esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Communication in Wireless Sensor Networks
## 5 Gathering Information Cooperation  
The cooperation of SNs that do not have enough energy (to transmit the MA) with their neighbors during information collecting sessions are detailed in the figure below. The source node C, which does not have enough energy, begins to seek cooperation near its neighbors of the itinerary when the previous source node B receives a mobile agent as shown in step (1) of Figure 6. The cooperation request is a short message, programmed for a single hop, and neighbors will be programmed not to reply to this message. Thus, node C waits during a fixed delay, if neighboring nodes do not have enough energy; the sensor node considers that these neighboring nodes are not cooperating sensors. In this case, the message is programmed for two hops, and so on, until a cooperating node is obtained (the source node or a simple neighbor node knowing that the priority of choice goes to the source node). Thus, the source node makes the decision to cooperate or not with a neighbor according to a specific energy threshold; which is in the itinerary of the list. After the appropriate decision is made, the source node C sends its processed information (useful and non-redundant) to the cooperating neighbor node C \". Then, the previous source node B sends the MA to the cooperating neighbor node C \", as shown in step (2) of Figure 6.  
Figure 6: Gathering information cooperation.  
The other following source nodes in Src_List list of MA iterate the same previous steps until they reach the base station; these steps are explained in Algorithm 3.  
```
Begin  Pass the MA to MA@Firs_Src;  While MA comes at SN Do ifMA@Nex_Src_IDdoes not have enough energy Then Fori = 1 to sensor@Nbr_neighbor Do // neighboring number of SN sensor diffuse ReqCoop EndFor If deadlines <= D and neighbor sensor cooperating responds Then // the waiting time to send the MA to the next If sensor receives ReqCoop Then IfE>S Then //if the energy remaining> at a predefined threshold neighbor sensor cooperating responds EndIf EndIf N:=N+\(\sum\)_0.Ri; //Add information to the package Else Fori = 1 to sensor@Nbr_neighbor Do //neighboring number of sensors neighbor Sensor diffuse ReqCoop If neighbor sensor = source node N:=N+ \(\sum\)_0.Ri EndIf EndFor EndIf EndIf EndIf Fill in the MA@Int_List //by the intermediate sensor between the two SN MA@Nex_Sensor = read the novel destination from MA@Int_List; Pass the MA at MA@Nex_Sensor; End.
```  
**Algorithm 3. Pass the MA among the SNs.**  
The rest of the energy of a battery is represented by E, so when a sensor contains enough energy, the latter participates in a large number of cooperation sessions.
# Analysis of Technologies for Carbon Dioxide Capture from the Air
## 2 DAC Technologies
### Adsorption  
While the absorption process mainly uses an aqueous solution of NaOH, KOH or Ca(OH)2, the adsorption process is based on the chemical or physical adsorption of CO2 with particular sorbents, such as amine-functionalized sorbents, metal organic frameworks (MOFs), zeolites, etc. [6]. Among chemisorbents, amine-functionalized sorbents and solid alkali carbonates sorbents (bulk and supported) have been proposed, while, among physisorbents, MOFs, zeolites and new materials, such as boron nitride nanosheets and nanotubes, have been investigated [18]. Amine-functionalized sorbents have received significant attention for DAC systems due to their stability under humidity, low regeneration temperatures (50-120 \({}^{\circ}\)C) and flexibility in the choice of low-cost support [39].  
Generally, in the adsorption system, a single equipment unit is used, where adsorption and desorption happen alternately after variations in temperature or pressure or a combination of the two. Generally, pressure swing adsorption (PSA) is not practicable in a real process because a pressure lower than 0.4 mbar is required. At lab scale, the temperature swing adsorption (TSA) is generally used, but the use of an inert gas purge results in a dilute output stream. On the other hand, temperature and vacuum swing adsorption (TVSA) could be used to produce a high-CO2 purity stream.  
