esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Comparison between the RVE Model and Published Laboratory Experiments
The reason for this difference was most likely the relatively large number of tensile activated contacts at the moment that the uniaxial loading phase started, in comparison to zero activated contact elements when an initial isotropic stress state of 10 MPa was applied, as shown in Figure 7a. In other words, the effect of the preceding unloading phase from (25, 25) MPa to (10, 25 MPa) was large, and this interval weakened the rock significantly. When looking at the case of an unloading interval of 10 MPa, until a stress level of 15 MPa occurred at the start of the successive loading interval, the effect of the unloading interval was smaller. As mentioned above (Figure 11c), at the moment of the change from unloading to loading in the other direction, 9.5% of all available contact elements had failed. At the moment of failure, the distribution of the different failure modes was more similar to what was observed for a uniaxial loading only from an initial isotropic stress state of 15 MPa (Table 2 (a) and Table 5 (b)). For the latter stress path, 18% of the failed contacts were in tension only, 52% were in shear only, and 30% were in mixed mode. For the unloading that was followed by loading in the other direction, these percentages were 26%, 56%, and 18%, respectively. In other words, in comparison to the large unloading interval of 15 MPa (Figure 11a), now there was far less activation in tension and much more activation in shear (Figure 11c), which resembled the stress path with only uniaxial loading more. The case with a change from unloading to loading at a minor principal stress of 12.5 MPa (Figure 11b) clearly showed the transition from the behaviour for 10 MPa to the one for 15 MPa. These additional simulations, referring to the stress paths applied in the laboratory [28], indicated that the modelling approach to quantify the effect of stress paths indeed has merits. The importance of (micro-)fracturing during a specific interval in a stress path is of the upmost importance when analysing the failure of rock. Even though the RVE model is relatively small and simple, the interpretation of the data shows a logic behaviour, e.g., when analysing the different failure modes, and it provides useful insights concerning the behaviour of the rock. The simulations also show that the behaviour and failure load are sensitive to the various stress levels. Unloading the minor principal stress over an interval of 10 or 15 MPa makes a significant difference in the micro-fracturing and thus in the final strength.
## 2 Methods
### Full-Wavefield Least-Squares Migration Imaging  
The use of least-squares migration to eliminate imaging artifacts, improve the spatial resolution, and balance the imaging amplitude has been widely studied. It obtains the optimal reflection coefficient through least-squares fitting of the observed data and simulated data, and the mathematical expression is as follows:  
\[\begin{array}{ll}J(\mathbf{m})=\sum\limits_{\omega}\lVert\Delta\mathbf{d} \rVert_{2}^{2}=\sum\limits_{\omega}\lVert\mathbf{d}_{obs}-L(\mathbf{m}) \rVert_{2}^{2}\\ \mathbf{m}=\operatorname*{argmin}\limits_{\omega}\lVert\mathbf{d}_{obs}-L( \mathbf{m})\rVert_{2}^{2}\end{array}, \tag{16}\]  
where Equations (5)-(9) define \(L\) as the full-wavefield forward operator, \(\mathbf{m}\) is the imaging result, and \(\mathbf{d}_{obs}\) is the observed data.  
The gradient term of the reflection coefficient can be expressed as follows:  
\[\mathbf{g}(\mathrm{x})=L^{\mathrm{T}}(\mathbf{d}_{obs}-\mathbf{d}_{cal}), \tag{17}\]  
where \(\mathbf{g}(\mathrm{x})\) is the imaging gradient, \(L^{\mathrm{T}}\) is the adjoint operator of the forward operator \(L\), and \(\mathbf{d}_{cal}\) is the demigration data. For stability, we chose the steepest descent method:  
\[\mathbf{m}_{k+1}(\mathrm{x})=\mathbf{m}_{k}(\mathrm{x})+\alpha_{k}\mathbf{g}_ {k}(\mathrm{x}), \tag{18}\]  
where \(\alpha_{k}\) is the linear search step size for the kth iteration.  
