esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Precipitation Data
#### 2.1.3 U.S. Climate Reference Network  
The U.S. Climate Reference Network (USCRN) is a systematic and sustained network of climate monitoring stations distributed across the contiguous U.S., Alaska, and Hawaii. It has provided high-quality measurements of precipitation and other climate variables at about 225 sites since the year 2000, to monitor climate change and its impacts [51]. These stations are distributed to ensure that all major nodes of regional climate variability are captured while taking into account large scale regional topographic factors. Advancedinstruments are equipped to continuously provide hourly observations with accuracy and reliability, by improving the measurement accuracy, sensor stability, and the optimization of data processing algorithms. These improvements enable the network to play an important role in monitoring precipitation trends as well as providing reliable data support for climate research, water resource management, and weather warning [51]. The USCRN stations are widely distributed across various geographical regions, including mountainous areas, plains, and coastal areas. Therefore, they can monitor a wide range of local climate conditions and trends, considering the influence of geographical characteristics, climate types, and population distribution.
## 4 Design and Implementation of the Optimization Framework
### Architecture of the Framework (iMASKO)
\begin{table}
\begin{tabular}{l} \hline \hline
**iMASKO Optimization Process** \\ \hline
**Input** initial solution: \(X\), \(P\) \(\#\)\(X\)-parameters space, \(P\)-performance space \\
1: _assume that:_\(X_{best}\)\(\bigstar\)\(X\), \(P_{best}\)\(\bigstar\)\(P\) \\
**\# Starting optimization until condition met** \\
2: **while** stop condition not met then **do**\#condition (e.g., max generation exceeded) \\
3: \(X_{next}\)\(\bigstar\)\(\mathrm{generate}(X)\) _\#GA operations: selection, crossover, mutation_ \\
**\# Evaluations are proceeded in parallel** \\
4: \(P_{next}\)\(\bigstar\)\(\mathrm{evaluate}(X_{next})\) _\#evaluate results from SystemC simulations_ \\
5: \(\Delta\mathit{cost}\)\(\bigstar\)\(\mathrm{compare}(P_{\mathit{next}},P)\)\(\#\Delta\mathit{cost}=P_{\mathit{next}}-P\) \\
5: **if**\(\Delta\mathit{cost}\)\(\leq\) 0 \\
6: \(X\)\(\bigstar\)\(X_{\mathit{next}}\), \(P\)\(\bigstar\)\(P_{\mathit{next}}\) \\
7: **if**\((P\)\(\leq\)\(P_{\mathit{best}})\) \\
8: \(X_{best}\)\(\bigstar\)\(X\), \(P_{\mathit{best}}\)\(\bigstar\)\(P\) \\
9: **end if** \\
10: **end if** \\
11: **end while** _\# when condition is met_ \\
12: **return/output**: \(X_{\mathit{best}}\), \(P_{\mathit{best}}\) \\ \hline \hline \end{tabular}  
*Note: Process 5 is not necessary, when considering the case in Section 5.1, where one population size is used.  
\end{table} 
Table 2: Pseudo code of iNL@MATLAB GA-based Sensor Network**K**O**ptimizer (iMASKO) optimization process.
# Monitoring of Preload Variation of Linear Guide Positioning Stage Using Artificial Neural Network
## 4 Prediction of Linear Guide Preload--ANN Modeling
### Artificial Neural Network Model  
#### 4.2.1 ANN Architecture  
A neural network algorithm was used to establish the correlation between the vibration of the feeding stage in motion and the preloaded status of the linear guides, and to develop a prediction model to identify the preload state or failure of the linear guides. The basic structure of a typical neural network is shown in Figure 14, which includes an input layer, a hidden layer, an output layer, and neurons inside a layer. For a simple neural network model, weights (\(w\)), bias (\(b\)), and activation functions exist in each layer. The weight indicates a correlation or multiplier value between adjoining neurons. A weight value on the neural network is significantly higher than other weights if the input parameter, which is given to the weight, has a greater influence on the output value.  
