esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Example Analysis
### Communication Technology Performance Index Data  
Based on the adaptability evaluation system proposed in this paper, indexes such as latency, bandwidth, bit error rate, security, cost, and transmission distance are selected for adaptability analysis. This paper obtains remote and local communication technology performance index data based on reference [19; 20; 21], as shown in Table 2. Notably, a hundred-point scale is used to quantify qualitative indexes such as security and cost.
## 4 Discussion
### Seasonal Variations in ET and WUE
A decline in WUE with drought in semi-arid/sub-humid ecosystems was also reported in an overview [31], where a decrease in ET that was smaller than the GPP response during the dry period led to lower WUE in drier years. Seasonal variations in WUE are complex and difficult to analyze, since WUE results from a trade-off at the ecosystem level between water loss and carbon gain, and numerous factors affect these fluxes, and to different extents. Divergent patterns in WUE have indeed been revealed under contrasted soil water conditions. In a study of a long-term, extreme, experimental drought [9], the increase in WUE was indeed caused by a stronger decline in ET than in GPP; yet, the observed pattern was different in the control treatment (no rain exclusion) where the moderate decline in soil water availability did not actually affect ET. We show that the decrease in WUE during WD periods was the result of both an increase in ET (this study) and GPP ([25]), but to different degrees (rates of 4%-18% for ET and 2%-10% for GPP, [25]). The observed reduction in WUE during the WD period as compared to the rest of the year cannot be solely explained by expected patterns of change in ET and GPP when environmental conditions become extremely dry. Indeed, the decrease we found was driven mainly by light availability (Table 2) and by a greater positive effect of increased radiation during the WD period on GPP than on ET. This result is consistent with previous modeling approaches pointing out that tropical rainforests are mainly light-limited rather than water-limited [46] and that radiation is a major driver of both ET and WUE [35]. The asynchronous response of GPP and ET illustrates the decoupling that can occur between carbon and water cycles in this ecosystem. Simulations of tropical rainforest ecosystem responses to climate change should include this decoupling to better predict carbon and water cycling at the planetary scale.
# AMP-GSM: Prediction of Antimicrobial Peptides via a Grouping-Scoring-Modeling Approach
## Materials and Methods
### Proposed Model  
In our earlier studies, in order to improve classification performance, we proposed grouping-based feature elimination techniques, e.g., SVM RCE [39], SVM-RCE-R [40], and SVM-RCE-R-OPT [41]. Recently, we proposed numerous tools which incorporate biological information into the machine learning algorithm to accomplish feature selection or to choose groups of features. maTE [42], CogNet [43], miRcorrNet [44], miRModuleNet [45], PriPath [46], 3 Mint [47], and Integrating Gene Ontology-Based Grouping and Ranking [48] followed this strategy. This technique is known as the GSM approach [49], which is the primary motivation for the development of our proposed approach within this study.  
The workflow of the proposed approach, AMP-GSM, is presented in Figure 1. AMP-GSM includes three main components: grouping, scoring, and modeling.  
3.1 Grouping Peptides Based on Physico-Chemical, Sequence-Based, Structure-Based, and Linguistic-Based Features  
The grouping component generates a list of groups where each group is composed of a feature set. An example output of this component is shown in Table 1. In our study, we have 12 groups, including physico-chemical, amino acid composition, dipeptide composition, physico-chemical composition, physico-chemical transition, physico-chemical distribution, normalized Moreau-Broto autocorrelation, Moran autocorrelation, Geary autocorrelation, sequence order coupling number, quasi-sequence order, and pseudo-amino acid composition. Each group has its own feature set. In Figure 2, the distribution of the features into different groups are shown.  
The groups created within the Grouping step are utilized to generate sub-datasets from the initial data. Each sub-data is composed of the properties belonging to the features within a particular group, retaining the original class labels of the peptides.  
Let Group\({}^{\text{n}}\) represent the n-th group from the Gram-negative or Gram-positive AMP dataset, which includes x different features and is denoted as Group\({}^{\text{n}}\) = {AMPFeature\({}^{\text{1}}\), AMPFeature\({}^{\text{3}}\),..., AMPFeature\({}^{\text{x}}\), class label} and Let g_features_subdata\({}^{\text{s}}\) represent the s-th group created by the grouping part of the AMP-GSM model, which includes a number of groups (i), denoted as g_features_subdata\({}^{\text{s}}\) = {Group\({}^{\text{1}}\), Group\({}^{\text{3}}\),..., Group\({}^{\text{i}}\)}.  
Figure 1: AMP-GSM workflow based on grouping, scoring, and modeling.
# Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis
## 5 A Taxonomy for Gait, Balance, RoM Analysis
### Gait Analysis
#### 5.2.2 Postural Sway: Asymmetry and Variability  
Physical therapists often use Modified Clinical Test of Sensory Interaction and Balance (mCTSIB) when evaluating postural sway [135]. In the mCTSIB, subjects have to complete four trials in four different conditions: _eyes open-firm surface_, _eyes closed-firm surface_, _eyes open-foam surface_, and _eyes closed-foam surface_. After completing the trials, the physical therapists calculate the asymmetry and variability between the measurements extracted in the different conditions. The asymmetry and variability extracted from the trials between eyes open vs eyes closed are known as the visual  
Figure 6: Convex Hull Example.  
dependence, while the asymmetry and variability extracted from the trials between hard surface vs foam surface are known as the surface dependence.
# Legal Judgment Prediction via Heterogeneous Graphs and Knowledge of Law Articles
## 3 Our Method  
As shown in Figure 2, we propose a graph neural network-based LJP method. Firstly, six kinds of text graph were constructed based on judicial decision documents, including the following: co-occurrence graph, point-wise mutual information (PMI) graph, semantic graph (using cosine similarity, Euclidean distance, Manhattan distance and Chebyshev distance). The nodes in the graph are words of the document, document virtual nodes; they then went through a GCN for intra-graph information passing. For the law article graph, we used the law article (LA) distinction extractor to obtain the information of different point features of similar law articles. Then, we input the text graph features and the law article graph features into the graph fusion layer to predict the results of LJP.
## 3 Experimental Results Analysis
### Effect of Loading Rate and Inclination Angles on Peak Axial Stress
Eventually, the peak axial stress versus loading rate curve shows the characteristics of first increase and then decrease at low inclination angle and the continuous nonlinear decrease at a high inclination angle.
## 2 Constructing the Evaluation Index System
### Selection of Mapping Units  
According to the summary by the Italian scholar [PERSON], landslide susceptibility assessment units comprise five types [20]: raster units, slope units, terrain units, unique condition units, and sub-basin units. Among these five types, raster units and slope units are the most widely applied.  
Raster units are regularly partitioned, ensuring that the area of each computational unit is the same. For regional landslide susceptibility assessment, the number of computational units is often large, making it suitable for evaluation models requiring large datasets. The advantages of raster units include regular shapes and simple and clear calculations, making them suitable for large-area landslide susceptibility assessment. The determination of raster size is based on the empirical formula proposed by scholars such as [PERSON] et al. [21]:  
\[\mathrm{G_{s}}=7.49+0.0006\times\mathrm{S}-2.0\times 10^{-9}\mathrm{S}^{2}+2.9 \times 10^{-15}\times\mathrm{S}^{3}\]  
In the formula, \(\mathrm{G_{s}}\) represents the suggested size of raster units, and \(\mathrm{S}\) represents the denominator of the scale of the study area.  
The division of slope units is primarily based on the delineation of mapping units formed by ridgelines and valley lines in the study area. This enables the terrain undulations and hydrological characteristics of the study area to be represented effectively. Compared to raster units, slope units are much fewer in number, resulting in higher computational efficiency. Additionally, compared to raster units, slope units can reflect the physical relationship between landslides and basic terrain features a priori [22]. However, obtaining slope units is indeed a time-consuming and labor-intensive task, requiring collaboration between computers and manual efforts. Therefore, slope units are suitable for detailed landslide susceptibility assessments of small areas [19]. Slope units can effectively reflect the relationship between landslides and terrain features, greatly enhancing the efficiency of landslide susceptibility assessment. Common methods for extracting slope units include hydrological methods, r.slogeunits, and MSS (multi-scale slope selection) methods. These methods have significantly promoted the application of slope units in landslide susceptibility assessment [23; 24; 25]. Table 1 lists the mapping unit types and acquisition methods used in the relevant literature.
## 2 Numerical Study
### Flow Field Model  
The plugging-induced vibration of the PIPR is the result of multi-physics. During the plugging operation, in-pipe fluid decreases rapidly with the increase in the plugging process. The sudden contraction of the in-pipe flow field induces intermittent de-flow of the fluid near the plugging area. It can cause resonance damage and wake vortex of PIPR, resulting in a large vibration phenomenon [17; 18]. The in-pipe fluid makes the pipeline structure vibrate, and the moving pipeline structure interacts with the PIPR, which in turn affects the vibration form of the in-pipe fluid. Therefore, a complex fluid-structure coupling phenomenon is formed.  
