]. \({}^{2}\) Results from Reference [8]. \({}^{3}\) Results from Reference [22]. \({}^{4}\) Results from Reference [32]. \({}^{5}\) Results from Reference [12]. \({}^{6}\)\(\Delta\varepsilon=\varepsilon_{//}-\varepsilon_{\perp}\).  
\end{table} 
Table 1: Birefringence, absorption coefficients, and dielectric anisotropy of LC1. For comparison, reported values of various LCs are given. Figure 7: Simulation results of the polarization conditions of a transmitted terahertz wave. We set the absorption values for (**a**) zero (no loss), (**b**) LC1, and (**c**) E44. Retardation of the LC device was \(\lambda\), \(\lambda/2\), and \(\lambda/4\). 2.5 THz because there was no dichroism. We believe that the hydrogen-bonded LC will be particularly useful in future LC-based terahertz devices.
# Signal Quality Assessment for Transdermal Optical Wireless Communications under Pointing Errors +
## Appendix A  
According to Equation (18), the instantaneous SNR is a random variable (RV) that follows the same distribution as \(h_{p}^{2}\). Therefore, to derive the average SNR of the optical link, we first need to identify the distribution of \(h_{p}^{2}\).  
The CDF of \(h_{p}^{2}\) can be obtained as  
\[F_{h_{p}^{2}}\left(x\right)=P\left(h_{p}^{2}\leq x\right)=P\left(h_{p}\leq \sqrt{x}\right)=P\left(h_{p}\leq\sqrt{x}\right)=F_{h_{p}}\left(\sqrt{x}\right),\]which, by using Equation (11), can be rewritten as  
\[F_{h_{p}^{2}}\left(x\right)=\left\{\begin{array}{cc}\frac{1}{A_{0}^{2}}x^{ \frac{\xi}{2}}&0\leq x\leq A_{0}^{2}\\ 1,&x\geq A_{0}^{2}\end{array}\right., \tag{11}\]  
while the PDF of \(h_{p}^{2}\) can be obtained as  
\[f_{h_{p}^{2}}\left(x\right)=\frac{\mathrm{d}F_{h_{p}^{2}}\left(x\right)}{ \mathrm{d}x}=\frac{\xi}{2A_{0}^{\xi}}x^{\frac{\xi}{2}-1}. \tag{12}\]  
The average SNR can be defined as \(\hat{\gamma}=\mathbb{E}[\gamma]\), which by using Equations (18) and (12), can be written as  
\[\hat{\gamma}=\frac{R^{2}\exp\left(-\alpha(\lambda)\delta\right)\bar{P}_{s}}{2 |\epsilon|RP_{b}+2|\epsilon|I_{\mathrm{DC}}+N_{0}}\frac{\xi}{2A_{0}^{\xi}} \int_{0}^{A_{0}^{2}}x^{\frac{\xi}{2}}\mathrm{d}x. \tag{13}\]  
By performing the integration, Equation (13) can equivalently be written as Equation (19). This concludes the proof.
## 2 VDES Data Link Credibility Monitoring Approach
### Radio Interference Detection Based on Message Characteristics
#### 2.1.1 VDES Message Integrity Verification Method Based on the MD5 Algorithm
A graphical user interface is created using C# and the WPF framework to facilitate communication between the server and client, with the verification results depicted in Figure 19:3

As shown in Figure 26, with the listening port set to 123, the client and server display specific message information for transmission and reception in their respective message exchange areas: \"I:ABTDB,1,1,0,A,403t? ]lvLohh<JAJH=Tdp500D0G,0\"6E\" and \"SAIVEM,1,1,0,A,403t? ]lvLoLAFbV4 vF:dsG00D0<,0\"7D\". This signifies successful communication between the client and server, validating the functionality of the VDES communication module. #### 3.1.2 VDES Message Processing Module

The VDES message processing module accomplishes the parsing and encapsulation of VDES messages. VDES message parsing involves dissecting received messages to extract various field parameters. Conversely, VDES message encapsulation packages outgoing information in a fixed data format to facilitate data exchange and communication between systems. (1) VDES Message Encapsulation

The process of VDES message encapsulation is illustrated in Figure 19:4 The VDES message encapsulation module transforms outgoing information into messages that include cyclic redundancy check (CRC) codes, following the ITU-R M.2092 international standard. These messages consist of a header, payload data, and a checksum, along with the message length, VDES channel number, and additional relevant information. The payload data contains encoded navigational safety-related information. The VDES checksum is a two-digit hexadecimal number calculated by CRC to ensure the integrity and accuracy of the received messages. As depicted in Figure 27, the VDES message encapsulation submodule initially inputs data such as the Maritime Mobile Service Identity (MMSI) code, Coordinated Universal Time (UTC), and latitude-longitude coordinates into the VDES message encapsulation information exchange interface. The DecimalToBinary() function is then invoked to convert decimal numbers into binary, followed by partitioning the binary numbers into groups of six bits each. In cases where the data points are less than six bits, zero-padding is applied to reach the required length. Subsequently, using a conversion table that maps six-bit binary to ASCII characters, each group of binary data is transformed into the corresponding ASCII code. Finally, the CalculateChecksum() function is called to compute the message checksum, completing the encapsulation process. The pseudocode implementation of the VDES message encapsulation algorithm utilized in this paper is presented in Algorithm 2. Figure 19:5 Flowchart for VDES message encapsulation.
