esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Relationship Between Spatial and Temporal Distribution of Sites and Environmental Factors  
#### 3.2.1 Elevation  
The elevation range of the study area spans from 107 m to 7473 m. In ArcMap, site points from different periods were overlaid onto a digital elevation model (DEM) map. The number and percentage of sites within various elevation ranges were calculated and visualized in statistical charts (Figures 5 and 6). During the Stone Age, the average elevation of site distribution was 1866.5 m. Approximately 20% of the sites were located below 500 m, followed by 18% within the 500-1000 m range and 14% within the 3000-3500 m range. Apart from these ranges and a small cluster (3%) within 4000-4500 m, site distribution across other elevation intervals was relatively uniform. In the Shang to Spring and Autumn periods, the average elevation of site distribution significantly decreased to 977.8 m. Human settlements were predominantly concentrated in areas below 1000 m, accounting for 76% of the total sites, with only a few scattered at higher elevations. This lower elevation distribution was mainly attributed to the concentration of sites in the Chengdu Plain. During the Warring States period, the average elevation of site distribution increased again to 1561.5 m. The most densely distributed sites were within the 1500-2500 m range, accounting for 48% of the total, while only a few sites were found above 3000 m. Overall,  
Figure 4: Statistical chart of the number of heritage sites in different periods in various cities of Sichuan Province.  
the analysis revealed a general downward trend in site distribution elevation from the Stone Age to the Warring States period. After dropping below 1000 m during the Shang to Spring and Autumn periods, the average elevation of site distribution rose again, which may be related to environmental changes in the Chengdu Plain.  
Figure 5: Distribution of heritage sites in different elevations.  
Figure 6: Percentage of heritage sites in different elevations.
# Viability of ABO Blood Typing with ATR-FTIR Spectroscopy
## 3 Results and Discussion
### Linear Discriminant Analysis (LDA)  
The whole pool of 111 samples was randomly divided into a training dataset (78 samples) and a test dataset (33 samples) and then the LDA training was performed. This procedure was repeated six times, each of them with different training and test datasets. Figure 3 (up) shows the visual representation of the LDA algorithm. It is obvious that the B-type samples are differently grouped than the A-type samples. However, the AB and O samples, although more or less self-grouped, overlap with either B (in the case of AB) or A (in the case of O). These results suggest that infrared is more sensitive to the B antigen, as the B-antigen-containing samples (B and AB) appear grouped and segregated from the samples not containing the B-antigen (A and O). The obtained results were significantly better than a pure random classification (\(p<0.02\), Table 4 up), obtaining 48% of correctly classified samples versus 29% of expected success in a mere random classification. Most  
Figure 2: PC representation of samples. Color represents ABO type (**up**) or Rh factor (**down**).  
difficulties are found in the O samples classified as A (15%) and A classes classified as O (7%), which is in clear agreement with the visual representation of Figure 3.  
Since the AB samples contain both A and B antigens and, therefore, spectroscopic characteristics common to both A and B types, the inclusion of the AB samples in the training dataset could probably lead to difficulties in the classification. Therefore, we trained a second model including A, B, and 0 samples only. The samples were randomly  
Figure 3: LDA representation for training (circles) and test (stars) datasets. Color of test dataset belongs to their real class, and not to the assigned one. (**Up**): A, B, AB, and O blood types used; (**down**): A, B, and O only.  
split into training and test datasets, and the procedure was repeated six times. The graphical representation of this LDA classification is shown in Figure 3. The training samples from A and B sets clearly segregate, establishing two well-differentiated areas; training samples of O type, however, overlap with those of A type, but not with those of B type. Only the B-type samples achieve an improvement in all categories, with an increment in the rates of true positives and negatives, and a drastic reduction in the percentage of false positives and negatives. However, and despite the improvement in the ratio of true positives of both A and O samples, the ratio of false positives (for O type) and of false negatives (for A type) also increases. In this case, 63% of the samples are correctly classified with, again, a high wrong classification in the O samples taken as A and the A samples taken as O. This situation is not likely to be related to the chemical difference on the antigens, since there is a complex interrelationship between blood type and blood chemistry, which can participate in the ABO-type identification without being antigens themselves.