Most studies reported in the literature on the adsorption process are on the investigation of sorbents, evaluating adsorption capacities and performances, as discussed below.
# Parameter Analysis of Resilient Precast Concrete Beam-Column Joints
###### Abstract  
Conventional precast structures often face the challenge of post-earthquake repair, especially at beam-column joints. A new type of precast concrete beam-column joint with a replaceable energy dissipation device consisting of cross-shaped and H-shaped steel was proposed in this paper, which was characterized by the use of replaceable energy-dissipating devices to improve the seismic capacity. Based on the previous test results of the group, this paper used ABAQUS to investigate how factors like the thickness of the H-shaped steel webs and the size and number of openings affected the seismic performance of precast concrete beam-column joints with replaceable energy dissipation devices. The results showed that (1) an increase in the H-shaped steel's thickness in the REDDC could improve the load-carrying capacity of the node, but the energy dissipation capacity was weakened, and (2) the length and width of the H-shaped steel openings had almost no effect on the ultimate load-carrying capacity within a certain range, but increasing the size of the openings could improve the energy dissipation capacity and reduce the ultimate load-carrying capacity at the same time. Compared with the length of the openings, the width of the openings had a more significant impact on the energy dissipation capacity. (3) The peak load-carrying capacity decreased with an increase in the number of openings in the H-shaped steel.  
beam-column joint; energy dissipation devices; seismic performance; replaceable; ABAQUS Article
## 5 Experimental Results
### Datasets and Models  
Our experiments were conducted on two well-known public benchmark image datasets for image classification: CIFAR-10 [64] and MNIST [22]. We utilized four distinct DNN architectures as target models: three previously experimented on with DeepSigns [51] (MLP, CNN, and ResNet-18) and one with RIGA [57] (LeNet). Table 2 summarizes their topologies, training epochs, and corresponding baseline accuracies.  
Throughout the experimental section, we use the following abbreviations for our four benchmark configurations: **BM1**: MLP architecture on the MNIST dataset, **BM2**: CNN architecture on the CIFAR-10 dataset, **BM3**: ResNet-18 architecture on the CIFAR-10 dataset, and **BM4**: LeNet architecture on the MNIST dataset. The implementations of DeepSigns [51], ResEncrypt [55], and DICTION, along with their configurations, are publicly available at [[https://github.com/Bellaqira/DICTION](https://github.com/Bellaqira/DICTION)]([https://github.com/Bellaqira/DICTION](https://github.com/Bellaqira/DICTION)), accessed on 15 June 2025.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Benchmark ID** & **Dataset** & **Baseline Acc** & **Training Epochs** & **Model Type** & **Architecture** \\ \hline
1 & MNIST & 98.06\% & 50 & MLP & 784-512 FC-512 FC-10 FC \\ \hline
2 & CIFAR-10 & 87.17\% & 100 & CNN & 3x32\(\times\)32-32C3(1)-32C3(1)-MP2(1)-64C3(1)-64C3(1)-MP2(1)-512 FC-10 FC \\ \hline \hline \end{tabular}
\end{table}
Table 2: Benchmark DNN: architectures, number of epochs, and classification baseline accuracies on two public datasets (CIFAR-10 [64] and MNIST [22]). Model notation: ‘64C3(1)’ denotes a convolutional layer with 64 output channels and \(3\times 3\) filters applied with stride 1; ‘MP2(1)’ indicates a max-pooling layer over \(2\times 2\) regions with stride 1; ‘512 FC’ refers to a fully connected layer with 512 output neurons; ‘BN’ stands for batch normalization. ReLU and sigmoid activation functions are used across all benchmarks.