## 5 Energy-Efficient Resource Matching Algorithm for D2D Relay Communications
### Implementation  
In the real system, the BS is a major work component of the implementation of the proposed algorithm. At the beginning, the transmitters send some packets with detection signals, and then, the BS would obtain the feedback of the CSIfrom each terminal, i.e., D2D and cellular users. After that, the four-dimensional matching process would be conducted at the BS. Finally, after the matching process ends, the control signals would be forwarded to each node, and there is no signaling interaction among the network nodes during the process of matching.
# Automated Real-Time Assessment of Stay-Cable Serviceability Using Smart Sensors
## 3 Automated Real-Time Cable Serviceability Assessment System Using Wireless Smart Sensors
The MEMS accelerometer with the ADC sensor can be installed in the mid-span of the stay-cable to measure the vibration of the cable, and these sensing devices are connected to the GPIO headers of the Raspberry Pi model through a jump cable. The software layer is designed depending on the functions of the gateway and leaf nodes. _BT Chat_ ([[https://play.google.com/store/apps/details?id=com.hardcopy.btchat&hl=ko](https://play.google.com/store/apps/details?id=com.hardcopy.btchat&hl=ko)]([https://play.google.com/store/apps/details?id=com.hardcopy.btchat&hl=ko](https://play.google.com/store/apps/details?id=com.hardcopy.btchat&hl=ko))), which is a free mobile software application used to implement Bluetooth connections between available devices, is installed in the gateway node. When the gateway node is connected to the leaf node through Bluetooth, it commands the leaf node to assess the serviceability level. After the leaf node carries out the assessment, the gateway node receives the status of serviceability from it wirelessly. Inspectors can monitor the serviceability level of a subject cable automatically and in real time using BT Chat, which communicates the maximum amplitude of the cable vibration and a warning message when serviceability is a failure. In the case of leaf nodes, the software algorithm is implemented using Python and embedded to the Raspberry Pi 3 Model B+ running on a Linux operating system. In detail, the software of the leaf node works as follows: when the gateway node commands the leaf node to start monitoring, the leaf node first measures the vibration of the cable in the mid-span using the MEMS accelerometer with an ADC sensor at a sampling rate of 100 Hz. It should be noted that in this study, the sampling time was set to 5 s to assess the serviceability, which can be changed depending on the users' requirements. Subsequently, the acceleration measured for 5 s is converted into displacement using the displacement estimation algorithm, and the leaf node evaluates whether the  
Figure 2: Design of wireless automated real-time cable serviceability assessment system. maximum amplitude of the estimated displacement exceeds the serviceability criterion which is twice the diameter of the target cable provided by the United States [16]. Thereafter, the leaf node transmits the results, i.e., the maximum displacement and serviceability status, to the gateway node through Bluetooth communication. As mentioned, this system is designed to monitor the serviceability level of the stay-cable every 5 s. The procedure for monitoring the serviceability is graphically described in Figure 3.
# Sillcom: A Communication-Efficient Privacy-Preserving Scheme for Indoor Localization
## 7 Efficiency and Security Analysis
### Cost Analysis
#### 7.1.2 Computation Cost  
We also performed a detailed analysis of the computational overhead. First, during the symmetric \(\Pi_{SED}\) computation phase, each server needs to compute three matrix-vector multiplications of an \(m\times n\) matrix and an \(n\)-length vector, resulting in a total of \(3 nm\) ring multiplications. In fact, optimized vector multiplication can be performed very quickly on  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scheme** & **Module** & **Rounds** & **P2** & **P0** & **P1** \\ \hline \multirow{4}{*}{\(\text{Sillcom}_{3R}\)} & \(\Pi_{SED}\) & 0 & 0/0 & 0/0 & 0/0 \\  & \(\Pi_{SC}\) & Merged & \(0/2 ml\) & 0/0 & 0/0 \\  & \(\Pi_{Shuffle}\) & 2 & \(10 ml/0\) & 0/ \(2 ml\) & 0/ \(2 ml\) \\  & \(\Pi_{rank}\) & 1 & \(m(m-1)\cdot k^{<}/0\) & \(0/ml_{r}\) & \(0/ml_{r}\) \\ \hline \multirow{4}{*}{\(\text{Sillcom}_{trd}\)} & \(\Pi_{SED}\) & 0 & 0/0 & 0/0 & 0/0 \\  & \(\Pi_{SC}\) & Merged & \(0/2(m+k\frac{m}{m^{2}})l\) & 0/0 & 0/0 \\ \cline{1-1}  & \(\Pi_{Shuffle}\) & \(2+2\) & \(10(m+k\frac{m}{m^{2}})l/0\) & 0/ \(2(m+k\frac{m}{m^{2}})l\) & 0/ \(2(m+k\frac{m}{m^{2}})l\) \\ \cline{1-1}  & \(\Pi_{rank}\) & \(1+1\) & \(\approx(mm^{\prime}+(\frac{m}{m^{2}})^{2})\cdot k^{<}/0\) & \(0/(m+k\frac{m}{m^{2}})l_{r}\) & \(0/(m+k\frac{m}{m^{2}})l_{r}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Communication cost of \(\text{Sillcom}\).  
