esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Calculation Method of Stable Bearing Capacity of Tower
### Stable Bearing Capacity of Tower Considering the Influence of Joint Axis Stiffness  
The tower connection joint can be equivalent to a spring element with elastic modulus \(E\) and cross-sectional area \(A_{j}\). The coefficient of the length of the joint in the general name of the members is \(k\) (0\(\rightarrow\)1.0). According to the energy method, the energy of a single joint under axial force can be expressed as following. Simplified calculation model of axial stiffness of double-limb double-plate is shown in Figure 11.  
Figure 11: Simplified calculation model of axial stiffness of DLDPJ.  
The strain energy of DLDPJ is shown in Equation (16).  
\[U=\sum\frac{F_{N}^{2}k\frac{b}{\cos\theta}}{2 EA_{j}} \tag{16}\]  
In Equation (16), \(k\) is the ratio of the length of the connecting joint to the total length of the member, referred to as the joint length coefficient; \(A_{j}\) is the cross-sectional area of the equivalent spring joint.  
After substituting Equations (4) and (5) into Equation (16), the following Equation (17) is obtained.  
\[U=\frac{1}{2E}\left(\sum_{1}^{4n}\frac{\left(F_{P}\frac{\theta}{2b}\sin\frac{ \pi x}{2l}\right)^{2}d}{A_{1}}+\sum_{1}^{4n}\frac{\left(F_{P}\frac{a\pi}{8\cos \theta}\cos\frac{\pi x}{2l}\right)^{2}(1-k)\frac{b}{\cos\theta}}{A_{2}}+\sum_ {1}^{8n}\frac{\left(F_{P}\frac{a\pi}{8\cos\theta}\cos\frac{\pi x}{2l}\right)^ {2}k\frac{b}{\cos\theta}}{2 EA_{j}}\right) \tag{17}\]  
The transformation formula of joint energy part is shown in Equation (18).  
\[\sum_{1}^{8n}\left(\cos\frac{\pi x}{2l}\right)^{2}d\approx 8\int_{0}^{l} \left(\cos\frac{\pi x}{2l}\right)^{2}dx=4l \tag{18}\]  
By transforming Equations (9), (10) and (18). Equation (19) is obtained as following.  
\[U=\frac{F_{P}^{2}la^{2}}{4E}\Bigg{[}\frac{1}{A_{1}b^{2}}+\frac{\pi^{2}}{32l^{ 2}\tan(\theta)}\cdot\frac{1-k}{A_{2}\cos^{3}\theta}+\frac{\pi^{2}}{32l^{2} \tan(\theta)}\cdot\frac{k}{A_{j}\cos^{3}\theta}\Bigg{]} \tag{19}\]  
From the Equation (13), the expression of the critical load can be obtained as following.  
\[F_{P\sigma}=\frac{\pi^{2}EI}{l^{2}}\frac{1}{4+\frac{\pi^{2}}{8}\left(\frac{b} {l}\right)^{2}\frac{(1-k)A_{1}}{A_{2}\sin\theta\cos^{2}\theta}+\frac{\pi^{2}k }{4}\left(\frac{b}{l}\right)^{2}\frac{A_{1}}{A_{j}\sin\theta\cos^{2}\theta}} \tag{20}\]
## 4 Proposed Model Design
### Apache Spark  
Spark is a distributed computational model and an open-source framework used to analyze and process massive amounts of data on cluster computing platforms [54; 55]. It supports in-memory computing that allows data to maintain and processes in shared physical memory. When the entire memory is occupied, and the system cannot store more data, the data is split by Spark into secondary storage. Because of this feature, Spark provides a better computational environment for iterative methods like machine learning methods. The Spark framework comprises a cluster manager, worker node, SparkContext, and driver program. With the support of a cluster manager, a user job can use SparkContext to allocate resources to it and access the recourses of the cluster. The primary method that creates SparkContext is in the driver program, a Java process. Cluster Manager is a resource management module that schedules, allocates, and shares resources across multiple jobs. Various cluster managers, including Mesos, Hadoop Yarn (yet another resource negotiator), and Standalone Cluster Manager, can be used to deploy the Spark Framework. The master/slave architecture is used in the Spark framework. The driver is the master node, which serves as the central coordinator, while the slave nodes serve as the distributed workers (exectives). Spark can access data from various sources, including Cassandra, HBase, S3, and HDFS. In addition, the Spark framework comes with various built-in libraries like MLib, Spark Streaming, GraphX, and Spark SQL to facilitate application developers across multiple domains. MLib is a scalable library that supports machine learning methods like random forests, K-nearest neighbors, SVM, etc.  