## 3 Results and Discussion
#### 3.1.1 Film Thickness  
Thickness is an important parameter of the film and can be affected by the film-forming material and its composition [40]. In this study, thickness of the seaweed hydrocolloid/gac pulp edible films ranged from around 0.06 mm to 0.10 mm and was significantly (\(p\) < 0.05) affected by the inclusion of essential oils and plant extracts (Figure 1). GPP films had the lowest thickness, with of 0.06 mm. Overall, the GPP-based films were thinner than other films. These findings are in alignment with a previous study by [PERSON] and [PERSON] [41] who reported that the thickness of pectin-based edible films was lower than that of other polysaccharide-based edible films. The results also showed that the thickness of edible films which incorporated gac oil, blueberry ash and macadamia extract was approximately 0.1 mm, which was thicker than the films without additives, which had a thickness of less than 0.08 mm. These findings are in agreement with a previous study by [PERSON] and [PERSON] [42] who reported that edible films incorporated with plant extracts were thicker than the films without additives. In addition, the results also indicated that the type of essential oils and plant extracts affected the thickness of the films. The films incorporated with gac oil were thicker than the films incorporated with other essential oils. In the literature, film thickness has been reported to depend not only on processing parameters, but also on film composition [43]. Therefore, the nature of solid material of particular additives may effect the way they enter and distribute in the film network differently [44]. As a result, film which incorporated gac oil, blueberry ash extracts and macadamia extract were thicker than films prepared without additives, while all other additives showed no significant effect on the thickness of the film.
# Efficient QC-LDPC Encoder for 5G New Radio
## 3 LDPC Encoding Algorithms
### LDPC Encoding with Gaussian Elimination  
The Gaussian elimination is the most conventional method of encoding LDPC codes, which is carried out by the multiplication of the generator matrix \(G\), and contains a complexity quadratic in the block length [19]. The unknown generator matrix \(G\) can be derived from the parity check matrix \(H\). A generator matrix for code with a parity check matrix \(H\) can be obtained by carrying out Gauss-Jordan elimination on \(H\) in the following form:  
\[H=\begin{bmatrix}A&I_{N-K}\end{bmatrix}, \tag{6}\]  
where \(A\) is an \((N-K)\times K\) binary matrix and \(I_{N-K}\) is the identity matrix of order \((N-K)\). The generator matrix is as follows:  
\[G=\begin{bmatrix}I_{K}&A^{T}\end{bmatrix}. \tag{7}\]  
The codeword \(C\) is then obtained by multiplying the generator matrix \(G\) by the systematic bits \(S\) as follows:  
\[C=SG. \tag{8}\]  
The sequential LDPC encoder based on the multiplication of the \(G\) matrix requires a ROM to store the generator matrix used to compute the codeword \(C\). The main drawback of this approach is that, unlike parity check matrix \(H\), the corresponding generator matrix \(G\) will most likely not be sparse. The complexity of this straightforward encoding algorithm is \(O(N^{2})\), where \(N\) is the number of bits in a codeword. Therefore, the implementation of the matrix multiplication at the encoder results in a very high complexity. For an arbitrary parity check matrix, the construction of \(G\) should be avoided and encoding should be carried out using back substitution with \(H\).
## 3 Results and Discussion
### Effect of the Pore Water of Concrete  
Moisture within the concrete's pores is essential for the passage of electrical current, through which resistivity can be determined. Lowering the moisture content will restrict the current flow and will consequently increase the measured resistivity. For this reason, the measurement of electrical resistivity is strongly influenced by the amount of water in the materials (Figure 6). According to [PERSON] et al. (2014) and [PERSON] (2010), the electrical resistivity of water in the pores is about \(5\times 10^{-2}\) K\(\Omega\)-cm. According to [PERSON]'s experimental results ([PERSON] et al., 2016), completely dried concrete does not allow ions or charges to move. Therefore, the small amount of pore water in concrete can lead to high conductivity and low resistivity.  
Figure 7 shows the relationship between the electrical resistivity and the volumetric fraction of the capillary pore water of the specimens. The amount of capillary water in the pores was estimated by the cement hydration model proposed by [PERSON] (2014). The maximum volumetric fraction of the capillary pore water was approximately 0.6 and the ratio of the electrical resistivity of the air-dried concrete to that of the saturated concrete decreased dramatically as the amount of pore water increased. The relationship between the electrical resistivity ratio of the concrete and the volumetric fraction of the capillary water in the pores is expressed in Equation.  
In particular, the electrical resistivity ratio of air-dried to saturated concrete is very sensitive to the amount of capillary water in the pores. A sudden decrease in the resistivity ratio from 0.3 to 0.45 was observed. However, a gradual decrease of the ratio of air-dried to saturated concrete was observed as a function of the volumetric fraction of the capillary water in the pores between 0.45 and 0.6. From these observations, it can be concluded that the capillary pore water in concrete significantly influences its electrical resistivity and its relationship can be expressed as follows:  
\[\rho_{air}=1.822\times 10^{4}\exp\left[-17.079\left(\frac{V_{water}}{V_{ pore}}\right)\right]\cdot\rho_{sat} \tag{2}\]  
Figure 5: Electrical resistivity measurements of the air-dried concrete without chloride.
