esa-sceva/satcom-chunk-collection huggingface dataset:
# Partitioning Evapotranspirationiration in a Cotton Field under
## 4 Discussion
### Causes of Changes in ET and GPP  
Comparing Figures 3 and 4, it can be found that on an annual scale, the change process of \(ET\) and \(GPP\) is similar to the change process of \(T_{air}\) and \(R_{n}\), with an upward trend from seedling stage to blooming and boll stage and then a downward trend until the end of the growth stage, presenting a unimodal curve overall. Hyperbola fitting was carried out between \(ET\), \(GPP\), and different environmental factors, as shown in Figures 8 and 9, and it was found that they had the most obvious correlation with \(T_{air}\) and \(R_{n}\). The diurnal dynamic changes of \(ET\) and \(GPP\) on a daily scale at the budding, blooming, and boll stages presented a bimodal curve. The reason is that \(ET\) is a comprehensive expression of \(T\) and \(E\) in cotton fields. At budding and blooming boll stages, cotton plants had a larger body size, a large number of leaves, and a large leaf area, while \(T_{air}\), \(RH\), \(R_{n}\), and \(VPD\) were also higher. At 14:00-16:30, excessive levels of \(T_{air}\) and \(R_{n}\) would reduce the stomatal conductance of cotton leaves, resulting in a decrease in \(T\) at this time. When \(T_{air}\) and \(R_{n}\) slowly decreased, \(T\) rebounded, so the diurnal dynamic change process of \(ET\) in the two growth stages was bimodal. \(GPP\) represented the amount of CO\({}_{2}\) that cotton fixed into organic matter through photosynthesis, similar to the cause of \(ET\) changes. Excessive levels of \(T_{air}\) and \(R_{n}\) inhibited the open-close degree of stomata on cotton leaves, resulting in weakening the absorption and utilization of CO\({}_{2}\) by the plant, causing \(GPP\) to decrease at noon and then rise. In general, the changes of \(ET\) and \(GPP\) are synchronous on both annual and daily scales. When \(ET\) and \(GPP\) increase simultaneously, the \(u\)_WUIE\({}_{a}\)_ at each growth stage will also increase. Therefore, when \(ET\) and \(GPP\) are at their maximum, the \(u\)_WUIE\({}_{a}\)_ will also be maximum.  
Figure 8: Hyperbola fitting of \(ET\) and different environmental factors.
## 4 Case Study  
The study site is situated in Xiamen City along China's eastern coast (Figure 6). Xiamen Island occupies a strategic position within the \"Yanshan Fault Depression Zone in Eastern Fujian\", where it interfaces with the metamorphic belt along the coastal region of Eastern Fujian. This geological setting has led to a widespread distribution of granite across the area. Granite formations in Xiamen have been significantly influenced by multiple joint sets, resulting in their fragmentation into angular blocks. Over time, prolonged exposure to physical and chemical weathering processes has transformed these angular blocks into spherical rock masses known as granite spheroidal weathering bodies. These weathering bodies, colloquially referred to as \"boulders\", are a distinctive and prominent geological feature within granite-rich regions. Their presence not only underscores the complex geological history of the area but also poses significant challenges in engineering and construction due to their variable sizes and resistivity characteristics. Understanding and accurately detecting these granite spheroidal weathering bodies are crucial for effective urban planning, infrastructure development, and environmental management in Xiamen City and similar geological contexts along China's eastern coastline.  
Specifically, the metro exploration test area, strategically positioned within a precisely delineated rectangular zone proximate to drill hole MIZ3-TGJ-18 along Haidi Road, spans the vital link between Gaoqi Station and Jimei Xuecun Station of Xiamen Metro Line 1. As depicted in Figure 7, this comprehensive testing zone encompasses four strategic  
Figure 5: Resistivity results of physical test model.  