The flow field model around the PIPR is shown in Figure 3. The length of the pipeline model is 10 m. The in-pipe medium is single-phase incompressible liquid gasoline, and the centerlines of the pipeline and PIPR are coincident. Numerical simulation of the plugging process is performed using ANSYS 18.1. The main parameters of the flow field model are shown in Table 1.  
The boundary condition of the pipeline entrance is the velocity entrance, and the flow velocity is 3 m/s. The boundary condition of the pipeline outlet is the fully developed flow. The walls of the pipeline and PIPR are non-slip walls. The grids of the flow field model are divided using tetrahedral meshes, as shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Symbol** & **Value** \\ \hline The diameter of the pipeline & \(D\) & 0.8 m \\ Flow density & \(\rho\) & 830 kg/m\({}^{3}\) \\ Operating pressure & \(P_{0}\) & 5 MPa \\ Dynamic viscosity & \(\mu\) & 0.000332 kg/(m-s) \\ The length of PIPR & \(L\) & 1110 mm \\ The length of upstream & \(L_{1}\) & 4445 mm \\ The length of downstream & \(L_{2}\) & 4445 mm \\ The diameter of PIPR & \(d\) & 720 mm \\ The diameter of sliders & \(d_{1}\) & 640 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: The main parameters of flow field model.  
Figure 3: The flow field model around the PIPR.  
Figure 2: The schematic diagram of plugging process.
# Shock Vibration Control of SDOF Systems with Tubular Linear Eddy Current Dampers
## 3 Analytical Model for Transient Responses of an SDOF System with the ECD
### Equation of Motion  
As the SDOF system can reflect relevant dynamic characteristics of the relevant multi-degree-of-freedom (MDOF) system in a specific movement pattern, it is a common perception in both the design and retrofit of structures to simulate the latter by creating an equivalent or approximate one for the former. Therefore, in the initial design stage of a supplement damping system for a structure, the MDOF system could be simplified into an SDOF. Figure 4 depicts an SDOF system with the ECD subjected to external loading.  
Figure 4: Sketch map of the SDOF system with the ECD subjected to external loading.  
Figure 3: Three types of idealized shock excitation: (**a**) Half-cycle sine; (**b**) Initial peak triangular; (**c**) Rectangular.  
The equation of motion for the SDOF system (with a mass \(m\) and stiffness \(k\)) equipped with an ECD subjected to shock excitation can be expressed as:  
\[\ddot{u}+2\xi\omega_{n}\dot{u}+\omega_{n}^{2}u+\frac{2F_{max}V_{cr}\dot{u}}{m \Big{(}V_{cr}^{2}+\dot{u}^{2}\Big{)}}=\frac{p(t)}{m} \tag{5}\]  
In this equation, the upper dots represent time derivatives; and \(\omega_{n}=\sqrt{k/m}\), \(\xi\), and \(p(t)=ma(t)\) represent the natural frequency, natural damping ratio, and shock loading, respectively. The system's response, \(u(t)\), is determined by the external excitation, as evidenced by the equation's nonlinearity.  
By integrating the individual force terms in (5) over the entire relative displacement history, the energy representation can be formed as:  
\[E_{K}+E_{ID}+E_{D}+E_{S}=E_{I} \tag{6}\]  
where  
\[E_{I}=-\int pdu \tag{7}\]  
\[E_{K}=\int m\ddot{u}du=\frac{m\dot{u}^{2}}{2} \tag{8}\]  
\[E_{ID}=\int\dot{cu}du=\int\dot{cu}^{2}dt \tag{9}\]  
\[E_{D}=\int f\big{(}\dot{u}\big{)}du \tag{10}\]  
\[E_{S}=\int kudu=\frac{ku^{2}}{2} \tag{11}\]  
The left-hand-side of (6) represents the kinetic energy of the mass (\(E_{K}\)), the dissipative energy caused by inherent damping in the structure (\(E_{ID}\)), the dissipative energy caused by the extra damper (\(E_{D}\)), and the elastic strain energy (\(E_{S}\)). These energies can balance the input energy (\(E_{I}\)) imposed on the structure by external forces. In this study, the shock excitation is considered as shock acceleration and \(p(t)=ma(t)\) represents the shock excitation. It depends on three parameters including the maximum value \(a_{0}\), duration \(t_{d}\), and shape.  
It is easy to numerically solve Equation (5). The Newmark-\(\beta\) average method is an unconditionally stable integration method and it is used in this study to calculate the transient shock-excited response of the SDOF system with the ECD. In order to obtain the accurate shock-excited response, a quite small time-step of shock loading (\(\Delta t/t_{d}=10^{-4}\)) is taken in the step-by-step solution of this study.
