esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Analyses
#### 2.4.2 Identifying Leaders from Movement Patterns  
Detection of leading sheep during movement around the paddock was based on the GNSS data showing animal movement during the grazing period. This period was within the first two hours of introducing animals into the paddock. The collected data during days 2 and 3 of the study were used as all sensors worked well for these two days as mentioned in Section 2.4.1. The first two hours of the day were selected as animals displayed their normal diurnal behaviour of movement (based on their graphical movement plots) and grazing following a period of overnight feed restriction (although visual confirmation of grazing was not applied for the entire 2-h period (see Section 2.4.4 for video records of behaviour for a portion of the grazing period). Each two-hour period within each study day was divided into one-hour periods, and individual animal movement was drawn at 10 min intervals to see which sheep moved first. The individual or individuals moving first were visually identified as separate from the other animals by approximately 1 m distance. Animals were ranked based on their movement trajectory relative to group members by assigning the highest ranks to the animal (s) that moved first and the lowest ranks to the last animals to move (for example, rank 7 to animal F, and rank 1 to animal E in Figure 5a). Animals were ranked the same if they moved together (Figure 5b) or received a zero if they did not move (animal B, Figure 5c) across the 10 min time intervals. The rank of individual animals was calculated for each one-hour period for a total of four hours across the two study days. The 10 min values (\(n=24\) values/animal) were summed to provide a score for each animal. Individual ranks were then drawn in the group's social network using ggnet2 function ('Ggally' package in R, [40]) for each one-hour period for a total of four hours. To provide better estimates of individuals' movement leader scores in the network, bar charts of scores for individuals across each hour were also plotted.
## 2 Materials and Methods
### Elimination of Rotations  
Both the integration schemes proposed in the previous Subsections propagate nine variables, although only six degrees of freedom account for shape changes of the system cell. In order to eliminate the three redundant degrees of freedom, associated with global rotations, two possible strategies adapted from what was proposed in Ref. [8] could be applied. The first possibility was to symmetrize the deformation matrix defined in Equation (11) or (16), without fixing the cell matrix \(\mathbf{h}\) to any particular shape. The second strategy was to constrain \(\mathbf{h}\) to be a triangular matrix. This could be achieved either by propagating only six components of \(\mathbf{h}\), and fixing the other elements to zero, or by first evolving all the nine variables and then applying a rotation to the columns of the deformation matrix, in order to impose a triangular shape. Note that this last method is equivalent to maintaining the global rotations and co-rotating the reference frame with the system, without affecting the target \(N\)ST distribution. The last method was the one implemented and tested within this work (see Ref. [25] for further details). Preliminary tests employing the other strategies did not show any significant differences in the results.
# Probabilistic Forecasting of Electricity Demand Incorporating Mobility Data
## 2 Materials and Methods
### Evaluation Metric
#### 2.3.3 Metrics for Probabilistic Forecasting  
Probabilistic forecasting is typically evaluated in terms of its reliability, sharpness, and competence (skill). The prediction interval coverage probability (PICP) measures the reliability of the predictions via coverage rate [59]. It shows the percentage of the actual values that will be covered within certain prediction interval limits [59]. The considerably larger the PICP, the more likely it is that the real values will be within the prediction interval (PI). PICP is defined as follows:  
\[PICP=\frac{1}{N}\sum_{i=1}^{N}C_{i}, \tag{18}\]  
where \(N\) is the number of samples, and \(C_{i}\) is a Boolean variable defined as follows:  
\[C_{i}=\begin{cases}1,&y_{i}\in[L_{i}U_{i}];\\ 0,&y_{i}\
otin[L_{i}U_{i}],\end{cases} \tag{19}\]  
