esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhanced Multicast Repair Fast Reroute Mechanism for Smart Sensors IoT and Network Infrastructure
## 2 The Fast ReRoute
### Precomputation Approach of Fast ReRoute  
A key feature common to Fast ReRoute mechanisms is that they calculate the backup path in advance and therefore offer faster network recovery [32; 33]. The precalculated backup path in the FRR terminology is also referred to as a precomputed backup path [34; 35]. To ensure correct network recovery, the backup path cannot pass through the failure point. Depending on the FRR mechanism, a given router may also calculate several backup paths. When calculating and installing a pre-calculated alternative route, each router decides independent of other routers. The principle of precalculated alternative routes is currently used by all FRR mechanisms. This proactive approach is an important factor in minimizing the time required for fast network recovery after failures [32].
###### Abstract  
Reversible data hiding (RDH) is a recently emerged research domain in the field of information security domain with broad applications in medical images and meta-data handling in the cloud. The amount of data required to handle the healthcare sector has exponentially increased due to the increase in the population. Medical images and various reports such as discharge summaries and diagnosis reports are the most common data in the healthcare sector. The RDH schemes are widely explored to embed the medical reports in the medical image instead of sending them as separate files. The receiver can extract the clinical reports and recover the original medical image for further diagnosis. This manuscript proposes an approach that uses a new lossless compression-based RDH scheme that creates vacant room for data hiding. The proposed scheme uses run-length encoding and a modified Elias gamma encoding scheme on higher-order bit planes for lossless compression. The conventional Elias gamma encoding process is modified in the proposed method to embed some additional data bits during the encoding process itself. The revised approach ensures a high embedding rate and lossless recovery of medical images at the receiver side. The experimental study is conducted on both natural images and medical images. The average embedding rate from the proposed scheme for the medical images is 0.75 bits per pixel. The scheme achieved a 0 bit error rate during image recovery and data extraction. The experimental study shows that the newly introduced scheme performs better when compared with the existing RDH schemes.  
medical image transmission; clinical reports; reversible data hiding; bit plane compression; healthcare data Article
## 4 Results
### System with PV Panels  
The main issue concerning CO\({}_{2}\) emissions is the demand for electrical energy, which reaches 11,790 kWh annually for the installation with an electric heater (AHP + EH), while for the installation with a gas boiler (AHP + GB), this value is only 62.2% of the above value, i.e., 7328 kWh. Assuming that the demand for electrical energy would be covered by an additional photovoltaic (PV) installation designated solely for the purposes of the heat pump (excluding the demand for electrical energy for building-lighting and other purposes such as induction stoves, refrigerators, washing machines, dryers, etc.), the size of the PV installation can be roughly determined, assuming 1 kWp for every 1300 kWh of demand. Thus, the size of the PV installations for the respective systems can be estimated at about 15.5 kWp for the AHP + EH system, and about 9.5 kWp for the AHP + GB system. Figures 5 and 6 show the schematics of the analyzed systems.  
Figure 4: The annual CO\({}_{2}\) emissions for each installation variant.  
The estimated installation costs for these variants are 14,800 Euros for the AHP + EH system, and 9500 Euros for the AHP + GB system. The installation of a PV system would eliminate costs associated with electricity charges, which amount to 2947 Euros per year for the AHP + EH system, and 1832 Euros per year for the AHP + GB system. This means that the investment in PV panels would pay for itself after about 7.0 years for the variant with the electric heater. A similar payback period, estimated at about 6 years, is expected for the installation equipped with the GB gas boiler, as calculated from the cash flows (Table 5). This indicates that, in both cases, after approximately 6-7 years, the operating costs of the system will reduce to 0 Euros for the AHP + EH system, which represents a 100% reduction in annual bills, and to 489 Euros for the AHP + GB system, which represents a 79% reduction in annual bills (remaining costs are associated with the gas fuel needed for the gas boiler).  
After an average operational period of about 15 years for this type of installation, it is estimated that the final savings amount to \(\copyright\)23,516 for the AHP + EH installation, and \(\copyright\)20,668 for the AHP + GB gas boiler installation.  