# An Image Edge Detection Algorithm Based on an Artificial Plant Community
## 4 Algorithm Design
### End Judgment  
The artificial plant community algorithm uses the neighbor information obtained by evolutionary learning to organize and search for feasible solutions. Among them, the artificial plant individuals with greater fitness levels have a greater survival probability, and a more adaptive plant community is gradually obtained. Through three main operations, seeding, growing, and fruiting, the solutions of the image edge detection algorithm can be judged, either by a predefined error, the maximum number of iterations, or the total computational time, so that an optimal solution will be produced.  
At iteration \(k\), the best artificial plant individual is \(x(k)\) with a score of Obj_fun (\(x(k)\)) by Equation (16). In the previous iteration, the best artificial plant individual is \(x(k-1)\) with a score of Obj_fun (\(x(k-1)\)). The error between the two iterations can be calculated.  
\[e=|\text{Obj\_fun}\ (x(k))-\text{Obj\_fun}\ (x(k-1))| \tag{18}\]  
Subsequently, a predefined error is used to find a globally optimal solution. For example, an error threshold \(e_{th}\) can be predefined as an end judgment.  
\[\left\{\begin{array}{l}\text{if}e<e_{th},\text{ then go to end}\\ \text{else, perform the next iteration}\end{array}\right. \tag{19}\]  
If the error \(e\) calculated by two iterations is less than the preset error threshold \(e_{th}\), the calculation is completed, and the optimal solution \(x\) and the corresponding optimal edge image will be output. Otherwise, the optimal solution is taken as the seed. Then, we return to the seeding operation and perform the next iterative calculation to search for the optimal solution again.
# Agriculture 4.0: Is Sub-Saharan Africa Ready?
## 2 Prospects and Challenges for Agriculture 4.0 Adoption in Sub-Saharan Africa
### Education and Skills  
Younger and more educated farmers have been found to be more likely to adopt agriculture 4.0 technologies [69], and more broadly farmers are likely to need ICT skills and capabilities to fully embrace agriculture 4.0 [70]. Yet farmers in SSA are less literate than those in HICs; farm workers less literate than non-farm workers [7; 71], and many farmers lack the skills to operate sophisticated technologies and to collect and manage the high volume of data used to improve decision-making [72]. This suggests that lack of education and skills in data management as a barrier to agriculture 4.0 adoption is likely to be an issue in some African countries. The gap in the quality of education and the need for more training in Africa have previously been highlighted in several African Union Commission Policy frameworks, see [72; 73; 74].
## 3 Results and Discussion
### Infrared Spectrum Analysis  
The structures of magnetic nano-microspheres, magnetic polystyrene microspheres and functionalized magnetic polystyrene microspheres were characterized by a Thermo Fisher Nicolet -iS5 infrared spectrometer, with KBr as the substrate. It can be seen from Figure 3a that 560 cm\({}^{-1}\) is the Fe=O stretching vibration of magnetic nano-microspheres. Compared with Figure 3a, Figure 3b presents three additional peaks in the skeletal vibration frequencies of benzene ring at 1445 cm\({}^{-1}\), 1488 cm\({}^{-1}\), and 1594 cm\({}^{-1}\), respectively. The absorption peak at 3024 cm\({}^{-1}\) is attributed to the C-H stretching vibration of the benzene ring. There are two strong absorption peaks at 696 cm\({}^{-1}\) and 754 cm\({}^{-1}\), respectively, which proves the presence of styrene. There is a stronger absorption peak at 1718 cm\({}^{-1}\), which is the characteristic absorption peak of C=O. There is a broadened absorption peak at 3425 cm\({}^{-1}\), which is the stretching vibration frequency of OH; this may be caused by the moisture contained in the sample. The absorption peak at 538 cm\({}^{-1}\) is presumed to be the absorption peak of Fe\({}_{3}\)O\({}_{4}\) powder, which is inconsistent with that at 560 cm\({}^{-1}\) reported in a related article [36]. It may be caused by the particle size of Fe\({}_{3}\)O\({}_{4}\) powder in the micron scale; hence the deviation is induced. The obtained products were characterized by infrared spectroscopy. Compared with Figure 3b, Figure 3c presents three additional new peaks, including a broad peak at 3400 cm\({}^{-1}\), which is induced by the vibration of the water molecules adsorbed by the sulfonic acid group, and two peaks at 1164 cm\({}^{-1}\) and 1001 cm\({}^{-1}\), which may result from the C-S bond. This indicates the introduction of the sulfonic acid group on the benzene ring of the product. Moreover, the absorption peak of the C-H bond of the benzene ring at 1687 cm\({}^{-1}\) is significantly enhanced. The higher the sulfonation degree, the wider the absorption band.  
