esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Relationship Relating Climatic Factors, LOS and NPP  
#### 3.5.1 Correlation Analysis  
Climatic factors were generally positively correlated with NPP on a national scale (Figure 7). Positive partial correlations were observed for 62.66%, 70.79% and 57.60% of the total pixels with temperature, precipitation, and solar radiation, respectively. The positive correlation coefficients for the climatic variables ranged from 0.1-0.7. About 56.23% of the total pixels showed positive partial correlations between NPP and LOS, and the positive partial correlation coefficient was mostly distributed between 0.1 and 0.5.  
At a regional level, 62.97% of the total pixels in Northern China displayed a negative relationship between NPP and temperature, while precipitation, solar radiation and LOS all generally showed a positive relationship with the NPP (Table 1). Over 57% of the total pixels in Southern China indicated a positive relationship between the NPP and climatic factors as well as LOS. However, the relationship in the Tibetan Plateau was diverse. Specifically, over 83% of the total pixels for temperature and about 58% of the total pixels for precipitation displayed a positive relationship with the NPP, while in general, an opposite relationship occurred between the NPP and solar radiation. In addition, the relationship between the NPP and LOS in the Tibetan Plateau was ambiguous.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **Relationship** & **Northern China** & **Southern China** & **The Tibetan Plateau** \\ \hline \multirow{2}{*}{**Temperature**} & + & 37.03\% & 67.56\% & 83.14\% \\  & \(-\) & 62.97\% & 32.44\% & 16.86\% \\ \hline \multirow{2}{*}{**Precipitation**} & + & 84.55\% & 62.04\% & 57.83\% \\  & \(-\) & 15.55\% & 37.96\% & 42.17\% \\ \hline \multirow{2}{*}{**Solar radiation**} & + & 66.93\% & 61.06\% & 34.75\% \\  & \(-\) & 33.07\% & 38.94\% & 65.25\% \\ \hline \multirow{2}{*}{**LOS**} & + & 57.83\% & 57.56\% & 50.73\% \\  & \(-\) & 42.17\% & 42.14\% & 49.27\% \\ \hline \hline \end{tabular} + Represent positive relationship; – Represent negative relationship.  
\end{table}
Table 1: Partial correlation between the Net Primary Productivity (NPP) and climatic factors as well as Length of Season (LOS).  
Figure 7: The partial correlation coefficients and corresponding frequency distribution between the NPP and temperature (**a,b**), precipitation (**c,d**), solar radiation (**e,f**) and LOS (**g,h**) in China from 2001 to 2014. The red lines in figures (b), (d), (f) and (h) represent boundary for negative relationship and positive relationship.
## 4 Distributed Hotspot Network Architecture
### Distributed and Decentralized Hotspot Network Layer
#### 4.2.3 Dew Server Computation  
Dew servers can be used to host such hotspot gateways. We can use a hotspot gateway and dew server separately to achieve higher effectiveness and a better user experience. We can also integrate both into a single system, where both activities can be performed simultaneously. The main goal of using a dew server is to leverage the dew computing paradigm in the periphery of the distributed hotspot network. Another idea behind the use of dew computing-enabled servers is to provide better performance capabilities in a way that is independent of Internet network services (cloud), so that the hotspot can operate seamlessly in a scenario with limited Internet access. For example, hotspots can enable nearby IoT devices to share sensor data with remote applications. Dew servers can also help IoT devices transmit sensor data when they are in rental mode. In lease mode, a dew server that is overloaded or performing other work can allocate its nearby hotspots to act as a gateway for said IoT device. In this way, an IoT device gains better access. If a dew hotspot is unable to serve the purpose of its nearby IoT device, it can forward the coverage to the nearest dew hotspot system, which will handle the message delivery for the IoT device. Another important aspect of using dew computing in this layer is the security of IoT-based messages. Such secure transmission can be ensured by incorporating a peer-to-peer (P2P) network technology, i.e., blockchain.
## 3 Materials and Methods
### Problem Definition  
Here, we consider a database that records the location information of mobile objects in the two-dimensional space on a finite area. Also, we assume that objects move with small velocities that lie in the range \([u_{min},u_{max}]\) starting from a specific location at a specific time-stamp and which move along a non-linear trajectory. In order to be able to store and handle queries in an efficient way, a mobile object's trajectory is approximated with a series of linear ones, as depicted in Figure 2.  
