esa-sceva/satcom-chunk-collection huggingface dataset:
# Two Optimization Algorithms for Name-Resolution Server Placement in Information-Centric Networking
## 3 System Model and Problem Statement
Let \(\alpha^{i}\) represent the cost of deploying a server at level \(i\). The server placement problem in multilayer overlay networks can be formulated as follows:  
Minimize  
\[Cost=\sum\
olimits_{i=1}^{L}\sum\
olimits_{v\in V}x_{v}^{i}*y_{v}^{i}*\alpha ^{i}, \tag{1}\]  
subject to  
\[\cup_{m=1}^{M^{i}}V_{m}^{i}=V,\quad\forall i\in\{1,2,\ldots,L\}, \tag{2}\]  
\[V_{m}^{i}\cap V_{n}^{i}=\varnothing,\ \forall m,n\in\left\{1,2,\ldots,M^{i} \right\},\ m\
eq n,\qquad i\in\{1,2,\ldots,L\}, \tag{3}\]  
\[V_{m}^{i}\subseteq V_{n}^{j},\ if\ \exists v\in V_{m}^{i}\ and\ v\in V_{m}^{j},\ m \in\left\{1,2,\ldots,M^{i}\right\},\qquad n\in\left\{1,2,\ldots,M^{i}\right\}, \tag{4}\]  
\[0\leq i<j\leq L,\]  
\[d\left(v,HM_{m}^{i}\right)<t_{i},\ \forall v\in V_{m}^{i},\ m\in\left\{1,2, \ldots,M^{i}\right\},\ \forall\ t_{i}\in T. \tag{5}\]  
Equation (1) represents the total placement costs of name-resolution servers. The constraint of Equation (2) guarantees the full coverage of the partitioned HEAs to the nodes in the network, which means all requesters can use name-resolution services in each latency level. The constraint of Equation (3) ensures that there is no overlap between HEAs in the same layer, so a tree structure can be formed and the management work becomes easy. Nested relationships between HEAs from different level layers are indicated in Expression (4). In Expression (5), the upper bounds of name-resolution response latencies are promised. If a solution of placing name-resolution servers is subject to the aforementioned constraints, it is a feasible solution in server placement in multilayer overlay networks. Finding the HM set of a single layer network with a minimum count is similar to the MDS problem in graph theory. [PERSON] et al. [51] first defined the multilayer control problem in terms of the MDS controllability framework, which is called MDS in multilayer. They demonstrated that even in special cases of networks in which the MDS is solved in polynomial time, the MDS in a multilayer (MDSM) problem is still NP-hard. There are several constraints to guarantee the structure characteristic of multilayer overlay networks in our model, and the server placement problem is NP-hard as well. A good algorithm gives a solution that outputs HEAs and HMs for every layer of \(G\) with minimum cost and a shorter execution time.
# Multi-Hypergraph Neural Networks for Emotion Recognition in Multi-Party Conversations
## 4 Experimental Setting
### Compared Methods  
For a comprehensive evaluation of our proposed ERMC-MHGNN, we compared it with the following baseline methods.  
**Recurrence-Based Models:** DialogueRNN [3], COSMIC [13], DialogueCRN [12], TOD-KAT [30], DialogXL [24], VAE-ERC [29], DialogueRNN-RoBERTa [13], CoG-BART [32], and EmpCaps [28].  
**Graph-Based Models:** DialogueGCN [5], RGAT [15], RGAT-RoBERTa [17], DialogueGCN-RoBERTa [17], SKAIG [18], ERMC-DisGCN [6], MMGCN [35], TUCORE-GCN [36], and DAG-ERC [17].
## 3 Theoretical Background
### Proposed DCGOSM-Based Speech Enhancement Technique  
Figure 7 shows an illustrative block diagram of the proposed approach. Referring to this diagram, the proposed method can be divided into two parts. The first part is used to obtain the required sensing matrix, and the second part is used to enhance noisy speech.
# Energy Consumption Model for Sensor Nodes Based on LoRa and LoRaWAN
## 3 Sensor Node Design
### Processing Unit  
The processing manages all the resources utilized for the system operation [22]. This block is responsible for acquisition of output signal from the sensor unit, process the data after acquisition and communicate these data to the LoRa transceiver. Our embedded system is based on the _STM32L073_ microcontroller from ST Microelectronics [12]. This microcontroller can be optimized for very low power consumption.  