modern CPUs. Next, the main computation involved in \(\Pi_{SC}\) and \(\Pi_{Shuffle}\) is the shuffling of an \(m\)-length vector. Experimental results in [49] show that the amortized cost of one memory access for large-size vectors is approximately 31 ns. However, it is important to note that, when shuffling relatively smaller-size vectors, cache optimizations can be effectively leveraged, reducing the amortized cost of one memory access to just a few nanoseconds. This makes the computation time in these two modules much smaller than the impact of communication latency on the end-to-end time.  
The computational overhead of Sillcom is primarily concentrated during the secure comparison phase when computing the DDCF keys using fixed-key AES. In Sillcom\({}_{3R}\), during the online phase, both P0 and P1 need to compute \(2m(m-1)l\) instances of fixed-key AES. Similar to the case with communication overhead, an increase in \(m\) will gradually cause the computational overhead of computing DDCF keys to dominate the end-to-end time. Therefore, in Sillcom\({}_{trd}\), we can exchange a small amount of communication for a reduction in the computational bottleneck by a factor of \(\frac{m}{m^{\prime}}\), resulting in a tradeoff that allows the user to achieve faster query times.
# Optimizing Energy Efficiency in Cloud Data Centers:
## 4 System Model
### Power Model and QoS Metrics
#### 4.1.2 QoS Metrics  
A service level agreement (SLA) is a contract between an IT service provider and a customer or client that outlines the services to be provided and the expected quality of service. In cloud computing, SLA breaches occur when the service provider fails to meet the agreed-upon terms. SLAs are critical as they establish specific security standards for managing and storing data. To ensure compliance with the SLA, certain quality indicators must be clearly defined, measured, analyzed, and regularly monitored. If the service provider fails to fulfill their obligations, penalties may be applied. SLAs are typically used to define quality of service criteria, which are essential in cloud computing, and may include limitations such as maximum response time or minimum throughput time provided by the deployed system. In [25], the authors defined SLA violation using two metrics in the IaaS model: SLA\({}_{TAH}\) and PDM.  
SLA\({}_{TAH}\) (SLA violation time per active host): Represents the percentage of time in which servers experienced 100% CPU utilization. The function is shown in the equation below:  
\[\text{SLA}_{TAH}=\sum_{i=1}^{N}\frac{\text{T}_{si}}{\text{T}_{ai}} \tag{5}\]  
where \(N\) is the number of physical machines (i.e., servers), T\({}_{si}\) is the total time of experiencing 100% CPU utilization by server \(i\), resulting in an SLA violation, and T\({}_{ai}\) is the total time of server \(i\) being in the active state.  
PDM: The overall performance degradation by virtual machines due to live migrations (PDM) is shown in the equation below:  
\[\text{PDM}=\frac{1}{M}\sum_{j=0}^{Q}\frac{\text{P}_{dj}}{\text{P}_{rj}} \tag{6}\]  
where \(M\) is the number of virtual machines, \(P_{dj}\) is the estimate of performance degradation of virtual machine caused by migrations, and \(P_{rj}\) is the total CPU capacity requested by the virtual machine j during its life time. We set \(P_{dj}\) as 10% of CPU utilization in MIPS during all live migrations of virtual machine \(j\). We used the combined metric involving performance degradation due both to overloaded servers and to VM migrations. \(SLA_{V}\) (SLA violation) is computed as follows.  
\[SLA_{V}=SLA_{TAH}\times\text{PDM} \tag{7}\]
## 4 WSN Clustering
### A Low-Energy Adaptive Clustering Structure  
A lightweight yet low-energy adaptive clustering architecture is the most common energy-efficient routing protocol proposed in WSNs [72]. In LEACH, CH is selected from many rotating nodes to distribute the energy consumption. It is divided into two phases: the configuration phase and the stationary phase.  
