esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Collaborative Optimization Method for Real-Time Multi-Object Tracking
## 1 Introduction
Bi-Modal Dynamic Decision Method (BDDM): A hierarchical strategy for fusing appearance and motion modal information, utilizing a dynamic weight fusion function to optimize contributions from diverse information sources, thereby enhancing long-term trajectory stability. The You Only Look Once (YOLO) architecture, particularly YOLOv9, is a state-of-the-art solution for real-time object detection, balancing speed and accuracy. In this paper, we integrate YOLOv9 as our tracking model's detector due to its proven performance in diverse applications, ensuring scientific validity and reliability. Ref. [9], focusing on improving detection performance for autonomous vehicles in adverse weather conditions through the application of metaheuristic algorithms, exemplifies efforts to push YOLO's boundaries in difficult environments. Research [10] on the effectiveness of image augmentation techniques applied to YOLOv8 for detecting non-protective personal equipment highlights the importance of data-centric approaches and model optimization for specific detection tasks. Investigations [11] into heavy equipment detection on construction sites using YOLO-Version 10, incorporating Transformer architectures, further showcase the evolution and integration of advanced components within the YOLO family to handle complex scenes. Additionally, the development of specialized models like COTTON-YOLO [12] for enhancing cotton bolt detection and counting in complex environmental conditions underscores YOLO's versatility in adapting to specific, challenging real-world applications. These findings validate YOLOv9 as a robust, efficient detector for our tracking model, ensuring system-wide reliability.
## 3 Results  
### Nanoparticles Production Optimization  
The impact of the studied variables on the nanoparticles was evaluated through size and ZP analysis using DLS scattering. The results regarding the optimization parameters effect on particle size can be seen in Figure 1.  
Comparing the results obtained at both pH values tested, it is possible to see that at pH 4 (Figure 1(a2,b2,c2)), it was not possible to ascertain optimal conditions for the assayed parameters. As for addition time, reaction time and rotation speed, no significant differences (\(p>0.05\)) were registered. On the other hand, for pH 5 (Figure 1(a1,b1,c1)) it is possible to see that among the parameters assayed, the 4 min of addition time, 11 min of reaction time and 500 rpm of rotation speed presented NPs with significantly (\(p<0.05\)) smaller sizes. On a condition-by-condition analysis, one can see that when regarding the TPP addition time (Figure 1(a1)) from 4 min of addition time onwards, particle size increased, with the NPs' size increase reaching a maximum value of 345.1 nm (\(\pm\)7845 nm) at 7 min of addition time. From 7 to 10 min of addition time, the nanoparticles' average size fluctuated slightly, but this was not significant (\(p>0.05\)). When considering the reaction times assayed, the standout condition was 11 min, as it produced NPs that were significantly (\(p<0.05\)) smaller than NPs obtained in the remaining conditions assayed. Lastly, regarding rotation speed, a similar behaviour was observed, as at 500 rpm, the NPs produced were statistically significantly smaller (\(p<0.05\)), with size differences reaching almost 100 nm compared to the next smallest particles produced.  
When considering the impact of production conditions on the chitosan NPs' charge (Figure 2), it is possible to see that for both tested pH values, it was not possible to ascertain any optimum production conditions, as no significant (\(p>0.05\)) differences were registered.  
Figure 1: Evaluation of the studied parameters on nanoparticles’ size at pH 5 (1) and pH 4 (2). (**a**)\(-\)TPP addition time; (**b**)\(-\)Reaction time; (**c**)\(-\)rotation speed. Different letters represent the statistical differences found between conditions assayed.  
Figure 2: Evaluation of the studied parameters on nanoparticles’ charges at pH 5 (1) and pH 4 (2). (**a**)\(-\)TPP addition time; (**b**)\(-\)Reaction time; (**c**)\(-\)rotation speed. Different letters represent the statistical differences found between conditions assayed.  
Based on the results obtained both for NP size and charge, the optimum conditions for their production were defined as being pH 5, 4 min of addition time, 11 min of reaction time and 500 rpm of rotation speed, as they individually produced the smallest particles in each of their individual assays. The conjunction of these parameters produced NPs with an average size of 226.6 \(\pm\) 5.2 nm, a PDI of 0.339 and a ZP of +27.1 \(\pm\) 3.1. Nanoparticles produced under these conditions were used for all of the remaining assays.
## 2 The Numerical Tool Development
### The Development of the Simulation Strategy
#### 2.1.6 The Experimental Approach to Manufacturing Process Monitoring  
Monitoring critical process parameters is the most dependable method for comprehending the extent of distortion mechanisms, particularly during the manufacturing phases. An experimental program was conducted using composite coupons. Figure 6 is a graphic description of the experimental program strategy for obtaining the much-needed curing process data.  
The manufacturing process was meticulously observed through the integration of fiber Bragg grating (FBG) optical sensors and dielectric sensors (DCs). The data collected from this monitoring were utilized to calibrate a finite element method (FEM) simulation tool aimed at predicting shape distortion in large integral composite wing structures [29]. The experimental program utilized FB sensors to assess temperature and strain in the coupons and test specimens, while DC sensors were employed to evaluate the degree of resin curing [29].  
Figure 7 illustrates the spatial arrangement of the sensors on each coupon geometry. The FBG sensors were organized into three arrays (labeled A, B, and C), each comprising five sensors (designated S1 through S5). An identical configuration was applied to both sides of the laminate: the surface in contact with the tool (lower side) and the surface interacting with the infusion consumables (upper side). No sensors were installed between the layers to prevent damage to the previously consolidated laminate. Furthermore, two DC sensors were strategically placed adjacent to the resin inlet and the vent to facilitate in-plane impregnation monitoring. Composite tools were shaped and manufactured to minimize part-tool interaction during the cooling phase. Ultimately, the shape distortions of the manufactured coupons were assessed using a 3D coordinate measurement machine (CMM) alongside FBG sensors to monitor residual stresses [29].  