In this analysis, the input neurons were RMS vibration levels in each axial and rotational direction, initial preload level, and initial stage rigidity. The output was the current rigidity or preload variation of the positioning stage. In addition, the activation function was responsible for counting or conditioning the output layer value and transmitting signals to the next layer in a neural network. To obtain a smooth continuous function system, the activation function commonly used in a neural network can be tan-sigmoid, log-sigmoid, linear, and others. In this analysis, tan-sigmoid was selected as the activation function for all neurons in the hidden layer, and a linear function was used as the output layer activation function.  
In this neural network analysis model, the predicted variable was positioning stage rigidity. The input parameters included the vibration features, such as RMS values in three linear axes and the vibration RMS values in three angular axes (yawing, pitching, and rolling). The input parameters were labeled as X-RMS, Y-RMS-, Z-RMS, XYZ-RMS, YA-RMS, PI-RMS, and RO-RMS. In addition, the initial preload could also serve as an indicator of the preload state of linear guides at the initial assemblage of the stage. It could be assumed to have the same preload level, such as a high or medium level, depending on the scenarios to be simulated in application modeling. Stage rigidity was the target to be predicted from the measured vibrations. In the establishment of the predictive model, this rigidity value was given as the preload amount of linear guides assembled with the stage for motion tests. The data samples are listed in Table 5.
## 3 Modeling Procedures of Extended Finite Element Method (XFEM)-Based Multiscale Analysis  
### Theoretical Foundation of XFEM  
The XFEM is based on the concept of partition of unity method (PUM) [9], which is an extension of conventional finite element methods. The singularity around the crack tip is described by a near-tip asymptotic function. A discontinuous Heaviside function is used to express the displacement jump at the fracture surface. The enrichment function is composed of near-tip asymptotic functions and discontinuous function. After integrating the partition of unity enrichment, the displacement vector function \(u\) can be approximately expressed as Equation (1) [24].  
\[u=\sum_{I=1}^{N}N_{I}(x)[u_{I}+H(x)a_{I}+\sum_{a=1}^{4}F_{a}(x)b_{I}^{a} \tag{1}\]  
where \(N_{I}(x)\) is the shape function of node \(I\) and \(u_{I}\) stands for the displacement vector for the continuous part without cracks passing through the crack tip. \(a_{I}\) and \(H(x)\) represent the freedom vector of an enriched degree and the Heaviside function. \(b_{I}^{a}\) is the degree of freedom (DOF) related to elastic asymptotic crack-tip functions \(F_{a}(x)\). \(u_{I}\) is suitable for all nodes. \(H(x)a_{I}\) is only applicable for nodes where the element is passed through by cracks. \(\sum_{a=1}^{4}F_{a}(x)b_{I}^{a}\) is suitable to nodes where the corresponding element contains the crack-tip. When the element is not cut by cracks and does not contain crack-tip, Equation (1) degenerates into the displacement formula of conventional FEM. The normal and tangential coordinates for a smooth crack are illustrated in Figure 3.  
Figure 3: Illustration of normal and tangential coordinates for a smooth crack [24].  
Figure 2: Experimental instrumentation.  
The generalized Heaviside function is employed to describe the displacement discontinuity due to cracks. The mathematical expression of \(H(x)\) can be described as Equation (2).  
\[H(x)=\left\{\begin{array}{ll}+1&(x-x^{*})\cdot n\geq 0\\ -1&others\end{array}\right. \tag{2}\]  
where \(x\) stands for the sample point for investigation, \(x^{*}\) refers to the point on crack which is closest to \(x\). \(n\) represents the unit outward normal to crack at point \(x^{*}\).  
The asymptotic crack tip function \(F_{\alpha}(x)\) can be described as Equation (3)  
\[[F_{\alpha}(x)]=\sqrt{r}\Big{[}sin\frac{\theta}{2},cos\frac{\theta}{2},sin\ \theta\ cos\ \frac{\theta}{2},cos\theta sin\frac{\theta}{2}\Big{]} \tag{3}\]  
where \(r\) and \(\theta\) represent the polar coordinates. At crack tip, \(\theta\) is equal to zero.
