esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Proposed Community Detection Method
### Sparse Subspace Clustering  
The SSC algorithm flow is as follows [27]: (1) The sparse coefficient matrix \(C\) is obtained by solving the sparse representation of the data set and normalized to construct the adjacency matrix \(\mathrm{W}=|C|+|C|^{T}\). The matrix \(|C|\) is the absolute value of the corresponding element of the matrix \(C\). (2) The degree matrix \(\mathrm{D}\) of adjacency matrix \(\mathrm{W}\) is then calculated, and normalized Laplacian matrix \(\mathrm{D}^{-1/2}\mathrm{L}\mathrm{D}^{-1/2}=\mathrm{D}^{-1/2}(\mathrm{D}- \mathrm{W})\mathrm{D}^{-1/2}\) is computed according to the degree matrix \(\mathrm{D}\) and the adjacency matrix \(\mathrm{W}\). (3) The \(k\) minimum eigenvalues of the Laplacian matrix are solved and the corresponding eigenvectors are normalized by row. The k column vectors obtained above constitute an \(\mathrm{n\times k}\)-dimensional matrix. Each k-dimensional row vector of the matrix corresponds to each original data point. (4) The \(\mathrm{n\times k}\)-dimensional matrix is clustered by spectral clustering algorithm, and the clustering results are obtained.  
The spectral clustering algorithm does not need to know the original information of the data, but clusters directly in the adjacency matrix of the sample, which reduces the complexity of the algorithm. To ensure the sparseness of the data point relationship in the weight graph, only the points with high similarity are connected when constructing the undirected weighted graph. Sample points whose similarity is less than the threshold are not adjacent, and the sample points are only related to the nearest points. Through the above analysis, combined with the feature extraction results in the third part, the SSC-based Microblog network user grouping process is shown in Figure 2.
# TCNformer Model for Photovoltaic Power Prediction
## 4 Experiment
### Prediction Performance of Different Models  
In this experiment, each model was trained five times in a 24-h (96 prediction steps) scenario, and the average value was taken. The final test set prediction results are shown in Table 5.  
As shown in Table 5, the TCNformer performs best according to the three indicators of the MSE, MAE, and MAPE. Compared with the time series prediction model Informer, the MSE, MAE, and MAPE decreased by 81.90%, 50.03%, and 14.98%, respectively. The training time (153.43 s) and running time (1.29 ms) of TCNformer are relatively long, but  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **LSTM** & **SkipGRU** & **Transformer** & **Informer** & **TCNformer** \\ \hline
1 & 0.1409 & 0.1272 & 0.2188 & 0.2273 & 0.0395 \\
8 & 0.6859 & 0.3554 & 0.3135 & 0.3623 & 0.1080 \\
16 & 0.9509 & 0.38  
considering the 15-min sampling granularity and 24-h prediction scenario, the training time and running time do not affect the practical application of TCNformer.  
As shown in Figure 10, this we visualized the prediction results of TCNformer using the test data set. The prediction results shown in the figure are 30 sets of 24-h prediction results, with little deviation when compared with the real data. It can be seen that TCNformer has a high level of accuracy and low number of errors.
# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## 2 RS Applications in Ocean
### Sea Ice (SI)
#### 2.2.2 TIR Radiometer  
The images acquired by TIR radiometers, which can be interpreted as an indication of the heat emitted by the surface, have been utilized for SI studies, including SI condition monitoring [86], SI surface temperature estimation [72], SI thickness modeling [70], and SI lead detection [71]. Furthermore, thermal bands have proved useful for other applications related to SI, such as wildlife monitoring in polar regions [87]. Among various TIR systems, MODIS and AVHRR instruments have been frequently used for SI monitoring. Although the application of TIR images, especially for SI thickness retrieval, has also been proved in multiple studies [88, 89], the major problem is still the cloud cover in TIR images [90]. Furthermore, the temperature of the newly formed thin SI is very close to the freezing water, which makes it difficult to be distinguished from the surrounding water. During summer, the melting surface of SI also has a temperature close to the freezing point and would be very similar to the surrounding water that is also in the freezing point [58, 67].
