esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Simulation Results
Here also, the \(8\times 4\) antenna array achieved the best signal quality and a high cell-edge data rate of 7.63 Mbps, as compared with the \(2\times 2\) antenna configuration that showed 3.67 Mbps only. Furthermore, the spectral efficiency for the FI model is also shown in Figure 12(a) for varied antenna patterns. It predicted 2.10 b/s/Hz for the \(2\times 2\) array and then increased proportionally with respect to more antenna arrays, hence providing up to 75% enhancement for the \(8\times 4\) antenna array that gave 3.67 b/s/Hz. In a similar way as in Figure 11(b), Figure 12(b) shows peak user, average user, and average cell throughput for varying MIMO conditions. The peak user throughput liked from a minimum of 10.41 Mbps for the \(2\times 2\) antenna settings and predicted up to a maximum of 22.26 Mbps for the \(8\times 4\) antenna array case. Similarly, the average user throughput exponentially increased with more antenna array configurations. A minimum of 7.08 Mbps was attained at the \(2\times 2\) antenna array and increased to 11.38 Mbps for the \(8\times 4\) antenna case. Lastly, the average cell throughput is illustrated, which showed a massive improvement of 60% from the minimum for the \(2\times 2\) antenna setting (which achieved 70.84 Mbps) to the maximum for the \(8\times 4\) antenna formation (which approximated 113.85 Mbps). Thus, more MIMO configuration led to extra propagation paths between Tx and Rx, which gave better received signal quality, apart from achieving more throughput for the cell users. The most important reason for using the mmWave spectrum is to achieve a large bandwidth. Here, Figures 13 and 13 display the overall network performances of the CI and FI models at varying bandwidth settings, respectively. It presents the outcomes for fairness index, average cell throughput, cell-edge user throughput, peak user throughput, average user throughput, and spectral efficiency with respect to different MIMO antenna configurations. All the simulation parameters are similar in appearance to those tabulated in Table 4, when the antenna configuration was set at \(2\times 2\). On the basis of the data shown in Figures 13 and 13, the user throughputs increased drastically with increments in bandwidth, which indicated that more users would gain higher spectrum sharing in order to transmit information. The fairness index for various bandwidth settings also changed slightly, from 86 to 92% for the CI model and from 83 to 94% for the FI model, as increments in the bandwidth affected the spectrum sharing amongst the users. Spectral efficiency also increased with respect to bandwidth setting as higher spectrum gave extra propagation channel between Tx and Rx, which yielded better received signal quality. Figure 12: Overall network performance for FI model at various antenna configurations.
# Perturbations During Gait on a Split-Belt Treadmill: A Scoping Review
## 1 Introduction
In contrast, the split-belt treadmill offers a more sophisticated approach by operating its belts at different speeds, thus introducing specific and controlled asymmetrical perturbations to an individual's gait [PERSON] et al. (2018); [PERSON] et al. (2018). This advanced setup allows researchers to dissect the adaptive mechanisms of gait in response to asymmetrical walking conditions. The split-belt treadmill's ability to independently adjust the speed of each belt provides a nuanced understanding of how individuals respond to these conditions. By simulating real-world challenges in a controlled environment, this method proves invaluable for studying gait stability. The split-belt treadmill enables the examination of various adaptive strategies, such as altering step length, timing, and coordination between limbs, which are crucial for maintaining balance and preventing falls [PERSON] et al. (2018). These treadmills offer precise control of perturbation timing, magnitude, and direction, offering detailed insight into adaptive responses during walking. It is particularly relevant for populations vulnerable to gait instability, including older adults and individuals with neurological impairments. For example, older adults often exhibit slower and less adaptable responses to perturbations, making them more susceptible to falls [PERSON] et al. (2018). Individuals with neurological impairments, such as stroke survivors, may have asymmetric gait patterns and reduced adaptive capacity, which can be effectively studied using the split-belt treadmill [PERSON] et al. (2018). This scoping review aims to examine the types of perturbations used during split-belt treadmill gait, explore the methods used to induce them, and consolidate current knowledge on the effects of split-belt treadmill-induced gait perturbations. By examining various studies, we seek to understand the strategies employed by individuals to maintain balance and adapt their gait. Understanding these strategies can significantly improve the development of fall prevention programs for older adults and inform gait reeducation in post-traumatic rehabilitation. Research on gait perturbations has practical value in refining rehabilitation protocols, such as split-belt treadmill training for stroke survivors [PERSON] et al. (2018); [PERSON] et al. (2018), to improve balance and coordination. It also supports the development of assistive devices like exoskeletons [PERSON] et al. (2018) for those with mobility impairments and strengthens fall prevention strategies. Theseadvancements improve mobility, reduce injury risks, and enhance the quality of life for vulnerable populations.