# Performance Evaluation of MPTCP on Simultaneous Use of 5G and 4G Networks
## 2 An Overview of The MPTCP Schedulers and Congestion Control Algorithms
### MPTCP Schedulers
#### 2.1.1 Shortest RTT First  
Shortest RTT first (SRTT) sends packets through the SF having the lowest round-trip-time (RTT) among all the SF having an available CWND. CWND is a parameter decided by the CCA that determines how much data can be sent through a SF. When more than one SF has the same lowest RTT, SRTT systematically chooses an SF and continues sending through that SF until the CWND of that SF becomes full. Once the CWND of that chosen SF becomes available again, SRTT starts sending through that SF. SRTT is currently the default scheduler for the MPTCP Linux Kernel [27].
# Multicriteria Route Planning for In-Operation Mass Transit under Urban Data
## 4 Multicriteria Route Planning for Mass Transit
### The Strategy in BiasSpan
#### 4.2.1 Grid-like Graph Construction  
The BiasSpan algorithm relies on the grid-like graph for generating possible routes. The benefits are two-fold. First, the number of nodes can be significantly reduced since only some of the grids contain road segments. Using Chicago and Tainan as examples, based on the two cities' road networks, the number of nodes in the two cities can be reduced from 390,509 and 237,866 to 91,320 and 94,282, respectively. To be more specific, since routes are confined to existing roads only, grids that contain no existing road network (299,189 and 143,584 grids for the two cities, respectively, contain features but do not correspond to existing roads) will not be considered for further trajectory construction. It is worth mentioning that the features stored in \"no road\" grids can still be fetched by indexing, since they still contribute to the PF inference of some routes. Second, when extracting features in RAR for a given route, the computational load and the time needed can be reduced by grid indexing.
## 2 Methods  
### Sample and Participants  
The initial sample consisted of 253 individuals occupying responsibility positions--such as directors, department heads, or other strategic roles--within companies located in the Basque Autonomous Community (CAPV). These individuals were invited to participate in this study by completing an online questionnaire. Of those contacted, 66.4% submitted valid responses. To ensure data consistency and quality, only fully completed questionnaires were included in the final analysis, resulting in a sample of 168 managers.  
Among the final 168 participants, 54% were women. Regarding age distribution, 1.8% were under 25 years old; 11.9% were between 26 and 35 years old, 20.2% between 36 and 45, 32.7% between 46 and 55, 32.1% between 56 and 65, and 0.6% were over 66. In terms of professional roles, 61.3% were senior managers; 26.8% held middle management positions, and 11.9% had specific responsibilities without formal management roles.  
With respect to organizational tenure, 18.5% had less than 5 years of experience; 17.3% had between 5 and 10 years; 25.6% had between 11 and 20 years; 22.6% had between 21 and 30 years, and 16.1% had more than 31 years.  
Figure 1: Research Model: Analysis of Knowledge Transfer Protocols and Generational Handover Processes on Knowledge Transfer Behavior. Source: Own elaboration.  
Figure 2: Research model: Analysis of Knowledge Transfer Behavior and Organizational Variables. Source: Own elaboration.  
The CAPV stands out at the national level for its robust industrial sector and, at the European level, is recognized as one of the most dynamic hubs of technological innovation. According to the Spanish National Institute of Statistics [70], it is the second-highest region in Spain in terms of GDP, underscoring its economic strength. Furthermore, the European Commission's 2023 Regional Innovation Scoreboard [71] classifies the CAPV as a \"Strong Innovative Region\" based on indicators such as R&D expenditure, inter-firm collaboration, and patent generation. These features highlight not only its role as a strategic innovation hub but also its commitment to long-term economic resilience.  
The participating companies' headquarters were distributed as follows: Gipuzkoa (38.7%), Bizkaia (39.9%), Araba (5.4%), Nafarroa (1.8%), and other autonomous communities (14.3%). Regarding company size, 29.2% of the respondents were from small companies (<50 employees), 26.8% from medium-sized companies (50-150 employees), and 43.5% from large companies (>150 employees).  
The sample covered a broad range of economic sectors, including construction (4.8%), industry (30.4%), trade and hospitality (3.0%), transport and storage (3.0%), banking, finance, and insurance (3.0%), information and communication (4.2%), public administration (3.0%), education (9.5%), healthcare (1.8%), and other sectors (36.3%). This sectoral diversity enhances the breadth and representativeness of this study's analysis.