## 4 Discussion
### Subjective Assessment  
In this part, we first describe the subjective assessment of the experimental results. Figures 5a, 6a and 7a show these three test multispectral images selected from many tested images of the multispectral WorldView-3 remote sensing image of Rio de Janeiro, Brazil. Figures 5b, 6b and 7b present the corresponding reference images that were manually interpreted from these three test images in panchromatic version respectively. These reference images are regarded as ground-truth  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time Used (ms)** & **A** & **B** & **C** \\ \hline SRI [11] & 89 & 62 & 55 \\ NSVDI [18] & 60 & 42 & 38 \\ MC3 [20] & 90 & 49 & 56 \\ SI [14] & 263 & 258 & 277 \\ SDI [19] & 276 & 258 & 253 \\ MPSI & **72** & **49** & **47** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Time consumption of test images A–C shown in Figures 5a, 6a and 7a, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **PA (\%)** & **SP (\%)** & **EO (\%)** & **EC (\%)** & **OA (\%)** \\ \hline SRI [11] & **97.16** & 68.75 & **2.84** & 31.25 & 86.94 \\ NSVDI [18] & **99.94** & 11.15 & **0.06** & 88.85 & 68.01 \\ MC3 [20] & 79.20 & 88.52 & 20.80 & 11.48 & 82.55 \\ SI [14] & 62.08 & **93.70** & 37.92 & **6.30** & 73.45 \\ SDI [19] & **94.99** & **90.21** & **5.01** & **9.79** & **93.27** \\ MPSI & **95.63** & **91.76** & **4.37** & **8.24** & **94.24** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Shadow detection accuracy data for test image B in Figure 6a.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **PA (\%)** & **SP (\%)** & **EO (\%)** & **EC (\%)** & **OA (\%)** \\ \hline SRI [11] & **98.88** & 28.69 & **1.12** & 71.31 & 75.60 \\ NSVDI [18] & **99.72** & 24.87 & **0.28** & 75.13 & 74.89 \\ MC3 [20] & 84.52 & 76.17 & 15.48 & 23.83 & 81.75 \\ SI [14] & 64.19 & **96.94** & 35.81 & **3.06** & 75.06 \\ SDI [19] & 86.68 & **94.45** & 13.32 & **5.55** & **89.26** \\ MPSI & **97.19** & **92.10** & **2.81** & **7.90** & **95.50** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Shadow detection accuracy data for test image C in Figure 7a.  
images when assessments were applied over the resulting images using various shadow detection methods, such as the SRI-based method in [PERSON] [11], the NSVDI-based method in [PERSON] et al. [18], the MC3-based method in [PERSON] et al. [20], the SI-based method in [PERSON] et al. [14], the SDI-based method in [PERSON] et al. [19], and our proposed MPSI-based approach. To validate the effectiveness of our proposed approach in resolving the problems of shadow omission and vegetation misclassification, we chose these three test images because they mainly include many small shadow regions and high coverage of vegetation, named test images A, B, and C, as shown in Figures 5a, 6a and 7a, respectively. Assessments of test images A, B, and C are described in detail as follows.
## 2 Background
### Dynamic Characterization and Processing Techniques
between test and analytical modal vectors to ascertain the degree of consistency among mode shapes, as described in Equation (4) [22]:  
\[\text{MAC}(r,q)=\frac{\left|\{\varphi_{A}\}_{r}^{T}\{\varphi_{X}\}_{q}\right|^{2} }{(\{\varphi_{A}\}_{r}^{T}\{\varphi_{A}\}_{r})(\{\varphi_{X}\}_{q}^{T}\{\varphi_ {X}\}_{q})} \tag{4}\]  
where \(\{\varphi_{A}\}_{r}\) is the analytical modal vector corresponding to mode \(r\), \(\{\varphi_{X}\}_{q}\) denotes the test modal vector corresponding to mode \(q\), and \(T\) represents the transpose matrix. For the outreach of this paper, the considered MAC acceptance level was 0.80 as a typical value suggested in [23]. Larger values than 0.80 mean high significance consistency, whereas small values represent a poor correlation between mode shape vectors. Time-frequency analysis was employed to identify and assess the natural frequencies of the tested structures throughout time. This technique enables tracking the frequency content of civil structures over time windows to retrieve the external frequencies (as operational, ambient, and noisy frequencies) from the original dynamic system response. Spectrogram methods commonly are based on discrete Fourier transform (DFT), computed through [24]:  
\[x(n)=\sum_{k=0}^{N-1}c_{k}e\frac{J2\pi kn}{N} \tag{5}\]  
where \(x(n)\) is the discrete periodic signal in time \(n\), \(N\) is the signal period, \(J\) is the complex number, and \(c_{k}\) are the Fourier series coefficients.
