esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Computational Experiments
### Experimental Experiments and Comparative Analysis
#### 5.3.2 Heuristic Optimization Under Unlimited Capacity of CI  
This section eliminates capacity constraints in CI, assuming that both CI and BSI possess infinite service capacity. Table 5 presents the comparative results of four instance types solved using the HACO, BR-GRASP, SA, GA, and GA-GSK algorithms, with optimal solutions emphasized in bold.  
Based on the experimental results, we summarize the following conclusions:  
1. The HACO algorithm demonstrates superior performance in solving the EVRP with unlimited capacity of CI, achieving optimal solutions in 35 out of 40 instances and delivering the lowest cost values across all C15 instances. The SA algorithm consistently yields sub-optimal solutions, showing enhanced competitiveness with increasing problem scale and outperforming HACO in five medium-to-large instances. Conversely, the GA algorithm exhibits the least favorable performance.
2. The BR-GRASP algorithm reveals inherent limitations in its greedy and short-sighted approach. In smaller-scale C15 and C25 instances, it predominantly selects CIs due to their lower energy replenishment costs, neglecting BSI. However, the prolonged duration of CI significantly reduces vehicle availability for customer delivery, necessitating additional vehicle deployment and consequently increasing fixed costs. This ultimately results in total costs that may not be superior to scenarios employing both CIs and BSI. This phenomenon substantiates the cost-effectiveness of HERI in electric logistics fleet management.
3. GA and GA-GSK exhibit the poorest performance among all algorithms. As the problem scale increases, their excessive reliance on BSI leads to significantly higher energy replenishment costs, as well as total costs. These algorithms first construct a giant tour of customer nodes before inserting CI or BSI to meet energy constraints. However, crossover and mutation operations frequently introduce redundant HERI. Unlike HACO's Remove Infrastructure operator, they lack mechanisms to eliminate over-replenishment. Notably, GA-GSK consistently outperforms GA across all instances by employing GSK's balanced exploration-exploitation knowledge management strategy during mutation.
4. In the majority of instances, the proposed algorithm successfully fulfills all customer delivery requirements with the minimal fleet size. HACO's ability to minimize vehicle deployment demonstrates its effectiveness in leveraging the complementary advantages of HERI, optimally utilizing BSI when appropriate to achieve total cost minimization, even in scenarios with abundant CI.
# Superior Control of Spacecraft Re-Entry Trajectory
## 2 Optimization Mathematical Model for Re-Entry Trajectory and Superior Control Algorithms  
### Performance Indexes for Re-Entry Trajectory  
There are three performance indexes for the optimized re-entry trajectory system in this article: heating rate \(\dot{Q}_{S}\), overload \(n_{Y}\), and dynamic pressure \(q\). Minimize the following performance indexes:\[J_{min}=\min_{\tilde{U}(t)}\int_{t_{0}}^{t_{f}}\left[\begin{array}{c}\dot{Q}_{S} \\ n_{Y}\\ q\end{array}\right], \tag{1}\]  
Each performance index in the above equation is a real-valued function of the system state \(x(t)\) and control variables \(u(t)\). Traditional performance indexes require weight coefficients, while superior control algorithms do not have weight coefficients in their performance indexes.
## 1 Introduction
### Advancements in Cross-Lingual Settings  
It is now feasible to train neural architectures for cross-lingual NLP tasks that can achieve state-of-the-art performance, thanks to developments in cross-lingual settings. The creation of multilingual contextualized word embeddings, which seek to emulate universal language representations, has made this possible. Numerous multilingual downstream NLP tasks, including text classification, question answering, and text generation, have made use of these embeddings [1; 2; 3].  
Despite these achievements, there are still obstacles to overcome when it comes to lingual NLP. One such challenge is the availability of data for languages other than English. To address this issue, researchers are exploring techniques like translation and code-switching to create datasets for low-resource languages. However, it is important to acknowledge that these methods can introduce their own challenges by altering surface patterns within the data and potentially impacting the generalizability of models.  
To tackle these challenges, researchers are actively developing techniques for data augmentation and code-switching. These approaches aim to enhance the performance of models by making them more resilient against noise and variations in input data. Additionally, specialized models that cater specifically to lingual NLP tasks are also under development by researchers.  