Figure 5: Diagram of the heat pump system (AHP) with an electric heater (EH) cooperating with a photovoltaic installation (PV), where: 1—building installations; 2—heat pump (AHP); 3—electrical grid; 4—electric heater (EH); 5—inverter; 6—photovoltaic panels (PV); 7—electric power for the heat pump (NAHP); 8—electric power for the electric heater (EHM).  
Figure 6: Diagram of the cooperative system between the heat pump (AHP) and the gas boiler (GB) in a bivalent system, where: 1—building installations; 2—heat pump (AHP); 3—electrical grid; 4—gas boiler (GB); 5—inverter; 6—photovoltaic panels (PV); 7—electric power for the heat pump (NAHP).
## 3 Base Station Deployment and Self-Optimization Positioning Method
### Self-Optimization Positioning Method for Base Stations
#### 3.3.4 Judgment Logic  
According to the code displayed in Algorithm 1, the current moment's ranging information dd and the previous moment's coordinates \(point_{t-1}\) are inputted as the basis for determining which area the current location is in. Initially, it is checked whether the shortest ranging distance \(d\_min\) to the current moment's coordinates is less than \(x_{a}\). If so, it is determined that the location is within the C-type transition area, and the AreaC function is called. If not, the dist function is called to first identify the coordinates of the two base station nodes corresponding to the shortest ranging distance at the current moment. Based on the previous moment's coordinates \(point_{t-1}\) (\(\mathrm{x_{t-1}}\), \(\mathrm{y_{t-1}}\)), the perpendicular distance _dis_ to the line connecting the two base station nodes is calculated. If the distance _dis_ is less than 0.5 m, it is determined that the location is within the B-type transition area, and the AreaB function is called. Otherwise, it is determined that the location is within the A-type central area, and the AreaA function is called. After determining the area, subsequent weighted average coordinate calculations are carried out according to the procedures specific to each area.  
```
1:Input: Distance set \(d\), Time \(t-1\) coordinate set \(point_{t-1}\)
2:Output:Time \(t\) coordinate set \(point_{t}\)
3:\(d\_min\leftarrow\min(d)\)
4:If d_min < \(\mathrm{x_{a}}\)
5:AreaC;
6:else
7:\(Dis\leftarrow\) dist(\(d\), \(point_{t-1}\))
8:If dis < 0.5
9:AreaB;
10:else
11:AreaA;
12:End if
13:End if
```  
**Algorithm 1** Base station self optimization algorithm  
In terms of algorithm complexity, this algorithm only adds classification judgment; thus, compared to traditional algorithms, the time complexity has not changed, and it can also meet computing power requirements with common hardware.
# Image Watermarking between Conventional and Learning-Based Techniques: A Literature Review
## 2 Image Watermarking Background
### Transmission Phase
#### 2.3.1 Unintentional Attacks  
Any processing or manipulation performed by a non-malicious user is classified as an unintentional attack. The goal of these treatments is to damage the watermark by modifying or hiding some characteristics of the image but not to remove the mark. We will content ourselves to provide the most frequently encountered examples.  
**Compression:** Compression attacks are designed to reduce the amount of data encoded in the host document. This process consists of removing the perceptibly less important components while preserving the important components. JPEG and JPEG2000 are considered the most popular image compression algorithms currently in use [42].  
**Filtering:** Generally, filtering is used for noise reduction. This type of filtering basically has the effect of attenuating the high-frequency components of the image and therefore degrading the brand components inserted in these frequencies.  
**Volumetric transformations:** The basic idea behind these transformations is to adjust the brightness value of each pixel in an image using a linear or non-linear function to improve the visual aspect of the image. These transformations include histogram spreading and equalization, Gamma transformation, and so on.  
**Noise:** The addition of noise increases the uncertainty or ambiguity of the average information embedded in the image, which can have a masking effect on the watermark and therefore disturb its detection/extraction.  