**Definition 4**.: _A linear trajectory is a straight line that an object keeps track of, starting from a location \(l(t_{0})=[x_{0},y_{0}]\) at time \(t_{0}\). Then, its location for \(t>t_{0}\) will be \(l(t)=[x(t),y(t)]\), or \(l(t)=[x_{0}+u_{x}(t-t_{0}),y_{0}+u_{y}(t-t_{0})]\), where \(u=(u_{x},u_{y})\) is the object's velocity in each plane [12]._  
**Definition 5**.: _A trajectory partition or sub-trajectory segment is a line segment \(L_{i}L_{j}\), where for \(i<j\), both points belong to the same trajectory and are connected in order to form a partition denoted by \(TS_{i}\)[13]._  
**Definition 6**.: _Characteristic points are the points where the trajectory changes rapidly._  
**Definition 7**.: _The dual points array constitutes a set containing points of a trajectory that are represented in the dual space._  
**Definition 8**.: _A compressed trajectory path is a subset of the trajectory's points that indicate a significant change in the motion characteristics, that is, the speed or direction of a moving object._  
Figure 2: A raw trajectory approximation with a discrete number of \(R\) linear sub-trajectories. In the dual dimensional space, each one is represented as a dual point—for example, the linear sub-trajectory \([l(t_{0}),l(t_{1})]\) is represented as a dual point \(dp_{1}\), and the linear sub-trajectory \([l(t_{1}),l(t_{2})]\) is represented as a dual point \(dp_{2}\).  
**Definition 9**.: _Given a trajectory \(T\) of size \(|T|\) and a compressed trajectory \(T_{c}\) of \(T\) with size \(|T_{c}|\), the Compression Ratio (CR) is \(\frac{|T|}{|T_{c}|}\)._  
Authors in [14] claim that the compression ratio constitutes a common metric for evaluating the effectiveness of compression algorithms that can accurately reflect the change of a trajectory's data size. It is influenced by the original signal's data-sampling rate, as well as the quantization accuracy.
## 3 Results and Discussion
### Characteristics of Randomly Oriented Polycrystalline Samples
#### 3.1.1 Lattice Parameters and Microstructures
As shown in Figure 6, \(H_{ci}\) increases with the increase in \(x\). This variation in \(H_{ci}\) with \(x\) can be understood with the following relationship [43; 44]:  
Figure 1.4  
The effect of the microstructure on \(H_{ci}\) is characterized by two factors: \(\alpha\) and \(N\). The first term in Equation (5) represents the magnetic field required to nucleate a reverse domain, and \(\alpha\) increases as the grain size decreases. The second term includes \(N\), \(M_{r}\), and \(M_{s}\). Here, \(N\) is the demagnetization factor, which increases as the grains become more platelet-shaped (i.e., with a higher aspect ratio). As shown in Table 1, increasing \(x\) results in larger average grain sizes and higher aspect ratios, causing a reduction in \(\alpha\) and an increase in \(N\), respectively. On the other hand, the combined value of \(M_{r}\) and \(M_{s}\) in Table 2 slightly increases up to \(x\) = 0.15 and then slightly decreases. Therefore, if \(H_{a}\) remains constant with \(x\), the \(H_{ci}\) values should be continuously decreased, according to Equation (5). However, as shown in Table 2 and Figure 6, the \(H_{ci}\) values significantly increase with the increase in \(x\), suggesting that such an increase in \(H_{ci}\) originates from the increase in \(H_{a}\). The increase in \(H_{a}\) with the increasing \(x\) is ascribed to a continuous decrease in the crystal symmetry [14].
# Malicious URL Detection Model Based on Bidirectional Gated Recurrent Unit and Attention Mechanism
## 4 Experimental Results and Analysis
### Data Set  
The experimental data set used in this paper is a collection malicious phish URLs. The malicious phish URLs ([[https://www.kaggle.com/datasets/sid321](https://www.kaggle.com/datasets/sid321) axon/malicious-urls-dataset]([https://www.kaggle.com/datasets/sid321](https://www.kaggle.com/datasets/sid321) axon/malicious-urls-dataset)) come from the Kaggle community and are from a collection of URL data collected by [PERSON] across multiple communities. In this experiment, 65,536 benign URLs and 65,536 malicious URLs were randomly selected, for a total of 131,072 URLs. This article divides the URL data into a training set, a validation set, and a test set after randomly disrupting the order. The specific sample size is shown in Table 2.
