esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Previous Work  
Numerous spherical surface tessellations have been proposed to establish DGGSs. The most commonly used DGGS is the traditional latitude-longitude graticule, in which they are termed equal-angle grids. Another approach is the quadrilateral cells, which are called \"constant-area\" grids [24]. Constant-area tessellations begin with an arbitrarily sized quadrilateral cell at the equator and then define the parallel and meridian cell boundaries across the globe to achieve approximately equal-area cells. [PERSON]'s quad-tree data structure [21] and [PERSON]'s small circle subdivision [9] are both based on the recursive subdivision of spherical triangles obtained by projecting the faces of a regular polyhedron onto a sphere, and the edges of the cells are all great and/or small circle. The Hipparchus System [25] allows for the creation of arbitrarily regular DGGs by generating Voronoi cells on the surface of a sphere from a specified set of points. However, for many applications, it is desirable to have cells that consist of highly regular regions. The approaches discussed above are all a \(DSS\), but there are some popular map projection methods. In the common Gnomonic azimuthal projection, all straight lines in the plane correspond to great circle arcs on the sphere but exhibit relatively large area and shape distortions and the geodesic bisector method give the same results. The [PERSON] equal-area polyhedral projection [1] is defined on all regular polyhedra, but it has greater shape distortion. The Dymaxion projection [7] given in [3] has less shape distortion than [PERSON]'s projection, and it is not an equal-area projection. [PERSON]'s Quaternary Triangular Mesh (QTM) [26] is based on the 4-fold recursive subdivision of the octahedron, and two projections have been proposed for transforming the QTM from the plane to the sphere: [PERSON] and [PERSON]'s Plate Carree projection [2] and [PERSON]'s Zenithial OrthoTriangular (ZOT) projection [23]. The difference between these two projections is that they use different planar surfaces. [PERSON]'s method uses an isosceles right triangle, and [PERSON] and [PERSON]'s method uses an equilateral triangle; however, both projections give identical geographic coordinates when taken to the sphere.
## 4 Discussion
### Strengths of the Study  
This study benefits from a randomized controlled design, a relatively large sample size for an exercise intervention in this demographic, and comprehensive tracking of multiple health parameters, including anthropometric indices, body composition, and lipid profile biomarkers.  
The use of three distinct aerobic modalities allows for a comparative analysis of their relative efficacy, an approach that is rarely explored in similar studies. Training intensity was rigorously monitored and personalized, increasing interval validity. Furthermore, the 12-week duration proved sufficient to detect statistically significant changes across a broad range of outcomes.
## 4 Evaluation
### Experimental Setup  
#### 4.1.1 Datasets  
For evaluation, we used a dataset built by cybersecurity domain experts. For each target file type, the experts crawled 1000 samples from VirusTotal [42] and adjusted the numbers of malicious or benign samples for each file type to be the same (i.e., 500 malicious samples and 500 benign samples). Each dataset was split into five folds by using stratified sampling [43] to perform cross-validation [43]. Then, in each run of cross-validation, words with less than 2 occurrences and those with lengths higher than 30 characters were removed from the corpus of our training set. In the vectorization using BoW and TF-IDF, the length of a document vector (i.e., dimensionality) was set as the number of words remaining in the final corpus of the training set; in vectorization using Word2 Vec, it was set as 600, following [30].
## 6 Conclusions  
The examination of source-rock geochemistry and statistical modelling are critical elements in assessing hydrocarbon reserves. Geochemical parameters can be statistically represented to uncover the most essential common and affective qualities, which may take a long time to discover using conventional approaches. Thus, statistical modelling approaches make it quick and straightforward to gain a more detailed understanding of the results by simplifying access to the significance of residual elements and the extent of their interaction. This article examined both geochemical and statistical techniques for establishing the source rock's composition, based on the Triassic Montney Formation samples from northeast British Columbia's Western Canadian Sedimentary Basin. Our findings support the following assertions:  
1. The TOC for the source rock presented in this study ranged from 0.5 to 2.86 wt.%, which was interpreted as fair to good source rock.
2. The hydrocarbon linked with the Montney Formation in the examined samples ranged from oil to dry gas with a medium to a high level of conversion depending on the Tmax and PI, originating from Type IV kerogen.