**Geometric transformations:** This kind of transformation does not remove the mark, rather, it makes it undetectable while still remaining in the image. In the majority of watermarking algorithms, the watermark detector requires knowledge of the exact position of the mark in the image. These transformations cause a desynchronization between the mark inserted in the image and the detector [43]. There are several geometric transformations, for example, affine geometrical transformations (translation, rotation, and change of scale), and cropping, which consists of removing a part of the image and consequently a part of the mark. There are also local geometric transformations, such as the StirMark attack. This attack consists of a succession of random geometric distortions applied locally to several places in the image [44].
# Assessing the Applicability of Machine Learning Models for Robotic Emotion Monitoring: A Survey
## 2 Background
### Human Signal Sources
#### 2.1.3 Skin Signals  
Electrodermal activity (EDA) is the umbrella term for any electrical changes that occur within the skin. Like ECG, this is measured through surface electrodes, often placed on the body's extremities. One signal that is measured under EDA is the galvanic skin response (GSR). GSR, a measure of the conductivity of human skin, can provide an indication of changes in stress levels in the human body [30]. Depending on our emotional state's intensity, our skin produces sweat at different rates [31]. This results in the electrical conductance of our skin changing, which can then be measured.  
In addition to conductance, our skin temperature (ST) also changes in response to several internal and external stimuli. External stimuli, such as ambient temperature and any applied heat sources, are of little use to researchers studying physiological signals. However, internal causes of skin temperature change are of great interest. Emotionally charged music causes skin temperatures to rise or fall [32]. Therefore, emotional states are linked to skin temperature, and it may be possible to infer patients' emotions through skin temperature. Skin temperature is measured either through contactless or contact methods, but recently, researchers have taken great interest in contactless methods, such as infrared cameras, that measure temperatures by collecting radiation emitted by the surface [33].
## 5 Results and Discussion
### Geochemical Anomalies Identified by the S-A Method  
Given that unsatisfactory results were obtained using the statistical method in the Luliangshan area, which is characterized by a Gobi Desert landscape, the S-A method was used to identify weak anomalies related to mineralization. The S-A method does not require preprocessing of extreme or outlier values, thereby avoiding interference and extracting geochemical anomalies directly from the original element data structure. Its advantages are obvious: in addition to identifying the same anomalous areas as the statistical method, S-A can effectively delineate weak geochemical anomalies caused by metallogenic singularities while separating the anomaly field from the background field.  
S-A was used to decompose the background and anomalies and obtain abnormal information of chemical elements from grid images obtained through MIDW interpolation. The data processing was as follows: spatial raster data were transformed from the spatial domain to the spectral domain via positive Fourier transform, followed by double logarithmic processing of the energy spectrum density (S) and identification of densities greater than a certain threshold. The least squares method was then utilized to fit three-segment equations. This study focused on the geochemical element Cu and the combined elements Cu + Zn + Co. Figure 8 shows the double logarithmic curve fitting results, where it can be observed that each R\({}^{2}\) value of the fitting equation was greater than 0.98, indicating a significant fitting effect. For the individual geochemical element Cu (Figure 8a), the leftmost line (y = \(-1.32\)x + 21.09, 6.72 \(<\) InS \(<\) 8.78) represents the interference factor, the middle line (y = \(-1.42\)x + 21.75, 8.78 \(<\) InS \(<\) 9.74) represents the anomaly, and the right-most line (y = \(-1.44\)x + 21.92, 9.74 \(<\) InS \(<\) 10.88) represents the background. Similarly, for the combined elements Cu + Zn + Co (Figure 8b), three segments can be identified: the interference factor equation (y = \(-1.37\)x + 22.06, 9.90 \(<\) InS \(<\) 12.20), the anomaly equation (y = \(-1.62\)x + 25.18, 12.20 \(<\) InS \(<\) 12.81), and the background equation (y = \(-0.84\)x + 15.06, 12.81 \(<\) InS \(<\) 13.04).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Type** & **Ag** & **As** & **Au** & **Cu** & **Cd** & **Pb** & **Sb** & **Zn** & **Ni** & **Co** & **Cr** & **W** & **Mo** & **Hg** & **Bi** \\ \hline F1 & 0.73 & 0.10 & 0.38 & 0.25 & 0.01 & 0.76 & 0.25 & 0.31 & 0.46 & 0.29 & 0.37 & 0.16 & 0.29 & 0.75 & 0.06 \\ F2 & 0.14 & 0.38 & 0.02 & 0.71 & 0.02 & 0.35 & 0.17 & 0.51 & 0.23 & 0.77 & 0.09 & 0.48 & 0.36 & 0 Results of the S-A fractal analysis for single elements Cu, Zn, Co, and Cr are shown in Figure 9. The analysis results for the combined element anomalies of Au + Ag + Pb + Sb + Hg and Cu + Zn + Co are shown in Figure 10.  