These models can effectively understand the subtleties of languages and enhance the performance of tasks related to cross-lingual NLP.
###### Abstract  
Rate of force/torque development scaling factor (RFD-SF/RTD-SF) has been used as a tool for assessing neuromuscular quickness. The aim was to investigate strength capacities of two major shooting muscle groups and their relationship to basketball shooting performance, and to compare the RFD-SF as well as shooting performance between junior and senior basketball players, and finally to examine the differences in RTD-SF between elbow extensors and volar flexors. In 23 male basketball players (13 juniors and 10 seniors) we assessed maximal isometric torque (T\({}_{\text{MVC}}\)), maximal rate of torque development and RTD-SF slope (k\({}_{\text{RTD-SF}}\)) for elbow extensors and volar flexors. The subjects performed 10 throws at 2.3 m (short) and 8.9 m (long) from the basket. Our results showed similar k\({}_{\text{RTD-SF}}\) and T\({}_{\text{MVC}}\) in both groups. Better shooting performance from short distance was observed in senior players. Significant associations between k\({}_{\text{RTD-SF}}\), T\({}_{\text{MVC}}\) and shooting performance were found only in juniors. Elbow extensors T\({}_{\text{MVC}}\) was found to have a significant positive large association with shooting performance from long distance. It seems that muscle capacity has an important role in shooting performance in junior compared to players. Sufficient strength capacity of major shooting muscles is important for juniors' shooting performance from a long distance.  
wrist; elbow; shot; accuracy; RFD-SF +
Footnote †: journal: Applied Sciences
## 6 Results
### Evaluation for Real-Time Applications  
A key performance indicator of our system is latency which can support real-time streaming requirements. In Table 2, we present the inference times of onboard feature extraction and cloudlet-side object detection on different device platforms. We see that the feature extraction time is similar to the CPU-only drone and the GPU-powered cloudlet, which aligns with our design choice of running image classification (and feature extraction) on the drone to minimize bandwidth usage. Also, there is a significant overhead (5.9\(\times\) more than the cloudlet) incurred when running the entire object detection process on the drone. Most importantly, the use of the selector module and noise injection results in only a 2 ms increase in latency.  
Additionally, we present the trade-off between bandwidth efficiency and latency due to an additional encoding step. The extracted feature maps sent by the drone are 1.57 MB per video frame (image) without encoding or compression. A large packet size per frame is undesirable for real-time video analytics, and as such we encode the feature maps into images to obtain a 6\(\times\) improvement as shown in Table 2.  
In Table 3, we further examine the overall execution time for all processes on the drone and the cloudlet. In exchange for improved bandwidth efficiency, the drone has a 55 ms overhead to process the feature maps into PNG format, which is a reasonable trade-off considering the significant improvement in object detection latency due to the cloudlet.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Device**} & \multicolumn{2}{c}{**Feature Extraction Time**} & \multicolumn{2}{c}{**Object Detection Time**} \\ \cline{2-4}  & **MobileNetV3 Baseline** & **MobileNetV3 (Protected Feature Maps)** & **SSDLite Head** & **Packet Size** \\ \hline \multicolumn{4}{c}{Comparison of Baseline DNN vs. Noise-based Protection without encoding Feature Maps} \\ \hline Drone & 8.20 ms & 10.21 ms & 101 ms \\ \hline NVIDIA T4 & 6.14 ms & 8.38 ms & 17 ms \\ \hline \multicolumn{4}{c}{Comparison of Baseline DNN vs. Noise-based Protection using encoded Feature Maps} \\ \hline Drone & 8.20 ms & 65.39 ms & 101 ms \\ \hline NVIDIA T4 & 6.14 ms & 63.39 ms & 17 ms \\ \hline \hline \end{tabular}
\end{table}
Table 2: DNN model profiling and comparison of packet transfer methodologies at frame-level granularity. Here, NVIDIA T4 represents the cloudlet with the GPU.
