esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Proposed Method
#### 2.3.2 Algorithm Description  
Our algorithm operates in rounds, as outlined in Algorithm 1. We assume client datasets are non-independent and identically distributed (non-IID), local datasets \(\mathcal{D}_{i}\) are drawn from a distinct distribution \(P_{t}\), and that clients do not share raw data to preserve privacy. A central server coordinates the federated learning process, broadcasting the global model and aggregating updates from a subset of clients in each round. Clients perform local training on private datasets and communicate model parameters or gradients with the server. We additionally assume that the client can communicate with the global server with minimal lag, so that global updates may be computed efficiently. The client-level time complexity is \(\mathcal{O}(R\cdot m_{c})\), where \(m_{c}\) is the computation cost of the local loss function. At the global server level, the complexity is \(\mathcal{O}(T\cdot m_{g})\), where \(m_{g}\) is the computational cost of the server weight optimization. Therefore, our algorithm is linear in the number of global and local iteration steps. Unlike traditional knowledge distillation, where the teacher model is pre-trained on a dedicated dataset, our approach requires no server-side training data. Instead, the global model \(w\), which serves as the teacher, is optimized solely through aggregated gradients of the KL divergence computed on clients' local datasets \(\mathcal{D}_{i}\). This design aligns with conventional federated learning principles, such as FedAvg, ensuring privacy by keeping all training data decentralized while leveraging KD to enhance personalization in non-IID settings. The parameter \(\gamma\) controls the trade-off between local fit and global alignment, with \(\gamma=0\) reducing to standalone local training and \(\gamma=1\) prioritizing global consistency. Compared to pFedMe, which uses an \(\ell_{2}\)-norm penalty, our KD-based approach captures richer predictive relationships, potentially improving generalization on non-IID data.  
```
1:Input:\(T\) (rounds), \(R\) (local steps), \(S\) (clients per round), \(\eta\) (learning rate), \(\gamma\) (KD weight), \(w_{0}\) (initial global model)
2:for\(t=0\) to \(T-1\)do
3: Server samples subset \(\mathcal{S}_{t}\) of \(S\) clients
4: Server broadcasts \(w_{t}\) to all clients in \(\mathcal{S}_{t}\)
5:for each client in \(\mathcal{S}_{t}\) in parallel do
6:for\(r=0\) to \(R-1\)do
7: Sample mini-batch \(\mathcal{D}_{i,r}^{t}\) from \(\mathcal{D}_{i}\)
8: Compute loss \(F_{i}(\theta_{i,r}^{t},w_{t})\) using Equation (3)
9: Update \(\theta_{i,r+1}^{t}=\theta_{i,r}^{t}-\eta\
abla_{\theta_{i}}F_{i}(\theta_{i,r} ^{t},w_{t})\)
10:endfor
11: Set \(\hat{\theta}_{i}^{t}=\theta_{i,R}^{t}\)
12:endfor
13: Clients in \(\mathcal{S}_{t}\) send \(\hat{\theta}_{i}^{t}\) to server
14: Server updates \(w_{t+1}=w_{t}-\eta\frac{1}{S}\sum_{i\in\mathcal{S}_{t}}\
abla_{w}\text{KL}(p_ {w_{t}},p_{\hat{\theta}_{i}^{t}})\) with Equation (4)
15:endfor
16:Output: Global model \(w_{T}\), personalized models \(\{\hat{\theta}_{i}^{T}\}\)
```  
**Algorithm 1** pFedMe-WCL Algorithm
# Mobile Data-Mule Optimal Path Planning for Wireless Sensor Networks
## 2 Problem Description  
The case of a single MoEI [32] is extended to multiple \(M\)-MoEIs acting as data mules. The MoEIs are used either as an intermediate way to interconnect isolated WSNs or as an alternative to multi-hop relay-based approaches within a sparse WSN that suffers from poor link quality and requires data retransmissions. Thus, the data harvesting concept resembles that of Figure 1, where the mobile robots act as data mules within the \(N\)-node WSN.  