# A Novel Traffic Analysis Zone Division Methodology Based on Individual Travel Data
## 2 Data Preprocessing
### Data Cleaning  
In this paper, a model is built using individual travel data from public transport (specifically, bus smart-card data containing spatiotemporal information on individual travel). The spatiotemporal mapping and discretization of individual travel data are performed using spatial data to generate the spatiotemporal data required for the model.  
Currently, the main public transportation billing methods are divided into two categories: the one-ticket system and segmented billing. The former only requires passengers to swipe their cards when boarding and alighting, while the latter charges passengers based on the distance traveled, which is determined based on the boarding and alighting points. The 2016 Beijing public transportation smart-card data used in this study contain the following data abnormalities: (1) passengers boarding and alighting at the same station, resulting in the same starting and ending points; (2) passengers' alighting time being earlier than or equal to their boarding time, resulting in erroneous data; (3) passengers' travel time being inconsistent with their travel distance, leading to speeds outside the normal range; (4) data being duplicated due to system errors; (5) data being incomplete due to passengers forgetting to swipe their cards or failing to swipe them successfully; (6) the duration of a single trip exceeding 4 h (in 2016, Beijing implemented a segmented charging rule, which prevents passengers from swiping their cards on the same route within 4 h; otherwise, it results in abnormal charges).  
For the first five types of data quality issues, constraints and rules were applied directly in the database to filter and delete erroneous entries. For the sixth issue, following the method proposed by [PERSON] [28], card swiping behavior was divided into \"goodwill swipes\" and \"malicious swipes\". The corresponding technique was applied to correct anomalous data, and data that remained erroneous after correction were deleted. \"Goodwill swipes\" refers to when passengers swipe their cards in advance to avoid missing their stop, while \"malicious swipes\" refers to when passengers swipe their cards ahead of time to reduce the fare.
# Research on the Face Forgery Detection Model Based on Adversarial Training and Disentanglement
## 4 Experiments
### Experimental Setup
#### 4.1.2 Implementation Details  
As forgeries typically focus on facial regions, we extract 10 frames from each video in the FF++ training dataset, employing MTCNN [31] for accurate face cropping and DLIB [32] for reliable face alignment and resizing, which standardizes the image size to 256 \(\times\) 256 pixels to facilitate feature extraction and comparison.  
We choose the modified XceptionNet [33] as the backbone for our generator, encoder, and classifier, because this architecture has been extensively used for effective forgery detection. The model's parameters are pre-trained on ImageNet. We set the batch size to 32 and select Adam [34] as the optimizer for both the generator and discriminator networks. Following the method outlined in SLADD [23], we set the learning rates at \(2\times 10^{-4}\) and \(5\times 10^{-5}\) to optimize the balance between rapid convergence and training stability.  
The hyperparameters for the three self-supervised auxiliary tasks, \(\lambda_{2}\) is defined in Equation (6) as 0.1 respectively, based on SLADD [23]. For the hyperparameter \(\lambda_{1}\) of the forgery type classification loss, it is considered to be as important as the forgery classification, so it is set to 1. The value of the hyperparameter \(\lambda_{3}\) for the distribution classification loss is empirically set to 1.
