esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A
Averaging the polarization of \(\mathrm{P}^{(\omega;2\omega)}\) (\(\xi_{\
u}\tau\)) \(=\mathrm{q}\times\mathrm{p}^{(\omega;2\omega)}\) (\(\xi_{\
u}\tau\)) with respect to the crystal's thickness  
\[\mathrm{P}^{(\omega;2\omega)}\ (\tau)=\frac{\mathrm{q}}{\mathrm{d}}\int_{0}^{ \mathrm{d}}\mathrm{x}\mathrm{p}^{(\omega;2\omega)}\ (\xi_{\
u}\tau)\mathrm{d}\mathrm{x}, \tag{13}\]  
and taking into account (12a) and (12b), we obtain the following:  
\[\mathrm{P}\ (\tau)=\mathrm{P}^{(\omega)}\ (\tau)+\mathrm{P}^{(2\omega)}\ ( \tau). \tag{14}\]  
The B (14) polarization is set at the fundamental frequency of field \(\omega\)  
\[\mathrm{P}^{(\omega)}(\tau)=\frac{8 aqn_{0}\gamma}{\pi^{2}\Big{(}1-\frac{8 aqn_ {0}\lambda_{0}\gamma}{\pi^{2}\ell_{0}\epsilon_{\
u}\kappa_{0}}\Big{)}}\times \sum_{n=1}^{+\infty}\Bigg{[}\frac{\sin^{2}\big{(}\frac{\varpi n}{2}\big{)}}{n ^{2}\Big{(}\frac{1}{\tau_{n}}+\mathrm{i}\frac{\omega}{W(0)}\Big{)}}\Bigg{]} \times\exp\bigg{(}\frac{\mathrm{i}\omega\tau}{W(0)}\bigg{)},\] (15a) and at the even frequency of \(2\omega\) according to the identity \(\sin^{2}\big{(}\frac{\varpi n}{2}\big{)}\cos^{2}\big{(}\frac{\varpi n}{2} \big{)}=0\). We then obtain the following:  
\[\mathrm{P}^{(2\omega)}\ (\tau)=-\frac{32a^{2}\mathrm{q}n_{0}\gamma^{2}}{ \mathrm{d}\pi^{2}\Big{(}1-\frac{8 aqn_{0}\lambda_{0}\gamma}{\pi^{2}\ell_{0} \epsilon_{\
u}\kappa_{0}}\Big{)}}\times\sum_{n=1}^{\infty}\sum_{s=1}^{\infty} \Bigg{(}\frac{n^{2}\cdot\cos^{2}\big{(}\frac{\varpi n}{2}\big{)}\sin^{2} \big{(}\frac{\varpi n}{2}\big{)}\cdot\sin^{2}\big{(}\frac{\varpi n}{2}\big{)}} {(n^{2}-s^{2})\big{(}\frac{1}{\tau_{s}}+\mathrm{i}\frac{\omega}{W(0)}\big{)} \Big{(}\frac{1}{\tau_{n}}+2\frac{\mathrm{i}\omega}{W(0)}\Big{)}}\Bigg{)}=0. \tag{15b}\]  
From expressions (15a) and (15b), it is obvious that in a dielectric alternating electric field, only relaxation modes \(\rho_{\mathrm{k}}^{(\omega)}(\xi_{\
u}\tau)\), \(\mathrm{k}\ \geq\)1 participate in the formation of polarization generated at odd frequencies of the field multiples of \(\omega\). The relaxation modes \(\rho_{\mathrm{k}}^{(2\omega)}(\xi_{\
u}\tau)\), \(\mathrm{k}\ \geq\)2 and multiples of the even frequency \(2\omega\) do not affect polarization.