## 2 Related Works  
In recent years, the focus on PLS has intensified, particularly concerning untrusted relay networks. Traditional approaches have primarily employed higher-layer encryption techniques to secure communications, but these methods are increasingly being complemented or replaced by PLS strategies. PLS leverages the inherent properties of wireless channels, such as noise and fading, to ensure that data remains secure irrespective of an eavesdropper's computational power. This section reviews recent advancements in related areas, highlighting key contributions and identifying gaps our study aims to address.
## 1 Introduction
Adding of ground plane between them or adjusting the placement of via to conduct away the electromagnetic field are some examples. Irrespective of the methods employed, a method to evaluate the EMI in the stack before fabrication of the 3D stack dies will be necessary. Near-field (NF) measurement is the basic for the detection of EMI at IC level. In the work of [24], the correlation between near field scanning and electrostatic discharge susceptibility measurement of the CPU IC is studied, which is an initial study for the application of NF measurement. In another paper [25], a procedure is shown to characterize radiated EMI and nearby conducting objects using near-field scanning measurements. [PERSON] et al. [26] proposed a model from near-field measurement to calculate the magnitude and phase of the dipoles. In the work of [27], [PERSON] showed that near field scanning can be utilized to calculate far field from the IC. ICs are often the primary source of radiated emissions, and near field magnetic field can help engineers to track down EMI culprit and solve the problems with a robust IC design [28]. With the well-known rapid decrease of the intensity of EM wave with the distance [29], if the source for high EMI is from the die in the middle of the stack of a 3D-IC, such near field measurement will not be able to pinpoint the source of high EMI. Therefore, a computational method is required, and if the computation can be performed before the fabrication of the dies, the cost and time for re-design can be saved significantly, and it is this necessity that forms the motivation of this work. While the equation of the intensity of electromagnetic field strength versus distance is well known, the 3D distribution of the field strength in the presence of various materials within each die, as well as the 2D distribution of electromagnetic field over a given circuit on a die, are to be determined in order to evaluate the EMI. This work proposes a simulation method from a given GDSII file for such evaluation, and we aim to provide the evaluation before the fabrication of the 3D-IC. The example studied in this work on the stacking of power amplifier ICs is expected to be the future, and research is being done on the thermal management [30; 31; 32; 33]. While thermal management research is being considered, the impact of electromagnetic interference (EMI) among the stack dies has not been studied. This work represents the first of its kind. Advancements in the functionalities and operating frequencies of integrated circuits (IC) have led to the necessity of measuring their EMI. The stacking of power amplifier chips in this work can be considered as an extreme case so as to bring out the important of the EMI consideration in stack dies more vividly, and it is also a good example of generalization of ICs with multi functionality of communication, high performance, and high frequency IC technology. The methodology developed here can be applied easily to other cases of stack dies.
# Privacy-Preserving Byzantine-Resilient Swarm Learning for E-Healthcare
## 2 Related Work
### Blockchain-Based Federated Learning and Swarm Learning  
By heavily relying on a single server, traditional FL cannot protect against a single point of failure and resist malicious attacks. As the infrastructure of Bitcoin, blockchain has attracted a lot of attention because of its decentralization and reliability. Several blockchain-based FL protocols [44; 45; 46; 47] replace servers with blockchain, enabling privacy protection and enhancing trust among parties. BAFLE [44] uses blockchain to avoid single points of failure and aggregates local models using smart contracts (SCs). Similarly, through blockchain smart contracts, BlockFL [46] exchanges and validates local model updates. By providing incentives associated with training samples, BlockFL can integrate more equipment and more training samples. Meanwhile, as a state-of-the-art BCFL, swarm learning (SL) [13; 48] combines edge computing, blockchain networking, and federated learning while maintaining confidentiality without a central coordinator. In the e-health field, SL has been successfully applied to the diagnosis of COVID-19, tuberculosis, leukemia and lung diseases [13], skin lesion classification fairness [49], genomics data sharing [50], and risk prediction of cardiovascular events [51].  
