esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigation on Interference Test for Wells Connected by a Large Fracture
## 2 Methodology  
When an interference test is performed, a well is selected as an active well, and its production rate is changed to vary the pressure of the reservoir. At the same time, a neighboring well acts as an observation well, in which a high-precision pressure gauge is used to record the pressure variation. The production rate variation of the active well and the pressure in the observation well are shown in Figure 1. The connectivity is determined according to the pressure response in the observation well, and the pressure conductivity coefficient is calculated based on the time interval of the pressure signal received and other information [38].
## 4 Results  
### Analysis at the County Level  
Our estimated corn and soybean yields for 2011 agree well with the NASS reported survey data for counties dominated by rainfed cultivation (Figure 6). The coefficients of determination (R\({}^{2}\)), as measures of the correlations between estimated values and reference data, are 0.77 for corn and 0.66 for soybeans. The corn yields were slightly underestimated, as indicated by the mean error (ME) of \(-\)0.18 MT\(\cdot\)ha\({}^{-1}\), which was possibly due to the variation of parameters defined in Table 2. The variation in the parameters, as the linear coefficients in the GPP-Yield relationship (Equation (11)), has little influence on the R\({}^{2}\) values. The high R\({}^{2}\) values obtained for both the corn and soybeans demonstrate that the crop yields estimated from MODIS data strongly correlate with the NASS survey data at the county level.  
Table 3 compares our algorithm with the MODIS-GPP algorithm (in parentheses) in the period 2009 to 2011. Our algorithm demonstrates improved crop yield estimation over the MODIS-GPP algorithm, as indicated by higher R\({}^{2}\) values and lower root mean square errors (RMSE). Similar to the findings in other studies [18], the R\({}^{2}\) values based on the MODIS-GPP algorithm between estimated crop yields and NASS survey data were only 0.15\(\sim\)0.46 for corn and 0.35\(\sim\)0.53 for soybeans in rainfed counties. In addition, the yields were considerably underestimated for corn (negative mean error) but overestimated for soybeans (positive mean error). Our estimated biases were considerably smaller, as indicated by the mean errors. Because the soybean RUE (1.44 g\(\cdot\)MJ\({}^{-1}\) PAR) is close to the biome-wide value in MOD17  
(1.36 g\(\cdot\) MJ\({}^{-1}\) PAR), the GPP contributed by other vegetation was the major factor that resulted in overestimation in MOD17. In contrast, corn as a C4 plant has a much higher RUE value (3.35 g\(\cdot\) MJ\({}^{-1}\) PAR) based on a photosynthetic pathway different from soybean, a C3 plant. The low RUE value prescribed in MOD17 is the main contributor to the large underestimation of corn yields. Therefore, we need to consider the effects of mixed pixels and the RUE variation across crop types when estimating yields based on remotely sensed data.
## 3 Results and Discussion
### Adsorption Kinetics  
The adsorption kinetics of Nd(III), Ce(III), Er(III), and Y(III) on TK221 at room temperature were investigated, and the results are shown in Figure 3a,b. The equilibrium adsorption capacity was found to be 7.65 mg Nd/g, 9.90 mg Ce/g, 12.4 mg Er/g, and 8.99 mg Y/g, respectively, which were reached after 360 min of contact time. The experimental data were fitted with pseudo-first-order and pseudo-second-order kinetic models, and the fitting parameters obtained by linear fitting were listed in Table 3. The correlation coefficients (R2) of the pseudo-second-order model were found to be 1.00 for all the studied REEs, indicating that adsorption predominantly occurred via chemisorption. Notably, for Fe, the amount adsorbed and recovery at 360 min suggest a similar equilibrium time with the REEs, as seen in Table 4.  
Figure 3: (a) pseudo-first-order kinetic rate law fitting for Nd, Ce, Er, and Y adsorption, and (b) pseudo-second-order kinetic rate law fitting for Nd, Ce, Er, and Y adsorption.