# Grading Algorithm for Orah Sorting Line Based on Improved ShuffleNet V2
## 3 Results and Discussion
### Comparison Test of Different Network Models  
To evaluate the performance of the ShuffleNet_wogan model, its performance was compared with various classical classification models. The models selected for the comparative experiments included ResNet50, DenseNet121, AlexNet, EfficientNet B0, MobileNetV2, MobileNetV3-Large, and ShuffleNet V2_\(\times\)1.0. The results are shown in Table 6.  
As observed in Table 6, all models demonstrated good performance in determining the external grades of Orah, with accuracy exceeding 87%. Among the classical classification models, ShuffleNet V2_\(\times\)1.0 exhibited the lowest performance, achieving an accuracy of 87.20%. However, it had the lowest floating-point operations (FLOPs) and parameter count, indicating its suitability for high-speed grading requirements in Orah sorting lines. ResNet50 achieved a higher accuracy of 90.64%, but its complexity was significantly greater. Its FLOPs exceeded those of ShuffleNet V2 by more than 25 times, which could negatively impact grading speed. After 200 iterations of training, the ShuffleNet_wogan model achieved an accuracy of 91.12%, making it the best-performing model. Additionally, it  
Figure 21: Small black spots were not detected. The green squares indicate the black spots detected by the program.  
Figure 20: The stem was misidentified as a black spot. The green squares indicate the black spots detected by the program.  
demonstrated advantages in model complexity, with the lowest FLOPs and parameter count compared to other networks. These results indicate that the proposed model is an efficient and lightweight network with excellent overall performance, making it well-suited for the requirements of Orah grading (annotation: F1: F1 score; Par: parameters; and FLOPs: floating-point operations).
## 1 Introduction
One can imagine that it would provide extraordinary energy benefits if building envelopes (roofs or walls) can modify its thermal properties, such as thermal resistance or heat storage capacity, according to the heating and cooling demand daily profiles. For example, it will be interesting to remove the insulating layers of building envelopes (roofs and walls) in an office building during summer nights so the building can dissipate the heat stored from external and internal loads to outdoors. However, insulation is required to prevent peak cooling load during the day. Similarly, in winter it will be interesting to remove the insulation during the sunny hours so thermal massive elements (brick or concrete layers facing indoors) would be able to store solar heat and later provide space heating. Insulation layers have to be present after these sunny hours to avoid or limit thermal losses to outdoors. Within this context, some work has been done in the development of active insulating layers. [PERSON] et al. [21] tested numerically and experimentally  
Figure 1: Implementation of passive phase change materials (PCM) in static building envelope. thermally active walls with water pipes arranged in milled channels in the thermal insulation, which is able to alternate between a thermal barrier system to a space heating supplier. The ability to modify the thermal properties depending on the weather conditions and building use becomes even more interesting when phase change material (PCM) is integrated into the building envelope. This paper presents an innovative system, which is designed to solve the previously cited drawbacks and to convert PCM passive heating and cooling technology into a competitive and attractive solution for the building sector. The proposed system relies on the ability of roofs and walls to adapt the position of their layers and hence their morphology during its operation. This new ability of building components will revolutionize building designs as multiple technical, functional, and aesthetic options arise. This new system varies the position of the PCM with respect to the insulating layer to maximize solar benefits in winter and night free cooling in summer. [PERSON] [22] numerically analyzed the benefits and performance of such technology when used for passive cooling purposes, showing that the system cannot only reduce cooling loads from outdoors but can provide space cooling when required by the demand. The previous paper [22] evaluated the performance of the system during summer when it moves the PCM to face outdoors at night time to facilitate its solidification and once the PCM is charged, it moves the PCM back to face indoors to prevent peak cooling load during the following day. This paper will use the previously developed numerical tool to evaluate the potential of this new building system to reduce energy consumption for space heating taking advantage of thermal storage of solar energy and to optimize key design and control variables.
## 3 Materials and Methods
### Monodomain Solved with SPH  
The solution of the bidomain equations is usually done using standard FEM, or finite-difference (FD) methods. However, since the discretization of an object generates a mesh with many millions of nodes, the linear systems resulting from FD or FEM methods are very large. Several meshless methods have established the ability to provide a computational feasible model without the burden of mesh generation [65]. For this work, SPH is proposed to numerically solve the monodomain model of electrophysiology.  