Figure 1: Sensor node architecture.
# Object-Independent Grasping in Heavy Clutter
## 4 Discussion  
### Comparisons with Other Algorithms  
As seen in Table 1, DBP exhibited the highest success rate in all the experiments. The learning-based methods have a lower success rate, though they show similar performance. In particular, the PCA-based  
Figure 8: (a) Target objects; (b) grasping in 2D clutter; (c) grasping in 3D bin.  
Figure 9: (a) Bin picking filled by cosmetic containers and (b) estimated grasp poses in heavy clutter.  
grasping performed similarly to DBP in the 2D and 3D clutters with different objects, because the gripper collided rarely with other objects during the grasp attempts owing to the large space between the objects in those experiments. In fact, the success rate in the heavy clutter of cosmetic containers, where DBP outperformed the other algorithms, supports this assumption. Thus, the experiments indicated that grasping without collisions is important in heavy-clutter environments. Examples of grasp poses estimated by DBP are shown in Figure 9b.  
In all the experiments, the PCA-based grasping was twice as fast as DBP, and the ANN-based grasping was in turn 1.16 times faster than PCA. However, DBP needed 1.4 s to estimate the grasp pose, which is sufficiently fast for practical applications. After the robot grasps an object, it needs time to move the object to the designated position. Additionally, if an eye-to-hand camera is used instead of the eye-in-hand camera used in our experiments, the robot can estimate a new grasp pose while moving an object.  
In summary, the PCA grasping method had a good performance if the space around the object to be grasped was sufficiently large, and the performance deteriorated otherwise. The performance of the learning-based grasping was similar in all environments, but worse than DBP. In fact, DBP, which considers both the space around and the shape of the target object, obtained a good grasping success rate even when the space around the target object was small. Thus, DBP can be applied more widely than other grasping methods, because its performance is good in different environments.
## 2 Materials and Methods  
Characterization of the parameters of RF EMR from mobile communications in the near-field of the antenna covers measurements of electric field strengths. The RF EMF power density is measured in the far-field. We evaluated the measurement points by measuring the areas concerned with an EMF meter and we present the zones with the highest RF EMF values. The influence of the other base stations in the vicinity is very small (about 1%), so this was not taken into account.  
Evaluation of the electric fields measurements was done according to LST EN 50383:2010 and LST EN 50492:2009. Only at a sufficiently large distance from the source, in the so-called far-field region, is it sufficient to evaluate the RF EMF power density in microwatts per centimeter squared [50; 51]. The parameters of the investigated mobile telephony base station antennas are presented in Table 1.  
The chosen mobile telephony base station antennas are limited to bands around 900 MHz (GSM), 1800 MHz (further GSM services) as well as 1900-2200 MHz (UMTs). (GSM = Global System for Mobile Telecommunication, UMTS = Universal Mobile Telecommunications System).
###### Abstract  
The Henan area of the middle and lower reaches of the Yellow River is situated within the third sedimentary loess area, positioned as the southeasternmost segment within the transitional belt connecting the Loess Plateau with the North China Plain. Addressing concerns related to loess collapse, landslides, and subgrade settlement across various regions attributable to the collapsible nature of Malan loess in western Henan, this study undertook collapsibility testing of undisturbed Malan loess in the province. The different mechanisms of loess collapsibility in different regions were explained from the microstructure by using the indoor immersion-compression test double-line method, scanning electron microscope (SEM), and particles and cracks analysis system (PCAS). The relationship between quantitative factors of microstructure and collapsibility of loess was analyzed by linear regression analysis. The findings indicate that under identical overburden pressure and immersion conditions, the collapsibility of Malan loess in western Henan diminishes progressively from west to east. Microstructural tests were conducted on various loess specimens using scanning electron microscopy, revealing that the distribution of loess particles is notably concentrated in the Xingyang and Gongyi areas, leading to a reduction in pore area compared to the Shanzhou and Mianchi areas. While the Mianchi and Shanzhou areas exhibit a loose arrangement of loess particles, those in Xingyang and Gongyi are comparatively denser. Analysis of microstructural images through the particles and cracks analysis system elucidated that the pore arrangement in the Gongyi and Xingyang areas is more stable than in the Mianchi and Shanzhou areas. Additionally, there is a gradual concentration in particle distribution, accompanied by an increase in agglomeration degree. According to the analysis and comparison of microstructure and quantitative parameters of four groups of loess samples before and after collapsibility, it is revealed that the change mechanism underlying loess collapsibility in various regions of western Henan primarily stems from the external factors influencing the microstructural alterations within the loess. The microstructural determinants contributing to collapsibility changes in different regions encompass three principal aspects: Firstly, modifications in the grain morphology of the Malan loess skeleton in western Henan are notable. Secondly, variations in the internal pore characteristics of loess microstructure are observed. Thirdly, disparities exist in the interconnections between soil particles. The findings of this research hold significant worth for improving construction safety and geological hazard prevention within the Loess region of western Henan.  