## 3 System Design Requirements and Challenges
### Counting Elevator Occupants  
By installing cameras in the elevator waiting areas and capturing images from the external perspective toward the interior of the elevator, statistical information regarding the number of people waiting inside the elevator is obtained. The advantage of this people-counting method lies in the fact that it eliminates the need to install cameras inside each individual elevator to assess the passenger load. Instead, the information on the passenger count of each elevator is provided to users, allowing them to make informed decisions about whether to choose that particular elevator. However, this design also faces two challenges. Firstly, the issue of occlusion arises due to the camera's top-down angled perspective of approximately forty-five degrees, which may result in people standing in the back row being obstructed by those in the front row, leading to inaccurate counting. Secondly, calculating the number of people both inside the elevator and in the elevator  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Authors**} & \multicolumn{3}{c}{**Main Objectives**} & \multicolumn{2}{c}{**Methods**} \\ \cline{2-6}  & **Accuracy** & **Real-Time** & **Low Computation** & **Object Tracking** & **Feature Extraction** \\ \hline [PERSON] et al. [9] & V & & & & V \\ \hline [PERSON] et al. [10] & V & V & & & V \\ \hline [PERSON] et al. [11] & & V & V & & V \\ \hline [PERSON] et al. [12] & V & & & V & \\ \hline [PERSON]. et al. [13] & V & V & & V & V \\ \hline [PERSON] et al. [15] & V & V & & & V \\ \hline [PERSON]. et al. [17] & V & V & & V & V \\ \hline [PERSON] et al. [19] & & V & V & V & \\ \hline [PERSON] et al. [20] & V & & & & V \\ \hline [PERSON] et al. [21] & & V & V & & V \\ \hline Ours & V & V & V & V & V \\ \hline \hline \end{tabular}
\end{table}
Table 1: A comparison of people-counting methods.  
waiting area simultaneously presents the possibility of the elevator arriving at the waiting area without being able to accommodate all waiting passengers. Thus, distinguishing between the current number of people inside the elevator and those still waiting in the waiting area within the same frame is one of the problems that this study needed to address.
# Multi-Dimensional Fusion Attention Mechanism with Vim-like Structure for Mobile Network Design
## 2 Background and Related Work
### Feature Grouping  
Feature grouping in convolutional neural networks (CNNs) [24; 25; 26; 27; 28; 29; 30] is a critical area of research focused on enhancing the efficiency and effectiveness of feature extraction and representation. To address computational constraints, AlexNet [31] introduced the concept of splitting feature maps into two separate GPU (graphics processing unit) pathways, thereby reducing computational complexity. Building on this idea, ResNeXt [26; 32] employs grouped convolutions to create cardinality, referring to the number of groups in convolutions. This approach increases the diversity of learned features while maintaining manageable computational costs.  
Xception [33] further advances this methodology by utilizing depthwise separable convolutions, which divide the convolution operation into depthwise convolutions (applying a single convolutional filter per input channel and pointwise convolutions \(1\times 1\) convolutions to combine the outputs) [34]. Graph convolutional networks (GCNs) [35; 36; 37] extend convolutional operations to graph-structured data, modeling relationships and interactions within feature groups in CNNs for enhanced contextual understanding. BaGFN [38] models high-order feature interactions in a flexible and explicit manner and designs a new generalized attention mechanism to dynamically learn the important weights of crossing features.  
Despite these advancements, traditional modules often consider only a subset of channels, focusing on exploiting the inter-relationships of channels. While they aim to construct informative features by fusing both spatial and channel-wise information, there remains room for improvement in capturing comprehensive channel-wise dependencies.
# Expansion Lemma--Variations and Applications to Polynomial-Time Preprocessing
## Box 7. \(d\)-Set Packing Problem.  
\begin{tabular}{|l|} \hline \(d\)-Set Packing \\
**Input:**_A family \(\mathcal{F}\) of sets over a universe \(U\), where each set in \(\mathcal{F}\) has a size of, at most, \(d\), and an integer \(k\)._ \\
**Parameter:**_k._ \\
**Question:**_Does there exist a subset \(\mathcal{F}^{\prime}\subseteq\mathcal{F},|\mathcal{F}^{\prime}|=k\) such that every element in \(U\) is contained in at most one set in \(\mathcal{F}^{\prime}\)?_ \\ \hline \end{tabular}  
We formally state the definition of a _generic crown decomposition_ below.  