where \(L_{i}\), and \(U_{i}\) are the lower and upper PI boundaries of the target \(y_{i}\), respectively. PICP ranges between 0-100%. The PI is accepted as valid if the PICP value is greater than the prediction interval nominal confidence \(PINC=100(1-\alpha)\%\); here, \(\alpha\) indicated the probability of error. In this study, the PI limits are supposed to cover 95%, 90%, 80%, and 50% of the PDF of the forecast. The evaluation of the PICP alone is misleading since high PICP values can be easily reached when the width of the PIs is large. In contrast, a forecast with a narrow PI and a large PICP is more reliable. The effectiveness of PIs is determined by their widths. Thus, larger PIs are less informative for making decisions since it presents increased uncertainty [59]. Therefore, to evaluate the widths of the PI, a supplementary metric is required, and lower MPIW and high PICP values help make better decisions [59]. The metric is the Mean Prediction Interval Width (MPIW), which is defined as follows:  
\[MPIW=\frac{1}{N}\sum_{i=1}^{N}(U_{i}-L_{i}) \tag{20}\]  
To evaluate the calibration and sharpness of the forecasted PDF simultaneously, the continuous ranked probability score (CRPS) metric is used and can be expressed as follows [60]:  
\[CRPS=\int_{-\infty}^{\infty}{(F(x)-1\{x\geq y\})^{2}dx}. \tag{21}\]  
where \(F(x)\) represents the predicted cumulative distribution function (CDF) of the variable of interest \(x\), and \(y\) is the verifying observation. If the forecast variable \(x\) equals the observation \(y\), then the value jumps from zero to one. The squared difference between the two CDFs is averaged over the number of observation pairs. The smaller the CRPS metric, the better the accuracy of the PDF.
## 4 Discussion
### The Effects of Different Additives on the Fermentation Quality of Corn Straw Silage  
The pH value, NH\({}_{3}\)-N, and lactic acid (LA) content are key fermentation indicators used to assess silage quality. Typically, a lower pH value correlates with a higher LA content, signifying better fermentation quality [34]. In this study, after 60 days of corn straw silage, the groups treated with cellulase and xylanase effectively reduced the pH value and significantly increased the LA content. Among these treatments, the xylanase treatment proved more effective than the cellulase and _Lactobacillus treatments_. [PERSON] et al. [35] observed that adding cellulase (100 U/g) to a mixture of corn straw and soybean residue resulted in a significant increase in both lactic acid and acetic acid content after 56 days of silage. Similarly, [PERSON] et al. [36] reported that after 60 days of silage at room temperature, corn silage treated with cellulase and xylanase showed significantly higher LA content and lower pH values than the control group. This improvement is likely due to the ability of cellulase to degrade lignin and cellulose, releasing more soluble sugars for lactic acid bacteria fermentation. This, in turn, promotes the activity of homoffermatative lactic acid bacteria, inhibits the growth of harmful microorganisms [37; 38], lowers the pH, and increases lactic acid production [39; 40]. The NH\({}_{3}\)-N content reflects the extent to which proteins and amino acids in the silage are broken down by microorganisms. A lower NH\({}_{3}\)-N ratio indicates reduced nitrogen loss, which results in higher-quality silage [41]. The addition of cellulase and xylanase in the silage process can enhance protein content in the feed [42] and minimize protein hydrolysis into ammonia-containing compounds by plant proteinases and microorganisms [43]. [PERSON] et al. [44] reported that adding cellulase (50-100 U/g) to multiberry leaf silage significantly reduced NH\({}_{3}\)-N content. Similarly, in this study, after 60 days of corn straw silage, the NH\({}_{3}\)-N content in the additive-treated groups was significantly lower than in the CK group, indicating that cellulase and xylanase helped reduce protein loss during the silage process. This reduction is likely due to the breakdown of cellulose by cellulase and xylanase, which provides more substrate for lactic acid bacteria fermentation, while inhibiting harmful microorganisms and proteinases that hydrolyze proteins [45]. Therefore, this study highlights the significant advantages of using xylanase as an additive to enhance the nutritional quality of corn stover silage.
# Collaborative Caching for Implementing a Location-Privacy Aware LBS on a MANET
## 6 Performance Evaluation  
In this section, we present our experimental evaluation methodology, setup, and the results of our comparative analysis. Our evaluation addresses both the efficiency of our collaborative caching approach and its effectiveness in preserving user privacy in MANETs. We follow established practices in distributed systems research while considering the unique challenges of privacy-preserving location-based services.