The data harvesting optimization problem's objective for every MoEI is to visit as many static nodes as possible and receive their stored sensor measurements. The constraints that are used in the formulation of the optimization problem are: (a) the pairwise distance  
Figure 1: Data Mule Concept with 3 MoEIs and 13 static nodes (\(M=3,\ N=13\)).  
between the static nodes, (b) the maximum time interval between consecutive visits of each static node that is imposed by the sensor data generation rate and its hardware limitations, (c) the service time that is required for the collection of the sensor data by the MoEl that visits this sensor node, (d) the energy efficiency of the mobile nodes, and (e) the effective assignment of groups of static sensor nodes to each one of the data mules.  
Regarding the service time that each MoEl spends in order to gather the stored data from the static node that it has visited, real time delays from our previous experimental work [33] are taken into account. In this work, the Rime communication stack [34; 35] was adopted over the Contiki operating system [36]. The application was performed using embedded computing devices (Motes) equipped with hardware that supports the IEEE 802.15.4 communication protocol.  
A variation of the combinatorial optimization Traveling Salesman Subset-tour Problem (TSSP) is used to compute the paths for the \(M\) MoEls. TSSP is a relaxed variant of the TSP in which a feasible tour may not include a visit to every vertex. Such a tour varies from visiting no vertices (other than the start node) to visiting all the vertices. This approach is further extended to an MES scheme that accounts for the extraction of consecutive trajectories for the MoEls. These trajectories concurrently satisfy the priority, energy, and inter-visit duration constraints, in order to visit at least the set of nodes that have priority in each trajectory. Hence, an MILP-problem is generated along with the MES-scheme, and both assure that the MoEls return on time to the depot node for charging prior to their energy-drain, while eliminating data loss due to the nodes' buffer overflow.
## 2 Monitoring Protocols and Methods
### Laboratory Analyses  
Carbon-isotope ratios of the CO\({}_{2}\) soil-gas were determined on the same glass bulbs. After purifying the CO\({}_{2}\), the gas is analysed through a thermostated chromatographic column connected to the inlet of a mass spectrometer (Gas Bench system associated with a Delta + XP mass spectrometer). The \(\delta^{13}\)C\({}_{\text{CO2}}\) isotopic ratio is expressed with reference to the \"Vienna Pee Dee Belemnite\" standard (\(\%\)\(\%\) VPDB). Analytical precision is better than 0.2\(\%\).
## 6 Implementation and Discussion of Results
### State Representation  
The state represents a significant element in DRL. State, in this case, consists of different components. Several experiments are conducted with different combinations of components to achieve the best state for our problem. The final state in our proposed framework consists of several elements: first, the RSI technical indicator for the last nine days; second, the MOM technical indicator for the last nine days; third, the simple moving average for the last nine days; Fourth, the simple moving average for the last two days; fifth, the predicted prices for the upcoming five days are predicted from the VMD plus LSTM; and sixth, the sentiment analysis module generates the sentiment for the current day.
