esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Temperature Measurements  
The flat agar skin phantoms were subjected to 30 s of cooling using six different methods. The temperature measurements were taken at depths of 5 mm and 9 mm using the temperature sensors. The time interval from \(-\)5 to 0 s represents the steady-state phase. During this phase, the temperature at a depth of 5 mm remains approximately 33 \({}^{\circ}\)C, while at a depth of 9 mm, it remains around 37 \({}^{\circ}\)C. At 0 s, the cooling sequence is initiated, and after 30 s, the cooling load is removed, marking the beginning of the reheating phase. Figure 5 illustrates the cooling and reheating curves for the various cooling methods. Among them, ice proves to be the fastest at reducing the temperature at a depth of 5 mm. After 30 s of cooling, the temperature at 5 mm is approximately 24 \({}^{\circ}\)C and keeps decreasing to a minimum of approx. 21 \({}^{\circ}\)C. The cooling effect of the alcohol spray is insufficient for adequately cooling the skin phantom, the cooling is slow and the penetration depth of the cooling is not sufficient. The cryo cooler was employed at three different levels: level 1, level 3, and level 5. As depicted in Figure 5, level 5 demonstrates a more rapid cooling of the skin phantom compared to level 3 and level 1. At a depth of 9 mm, it becomes evident that both ice and the cryo cooler achieve the fastest and deepest cooling. Conversely, the conductive cooling methods, the vortex tube and alcohol spray, do not achieve the same cooling as the cryo cooler and ice.  
Figure 4: The figure presents the outcomes of the four remaining cooling methods, which were accompanied by their respective histograms. The circular area in the thermogram represents the ulcerating skin lesion. It is evident that the convective cooling methods demonstrate a more uniform cooling effect in contrast to the conductive cooling methods, which encounter challenges in effectively cooling the immediate region surrounding the bulge.  
Figure 5: The figure presents the results of applying six different cooling methods on the flat agar skin phantom for a duration of 30 s at 5 mm and 9 mm depth. Among the cooling methods, ice demonstrates the most rapid cooling rate, while alcohol spray shows the slowest cooling performance and fails to achieve sufficient cooling.
## 6 Experimental Evaluation
### Experiment II: Service Allocation  
We evaluated Ubunye's performance in terms of computational efficiency (a QoS-related parameter) and user experience (a QoE-related parameter). We benchmark it against two classic edge resource allocation algorithms (round robin and first-fit) and a state-of-the-art algorithm (double price double auction, DPDA), aiming to compare our proposal with established and recent advancements in edge resource allocation mechanisms.  
Table 3 shows the simulation configurations using the EdgeSimPy simulator and outlines the topology and scenarios simulated by varying the number of users from 30 to 150, keeping the other parameters fixed. The experiments involving allocation assume that the classifier has previously grouped the services into two distinct groups: high-bandwidth and low-latency requirements.  
The simulation applications follow edge computing models relevant to our classifier. High-bandwidth services mimic video streaming with packet sizes greater than 256 bytes, while low-latency services resemble IoT applications with packet sizes smaller than 256 bytes. These services are treated as distributed applications on edge servers, each with specific requirements for CPU, memory, and others supported by available edge resources.  
Figure 7: Ubunye service classification—confusion matrix.
## 3 Results and Discussion
### FTIR Results: Endorsing the Findings on Zeolitization  
The diagrams obtained by FTIR spectra are shown in Figure 4. Between 2950-3600 cm\({}^{-1}\) there is a slight variation in the absorbance of Dt-Z which is practically negligible for Dt. This band is related to hydration, namely to the H-O-H vibrations of stretching and bending of the free water molecules, as well as those absorbed in the pores and adsorbed on the particles' surface [24; 25; 26]. Similarly, at 1597 cm\({}^{-1}\) there is a band in Dt-Z, which is not observed in Dt, and is attributable to the bending of water molecules bound to the zeolite particles [2; 27].  