Figure 6: Map of Xiamen’s location.  
drill holes--ZK1, ZK2, ZK3, and ZK4--each bored with a uniform diameter of 90 mm to ensure consistency in data acquisition. ZK1 delves 15 m deep, while ZK2 reaches 14 m, showcasing slight variations in penetration depths tailored to geological nuances. ZK3 and ZK4 extend even further, with ZK3 piercing to 16.75 m and ZK4 achieving a depth of 16.8 m, demonstrating the detailed attention paid to capturing the full extent of subsurface variations. The spacing between these holes is meticulously planned, with ZK1 and ZK2 separated by 16 m, ZK2 and ZK4 by 15 m, ZK3 and ZK4 by a closer 4.6 m, and a triangular arrangement among ZK1, ZK3, and ZK4 maintaining a consistent 12.4 m distance, providing a dense and comprehensive network for geological analysis. Notably, during drilling operations, ZK2 uniquely encountered a layer of boulders between depths of 11 m and 14 m, necessitating adjustments and highlighting the unpredictable challenges encountered in subway construction geology. In contrast, the remaining three boreholes progressed smoothly, further emphasizing the intricate geological makeup of the area and the importance of rigorous exploration prior to construction.
# Clustering-Based Data Dissemination Protocol Using the Path Similarity for Autonomous Vehicles
## 3 Proposed Method
### Term Definitions  
The definitions of the main terms used in this paper are shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline
**Term** & **Definition** \\ \hline Unclustered node & Node not joined to any cluster \\ \hline Cluster head & A node that manages the cluster in a cluster \\ \hline Cluster member & A node belonging to a cluster but not a cluster head \\ \hline Path distance & Similarity distance between path information of each node \\ \hline Data dissemination rate & The percentage of messages disseminated across the network \\ \hline Survivability & The degree to which a cluster member can be maintained as a member of a cluster for a unit of time \\ \hline Data Dissemination & Delivering messages to the entire network so that as many nodes as possible receive \\ \hline \end{tabular}
\end{table}
Table 1: Term Definition.  
Figure 2: Difference between Highway and City Situations.
# BioSuiteT: A Unified Tool for Biological Sequence Analysis
## 2 Materials and Methods
### Validation of BioSuiteT
#### 2.6.1 Pairwise Sequence Aligner  
The sequence alignment of BioSuiteT is based on the algorithms Needleman-Wunsch for global alignment and Smith-Waterman for local alignment [51]; those algorithms maximize a punctuation that is defined by  
\[S(i,j)=max\left\{\begin{matrix}S(i-1,j-1)+\delta\big{(}a_{i},b_{j}\big{)}& \text{{Coincidence}}\,/\,\text{{Mismatch}}\\ S(i-1,j)+\gamma&\text{{Gap}}\,\,\text{insertion}\\ S(i,j-1)+\gamma&\text{{Gap}}\,\,\text{{erasing}}\end{matrix}\right.\]  
where  
* \(\delta\big{(}a_{i},b_{j}\big{)}\) is the punctuation of coincidence/mismatch (e.g., +1 for match, \(-1\) for mismatch).
* \(\gamma\) is the penalization for the gap (e.g., \(-2\)).
* BLOSUM and PAM substitution matrixes are integrated for protein analysis.  
In contrast to commonly utilized tools such as NCBI BLAST, the pairwise sequence aligner in BioSuiteT integrates both global and local alignment capabilities within a single interface. It employs well-established algorithms--Needleman-Wunsch for global alignment and Smith-Waterman for local alignment--ensuring precise and exhaustive comparisons. While heuristic methods like BLAST are optimized for speed at the expense of alignment completeness, BioSuiteT emphasizes accuracy by performing full matrix computations. Furthermore, the aligner supports customizable scoring schemes, including BLOSUM and PAM substitution matrices, as well as user-defined gap penalties, facilitating detailed and reliable analyses that are particularly suited to scenarios where alignment fidelity is critical, such as in small- or medium-scale comparative studies.
## 3 SF-PPP Performance Analysis of Multi-GNSS Stations  
To evaluate the SF-PPP implementation, daily GNSS data from 40 globally distributed MGEX stations were processed during the days of the year 295-301 in October 2020. The geographical distribution of the experimental stations is shown in Figure 1.
## 4 Problem Space for DL Applications
### IoMT Architecture Layers
#### 4.1.3 Application Layer  
DL in the application layer supports creating personalized treatment plans via analyzing individual patient data such as genetic, demographic, and clinical history. This layer enhances clinical workflows, patient engagement, and telemedicine services through AI-driven applications. Clinical decision support systems (CDSSs) assist healthcare professionals by analyzing patient data for accurate diagnostics, risk assessments, and treatment recommendations. AI-powered CDSSs can detect early signs of diseases and suggest optimal intervention strategies [152]. Remote patient monitoring (RPM) facilitates continuous home-based care, reducing hospital visits while ensuring timely interventions for patients with chronic conditions like hypertension, diabetes, and heart disease [153]. Telemedicine platforms facilitate virtual consultations, allowing real-time interaction between doctors and patients.  