# An On-Demand Charging for Connected Target Coverage in WSRNs Using Fuzzy Logic and Q-Learning +
## 4 Fuzzy Q-Charging Algorithm  
### Overview  
We follow the on-demand charging strategy, in which a sensor sends a charging request to the base station when its energy is below a predefined threshold \(E_{th}\). The base station then uses one-hop routing to transfer the request to the MC. The request is inserted into the waiting list at the MC. The MC then performs the following procedures to update the Q-table:  
* The MC leverages Fuzzy logic to calculate a so-called _safe energy level_ (denoted as \(E_{sf}\)), which is sufficiently higher than \(E_{th}\). The MC then uses the algorithm described in Section 4.3 to determine the charging time at each charging location. The charging time is optimized to maximize the number of sensors that guarantee the safe energy level.
* The MC calculates the reward of every charging location using Equation (12) and updates the Q-table using Equation (4).  
After finishing charging at a charging location, the MC selects the next charging location as the one with the highest Q-value. Finally, the MC moves to the next charging location and charges for the determined charging time. When the energy of the MC goes below a threshold, it returns to the depot to recharge itself. Figure 3 presents the overview of our charging algorithm. To facilitate the reading, we summarize all the used notations in Table 1.
## 3 Results and Discussion
### SEM Microscopy  
The SEM micrograph of the PLA filament cross-section shows a brittle fracture which results in smooth surfaces (Figure 4). In addition, the presence of some inorganic additives in the filament structure can be seen, presented as white spots [26]. The presence of air voids in the filament structure probably occurs due to the filament technology process, i.e., the filament extrusion process and the presence of moisture [27].  
The SEM micrograph of the interior of the 3D print made with 50% infill shows the transition from the initial layer to the 50% infill area (Figure 5). The fracture, upon closer examination, presents discernible irregularities and rough edges, providing clear indications of a fracture mode characterized by brittleness or partial brittleness. The observable Jagged features on the fracture's surface further underscore the nature of the breakage, suggesting a lack of ductility and a tendency toward brittle behavior during the impact.  
Figure 6a shows layers in the printed model made with 100% infill, while Figure 6b shows an enlarged micrograph of the layers' structure. It is obvious that the layers have a fibrous structure.  
Figure 4: SEM micrograph of PLA filament cross-section.  
Figure 5: SEM micrograph of the interior of the 3D print made with 50% infill.  
The printing was carried out using the FFF process in which a 3D print is formed through layer-by-layer deposition. In this process, the filament melts and extrudes through the nozzles. By leaving the nozzles, the filament is placed onto the printing bed. Due to its softness, the lower part is flattened while the top part forms convex structures, forming triangular voids (Figure 6c) [28]. These voids can cause an uneven bonding of the layers [29].  
Figure 7 shows the cross-section view of the 3D print made with 100% infill under a magnification of 200\(\times\). Triangular inter-track voids are formed at inter-layer boundaries due to neck growth phenomena during the solidification and sintering processes when molten tracks merge [30]. Additionally, irregular micro voids at the inter-layer boundaries can be observed. The average surface area values of the micro voids (71 \(\upmu\)m\({}^{2}\), 63 \(\upmu\)m\({}^{2}\)) are 100\(\times\) smaller compared to the area of the triangular inter-track voids (6564 \(\upmu\)m\({}^{2}\)) (Figure 7).
## 3 Results
### MuRXLS Framework Implementation
#### 3.1.3. Dynamic Example Selection and Prompt Construction  
The retrieval-based approach transforms traditional prompt engineering for cross-lingual summarization through an embedding-guided example selection process. Unlike conventional approaches that randomly select or prioritize shorter examples, our framework dynamically constructs prompts by integrating semantically-aligned demonstrations identified through multilingual embedding similarity, as shown in Figure 2.  
For \(k\)-shot settings where \(k>1\), we select the top-\(k\) most similar examples according to cosine similarity. The demonstration set contains the \(k\) most semantically relevant article-summary pairs that provide contextually appropriate guidance for the target task. The prompt follows the template presented in Table 2.  
The prompt construction ensures that each test article receives demonstrations that are semantically aligned with its content, enabling more effective knowledge transfer during  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Component** & **Content** \\ \hline Instruction & Please summarize the following text in [TARGET\_LANGUAGE] \\ Example 1 & Text: [RETRIEVED\_Article\_1] \\  & Translated summary: [RETRIEVED\_SUMMARY\_1] \\ \(\vdots\) & \(\vdots\) \\ Example \(k\) & Text: [RETRIEVED\_Article\_\_k] \\  & Translated summary: [RETRIEVED\_SUMMARY\_k] \\ Query & Text: [TEST\_Article] \\  & Translated summary: \\ \hline \hline \end{tabular}
\end{table}
Table 2: Template for XLS prompts with retrieved examples.  