At 1044 cm\({}^{-1}\) a band of high intensity is observed in Dt, which is associated with asymmetric bending/stretching vibrations in T-O-(T) bonds, where T is Si or Al [11; 28; 29]. Considering the chemical composition of the Dt sample (Table 1), it is expected that T is mostly silicon. In the case of Dt-Z this band appears somewhat shifted towards smaller wavelengths, specifically at 957 cm\({}^{-1}\). According to [PERSON] [30], this displacement towards shorter wavelengths could be directly related to the inclusion of Al atoms in the T-O-T bonds, to give rise not only to Si-O-Si bonds, but also Si-O-Al ones during the formation of the zeolite P obtained. However, it must be taken into account that the sample used has a low percentage of aluminum (3.8%; Table 1) and that no reagent has been incorporated to increase its content, so that this type of Si-O-Al bonds would be, in theory, less representative.  
Figure 3: BJH Desorption dV/dlog(w) Pore Volume vs. Pore Width for Dt and Dt-Z.  
In the wavelength range between approximately 400 and 800 cm\({}^{-1}\), a series of peaks are seen that differ depending on the sample. In the non-hydrothermally treated diatomic, bands at 777 cm\({}^{-1}\) and 447 cm\({}^{-1}\) stand out, with the presence also of a very low intensity band at 599 cm\({}^{-1}\). In contrast, the zeolitization process in Dt-Z is mainly associated with a band at 589 cm\({}^{-1}\) (of much higher intensity than that observed in Dt at 599 cm\({}^{-1}\)) and another at 415 cm\({}^{-1}\) (again shifted towards lower wavelengths with respect to that observed in Dt at 447 cm\({}^{-1}\)), while between 650-800 cm\({}^{-1}\) there is a succession of small peaks of low intensity, with no evidence of the band at 777 cm\({}^{-1}\) observed in Dt. All these bands are related to symmetric stretching vibrations of the T-O-T bridge bonds described above, with the exception of the bands at 447 cm\({}^{-1}\) (Dt) and 415 cm\({}^{-1}\) (Dt-Z), which are due to bending vibrations in O-Si-O bonds, and to a lesser extent in O-Al-O (in the latter case, linked mainly to the lower wavelengths of Dt-Z) [11, 26, 28].  
Therefore, the FTIR spectra shown in Figure 4 corroborate the changes in the bonds between atoms occurred during the alkaline hydrothermal treatment leading to the formation of zeolite P in the sediment obtained after treating the diatomic without any additional reagents other than NaOH.
## Appendix A  
Neural networks, autoencoders, and variational autoencoders  
Neural networks are machine learning models, which have gained popularity in recent years due to the accessibility of cloud computing and their vast range of applications. They are used for solving both supervised and unsupervised problems, classification, regression, clustering, and data augmentation; therefore, different neural network architectures exist, each of which is optimally suited for addressing these problems. The cornerstone of neural networks is the neuron. A vertical arrangement of several neurons forms a layer and a horizontal arrangement of many layers forms the neural network. The first layer of the neural network is referred to as the \"input\" layer, whereas the last layer is called the \"output\" layer. The layers in between the input and output layers are referred to as \"hidden\".  
Autoencoders (AEs) are neural networks that are usually used for input reconstruction, which is possible due to their unique architecture. They are composed of two distinct elements, the encoder and the decoder, which are linked via a latent space. Both the encoder and the decoder consist of the same number of hidden layers, but their order is reversed; thus, the decoder is a mirrored image of the encoder. The input data flow in a left-to-right manner from the encoder and are mapped into a fixed-point representation in a latent space. A dimension of the latent space that is smaller than the input's dimension will result in an \"incomplete\" autoencoder, whereas a larger dimension of the latent space compared to the input's dimension will result in an \"overcomplete\" autoencoder. The decoder receives as input the output of the encoder and forward propagation is performed, the output is compared to the input and the difference (i.e., reconstruction loss) is calculated, and throughout the backpropagation the error is minimized.  