## 1 Introduction
It introduces four performance levels, depending on the importance of the bridge and the consequences of the failure (effect on human life; short- and long-term economic effects of service disruption; repair and replacement costs). Generalized design equations aid in the early identification of critical performance levels and the selection of optimal designs with minimal computational effort, eliminating the need for iterative analyses. As highlighted in the brief overview, most of the cited studies were developed for specific technologies of the isolation system and therefore may not have general validity. Furthermore, most approaches require iterative procedures to account for the inherently non-linear response of isolation systems with high damping capacity, such as High Damping Rubber Bearings, Lead Rubber Bearings, and the Friction Pendulum system. Additionally, in the case of existing bridges, a preliminary analysis of the actual rehabilitation needs of the bridge, to verify that isolation is effective in reducing the vulnerability of the structure, is usually not performed. The Authors of this paper formulated a new conceptual approach for the seismic retrofitting of existing bridges using seismic isolation. The retrofit is implemented by isolating the deck from the substructures, by replacing the bridge bearings with seismic isolators, and is applied to bridges characterized by a static scheme of either a simply supported deck or continuous deck on multiple supports, where the deck rests on conventional bridge bearings. These static layouts appear to be the most widespread in the Italian panorama, where they represent over 90% of existing bridges ([PERSON], 2018; [PERSON], 2019). The retrofit, in line with current code provisions ([PERSON], 2018; [PERSON], 2019), aims at keeping the behavior of the substructures elastic during the design earthquake. The method consists of two phases: (i) the assessment of the effectiveness of deck isolation in reducing the bridge vulnerability, given the characteristics of the bridge and the seismic scenario, and (ii) the determination of the effective stiffness and effective damping of the isolation system to achieve the specified performance of the retrofitted structure. The procedure is in principle applicable to all technologies of isolation systems, and only after the definition of the mechanical properties of the isolators is it possible to choose the most effective devices from the catalogues of the manufacturers. This paper is dedicated to illustrating the first part of the method, which consists of assessing the effectiveness of the deck isolation to reduce the vulnerability of the bridge, while the preliminary design of the isolation system will be the subject of a future study. After introducing the criterion of effectiveness, a parametric study is developed considering different bridge prototypes obtained by varying the geometry of the piers and the type of deck. The bridge prototypes are analyzed for different sites, highlighting how various parameters affect the effectiveness of the isolation retrofit.
###### Abstract
NiTi files manufactured using CM-wires outperform conventional, M-wire, and R-phase-wire NiTi files in terms of flexibility and cyclic fatigue resistance at room temperature [9; 24; 25; 26; 27; 28]. Furthermore, CM-wire NiTi files exhibit higher cyclic fatigue resistance than conventional, C-wire, and T-wire NiTi files at body temperature [28; 29]. Although the angle of rotation to fracture (ARF) in torsional fracture resistance tests is higher in CM-wire NiTi files [14; 18; 25; 30], their ultimate torsional strength (UTS) is lower than those of conventional NiTi files [18; 25; 26; 30]. Gold heat-treated files exhibit reduced superelasticity, similar to CM-wire files. Although the manufacturer has not disclosed the exact process of proprietary heat treatment, gold-wire NiTi files appear golden after thermomechanical treatment [7]. According to DSC results, gold-wire NiTi files are mainly composed of martensite and R-phase at room and body temperature [31; 32]. The A\({}_{\text{f}}\) of gold-wire NiTi files is 49-52 \({}^{\circ}\)C [31; 32]. Additionally, some studies have shown that gold-wire NiTi files exhibit enhanced flexibility and superior cyclic fatigue resistance in relation to conventional files [32; 33]. 2 Shape (Micro-mega, Besancon, France) is a brand that fabricates files through heat treatment using T-wire technology. According to the manufacturer, T-wire heat treatment improves resistance to cyclic fatigue and enhances flexibility [34]. The asymmetrical cross-sectional design of 2 Shape files, comprising two main cutting edges and one secondary cutting edge, reduces fracture risk and promotes debris removal [34]. The goal of this study is to determine and compare the bending, buckling, cyclic fatigue, and torsional resistances of six types of heat-treated NiTi files with triangular cross-sectional shapes. ProFile, which is not fabricated via heat treatment, is used as the control. DSC was performed to analyze the phase transformation behavior of the tested NiTi files.
## 5 Results and Discussion
### Data Robustness Ratio  
The data robustness ratio (DAR) is measured to study the ability of MA route planning protocol to detect event regions and collect data records which match the consumer's query. A protocol offers benefits to remote-sensing applications if it is able to have a high DAR.  
According to Figure 6, MINDS increases DAR as compared to the benchmarks. This is rooted in the protocol's ability to visit event regions and LDSs which have data records matching the consumer's query. MINDS works according to a data-centric path planning that establishes a tree route with LDSs which have interesting data samples to collect. That way, MAs should be able to collect meaningful data samples if they only move throughout the path.  