## 1 Introduction  
Aerobic composting is a significant and sustainable technology that is extensively used for utilizing organic waste. Composing not only reduces the quantity of organic waste, but also the composting products can be used as an effective organic fertilizer [1, 2]. However, gaseous emissions of secondary byproducts including NH\({}_{3}\) and greenhouse gas during aerobic composting are a serious pollution threat to the environment [3]. Unwars discharge causes loss of nutrients, thus decreasing the compost product quality [4].  
NH\({}_{3}\) and greenhouse gases are produced as the chief secondary pollution of composting. NH\({}_{3}\) is volatilized from NH\({}_{4}\)+-N formation by the ammonification of degradable organic nitrogen at optimum pH and higher temperature [3, 5]. Previous studies [6, 7, 8] have reported the N losses of NH\({}_{3}\) emission accounted for 9.6-46% of the initial total nitrogen. CO\({}_{2}\) is regarded as the main byproduct of C losses during composting, 70-85% of total organic carbon is lost in the form of CO\({}_{2}\)[4]. Although N\({}_{2}\)O and CH\({}_{4}\) are emitted in small amounts during composting, studies based on a 100-year period have estimated that N\({}_{2}\)O and CH\({}_{4}\) are crucial CHGs, and the global warming potentials of them are 298 and 25 times higher than that of CO\({}_{2}\)[9]. CH\({}_{4}\) is emitted by extensive labile carbon compounds under strictly anaerobic conditions; therefore, proper aeration management is vital to reducing CH\({}_{4}\) emissions [3; 10; 11]. N\({}_{2}\)O can be produced not only by the denitrification pathway but also by nitrification pathway [12; 13; 14]. Different research indicated that N\({}_{2}\)O emissions occur at different stages. [PERSON] et al. [15] examined that vast quantities of N\({}_{2}\)O was produced during the thermophilic phase, while [PERSON] et al. [16] indicated that significant N\({}_{2}\)O was emitted primarily during the maturation phase.  
NH\({}_{3}\) and GHG emissions during aerobic composting are significantly affected by factors such as C/N ratio, temperature, water content, aeration condition, additives, pH, and pile scale. Among these factors, aeration condition is considered a critical factor determining the gaseous emissions and nitrogen transformation [3]. Deficient aeration rate and decreasing aeration frequency may result in anaerobic conditions and thus increase the emissions of CH\({}_{4}\) and N\({}_{2}\)O, while NH\({}_{3}\) volatilization leads to acidification and eutrophication of ecosystems [17; 18]. On the contrary, excess aeration rate can limit microbial activity due to the losses of heat and moisture of composting materials [4; 19]. This study aims to approach the effects of different aerated modes on gaseous emissions and reduce the losses of carbon and nitrogen caused by gas emissions. It provided useful strategies for reducing atmospheric environmental pollution and valuable information for solving problems of gaseous emission and nutrient losses during composting.
# A Method for 5G-ICN Seamless Mobility Support Based on Router Buffered Data
## 5 Performance Evaluation
### Results and Discussion
#### 5.3.2 Weighted Average Packet Loss Rate  
Figure 5 presents the comparison between weighted average packet loss rates and different buffer sizes for different methods in different handover scenarios. We can find that as the buffer size increases, the weighted average packet loss rate of all methods becomes smaller. This is because the larger buffer space allows more packets during handover to be buffered. As can be seen from Figure 5a,b, under the same buffer size, our proposal performs better than ProPull and LPBMMS in terms of weighted average packet loss rate. For example, in the cross-UPF handover scenario, when the buffer size is 500, compared with Pro-Pull and LPBMMS, BDMM reduces the weighted average packet loss rate by 71.17% and 67.64%, respectively. In the non-cross-UPF handover scenario, when the buffer size is 500, compared with Pro-Pull and LPBMMS, BDMM reduces the weighted average packet loss rate by 89.21% and 88.25%, respectively. As the buffer size increases, the advantage of BDMM in reducing the packet loss rate becomes more obvious. This is because the BDMM buffer uses DBRAS, which comprehensively considers the business types of mobile data flows and the rates of different types of businesses. DBRAS allocates more buffers to data flows with higher priority and higher flow rate. Both ProPull and LPBMMS adopt a CS strategy. In the CS strategy, when the mobile buffer is full, the packet  
Figure 4: Comparison of average packet delays in different scenarios: (**a**) cross-UPF handover scenario; (**b**) non-cross-UPF handover scenario.  