VAEs are an extension of conventional autoencoders. Instead of a fixed-point representation, the latent space is a probability distribution (typically Gaussian), where its mean and variance are calculated throughout the training of the neural network. This is achieved by adding the Kullback-Leibler loss to the reconstruction loss of AEs. VAEs are \"generative\" algorithms due to the ability to randomly sample data from the latent space, pass the sampled data through the decoder, and generate novel datapoints that are \"similar\" to the input data. The general architecture of a variational autoencoder is presented in Figure A1.  
Figure A1: Visual representation of the architecture of a variational autoencoder. The input data \(x\) flow from left to right from the encoder and are mapped to the latent space. The decoder processes the output of the encoder and produces the output \(\hat{x}\).
## 4 Conclusions  
In this paper, a superhydrophobic surface is prepared by Selective Laser Melting (SLM) 3D-printed technology, comparing the corrosion resistance of polished samples, unmodified 3D-printed samples, 3D-printed + modified and 3D-printed + corroded samples. The relevant experimental results are summarized as follows:  
(1) For samples not treated with fluoro-silane, the microstructure adsorbs the organic matter in the air and reduces the surface energy, which is the root cause of the sample surface changing from hydrophilic to superhydrophobic.  
(2) After 3D-printed + modified superhydrophobic samples are placed in outdoor natural conditions for 60 days, the contact angle of water droplets on the surface is 150.8\({}^{\circ}\), which still has superhydrophobic properties and excellent natural durability; when 3D-printed + corroded superhydrophobic samples are exposed to outdoor natural conditions for 10 days, the contact angle of the surface water droplets drops to 90.2\({}^{\circ}\), the superhydrophobic properties are lost, and the natural durability is not good.  
(3) 3D-printed + modified and 3D-printed + corroded samples have excellent corrosion resistance when immersed in simulated seawater for a short time (2 h), and the corrosion characteristics are much higher than polished samples. In the case of prolonged exposure to simulated seawater (7 days), the corrosion resistance of the 3D-printed + modified sample has slightly decreased, and the corrosion characteristic parameters are still higher than the polished surface, showing long-term corrosion resistance; the corrosion characteristics of the 3D-printed + corroded sample has decreased significantly, and the corrosion resistance on the 5 th day is already lower than that of the polished surface, and it does not have long-term corrosion resistance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **Soak for 1 Day** & **Soak for 3 Days** & **Soak for 5 Days** & **Soak for 7 Days** \\ \hline I\({}_{\text{corr}}\) (\(\mu\)A\(\cdot\)cm\({}^{-2}\)) & 0.386 & 0.785 & 3.254 & 8.95 \\ E\({}_{\text{corr}}\) (V) & \(-\)0.34 & \(-\)0.373 & \(-\)0.388 & \(-\)0.532 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Corrosion characteristics parameter table of 3D-printed + corroded samples immersed in simulated seawater for different times.  
Figure 15: Nyquist and Bode diagrams of 3D-printed + corroded samples immersed in simulated seawater for different times.  
**Author Contributions:** Conceptualization, [PERSON], [PERSON], [PERSON], [PERSON]; [PERSON] wrote the paper; [PERSON] and [PERSON] offered useful suggestions for the preparation. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the National Natural Science Foundation of China (grant No.51875425).  
**Institutional Review Board Statement:** Exclude this statement.  
**Informed Consent Statement:** Exclude this statement.  
**Data Availability Statement:** Exclude this statement.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 6 Discussion
### Conventional Control Strategies
#### 6.1.1 DTI Controllers  
For the discrete-time integrator (DTI) controller developed in Section 3.1, through a simple inspection of both the step responses represented in Figure 18a,b and the statistics extracted for the same step responses for evaporator temperature, depicted in Figure 18a, and liquid refrigerant level inside the condenser, reported in Figure 18b, the following performances during transient and steady state regimes were calculated: acceptable time responses (settling time, Ts) and rise time (Tr) for temperature Ts = 40, Tr = 5.5 s and for liquid refrigerant level Ts = 20, Tr = 1.7 s, an overshoot of \(\sigma\_\max\) = 45.27% for temperature, and a larger number of maximum amplitude oscillations, of about 10%, both reaching zero steady-state errors, therefore showing high tracking accuracy.  