## 3 The Proposed SSM-NTF
### Stable Sparse Model with Non-Tight Frame
Therefore, the RIP constraint can be enforced on the frames by limiting the maximum and minimum singular values. In this paper, we integrate non-frame to traditional sparse model to establish a stable sparse model with RIP. Let \(\mathbf{x}\) be a signal vector. Under the assumption of the sparsity prior of \(\mathbf{\Psi}^{T}\mathbf{x}\), we apply a soft thresholding operator \(\mathcal{S}_{\mathbf{\lambda}}(\cdot)\) (which shall be defined in the next subsection) on it such that  
\[\mathbf{x}=\mathbf{\Phi}\mathcal{S}_{\mathbf{\lambda}}(\mathbf{\Psi}^{T}\mathbf{x}) \tag{12}\]  
where \(\mathbf{\lambda}\) is a vector with elements \(\lambda_{i}\) corresponding to \(\mathbf{\psi}_{i}\), \(i=1,2,\ldots,M\). Therefore, we propose the stable sparse model with non-tight frame (SSM-NTF) as follows  
\[\mathbf{y}=\mathcal{S}_{\mathbf{\lambda}}(\mathbf{\Psi}^{T}\mathbf{x}), \quad\mathbf{x}=\mathbf{\Phi}\mathbf{y}, \tag{13}\] \[\text{s.t.}\ \ \|\mathbf{y}\|_{0}\leq\text{s.}\]  
Here, the correlation between the frame \(\mathbf{\Phi}\) and its dual frame \(\mathbf{\Psi}\) is formulated as \(\mathbf{\Psi}=\mathbf{F}^{-1}\mathbf{\Phi}\). The frame operator \(\mathbf{F}\) is formulated as \(\mathbf{\Phi}\mathbf{\Phi}^{T}\) which is indeed a gram matrix of \(\mathbf{\Phi}\). The singular values of \(\mathbf{\Phi}\) are constrained by \(\sqrt{A}\leq\eta_{\mathbf{\Phi}}\leq\sqrt{B}\) to satisfy the RIP. Actually, by constraining the singular values of \(\mathbf{\Phi}\), the elements of the gram matrix are also bounded which meets the theory of mutual coherence. In order to be consistent with the traditional sparse models, we refer to the frame \(\mathbf{\Phi}\) and its dual frame \(\mathbf{\Psi}\) as dictionary and its dual dictionary.
## 5 Performance Analysis
### Experiment 4  
Next, we increased the spatial diversity of the data by using four antennas for both transmission (\(N_{t}\)) and reception (\(N_{r}\)). We compared the BER performance of the proposed MIMO-OFDM-IM system with OSIC detection against the MIMO-OFDM-IM system with MMSE detection and the conventional MIMO-OFDM system. The simulation parameters used for the experiment are listed in Table 3.  
The results from Figure 6 in low \(E_{b}/N_{0}\) regions demonstrate close performance between the proposed MIMO-OFDM-IM system with OSIC detection and the MIMO-OFDM-IM system with MMSE detection. However, the proposed detector requires lower computational complexity. Compared to the conventional MIMO-OFDM system, the proposed system is not affected by burst errors caused by ICI, resulting in significant BER improvement of approximately 2.5 dB above 10 dB of \(E_{b}/N_{0}\). This result not only highlights a significant trade-off between computational complexity and achieved BER by increasing the number of antennas in the system but also demonstrates the feasibility of implementing the proposed system.  
Figure 4: BER-vs-\(E_{b}/N_{0}\) comparison of different detection algorithms for MIMO-OFDM-IM and MIMO-OFDM with \(N_{t}=2\), \(N_{r}=2\), and 4-QAM data modulation.  
Figure 5: BER-vs-\(E_{b}/N_{0}\) comparison of different detection algorithms for MIMO-OFDM-IM and MIMO-OFDM with \(N_{t}=2\), \(N_{r}=2\).  
Figure 6: BER-vs-\(E_{b}/N_{0}\) comparison of different detection algorithms for MIMO-OFDM-IM and MIMO-OFDM with \(N_{t}=4\), \(N_{r}=4\).