# Stable Throughput Analysis of Heterogeneous Hybrid FSO/RF Networks with Cognitive Radio Capability
## 6 Numerical Results
Figure 9 illustrates the maximum values of \(\lambda_{\texttt{RD}}\), \(\lambda_{\texttt{HY}}\), and \(\lambda_{\texttt{sum}}=\lambda_{\texttt{RD}}+\lambda_{\texttt{HY}}\) as a function of the detection probability for RF and hybrid FSO/RF pairs, assuming haze conditions. In the cooperative system with SAS, the maximum values of \(\lambda_{\texttt{RD}}\) and \(\lambda_{\texttt{HY}}\) decrease as the detection probability increases. In this cooperative system, both the RF and hybrid FSO/RF pairs compete to access each other's frequency. A higher detection probability reduces the likelihood of missed detections, but it also increases the false alarm probability, thereby diminishing the chance of successfully accessing the other frequency pair. A similar trend is observed for the maximum \(\lambda_{\texttt{RD}}\) in the cooperative system with SMP; however, \(\lambda_{\texttt{HY}}\) remains constant at 1. The maximum value of \(\lambda_{\texttt{sum}}\) in the cooperative system with SAS decreases as the detection probability increases. In contrast, the maximum \(\lambda_{\texttt{sum}}\) for the cooperative system with SMP initially increases and then decreases with a rising detection probability. Compared to SMP, SAS is less likely to interfere with other pairs. Consequently, the advantages of reducing interference from other pairs are outweighed by the disadvantages of decreasing transmission opportunities as detection probability increases. In the economic system, the maximum value of \(\lambda_{\texttt{RD}}\) remains unchanged as detection probability increases, since the RF pair cannot borrow the \(f_{2}\) frequency. However, the maximum values of \(\lambda_{\texttt{HY}}\) and \(\lambda_{\texttt{sum}}\) decrease with increasing detection probability, as this reduces the chance for hybrid FSO/RF pairs to access the \(f_{1}\) frequency.
# Evapotranspiration in Dynamic of Highland Barley Using PML ET Product in Tibet
## 1 Introduction
Generally, the ET, Ec, and the Ec/ET ratio in hot and humid areas were higher than those in arid and semiarid areas [23]. For example, ET, Ec and the Ec/ET ratio of wheat in eastern China [2] with humid climate were higher than those in western China with arid climate [4], and ET, Ec, and Ec/ET ratio of crops in southern China with hot temperature [24] were higher than those in northern China with cold temperature [25]. Previous studies revealed that Ec generally accounted for a large proportion of ET [6; 26], while Es became a major component in arid and semiarid areas [27; 28]. Compared with Ec and Es, Ei was not a major component of ET, while it was an important ET component of the soil-vegetation-atmosphere continuum, regulating the reallocation of rainfall reaching the canopy [26]. However, this component was often neglected in many studies on ET partitioning [4; 29]. Ei was significantly affected by the structure of the canopy and by precipitation; the crop with higher leaf area and vegetation cover generally yielded higher Ei [30]. Featured as a unique germplasm resource and occupying 70% of cropland in Tibet Autonomous Region, China [31], highland barley plays a critical role in guaranteeing the livelihood and promoting social stability of herdsmen in Tibet. Tibetan highland barley has the characteristics of a short-stalk, closed-system planting crop, and it is usually sown between March and April. The growth, development, traits, and yield of highland barley are dramatically affected by hydrological regimes in the Tibetan Plateau. However, limited rainfall, coupled with higher ET, resulted in a severe shortage of available water resources [32], which significantly affected the yield of highland barley in this area. Therefore, it became an important alternative to irrigate highland barley using the available water resources for maintaining the yield of highland barley [33]. Irrigation management was based on the available water at the beginning of the growing season, while no detailed knowledge of water deficit and irrigation demand was taken into account due to the lack of relevant studies. In fact, water depth and the number of irrigations initially proposed can be modified according to rainfall. ET is a critical process and the most important component of the water cycle in Tibet; therefore, quantifying ET components of highland barley and understanding their dynamic can help in the better and more efficient management of the limited water resources and in developing an optimal irrigation schedule. However, the changes and characteristics of ET components for highland barley are still not clear and need to be investigated. Therefore, this study was carried out to fill this knowledge gap; the main objectives of this study were to (1) investigate the characteristics and variations in ET components and their proportions with regard to highland barley and (2) explore the meteorological factors affecting the variations in ET components.