Malan loess; loess collapsibility change mechanism; microstructure +
Footnote †: journal: Applied Sciences
# DRL-Based Dynamic Destroy Approaches for Agile-Satellite Mission Planning
## 2 Problem Description  
AEOS mission planning is an oversubscribed problem which requires a satellite to schedule more tasks than its capacity. As depicted in Figure 1, agile satellites with three-axis maneuvering capabilities must schedule a considerable number of remote sensing missions within a limited time frame. Based on previous research, the general objectives and constraints of the AEOS schedule problem are widely discussed in [PERSON] et al. [7], [PERSON] et al. [13], and [PERSON] et al. [19]. Considering the constraints, the objective function of satellite mission planning is described as maximizing the total profit of all scheduled tasks. In this section, some problem assumptions and detailed mathematical formulations are set up for a specific scenario.
## 2 Literature Review on the Australian HSR Risks and Barriers
### System Integration and Feasibility  
Many publications call for widespread system integration to realise the benefits of HSR in Australia [2,40]. System integration is divided into two categories: the integration of the HSR network with current and future infrastructure pipelines and the integration with suburban/regional rail. This is difficult in Australia because each state has its own rail network, with different rolling stocks and infrastructure. [PERSON] [40] advocates for integrating HSR with existing rail infrastructure, citing its effectiveness in staging and providing early benefits. [PERSON] concluded that this integration is projected to serve approximately 75% of the Australian population, a similar figure of 69.2% corroborated by [PERSON] [41].  
[PERSON] raises concerns about the added topographical challenges in the Sydney to Newcastle route, the \"most difficult\" in the Eastern Corridor [40]. As the High-Speed Rail Authority proposed, it would possess an unfavourable benefit-cost ratio if it were to be implemented as a stand-alone network in the first phase. The High-Speed Rail Study conducted in 2013 [5] agrees that the Sydney to Melbourne route has a greater economic rate of return. However, it would need to be delivered in 2 phases (Sydney to Canberra and Canberra to Melbourne). This requires greater investment, which would substantiate the decision to opt for the lower-cost Sydney to Newcastle route instead, albeit with greater risk. This coincides with [PERSON]'s principle of the \"Hiding Hand\", that \"nothing would ever get built if people knew the real costs and challenges involved\" [14,42]. The decision to investigate the Sydney to Newcastle route is a significant risk, preventing the realisation of HSR in Australia.
# Development and Evaluation of an Image Processing-Based
## 2 Materials and Methods
### Second Phase  
In implementing and evaluating the system, we designed the research process following an explanatory sequential design, which started with a quantitative stage and continued with a qualitative stage to explain the quantitative results. Qualitative data were analyzed to help explain the quantitative results [9]. In the quantitative stage of the study, we calculated statistical significance, confidence intervals, and effect sizes and presented the main findings. The research was finalized by continuing with the qualitative stage to explain the quantitative results. In this process, a semi-structured interview method was applied to obtain the students' opinions about the system's effectiveness. Thus, this two-stage evaluation process built on itself helped us understand the effect of the image processing-based kinesthetic learning system more reliably.  