Figure 2: Examples of prompt for different 2-shot settings in Gujarati-to-English cross-lingual summarization. The figure shows how prompts are structured with the instruction, retrieved examples, and the test article.  
in-context learning. Upon receiving the prompt, the LLM interprets the retrieved examples as demonstrations guiding the execution of cross-lingual summarization, analyzing source-summary relationships to infer both content selection strategies and target-specific linguistic patterns.
###### Abstract  
Indirect bridge health monitoring is conducted by running an instrumented vehicle over a bridge, where the vehicle serves as a source of excitation and as a signal receiver; however, it is also important to investigate the response of the instrumented vehicle while it is in a stationary position while the bridge is excited by other source of excitation. In this paper, a numerical model of a stationary vehicle parked on a bridge excited by another moving vehicle is developed. Both stationary and moving vehicles are modeled as spring-mass single-degree-of-freedom systems. The bridges are simply supported and are modeled as 1D beam elements. It is known that the stationary vehicle response is different from the true bridge response at the same location. This paper investigates the effectiveness of contact-point response in reflecting the true response of the bridge. The stationary vehicle response is obtained from the numerical model, and its contact-point response is calculated by MATLAB. The contact-point response of the stationary vehicle is investigated under various conditions. These conditions include different vehicle frequencies, damped and undamped conditions, different locations of the stationary vehicle, road roughness effects, different moving vehicle speeds and masses, and a longer span for the bridge. In the time domain, the discrepancy of the stationary vehicle response with the true bridge response is clear, while the contact-point response agrees well with the true bridge response. The contact-point response could detect the first, second, and third modes of frequency clearly, unlike the stationary vehicle response spectra.  
contact-point response; fast fourier transform; finite element method; indirect method; bridge health monitoring; moving average filter +
Footnote †: journal: Applied Sciences
# Performance Analysis of Software-Defined Networks to Mitigate Private VLAN Attacks
## 2 Private VLAN
### Private VLAN Attack  
The main threat related to both PVLAN and PVLAN edge is private VLAN attacks, which may affect both the physical and virtual layer 2 architectures [18]. They allow for sending unidirectional traffic while avoiding the restrictions imposed by PVLANs. For example, a device connected to an isolated port may reach a device connected to a community port. Similarly, a device connected to a community port may send traffic to a device connected to a port belonging to a different community. An example of a PVLAN attack that exploits this vulnerability is shown in Figure 3.  
Figure 1: PVLAN configuration on the switch.  
Figure 2: PVLAN edge.  
In this example, a user connected to the port labelled as _Isolated_\(A\) is an attacker who aims to send malicious unidirectional traffic to _User B_, who is a member of the \(C_{1}\) community. As illustrated in Figure 2(a), the attacker crafts packets whose destination IP address is the IP address of _User B_, while the destination MAC address is the MAC address of the network gateway. This initial step is legitimate since communications between isolated ports and promiscuous ports are allowed in PVLANs. When the gateway receives the crafted packets, it routes them to _User B_, as depicted in Figure 2(b). Since the gateway knows the path towards the destination (_User B_) because both are located in the same network, the gateway forwards the crafted packets after changing the destination MAC address for the MAC address of _User B_. This final step is possible since communications from a promiscuous port to a community port are also allowed in PVLANs. Thus, the attacker can evade the restrictions imposed by PVLANs between different types of ports by relying on a promiscuous port.  
The traditional solution to mitigate the PVLAN attack in conventional networks is to configure access control lists (ACLs) in the network gateway [19]. Specifically, an access control rule should be implemented where all communications having both source and  
Figure 3: PVLAN attack.  
destination IP addresses within the LAN are denied, with the exception of the case when the destination IP address is that of the network gateway. This ACL must be applied to inbound traffic on the interface that the network gateway uses in the LAN. This approach is also applicable if a firewall is located between network levels, although the ACL in this case would be replaced by a firewall rule. Moreover, an intrusion prevention system (IPS) can be placed within the network to stop the PVLAN attack, but a networking rule similar to the ACL is needed to discard the traffic. In both cases, the operating principle is the same as for the traditional solution.