# Positioning Methods and the Use of Location and Activity Data in Forests
## 5 A Hierarchical Model for Processing and Sharing Data
For example, infrequent (e.g., daily) downstream flow of critical alerts or updated imagery or maps from IC to lower-levels may take high priority at fixed times during the day, while upstream aggregation and distillation of field condition data from line workers upward. This hierarchical model also applies to operational forestry and the wood supply chain, wildlife and fisheries management, ecology and ecosystem sciences, and other fields. In operational forestry, for example, data may be collected on logging equipment efficiency (e.g., wood production rate for each piece of equipment and associated stand conditions), current logging costs per hour, worker health and safety metrics, and the quantity and quality of products being processed in remote areas. This data may be summarized on smartphones and shared locally at the jobsite using miniaturized radios. Figure 16: Hierarchical approach to location and data sharing in wildland firefighting. Smartphones serve as hubs for Bluetooth, BLE, or ANT devices such as an environmental sensor (bottom left) and wearable activity-monitoring devices on individuals. These data are processed locally on phones and summary data are transmitted via miniaturized radios such as goTenna, first by Bluetooth, BLE or ANT to the radio device, and then by radio frequency to other crew members. Crew bosses at a higher level of supervision receive data on advanced digital radios with higher bandwidth (and cost). For emergency alerts that require complete connectivity independent of a local network, and for the highest level of Incident Command, devices with two-way satellite data transfer are used. As in the wildland fire example, data would then be transferred among operations foresters, wood procurement staff, or land managers using higher throughput radios. Critical summary information would ultimately be transmitted to large timberland owners, state or federal government agencies or sawmills in the top tier using high-bandwidth radios or satellite data communications. Feedback from the landowner or mill, such as product specifications, timing of operations, current product needs, or other information can be transmitted back to multiple jobsites with higher-level devices as well.
## 8 Restrictions and Future Work  
### Limitations  
While this paper presents a methodology for optimizing safety investment in coal mining equipment, there are several limitations that need to be addressed, particularly in terms of the model's generalization to other mining contexts.  
1. Data Acquisition Challenges: The accuracy and reliability of our model heavily rely on the quality and availability of data. However, the coal mining industry often faces difficulties in collecting comprehensive and accurate data due to the absence of a standardized safety investment accounting system. This challenge is exacerbated when attempting to apply the model to other mining environments, where data collection practices and standards may vary significantly.
2. Practical Implementation Barriers: Although the digital twin five-dimensional model proposed in this paper offers a theoretical framework for safety investment optimization, its practical implementation may encounter numerous barriers. These include, but are not limited to, technical challenges in integrating the model with existing mining operations, resistance to change from industry stakeholders, and the need for specialized training and resources to operate and maintain the system effectively.
3. Limitations of Input Feature Selection and Model Generalizability: The selection of five safety investment indicators in this study may not be exhaustive, potentially limiting the model's accuracy and generalizability to other mining contexts. Factors specific to different mining environments, such as geological conditions, mining methods, and regulatory frameworks, may not be adequately captured by the current set of indicators.  
Figure 17: Comparison of different optimization prediction algorithms.  
Nonlinear Relationship Challenges: Despite the use of the C-D production function for fitting, the complex and potentially nonlinear relationship between safety investment and accident loss may not be fully captured by this approach. This limitation could affect the model's predictive accuracy and its ability to generalize to diverse mining scenarios.
## 3 Materials and Methods
### Sentimental Analysis Using Single and Tetrad Feature Selection Techniques
#### 3.4.1 Filtered Classifiers  
WEKA, an open-source tool, is a collection of machine learning algorithms. In the WEKA tool, initially, the dataset is loaded. Under meta classifier, filtered classifiers were found.  
LibLINEAR  
LibLINEAR: An open-source software application that effectively classifies enormous sparse datasets with a high number of attributes and instances. For big, linear binary and multiclass classification, the tool offers L2-regularized logistic regression, L2-loss, and L1-loss linear SVMs. SVMs of \"L2-regularized, L2-loss Support Vector classification\" is used in LL, which offers efficient linear classification methods, and LibSVM, which generates non-linear SVMs. Both use SVMs, which WEKA already has as the SMO approach. The distinction is that LL is usually quicker than SMO (and can optionally minimize the sum of absolute values of errors rather than the sum of squared errors), whereas LibSVM is significantly more versatile. Using different kernels, SVMs may be used to build many types of non-linear decision boundaries, and the effect can be studied using WEKA's boundary visualizer. They benefit immensely from parameter optimization, which may be accomplished with WEKA's grid search meta-classifier.  
Figure 4: Text-based sentimental classification.  