# Non-Iterative Cluster Routing: Analysis and Implementation Strategies
## 3 Methods
### Proposed Routing Variations  
#### 3.4.1 Our First Routing Variation  
This deviated version originates from the multiple-channel version of cluster routing. Nevertheless, it remains compatible with the single-channel version, and we adhered to the agreement based on standard deviation. However, we omit the step of summation over the weighted centroids, thereby eliminating the need for the hyperparameter **N**, representing the number of clusters per channel. The process is illustrated in Figure 2.  
Figure 1: Single-channel quick implementation of cluster routing.  
The agreement vector \(\mathbf{a}_{i}\) in Equation (3) is simplified as the reciprocal of its standard deviation, as follows:  
\[\mathbf{a}_{i}=\frac{1}{\sqrt{\frac{1}{K}\sum_{k=1}^{K}(\hat{\mathbf{u}}_{i}^{k}- \mathbf{m}_{i}^{k})^{2}}} \tag{8}\]  
Apart from the weighted sum in Equation (5), the centroids of these vote groups are only weighted and fast-forwarded to the next layers, as follows,  
\[\mathbf{s}=\mathbf{c}_{i}\times\mathbf{m}_{i}, \tag{9}\]  
Figure 2: Our first routing variation with multiple channels. The bottom of the figure demonstrates the connections between different capsule layers. At the top of the figure, \(\mathbf{3}\times\mathbf{3}\) primary capsules are multiplied with each weight matrix of a weight cluster comprising \(\mathbf{K}\) matrices \(\mathbf{W}_{i}^{\{1,\cdots,K\}}\) to generate a vote cluster \(\mathbf{u}_{i}^{\{1,\cdots,K\}}\). This vote cluster consists of \(\mathbf{K}\) votes, depicted as a \(1\times 1\times\mathbf{K}\) block (colored cyan) in the right corner of the front middle block. Each cluster sends its weighted centroid \(\mathbf{m}_{i}\) based on the agreement vector \(\mathbf{a}_{i}\) to the next capsule layer. The agreement vector is the result of comparing the standard deviation of each vote cluster with other vote clusters within their smaller respective group, distinguished using different colors, where the number of groups is denoted as \(\mathbf{G}\). In the current scenario, there are three groups (G = 3), each with two centroids (denoted as \(\mathbf{m_{0}}\) and \(\mathbf{m_{1}}\)). These centroids are weighted using their corresponding standard deviations \(\mathbf{\sigma_{0}}\) and \(\mathbf{\sigma_{1}}\), resulting in transformed values (labeled as \(\mathbf{m_{0}^{\prime}}\) and \(\mathbf{m_{1}^{\prime}}\) for the cyan-colored group). This procedure is replicated for the other (red and blue) groups. The resultant weighted centroids are concatenated together to form the next capsule channel layer.  
where,  
\[\mathbf{c}_{i}=\frac{\mathbf{a}_{i}}{\sum_{j=1}^{C}\mathbf{a}_{j}} \tag{10}\]  
Then, layer normalization [6] is applied to the weighted centroids \(\mathbf{s}\). The fast-forward mechanism, when experimentally applied, exhibited comparable performance with fewer parameters than the weighted sum approach used in cluster routing for the _SVHN_ and _CIFAR-10_ data sets, as illustrated in Table 1. This method has the potential to reduce the number of parameters while preserving network performance.
## 3 Geological Setting  
The geological setting of the study area is presented in Figure 1. The Pingtung Plain extends over 1000 km southward toward the Taiwan Strait, bordered by two major faults. The Chishan Fault, a northeast-southwest trending thrust fault, forms the westernboundary. Meanwhile, the Chaochou Fault, a high-angle fault trending north to south, marks the eastern boundary, separating the Plain from the Central Mountain Range. The Chaochou Fault continues southward, extending beyond the land into the offshore area. The uplift along the Chaochou Fault is considered a key factor in the sediment transport from the eroded Central Mountain Range into the Pingtung Plain.  