Mean = 7.071 and standard deviation (std) = 1.708 for DTI temperature control can be extracted from Figure 18a, while mean = 44.43 and std = 7.759 for DTI liquid refrigerant level control is given in Figure 18b. It is also worth noting the strong control effort of the oscillating compressor and the almost smooth opening of the expansion valve.
## 5 Conclusions  
This study investigated the influence of sand particulate morphology on the shear strength characteristics using experimental and machine learning approaches. The findings can be summarized in the following points.  
* Across the range of poorly graded sand sizes, the large sand sample exhibits higher density and shear strength compared to both medium and fine sand.
* The shear strength of well-graded sand is higher than that of poorly graded medium and fine sand, but not as high as that of poorly graded coarse sand.
* The particle shape regularity, including its roundness and sphericity, is not related to the mean particle size.
* In a monodisperse system of glass beads with a similar shape and size, larger particles contribute to greater shear strength compared to their smaller counterparts.
* As the mean particle size of sand decreases, the specific gravity increases and the density decreases, leading to a sample with a higher void ratio. Therefore, finer sand has a higher coefficient of volume compressibility compared to coarse sand.
* The active lateral earth pressure increases as the particle size, density, and confining pressure increases.
* The machine learning models (MLR and RFR) show excellent accuracy in predicting the shear strength of sand based on different particle shapes, sizes, and gradations. In the case of MLR, the R-squared accuracy is 0.95 for the training data, 0.94 for the testing data, as well as 0.93 when using the entire dataset with 10-fold CV method. Similarly, for RFR, the R-squared accuracy is 0.98 for the training data, 0.97 for the testing data, and 0.90 when employing the entire dataset with the 10-fold CV method.
* When using the train-test split, the machine learning models (MLR and RFR) agree on the importance of the following input features in sequence: normal stress and dry density. However, when using the 10-fold CV, the importance of the input features shifts to mean particle size and coefficient of uniformity.  
Figure 17: Feature importance analysis comparing multiple linear regression and random forest regression with and without 10-fold cross-validation: (**a**) MLR without 10-fold cross-validation, (**b**) RFR without 10-fold cross-validation, (**c**) MLR with 10-fold cross-validation, (**d**) RFR with 10-fold cross-validation.  
* Future research could address different types of soil (silt and clay), different parameters that could influence the shear strength (moisture content, temperature, strain rate, and stress history), as well as different machine learning algorithms for further exploration.  
Conceptualization, F.D. and H.A.-N.; methodology, F.D.; software, F.D.; writing--original draft preparation, F.D.; writing--review and editing, F.D. and H.A.-N.; supervision, H.A.-N. All authors have read and agreed to the published version of the manuscript.  
The authors declare that no funds, grants, or other financial support were received during the preparation of this manuscript. This paper is part of the PhD of the first author.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Texture Feature Map Computation  
The original DIR images were analyzed using OsiriX (Pixmeo) on a Mac OS-based platform. The DIR images underwent texture preprocessing in the MATLAB environment (MathWorks, Natick, MA, USA) on Windows. The clustering of image colors was implemented using the clustering with a k-means approach (CLU). The co-occurrence matrices (COM) [21], first-order features (FOF), gray-tone difference matrices (GTDM) [22], run-length matrices (RLM) [23], and local binary patterns (LBP) [24; 25] were applied. The details of the texture methods are described in [26]. Most of the mentioned methods (COM, FOF, GTDM, and RLM) in the original version compute several features to describe the whole image content. However, such an approach would not be useful for diagnostic purposes, so a new image (called a \"texture feature map\") reflecting the feature values calculated in a small region of interest was computed. As a consequence, several texture feature maps (depending on the number of features designed for each texture operator) were generated using this technique. In the current study, each texture feature map was generated using the \"moving window\" approach where, for each pixel, the new feature value was calculated on the basis of data collected in a square window (with sides of an odd number of pixels in length), to ensure that the considered pixel is in the center. In the current study, a \(21\times 21\) pixel square was used. This size achieves a consensus between computational overhead resulting in image processing time (which grows exponentially with the size and statistical stability of the results, where 441 elements used to fill a histogram of 256 bins is sufficient to achieve statistically reliable results) and image quality. For the LBP texture operator, the radius, R, was in a range from 3 to 15 pixels and there were 8 samples taken in the circular  
Figure 1: Image processing system schema. DIR, digital intraoral radiography. ROI, region of interest.  