The first stage of the evaluation was conducted on the experimental and control groups formed in accordance with the quasi-experimental design, one of the quantitative research designs. The effects of the developed image processing-based kinesthetic learning system on students' foreign language learning levels were determined with the experimental group's applications. The experimental and control groups, whose application conditions were equalized, were administered the pre-test developed by the researchers regarding the \"Fun With Science\", \"Prepositions\", and \"Jobs\" units before the procedure. Then, the experimental group students were taught language through the image processing system, while the control group was taught in accordance with the curriculum. After three weeks of implementation, a post-test was administered to both groups, and student scores were compared in terms of significant differences. After analyzing the quantitative data in the first stage of the evaluation, the second stage, the qualitative stage, was started. In the qualitative stage, the data on the implementation, usability, and perception of the image processing-based kinesthetic learning system were collected and evaluated through focus group interviews with the experimental group students.
## 5 Discussion
### Comparative Analysis with Prior Studies  
The outcomes of this study corroborate the prior research that underscored the efficacy of ML and DL models in lung cancer diagnosis (Table 10). The current research has shown that ML classifiers, namely DT, NB, and RF, attain accuracy rates beyond 85% when used on medical datasets [1; 14; 16]. [PERSON] [14] achieved an accuracy of 81.25% using SVM, among other algorithms, while [PERSON] [22] reported 83% accuracy using KNN, Naive Bayes, and Decision Trees. [PERSON] et al. [1] reached a maximum of 91.07% with [PERSON], and [PERSON] et al. [16] demonstrated a 90% accuracy using Gradient-Boosted Trees. These findings illustrate a gradual improvement in the prediction accuracies over time as model architectures and feature engineering techniques have advanced.  
The current study notably surpasses these benchmarks. Its NN model, featuring a single hidden layer trained over 800 epochs, achieves an accuracy of 92.86%. This outcome underscores the effectiveness of DL architectures, particularly when paired with rigorous data preprocessing and optimal hyperparameter tuning strategies like learning rate and epoch selection.  
Importantly, this study further illustrates the advantage of DL in predicting the accuracy, in contrast to past studies that focused only on the classical ML models [16; 22]. The findings indicate that NN, when well adjusted, surpasses the classical classifiers in lung cancer detection, making them a significant asset for early diagnosis based on symptomatic and lifestyle data.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**References** & **Model and Method** & **Accuracy** \\ \hline [PERSON] [14] & SVM, LR, RF, DT, NB, KNN, RBF & SVM = 81.25\% \\ \hline [PERSON] [22] & KNN, NB, DT & 83 \\ \hline [PERSON] et al. [1] & LR, NB, SVM, RF, Bernoulli Naïve, KNN, Extra Tree, AB, MLP & RF = 85.71; LR = 87.5; NB = 91.07 \\ \hline [PERSON] et al. [16] & GBT, SVM, C4.5 DT, MP, NN, NB & GBT = 90 \\ \hline [PERSON] et al. [23] & SVM & 94.45 \\ \hline [PERSON] et al. [24] & RF & 88 \\ \hline [PERSON] et al. [25] & LR & 70 \\ \hline Current study & DT, KNN, RF, NB, AB, LR, SVM, NN, DL (with varying architectures) & KNN = 91.07; RF= 89.29; NB = 91.07; SVM= 87.50 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison between current study and previous studies on lung cancer prediction.
# Toward Sustainable Mars Exploration: A Perspective on Collaborative Intelligent Systems
## 4 Communication, Navigation, and Observation
### Multi-Hop Communication  
The lack of a wired backbone communication infrastructure on Mars presents communication challenges related to long-range communication, necessitating multi-hop data relays through intermediary nodes. Tailored communication protocols strategically address these factors to optimize data transmission and seamless swarm connectivity. Simultaneously, the implementation of efficient routing algorithms plays a crucial role in enabling intelligent path selection. These algorithms consider dynamic elements, such as energy consumption and resource availability, ultimately leading to optimal data dissemination routes. An ad hoc network approach for Mars sensor networks utilizes a multi-path routing scheme with energy-aware and load-balanced node selection [46]. The base station, operated by a Mars lander or rover, polls sensor nodes periodically, establishing multiple paths with equal minimal hop distance. Path selection, based on random or neighbor energy usage, enhances data delivery reliability and reduces congestion. Simulation results showcase energy conservation and a more equitable distribution of energy consumption, significantly extending the network's lifetime, particularly with the superior performance of the energy-aware multi-path scheme.  