3. The higher values of the S\({}_{1}\) compared with the S\({}_{2}\) are likely to be interpreted as stained or contaminated source rock.
4. The Spearman's correlation coefficient showed that the TOC had positive correlations with each of S\({}_{1}\), S\({}_{2}\), and GP, indicating that S\({}_{1}\) and S\({}_{2}\) contributed to TOC; additionally, the positive correlation with HI and negative correlation with OI, along with weak correlations with both Tmax and PI, indicated that the maturity of the source rocks was independent of the amount of organic matter.
5. The PCA application categorized the pyrolysis parameters into three factors that influenced the source rock evaluation. TOC, S\({}_{1}\), S\({}_{2}\), and OI were all part of Factor 1, which determines the richness and potentiality. Factor 2 consisted of PI and S\({}_{3}\), which reflected the maturity of the source rock. Tmax and HI of Factor 3 indicated the kerogen's maturity level, source, and type.
6. The clustering methods used in this study showed the efficiency and reliability of the TwoStep algorithm in contrast to the HCA algorithm.  
Figure 10: Predictor importance according to the TwoStep clustering technique.  
Using the TwoStep clustering method showed that there were two clusters within the Montney Formation. The first lay in the oil window zone with relatively high TOC, S\({}_{1}\), S\({}_{2}\), S\({}_{3}\), HI, PI, and GP, accompanied by low Tmax and neutral OI (Figure 8). The second cluster was in the dry gas window with relatively low TOC, S\({}_{1}\), S\({}_{2}\), S\({}_{3}\), HI, PI, and GP, accompanied by high Tmax and neutral OI.  
Statistical modelling demonstrates that it is a valuable tool for uncovering previously unknown relationships between variables.
## 5 Structure Design Based on Universal Joint Coupling Mechanism
In addition, each petal is deployed by a single motor, which can reduce the position error of the petal to a large extent; therefore, the second approach is adopted. It is known that the universal joint coupling (UJC) is a compact structure capable of bearing high torque loading, which is appropriate to use for deploying the RRSAs due to their heavy weight and narrow space in placing deployment mechanisms. Figure 10a illustrates the deployment mechanism, from which it can be observed that each petal is mounted on the output rod of the UJC, and the input rod is connected through a hole with a reducer linked to the driving motor. The reducer is indispensable because of the extremely slow deployment process. According to the related reports, the deployment of Spektr-R takes one and a half hour, as shown in Figure 1. This operation can offer larger driving torque, which is of vital importance for large SSDAs due to their heavy petals. In this way, if all motors drive the input rods synchronously, the antenna deployment can be fulfilled without concerning the interference among petal deployment mechanisms. (4) Supporting and axis-adjusting deviceThe EM performance of the antenna is influenced not only by the surface error caused by manufacturing but also by the position error largely induced by the rotation axis, which is equivalent to additional surface error. Therefore, strict rotation-axis accuracy must be guaranteed, to which a device capable of adjusting the axis position is introduced. A line in 3D space has five DOFs [70]; hence, the device must supply the output rod with at least five DOFs in order to precisely install the output rod. The ability of the input rod to move slightly on the hole plane provides two DOFs, and the telescopic function of the input rod provides an extra DOF, by which three translational DOFs are thus obtained. In addition, the universal joint provides the two other rotational DOFs, thereby verifying that adopting the UJC can guarantee the expected position of the output rod.