## 2 Materials and Methods
### Machine Learning Models  
In this study, three models were adopted to estimate fine dead fuel load: Multiple Linear Regression (MLR), Support Vector Regression (SVM), and Random Forest (RF). In recent years, machine learning applications in wildfire science have increased. In particular, RF and SVM, two supervised machine learning algorithms, have been widely and extensively used for both classification and regression problems [32]. RF is an ensemble model of decision trees, usually trained with the \"bagging\" method [51]. In this study, the RF model was created using the _randomForest()_ function of the randomForest package [51]. To tune the RF algorithm, the \"ntree\" and \"mtry\" parameters have been set. The \"mtry\" parameter is the number of variables randomly sampled as candidates at each split, while the \"ntree\" parameter is the number of trees to be grown. For the regression problem, the default value for \"mtry\" is calculated by p/3, where p is the number of independent variables of the database. In our study, the \"mtry\" number was set to 2, whereas for \"ntree\" the default value of 500 was used. SVM is a related kernel-based method that finds the optimal hyperplane for dividing data in space [52]. The SVM model was trained using the _svm()_ function of the e1071 package [53]. The kernel used in training and predicting is radial, and the regression function used in the model is the eps-regression default function. To fit the MLR model, the _lm()_ function of a stats package was used [54]. All models have been developed with R software [55].  
Figure 4: Flow chart of the methodology employed in this study. RSME = Root Mean Squared Error; MSE = Mean Squared Error; R\({}^{2}\) = coefficient of determination.
# Waterproof Performance of Sealing Gasket in Shield Tunnel: A Review
## 5 Thermal-Mechanical Coupling Analysis Model  
In terms of the thermal-mechanical coupling analysis model, the existing research focuses on mathematical models and corresponding calculation methods for the shield tunnel lining under high temperatures caused by fire.  
[PERSON] et al. [1] made an in-depth and detailed analysis and reviewed the research progress in the theoretical analysis model for segment joints and lining structures under high fire temperatures. Representative research achievements in recent years include [PERSON] et al. [69], who established a layered Beam-Spring model that can characterize the bursting of lining concrete under fire; [PERSON] et al. [70], who discussed the temperature field distribution of lining structure at high temperatures based on the Homogeneous Ring model. [PERSON] et al. [71] used a thermal-mechanical coupling numerical model of tunnel lining structure to analyze the influence of different heating curves on stress. Further, [PERSON] et al. [72] developed a strong coupling numerical model of heat, water, and steam. [PERSON] et al. [15] constructed a fine thermal-mechanical coupling model for lining structures, considering the cracking and crushing characteristics of concrete at high temperatures. In addition, some scholars have also modeled the mechanical behavior of the structure and waterproofing weak point-segment joints. [PERSON] et al. [73] established the thermal-mechanical coupling finite element model for reinforced concrete and fiber-reinforced  
Figure 3: Flowchart of the Mesh Regeneration method [7] (Reprinting with permission from [PERSON] et al., 2018).  
segment joints at high temperatures in fine. [PERSON] et al. [15] used the joint thermal-mechanical coupling numerical model to predict the opening amount of joint gaskets and explore the waterproof performance at high temperatures.  
In general, compared with the research on mechanical behavior models of shield tunnel lining structures and joints, there are few reports on the sealing capacity and response of lining structures at different joint positions under the effect of temperature.
# Late Fusion-Based Video Transformer for Facial Micro-Expression Recognition
## 3 Proposed Method
### Late Fusion  
In prior work [21], researchers called extracting features of each frame through one shared 2 DCNN which could not model the temporal relations, and combining features before classification, Late Fusion (LF). In contrast to LF, they called extracting a combined feature of all frames through one 3 DCNN early fusion (EF).  
The definitions of LF and EF above are slightly different from our research. However, we used the names because the positions that combine each feature are the same. In our research, we named LF the process that extracts two features individually from two different inputs, video and optical flows, through two different backbones and combines  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Interpolation Time \({}^{a}\)**} & \multirow{2}{*}{**UF1**} & \multirow{2}{*}{**UAR**} & **Acc.** \\  & **(ms, millisecond)** & & & **(\%)** \\ \hline TIM [16] & 602 \(\pm\) 90.4 & 0.6516 & 0.6565 & 65.85 \\ Linear Interpolation & 22.3 \(\pm\) 29.7 & 0.6590 & 0.6629 & 65.85 \\ \hline \hline \end{tabular}  
In this experiment, we set video length as 8 and do not use Late Fusion. \({}^{a}\) is the average time measured 500 iterations.  