Sediment types in the Pingtung Plain range from fluvial deposits to marine sediments, spanning the Holocene to Miocene epochs. The surface is predominantly covered by Holocene alluvium, which overlies Pleistocene sediments. These Quaternary sediments form the water-bearing aquifers and aquitards of the Plain. Hydrogeological studies indicate that the aquifer system is notably thick, with aquifers primarily composed of gravel and sand from the continental sequence. In contrast, the aquitard, primarily made of silt and clay from the marine sequence, is mostly found in the distal fan region [35; 36].  
Stratigraphical records, as presented in Table 1[13; 37; 38; 39; 40], show that the Holocene alluvium is relatively thin. It overlies the Pleistocene conglomerates consisting of loose sediments, predominantly pebbles and cobbles. Below these are Neogene sediments, including the early-to-late Pliocene Nanshilun Sandstone, which consists of medium-to-coarse-grained sandstone with slate fragments. The Nanshilun Sandstone formation overlies the Kaitzuliao Shale, a deep marine mudstone interbedded with sandstone. The Pliocene rocks are considered the bedrock of the Plain, deepening from north to south [41; 42; 43]. The Wushan Formation, which consists of Miocene very fine-grained sandstone and mudstone interbedded with shale from shallow marine environments, is present below this layer.
# XLNet-Based Prediction Model for CVSS Metric Values
## 1 Introduction
### Background
#### 1.2.3 XLNet Model  
XLNet is an autoregressive language model that can acquire bidirectional contextual information. To achieve bidirectional contextual information, XLNet mainly adopts three methods, i.e., the permutation language model, two-stream self-attention, and the circulation mechanism [12]. The core of XLNet is the permutation language model. To extract bidirectional contextual information, the algorithm uses the random ordering of the initial input order while maintaining the one-way model of the autoregressive model. For a text of length \(n\), there are \(n!\) different sorting methods; the bidirectional contextual information can be obtained indirectly by considering the whole ranking order of the text. However, computing all the ordering would consume a lot of arithmetic power. Therefore, XLNet only predicts a partial sequence. The mathematical expression of its loss function is as follows:  
\[\underset{\theta}{max}E_{Z\sim Z_{T}}\left[\sum_{l=1}^{T}\;\log\;p_{\theta}(x _{z_{t}}\;\mid\;x_{Z_{<l}})\right] \tag{1}\]  
where \(Z_{T}\) denotes the collection of total permutations of the text with a length of \(T\), \(Z\) is one of the total permutations, \(x_{z_{t}}\) denotes the \(t\)th element, and \(x_{Z_{<l}}\) represents the first \(t-1\) elements.  
There remains one oversight to address in applying the permutation language model. For example, predicting 2 in the sequence of [1, 3, 2, 4] requires the semantic position information of 1 and 3, but only the position information of 2. However, predicting 1 in the sequence of [2, 3, 1, 4] requires the semantic information of 2, 3, 1 and the position information. In two different permutations, sometimes only the location information of 2 is needed. Sometimes, both the semantic and location information of 2 are required. In response to this issue, XLNet proposes two-stream self-attention that combines the two types of information. Figure 2 depicts the structure of two-stream self-attention, where \(h_{\theta}\) represents a unit containing both semantic and location information; \(g_{\theta}\) represents a unit with only location information.  
Moreover, XLNet integrates the current optimal autoregressive language model Transformer-XL into XLNet and introduces two key techniques of Transformer-XL into XLNet, namely the relative positional encoding scheme and the segment recurrence mechanism.
# Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series
## 3 Material and Methods
### Benchmark Algorithms
#### 3.4.1 Random Forests  
The remote sensing community has assessed the performance of different algorithms for SITS classification showing that Random Forest (RF) and Support Vector Machine (SVM) algorithms dominate other traditional algorithms ([PERSON] et al., 2014; [PERSON] et al., 2014). In particular, the RF algorithm can handle the high dimension of the SITS data ([PERSON] et al., 2014), is robust to the presence of mislabeled data ([PERSON] et al., 2014), has high accuracy performance on large scale study ([PERSON] et al., 2014), and has easy-to-tune parameters ([PERSON] et al., 2014).  