However, the above schemes impose heavy computation and communication costs on the blockchain nodes and cannot distinguish the malicious gradients. To address these issues, the blockchain-based federated learning framework with committee consensus (BFLC) [45] can effectively reduce consensus computing and prevent malicious attacks through innovative committee consensus mechanisms.  
Finally, Table 1 shows a comparison of our PBSL scheme with the various FL schemes described above. The results show that our scheme can not only resist poisoning attacks and be robust to users dropping out, but also has high privacy and computational efficiency. The compared results point out that PBSL can not only resist Byzantine attacks and be robust to parties dropping out, but also protect privacy and improve computing efficiency.
# Novel Applications of Microbial Fuel Cells in Sensors and Biosensors
## 4 MFC-Based Biosensors for BOD Detection
Investigations addressed to identify versatile and highly active electrogenic microorganisms, capable of effectively oxidizing various types of organic substances, is currently considered as the most viable approach to enhance the performance and scope of MFC-based sensors for BOD detection [143]. BOD control is of particular interest for the monitoring of microbial activity and pollutants in groundwater [144]. As most of the monitoring approaches are often costly, time consuming, and not applicable to the subsurface environment [145], MFC BOD sensors offer rapid, simple, non-invasive, and cost-effective solutions. With this aim, [PERSON] et al. designed a novel submersible microbial fuel cell [131]. The cell was used as a self-powered sensor for in situ monitoring of BOD and microbial activity of groundwater. Using real contaminated groundwater, the cell detected microbial activity and BOD in less than 3 hours, with deviations ranging from 15% to 22% and 6% to 16%, respectively. More recently, [PERSON] et al. developed an MFC-based BOD sensor with long-term stability using MnO\({}_{2}\) catalysts [146]. Specifically, manganese dioxide was used as a novel, innovative, and low costing cathode to reduce MFC production costs, aiming towards possible scale-up applications. The cell was tested using both sodium acetate solution and real domestic wastewater. BOD values predicted by the biosensor were in agreement with the BOD\({}_{5}\) values, with slight variations in the range from 3% to 12%. The MFC BOD biosensor at issue was demonstrated to be stable for over 1.5 years.
## 3 Numerical Results
### Deformation Characteristics under Different Rotation Rates  
This section presents the equivalent stress and radial displacement curves of \(\rm A_{e}\), \(\rm B_{e}\), and \(\rm D_{e}\) nodes in the simulated feed experiment at different rotational speeds, and it conducts a preliminary analysis of the relationship between the changes in rotational speed and the equivalent stress and radial displacement. It was proposed to use the feed performance efficiency to evaluate the energy absorption efficiency of the roller.  
Figures 9 and 10 show the equivalent stress and radial displacement changes of nodes \(\rm A_{e}\), \(\rm B_{e}\), and \(\rm D_{e}\) at different linear speeds (100-320 mm/s) at a compression rate of 15%. The figure shows that under a 15% compression rate and a linear velocity range of 100 mm/s to 320 mm/s, the change in linear velocity had no significant effect on the equivalent stress and radial displacement of the \(\rm A_{e}\) node. There were minor differences among them, such as small fluctuations occurring within the 200\({}^{\circ}\)-225\({}^{\circ}\) range of the radial displacement curve of \(\rm A_{e}\) and \(\rm D_{e}\) nodes and the 137.5\({}^{\circ}\)-156\({}^{\circ}\) range of \(\rm B_{e}\) nodes.  
In order to evaluate the feed performance of roller honeycomb better, an evaluation index of feed performance efficiency (EF) was proposed, which can represent the node's performance \(\rm\alpha\)-C to \(\rm\beta\)-R, the energy absorption characteristics per unit area, and per unit time during the process. Figure 11 shows the variation of node EF at different linear speeds (100-320 mm/s) for nodes \(\rm A_{e}\), \(\rm B_{e}\), and \(\rm D_{e}\) at a compression rate of 15%. From the figure, it can be seen that under the condition of a 15% compression rate and a linear speed range of 100 mm/s to 320 mm/s, the EF also increased with the increase in rotational speed. Due to the limited numerical values of the experimental conditions, the EF curve in Figure 11 does not exhibit extreme values.  