Also, AI-driven chatbots and virtual assistants further support triage, symptom analysis, and medication adherence. Personalized treatment systems use AI to facilitate treatment plans, medication dosages, and lifestyle recommendations according to individual patient data for improved outcomes. Interaction in the IoMT ecosystem: Healthcare providers receive real-time notifications and reports via mobile and web-based dashboards, improving decision making and response times. Patients interact with AI-powered apps, improving engagement, treatment adherence, and self-management of health conditions. By integrating AI-driven analytics, real-time alerts, and patient engagement tools, the application layer bridges the gap between IoMT-generated data and actionable healthcare decisions, enhancing efficiency, accessibility, and personalized patient care. Table 6 lists some examples of applications with DL to satisfy personalized treatment.
## 2 Anisotropic Damage Model
### Damage Evolution Equations
#### 2.3.2 Shear Damage  
An elastic-brittle-plastic strain-softening model was used for shear damage, as shown in the shear stress-shear strain relationship in Figure 3.  
Hence, the damage variable can be obtained as follows:  
\[D=\left\{\begin{array}{rl}0&\gamma_{\mathrm{eq}}<\gamma_{0}\\ 1-\frac{\lambda_{\mathrm{s}}\gamma_{0}}{\gamma_{\mathrm{eq}}}&\gamma_{\mathrm{ eq}}\geq\gamma_{0}\end{array}\right., \tag{18}\]  
where \(\lambda_{\mathrm{s}}=\tau_{\mathrm{r}}\) / \(\tau_{0}\) is the residual shear strength coefficient, \(\tau_{\mathrm{r}}\) is the shear residual strength, and \(\tau_{0}\) is the shear strength.  
Figure 3: Shear stress–shear strain relation for elastic–brittle–plastic shear damage.  
Figure 2: Stress–strain relationship under uniaxial tension for elastic–brittle tensile damage.  
Owing to the nonlinear relationship between the equivalent shear strain \(\gamma_{\text{eq}}\) and principal strain component, the nonlinearity of the damage evolution equations is further increased compared with the conventional damage mode. Moreover, the complexity and uncertainty of the damage problem are also significantly increased.
# Delay-Sensitive Multi-Sensor Routing Scheduling Method for Underground IoT in Mines
## 2 Related Work
### Energy-Based Wireless Sensor Routing Algorithms  
Energy consumption is a critical factor in the design and operation of WSNs, especially when sensor nodes are deployed in remote or inaccessible areas. Numerous studies have developed energy-efficient routing algorithms to reduce power consumption while maintaining network performance, each offering unique strategies to tackle this challenge.  
One approach utilizes optimization techniques, such as the genetic algorithm presented in [10], which targets key metrics like network lifetime, throughput, delay, and reliability. This algorithm identifies optimal routing paths in a graph structure, effectively balancing energy consumption with transmission performance. Similarly, regional management schemes, such as the divide-and-conquer strategy proposed in [11], focus on subdividing the network into smaller regions to manage energy consumption locally. By distributing energy demands across these regions, this method extends the operational life of the network and adapts well to large-scale WSNs with varying node energy levels.  
Machine learning has also been applied to enhance energy efficiency, with methods like the Q-learning-based, data aggregation-aware algorithm in [12]. This approach leverages the aggregation degree of neighboring nodes to reduce redundant transmissions, thereby conserving energy and dynamically adjusting routing paths for optimal network longevity. The combination of learning-based and optimization strategies demonstrates that both dynamic adaptation and regional energy management are effective in reducing power usage across WSNs.  
However, these strategies often assume relatively stable topologies and do not fully account for the complexities of challenging environments, such as underground mines. In such settings, factors like sensor mobility, dynamic topology changes, and harsh conditions pose additional challenges that require both energy efficiency and robustness for sustained operation. While these existing methods provide foundational techniques, they fall short of addressing the unique demands of underground mining applications, where continuous operation under extreme conditions is essential.