# Analysis of 5G Channel Access for Collaboration with TSN Concluding at a 5G Scheduling Mechanism
## 5 Analysis of the Channel Access Prioritization of Packets
### Conditions for TSN-Compliant Scheduling
#### 5.3.2 Time-Variant Logical Channel Prioritization  
The requirement that a packet be forwarded related to the conditions requested by the TSN control plane relies on both the TSN stream it belongs and on the current point in time. Therefore, a time invariant LCP is not appropriate for the forwarding of packets belonging to a TSN traffic class. Instead, the priorities of the logical channels, i.e., instances of the Dedicated Traffic Channel (DTCH), have to be updated for every TTI matching the current status of the applicable GCL. At that point, the periodicity of the GCL can be exploited to define a finite set of LCPs described by a vector for every logical channel.
# _CoVSCode_: A Novel Real-Time Collaborative Programming Environment for Lightweight IDE
## 5 Major Technical Issues and Solutions
### Consistency Maintenance of Replicated Source Code Copies
#### 5.3.2 Consistency Maintenance in Project-Level Sessions  
Similar to file-level editing operations, when two project-level editing operations target source code items in one directory or in hierarchically-related directories, they may also conflict with each other and result in inconsistent source code trees. To support consistency maintenance in project-level sessions, we have adopted conflict resolution and consistency maintenance techniques that have been proposed and widely applied in prior studies [39; 40; 41].  
In essence, three primitive project-level editing operations can be performed on the source code tree of a project:  
* _createSourceCodeItem_ (_n_, _parent_): to create a new source code item \(n\) in the directory _parent_.
* _removeSourceCodeItem_ (_n_, _parent_): to remove an existing source code item \(n\) from the directory _parent_.
* _renameSourceCodeItem_ (_on_, _nn_, _parent_): to rename an existing source code item from _on_ to _nn_ in the directory _parent_.  
The registered listener (item \(a\) in Table 1) captures changes in the source code tree and produces primitive operations as defined above. Consequently, based on the conflict/compatible relationships among project-level editing operations, the operational transformation mechanism (deployed in the operational transformation controller) processes local and remote editing operations in a similar fashion to that in file-level sessions, and ensures source code consistency.
# Deep Learning- and IoT-Based Framework for Rock-Fall Early Warning
## 4 Methodology
### Rock-Fall Risk Assessment  
The likelihood that a rock-fall event will occur at a specific location and at a specified time and impose a specific level of damage to roads, automobiles, and pedestrians was characterized as rock-fall risk. Then, the risk was calculated in terms of the temporal and spatial information on the effect of precipitation. Based on the possibility that cars are available in a certain position and time period affected by rocks falling, the temporal-spatial probability and susceptibility were determined [34]. Equation (12) shows the risk probability's value.  
\[P_{(Risk)}=f_{r}\times P(S:T)\times P(r)\times V(u) \tag{12}\]  
where \(P_{(Risk)}\) denotes the likelihood that a rock-fall incident will occur within a given hour and _fr_ denotes the frequency of rock falls. The possibility that a rock will fall and impact the car is \(P(r)\). The vulnerability of the vehicle regarding rock-fall incidents \(V(u)\), has two possible values: 1 if the rock actually hits the car, and 0 if it does not. The likelihood that automobiles will be accessible at a particular location and time is known as \(P(S\cdot T)\). There is a temporal-spatial probability that a car traveling the entire path will be impacted at the moment of impact. It is determined using Equation (13) [35].  
\[p_{(S\cdot T)}=\frac{N_{V}}{24}\times\frac{L_{v}}{1000}\times\frac{1}{V_{v}} \tag{13}\]  
where \(N_{V}\) is the average daily number of vehicles, \(L_{v}\) is the average length of a vehicle in meters, and \(V_{v}\) is the average speed of a vehicle in kilometers per hour.