Furthermore, the significance of fault tolerance and network resilience takes center stage, with a focus on integrating error correction mechanisms and self-healing strategies  
Figure 3: Based on our study, machine learning algorithms combine historical data of energy harvesting techniques and atmospheric conditions to predict available energy levels from different sources of energy on Mars.  
to uphold communication integrity amid potential system disruptions. Another challenge is that these swarms of vehicles will be fully decentralized and will require very scalable and dynamic mesh or multi-hop communications as the swarm can be large, dense, and full of mobile nodes. To augment capacity utilization and network performance, strategies encompassing traffic management, network planning, and resource allocation tailored to the Martian environment come to the forefront. As mobility gains prominence, seamless hand-offs to facilitate adaptive and dynamic network topologies become indispensable. Cumulatively, these aspects spanning protocol design, routing algorithms, fault tolerance, mobility support, and network optimization underscore the pivotal role of multi-hop communication in empowering collaborative swarms to adeptly traverse and explore the dynamic landscape of Mars, enhancing the potential for successful exploration and efficient resource utilization.
# A Brazilian Space Launch System for the Small Satellite Market
## 2 Methodology and Launch Vehicle Configuration
### Cost Analysis
#### 2.2.1 Launch System Development Costs  
For the system development cost, we considered cost estimate relationships restricted to the launch vehicle and its engines, disregarding the application of boosters. The vehicle should be composed of at least two stages, each comprised of a liquid or solid rocket propellant engine or cluster of them. Stages or engines previously developed are not included in this cost assessment. The total development cost is, therefore, the cost addition to develop stages and engines, multiplied by some correcting factors, as detailed in Equation (18). More specifically, the sub-model takes into consideration cost growth for deviation from optimum time schedule (\(f_{6}\)), cost growth for development by parallel contractors (\(f_{7}=N_{O}^{0.2}\)) and a correction for productivity (\(f_{8}\)). \(N_{O}\) refers to the number of organizations contracted to develop the launch vehicle. The cost growth by deviation from the optimum schedule varies almost linearly from 1.0 (no deviation) to 1.5, with the latter value referring to a 70% increase in the relative development schedule. In the model, productivity refers the local industry working hours, level of education and dedication to work. The reference value is 1.0 (USA), spanning from 0.7 (Japan) to 2.11 (Russia). Higher values of (\(f_{8}\)) increase the cost of development or fabrication of a given space system.  
System engineering integration factor (\(f_{0}=1.04^{N_{s}}\)) is directly related to the number of vehicle stages (\(N_{s}\)). All the costs are estimated in WYr. Launch system development cost is given by  
\[C_{dev}=f_{0}\left(\sum_{1}^{N_{s}}H_{S}+\sum_{1}^{N_{e}}H_{E}\right)f_{6}f_{7} f_{8} \tag{18}\]  
where,  
\(f_{0}\)--system engineering integration factor;  
\(H_{S}\)--vehicle stage development cost;  
\(H_{E}\)--engine development cost;  
\(N_{s}\)--number of stages to be developed;  
\(N_{e}\)--number of engines to be developed;  
\(f_{6},f_{7},f_{8}\)--programmatic cost impact factors.  
Engine development is restricted to the S-50 solid rocket motor. In Equation (19), the engine development cost for a solid propellant rocket engine, based on its mass (\(m_{ES}\)), in kilogram, is calculated by  
\[H_{ES}=16.3m_{ES}^{0.54}f_{1}f_{3} \tag{19}\]  
For a liquid propellant propulsion system, stage development based on its dry mass (\(m_{S}\)), can be estimated using the following equation  
\[H_{SL}=14.2m_{S}^{0.577}f_{1}f_{2}f_{3} \tag{20}\]  
Stage development cost based on solid propellant rocket engines is not predicted by the TRANSCOST model. We proposed that stage development cost should be a fraction of the engine development cost. Therefore, the effort is estimated by the following relationship  
\[H_{SS}=0.05H_{E}N_{e} \tag{21}\]  
Stage development effort is, therefore, estimated as 5% of the cost to develop the related engine since very little extra hardware is needed for the proper integration of that stage, multiplied by the number of engines employed (\(N_{e}\)).