MINDS outperforms ZMA because of utilising clustering technique and NDN routing. Clustering forms data zones which are interesting for MAs to visit. However, NDN routing forms data-centric paths which interconnect LDSs whose data records match the consumer query. That way, MAs should be able to find event regions and capture data samples if they move through the paths. Indeed, MAs avoid random walking. They visit only the minimum number of LDSs whose data records match the consumer's query.  
TBID underperforms compared to MINDS and ZMA for DAR. This is because of TBID establishing a tree structure according to an address-centric manner. This utilises no data-centric feature to establish the routes. For this, the tree is formed with a number of LDSs which might have no data records matching the query. This results in MA migration to a hole which has no meaningful data records to report. Hence, DAR is reduced especially when data density is low.  
Figure 5: Average end-to-end delay.
## 3 Preliminaries
### Attack Model  
In sensor networks, data routing uses hop-by-hop transmission because of the limited communication range of each sensor node. The attacker can trace the data to the sink or source node according to the time correlation of data packet transmission and the traffic pattern of different communication nodes. We assume that the attacker has the following characteristics: (1) local, that is, the attacker's surveillance range is its neighboring sensor node; (2) passive, that is, the attacker can only listen for packets in the network and cannot control or destroy sensor nodes, it will not have any functional impact on the network; (3) mobile, the attacker will start from different locations in the network to find the location of the source node.  
In a typical SLP protection problem, there are two main types of attackers: (1) The patient attacker moves to the direction in which the packet is sent when new data is captured, otherwise it remains in the in-place wait state; (2) The curious attacker will walk randomly if it does not receive any packet  
Figure 1: The problem of STD algorithm.  
within the limited waiting time. Curious attackers are more flexible when they cannot receive any data. Therefore, in this paper, we implement the attacker model [19] based on a curious attacker, as shown in Algorithm 1.  
```
input:\(R\), \(H\), \(M\), \(s_{0}\), \(D\) output:NULL
1. msgs, moves, num, curLoc \(\leftarrow\emptyset\), \(0\), \(0\), \(s_{0}\);
2. history[] \(\leftarrow\) array of node ids of size \(H\);
3. IF msgs.size() \(<\)\(R\)THEN
4. Msgs \(\leftarrow\)msg\(\cup\){\(l\)};
5. END IF
6. IF (msgs \(\
eq\emptyset\))
7. IF (\(moves<M\))
8. IF (\(H>0\))
9. history[\(num\)] \(\leftarrow\)curLoc;
10. num \(\leftarrow\) (\(num+1\)) % \(H\);
11. END IF
12. curLoc \(\leftarrow\) D(msgs, history);
13. moves \(\leftarrow\) moves \(+1\);
14. msgs \(\leftarrow\emptyset\);
15. END IF
16. END IF
```  
**Algorithm 1** Attacker model.
## 2 Materials and Methods  
### YOLOv5  
Ultrayltics developed the YOLOv5 object detection algorithm, which is based on the You Only Look Once (YOLO) family of models. Due to its high accuracy and quick inference speed, YOLOv5 has quickly gained popularity since its initial release in June 2020. YOLOv5 is available in the following architectures: YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x. These architectures vary in terms of the network depth and feature map width, allowing users to select the one that best meets their particular requirements. To meet the industrial requirements for bus detection algorithms, such as real-time detection and simple deployment, we chose YOLOv5n, which has the smallest model size and fastest detection speed, as the infrastructure. Figure 1 depicts the network architecture of YOLOv5, which consists primarily of four components: Input, Backbone, Neck, and Head.  
Input: The Input component is in charge of receiving and preparing the input image for processing. As part of the input, an image preparation step, such as adaptive image scaling, mosaic data enhancement, or adaptive anchor frame calculation, is frequently performed. When images of varying sizes are entered into the Input, the adaptive image scaling technique first normalizes the image size to 640 \(\times\) 640. It then chooses four images at random for cropping, scaling, and layout processing using mosaic data.  