Spark Streaming is an API that is language-integrated, which helps developers to build quickly fault-tolerant, scalable, real-time, and streaming data processing systems. GraphX is a built-in library that is built for graphic iterative parallel computations. Application developers can use SparkSQL to query structured data employing the Spark system. In Spark, a significant memory abstraction is provided in the form of an in-memory Resilient Distributed Dataset (RDD) that enables fault tolerance and capabilities of high scalability [56] in addition to the built-in libraries. RDD is the set of read-only objects split over several cluster processing nodes to enable parallel processing. Furthermore, by implementing RDD Lineage [57; 58; 59] service, the RDD acquires fault-tolerance capability. If a node fails during execution, RDD automatically recalculates RDD distribution using parent RDD.
# Fundamental Spacetime Representations of Quantum Antenna Systems
## 5 On the General Structure of Radiation Processes in Linear Quantum Antenna Systems
### On the Causal Spacetime Structure of Radiation Emitted by Quantum Antenna Systems
Then, we have_  
\[C^{+}(D_{\mathrm{s}}):=\bigcup_{x^{\prime}\in D_{\mathrm{s}}}C_{x^{\prime}}^{ +},\quad C^{-}(D_{\mathrm{s}}):=\bigcup_{x^{\prime}\in D_{\mathrm{s}}}C_{x^{ \prime}}^{-}, \tag{52}\]  
_and_  
\[C(D_{\mathrm{s}})=C^{+}(D_{\mathrm{s}})\cup C^{-}(D_{\mathrm{s}}). \tag{53}\]  
_Note that the same constructions can be laid out for the receiving q-antenna case when the detector is in the receive domain \(D_{\mathrm{r}}\)._  
**Remark 14**. : _Note that it is still possible to incorporate gravitation into our model, since the effect of gravitational fields is mainly to tilt the lightcones locally, where the tilting is directly determined by the gravitational potential/metric tensor \(g_{\mathrm{zw}}\)[41]. For applications of q-antennas in deep space communications, these gravitational effects may have to be taken into account. Consequently, the constructions provided by Definition 5 may become useful in astronomical and cosmological applications of either classical or quantum antennas. That is especially true when the large-scale structural impact of the gravitational field on a planned solar or even future interstellar communication links is important._  
**Remark 15**. : _The causal domain \(C(D_{\mathrm{s}})\) is a function of the source region \(D_{\mathrm{s}}\). Moreover, it should be emphasized that in general, the antenna causal domain, as defined above, need not constitute a simple cone in itself. In addition, in principle, the receivers located inside \(C(D_{\mathrm{s}})\) can receive information transmitted by radiation emitted from inside \(D_{\mathrm{s}}\). On the other hand, noncausal receivers, e.g., see some cases schematically depicted in Figure 4b, can never receive information from an antenna system whose source is supported by the region \(D_{\mathrm{s}}\)._  
Overall, the analysis above has highlighted an organic interlinking of spacetime with causality within the context of generic quantum communication systems utilizing quantum antennas. This suggests the need to pay closer attention to the _global_ (i.e., topological) structure of spacetime domains when such future quantum technologies are incorporated in the analysis, design, and construction of long-distance space communication systems at the solar, extra-solar, astronomical, or even cosmological scales.
## 3 Result and Discussion
### Stress-Strain Behavior  
This study used BJPA and GGBFS as the base materials for geopolymer production to determine the effects of distinct L/S and BJPA-substitution ratios on stress-strain behavior through an examination of modulus of elasticity (Table 4). Figure 6A-C displays the stress-strain curve results, whereby the stress-strain behavior of OPC was between that of geopolymers with BJPA-substitution ratios of 40% and 60%. The trend of stress-strain behavior was consistent with that of compressive strength: its modulus of elasticity decreased by 5.7% when L/S decreased from 0.5 to 0.3; whereas the modulus of elasticity decreased by 50.6% when the BJPA-substitution ratio rose from 0% to 100%.  
Figure 5: The compressive strength of the BJPA–GGBFS composite geopolymer.  
Figure 6A indicates that the strain of the pure GGBFS geopolymer dropped from 0.44% to 0.15% (a 55% decrease) when its BJPA-substitution ratio reached 100%. In other words, an increase in the BJPA-substitution ratio reduced strain, and the same applied to stress: stress decreased by approximately 73% when the BJPA-substitution ratio increased from 0% to 100%. The strain of the pure GGBFS geopolymer, as well as those with 20% and 40% BJPA-substitution ratios, exceeded that of OPC by 33.3%, 25.7% and 8.7%, respectively. The stress-strain curve showed a larger drop when the BJPA-substitution ratio rose from 40% to 60%. Figure 6B shows that stress-strain behavior exhibited signs of decrease when the L/S increased. Strain decreased by approximately 61% when the BJPA-substitution ratio rose from 0% to 100%, and the stress-strain curve showed a larger drop when the substitution ratio rose from 40% to 60%. The stress and strain of the pure GGBFS geopolymer decreased by 1.2% and 5.8%, respectively, when the L/S increased from 0.3 to 0.4. Figure 6C indicates that when the L/S rose from 0.4 to 0.5, the overall stress and strain decreased by an average of 5.9% and  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**BJPA (\%)**} \\ \hline
**L/S** & **0** & **20** & **40** & **60** & **80** & **100** \\ \hline
0.3 & 53.1 & 46.5 & 42.1 & 34.8 & 29.6 & 22.5 \\
0.4 & 51.5 & 47.2 & 42.2 & 35.8 & 31.4 & 27.4 \\
0.5 & 43.2 & 40.2 & 33.6 & 24.6 & 29 & 29 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Modulus of elasticity of BJPA–GGBFS composite geopolymer. (GPa).  
Figure 6: Stress–strain behavior of the BJPA–GGBFS composite geopolymer.  
5.1%, respectively. Stress and strain increased by 10.7% and 4.4%, respectively, when L/S decreased from 0.5 to 0.3. Figure 6C also displays a larger drop in the stress-strain curve when the BJPA-substitution ratio increased from 40% to 60%.  
BJPA-substitution ratio had a greater influence on stress-strain behavior than L/S did, whereby the decrease in stress was more obvious. Both stress and strain substantially decreased at a BJPA-substitution ratio of 60%, thus forming a considerable drop in the stress-strain curve.
# A Multi-Task Learning and Multi-Branch Network for DR and DME Joint Grading
## 4 Experiment and Results  
### Experimental Settings  
#### 4.1.1 Datasets  
The grading performance is tested on the IDRiD [39] dataset, which has 516 fundus images and is divided into a training set and a test set. The training set includes 413 images, and the test set contains 103 images. Each image in the training set and test set has two types of labels: DR-graded labels and DME-graded labels. According to the severity, DR has five levels of labeling (0,1,2,3,4) and DME has three levels of labeling (0,1,2).
# Unsupervised Vehicle Re-Identification Method Based on Source-Free Knowledge Transfer
## 2 Method
### Pre-Trained Source Model and Target Model  
Among the existing unsupervised domain-adaptive methods [16], the usual practice is to train the model \(F(\cdot\mid\theta)\) on the source domain first, where \(\theta\) represents the parameters of the current model, and then transfer the model to the target domain for learning.  
The method in this paper does not need to access the source data, and its source domain model is expressed as shown in Equation (1):  
\[\mathbb{D}_{s}=\{x_{i}^{s},y_{i}^{s}\}\mid_{i=1}^{N_{s}} \tag{1}\]  
where \(x_{i}^{s}\) represents the i-th training sample, \(y_{i}^{s}\) represents its identity label, and \(N_{s}\) represents the total number of samples. The target domain model is expressed as shown in Equation (2):  
\[\mathbb{D}_{t}=\{x_{i}^{t}\}\mid_{i=1}^{N_{t}} \tag{2}\]  
which does not have any associated labels. The experimental steps are as follows:  
First, the source domain is accessed to train the source model as well as a learnable source domain classifier \(C^{s}:f^{s}\rightarrow\{1,2...M_{s}\}\), where \(M_{s}\) represents the number of sample identities.  
Figure 1: Schematic diagram of unsupervised domain-adaptive vehicle re-identification method based on source-free knowledge transfer.  
Second, it is optimized using the identity classification loss \(\mathcal{L}_{id}^{s}(\theta)\) and triplet loss \(\mathcal{L}_{tri}^{s}(\theta)\)[17] composed of the cross-entropy loss function \(L_{cc}\), as shown in Equations (3) and (4):  
\[\mathcal{L}_{id}^{s}(\theta)=\frac{1}{N_{s}}\sum_{i=1}^{N_{s}}\mathcal{L}_{cc} \big{(}\mathcal{C}^{s}\big{(}F^{s}\big{(}x_{i}^{s}\mid\theta\big{)}\big{)},y_{ i}^{s}\big{)} \tag{3}\]  
\[\mathcal{L}_{tri}^{s}(\theta)=\frac{1}{N_{s}}\sum_{i=1}^{N_{s}}max(0,||\;F^{s} _{i}\mid\theta)-F^{s}(x_{i,p}^{s}\mid\theta)\;||\;+ \tag{4}\]  
\[m-\;||\;F^{s}(x_{i}^{s}\mid\theta)-F^{s}(x_{(i,n)}^{s}\mid\theta)\;||)\]  
where \(||\cdot||\) represents the \(L2\)-norm; the subscripts \((i,p)\) and \((i,n)\) represents the positive sample and the negative sample of the \(i-\)th sample, respectively; and \(m=0.5\) represents the distance margin of triplet loss. The overall loss \(\mathcal{L}^{s}(\theta)\) of the pre-trained source domain model is therefore calculated as shown in Equation (5):  
\[\mathcal{L}^{s}(\theta)=\mathcal{L}_{id}^{s}(\theta)+\lambda^{s}\mathcal{L}_ {p}^{s}tri(\theta) \tag{5}\]  
where \(\lambda^{s}\) represents the weight of two losses. After obtaining the source model, we train a target model by loading the source model parameters, clustering the target domain, and then predicting the pseudo labels \(\tilde{y}_{i}^{t}\). The overall loss \(\mathcal{L}^{t}(\theta)\) of the target domain model is therefore calculated as shown in Equation (6):  
\[\mathcal{L}^{t}(\theta)=\mathcal{L}_{id}^{t}(\theta)+\lambda^{t}\mathcal{L}_{ tri}^{t}(\theta) \tag{6}\]
## Abbreviations  
AA, Acrylic acid; AAm, Acrylic amide; AFP, \(\alpha\)-Fetoprotein; BPA, Bisphenol A; BSA, Bovine serum albumin; CFX, Cefalexin; Cyt C, Cytochrome C; DA, Dansylamide; DBTS, Dibenzothophene sulfone; DEDTC, \(N\),\(N\)-diethyldithiocarbamate; DMAEMA, 2-(Dimethylamino)ethyl methacrylate; DMAPMA, \(N\)-(3-(dimethylamino)propyl)methacrylamide; DMF, Dimethyl formamide; DMSO, Dimethylsulfoxide; Dox, Doxorubicin; DVB, Divinylbenzene; EGDMA, Ethylene glycol dimethacrylate; HEMA, 2-Hydroxy methacrylate; LCST, Low-critical solution temperature; Lys, Lysozyme; MAA, Methacrylic acid; MBAA, \(N\),\(N\)-methylenebisacrylamide; MIPs, Molecularly imprinted polymers; MIH, Molecularly imprinted hydrogel; M-MIPs, Magnetic responsive MIPs; MPABA, 4-((4-Methacryloyloxy) phenylazo)benzoic acid; MAPASA, 4-((4-Methacryloyloxy)phenylazo) benzenesulfonic acid; MRS, Molecular recognition sites; QDs, Quantum dots; PCL-T, Polycaprolactone-triol; PCS, Photocatalytic sites; PEG, Poly(ethylene glycol); PES, Polyethersulfone; PhAAAn, \(p\)-phenylazoacrylanilide; NIPAAm, \(N\)-isopropylacrylamide; RAFT, Reversible addition-fragmentation chain transfer; RGDS, Arg-Gly-Asp-Se peptide; SRP, Stimuliresponsive polymers; tBAm, _N-tert_-butylacrylamide; TRIM, Trimethyllopropane trimethacrylate; TTT, Tetramethyloylyl triethylene tetramine; VBIDA, _N_-(4-vinyl)-benzyl iminodiacetic; 4-HA, 4-Hydroxybenzoic acid; 4-VP, 4-Vinylpyridine.
# Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications
## 3 Sentinel-2 for Precision Agriculture
Information on the nitrogen absorbed by the crops and nitrogen nutrition index is provided; this information allows Belgian farmers to decide on precise N applications. Furthermore, geolocalized fertilizer spreaders can make applications on fields following Sentinel-2 sensed data in other to balance the applied amount of fertilizer depending on the crops needs per zone. Regarding biotic stresses several approaches have been studied. Despite the insufficient resolution of Sentinel-2, 10 or 20 m, to analyze crops at leaf and individual plant scale, different strategies have been developed. On the one hand, the use of VIs to monitor effects of pests at canopy level in crops have been used in coffee [83] and wheat [84], as well as new VIs proposed with Sentinel-2 multispectral bands that have shown to sense wheat yellow rust using bands 4, 5, and 7, the so-called Red Edge Disease Stress Index (REMSI) [85]. On the other hand, classification algorithms using 2, 3, 4, and 8 bands have shown to correctly separate cotton rot root affected areas with 94.1% accuracy [86]. Furthermore, the combination of UAV and Sentinel-2 for western swamphen monitoring in rice crops have shown a relative high potential (57% of accuracy) for detecting infested fields [87]. The detection of biotic stresses can drive precise application of pesticides for both regional and field-level, furthermore the current drones' on-board carrying capacity has increased and there are various drone-based spraying experiences for crop protection against biotic stresses [88; 89]. In this case, the application within the field could be driven by Sentinel-2-derived information on the most affected sites by the biotic stresses. Concerning abiotic stresses, heavy metal stress in rice fields produced by cadmium (Cd), lead (Pb), and mercury (Hg) have shown to be effectively sensed using red edge features of Sentinel-2 [90; 91; 92]. In the case of salinity, Sentinel-2 bands 2 and 4 showed higher correlation (R\({}^{2}\) = 0.67) with the electrical conductivity of fields than Landsat-8 bands [93]. Drought monitoring at 20 cm soil-depth, using combined soil moisture models, and Sentinel-2-derived NDVI has shown to correlate strongly, with determination coefficients (R\({}^{2}\)) ranging between 65% and 83% [94; 95]. Detecting areas affected by abiotic stresses can guide determination of suitable zones for agriculture or, on the other hand, in need for phytoremediation activities or the implementation of halophyte cultivars. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**S2 for Agriculture** & **Sensed Trait** & **Method** & **Ref.
# HiGoReg: A Hierarchical Grouping Strategy for Point Cloud Registration
## 4 Results
### Simulated Datasets
#### 4.1.2 Computational Efficiency  
Table 3 illustrates that HiGoReg consistently delivered substantial speedups across all tested simulated datasets. Specifically, for the Bunny dataset, HiGoReg reduced the average runtime from 1.4151 s (using the batch method) down to 0.08748 s, which corresponded to an improvement of 38.18%. For the Monkey dataset, the runtime decreased from 4.9710 s to 1.3517 s, achieving a notable 72.81% acceleration. When processing the Dragon dataset, HiGoReg completed the task in 3.6131 s compared to the 20.8628 s required by the batch approach, resulting in a speedup of 82.68%. The largest gain was observed on the large-scale synthetic dataset: HiGoReg finished in just 4.3611 s, whereas the batch method took an  
Figure 12: Comparison of calculating efficiency of two schemes for statue point cloud data under different stochastic models. “Running index” denotes the index of execution counts, where each execution sequentially completed the batch solving process and HiGoReg algorithm workflows under different groups, with a total of 10 executions. The suffixes “E” and “N” indicate equal weighting and nominal accuracy weighting methods.  