Figure 2: Transmission electron micrographs of Fe\({}_{3}\)O\({}_{4}\) (a) and Fe\({}_{3}\)O\({}_{4}\)@PS (b).
## 6 Conclusions  
The time domain and frequency domain preamble-based SNR estimators, known as CAZAC-TD and CAZAC-FD SNR estimators, respectively, that make use of the modified CAZAC preamble structure are presented in this study. These SNR estimators are compared to the Milan-FD SNR estimator proposed in [18] when used in a non-adaptive \(2\times 2\) STBC-assisted MIMO-OFDM system. The performance of the CAZAC-TD and CAZAC-FD SNR estimators is further examined when employed in the non-adaptive \(4\times 4\) STBC-assisted MIMO-OFDM system. The estimated SNR using the CAZAC-TD SNR estimator matches the actual SNR for non-adaptive \(2\times 2\) and \(4\times 4\) STBC-assisted MIMO-OFDM systems transmitting over the AWGN channel. However, in the \(2\times 2\) STBC-assisted MIMO-OFDM system, the differences between the actual SNR and the estimated SNR using CAZAC-FD and Milan-FD SNR estimators are 0.12 dB and 0.16 dB, respectively. However, the SNR estimator performance deteriorated for transmission over the SUI-5 channel where the Milan-FD SNR estimator shows estimated SNR differences of about 0.26 dB, but the CAZAC-TD and CAZAC-FD SNR estimators used in the \(2\times 2\) and \(4\times 4\) STBC-MIMO-OFDM systems show estimated SNR differences below 0.2 dB. It was also demonstrated that the NMSE performance of the CAZAC-TD SNR estimator approached the theoretical limit set by NCRB for the non-adaptive \(2\times 2\) and \(4\times 4\) STBC-assisted MIMO-OFDM systems. Subsequently, the CAZAC-TD SNR estimator is invoked in an adaptive modulation scheme for a \(2\times 2\) STBC-assisted MIMO-OFDM system employing \(M\)-PSK, denoted as the AM-CAZAC-TD-MIMO system. The AM-CAZAC-TD-MIMO system outperformed the non-adaptive STBC-assisted MIMO-OFDM system using 8-PSK by about 2 dB at BER = \(10^{-4}\). The adaptive SISO-OFDM system with \(M\)-PSK, also known as the AM-CAZAC-TD-SISO system, which invokes the CAZAC-TD SNR estimator, was compared to the proposed AM-CAZAC-TD-MIMO system in terms of channel capacity and BER. In contrast to the AM-CAZAC-TD-SISO system, the AM-CAZAC-TD-MIMO system demonstrated an SNR gain of about 4 dB. It was shown that the spatial diversity of the MIMO-OFDM system is key for the AM-CAZAC-TD-MIMO system's improved performance.  
Figure 20: A comparison of the channel capacity performance of the AM-CAZAC-TD-MIMO system and AM-CAZAC-TD-SISO system employing \(M\)-PSK for SUI-5 channel.
# Progress in Transparent Nano-Ceramics and Their Potential Applications
## 3 Magneto-Optical Transparent Nano-Ceramics
### Others  
Some sesquioxides also have magneto-optical effects and generally have high thermal conductivity, which is also a potential application value, such as in high-energy lasers [38; 55; 56; 57]. For example, at 1064 nm wavelength, the optical transmittances of fully doped and 10% doped Y\({}^{3+}\):Y\({}_{2}\)O\({}_{3}\) ceramics were 63.4 and 79.3%, respectively, but both were lower than their theoretical transmittances of 81.1% [55]. The Ho\({}_{2}\)O\({}_{3}\) magneto-optical transparent nano-ceramic was successfully prepared by the SPS sintering method, and its transmittance at 1 \(\upmu\)m was measured to be about 60%, but its Verdet constant at the wavelength of 1064 nm was \(-\)46.3 rad/(T\(\cdot\)m), which was close to that of TAG, which was about 1.3 times that of TGG [56]. These studies [55; 56; 57] showed that yttrium oxide (Y\({}^{3+}\):Y\({}_{2}\)O\({}_{3}\)), holmium oxide (Ho\({}_{2}\)O\({}_{3}\)) [58], dysprosium oxide (Dy\({}_{2}\)O\({}_{3}\)), and other nano-transparent ceramics still need to be further optimized to improve their optical properties.