# Perspectives and Challenges in Robotic Neurorehabilitation
## 2 An Overview of Robotic Devices for Neurorehabilitation  
Robotic devices for neurorehabilitation can be classified into two main categories based on the different types of physical human-robot interaction: end-effector devices and exoskeletons.  
End-effector-based systems are robotic devices provided with a specific interface that mechanically constrains the distal part of the human limb (e.g., the human wrist). These systems do not control the whole kinematic chain and the human limb is free to completely adapt either to external disturbances or to movements applied by the end-effector robot. In this type of device, it is thus only possible to directly control the distal body segment that is attached to the end-effector; further information about forces and/or positions of the remaining parts of the human limb can be obtained indirectly.  
Exoskeletons, on the contrary, exactly reproduce the kinematics of the human limb and support its movements through the control of the position and the orientation of each joint. The devices are designed with the specific purpose of coupling and aligning the mechanical joints to the human ones. As an example, in the context of upper limb robotics, the devices are linked to the limb either at the level of the arm and/or at the forearm. In addition, the range of motion (ROM) and the number of the actuated joints are appropriately chosen to optimize the control. Therefore, by using the exoskeleton, the patient's movements are more supervised but at the cost of a higher complexity for the control of the degrees of freedom (DOFs).  
In the following section, an overview of the available robotic devices for the upper and lower limb is provided, including a detailed description of two newly developed systems treated as case studies.
# Accelerating Electromagnetic Field Simulations Based on Memory-Optimized CPML-FDTD with OpenACC
## 4 Simulation Settings and Results  
Two GPUs were used an NVIDIA GeForce RTX 3060 with a compute capability of 8.6 with 12 GB and an NVIDIA TITAN RTX with a compute capability of 7.5 with 24 GB of NVRAM and the NVIDIA HPC FORTRAN, C++, and C Compilers with OpenACC version 22.9. The processor is an Intel Xeon E5-2630v4 2.2 Ghz with ten physical cores.  
Simulations were carried out on domains that require computational demand in single and double precision, and the reference solution was calculated with the CPU in double precision to check solutions accuracy from GPU. Clearly, single precision reduced memory storage by a factor of almost two, and the GPU performed the floating point operations faster. Indeed the first GPUs for general purposes applications only supported single precision.  
Single precision is very convenient for memory saving but can introduce precision errors. Notwithstanding there are no studies reported in the literature using single precision for FDTD solving Maxwell with GPU. Long-term simulations require a lot of time steps, potentially introducing oscillations or spurious returning waves to the computational domain, for this reason the simulations results has been compared versus the CPU obtained.  
For a fair comparison experiments carried out on the CPU were highly optimized with vectorization, the flag compiler used to generate the vectorization report is -Minfo=vect and according to the report, all the cycles were successfully vectorized. The versions in the CPU are labeled CPU-M (Memory Saving Version) and CPU-V (Vectorized Version), respectively. On the GPU, experiments with memory saving (GPU-MD) and without memory saving (GPU-VD) are executed in double precision, as well as in single-precision for memory saving (GPU-MS) and without memory saving (GPU-VS).  
This work considered three simulation scenarios at different frequencies, giving seven experiments performed in two cards:  
* Free space propagation at 2.45 Ghz, 5.00 Ghz and 20 Ghz.
* Parabolic reflector at 2.45 Ghz, 5.00 Ghz and 20 Ghz.
* Coplanar nano-waveguide (CNWG) at 100 Thz.  
Figure 5: Flux diagram of design in OpenACC maintaining persistent data regions between loops.
## 4 Trust Assessment Using Various Approaches
### Statistical Methods: Weighted Sum  
Weighted sum methods play a pivotal role in assessing the level of trust in 6G networks, offering several advantages in trust evaluation. These methods aggregate diverse trust parameters, such as reliability, honesty, and context-awareness, enabling a more adaptive and context-specific trust evaluation process [52]. By incorporating subjective opinions and uncertainty, weighted sum models enhance the flexibility and robustness of trust assessment in dynamic 6G network environments [53]. Moreover, these models can be tailored to specific application domains, allowing for the customization of trust evaluation criteria based on the unique requirements of 6G networks [54]. However, weighted sum methods also present challenges, such as difficulty in determining appropriate weights for trust parameters, which may introduce bias and inconsistency. Additionally, the interpretability of the weighted sum approach may be limited, as the rationale behind assigned weights may not be transparent, potentially hindering trust establishment [55; 56].