When a node enters the configuration phase, it decides whether to become a CH for the current round or not. The decision also depends on the suggested CHs by considering if it has been a CH node in the previous rounds. An integer between 0 and 1 is chosen at random. If the value is less than the threshold, it will be considered a cluster leader node. The chosen CH broadcasts messages to the entire network. The nodes will decide which cluster to join and which affiliation message to transmit based on the signal power received from the chosen CH. To make better use of available energy, the role of CH has been reversed.  
In the stationary phase, nodes were discovered. The collected data from the notes are sent to the appropriate CH for accumulation and transmitted directly to the BS. The use of TDMA/CDMA MAC will prevent collisions. Because of the distributed nature of LEACH's technique, it does not require global information.  
Figure 6: Classification of energy-efficient routing protocols.
## 4 Performance Evaluation and Comparison
### Evaluation Metrics
#### 4.1.6 Confusion Matrix  
An integral aspect of our performance evaluation entails the utilization of a confusion matrix. This tabular representation allows for a succinct summary of the classification model's performance by displaying the count of true positive (TP), false positive (FP), true negative (TN), and false negative (FN) predictions. The confusion matrix acts as a critical tool in acquiring a comprehensive understanding of the model's capabilities, identifying its strengths, and highlighting potential areas for improvement.  
The confusion matrix is typically presented in the following format Table 1.  
Through an in-depth analysis of the values within the confusion matrix, we can derive various evaluation metrics, including accuracy, precision, recall, and F1-score, which enable us to glean valuable insights into the model's performance for each class.  
In the forthcoming sections, we embark on a detailed exposition of the results attained through the evaluation metrics and confusion matrices for our LSTM-based IDS, leveraging the CICIDS2017, NSL-KDD, and UNSW-NB15 datasets. This comprehensive analysis serves as a validation of the efficacy and adaptability of our model in precisely detecting network intrusions across a diverse array of real-world scenarios.
# Damage Identification Method Using Additional Virtual Mass Based on Damage Sparsity
## 4 Numerical Simulation of Simply Supported Beam and Space Truss
### Simply Supported Beam Model
#### 4.1.4 Susceptibility to Noise  
As mentioned in Section 4.1.1, the damaged substructures are the 3 rd, 5 th, and 8 th, and both the methods could select the damaged substructures. So, the stability of the IOMP method to noise is verified by comparing the order and identification results of damaged substructures under different level noise. The order and identification result of selected damaged substructures with the IOMP method based on the residual variance criterion is almost same as that one of the sensitivity correlation criterion. Therefore, taking IOMP(S) as representative. The various noise level is 5%, 10%, 20%, and 30% rms. The results are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Substructure** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline OMP & 0.5087 & 1 & 0.4675 & 1 & 1 & 1 & 1 & 1 & 0.9531 & 0.7076 \\ \hline IOMP(S) & 0.5049 & 1 & 0.5658 & 0.6860 & 1 & 1 & 1 & 1 & 1 & 0.6076 \\ \hline Actual value & 1 & 1 & 0.7 & 1 & 0.8 & 1 & 1 & 0.6 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Identification results of OMP and IOMP(S) method with nonlinearity iteration.  
Figure 8: Contrast with sparsity in simply supported beam simulation.  
As known from Table 2, the identification results and selected order of damaged substructures are worse with noise increasing and that one of IOMP(S) is always better than OMP, which confirms that the susceptibility to noise of the IOMP methods is better. When the noise is in low level like 5% rms, the OMP method is accurate. However, the IOMP method has good results even in the noise level of 30% rms.
## 3 System Model
### Threat Model and Security Assumptions
#### 3.2.1 Attacker Capabilities and Objectives  
We consider two primary attack vectors within our threat model:  
* Availability Attacks: Adversaries may exploit unknown vulnerabilities within executors to manipulate their operational behavior, causing them to produce erroneous  
Figure 2: Proposed polymorphic heterogeneous security architecture.  
computation results or control outputs. The ultimate objective of such attacks is to compromise system availability by inducing inconsistent or harmful control actions. The attacker possesses the capability to infiltrate individual executors but faces the challenge of compromising a sufficient number of heterogeneous executors simultaneously to overcome the arbitration mechanism.
* Privacy Attacks: We assume sophisticated adversaries can mount side-channel attacks, memory analysis techniques, or advanced persistent threats (APTs) against the arbitration and scheduling module. While unable to alter the module's functionality, these attacks aim to extract sensitive information processed during arbitration, potentially exposing proprietary algorithms, control parameters, operational data, or system state information that could be leveraged for future attacks.