By incorporating the multi-order wavefield global normalization imaging condition, the gradient solution for full-wavefield migration becomes  
\[L^{\mathrm{T}}\mathbf{d}_{obs}=\sum\limits_{\omega}\frac{\sum\limits_{nshot}U _{B}(x,\omega)\left(D_{F}^{0}(x,\omega)\right)^{*}}{\sum\limits_{nshot}D_{F}^{ 0}(x,\omega)\left(D_{F}^{0}(x,\omega)\right)^{*}}, \tag{19}\]\[L^{\rm T}(\mathbf{d}_{\rm obs}-\mathbf{d}_{\rm cal})=\sum\limits_{\omega}\frac{ \sum\limits_{nshot}\Delta U_{\rm B}(x,\omega)\big{\{}D_{\rm F}^{0}(x,\omega)+D_{ \rm F}^{1}(x,\omega)+\cdots\big{\}}^{*}}{\sum\limits_{nshot}\big{\{}D_{\rm F}^{0 }(x,\omega)+D_{\rm F}^{1}(x,\omega)+\cdots\big{\}}\big{\{}D_{\rm F}^{0}(x, \omega)+D_{\rm F}^{1}(x,\omega)+\cdots\big{\}}^{*}}, \tag{20}\]  
where \(\delta U_{\rm B}(x,\omega)\) is the residual data backward-propagating upward wavefield.  
By applying the above algorithm, we established a least-squares migration method with a multi-order wavefield global normalization imaging condition, improving the resolution of the gradient term and providing additional illumination compensation for deep areas, which is beneficial for the inversion problem.
# Optimal Resource Allocation Policies for Multi-User Backscatter Communication Systems
## 2 System Model  
As shown in Figure 1, we consider a BackCom system consisting of one two-antenna reader and \(N\) single-antenna tags. BackCom is studied based on frame slots. The time duration of each frame is given as \(NT\) seconds. Each tag is equally allocated with a fixed time duration (given as \(T\) seconds), and can only work during its allocated duration. We assume that full-duplex mode at the reader is enabled by employing the perfect self-interference cancellation. During the duration of each tag, the reader transmits excitation signals to the tag and concurrently receives backscattered signals modulated by the tag.
# Device Free Detection in Impulse Radio Ultrawide Bandwidth Systems
## 2 System Model
### Channel Model  
The Saleh-Valenzuela (SV) channel model has been proposed for UWB communication in the 3 GHz to 10 GHz range [31; 50; 51; 52; 53; 54]. The impulse response of the SV channel model is represented as  
\[h(t) = \sum_{v=0}^{V-1}\sum_{u=0}^{U-1}h_{u,v}\delta(t-T_{v}-T_{u,v}), \tag{2}\] \[= \sum_{l=0}^{L-1}h_{l}\delta(t-T_{l}),\]  
where \(h_{u,v}\) is the tap weight of the \(u\)-th ray in the \(v\)-th cluster, \(T_{v}\) represents the delay of the \(v\)-th cluster, \(T_{u,v}\) is the delay of \(u\)-th ray in the \(v\)-th cluster, \(U\) is the total number of rays and \(V\) is the total number of clusters experienced by the transmitted signal. As shown in (2), the rays of all the clusters can be expressed as \(L\) multipaths, where \(L=UV\) and \(T_{l}\) represent the delay of the \(l\)-th multipath [55; 56].
## 6 Implementation of Structural Reliability Assessment on the OWT Support Structure
### Stochastic Variables and FEA  
The stochastic variables considered in this study are presented in Table 8. Eight stochastic variables were considered for all cases (i.e., wind thrust, tilting moment, torsional moment, the weight of RNA, hydrodynamic load, and three Young's moduli were considered at three soil strata). The total hydrodynamic load is considered differently, based on its dependence on the wave height, wave period, and current speed. To find the peak hydrodynamic force acting on the system, a Weibull distribution is applied to the significant wave height and the current speed, while the lognormal distribution is applied to the wave period [2; 90; 91]. The distribution is then fitted into a Weibull-equivalent distribution. In the presence of observed data, distribution fitting algorithms (such as Akaike information criterion, Bayesian information criterion, and Kolmogorov-Smirnov) are often employed to determine the shape coefficients of the most appropriate statistical distributions [19]. The coefficient of variation (COV) of all stochastic variables was assumed to be 0.1 except for the stochastic variables with Weibull distribution. The mean values of these stochastic variables are presented in Table 2, Table 4, and Table 5. This is summarized in Table 9, which depicts the means and the corresponding standard deviations. Having defined the stochastic variables, the FEA model presented in Section 5.1 is then used to perform FEA simulations of the OWT support structure with the help of the design of experiments (DoE) module in ANSYS. It enables the input parameters to be designated as stochastic parameters, having different types of distributions. DoE indicates the variation of design points with the output parameter.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Description** & **Distribution Types** \\ \hline Wind thrust & Normal [2; 90; 91] \\ Tilting moment & Normal [2] \\ Torsional moment & Normal [2] \\ Weight of RNA & Normal [90; 91] \\ Young’s modulus of soils & Normal [2; 90; 91] \\ \hline \hline \end{tabular}
\end{table}
Table 8: Stochastic variables.