The monodomain model can be written as:  
\[\frac{\partial V_{m}}{\partial t}=\frac{1}{C_{m}}\left(\frac{1}{\chi}\left( \
abla\cdot\sigma\
abla V_{m}\right)-I_{ion}+I_{ext}\right), \tag{24}\]  
where \(I_{ext}\) represents the stimulus current that is applied to the tissue.  
Applying the SPH formulation [53] to the rewritten monodomain Equation (24), and using XSPH to reduce particle oscillations with \(\varepsilon=1\) yields:  
\[\frac{\partial V_{m}}{\partial t}=\frac{1}{C_{m}}\left(\frac{\sigma}{\chi} \bigg{(}\sum_{j}m_{j}\frac{V_{m,j}-V_{m,i}}{\rho_{j}}\
abla^{2}W(\mathbf{r}- \mathbf{r}_{j},h)\bigg{)}-I_{ion}+I_{ext}\right). \tag{25}\]  
The second derivative of the kernel, which is necessary to solve Equation (25), is the following:  
\[W^{\prime\prime}(\mathbf{r}-\mathbf{r}_{j},h)=\frac{\alpha_{d}}{h^{n}}\begin{cases} -3+\frac{9}{2}q&0\leq q<1\\ \frac{3}{2}(2-q)&1\leq q<2\\ 0&\text{otherwise}\end{cases}. \tag{26}\]  
For time integration, a forward Euler method was used:  
\[y_{n+1}\approx y_{n}+\Delta tf(y_{n},tn). \tag{27}\]
# Evaluation of Urban Transportation Resilience under Extreme Weather Events
## 3 Method
### Evaluate the Transportation Performance under EWEs  
When EWEs occur, the performance of urban transportation can change over time. In order to measure the instantaneous perturbations of the transportation performance during EWEs at any moment, the performance indicator (PI) is introduced. The PI is a normalized indicator, which is calculated to evaluate the deviation of the transportation performance from its normal state at any moment during EWEs.  
Figure 3: The bus trajectory data used for calculating transportation resilience.  
Displacement (d) refers to the change of position and is one of the indicators to measure the change in the human trajectory. By accumulating the continuous displacement of all individuals (here, individual refers to a bus), the total displacement (TD) can be calculated, which is an indicator widely used in transportation. TD is calculated according to Equation (1) [39]:  
\[\text{TD}=\sum_{j=1}^{m}\sum_{i=1}^{n-1}d_{ij} \tag{1}\]  
where \(m\) refers to the number of individuals, \(n\) refers to the number of locations visited by the \(j\)th individual in a time span (taken as an hour here), and \(d_{ij}\) is the \(i\)th displacement in the \(j\)th individual's trajectory, and is calculated as shown in Equation (2) [41]:  
\[d_{ij}=2r\sin^{-1}\left(\sqrt{\sin^{2}\left(\frac{\lambda_{i+1,j}-\lambda_{i,j }}{2}\right)+\cos\lambda_{i+1,j}\cos\lambda_{i,j}\sin^{2}\left(\frac{\xi_{i+1,j }-\xi_{i,j}}{2}\right)}\right) \tag{2}\]  
where \(r\) is the radius of the earth (6371 km); \(\lambda_{i,j}\) and \(\lambda_{i+1,j}\) denote the latitude of the first point and the second point, respectively, of the displacement in the individual's trajectory in radians; \(\xi_{i,j}\) and \(\xi_{i+1,j}\) denote the longitude of the first and second points, respectively, of the \(i\)th displacement in the \(j\)th individual's trajectory in radians.  
A benchmark is needed to evaluate the deviation of bus trajectories from their normal state after TD calculation. The benchmark is denoted as \(\overline{TD}\), which refers to the total displacement of all individuals over a time span in the absence of EWEs. \(\overline{TD}\) can be calculated based on the TD value under normal conditions following Equation (3) [39].  
\[\overline{TD}=\sum_{i=1}^{n}TD_{i} \tag{3}\]  
where \(TD_{i}\) is the TD value of the \(i\)th time span without EWEs. Based on the \(TD_{i}\) value, PI can be calculated by normalizing TD with \(\overline{TD}\), as shown in Equation (4).  
\[\text{PI}=\text{TD}/\overline{TD} \tag{4}\]  
Figure 4: A flowchart of the evaluation method.
## 2 Overview of Modern Technologies and Frameworks in Smart Healthcare Systems
### Unmanned Aerial Vehicles in Healthcare  
With the evolution of Information and Communication Technology (ICT), the hospital management have adopted the smart wearable technologies for the timely and efficient treatment of patients. However, various innovative technologies such as DL, AI, swarm intelligence, unmanned aerial vehicle (UAV) have been exploited to combat the severe medical conditions of patients. For example, we can consider the scenario of coronavirus disease which has had sever impacts on the people's health, can be tackled using the usage of aforementioned promising technologies. However, the main reason for for widespread incidence of coronavirus is the increase in human interaction which occurs other during, and which can be reduced with the help of drone (unmanned aerial vehicles--UAV) technology. The usage of UAV minimizes the human contact and helps with clinical treatment by providing the medical equipment and medicines within the threshold time required for the patient's early treatment. For example, the authors of [54] studied the role of drone technology in tackling the surge of coronavirus disease worldwide. The authors analysed various promising technologies such as IoT, edge computing, DL, and virtual reality (VR) to mitigate the effect of coronavirus disease. Moreover, they have focused on the advantages and limitations of adopting the drone technology in healthcare. Next, [PERSON] et al. [27] utilized the blockchain technology to enable a secure and preserve outdoor delivery scheme for Healthcare 4.0 with the help of UAVs. They mainly focused on securing the medical supplies during the outdoor delivery process by performing a security analysis using the on MyThril security tool. Further, the authors of [55] implemented the IoT and UAV-enabled wireless body area networks (WBAN) for healthcare applications. They have considered various sensors to ensure the smooth and faster interaction between patient and healthcare professionals with the usage of UAVs, especially in cases of emergency.
## 2 Related Work  
The problem of secure underwater acoustic communication is quite a new research field [1; 2]. [PERSON] presents a survey of security issues, and possible countermeasures. Similarly, [6; 7] analyze the security and threats of UASN. [PERSON] _et al._ make a taxonomy of the attacks against a UASN [8]. They classify the attacks into three categories: (a) physical attacks against nodes; (b) attacks against the network; and finally, (c) attacks against the protocols. Although they give guidelines to contrast such attacks, they do not propose any concrete solution.  
Some solutions to specific security problems have been presented. [PERSON] _et al._ have proposed mechanisms for network reconfiguration when one or more nodes get destroyed [9; 10]. Solutions against wormholes have been presented in [11; 12]. [PERSON] _et al._ study the effects of denial-of-service jamming attacks on UANs using real-world field tests by building their own hardware jammer [13]. They show that underwater acoustic networks can be easily jammed by carefully using timed attacks, which are energy efficient. Finally, [PERSON] and [PERSON] have proposed a key establishment scheme that is based on the channel characteristics [14] whereas [PERSON] _et al._ have proposed a key distribution scheme based on the mobility model of underwater mobile sensors [15].  
In this paper, we face with an orthogonal problem, namely secure end-to-end communication. We present a secure communication suite that: (i) protects end-to-end communication; (ii) supports both the one-to-one and one-to-many communication model; (iii) allows secure network reconfiguration due to nodes mobility, joining and leaving; and finally, (iv) has been evaluated in the real-world field. To the best of our knowledge, no such a contribution has been given so far. [PERSON] _et al._ have proposed a cross-layer scheme for secure communication but they do not go further than illustrating the basic ideas underlying their proposal [16]. In particular, they do not provide an implementation and a performance evaluation.  
The communication suite is composed of two components, a set of cryptographic primitives and a secure routing protocol. As to the cryptographic primitives, they have been initially proposed in [17]. Then, in [18] we have employed them to protect application-level communications. Here, we present the integration of the primitives with the routing protocol and show that they are adequate at the network-layer too. As to the secure routing protocol, the basic ideas behind it have been sketched in [19]. Here, we provide a complete and detailed description of the protocol as well as an evaluation of its performance.