Figure 9: Equivalent stress changes of nodes at different linear speeds (100–320 mm/s): (**a**) \(\rm A_{e}\) node. (**b**) \(\rm B_{e}\) node. (**c**) \(\rm D_{e}\) node.
# Error Correction of Water Vapor Radiometers for VLBI Observations in Deep-Space Networks
## 3 Error Source Analysis and Calibration
### Absolute Calibrations  
It takes approximately 30 min for the system to reach temperature control after each re-energization. With a stable temperature, it is necessary to calibrate the entire system once a month, or, preferably, before each mission. The overall calibration can eliminate most of the errors of the radiometer hardware system at one time. The water vapor radiometer mainly uses an improved dumping method developed independently. Liquid nitrogen calibration, which can be performed in any weather, was not selected because the antenna was too large to design a convenient and feasible source of liquid nitrogen calibration. Tipping calibration is an important technique for the absolute calibration of ground-based microwave radiometers [1]. A new tilt calibration method for ground-based microwave radiometers has been improved to eliminate the radiation from cloudy liquid water and to effectively calibrate the radiometers under cloudy sky conditions. Since the inclination calibration part has been published in [10], we will not go into details here. Through extensive testing, we have verified the absolute calibration accuracy of the water vapor radiometer. The accuracy of the brightness temperature measurement can reach 0.2 K, even when the calibration is performed on a cloudy day.  
Since the absolute calibration can only guarantee the accuracy for a short time after the calibration is completed, in order to ensure a long-term stable measurement accuracy, the influence of the receiver gain, antenna system, and environmental factors must be considered. The following is a set of data. Tipping calibration was performed on the evening of 4 March 2017, followed by a 15-day observation experiment. The object observed on March 5 was a black body at room temperature, which was used to determine the accuracy of the brightness temperature measurement during the day. As shown in Figure 3, the brightness temperature measured by the water vapor radiometer deviated from the measured blackbody brightness temperature when the ambient temperature changed. As can be seen from the figure, the difference between the measured value and the theoretical true value is quite small (approximately 0.2 K) when observing the blackbody reference source from early morning to 7:00 a.m., but, after 7:00 a.m., as the sun rises (change in ambient temperature), the measured value gradually deviates from the theoretical true value, reaching a maximum value of 6 K at 9:30 a.m., and then the error gradually decreases. Figure 4 shows the relationship between the measurement error and the ambient temperature, and shows a clear correlation. Thus, the measurement error of the brightness temperature increases with the change in ambient temperature. The reason is that the change in ambient temperature will lead to fluctuations in the receiver gain and an increase in the antenna noise temperature. Later, we will discuss the errors caused by these cases and the approaches to eliminate them.
# Research on Interference Mechanism of 25 Hz Phase Sensitive Track Equipment from Unbalanced Current
## 2 Cause Analysis of Abnormal Red Ribbon
### Disturbance Mechanism Caused by Grounding Wire  
During the daily maintenance work on the track circuit site, in order to ensure the safety of the maintenance personnel, it is necessary to construct an absolute non-electric zone with the earth as the zero point, so the grounding wire should be installed on the relevant equipment. However, in the process of track circuit section maintenance, the grounding wire is often accompanied by induction discharge. The large current generated on the track causes the unbalanced current to invade the track, then interferes with the signal in the track circuit.  
The essence of the disturbance of the track circuit caused by the grounding wire is to change the impedance of one side of the track, resulting in the impedance imbalance of the two rails. When the catenary is overhauled, it is necessary to hang the grounding wire on the catenary to the single-sided grounding track, which is equivalent to the catenary in parallel to the single-sided grounding track. This will reduce the equivalent impedance  
Figure 2: Magnetic field coupling in adjacent orbit.  
of the rail of the single-sided grounding track, and the potential difference between the two rails will cause the voltage of the rail to fluctuate, causing interference to the track receiving the end equipment and causing misoperation. At the same time, part of the traction current in the non-working section will flow into the working section through the rail, resulting in an increase in the reflow imbalance between the two rails, increasing the degree of interference with the track circuit equipment.