**Definition 5** ([19]).: _Let \(G=(V=A\uplus B,E)\) be a bipartite graph such that for all \(x\in B\), \(deg_{G}(x)=d\). Then, a generic crown decomposition \((C,H,X)\) of \(G\) is given by a head \(H\subseteq A\) and a crown \(C\subseteq(A\uplus B)\setminus(H\cup C)\) satisfying the following properties_  
* \(H\) _is a separator in_ \(G\)_, one collection of connected components being_ \(C\)_._
* \(N(H)\supseteq C\uplus B\)_._
* _There is an injective mapping_ \(M:H\to C\cap B\)_, such that_
* _For all_ \(x\in H\)_,_ \(xM(x)\in E(G)\)_, and_
* _Each_ \(v\in A\) _is a neighbor of, at most, one_ \(x\in M(H)\)_._
* \(X=(A\uplus B)\setminus(H\cup C)\) _are the remaining vertices._  
The authors proved that \((G,k)\) is a YES instance for \(d\)-red/blue dominating set (or \(d\)-red/blue distance-3 packing, resp.) if and only if \((G-(H\cup C),k)\) is YES instance where \((C,H,X)\) is a _generic crown decomposition_ of \(G\).  
The authors devised a framework for obtaining crown rules for covering and packing problems in graphs. They defined a notion of crown decomposition specific to the problem. They then reduced the problem to \(d\)-red/blue dominating set (defined in Box 8) or \(d\)-red/blue distance-3 packing (defined in Box 9) where the problem-specific crown decomposition corresponded to a generic crown decomposition. From the safety of the reduction corresponding to the generic crown decomposition, the safety of the problem-specific crown decomposition follows.
## 4 Results and Discussion
### Flow Characteristics of Brake System and Torque Link
#### 4.2.2 Analysis of Flow Characteristics of Torque Link  
Due to the existence of many cavities in the landing gear, the flow is complicated. Figures 19 and 20 are the flow and vorticity magnitude changes of the torque link at different wind speeds.  
According to Figure 19: I. After the fluid flows through the torque link, part of the fluid flows back to the cavity due to the pressure difference. The returned fluid is divided into two branches, \(T_{1}\) and \(T_{2}\). First, part of the fluid flows to \(T_{1}\)\(\rightarrow\)\(T_{3}\)\(\rightarrow\)\(T_{4}\). Secondly, another part of fluid \(T_{2}\) flows to the lower hole and interacts with \(T_{4}\). II. In the upper hole area: as the speed increases, more air flows into the upper hole. In the lower hole area: (1) when the incoming flow velocity is 34 m\(\cdot\)s\({}^{-1}\), the fluid \(T_{4}\) cannot flow out smoothly due to the obstruction of \(T_{2}\), resulting in large part of the fluid \(T_{6}\) flows back to \(T_{4}\). (2) When the speed is 55 m\(\cdot\)s\({}^{-1}\)-75 m\(\cdot\)s\({}^{-1}\), \(T_{4}\) is hindered by \(T_{2}\) and gradually decreases as the speed increases, and most of the fluid in the cavity flows out along \(T_{4}\)\(\rightarrow\)\(T_{6}\).  
Figure 19: Streamline distribution at the cross section of the torque link.  
Figure 20: The vorticity magnitude changes with time at different speeds at the center section of the torque link.  