# Energy-Efficient Multi-User Routing in a Software-Defined Multi-Hop Wireless Network
## 2 Related Work
Then, in order to achieve load balance and reliable transmission service, the author used an improved heuristic search algorithm to select disjoint paths and backup paths. The multi-objective optimization problem can also be solved by many other methods. For example, in [23], [PERSON] et al. proposed to solve DCLC problems with the knowledge of the least-cost (LC) tree and the least-delay (LD) tree that can reach the destination. This is a simple distributed heuristic solution called the delay constrained unicast routing (DCUR) algorithm. At each node, this algorithm builds the path node by node and makes a choice between the LC path and LD path according to the current delay. If it satisfies the constraint, we select the LC edge to add to the path, otherwise we add the LD edge. If a loop occurs, the algorithm will go back to the node that selected the LC path and select the LD edge instead. Backtracking guarantees loop elimination and the integrity of the algorithm, but it is not optimal. In [24], a new Lagrangian based on minimum cost QoS routing algorithm (MCQRA) for SDMWN was proposed to find the path more effective at a lowering the cost and meeting the requirements of QoS. Despite the above research works having different solutions to multi-objective problems, they can find a path to meet the requirements of QoS at a lower cost. However, all the studies only focused on a single user. In practical application scenarios, multiple users often submit service requests at the same time and need to use the same range of network resources for data transmission. At present, there are not many related works on global minimization routing designed for multiple users, and these works have achieved different purposes by considering diverse system models, optimization parameters, and methods. To our best knowledge, there is little work to consider energy-efficient multi-user routing in SDMWN. After all, mobile devices are widely used, and their transmission capabilities cannot be ignored. It is of great significance to design routing algorithms in this scenario. Accordingly, aiming at the multi-user routing problem of SDMWN, a global energy minimization algorithm with delay constraints is proposed. In addition, considering the congestion control problem, we assume that each intermediate node only provides transmission service for one user to avoid queuing delay effectively.
# Crop Land Change Detection with MC&N-PSPNet
## 2 Materials and Methods
### Attention Mechanism
#### 2.2.1 Cbam  
Attention refers to important spatial and channel information within feature channels. It is commonly assumed that the feature channels obtained through convolutional network pooling possess equal importance. However, in reality, the importance of features in each channel varies. Compared to using channel attention mechanisms or spatial attention mechanisms separately, the CBAM (Convolutional Block Attention Module) module (illustrated in Figure 3) integrates both channel and spatial attention mapping processes. This integration allows for the preservation of more useful feature information.  
The network embedded in the CBAM mechanism first uses channel attention mapping to globally pool and maximally pool the feature map F generated by convolution, and then inputs the pooling results into the multilayer perceptron to perform the summation operation, generates the channel weight coefficients through the Sigmoid activation function, and then multiplies the weight coefficients by the original feature map F to get the feature  
Figure 3: CBAM structure.  
map adjusted by the channel weights. The process of the channel attention mapping is shown in Equation (2).  
\[\mathrm{M_{c}(F)}=\sigma(\mathrm{MLP(AvgPool(F))}+\mathrm{MLP(MaxPool(F))}) \tag{2}\]  
where MLP denotes multilayer perceptron, which is the activation function. Subsequently, spatial attention mapping is performed on the weighted feature maps by serial concatenation of global maximum pooling and average pooling, using convolution to downsize into single-channel feature maps, using the Sigmoid function to activate and to generate the spatial feature matrices, and then the weight matrices and the feature maps undergo the dot-multiplication operation to get the final required spatial feature maps. The process of spatial attention mapping is shown in Equation (3).  
\[\mathrm{M_{s}(F)}=\sigma\Big{\{}\hat{f}^{\gamma\times 7}[\mathrm{AvgPool(F)}; \mathrm{MaxPool(F)}]\Big{\}} \tag{3}\]  
where f denotes a convolutional layer with a 7 \(\times\) 7 convolutional kernel which is the Sigmoid function and serial concatenation.  
Finally, the feature map F of the output of the previous convolutional layer is summed with the feature map via the CBAM mechanism to obtain the input of the next convolutional layer.
## 3 Results and Discussion
### Thermal Stability  
Figure 8 showed the thermogravimetric curves of pure ZnO, pure TOCgel and TOCgel/ZnO composite which were recorded under inert atmosphere. Both materials contain a few layers of moisture, which were eliminated at 105 \({}^{\circ}\)C. As can be seen in the thermogram curve, the decomposition of the pure TOCgel between 250 and 300 \({}^{\circ}\)C was attributed to the destruction of the crystalline region of the cellulose fiber and decomposition of amorphous region into a monomer of D-glucopyranose and followed by the major thermal-oxidative degradation of the organic part from 580 \({}^{\circ}\)C [57, 65].  
Figure 8: Representative TGA curves (**A**) and DTGA curves (**B**) of pure TOCgel and TOCgel/ZnO composite.  
In comparison with the TOCgel/ZnO composite film, TG curves demonstrated clearly that the latter exhibits higher thermal stability than pure TOCgel, which can be clearly highlighted by the ability of ZnO superstructures well-dispersed within the fibrous network to protect TOCgel nanofibers. Several works indicated that the thermal stability of carbohydrate (_i.e._, starch, cellulose) did not change after incorporation of ZnO [66,67].  