# Cross-Layer Optimization for Heterogeneous MU-MIMO/OFDMA Networks
###### Abstract  
To enable the full benefits from MU-MIMO (Multiuser-Multiple Input Multiple Output) and OFDMA (Orthogonal Frequency Division Multiple Access) to be achieved, the optimal use of these two technologies for a given set of network resources has been investigated in a rich body of literature. However, most of these studies have focused either on maximizing the performance of only one of these schemes, or have considered both but only for single-hop networks, in which the effect of the interference between nodes is relatively limited, thus causing the network performance to be overestimated. In addition, the heterogeneity of the nodes has not been sufficiently considered, and in particular, the joint use of OFDMA and MU-MIMO has been assumed to be always available at all nodes. In this paper, we propose a cross-layer optimization framework that considers both OFDMA and MU-MIMO for heterogeneous wireless networks. Not only does our model assume that the nodes have different capabilities, in terms of bandwidth and the number of antennas, but it also supports practical use cases in which nodes can support either OFDMA or MU-MIMO, or both at the same time. Our optimization model carefully takes into account the interactions between the key elements of the physical layer to the network layer. In addition, we consider multi-hop networks, and capture the complicated interference relationships between nodes as well as multi-path routing via multi-user transmissions. We formulate the proposed model as a Mixed Integer Linear Programming (MILP) problem, and initially model the case in which each node can selectively use either OFDMA or MU-MIMO; we then extend this to scenarios in which they are jointly used. As a case study, we apply the proposed model to sum-rate maximization and max-min fair allocation, and verify through MATLAB numerical evaluations that it can take appropriate advantage of each technology for a given set of network resources. Based on the optimization results, we also observe that when the two technologies are jointly used, more multi-user transmissions are enabled thanks to flexible resource allocation, meaning that greater use of the link capacity is achieved.  
MU-MIMO; OFDMA; heterogeneous networks; cross-layer optimization +
Footnote †: journal: Physics Letters A  
1
Footnote 1: email: [EMAIL_ADDRESS]
## 3 Results and Discussion  
### Electrografting of Gold Electrodes with 4-NBD\({}^{+}\): Spectroscopic Characterization  
The covalent grafting of nanometric thick organic layers onto the pretreated electrode surfaces involves the electroreduction of 4-NBD\({}^{+}\) in an aqueous acidic medium. The cyclic voltammograms (CVs) of the electrochemical reduction of the 2 mM 4-NBD\({}^{+}\) solution on activated AuAT-SPE (Figure 2a) and AuBT-SPE (Figure 2b) exhibited similar electrochemical behavior in both electrodes. The first scan shows a characteristic reduction peak, associated with the formation of an aryl radical that covalently bonded to the electrode surface, accompanied by the loss of N\({}_{2}\). The voltammograms of AuAT-SPE and AuBT-SPE exhibit cathodic peaks of \(-\)14.29 \(\upmu\)A at 0.134 V and \(-\)22.19 \(\upmu\)A at 0.137 V, respectively. Although the cathodic peak potentials were comparable for both gold electrodes, the higher peak current observed for AuBT-SPE suggests that its surface morphology enhances the electrochemical properties of the electrode [46].  
Figure 2: (**a,b**) CVs of the electrografting of 2 mM 4-NBD\({}^{+}\) in 25 mM HCl (during five scans at a scan rate of 100 mVs\({}^{-1}\)) on an activated AuAT-SPE (**a**) and on an activated AuBT-SPE (**b**). Raman-SERS spectra of AuAT-SPE (**c**) and AuBT-SPE (**d**) before and after electrografting with 2 mM 4-NBD\({}^{+}\) in 25 mM HCl, using an excitation wavelength of 785 nm and an excitation time of 10 s.  