Large linear classification (Binary or Multiclass) can be done through LibLINEAR, which supports two popular binary linear classifiers: linear support vector machine (SVM) and logistic regression (LR). Given a set of instances with label pairs \((v_{k},w_{k})\):  
\[v_{k}\in R_{l}\ for\ k=1,\ldots,\mathsf{s}, \tag{1}\]  
\[w_{k}\in R_{l}\ for\ k=1,\ldots,\mathsf{s}, \tag{2}\]  
Both methods solve the following unconstrained optimization problem with different loss functions \(\mathsf{\Delta}(t:v_{k},w_{k})\):  
\[\min_{t}\frac{1}{2}t^{x}t+D\sum_{k=1}^{s}\mathsf{\Delta}(t:v_{k},w_{k})\text{ for }D>0 \tag{3}\]  
In this case, D denotes a penalty parameter. The two most often used loss functions in SVM are:  
\[\max(1-w_{k}t^{x}v_{k},0) \tag{4}\]  
and  
\[\max(1-w_{k}t^{x}v_{k},0)^{2} \tag{5}\]  
The earlier is known as L1-SVM, while the latter is known as L2-SVM. The loss function for LR is as follows:  
\[\log\Bigl{(}1+e^{-w_{k}t^{x}v_{k}}\Bigr{)} \tag{6}\]  
It is calculated using a probabilistic model. In some circumstances, the classifier's discriminant function contains a bias factor, m. LibLINEAR implements this term by adding a feature to the vector \(t\), and each instance \(v_{k}\):  
\[t^{x}\leftarrow[t^{x},m],v^{x_{i}}\leftarrow[v^{x_{i}},M] \tag{7}\]  
where \(M\) is a user-specified constant. For L1-SVM and L2-SVM, a coordinate descent algorithm is used. LibLINEAR includes the trust region Newton algorithm for LR and L2-SVM. It predicts a data point \(\mathsf{v}\) as positive if \(t^{x}t>0\), and negative otherwise during the testing phase. It incorporates the one-vs-the-rest approach and a Crammer and Singer method for multiclass issues.
## 6 Conclusions  
In this study, the effect of water content on the tensile behavior of a clayey loess was investigated by conducting a series of tensile tests on remolded loess specimens with varying water contents that were prepared with two different methods (the DM and WM). PIV, SEM and NMR analysis were performed in order to assist with the interpretation of the underlying mechanism. The following conclusions can be drawn.  
1. The tensile characteristics of the DM and WM specimens are different. The WM specimens, in general, had lower tensile strength than the DM specimens and the former presented a ductile tensile failure mode while the failure process for the latter shifted from brittleness to ductility as the water content increased. The PIV results revealed the correlation between the tensile stress-strain response, the distribution of strain field and the development of tensile cracks.
2. The tensile strength of the WM specimen initially increased with the water content and then decreased with a further increase in the water content, whereas the tensile strength of the DM specimens increased with the decrease in water content at two distinct rates. SEM observation and NMR tests indicated that the different trends for the tensile strength to vary according to the water content derived from the differences in the soil microstructure and the relative amount of capillary and adsorbed water, as well as the interaction between the clay and water.
3. The effect of the water content's variation on the tensile strength of a remolded clayey loess is essentially linked to the evolutions of the clay's hydration/cementation, the development of capillary and adsorption suction and the accompanied microstructure change. This complicated linkage can be depicted using the proposed conceptual model that is presented in Figure 15.  
Experimental design: [PERSON], J.W. and Q.L.; Tensile experiment: P.Z. and W.H.; SEM and NMR test: [PERSON], J.W. and W.H.; Experimental analysis: [PERSON], Z.W. and C.L.; Data analysis: P.Z., W.H. and C.L.; Writing--original draft preparation: P.Z.; Funding acquisition: Q.L. and C.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (NSFC), funding number 41972298.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Action Registration in the System
#### 2.3.2. Sending Messages to the User  
Also, to keep the user engaged in the process, in the Planking challenge, the following messages were identified as pertinent based on the model described in [PERSON], [PERSON], et al. (2019) to be sent to the user:  
* If no training has occurred within the first day of the challenge, the user will receive a generic message emphasizing the benefits of physical practice: \"Engaging in physical activity improves both your physical and mental health\";
* If three days have passed since the start of the challenge without any training, the user will receive a specific message highlighting the consequences of neglecting core strength training: \"Failing to complete your core-strength training sessions can adversely affect your health\";
* Upon registering the completion of a plank on the first day, users will receive a personalized message focused on the benefit: \"Well done! You've completed your daily training. Keep it up to enhance your quality of life\";
* Upon completing a full week of performing the abdominal plank, users will receive a personalized message emphasizing the benefits of physical activity: \"Great job, [user's login!] You've completed half of this challenge. Keep it up to further enhance your balance\";
* Finally, when the users reach their goal of 15 consecutive days of completing the challenge, upon completing the challenge of performing the abdominal plank consistently, users will receive a personalized message highlighting the benefits of physical activity: \"Congrutulations, [user's login!] You've completed this challenge. Keep it up! You are reducing the likelihood of experiencing lower back pain\".  