## 2 Background
### Explainability in AI  
Deep learning models have shown to be reliable, highly effective and accurate in a wide range of research fields, but we do not exactly know how these models make predictions and why specific conclusions are reached; these are concerns that limit our trust in them. We can think of deep learning models as black boxes that receive input and produce output, but we do not understand the complex processes that happen inside. We would like to have models that are reliable, accurate and transparent, so they can be trusted. There are several reasons why explainability in AI is essential:  
1. Enhances understanding of models output. Users of AI models can make informed decisions if they understand how the models work.
2. Reduces the number of errors. Explainability can help spot model anomalies that allow us to design more accurate models. Explainability also helps to learn from mistakes and train the models to prevent them.
3. Provides clarity about models output and strengthens our confidence in them. This is essential to build trust and have AI models adopted and accepted. XAI explanations can be classified into the following three categories [3; 29]: visual explanations, textual explanation and example-based explanations.  
1. **Visual Explanations** A visual explanation of a medical image is vital to a reliable analysis of the image. Visual explanations that show important parts of an image and can be used to justify decisions are called _saliency_ or _heat maps_[5; 23; 30].
2. **Textual Explanations** Text-based explanations provide descriptions of model output. A description may consist of a simple labeling of an image's contents or an entire medical report [19; 31].
3. **Example-based Explanations** In example-based explanations, examples are provided to help understand why predictions are made by deep learning models. For instance, a previously diagnosed patient who had the same symptoms as a new patient can be used to understand a diagnosis made by the model [32].  
XAI methods can be classified into two groups: ante hoc and post hoc. The term post hoc refers to methods that are used to generate explanations for a model's output using the trained model, whereas ante hoc techniques create explanations during the training stage of the model.  
XAI methods can also be classified as global or local. A local approach provides explanations for the output produced by a deep learning model for a single, specific instance. Most local approaches are model agnostic, which means that they do not require knowledge of how the deep learning model works. Global approaches aim to explain the logic behind the functioning of a deep learning model, and therefore, these approaches are model-dependent.
# Machine Learning and rs-fMRI to Identify Potential Brain Regions Associated with Autism Severity
###### Abstract  
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized primarily by social impairments that manifest in different severity levels. In recent years, many studies have explored the use of machine learning (ML) and resting-state functional magnetic resonance images (rs-fMRI) to investigate the disorder. These approaches evaluate brain oxygen levels to indirectly measure brain activity and compare typical developmental subjects with ASD ones. However, none of these works have tried to classify the subjects into severity groups using ML exclusively applied to rs-fMRI data. Information on ASD severity is frequently available since some tools used to support ASD diagnosis also include a severity measurement as their outcomes. The aforesaid is the case of the Autism Diagnostic Observation Schedule (ADOS), which splits the diagnosis into three groups: 'autism', 'autism spectrum', and 'non-ASD'. Therefore, this paper aims to use ML and fMRI to identify potential brain regions as biomarkers of ASD severity. We used the ADOS score as a severity measurement standard. The experiment used fMRI data of 202 subjects with an ASD diagnosis and their ADOS scores available at the ABIDE I consortium to determine the correct ASD sub-class for each one. Our results suggest a functional difference between the ASD sub-classes by reaching 73.8% accuracy on cingulum regions. The aforementioned shows the feasibility of classifying and characterizing ASD using rs-fMRI data, indicating potential areas that could lead to severity biomarkers in further research. However, we highlight the need for more studies to confirm our findings.  