will be lost directly. When the buffer is not full, the CS strategy is only related to the flow rate. Initially, the rates of real-time and non-real-time data flows are comparable. After 1.05 s of simulation, the rate ratio of real-time data flow and non-real-time data flow becomes 1:4. In this case, the CS strategy allocates more buffer resources to non-real-time data flows, resulting in a larger weighted average packet loss rate. In summary, BDMM uses DBRAS to allocate more buffer resources to higher-priority real-time data flows, thereby significantly reducing the weighted average packet loss rate.
# Progress of Machine Vision Technologies in Intelligent Dairy Farming
## 2 Application of Machine Vision in Dairy Cow Identification
also extracted body contours from the side view of Holstein cows, using the novel CORF3D contour maps to suppress environmental noise, resulting in an average recognition accuracy of 99.64%. However, this method works better for cows with special body patterns; the recognition rate for pure-colored cows is poor [33]. In addition to the side view, images of the back and rear-view of cows can also be used as feature information for identity recognition. [PERSON] extracted features from 516 rear-view cows' videos and used the LSTM model trained with video data to identify cattle identity [34]. The accuracy of this method can reach 88% and 91% when the video length is 15 frames and 20 frames, respectively.Yukun et al. obtained a back-view of the cow from the top camera to determine the cow's identity information when evaluating cows' Body Condition Score (BCS) [35]. Utilizing convolutional neural networks and linear regression models, they achieved a recognition rate of 93.7%. [PERSON] et al. separated the head, trunk, and legs of the cow's body from the original image, and then fused these three features;finally, a support vector machine classifier was used to classify the fused features to identify the identity of cattle [36], achieving a 98.36% accuracy. The comparison of the accuracy of three types of cow identity recognition is shown in Figure 2. From the figure, it can be seen that the recognition accuracy of muzzle print features and body features is slightly better than that of facial features. Cow identity recognition based on body features includes side-view, rear-view, back-view, and multi-part fusion, all of which have high recognition accuracy. The body feature is more applied to the Body Condition Score (BCS), and this part of the content is not discussed in this paper. The above research sufficiently proved the feasibility of machine vision in dairy cow identification. Moreover, machine vision, with its advantages of being non-invasive and low-cost, and having multi-behavior recognition, will have a broader application prospect in dairy cow identification.
# Information-Theoretic Analysis of a Family of Improper Discrete Constellations
###### Abstract  
Non-circular or improper Gaussian signaling has proven beneficial in several interference-limited wireless networks. However, all implementable coding schemes are based on finite discrete constellations rather than Gaussian signals. In this paper, we propose a new family of improper constellations generated by widely linear processing of a square \(M\)-QAM (quadrature amplitude modulation) signal. This family of discrete constellations is parameterized by \(\kappa\), the circularity coefficient and a phase \(\phi\). For uncoded communication systems, this phase should be optimized as \(\phi^{*}(\kappa)\) to maximize the minimum Euclidean distance between points of the improper constellation, therefore minimizing the bit error rate (BER). For the more relevant case of coded communications, where the coded symbols are constrained to be in this family of improper constellations using \(\phi^{*}(\kappa)\), it is shown theoretically and further corroborated by simulations that, except for a shaping loss of 1.53 dB encountered at a high signal-to-noise ratio (snr), there is no rate loss with respect to the improper Gaussian capacity. In this sense, the proposed family of constellations can be viewed as the improper counterpart of the standard proper \(M\)-QAM constellations widely used in coded communication systems.  
Keywords:improper signals; mutual information; Gaussian noise channels; discrete constellations +
Footnote †: journal: