esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Localization-Free Routing Protocols  
The authors in [11] show a routing protocol that proficiently handles complex networks. Moreover, local knowledge is required to allow the depth of transfer packets greedily in the direction of the sink. In this paper, the proposed protocol for each qualifying node forwards priority-based packets. Priority is determined by the depth of forwarder nodes that have never sent the same packets together before. The DBR has better network life and a high PDR. However, this greedy method forces the immutable collection of forwarder nodes by which network nodes are partitioned and services are still underused due to being expensive and consuming exorbitant energy [12].  
In [13], a routing protocol is proposed where they used two mobile sink nodes to effectively collect the data. Moreover, a metric called \"Mobile Sink Efficiency Ratio (MUR)\" was presented to quantify the use of the mobile sink node during data collection. This system provided successful results for network lifespan, loss of packets, throughput, and the new MUR metric. However, a high end-to-end delay resulted from this system. The authors suggested a scheme to address the current shortcomings of the schemes in [14] so that they concentrated on the consistency of the connection and routing path problem.  
Connection Efficiency Indication (LQI), Signal-to-Noise Ratio (SNR), and Packet Reception Probability (PRP), an on-site triangular metric routing combination, is used to effectively select the next forwarder, resulting in low routing costs, minimal energy consumption, and increased network life.  
The authors suggested \"Localization-Free Energy-Efficient Routing (LFEER)\" and \"Cooperative localization-Free Energy-Efficient Routing (Co-LFEER)\" in [15]. First, the residual energy, hops, and failure rate of the link bit at which the data packets were sent were taken into account when selecting a relay. In the latter, the sink receives several data replicas from which it can decide on the data quality. These schemes provided better outcomes for energy optimization and increased PDR. However, as these schemes know the exact positions of sensor nodes, they result in computational complexity and extra use of resources.
# Potential of Halophytes-Associated Microbes for the Phytoremediation of Metal-Polluted Saline Soils
## 1 Introduction
### Potential of Halotolerant and Halophilic Microbes in the M mediation of Metal Stress in Plants
The bioavailability of metals determines the phytoremediation yield and depends on parameters such as pH, salinity, the total organic matter, and soil porosity. External factors framed in dotted line are specific of coastal environments and influence these parameters. On the left side, microbes favor the metal phytoextraction and translocation by enhancing metals mobilization via the production organic acids and metal-affine molecules. On the right side, microbes help plants to stabilize metals via their bioaccumulation, the production of metals immobilizing agents (extracellular polymeric substances and melanins), and the precipitation of metals by sulfate-reducing bacteria. In the middle, microbes can enhance plant growth by producing phytohormones, enhancing nutrients’ availability, and reducing oxidative stress.
## 7 Current Trends and Future Directions
This increases system cost and complexity, making them less practical for small-scale or cost-sensitive applications. Developing algorithms with minimal hardware while maintaining performance is a challenge. Additionally, these algorithms must adapt quickly to changing environmental conditions, ensuring efficiency and stability in real-time systems without delays, which is difficult for computationally intensive methods. There are indeed unresolved challenges that need further exploration. One is improving their robustness under dynamic and non-uniform conditions, such as rapidly changing solar radiation, partial shading, and temperature fluctuations. While advanced techniques perform well in stable conditions, they struggle in real-world scenarios. Another area is optimizing computational complexity. AI-based algorithms are efficient but can be limiting in low-cost or low-power systems. Future research could focus on simplifying these methods without sacrificing performance. Additionally, more work is needed on hybrid MPPT techniques that combine strengths from multiple approaches and improve real-time adaptability to environmental changes.
## 4 Results and Discussion
### Influence of System Size on HIPE Formation  
To examine the effect of system scaling on emulsification behavior, a larger simulation cell with dimensions of 200 \(\times\) 200 \(\times\) 200 Rc was constructed. Under these expanded conditions, none of the models succeeded in forming a HIPE. This outcome suggests a pronounced size effect, where the balance between droplet separation distance and emulsifier availability plays a pivotal role in emulsion stability. As observed previously in Figure 7, the emulsifier AS2 forms structural bridges across droplets through dimeric and trimeric \(\pi\)-\(\pi\) stacking arrangements. In smaller systems, these stacked emulsifier assemblies can successfully span the inter-droplet gaps. However, as the cell size increases, the average distance between droplets also increases, necessitating a higher quantity of emulsifier to maintain interfacial integrity and continuity. When this bridging threshold is not met, droplet coalescence becomes energetically favorable, inhibiting stable HIPE formation.  
It is also worth noting that the emulsifier structures used in this study possess larger molecular dimensions than those described in the classical Yen-Mullins model for asphaltenes [31; 32]. The conventional asphaltene model suggests smaller aromatic cores and limited side-chain functionality, which may be insufficient to provide the steric coverage and bridging capability required for HIPE stabilization, particularly in larger systems. This observation provides a plausible explanation for the limited emulsification capacity of conventional asphaltenes in macroscale oil-water systems. The detailed relationship between droplet size, emulsifier molecular architecture, and interfacial behavior under different cell dimensions remains an area of active investigation. Further parametric studies involving size-scaling, emulsifier concentration variation, and interface curvature effects are warranted to generalize the size-emulsification correlation.
## 3 Optimization Model and Evaluation Indexes
### DEWGO Algorithm  
#### 3.2.1. Differential Evolutionary  
[PERSON] and [PERSON] [37] proposed the differential evolution (DE) algorithm to solve global optimization, which has been widely applied in engineering fields [38; 39]. DE is a greedy-based search method. After randomly initializing the population, some better individuals are selected through the fitness function, and the selected individuals are subjected to mutation, crossover, and selection operations to generate a new population. The fitness function is applied again to select a new optimal individual and update the population. The iteration cycle is not terminated until the expected optimal is achieved. As such, the DE algorithm is utilized to search for the weight and bias of BPNN, which can improve the prediction accuracy of the model. The mutation, crossover, and selection operations of the algorithm are performed according to Equations (2)-(4).  
\[v_{i}(g)=x_{r_{1}}(g)+F\times(x_{r_{2}}(g)-x_{r_{3}}(g)) \tag{2}\]  
where \(r_{1}\), \(r_{2}\), and \(r_{3}\) are different positive integers and are not equal to \(i\) representing the \(i\)-th individual in the evolution process, \(g\) is the evolution number, and \(F\) is the scaling factor, limited in (0, 1.0).  
\[\mathrm{u}_{i,j}(g)=\left\{\begin{array}{ll}v_{i,j}(g)&\text{ if }rand\;(0,1.0)\leq crossover \;rate\;(CR)\;\text{or }j=j_{rand}\\ x_{i,j}(g)&\text{ otherwise}\end{array}\right. \tag{3}\]  
where \(j\) = 1-D, \(j_{rand}\) is a random integer of 1-D, \(rand\) is a random number of (0, 1.0), crossover rate (\(CR\)) is limited in (0, 1.0), and \(u_{i,j}(g)\) is the \(j\)-th gene of the \(i\)-th individual in the evolution number g.  
\[x_{i}(g+1)=\left\{\begin{array}{ll}u_{i}(g)&\text{ if }f(u_{i}(g)\leq f(x_{i}(g)) \\ x_{i}(g)&\text{ otherwise}\end{array}\right. \tag{4}\]  
In Equation (4), \(x_{i}(g+1)\) is the target vector of the evolution algebra(\(g+1\)), and \(i\) stands for the \(i\)-th individual in the evolution process.
## 4 Foundations of Recommendation Systems and LLM Integration
### Large Language Models: Capabilities and Evolution
#### 4.2.12 FLAN (Fine-Tuned LAnguage Net)  
FLAN (Fine-tuned LAnguage Net) is a language model by Google that improves zero-shot and few-shot learning through _instruction tuning_. This tuning helps in memorizing the previous model and can easily generalize into different natural language instructions, assisting it in performing unseen tasks in the future. In contrast to OLTP models that need specific tuning for each task, FLAN task tuning uses keywords and phrases to teach the model to make decisions on tasks it has never faced before. The above is why it surpasses other models, such as the GPT-3, after constructing the FLAN during zero-shot learning in qualitative tasks. The features and contributions of FLAN are buried in the possibilities of instruction tuning, impressively showing a pathway to the capabilities of instruction-tuned models that require less and less user help [60].  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Aspect** & **ERNIE** & **FLAN** & **DeBERTa** & **UniLM** \\ \hline Training Objective & Knowledge-Enhanced (entity and masking) & MLM phrase & Instruction-tuned language modelling & Decoding-enhanced MLM with disentangled attention & Unified language modelling (multi-mask mode) \\ \hline Pre-training Method & Entity- and phrase-level masking with external knowledge & Task-specific instruction tuning & Content and position disentangled representations + enhanced decoding & Bidirectional, unidirectional, and seq2 seq masking patterns \\ \hline Bidirectional Context & Yes (BERT-based) & Yes (fine-tuned on prompts) & Yes (with disentangled attention) & Supports multiple attention modes \\ \hline Multilingual Support & Chinese (ERNIE 1.0); extended in ERNIE 2.0+ & Yes (FLAN-T5, multilingual-tuned) & Yes (via XLM-DeBERTa) & Not standard, but adaptable \\ \hline Fine-tuning Requirement & Yes (for knowledge-specific tasks) & Often not needed (zero/few-shot capable) & Required but sample-efficient & Required per task type \\ \hline Key Applications & QA, semantic similarity, NER, cloze prediction & Generalization across unseen instructions/tasks & Classification, QA, NLI, SQuAD & Translation, summarization, generation & QA, generation \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of Transformer-based models: ERNIE, FLAN, DeBERTa, UniLM. Source: Author’s own design.
## 3 System Optimization Model
### Similarity Criterion and Node's Classification
The sample of category \(j\) is denoted by \(s_{1}{}^{(j)},s_{2}{}^{(j)},\ldots,s_{n_{j}}{}^{(j)}\), then the center clustering vector of the \(j\)-th category is \(\overline{s}^{(j)}=\left(\overline{s}_{1}{}^{(j)},\overline{s}_{2}{}^{(j)}, \ldots,\overline{s}_{n}{}^{(j)}\right)\), \(\overline{s}_{u}{}^{(j)}=\sum_{n=1}^{\infty}s_{u}{}^{(j)}/n_{\mu},u=1,2,\ldots,m\). Hence, the \(F\)-statistic can be obtained:  
\[F=\frac{\sum\limits_{j=1}^{t}n_{j}\Big{|}\Big{|}\overline{s}^{(j)}-\overline {s}\Big{|}\Big{|}^{2}/(t-1)}{\sum\limits_{j=1}^{t}\sum\limits_{u=1}^{n_{j}} \Big{|}\Big{|}\Big{|}\overline{s}^{(j)}_{u}-\overline{s}^{(j)}\Big{|}\Big{|} ^{2}/(n-t)} \tag{15}\]  
After clustering, the nodes in a single grid are divided into several categories, and the nodes with similar average observation results are classified into a same class. Intuitively, the less nodes determined as redundant nodes and then put into sleep mode, the greater the amount of information retained. For the selection of redundant nodes, we will comprehensively take into account multiple factors, such as the category of the node, residual energy, redundancy degree. In the next section, a highly distributed and scalable mechanism is proposed to increase network lifetime as well as maintain sufficient sensing coverage and the complete connectivity of the network.
# Energy Generation and Attenuation of Blast-Induced Seismic Waves under In Situ Stress Conditions
## 4 A Case Study
### blasting Design and Vibration Monitoring  
During the upper pilot tunnel blasting in No. 4 experimental cavern, blasting vibration monitoring was conducted within the surrounding rock mass. The pilot tunnel is blasted by using the millisecond-delay blasting method, in which the cutting holes are first fired, followed by the stopping holes and contour holes with time intervals in milliseconds. The blastholes have a length of 3.5 m and a diameter of 0.05 m. The explosive columns filled within the blastholes are 0.032 m in diameter. The cutting hole blasting has a maximum charge weight per delay of 60 kg.  
In order to monitor the blasting vibration within the rock mass, four instrument installment holes that are 10 m in length are drilled horizontally from the traffic tunnel wall into the surrounding rock mass, as shown in Figure 12. These holes are located almost  
Figure 11: Layout of the CJPL and cross-section of No. 4 experimental cavern (unit: m).  
at the same elevation, that is, 1.2 m above the traffic tunnel floor. The straight distance between the instrument installment holes and the experimental cavern wall varies from 10 to 67 m. Eight triaxial velocity sensors are installed in the instrument holes. Among them, Nos. 1-4 sensors are placed at a depth of 3 m from the traffic tunnel wall, and Nos. 5-8 sensors are fixed at a depth of 10 m from the traffic tunnel wall. The seismic wave signals collected by the velocity sensors are synchronously transmitted to the data loggers. In order to ensure the high-frequency seismic waves near the blasting source can be recorded, the sampling frequency of the data loggers is set to 10 kHz.
# Closed-Form Analytical Solutions for the Deflection of Elastic Beams in a Peridynamic Framework
## 3 Analytical Solutions to Peristatic Problems
### Clamped-Simply Supported Beam  
For the clamped-simply supported beam, Figure 6a shows the solution that can be applied. The rotational constraint of the clamped end can be released by introducing the moment \(M_{0}\), as shown in Figure 6b. By applying [PERSON]'s theorem, moment \(M_{0}\) can be computed as follows:  
\[M_{0}=-\frac{1}{L}\int\limits_{0}^{L}\left[\int\limits_{0}^{x}(x-a)p(a)da \right]\mathrm{d}x-\frac{3}{2L^{2}}\int\limits_{0}^{L}\left[\int\limits_{0}^{ x}(x-a)p(L-x)da\right]x\mathrm{d}x+\frac{t}{2}\int\limits_{0}^{L}p(x)\mathrm{d}x \tag{28}\]  
In the case of the shear rigid beam, the bending moment can be transformed into body force associated with a derivative of the Dirac delta. Indeed, a moment can be replaced with the force couple, as shown in Figure 7.  
Figure 5: Periodic extension of the deflection field for a simply supported beam.  
For such a case, the external load term can be formulated as follows:  
\[p(x) = \lim_{\Delta x\to 0}F\bigg{[}\delta\bigg{(}x-\bigg{(}x_{0}-\frac{ \Delta x}{2}\bigg{)}\bigg{)}-\delta\bigg{(}x-\bigg{(}x_{0}+\frac{\Delta x}{2} \bigg{)}\bigg{)}\bigg{]}\] \[= \lim_{\Delta x\to 0}M\frac{\bigg{[}\delta\bigg{(}x-x_{0}+\frac{ \Delta x}{2}\bigg{)}-\delta\bigg{(}x-x_{0}-\frac{\Delta x}{2}\bigg{)}\bigg{]}}{ \Delta x}\] \[= M\delta^{\prime}(x-x_{0}).\]  
With Equations (27)-(29), the solution to the clamped-simply supported beam can be formulated as follows:  
\[w(x)=\frac{2\delta^{2}}{LEI}\sum_{n=1}^{\infty}\int\limits_{0}^ {L}\big{[}p(x)+M_{0}\delta^{\prime}(x-0)\big{]}\text{sin}\frac{n\pi x}{L} \text{d}x\left[\int\limits_{-\delta}^{\delta}\frac{1}{\xi^{2}}\bigg{(}1-\text{ cos}\frac{n\pi\xi}{L}\bigg{)}\text{d}\xi\right]^{-2}\text{sin}\frac{n\pi x}{L} \tag{30}\] \[=\frac{2\delta^{2}}{LEI}\sum_{n=1}^{\infty}\left[\int\limits_{0}^ {L}p(x)\text{sin}\frac{n\pi x}{L}\text{d}x+M_{0}\Bigg{[}\delta(x-0)\text{sin} \frac{n\pi x}{L}\text{d}x\bigg{]}^{L}_{0}-\int\limits_{0}^{L}\frac{n\pi x}{L} \delta(x-0)\text{cos}\frac{n\pi x}{L}dx\right]\Bigg{\}}\] \[\qquad\qquad\times\left[\int\limits_{-\delta}^{\delta}\frac{1}{ \xi^{2}}\bigg{(}1-\text{cos}\frac{n\pi\xi}{L}\bigg{)}\text{d}\xi\right]^{-2} \text{sin}\frac{n\pi x}{L}\] \[=\frac{2\delta^{2}}{LEI}\sum_{n=1}^{\infty}\left[\int\limits_{0}^ {L}p(x)\text{sin}\frac{n\pi x}{L}dx-M_{0}\frac{n\pi}{L}\right]\left[\int \limits_{-\delta}^{\delta}\frac{1}{\xi^{2}}\bigg{(}1-\text{cos}\frac{n\pi\xi} {L}\bigg{)}\text{d}\xi\right]^{-2}\text{sin}\frac{n\pi x}{L}.\]
# BeiDou Short-Message Satellite Resource Allocation Algorithm Based on Deep Reinforcement Learning
## 2 System Model
### Satellite Load Balancing Model  
Satellite load balancing is an essential index for the rational utilization of satellite resources and the efficient processing of short-message tasks. We use the load balancing index \(LI\) to characterize the load balancing degree of the SMSCS.  
Let \(SAT_{j}\) respond to the number of short-message tasks \(\psi\), recorded as the set \(\textbf{Task}=\left\{task_{j1},task_{j2},\cdots,task_{j\psi}\right\}\). A task \(task_{j\psi}\) will continuously occupy \(n_{j\psi}\) time slices. Since each satellite has, at most, \(n_{t}\) time slices in a snapshot, the satellite resource utilization of \(SAT_{j}\) in that snapshot is:  
\[SRR_{j}=\frac{\max\left(n_{t},\sum\limits_{i=1}^{n}n_{ji}\right)}{n_{t}} \tag{10}\]  
After the short-message satellite responds to the short-message task, the processing of each short-message does not affect the processing of others; they are processed in parallel. Therefore, for \(LI\):  
\[LI=\frac{1}{m}\sum_{j=1}^{m}\left(SRR_{j}-\frac{1}{m}\sum_{j=1}^{m}SRR_{j} \right)^{2}. \tag{11}\]  
Intuitively, the smaller \(LI\) is, the more balanced the load of the short-message satellite system is.
# Feature Residual Analysis Network for Building Extraction from Remote Sensing Images
## 3 Experiments and Results  
### Hyperparameter Settings  
The hyperparameter settings of the FRA-Net in this experiment are shown in Table 1. At the same time, the meaning of these hyperparameters is briefly explained.  
(1) Initial learning rate: The learning rate is a very important parameter in the training process, and its setting largely determines whether or when the objective function of the model can converge. Therefore, choosing a learning rate suitable for the model is crucial to the training process of the model. Generally speaking, a larger learning rate is selected at the beginning to make the loss value decrease faster to ensure that the model has a faster learning rate, but when the model learns for a long time, the learning of the model should be slowed down. At this time, the learning rate needs to be adjusted appropriately to ensure that the model does not \"over-learn\".  
(2) Batch size: When training the model, due to the limitation of the memory of the graphics card, generally, all data will not be input into the network for training. At this time, a certain amount of data needs to be entered in batches. The preprocessed images in this paper have a resolution of 256 \(\times\) 256, and the batch size is set to 10.  
(3) Training steps: refers to the number of steps in network training; each step will select 10 images from the data set for training.  
(4) Loss function: Since it is necessary to judge whether to continue learning according to the deviation between the actual output and the expected value in the process of forward propagation, a loss function is needed to evaluate the quality of the model. This paper uses the cross-entropy loss function plus the L2 regularization loss function (L2 loss) to complete the gradient descent.  
(5) Optimization method: Adam is an effective first-order optimization algorithm, which iteratively updates the training weights on the basis of training data.
###### Abstract  
In deep excavation engineering, the implementation of cut-off walls stands as a crucial measure to ensure structural support stability. However, the existing theories for dewatering design often overlook the variations in cut-off wall penetration depths, potentially compromising the efficacy of groundwater control strategies. Addressing this gap, this study conducts a comprehensive investigation into the dynamics of groundwater levels within confined aquifers during the dewatering process of strip excavation pits with cut-off walls. Central to this inquiry is the conceptualization of the entire excavation pit as a singular large-diameter well, with the open part beneath the cut-off wall in the confined aquifer serving as a constant-flux boundary. Employing an advanced analytical modeling approach, the study formulates a robust framework to describe the intricate interplay of unsteady groundwater flow phenomena. Leveraging the techniques of the Laplace and finite cosine transform methods, a semi-analytical solution is derived to elucidate groundwater drawdown patterns over time. The validation of the proposed solution against finite element method results underscores its fidelity and applicability. The parametric analysis reveals a dynamic evolution in drawdown characteristics within the confined aquifer, transitioning from initially cone-shaped distributions to more linear profiles that eventually stabilize with prolonged dewatering. This evolution is governed by the aquifer's inherent anisotropy and the barrier effect exerted by the depth of cut-off wall penetration. The parametric research also underscores the critical role of lateral boundary distance in influencing groundwater drawdown patterns. The presented solutions can be used to identify optimal penetration depth ratios tailored to specific parameters, thereby offering insights for optimizing dewatering strategies for deep excavation groundwater control. Moreover, a case study was included in this study using the proposed analytical solution, and a comparison was made with field data to validate the practical applicability of the approach.  
semi-analytical solution; large well method; integral transform; cut-off walls; strip excavation pits; confined aquifer +
Footnote †: journal: Applied Sciences  
1
# Hybrid Chaotic-Based PRNG for Secure Cryptography Applications
## 2 Known 1D Chaotic Maps  
### Logistic Map  
Logistic maps make powerful tools for the examination of nonlinear systems, which have been applied to designing encryption schemes in various fields [27]. The simplicity of a logistic map allows for a comprehensive understanding of the underlying dynamics [28], while its complexity can reveal random-like behavior [29]. It is represented by the discrete-time iterative Equation (1), such that each iteration generates an iterate value:  
\[x_{n+1}=rx_{n}(1-x_{n}) \tag{1}\]  
where \(0<x_{n}<1\) denotes the state of the system at the \(n\)th iteration, and \(r\) is the control parameter that modulates the behavior of the system.  
The behavior of the logistic map is contingent upon the value of the control parameter \(r\). For \(r<1\), the logistic map converges to a fixed point of \(zero\). As shown in Figure 1a,b, the various forms illustrate that, for \(1<r<4\) values, the map oscillates between fixed points in a bifurcation behavior. For \(r=4\), the solutions of the logistic map demonstrate chaos, implying that small variations in the initial conditions can result in fundamental differences in the long-term behavior of the system.