The RF algorithm builds an ensemble of binary decision trees ([PERSON] et al., 2014). Its first specificity is to use a bootstrap sample of the data at each tree--i.e., training instances are selected randomly with replacement ([PERSON] et al., 2014)-- in order to increase diversity among the trees. The second specificity is the use of random subspace technique for choosing the splitting criterion at each node: a subset of the features is first randomly selected, then all the possible splits on this subset are tested based on a feature value  
Figure 7: Three types of normalization of four Normalized Difference Vegetation Index (NDVI) temporal profiles.  
test, e.g., maximization of the Gini index. It results in a split of the data into two subsets, for which previous operations are recursively repeated. The construction stops when all the nodes are pure (i.e., in each node, all the instances belong to the same class), or when a user-defined criterion is met, such as a maximum depth or maximum number of instances at the node.  
In the experiments in Section 4, we use the implementation from Scikit-Learn with standard parameter settings [16]: 500 trees at maximum depth, and a number of randomly selected variables per node equals to the square root of the total number of features.
## 2 Mathematical Model
### Physical Model and Basic Assumptions  
As shown in Figure 2, a partially penetrated vertical well is located in the center of a circular naturally fractured gas reservoir. The reservoir is assumed to have two regions: Inner region and outer region, in which reservoir properties are drastically different. The radius of the inner region is \(R_{1}\), and the radius of the whole system is \(R_{e}\). The inner region consists of fractures and matrix while the outer region consists of matrix only, provided that the transfer flow between matrix and fractures in inner region is pseudosteady state. Besides, both of the two regions possess permeability anisotropies. Apart from the aforementioned description, some other assumptions are elaborated as:  
(1) The reservoir is horizontal with uniform thickness of \(h\);  
(2) The upper and bottom boundaries of reservoir are impermeable, and outer boundary is also closed;  
(3) The water phase is assumed to be immobile, which means the gas production process is single phase flow;  
(4) The well is a line source and only part of it produces, and its perforation interval is \(h_{\rm w}\);  
(5) The effect of gravity and capillary pressure are ignored.  
Figure 2: A schematic of partially penetrated well in composite naturally fractured gas reservoir.
## 2 Materials and Methods
DSC measurements showed such high sensitivity that even a slight humidity of samples noticeably affected the final measurement results. Ammonium nitrate, min. 99.0% pure, was used in the analysis. This choice was made to ensure the repeatability  
Figure 1: Differential scanning calorimeter (DSC) analysis of pure polymers used in the case study: polyvinyl chloride (PVC), including hard PVC (uPVC) and soft PVC (PVC-P); polyethylene (PE); polypropylene (PP); polyamide (PA); and poly(methyl methacrylate) (PMMA). of measurements. The influence of additives used with AN for agricultural purposes (content <28% N), such as calcium salts, magnesium, dolomite flour, or chlorides, was avoided. The mixture was prepared just before performing the analysis, thanks to which the risk of a chemical reaction between its components and the vessel walls was minimized. An analytical balance with an accuracy of \(\pm\)0.01 mg was used for its preparation. Measured samples weighing 5.00 \(\pm\) 0.15 mg were placed in a designated aluminum vessel with a volume of 25 \(\mu\)L with a lid with a hole of 0.3 mm in diameter. The presence of an opening in the upper part of the vessel prevented a gradual increase in pressure in the analyzed sample due to the released gases. This is important due to the fact that the decomposition parameters of AN change with the change of pressure: In the case of a significant increase, an explosion may occur. The sample (mixture) was tamped using a PTFE rod to remove air and improve the thermal conductivity of the material. The prepared sample was then placed in the DSC 3500 Sirius NETZSCH apparatus and the analysis began in the range of 25-400 \({}^{\circ}\)C under nitrogen atmosphere with a constant flow of 50 mL/min. The sample was heated at a standard rate of 10 \({}^{\circ}\)C/min.