ABIDE; ASD; autism spectrum disorder severity classification; fMRI; machine learning +
Footnote †: journal:  
Article
## 4 Performance Evaluation
### TCN-TS-SW versus Other Machine Learning (ML) Algorithms  
To evaluate the performance of the proposed TCN-TS-SW in terms of training loss value as compared to other machine learning algorithms namely multi look-back LSTM (MLB-LSTM) [32], gated recurrent unit (GRU), and LSTM. A dataset is accumulated by utilizing the experiment setup described in Table 1. Note that the sampling rate of Arduino uno is set to 3000 samples/min. As a result, the dataset contains 328,470 samples from just over 109 min of execution time. It is then assigned to the training set and validation set on a 75/25 ratio.  
The performance of the proposed TCN-TS-SW in terms of training loss value as compared to other machine learning algorithms namely multi look-back LSTM (MLB-LSTM) [32], gated recurrent unit (GRU), and LSTM is presented on the Figure 4a.  
The results showed that TCN-TS-SW reaches the lowest training loss value over 40 epochs, and a good performance in terms of accuracy is illustrated in Figure 4b. Although the proposed scheme's loss is the highest in the first epoch, it was able to converge with the lowest training loss after 40 epochs, outperforming the MLB-LSTM, GRU, and LSTM.  
As presented in Table 2, the proposed TCN-TS-SW resulted to the lowest RMSE and MAE values which indicates a good fitting prediction model. The proposed model also got the highest r-squared score indicating a low variation between the forecasted values compared to the actual temperature readings, outperforming the other algorithms in all performance metrics presented.
# Spaceborne LiDAR Systems: Evolution, Capabilities, and Challenges
## 7 The Future of the Spaceborne LiDARs
### Future Missions
#### 7.1.2 Aeolus2  
Regarding the wind measurement, there are discussions within ESA of a follow-up operational mission of Aeolus, potentially Aeolus-2. This mission would deploy multiple Doppler LiDARs to provide continuous global wind data for weather forecasting, essentially moving from a demo to an operational system. Such a system might address Aeolus's weaknesses by using improved lasers (maybe in the UV or even eye-safe bands) with longer lifetimes and possibly two satellites for morning/evening orbit coverage [169; 170].
## 3 Results and Discussion  
### Determining the Number of Clusters  
The results of performing silhouette and elbow analyses on the 51 geospatial layers are shown on the left and right of Figure 1, respectively. Recall that a higher average silhouette score is indicative of better clustering, so the silhouette analysis identifies eight clusters as the optimal number. The results of the elbow analysis are more challenging to interpret because identifying the \"elbow\" in the plot (i.e., the location at which adding another cluster begins to only marginally reduce the inertia) is somewhat subjective. However, the \"elbow\" appears to be around seven or nine clusters. Given the subjective nature of determining the location of the \"elbow,\" we gave more weight to the results of the silhouette analysis. Therefore, we used eight as the optimal number of clusters since both silhouette and elbow analyses indicate this is a reasonable choice. We note that more advanced methods of determining the number of clusters exist [39; 40; 41] and may be worth exploring in future work.
## 2 Materials and Methods
### Residual Stresses  
Residual stress measurement techniques fall mainly into either of the two categories of destructive or non-destructive methods. Destructive techniques include sectioning, hole drilling, nanoindentation, cut compliance methods, etc. [31; 32; 33; 34]. The fundamental concept behind these techniques is to mechanically remove a section of the workpiece by slitting or sectioning and measure the resulting deformation, usually using strain gauges, and then reconstruct the initial residual stress field. Non-destructive methods include diffraction using X-ray or thermal neutrons from reactors. The principle is to use the lattice structure of crystalline materials as an intrinsic strain gauge. X-rays generated in the lab using standard X-ray sources have an attenuation length of a few micrometers, whereas X-rays from synchrotron sources and neutrons can penetrate the material up to a 7-8 mm [35; 36].  