Backbone: The Backbone component is the network's main feature extractor. It is in charge of extracting features from the input image through the use of a series of convolutional layers. The backbone component in YOLOv5 consists of a modified version of the  
Figure 1: The YOLOv5 model structure.  
Conv module, C3 module, and Spatial Pyramid Pooling--Fast (SPPF [17]) module. Convolution, batch normalization, and SiLU activation are all included in the Conv module. The C3 module is based on the CSPNet [27] concept and includes three standard convolutional layers as well as multiple bottleneck modules. The SPPF module employs serial maxpool to perform multiscale fusion in order to expand the receptive field's feature map.  
Neck: The Neck component is in charge of fusing the backbone features and improving their representational power. For its Neck section, YOLOv5 employs the Feature Pyramid Network (FPN [28]) and Pixel Aggregation Network (PAN [29]) structures. The FPN structure transmits semantic information from top to bottom via an upsampling operation, while the PAN structure transmits location information from bottom-to-top via a downsampling operation. By combining the two structures, the network is able to fuse more feature information, forming a multiscale feature fusion module that can retain both large-scale and small-scale target feature information.  
Head: The Head component is in charge of predicting the bounding boxes and class probabilities for each object in the input image. The loss function of the bounding box is CIOU Loss [30], and the Non-Maximum Suppression (NMS [31]) is used to screen the multi-object box and output the predicted image.
# Collision Avoidance from Multiple Passive Agents with Partially Predictable Behavior
## 6 Implementation and Results
### Collision Avoidance with Multiple Passive Agents
#### 6.3.1 Performance Results  
In Figure 8, the performance of the proposed algorithm for a car-like agent is presented for the set of three parameters, the number of passive agents present, minimum safrey margin \(\beta\), and the length of the time step. The effect on computation time, success rate, and elapsed time are investigated. The computation time is the time required to compute optimal control input. A successful run is a trial in which agent successfully reaches the goal without a collision. A collision might occur when a passive agent changes its direction and traps the robot. In a successful trial, elapsed time is the amount of time passed, starting from the point in time when the robot was at its initial position, to the point in time when robot reaches the goal position. Figure 8a shows an average computation time taken when the number of obstacles present grows. The computation time increases approximately linearly with the increase in a number of passive agents, while Figure 8b shows the success rate drops with the growth in the number of passive agents. For these experiments the step time was set to 5 ms, the margin was set to 0.4, and 256 samples was taken in \(U_{ad}\). In Figure 8c, the success rate is shown for selected values of \(\beta\), when 20 passive agents are present. It shows that the success rate increases with the increase in \(\beta\), while Figure 8d shows that the elapsed time also increases with the increase in \(\beta\). For larger value of \(\beta\), the robot takes comparatively longer time to reach the goal because it selects a more safe path to the goal rather than a direct path to it. Finally, Figure 8e shows the success rate when \(\beta\) is set equal to 0, and 0.4, for varying step time. It shows that the increase in step time will lead to a drop in success rate, whereas the success rate is comparatively higher for \(\beta=0.4\) than that for \(\beta=0\).  
Figure 7: Avoiding multiple passive agents. Multiple snapshots at different time instances show a car-like robot, in green, navigates among multiple passive agents, in red, while moving towards a goal \(p_{g}\), in yellow. The passive agents are moving with arbitrary velocities. The probability that a passive agent will change its velocity within one second is 0.2. \(|\beta=0.4\) is set for the simulation).  
The results in Figure 8c,d show that the car-like robot reaches the goal with relatively high success rate when \(\beta=0.4\) is selected, while the elapsed time is comparatively reasonable. The next subsection will compare the performance of the proposed approach (\(U_{safe}\)) with \(\beta=0.4\) to that of GVO.