## 2 Adaptive Streaming Algorithm for Internet of Autonomous Vehicles
### Streaming Service Support for an Autonomous Vehicle
Upon receiving this notification, the managing RSU triggers the \"Real-Time Streaming Bandwidth Allocation\" process to facilitate the allocation of the deficient bandwidth required for the real-time streaming service of the autonomous vehicle passenger during the time slot \(t_{j}^{\sigma,A}\). Following this adjustment, the RSU conveys the outcomes of the bandwidth allocation back to this module for further processing. * If the allocation of bandwidth in the preceding step meets the minimum bandwidth requirement for the real-time streaming service, we return to Step 5 and continue the execution. If not, continue to the next step. * When the base stations along a specific road segment cannot fulfill the real-time streaming service's minimum bandwidth requirements, and if the streaming service resolution for that segment has already been lowered to the predetermined lower limit, this module informs the autonomous vehicle passenger of the situation. Then, the process returns to Step 5 to continue with further execution. On the other hand, if the streaming service resolution for that segment has not yet reached the predefined lower limit, the module advances to diminish the quality prerequisites for the real-time streaming service of the autonomous vehicle passenger, in accordance with the following equations: \[uvv_{t_{j}^{\sigma,A}}^{\sigma,A}= \begin{cases}\forall\mathbf{r}_{q^{\sigma,A}-1}^{\sigma,A}&\text{if }ursu_{t_{j}^{\sigma,A}}^{\sigma,A}>0,\;q^{\sigma,A}>1,\;at_{t_{t_{j}^{\sigma }}}\leq t_{j}^{\sigma,A}\leq at_{t_{t_{j}^{\sigma}+1}},\;1\leq j\leq e^{\sigma,A },1\leq i<h^{\sigma}\\ \underline{\text{v}}\mathbf{r}^{\alpha}&\text{else}\end{cases} \] (22)\[dvr^{\sigma,\alpha}_{t^{\sigma,\alpha}_{j}}=\left\{ \begin{array}{ll}\mathrm{vr}^{ \sigma,\alpha}_{g^{\sigma,\alpha}-1}&\mathrm{if}\;drsu^{\sigma,\alpha}_{t^{ \sigma,\alpha}_{j}}>0,\;q^{\sigma,\alpha}>1,\;at_{p^{\sigma}_{1}}\leq t^{ \sigma,\alpha}_{j}\leq at_{p^{\sigma}_{1}+1},\;1\leq j\leq e^{\sigma,\alpha},1 \leq i<h^{\sigma}\\ &\mathrm{else}\end{array} ,\right. \tag{23}\]  
where \(\mathrm{vr}^{\alpha}\) represents the system-defined lower limit for the streaming resolution of \(\alpha\). This module notifies the autonomous vehicle passenger and returns to Step 3 for further execution. * If the autonomous vehicle passenger's streaming application involves on-demand streaming or 360-degree streaming, this module calculates whether the autonomous vehicle can smoothly play each video clip of the streaming service during its journey. This means that the video clips can be preloaded to the onboard storage facilities of the vehicle before their playback.
## 2 The Overall Architecture Design of the System
### The Database Subsystem  
The database subsystem is between the field monitoring subsystem and the remote monitoring subsystem and is a communication bridge between the two. Environmental parameter information,  
Figure 1: Overall design of the remote monitoring system for henhouse.  
equipment state, equipment control record, short-message warning record, user account and so on collected by the system are stored in the database for user query and analysis.
# TSA-GRU: A Novel Hybrid Deep Learning Module for Learner Behavior Analytics in MOOCs
## 4 Experimental Results and Comparaison
### TSA-GRU Performance Across Epochs and with Other Models  
The evaluation of TSA-GRU is conducted in two phases: first, its performance across training epochs, and second, its comparison to other models in knowledge tracing.  
In phase one, the TSA-GRU model depicts significant decreases in training and test loss and steady improvements in accuracy while training. Certainly, the best performance is attained at epoch 40, as shown in Figure 3, where minimal test loss and maximum accuracy are recorded. This is an indication for TSA-GRU: the performance captures the sequential dependencies in student interactions with no overfitting or underfitting. This stability at that time provides further justification for its robustness while processing fixed-length sequences of student learning data.  
The second phase considers the comparison of TSA-GRU against various other knowledge tracing systems, specifically TSA-Transformer, MSA-GRU, BKT, and Performance Factor Analysis (PFA). The attention-based models, TSA and MSA-GRU, performed fairly well, but TSA-GRU performs better by achieving high predictive accuracy along with considerably lower test loss values. On the other hand, classical models such as BKT and PFA incur higher losses but lower accuracies, indicating that it is very hard to completely characterize inherently complicated human learning behaviors. The relative comparisons confirm that TSA-GRU can show us the best representation concerning student learning paths, thus backing up its argument in favor of high-level knowledge tracing activities.