# Voice Assistant Application for Avoiding Sedentarism in Elderly People Based on IoT Technologies
## 3 Proposed System
### Architecture  
The architecture developed, shown in Figure 2, is divided between the elements that act in the local area network (the so-called edge), composed of those with which the end user interacts directly, and on the other hand, the services that run in the cloud, which will resolve the requests made by the different users that make use of the system.  
* Intelligent voice assistant: A device with an integrated intelligent assistant, additionally, it could have an LCD screen on which informative images can be displayed. It constitutes the main communication interface between the user and the system, allowing all interaction between the user and the software to be carried out by voice, making it as transparent and fluid as possible. This assistant communicates with the cloud services in order to complete the requested actions carried out by the user.
* Edge computing device: It consists of an SBC (Single Board Computer) that will be located in the home of the senior, next to a VA. It is a device with enough computational capabilities to run the programs responsible for monitoring the smart bracelet (explained below) via Bluetooth Low Energy (BLE) and allowing interaction with the user through the VA when it detects that this is close. It is able to communicate with cloud services through the Internet.
* BLE smart bracelet: Bluetooth smart bracelet to be worn by the user, with the capability to monitor the physical activity performed throughout the day. This will serve both to monitor in a nonintrusive way if the user is close to the ECD and the VA, and to periodically collect information on the user's physical activity (number of steps,calories burned, heart rate, oxygen saturation, etc.). In this hardware, it will be necessary to take into account the power consumption of the device since some of these sensors have a high energy consumption.
* Cloud services: Services that will allow communication between the VA and the ECD through the Internet. They consist of conversational agents and an NoSQL real-time database. This service will be responsible, on the one hand, for monitoring the gamification tasks related to the progress of the user and challenges, and on the other hand, for interacting with the ECD, which will proactively notify the user's progress or the achievement of the challenge. Moreover, it allows the storage of a history of the user's physical activity for further analysis.
* Conversational agents: Software agent implemented as an application for VAs--under the name EMERITI--which, through the intelligent VA, allows users to set the challenges they consider appropriate and check, when requested, their daily progress through a conversation.
# Implementation of MS Circle Map in Digital Image Encryption
## 2 Materials and Methods
### Encryption and Decryption Algorithms
Endfor i
```  
**Algorithm 1**Image Encryption Process  
Next, to decrypt the image, the process is the reverse of the image encryption process as shown in Figure 4 and can be implemented using Algorithm 2. The decryption process was carried out by using the same key parameter values as those used during the encryption  
Figure 4: Digital image decryption process based on MS Circle Map. process, and the cipher image became input data. All steps carried out in Algorithm 2 are the same as in Algorithm 1. The only difference is the decryption process in step 11, which applies Equation (6). \[D_{ij}=C_{ij}\bigoplus KS_{n} \tag{6}\]  
where \(KS_{n}\) is the \(n\)th key in the keystream \(KS\), and \(C_{ij}\) and \(D_{ij}\) are the pixel values of the cipher image and decrypted image, respectively. ```
Input: parameters \(x_{0},\;\lambda,\Omega,\;K,\;r,t\) and plain image \((C_{ij})\) Output: Encrypted Image \((D_{ij})\)
1. Enter all the parameter values of MS Circle Map and cipher image \((C_{ij})\) with size \(M\times N\)
2. \(n=0\)
3. While \(n\leq t\)do
4. Calculate \(x_{n+1}\) use Equation (4)
5. \(n=n+1\)
6. Endwhile
7. For i = 1 to M
8. For j = 1 to N
9. Calculate \(x_{n+1}\) use Equation (4)
10. \(KS_{n}=\lfloor x_{n+1}\times 10^{6}\rfloor\;mod\;256\)
11. \(D_{ij}=C_{ij}\oplus KS_{n}\)
12. \(n=n+1\)
13. Endfor j
14. Endfor i
```  
**Algorithm 2**Image Decryption Process