According to Figure 21 and Table 3, it can be concluded that: (1) With the increase of speed at the three points, the frequency of pressure fluctuation increases, and the frequency of the three points is roughly the same at 75 m\(\cdot\)s\({}^{-1}\); (2) At the point \(T_{a}\), since the vortex is mainly concentrated in the upper half when the speed is 34 m\(\cdot\)s\({}^{-1}\), the pressure fluctuates greatly, resulting in the same pressure fluctuation amplitude between 34 m\(\cdot\)s\({}^{-1}\) and 55 m\(\cdot\)s\({}^{-1}\). When the speed reaches 75 m\(\cdot\)s\({}^{-1}\), a large amount of fluid flows in, and the pressure fluctuates obviously at \(T_{a}\), so an obvious peak appears at 75 m\(\cdot\)s\({}^{-1}\). (3) At the point \(T_{\rm b}\) and \(T_{c}\), since the vorticity in this area is less at 34 m\(\cdot\)s\({}^{-1}\), the pressure fluctuation amplitude is quite different from 55 m\(\cdot\)s\({}^{-1}\) and 75 m\(\cdot\)s\({}^{-1}\). From Figures 19 and 20, the obstructive effect of \(T_{2}\) on \(T_{4}\) is significantly reduced at the lower hole, and many vortex structures are discharged and dissipated at the lower hole. Therefore, the pressure fluctuation amplitude in the middle and lower regions at 75 m\(\cdot\)s\({}^{-1}\) does not increase compared with that at 55 m\(\cdot\)s\({}^{-1}\).
# Channel Code-Book (CCB): Semantic Image-Adaptive Transmission in Satellite-Ground Scenario
## 2 System Model
### Proposed Semantic Image-Adaptive Transmission Framework
#### 2.3.2 Denoiser  
Beyond the encoder, \(\mathbf{x}\) is sent to the satellite-ground channel for transmission. Due to the existence of channel fading and noise, the feature vector \(\mathbf{x}\) will change to \(\mathbf{x}^{\prime}\) after passing through the channel. The fading process is modeled as  
\[\mathbf{x}^{\prime}=\mathbf{h}\mathbf{x}+\mathbf{n}, \tag{3}\]  
where \(\mathbf{n}\) is random Gaussian noise, \(\mathbf{n}\sim(0,\mathbf{e}^{2})\), and \(\mathbf{h}\) is the channel gain.  
To reduce the error caused by noise interference in the transmission process, the denoiser is used in this architecture. The denoiser adopts different model parameters in different channel environments that are trained in CCB MappingNet and chosen by the proposed parameter selection mechanism. In the denoiser, \(\mathbf{x}^{\prime}\) is transformed into \(\mathbf{x}^{\prime\prime}\), which is similar to \(\mathbf{x}\), and the process is modeled as  
\[\mathbf{x}^{\prime\prime}=f_{dt}(\mathbf{x}^{\prime},\theta), \tag{4}\]  
where \(\theta\) contains the model parameters of the denoiser.
## 3 Results
### Insights from the Video Monitoring of Pig Behaviors
#### 3.4.2 Video Monitoring and Sensor Detection of Pig Behaviors  
The comparison between the video recordings and sensor data confirmed that the sensor could detect changes in pig behaviors sensitively and rapidly. The comparison validated the sensor capacity to detect pig behaviors that were uncaptured by instantaneous visual observations within the 5 min time windows as shown in the 15 min example in Figure 8.  
The sensor clearly distinguishes between the pig lying and standing. It produces low output below 0.1 V when the sow and piglets are lying between 07:40 and 07:45 in Figure 8. As the sow starts to stand at 07:45:10, the sensor output soars, peaking at 0.28 V within one minute. Then, the output soars even further to 0.57 V when the sow starts drinking at 07:46:55.  
The sensor produces a distinct signal pattern for the sow's standing and drinking in Figure 8. The sow standing produces a smaller spike of about 0.3 V, sustained for just 1 min, while drinking produces bigger spikes above 0.5 V, lasting for 6 min from 07:46 to 07:52. During this period, there are times when the sow is actively sucking on the water nipple (i.e., drinking) or when the sow is just standing. The sow actively drinks three times at 07:47:10, 07:48:55, and 07:51:40. These drinking events are promptly recorded by the three different sensor output spikes.  