Figure 8: Double logarithm curve fitting results for the S-A model for Cu (**a**) and Cu + Zn + Co (**b**).  
Figure 9: Spatial distributions for anomalies of Cu (**a**), Zn (**b**), Co (**c**), Cr (**d**) based on the S-A method.
# Robust Beamforming Based on Graph Attention Networks for IRS-Assisted Satellite IoT Communications
## 5 Conclusions  
In this paper, we investigated the IRS-aided LEO satellite communication system. Specifically, we formulated a sum-rate maximization problem by optimizing the satellite precoding and IRS beamforming jointly. To tackle the time-varying network topology and high transmission delay of satellite communication, an RBF-GAT was presented to establish a direct mapping from the received pilots and network topology to the satellite and IRS beamforming, and the unsupervised learning mechanism was used to train this network offline. Compared with traditional beamforming methods, the proposed approach has the ability to capture the dynamic network topology and lower computational complexity. Therefore, it is more suitable for dynamic LEO satellite communication scenarios. The simulation results corroborated that the proposed scheme can achieve approximate performance compared with an optimal solution.  
Figure 6: The sum-rate versus the number of IRS elements \(N=N_{t}\times N_{t}\).  
**Author Contributions:** Conceptualization, H.C.; formal analysis, W.Z. and W.F.; investigation, W.Z.; writing--original draft, W.Z. and J.F.; writing--review and editing, H.C., J.F. and W.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Natural Science Foundation of China under Grant Nos. 51877015, U1831117, U20A20157, and U1931129.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 DV-Hop and Error Analysis
### Error Analysis  
From the above analysis, it can be concluded that there is an extensive range of deviation from DV-Hop utilizing hop distance instead of straight-line distance [37]. Figure 3 is formed by eight unknown nodes and four anchor nodes randomly deployed; the radio communication radius of nodes in this network is 50 m. The average hop distance of anchor nodes _A1_ and _A2_ is 29.73 m and 26.60 m, respectively, according to Equation (1) for the following calculation.  
As can be seen from the figure, anchor node _A2_ can communicate with nodes 3 and 6, and the hop counts are both recorded as 1. However, the difference between \(l_{A2,3}\) and \(l_{A2,6}\) has a large impact on the calculation of the average hop distance of anchor node _A2_. This mechanism of calculating the minimum hop count between nodes also leads to errors in calculating the minimum distance between unknown nodes and anchor nodes, and further errors accumulate during the localization process. Therefore, this paper considers the graded refinement of the hop count between the anchor node and the neighboring nodes, and the specific scheme is shown in Section 4.  
During the traditional second phase, the unknown node receives the average hop distance estimated by the closest anchor node. For example, unknown node 1 calculates its location based on the anchor node A1. Nevertheless, anchor node A1 does not have a complete description of the full array of nodes deployed.  
The least-squares method used in the third stage is computationally intensive. Moreover, if the quantity of nodes is substantial to be deployed in a broad environment, the primary least-squares method seriously affects the node survival rate. In addition, the least squares do not provide a continuous and stable localization process when some nodes run out of energy or when new nodes try to join the network.