neighborhood in the described experiments. In the case of clustering, from 10 up to 50 clusters were considered. The RLM calculated the matrix for images quantized to 32 and 64 colors, while the matrix stored the maximal length of 10 elements in a run. The square neighborhood applied to calculate the GTDM matrix was evaluated for sides 3, 7, and 11, yet the smallest one returned the best result. Some examples of texture feature maps achieved with the techniques described here are presented in Figure 2.
# Performance Analysis of DF Relay-Assisted D2D Communication in a 5G mmWave Network
###### Abstract  
Enabling D2D communication in the mmWave band has many obstacles that must be mitigated. The primary concern is the introduction of interference from various sources. Thus, we focused our work on the performance of decode-and-forward (DF) relay-assisted D2D communication in the mmWave band to increase the coverage probability and energy efficiency (EE). Three modes are proposed for D2D communication to prevail. The bitwise binary XOR operation was executed at the relay node, which increased the security feature. The radius of coverage was derived, which indicated the switching of the modes. The diffused incoherent scattering power was also considered as part of the power consumption. Furthermore, a unique relay selection scheme, the dynamic relay selection (DRS) method, is proposed to select the optimal relay for information exchange. A comparison of the proposed DF relay scheme with the amplify-and-forward (AF) scheme was also made. Finally, the simulation results proved the efficacy of the proposed work.  
dynamic relay selection; decode-and-forward; D2D communication; radius of coverage; coverage probability; mode selection; mmWave network; uplink channel Article
# Coupling Secret Sharing with Decentralized Server-Aided Encryption in Encrypted Deduplication
## 3 Design
### Content-Based Coding Matrix
The client can then encode the data chunks into the coded chunks using the coding matrix and send the coded chunks to the cloud servers for storage. Figure 3: An example of the fixed-size sampling. **Invertibility of generated matrices:** We need to ensure that the generated Cauchy matrix is invertible, which is a necessary condition for the RS code to be able to decode the coded chunks back to the original data chunks. We can ensure the invertibility of the generated matrix by checking the determinant of the matrix. If the determinant is not zero, then the matrix is invertible. If the current matrix is not invertible, we perform an elementary transformation and then add 1 to the elements in the pivot positions (diagonal positions) that are 0 to make the matrix invertible. Algorithm 1 details the steps of handling the matrix to be full ranked. Note that only the determinants of the square matrices can be calculated, which means that this generation scheme can only generate the \(k\times k\) square matrices. This enables the sharing of the matrix to achieve decentralized server-aided encryption, yet it can not generate additional \(n-k\) parity chunks to achieve fault tolerance. We will discuss how to optimize it and generate \(n\) parity chunks in Section 3.4. ```
1:original matrix M
2:full rank matrix M\({}^{\prime}\)
3:procedureHandleFullRank(M)
4:\(rank,flag\leftarrow\textsc{CheckFullRank}(M)\)
5:while\(flag==false\)do
6:for\(i\gets 0\) to \(len(M)-2\)do
7:\(M[(rank+i)\mod len(M)][(rank+i)\mod len(M)]\gets M[(rank+i)\mod len(M)][( rank+i)\mod len(M)]+i+1\)
8:endfor
9:\(rank,flag\leftarrow\textsc{CheckFullRank}(M)\)
10:endwhile
11:return M\({}^{\prime}\)
12:endprocedure
13:functionCheckFullRank(M)
14:\(rank\leftarrow\) the rank of M
15:if M is not full rank then
16:return\(rank,\) false
17:else
18:return\(-1,\) true
19:endif
20:endfunction
```  
**Algorithm 1** Handle Full Rank