\end{table}
Table 1: TIM vs. linear interpolation using Early Fusion Video Transformer.  
those features before classification. Additionally, we named EF the process that extracts a combined feature through one backbone from one input in which video and optical flows are concatenated channel-wise, and classifies the feature into emotional classes.  
In the proposed method, we used LF even though EF has a low amount of computation due to its shared backbone. This is because we thought that extracting a feature from the concatenated input would degrade performance due to dependency, caused by the optical flows calculated from the video.
## 4 Results and Discussion
### Effect of Vibration on the Pressure Drop
#### 4.1.4 Effect of Different Vibration Amplitude  
Figure 7 compares the analytical and simulated results of the effective value of the pressure drop without and with different vibration applied, whose amplitude is, separately, 0.25, 0.3 and 0.35 g. Generally, the predictions agree well with the simulations. When vibration is applied, the pressure drop is reduced. The value is further reduced when the amplitude of applied vibration is growing. For example, when the amplitude of applied vibration is 0.25 and 0.35 g at 300 Hz, the effective value of the analytical pressure drop is 10.3 and 8.3 MPa, respectively. Compared with that under the no-vibration field (16.1 MPa), the pressure drop is decreased separately by 36.0% and 48.4%. This can be explained by the fact that increasing vibration amplitude will decrease the inlet velocity according to Equation (A4), and the pressure drop is then reduced as referred to in Equation (A20). Therefore, increasing the amplitude of applied vibration can reduce the pressure drop inside the extrusion liquefier of ME, which helps enhance the forming quality of ME products.
# Enhancing Safety in Automatic Electric Vehicle Charging: A Novel Collision Classification Approach
## 3 Materials and Methods
### EMD Concept  
Empirical mode decomposition (EMD) is a signal processing technique that assumes a signal, \(y(t)\), can be decomposed into independent oscillation modes known as intrinsic mode functions (IMFs). IMFs are characterized by certain conditions to ensure meaningful instantaneous physical frequencies. Specifically, each IMF must satisfy the following two conditions [33]:  
1. The counts of zero crossings and extreme points should either be equal or differ by, at most, one across the entire data range;
2. At each data point, the average value of the upper envelope and the lower envelope of the local data should be zero.  
The EMD method decomposes a multicomponent signal into a series of IMFs arranged from high to low frequencies, along with a trend item. Each IMF is a signal that satisfies the physical interpretation of a single oscillation mode and a single component signal. [PERSON] proposed a sifting process for EMD, where each round of sifting separates one IMF. The process involves subtracting the obtained IMF from the original signal and repeating the sifting process until termination criteria are met. The specific decomposition process is as follows:  
\[\widetilde{x}(t)=x(t)+n(t), \tag{5}\]  
where \(x(t)\) represents the noise-free signal and \(n(t)\) represents the additive noise in the signal. Third-order spline curve fitting is employed for both the lower envelope (\(e_{l}(t)\)) and upper envelope (\(e_{u}(t)\)), ensuring that the signal remains bounded between these two envelopes. Subsequently, the mean curve (\(m_{1}(t)\)) for the maxima and minima envelopes is obtained as follows:  
\[m_{1}(t)=\frac{e_{l}(t)+e_{u}(t)}{2}, \tag{6}\]  
Then, the difference between the signal to be decomposed and the mean curve can be defined as follows:  
\[h_{1}(t)=\widetilde{x}(t)-m_{1}(t), \tag{7}\]  
At this point, it is necessary to determine whether \(h_{1}(t)\) satisfies the conditions for being a valid IMF. If \(h_{1}(t)\) meets the criteria for being an IMF, it is defined as a first-order IMF component (\(c_{1}(t)\)). If it does not meet the conditions for being an IMF, \(h_{1}(t)\) is considered as the signal to be further decomposed, and the process outlined in Equations (6) and (7) is repeated until the resulting signal satisfies the IMF conditions. This newly obtained signal is then defined as the first-order IMF component \(c_{1}(t)\). Subsequently, based on this, the residual signal, stripped of the IMF component, can be represented as  
\[r_{1}(t)=\widetilde{x}(t)-c_{1}(t), \tag{8}\]  
Then, using the residual signal as the new signal to be decomposed, the process from Equations (5)-(8) is repeated until the resulting signal after the \(m\)th decomposition cycle, denoted as \(r_{m}(t)\), becomes a monotonic function or a constant. In the context of EMD, at this point, the initial noisy collision vibration signal can be represented as  
\[\widetilde{x}(t)=\sum_{i=1}^{m}c_{i}(t)+r_{m}(t) \tag{9}\]
## 5 HTS Tapes Studies Using Scanning Electron Microscope
### Changes to HTS Tape Images  
Based on the analysis of the microscopic images, characteristic changes in the appearance of the HTS tapes can be seen for the samples subjected to prospective short-circuit currents. Figure 11 shows photos of the SF12100-CF tape with a 4 \(\upmu\)m silver layer--Figure 11a shows a cross-section of the control sample and Figure 11b shows a sample subjected to prospective short-circuit currents. The differences concern changes at the silver-superconductor boundary. In the control sample, the boundary between the layers is clearer, and the shades of both layers are uniform. In the second sample, the proportions of the layer thicknesses have changed, and cracks in the superconductor layer are visible (vertical arrows) and have clearly lighter areas (horizontal arrow).  