The near-surface residual stresses in this work were measured by X-ray diffraction (XRD) using the device Xstress3000 with a G2R goniometer from the manufacturer stresstech (Vaajakoski, Finland). The experimental parameters for the stress measurements with XRD are shown in Table 2. The residual stresses were measured in between the non-uniform ribs of the rebars using the \(\sin^{2}\psi\) method [36; 37]. The penetration depth of the Cr K\({}_{\alpha}\) radiation was calculated to be 11 um in this alloy using the software xraylib [38]. Thus, the effective depth of measurement is about half of that, i.e., 5 um when the radiation is directed perpendicular to the surface. The in-depth stress profile was determined by removing thin layers of material using electrochemical etching and measuring the stresses on the new surface using XRD. The depth increment was measured using a high-precision dial gauge with a resolution of 1 um. Due to the roughness on the etched surface and the need to remove the specimen between etching and measuring steps, the accuracy reduced so that the realistic accuracy was ca. 10 um. The stress relaxation due to the removal of layers was corrected by using the stresses measured before the removal and the amount of material removed. The foundations for these corrections were laid by [PERSON] and [PERSON] [39] and are integrated into the software of the Xstress device.
# Hardware-Accelerated Data Readout Platform Using Heterogeneous Computing for DNA Data Storage
## 5 Modules of Hardware Acceleration Kernel
### Deinterleaver Module  
The deinterleaver module restores the original codeword order by applying row-column deinterleaving to the consensus sequence and erasure marker sequence. The module employs a dual RAM partition ping-pong architecture, as shown in Figure 9. It utilizes two RAM units, each with a depth of 64,800 and a width of 1 bit, to construct a 180-row \(\times\) 360-column interleaved matrix. The workflow of the deinterleaver module is as follows. Taking the 120-bit-wide consensus sequence deinterleaving as an example, the consensus is first converted from parallel to serial and written column-wise to partition A of RAM. Then, after partition A is filled with 64,800 bits, the consensus is written column-wise to partition B of RAM, while partition A reads out the data row-wise with a width of 8 bits. Finally, the codeword data are reassembled by a serial-to-parallel conversion unit into a 360-bit-wide parallel output.
## 4 Evaluation of Current Formula for Spectrum Usage Fee
### Problem of the Current Formula Used for 5G-mmWave  
In our previous work [41], we simulated the amount of the spectrum usage fee for 5G-mmWave that should be paid by the three biggest Indonesian service operators using the current formula. Figure 7 shows that when the Indonesian service operators use a bandwidth of more than 100 MHz; their profit tends to be dynamically under turbulence, with only Telkomsel (the biggest service operator in Indonesia) seeing positive growth up to the eighth year but then declining significantly.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Frequency Range (MHz)** & **Units** & **Basic Price Index (IDR)** \\ \hline
3400–4500 & per MHz & IDR 4508 \\
4500–5000 & per MHz & IDR 4393 \\
5000–8500 & per MHz & IDR 3811 \\
8500–11,700 & per MHz & IDR 3461 \\
11,700–12,750 & per MHz & IDR 3367 \\
12,750–15,400 & per MHz & IDR 3160 \\
15,400–22,000 & per MHz & IDR 2769 \\
22,000–31,300 & per MHz & IDR 2383 \\
31,300–52,600 & per MHz & IDR 1814 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The Indonesian basic price index value (I) for radio frequency bands [2].  
Figure 7: The trend of the service operators’ profit as a result of 5G implementation in Indonesia [41].  
Based on research by [49], if the service operators have a spectrum usage fee that is greater than their gross income, their business will become unsustainable.  
By using the current formula, some Indonesian service operators will lose profit even from the start of the 5G implementation. This phenomenon is due to the fact that the current formula uses the national population instead of the specific number of service subscribers. However, because the 5G private network provides limited coverage, it can serve only a certain part of the population. In short, the current spectrum usage fee formula is no longer feasible for the 5G private network, making the fee very expensive.  
This paper presents proposed alternative representative measures to assess the location value with limited geography for 5G-mmWave private network licenses. The opportunities lie in the fact that not only do mobile applications today and in the future need wider bandwidth, but also that their coverage area can be developed within a small area, rather than on a national scale, and used by companies in industrial areas.