# Comparison of Tutorial Methods in Virtual Reality Games for a Better User Experience
## 2 Literature Review
### VR Tutorial  
[PERSON] et al. [20] compared three VR game tutorial design types: text, text + diagrams, and text + spatial. For text + diagrams, the controller diagram and text were displayed in front of the field of view (FOV) of the user, whereas in the text + spatial method, a tooltip was provided on the virtual controller of the player and text was displayed in front of the FOV. In third-person shooter games, the text+spatial method demonstrated the highest control learnability and led to higher performance, player experience, and intrinsic motivation compared with those resulting from the text method. In puzzle games, the text + spatial method exhibited higher control learnability and performance compared with the text method, whereas the text + diagram method had higher control learnability compared with the text method [20]. Similarly, in another study, VR wave shooter games were implemented with instruction-screen and context-sensitive tutorials, and the player's experience concerning the tutorial method was evaluated [5]. The results from the usability evaluation demonstrated that the context-sensitive tutorial method led to higher positive emotions and motivation and lower negative emotions than those resulting from the instruction-screen tutorial method [5]. [PERSON] et al. [27] developed immersive tutorials in a 3D video format and conducted usability evaluations for medical school students. The groups that used immersive tutorials achieved more positive learning outcomes than those that only read technical notes. Another study evaluated the learnability, effectiveness, and satisfaction of VR tutorials for users classified into beginner and expert groups. The beginner group demonstrated a relatively high workload compared with that of the expert group; however, learnability, effectiveness, and satisfaction were all high for both user groups [28].
## 1 Introduction
### Research Questions and Hypotheses  
Formulated in alignment with the overarching objectives, the following subsequent research questions were defined:  
* Research Question 1 (RQ1): Can the JudgED system's testing phases be considered reliable?
* Research Question 2 (RQ2): To what extent does the decision accuracy of the JudgED system tests align with the decision accuracy observed in actual, real-life scenarios? In pursuit of these inquiries, the following hypotheses were subjected to empirical analysis:  
**Hypothesis 1 (H1)**.: _There are no significant differences between the test and the re-test, using the JudgED system._  
**Hypothesis 2 (H2)**.: _There is a significant interrater-reliability agreement between the judges, during the gameplay._  
**Hypothesis 3 (H3)**.: _There is a significant interrater-reliability agreement between the judges, judging real tournaments._
# Recent Advances in Efficient Dynamic Graph Processing
## 6 Future Work
### Dynamic Algorithms for Various Types of Graphs  
Most existing works focus on general graphs without any detailed informations such as labels, attributes, and signs. This becomes an urgent issue for dynamic algorithms on various types of graphs.  
* _Attributed graphs_--extend algorithms to account for attributed graphs, which are critical for applications such as social network analysis where metadata can provide important context.
* _Uncertain graphs_--develop methods that can handle uncertainty in graph data, such as probabilistic edges, which are common in real-world networks like communication or transportation systems.
* _Signed graphs_--incorporate positive and negative relationships between nodes into the analysis, which is essential for understanding dynamics in social networks, recommendation systems, and more.
* _Temporal graphs_--analyze graphs that change over time, capturing the evolution of relationships and identifying patterns or anomalies that occur at different vertices in time.
# Total Internal Reflection Lens for Optical Wireless Receivers
## 4 Discussion
### Comparison with CPC and DTIRC  
The planar SMD package might be suited for a CPC or DTIRC. Compared to a DTIRC, the TIR lens covers the PD chip completely and reduces the ambient light photocurrent to a minimum by material absorption. The CPC and DTIRC most likely achieve a higher concentration ratio due to the direct contact to the PD package. Nevertheless, they feature challenges including precise alignment, the need for an index matching gel, and excess glue at the output aperture. Moreover, CPCs and DTIRC are higher and might not be suited for all applications. For the present setup, a realistic height is in the range of multiples of tens of millimeters depending on the realization. In comparison, the presented TIR lens is a compact concentrator with height of only 6.9 mm. It seems especially favorable for PD packages with non-flat surfaces, such as packages with integrated lenses that are not suited for the CPC or DTIRC. It allows for an easy assembly because the glue is not applied into the ray path and there is no issue with excess glue. The flat top of the TIR lens enables integration into a transceiver case without the need for an additional front plate.