However, our result demonstrated the thermal stability enhanced with TOCgel after the incorporation of ZnO flower-like superstructures. This contradiction could be explained by the strong interaction electrostatic/hydrogen bonding between the carboxylate moieties of TOCgel and ZnO flower-like superstructures, which contributes to the shift observed in the thermal degradation temperature of TOCgel from 300 to 335 \({}^{\circ}\)C which, in turn, offer thermal processing advantages. We also observed a low transition (at 162 \({}^{\circ}\)C) in the DTG curve of the final composite film. According to the literature, this transition can be assigned to the loss of water trapped in the composite film and the decomposition of some incorporated HMT or nitrate ions possibly remained in the composite [66]. Moreover, after combustion of all organic parts in the TOCgel/ZnO composite (under air atmosphere), the residual amount (\(\sim\)18% by weight) corresponds to ZnO as shown in Figure 8.
## 3 Results
### Residual Statistics  
A scatter plot matrix was used to assess the independence of residuals. The scatter plots depict the dispersion of points with respect to the dependent variable across various movement speeds, and that is for HR parameters (Figure 1), VO\({}_{2}\)max parameters (Figure 2), and lactate parameters (Figure 3).  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline  & **Mean** & **SD** & **Min** & **Max** & **Range** & **Skew** & **Kurt** \\ \hline HRmax (bpm) & 192.90 & 7.95 & 174.00 & 207.00 & 33.00 & \(-\)0.30 & \(-\)0.68 \\ HR AT (bpm) & 167.20 & 8.02 & 152.00 & 180.00 & 28.00 & \(-\)0.07 & \(-\)0.92 \\ HR 1\({}^{\prime}\) (bpm) & 170.05 & 12.67 & 140.00 & 193.00 & 53.00 & \(-\)0.48 & \(-\)0.06 \\ HR 2\({}^{\prime}\) (bpm) & 129.44 & 17.52 & 103.00 & 174.00 & 71.00 & 0.70 & \(-\)0.01 \\ VO\({}_{2}\)max (ml/kg/min) & 61.15 & 3.89 & 53.40 & 69.20 & 15.80 & 0.16 & \(-\)0.27 \\ VO\({}_{2}\)max/v & 2.93 & 0.21 & 2.59 & 3.38 & 0.79 & 0.48 & \(-\)0.32 \\ (mL-kg\({}_{2}\)min/km/h) & & & & & & & \\ VO\({}_{2}\)max/HR & 0.32 & 0.02 & 0.27 & 0.36 & 0.09 & 0.15 & \(-\)0.60 \\ La 4\({}^{\prime}\) (mmol/L) & 9.42 & 1.73 & 6.50 & 14.10 & 7.60 & 0.36 & \(-\)0.25 \\ La 10\({}^{\prime}\) (mmol/L) & 6.94 & 1.37 & 3.80 & 11.70 & 7.90 & 0.67 & 2.74 \\ Index LA & 39.01 & 31.77 & \(-\)2.35 & 165.79 & 168.14 & 1.79 & 5.01 \\ Index ME & 2.29 & 0.45 & 1.56 & 3.33 & 1.77 & 0.52 & \(-\)0.43 \\ \(<\)8 km/h (m) & 4522.56 & 450.43 & 3301.36 & 5243.99 & 1942.63 & \(-\)0.58 & 0.34 \\
8.1–15 km/h (m) & 4193.87 & 831.19 & 2580.46 & 5850.53 & 3270.07 & 0.08 & \(-\)0.57 \\
15.1–19 km/h (m) & 983.55 & 247.63 & 502.00 & 1393.53 & 891.53 & \(-\)0.22 & \(-\)1.27 \\
19.1–23 km/h (m) & 611.45 & 154.36 & 319.33 & 885.60 & 566.27 & 0.22 & \(-\)0.75 \\ \(>\)23 km/h (m) & 488.38 & 144.99 & 261.26 & 893.85 & 632.59 & 0.76 & 0.36 \\ Total (m) & 10,799.79 & 1143.70 & 8429.52 & 12,602.25 & 4172.73 & \(-\)0.11 & \(-\)0.71 \\ \hline \end{tabular}
\end{table}
Table 4: Cardio-respiratory, metabolic, and match running performance parameters of national-level football players.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **\textless{} 8 km/h** & **8.1–15 km/h** & **15.1–19 km/h** & **19.1–23 km/h** & **\textgreater{}23 km/h** & **Total** \\ \hline HR max & 0.27 * & 0.18 & 0.10 & 0.26 & 0.01 & 0.30 * \\ HR AT & 0.09 & \(-\)0.21 & \(-\)0.20 & \(-\)0.16 & \(-\)0.14 & \(-\)020 \\ HR 1\({}^{\prime}\) & 0.25 & 0.18 & 0.07 & 0.23 & 0.11 & 0.29 * \\ HR 2\({}^{\prime}\) & \(-\)0.16 & \(-\)0.42 ** & \(-\)0.22 & \(-\)0.50 ** & \(-\)0.10 &Figure 1: Scatter plot of standardized residuals; HR parameters for dependent MRP variables.  