overwhelming 2081.1157 s, yielding an exceptional 99.79% boost in efficiency. These results underscore HiGoReg's pronounced advantage, especially when handling large-scale data. Besides the improvements in average values, similar enhancements were also observed in terms of maximum and minimum values.  
As shown in Figures 5 and 6, grouping observations significantly reduced computation time compared to the batch solution. However, as the number of groups increased, the runtime exhibited a U-shaped trend--initially decreasing and then increasing--due to the trade-off between per-group computation and recursive update overhead. This behavior allowed the optimal number of groups, corresponding to the highest computational efficiency, to be easily identified. The optimal group configuration varied across datasets. For the Bunny dataset, the best balance was achieved with three groups, while the Monkey dataset performed similarly with three or four groups. The Dragon dataset, which contained substantially more data, reached peak efficiency with nine groups. In the case of the large-scale synthetic dataset, the lowest runtime was observed when the number of groups was set to 70. Clearly, these represent their optimal numbers of groups.
## 4 Optimal Frame Generation Time Threshold Calculation  
Real-time frame generation in AOS faces a fundamental trade-off between latency and transmission efficiency. Fixed threshold-based strategies are unable to adapt to dynamic traffic patterns and often fail to meet delay constraints for delay-sensitive services such as voice and control commands. Therefore, this section investigates a dynamic threshold adjustment method that minimizes queuing delay while maintaining high frame utilization. Among them, the determination of the optimal framing time threshold is the basic condition for the implementation of the real-time framing algorithm. Therefore, we first explain the framing time threshold calculation algorithm. To this end, we formulate the frame generation problem as a Markov decision process and propose a reinforcement learning-based solution using the Proximal Policy Optimization algorithm. The agent learns to select the optimal frame generation threshold dynamically by interacting with the queuing environment and receiving feedback based on performance metrics, such as delay and frame efficiency.
## 6 Performance Evaluations
### Simulation Results
The comparison of ACFTR among the six clustering protocols for different sensor field sizes is given in Figure 9. This figure shows that the ACFTR of MENC is bigger than other five protocols when \(S\) is equal to \(2\times 10^{4}\) m\({}^{2}\), and it tends to be zero when \(S\) increases. This is because multi-hop communication method is adopted in MENC. In addition, MENC can work well in large-scale networks as sensor nodes need not to transmit data to BS directly for a long distance. However, when the network size is small, data from the nodes farther from BS still has to be relayed by the ones closer to BS that may result in performance degradation of the protocol. From this figure, we can also find that the ACFTR of ENC, EP-LEACH, AEHAC, P-29 and LEACH increase with the increase of sensor field size \(S\). This is because single-hop communication model is adopted by these five protocols. In addition, with the increase of \(S\), CHs or RNs have longer data transmission distance to BS which increases the probability of cluster failure. Figure 10 gives the comparison of ANTR among the six clustering protocols for different sensor field sizes. The ANTR of all the six protocols increase with the increase of sensor field size \(S\). This is because the total number of sensor nodes is proportional to \(S\). More data can be collected and transmitted to BS with the increase of \(S\). In addition, when \(S\) is equal to \(2\times 10^{4}\) m\({}^{2}\), MENC has smaller ANTR than other five protocols since its ACFTR is the smallest among these protocols. Figure 10: The comparison of ANTR among MENC, ENC, EP-LEACH, AEHAC, P-29 and LEACH for different sensor field sizes. Figure 9: The comparison of ACFTR among MENC, ENC, EP-LEACH, AEHAC, P-29 and LEACH for different sensor field sizes.