An independent agent in the system is used, which is responsible for these messages and carries out the dispatch of messages to the users automatically. To do this, it combines the information extracted from the events in the BC with existing information in DBMS of the system. Figure 8 shows an excerpt of the code that searches the BC records for relevant events and, after a superficial analysis, sends the data to the function that will carry out the final sending of emails to registered users using the information stored in the DBMS. Those users who do not have records on the network will also receive the corresponding email. This feature is external to the BC itself. Messages within the platform are sent to the user via channels external to BC, in particular, via email. This is a strategy to  
Figure 7: Smart contract verification code of compliance with the challenge requirements.  
improve user engagement with the proposed solution using the gamification principles on several channels.
## 3 Numerical Results and Experimental Validation
### Experimental Validation  
To validate the numerical results, a practical experiment was conducted using real traffic vehicles under field conditions. The measurement system employed two PIR sensor modules arranged in a linear configuration with a fixed spacing of 1 m, as shown in Figure 2a. The sensors were mounted at a height of approximately 1.5 m above ground level and aligned horizontally along the direction of vehicle movement (Figure 2b). The distance from the measurement system to the vehicle movement line varied from 5 to 15 m. The axes of the sensors were adjusted to maintain their parallelism, as shown in our previous works [27, 28].  
The analog output of each PIR sensor was digitized using a data acquisition module based on a microcontroller STM32L476 RG [35] and sampled at 1024 Hz. A high-speed camera (200 fps) was simultaneously used as a reference measurement system, enabling the extraction of ground truth vehicle speeds by tracking vehicle position across successive video frames.  
A total of about 100 random vehicles, including cars and motorcycles, with different traveling speeds ranging from 20 to 100 km/h, were recorded. The time period between the sampled output signals of the two PIR sensors was extracted using both the conventional cross-correlation function (CCF) and the Hilbert transform-enhanced cross-correlation function (CCFHT). The estimated vehicle speeds were then calculated using the known distance between the sensors' axes and compared against the camera-based reference.  
The experimental results, shown in Figure 7, indicate that both methods provide reasonable estimates of vehicle speed with absolute estimation errors under 5 km/h over the speed range of 20 to 100 km/h; particularly, that the deviation between the estimated vehicle speed and the reference speed remains within 5% across the specific speed range when using the conventional cross-correlation method (CCF), and within 3.5% when using the Hilbert transform-enhanced cross-correlation method (CCFHT).  
Figure 7: Comparison between reference vehicle speeds (obtained from video) and estimated speeds using the CCF and CCFHT methods (**a**); and estimation errors of vehicle speeds using the CCF and CCFHT methods under real conditions (**b**).
## 3 Method
### Dynamic Depth Range Sampling  
An appropriate depth sampling range is crucial for comprehensive coverage of real depth values, playing a vital role in generating high-quality depth maps. Conventional methods typically focus on the distribution of individual pixels in the probability volume, adjusting the depth sampling range for the next stage based on this information. [PERSON] et al. [37] introduced a novel approach leveraging the information entropy of a probability volume to fuse feature volumes from different perspectives. Motivated by this, we propose an uncertainty module to adapt the depth sampling range. This module takes the information entropy of the probability volume from Stage 1 as input to assess the reliability of depth inferences. A higher output from the Uncertainty Module indicates greater uncertainty in the current pixel's depth estimation. Consequently, in Stage 0, the depth sampling range is expanded correspondingly to comprehensively cover true depth values, as illustrated in Figure 1. The module comprises five convolutional layers and activation functions, producing output values between 0 and 1. Higher values signify increased uncertainty. The uncertainty interval \(D(x)\) for the pixel \(x\) in the next stage is calculated using Equation (6).  
\[D(x)=[D_{est}-\lambda\times U(E_{est}),D_{est}+\lambda\times U(E_{est})] \tag{6}\]  
where \(\lambda\) is the hyperparameter defining the confidence interval, \(E_{est}\) represents the entropy map of the probability volume, \(U(\cdot)\) denotes the uncertainty module for the probability volume, and \(D_{est}\) is the predicted depth value for the current pixel.