Similar changes were observed for the SF12100-CF tape with a 2 \(\upmu\)m silver layer. Figure 12 shows a photo of the sample subjected to the prospective short-circuit currents, and shows cracks in the superconductor layers and brighter areas, marked with red arrows, as well as discontinuities (cavities) in the buffer layers, marked with green arrows. The identification of the changes required an analysis of the chemical composition of the HTS tape layers.  
Figure 10: Sample photos of the HTS tape cross-section (4 \(\upmu\)m Ag)—control sample: (**a**) the entire width of the cross-sectional area, (**b**) the upper Ag layer, (**c**) the YBCO layer, and (**d**) the lower Ag layer.
## 2 Results
### Antisense Transcription in S. ambfaciens
six 505 ribosomal protein-encoding genes _rplA_ (L1) (RS21180); _rplK_ (L11) (RS21175); _rplM_ (L13) (RS21490); _rplU_ (L21) (RS12440); _rpmG_ (L33) (RS21110); and _rpmJ_ (L36) (RS21450); iii.) _infA_ encoding translation initiation factor IF-1 (RS21445); and iv.) phenylalanine-tRNA ligase subunit beta and alpha-encoding genes, respectively, _pheT_ (RS07660) and _pheS_ (RS07665). Among asRNAs having as putative targets, mRNAs of genes encoding proteins of the transcription machinery, the presence of two asRNAs may be relevant, that are transcribed complementary, respectively, to _nusA_, coding for the transcription elongation factor NusA, and _cdnL_, coding for an AmiR and NasR Transcriptional Antiterminator Regulator domain (ANTAR)-containing protein. In addition to r-protein genes, we detected several asRNAs that are transcribed complementary to the 5 tRNAs, i.e., tRNA\({}^{\rm Cys}\) (GCA, codon UGC) (RS28910), tRNA\({}^{\rm Leu}\) (CAG, codon CUG) (RS28915), tRNA\({}^{\rm Pro}\) (UGG, codon CCA) (RS28920), tRNA\({}^{\rm Gly}\) (CCC, codon GGG) (RS18700), and tRNA\({}^{\rm Met}\) (CAU, codon AUG) (RS21105) (Supplementary Table S4). One of the 5 tRNAs recognizes a specific codon for proline (CCA) that is less represented in _Streptomyces_ CDSs with respect to other codons specifying the same amino acids (Supplementary Figure S2). We found many putative asRNAs that are transcribed complementary to genes coding for regulatory proteins that oversee morphological and biochemical differentiation. In particular, an asRNA is transcribed complementary to _phoR_ (RS16370) coding for the sensor kinase of the two-component PhoR-PhoP system [54]. Moreover, another asRNA is transcribed complementary to _whiB_ coding for the regulatory protein WhiB that is essential for morphological differentiation in _Streptomyces_[55; 56]. In addition, some asRNAs appear to also target genes coding for proteins involved in the control of secondary metabolite biosynthesis, and, in particular, spiramycin production and resistance. Precisely, asRNAs #88 is transcribed complementary to _smrR_ mRNA (Supplementary Table S4). As mentioned above, _smrR_ codes for a transcriptional activator of the spiramycin gene cluster [57; 58]. Another asRNA, asRNAs #87, is transcribed complementary to _smrB_ mRNA (Supplementary Table S4).