Figure 7: Comparison between the pig moving scores in visual observation data (**top charts**) and the corresponding sensor detection outputs (**bottom charts**) under heat stress (**left-hand charts** with red curves) and cooling (**right-hand charts** with blue curves).  
The sensor successfully detects transitions from standing to lying in Figure 8. At 07:52:25, when the sow transitions from standing to lying, the signals gradually drop below 0.1 V. Although the sow changes to a lying position within 10 s, the signals do not return to a low output until in about 2 min from 07:53 to 07:55. This delayed signal drop can be attributed to the sow subsequently moving to find a comfortable posture and the piglets walking inside the pen.  
Furthermore, the video analysis confirmed that the sensor can capture the very subtle movements of the pigs. The sensor can even detect the sow's small body part movements, such as its lifting ears, moving its tail, shaking its head, and sniffing. Therefore, the sensor can provide valuable information about the pig's small movements.
## 3 Proposed System Model
### Selection and Application Methods of Machine Learning Algorithms
#### 3.3.5 Random Forest Algorithm  
Random Forest is a machine learning algorithm used for classification and regression [67]. It combines multiple Decision Trees, aggregating their predictions to improve accuracy and reduce overfitting. The algorithm uses bootstrap sampling and random feature selection to diversify trees and balance predictions [68]. Random Forest performs well with high-dimensional datasets, even with missing values, and is resilient to class imbalance and outliers. However, its complexity requires substantial computational resources [69].
## 3 Receiver Model  
The coherent receiver aims at improving the sensitivity taking the advantage of the local optical oscillator. After the coherent mixing of the optical signal and the local laser, the output current of detector \(i\) includes DC and AC terms expressed by \(i_{d}\) and \(i_{a}\)[3]:  
\[i(t)=i_{d}+i_{a}(t)+n(t) \tag{7}\]  
where  
\[i_{d}(t)=R_{d}(P_{s}+P_{LO}) \tag{8}\]  
\[i_{a}(t)=2R_{d}\sqrt{P_{s}P_{LO}}\cos(w_{if}t+\Delta\varphi+\varphi) \tag{9}\]  
\(R_{d}\), \(P_{S}\) and \(P_{LO}\) are the detector responsibility, the power of optical signal and local laser respectively. \(n(t)\) is the shot noise considered as AWGN (addictive white Gaussian noise). The \(w_{if}\) and \(\Delta\varphi\) are the frequency offset and phase offset between the optical carrier and the local oscillator. \(\varphi\in\{0,\pi\}\) is the phase information of BPSK.  
The SNR of coherent receiver is defined as [18]  
\[\gamma=\frac{\left\langle i_{a}^{2}(t)\right\rangle}{\sigma_{n}^{2}}=\frac{2R _{d}^{2}P_{s}P_{LO}}{2 qR_{d}P_{LO}\Delta f}=\frac{R_{d}AI}{q\Delta f} \tag{10}\]  
where  
\[P_{s}=AI \tag{11}\]  
\(\sigma_{n}^{2}\) is the total variance of the shot noise. \(A\) and \(I\) are the aperture area and normalized optical radiance through the receiving aperture. \(\Delta f\) and \(q\) are the noise equivalent bandwidth and the electronic charge respectively.  
Assuming the received optical signal as plane wave, the average SNR in unit aperture area is  
\[\rho_{0}=\frac{R_{d}}{q\Delta f} \tag{12}\]  
Considering the aperture diameter as a constant, the average SNR is  
\[\gamma_{0}=\frac{AR_{d}}{q\Delta f} \tag{13}\]  
According to this section, large aperture area can improve the average SNR of the receiver efficiently. In addition, the noise equivalent bandwidth and the electronic charge should be limited in a suitable range.