# Analysis and Optimization of the Stray Capacitance of Rogowski Coils
## 4 Optimization of the Skeleton Cross-Section
### Optimal Design of the Skeleton Cross-Section
#### 4.2.1 The Constrained Variational Problem
The magnetic flux through the area surrounded by the upper part of the curve \(z(r)\) and the segment \(\overline{R_{1}R_{3}}\) is  
\[\frac{\Phi_{\mathrm{m}}}{2}=\int_{R_{1}}^{R_{3}}\frac{\mu i}{2\pi r}z(r)\mathrm{ d}r. \tag{24}\]  
With the definition \(A=\frac{\pi\Phi_{\mathrm{m}}}{\mu i}\), Equation (24) can be rewritten as  
\[\int_{R_{1}}^{R_{3}}\frac{1}{r}z(r)\mathrm{d}r=A. \tag{25}\]  
In the interval \([R_{1}\leq r\leq R_{3}]\), the length of the upper half of the curve \(z(r)\), which is denoted by \(\frac{l}{2}\), is calculated as  
\[\frac{l}{2}=z_{0}+\int_{R_{1}}^{R_{3}}\sqrt{1+[z^{\prime}(r)]^{2}}\mathrm{d}r. \tag{26}\]  
So far, the shortest perimeter problem has been attributed to the problem of determining the extreme value of the functional (26) under the constraints described by Equation (25). In summary, it can be formulated as the following conditional variational problem  
\[\text{minimize}\;I[z(r)]=z_{0}+\int_{R_{1}}^{R_{3}}\sqrt{1+[z^{\prime}(r)]^{2} }\mathrm{d}r\;\text{under}\left\{ \begin{array}{l}A=\int_{R_{1}}^{R_{3}} \frac{1}{r}z(r)\mathrm{d}r=\text{const}\\ \lim\limits_{r\to R_{1}^{+}}z(r)=z_{0}\;\;\;,\;\;\;z(R_{2})=z_{\mathrm{ max}}\;\;\;,\;\;\;z(R_{3})=0\\ z^{\prime}(R_{1})=+\infty\;\;\;,\;\;\;z^{\prime}(R_{2})=0\;\;\;,\;\;\;z^{ \prime}(R_{3})=-\infty\end{array} \right. \tag{27}\]
## 6 Discussion on Gaps, Challenges, and Links to Future Works
### Application of Available Excuation Software to Wildfire Evacuation
#### 6.5.1 Wildfire Behavior Integration  
One of the primary challenges is the limited integration of wildfire behavior into the evacuation simulation process. Most of the mentioned software focus on the traffic and transportation aspects of the evacuation process, neglecting the dynamic and evolving nature of wildfires. To create comprehensive evacuation strategies, it is crucial to incorporate real-time wildfire behavior data, including fire spread, smoke production, and fire intensity, into the models to better understand their influence on evacuation decisions and routes.
# Communication Fault Maintenance Decision of Information System Based on Inverse Symmetry Algorithm
## 4 Discussion
### Information System Communication Technology Troubleshooting Measures  
#### 4.3.1 Daily Maintenance of Hardware Equipment  
For the effective use of information system communication technology means, the fault problems shown are various [28]. In view of the defects and faults in hardware, we must focus on strengthening daily maintenance and repair, so as to effectively prevent the related problems, timely detect the generated threats and control the degree of loss. In the process of daily maintenance and management of hardware equipment, first of all, we should ensure that it has an ideal comprehensive effect and can focus on all the hardware components of the entire information system in an all-round way. In this way, we can better solve all kinds of potential problems and promote the operation of related hardwarein a more reliable and orderly manner, especially for the more critical network cables. The common components such as computer equipment and switch equipment need to be checked and replaced regularly, so as ensure they have ideal working conditions and avoid possible big threats. In addition, the daily maintenance of such hardware equipment also needs to focus on relevant organizational arrangements, conduct timely inspection and analysis around the corresponding management and monitoring objectives whether there are obvious corrosive problems and damage phenomena, so as to prevent and deal with failure problems, strangle all kinds of hidden dangers in the cradle state, and avoid the formation of large losses. Finally, in the maintenance and management of hardware equipment, we need to pay attention to the introduction and update of all kinds of new equipment, so as to promote the corresponding computer network communication system. It is reliable and efficient to realize the maximum value.