To confirm the successful grafting of the nitrophenyl groups onto the gold electrodes, direct evidence was obtained using SERS. Figure 2c,d depict the SERS Raman spectra for gold electrodes that were electrografted with 4-NBD\({}^{+}\). The spectra show a signal at 412 cm\({}^{-1}\), which can be attributed to the formation of an Au-C covalent bond between the gold surface and the carbon atom of the aromatic ring [47; 48; 49]. Furthermore, as shown in the figure, this signal is absent in the spectra recorded for the activated electrode (no grafting), confirming that the aryl radical was successfully bonded to the surface. In addition, the spectra exhibit the presence of signals at 1350 cm\({}^{-1}\) and 1590 cm\({}^{-1}\), corresponding to the symmetrical stretching of the N-O bond and the stretching of the C-C bond in the aromatic ring, respectively. The absence of these bands in the spectra of bare electrode confirms the presence of aromatic molecules with nitro functional groups on the surface. This provides strong evidence that the electrode surface has been successfully modified with nitrophenyl functionalities [50].
# Satellite Image Compression Guided by Regions of Interest
## 2 Materials and Methods
### Baseline Algorithm for Change Detection
Finally, the image (tile) is labeled as containing changes if the binary output matrix \(I_{bin}\) contains at least one value equal to 1.
# Vibration Signal Classification Using Stochastic Configuration Networks Ensemble
## 3 Fault Diagnosis Based on Ensemble SCNs
### SCN Model with Hybrid Data  
As shown in Figure 2, the monitor vibration signal is sampled by a sliding window with a certain size, then the time-frequency analysis methods such as Fourier transform and short time Fourier transform (STFT) are employed to analyze the sample signal and obtain the frequency spectrum from the frequency domain and the time-frequency image from the time-frequency domain, respectively. Fault data in the frequency domain and the time-frequency domain describe the fault features from different perspectives. The hybrid data that consist of samples from these two domains are used to build a SCN model in order to achieve better classification results.  
Given a dataset \(\{\mathbf{X},\mathbf{D},\mathbf{T}\}\) that consists of time-frequency image \(\mathbf{X}\), the frequency spectrum signal is \(\mathbf{D}\) and the target label is \(\mathbf{T}\). In order to combine the samples from these two domains, the 2D images in \(\mathbf{X}\) are vectorized to one-dimensional data denoted as \(\mathbf{A}\), i.e.,  
\[\mathbf{A}=Vec(\mathbf{X}). \tag{14}\]  
The hybrid data \(\mathbf{Z}=[\mathbf{D},\mathbf{A}]\) are obtained and will be used to build the SCN model. Because the hidden parameters \(w_{l},b_{l}\) are randomly configured under the constraint condition (5), the hidden layer output can be evaluated by  
\[h_{l}=g(w_{l}^{\mathrm{T}}\mathbf{Z}+b_{l}). \tag{15}\]  
The output weight \(\beta\) can be estimated by solving the following optimization problem:  
\[\mathbf{E}(\beta)=\min_{\beta}||\mathbf{H}\beta-\mathbf{T}||_{2}^{2}+\alpha|| \beta||_{1}. \tag{16}\]  
where \(\mathbf{H}=[h_{1},h_{2},...,h_{L}]\) is a non-linearly transformed feature matrix, \(\mathbf{T}\) is the target label, and \(\alpha\) is the regularization parameter. The detailed modeling process of the SCN with hybrid data is described in Algorithm 1.
## 3 Proposed Methodology
### Data Collection  
The process of data collection for PD diagnosis using EEG and MRI involves a meticulous and multi-faceted approach. EEG (Electroencephalography) data collection entails recording the electrical activity of the brain using sensors placed on the scalp. This method captures the brain's electrical fluctuations, which are crucial in understanding the neural dynamics associated with PD.  