## 1 Introduction
The primary objective of this paper was to develop models that accurately forecast the air quality in industrial regions by considering the correlation and interaction between meteorological and environmental factors. Furthermore, this research also aimed to extend the applicability of the proposed models by implementing them across both edge and cloud computing platforms, ensuring adaptability to diverse operational needs. Finally, this study aimed to compare the performance of all the developed models to identify the one that yielded the most optimal and robust results, both in terms of predictive accuracy and practical relevance. This comparative analysis pinpointed which model offers the most significant potential for real-world applications and decision making in air quality management. This paper is organized as follows: Section 2 presents the proposed AQI forecasting framework, which utilizes particulate matter and meteorological data, along with its corresponding slide and variable-length GRU models. Section 3 presents the authors' experimental scenarios that were used for evaluating the framework models. Section 4 outlines the experimental results of the proposed models, and Section 5 concludes this paper.
## 3 Communication Technologies and Wireless Sensors Networks
### Wireless Sensors Networks Categorization
#### 3.1.2 Categorized by Different Network Topologies  
* Star: in a star topology, all sensors communicate directly with a central hub or gateway, offering simplicity in deployment.
* Mesh: sensors in a mesh network communicate through neighboring nodes, ensuring self-healing capabilities and redundancy.
* Tree: with a hierarchical structure, data flow from leaf nodes to a central sink node, enabling efficient data aggregation.
* Hybrid: combine elements of various topologies to strike a balance between reliability, efficiency, and network coverage [23; 24; 25].
# V2X Communication over Cellular Networks: Capabilities and Challenges
## 4 Delay Analysis for V2X Communications
### UC Delay Evaluation
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Uu Interface**} \\ \cline{2-5}
**UC** & **Unicast\({}^{\textbf{Note1}}\)** & **Unicast\({}^{\textbf{Note2}}\)** & **Multicast\({}^{\textbf{Note3}}\)** & **Multicast\({}^{\textbf{Note3}}\)** \\  & & **(Localized)** & **(MBSFN)** & **(SC-FTM)** \\ \hline
**Delay (ms)** & 18.59 & 12.59 & 35.8 & 16.8 \\ \hline Platooning & & & & & \\ Sensor and State Map Sharing & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ \hline Remote Driving & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ \hline Lane Change & & & & \\ \hline Infrastructure-based Perception of & & & & \\ Environment & & & & \\ \hline Collision Avoidance & & & & \\ \hline Clot & & & & \\ \hline Clot & & & & \\ \hline \hline \end{tabular} \(\times\) Cannot fulfil. \(\bigtriangledown\) Fulfil only the lower levels of automation. \(\bigtriangledown\) Fulfils  
\end{table} 
Table 11: 3 GPP UC evaluation for 5G. because multiple delay-contributing network components (e.g., crossing multiple network interfaces among network components in the core network and RRC establishment) have been improved, but not to the extent that a total end-to-end delay of less than 10 ms is reached. Therefore, innovative solutions are still needed for beyond 5G/6G networks to reach these desired delay values.
## 4 Solution Method
### Hybrid Genetic Algorithm  
#### 4.2.1 Algorithm Overview  
The proposed HGA for the MTHVRP-PCIC consists of the traditional genetic operators (crossover, mutation and selection) in addition to the new strategies: (1) local search (LS), (2) split-feasibility procedure (SFP), and (3) simulated annealing (SA). The LS is responsible for exploring the local optimal neighborhood of this current solution. The SFP not only makes the improved solutions feasible for the constraints in the model after crossover and mutation, but also enhances search intensification and diversification. The SA avoids the algorithm falling into local optimum too early. Algorithm 2 presents the pseudo-code.  
```
Input: initial population _M*_ and other parameters; Output: the best solution found BestSol; while stop criteria not reached do  Apply crossover operator to generate offspring individual _st_  Apply mutation operator to generate offspring individuals _st_  Apply local search strategy to generate improved neighborhood solution _st_  Apply split-feasibility procedure to make new solution feasible for each new individual generated at each step above do if\(c(\textit{st})\leq c(\textit{s})\)then \(s:=\textit{st}\) else if\(c(\textit{st})>c(\textit{s})\)then \(s:=\textit{st}\) else \(s:=\textit{st}\) with probability \(p\) defined in Section 4.2.5 endfor endwhile Return BestSol
```  
**Algorithm 2** HGA