Figure 3: Scatter plot of standardized residuals; lactate parameters for dependent MRP variables.  
Figure 2: Scatter plot of standardized residuals; VO2 max parameters for dependent MRP variables.
# AR in the Architecture Domain: State of the Art
## 5 AR System
### Devices
#### 5.1.1 Display  
The display classification depends on the technology, the digital content, and the supported sensory channel [39, 84]. Most of the applications related to architecture are addressed to the optical channel (Figure 11). The first type is the head-mounted display (HMD), a single-user device based on a video or optical operation [85]. The video-based HMD, called VST-HMD (video see-through), calculates the cameras' images and augments the acquired scene with virtual information, creating mixed images. This process requires a high data computation, but the result is very faithful and less tied to latency time [86]. The single-user mode can be applied to a collaborative scenario using projection screens or caves, much more commonly used in VR. The optical-based HMD, named OST-HMD (optical see-through), allows the user to view the real scene directly while virtual content is overlaid on the glass. This kind of display must present a low latency in the overlap between virtual and real data.  
The second type of display is the handheld device (HHD), which combines a digital camera, inertial sensors, GPS, and display. This type of device is based on a VST approach by overlaying a camera-mediated view of reality with digital information. Most AR domain applications rely on these vision tools [20, 43, 45], which allow a collaborative approach and application in particular contexts, such as underwater [87].  
A third type is spatial AR (SAR), based on the layering of virtual information on the real environment [3, 88, 89] using cameras, video projectors, or holography, basing their operation only on low latency, markerless tracking. This is a collaborative visualization with external devices and a direct in situ interaction at a 1:1 scale. Recent technological developments have allowed a remarkable development in the field of CH [90], or examples in the archaeological field [91] or BIM and collaborative design [92]. A critical evaluation of the whole system concerns the different immersive and collaborative display capacities. The wearable systems allow a low group experience but a higher immersion in mixed reality. For this reason, some projects--such as HySAR [93]--integrate multiple displays, obtaining the best performances from each instrument.  
Figure 11: Functional schema of the different AR displays.
## 3 Spectrum Management Issues
### Cognitive Radio
#### 3.2.3 Spectrum Allocation  
In [120], the authors have worked on expanding the utilization of the available spectrum by dynamically assigning channels to SUs, and this problem is considered a spectrum allocation problem. A method called Chaotic Biogeography-Based Optimization (CBBO) evolutionary algorithm is derived. The results show that CBBO is a generally suitable optimizer for solving combinational optimization problems. Therefore, the technique enhances or at least maintains the performance of the other algorithms in the CR network. In the future, one can work on the same model that can use QoS parameters for nonlinear migration models and on the application of BBO in next-generation technologies. A study [121] has suggested to resolving the issue of spectrum management requirements and heterogeneity for the CR users. The study has provided an innovative Channel Management Framework (CMF) for CR sensor networks. The framework is strongly constructed on Opportunity Scheduler (OSR), Opportunity Detector (ODR), and Opportunity Ranker (ORR) to tackle the shortcomings of CR sensor network-aided IoT. Consequently, the proposed CMF scheme surpasses all  
Figure 3: Cognitive Radio (CR)-based spectrum sensing.  
current methods with respect to blocking and detection probability, collision probability, and idle time probability, and throughput. In the future, a system that uses a single optimization problem to execute ORR and ODR can be designed.