MRI (Magnetic Resonance Imaging) provides images of the brain's structure. In PD diagnosis, MRI is particularly useful for visualizing changes in brain regions affected by the disease, such as the basal ganglia. The combination of EEG and MRI data offers a comprehensive view of both the functional and structural aspects of the brain when gathering these data. The combined data collection approach in this research enhances the understanding of PD's impact on the brain, leading to a more accurate diagnosis and tailored treatment strategies. Figure 3 shows the snippets of EEG, and Figure 4 depicts MRI of PD and non-PD patients. In a normal EEG, a balanced distribution of various frequency bands, including delta, theta, alpha, beta, and gamma, is observed depending on the individual's state of consciousness. However, in [PERSON]'s disease, EEG recordings often show an increase in beta frequency band activity, particularly in the range of 13-30 Hz, especially during resting conditions. This exaggerated beta band activity, known as \"resting tremor,\" is a hallmark feature of [PERSON]'s disease and distinguishes it from normal brain activity patterns. A clear MRI image typically displays a healthy brain with normal anatomical structures and no signs of pathology. In contrast, an MRI Image of a brain affected by [PERSON]'s disease may reveal specific abnormalities. These can include changes in the size and shape of certain brain regions, such as the substantia nigra, as well as the presence of characteristic abnormalities.  
Figure 2: Proposed multimodal PD diagnosis architecture.
# Mechanical Properties of Sandstone Cement-Stabilized Macadam
## 3 Test Results
### Unconfined Compressive Strength
#### 3.1.2 Influence of the Aggregate Type  
It is well known that the UCS of CSM is closely related to the aggregate strength in the mixture. Mixtures of sandstone A and limestone with a cement content of 4.0% were tested. It can be observed from Figure 4 that the compressive strength of the SCSM mixture is lower than the compressive strength of the CSM mixture for the same curing period. The UCS of the limestone sample is approximately twice that of the sandstone sample; correspondingly, the compressive strength of the limestone parent rock is 2.0 times that of the sandstone A parent rock. This finding means that the characteristics of the parent rock, including its chemical composition and physical mechanics, are significant factors influencing the UCS of the CSM with this rock as the aggregate. Meanwhile, the strength of the SCSM is lower than that of the CSM, which may be because the dust attached to the surface of the sandstone weakens its bond with the cement slurry.
## 3 System Design
### Doppler Prediction
The method outlined in Table 7 is effective provided that, during the execution of stage 1, the change in the Doppler frequency of the received signal remains within the Doppler search range specified for stage 2. Otherwise, the signal detected in stage 1 may not be detected in stage 2. The probability of detection may also be affected by the need to detect the same signal twice.
# Ransomware-Resilient Self-Healing XML Documents
## 4 Proposed Version-Aware Ransomware Recovery Framework
### Details of the Proposed SH-VARR Framework
#### 4.1.2 Access-Control Module  
The access-control module is implemented as a root daemon that performs write/delete protection for the files produced by the version-control module each time a file version is created. This is achieved by running the chattr command (Change Attribute) with root privileges. chattr is a command line in Linux that is used to set/unset specific attributes to a file in a Linux environment to secure accidental deletion or modification of important files and folders, even by root users. Through this process, file snapshots are protected from corruption or deletion by using the change file attribute permissions with the immutable flag (i) under the Linux environment, preventing any user, including the root, from accidentally modifying and/or deleting files. An example using this command is shown in Figure 4.  
It is important to note that the default setting for standard users is assumed to be non-admins, with the access-control module configured as a system daemon with root access privileges executing the chattr command; this would inherently ensure the protection of newly created versions in the version-control directory. Any attempt to modify or delete a protected file will not be permitted, as shown in the example in Figure 5. This is considered a valid setting for two reasons: (i) users usually do not log into their systems as admins. In fact, one of the best practices of computer usage emphasizes that users never log in as admins. (ii) A recent report showed that 90% of ransomware instances in the wild could infect systems and encrypt files without administrative privileges [58]. This indicates that while users log in as non-admins, there is still a high possibility that Ransomware may encrypt their files. In our proposed solution, ensuring a specific access control process with administrative privileges will protect files created/edited by non-admin users.
## 3 Distributed Transmission Rate Adjustment
According to the channel state model, when the transmission rate of node \(i\) is \(\Delta^{*}/t_{i}\), the busy channel probability and the transmission rates of node \(j\) for \(\forall j\
eq i\) can be related as follows:  
\[\beta^{*}=\frac{(1-\prod_{j\
eq i}(1-\frac{1-\beta^{*}{}^{m+1}}{1-\beta^{*}} \frac{\Delta^{*}}{t_{j}}))(1+L)}{1+(1-\prod_{j\
eq i}(1-\frac{1-\beta^{*}{}^{m+ 1}}{1-\beta^{*}}\frac{\Delta^{*}}{t_{j}}))(1+L)} \tag{9}\]  
Using the same approximation technique as in Equation (7), Equation (9) can be rewritten as:  
\[\beta^{*}=\frac{\frac{1-\beta^{*}{}^{m+1}}{1-\beta^{*}}\ \Delta^{*}\sum_{j\
eq i}(1/t_{j}) \left(1+L\right)}{1+\frac{1-\beta^{*}{}^{m+1}}{1-\beta^{*}}\ \Delta^{*}\sum_{j\
eq i}(1/t_{j}) \left(1+L\right)} \tag{10}\]  
Solving Equation (10) for \(\Delta^{*}\) yields:  
\[\Delta^{*}=\frac{\beta^{*}}{\left(1-\beta^{*}{}^{m+1}\right)\ \sum_{j\
eq i}(1/t_{j}) \left(1+L\right)} \tag{11}\]  
In the meantime, \(\Delta^{*}\) should satisfy \(\Delta^{*}p^{*}_{PSR_{\hat{i}}}=1\) according to Equation (1). The packet success rate \(p^{*}_{PSR_{\hat{i}}}\) of node \(i\) can be expressed as follows [34]:  
\[p^{*}_{PSR_{\hat{i}}}=(1-\beta^{*}{}^{m+1})\prod_{j\
eq i}(1-\alpha^{*}_{j}) \tag{12}\]  
where \((1-\beta^{*}{}^{m+1})\) is the probability that node \(i\) has access to an idle channel for a certain packet and \(\prod_{j\
eq i}(1-\alpha^{*}_{j})\) is the probability that at the same time there is no other node attempting to access the channel. Combining Equations (1), (7) and (12), one can obtain:  
\[(1-\beta^{*}{}^{m+1})\left(1-\frac{1-\beta^{*}{}^{m+1}}{1-\beta^{*}}\Delta^{ *}\sum_{j\
eq i}(1/t_{j})\right)=\frac{1}{\Delta^{*}} \tag{13}\]  
The objective here is to estimate \(\beta^{*}\) from \(\sum_{j\
eq i}(1/t_{j})\). As such, one can substitute Equations (11)-(13) to eliminate \(\Delta^{*}\) as follows:  
\[\prod_{j\
eq i}\bigl{(}1-\alpha_{j}\bigr{)}=\prod_{j\
eq i}(1-\frac{1-\beta_{j }^{m+1}}{(1-\beta_{j})\lambda_{j}}) \tag{5}\]0  
From Equation (14), it can be shown that \(\sum_{j\
eq i}(1/t_{j})\) is monotonically increasing in \(\beta^{*}\) when \(0<\beta^{*}<\frac{L-(L+2)^{1/2}+2}{L+2}\) by differentiating \(f(\beta^{*})\). Thus, an inverse function \(\beta^{*}=f^{-1}(\sum_{j\
eq i}(1/t_{j}))=h(\sum_{j\
eq i}(1/t_{j}))\) exists in this region. In conclusion, given the values of \(L\), \(\sum_{j\
eq i}(1/t_{j})\) and \(m\), a unique \(\beta^{*}\) can be readily solved and so is \(\Delta^{*}\)(using Equation (11)). Node \(i\) can then use \({\lambda_{i}}^{*}=t_{i}/\Delta^{*}\) to determine its optimal transmission rate with respect to its throughput demand. In the actual implementation, the function \(\beta^{*}=h\big{(}\sum_{j\
eq i}\big{(}1/t_{j}\big{)}\big{)}\) can be discretized and computed using numerical methods and stored in a look-up table beforehand to simplify the computation.