Figure 5: Corner thickening due to resin flow from arms to corners.  
The observation of variations in the Bragg wavelength allows for the assessment of multiple parameters. Changes in strain and temperature will alter the characteristics of the grating, resulting in a shift in wavelength. To distinctly identify the impact of each parameter, two distinct fiber Bragg grating (FBG) sensors were employed, one exhibiting a different thermal sensitivity. The RTM6 and RTM6-2 time-temperature resin curing cycle is described in Figure 8, along with the evolution of the DC sensors' readings.  
Figure 6: Illustration of the overall aim of the experimental program.  
Figure 7: (a) Sensor distribution on skin coupons: FBG sensors (green dots) and DC sensors (squares). (b) Sensor distribution on C-spar coupons: FBG sensors (orange dots) and DC sensors (squares) [21].
This paper proposes an adaptive ML-based framework for automatic load optimization in Connected Smart Green Townhouses (CSGTs). The building considered is located in Burnaby, BC, Canada, and consists of four connected townhouses with 1-, 2-, 3-, and 4-bedrooms, connected water systems, and shared party walls [4, 5, 7]. Each townhouse type is associated with typical occupancy profiles to reflect realistic load usage patterns. These profiles were informed by publicly available datasets (e.g., from [7, 8]) and assumptions based on typical residential load usage. The 1-Bedroom (1-Bd) SGT is designed for a young couple, focusing on compact living with minimal load consumption, representing the lowest load usage. The 2-Bedroom (2-Bd) SGT is suitable for a couple with one young child, providing extra space while remaining load efficient, with moderate load usage. The 3-Bedroom (3-Bd) SGT is designed for a family with two teenage children, prioritizing efficient zoning and load distribution, resulting in higher load usage. Finally, the 4-Bedroom (4-Bd) SGT accommodates a family of five, representing the highest load consumption. The system leverages IoT data, including occupancy, weather, and energy prices, and employs a deep hybrid Long Short-Term Memory-Convolutional Neural Network (LSTM-CNN) model. Multi-Objective Particle Swarm Optimization (MOPSO) is used to balance costs, carbon emissions, and efficiency. This enables adaptive load optimization and automatic mode transitions to improve performance.  
The remainder of this paper is organized as follows. The methodology is presented in Section 2 including the proposed ML model and MOPSO algorithm. Section 3 gives the performance results and discussion. Section 4 summarizes the paper including the implications for sustainable urban living.
## 1 Introduction
### Theory of Detection Method  
Figure 1 shows a schematic diagram of a thin-plate lap weld with blowholes in the cross-section and the generation and detection points of the laser ultrasonic. Ultrasonic waves excited on the surface of the bead reflect off the back of the lower plate and reach the surface of the lower plate. Its signal strength attenuates when it passes through an inter  
nal blowhole. The proposed method detects defects by capturing this attenuation [17; 18]. Specifically, a B-scope (2D data of ultrasonic waveforms with the coordinates of the generation point on the horizontal axis and the ultrasonic propagation time on the vertical axis) was created for a single cross-sectional area. The B-scope was with multiple generation points on the bead by the pulsed laser and a single detection point on the lower plate surface by the laser interferometer. Focusing on the relevant signal area, a defect detection indicator representing attenuation was calculated, and the presence or absence of a defect was determined according to its value. The detailed results are described in the following sections.
# A Survey on Machine-Learning Based Security Design for Cyber-Physical Systems
## 3 Taxonomy of Cyber-Physical Attacks
### Application Layer
#### 3.3.2 Computing Hardware Attack  
The computing hardware attack is defined as disrupting a component of the computing system, such as the power supply, dynamic random access memory (DRAM), the CPU, and storage systems, directly or indirectly. We classify computing hardware attacks into two types of fault: one is a computer system-down attack, and the other is a data manipulation attack in memory via unauthorized accesses. Criteria for the computing hardware attack classification are determined by the impacts of the attacks on the physical system. A computer system-down attack refers to the impossibility of executing computing processes by intentional computer hardware faults, such as shutting down the power supply. Meanwhile, a data manipulation attack refers to modifying computing processes by the exploitation of a hardware vulnerability, but this does not break the computing hardware.  
From the perspective of physical dynamics, a system crash from a computing system-down attack is equal to a DoS attack. When the computing system is disrupted by a malicious power-off, the control application software also stops, and therefore the physical system is no longer controlled by the computing system. Furthermore, the data manipulation type of attack is similar to an application software attack, where a physical bit-flip in DRAM is considered to represent the manipulation of controller gain \(K\) in (2).  
Computer system-down attacks are implemented by injecting malware and requesting intensive tasks to be performed by the computing system maliciously. The power virus [91] leads to faults in the power supply of the computer by generating power surges that physically disrupt computer components. A CPU thermal attack [92] forces thermally intensive workloads to the CPU and leads to the physical disruption of the CPU or brings it into fail-safe mode, stopping the operation of the computing system. Meanwhile, data manipulation attacks are implemented by exploiting hardware vulnerabilities, but this does not break the computing hardware; an example of this is the row hammer attack [93]. The row hammer attack manipulates specific memory locations by intentional high-frequency access to rows of DRAM, exploiting an electromagnetic vulnerability [93, 94]. The row hammer attack flips some bits in DRAM, which affects software operations in real time. In [95], a row hammer attack targeting software operation is proposed, which modifies a neural network model for image classification. This attack deteriorates image classification performance, which can do severe damage to a real CPS, such as for obstacle detection software in an autonomous driving system. Moreover, the authors in [96] analyze the performance deterioration from atomic-level bit-flips induced by computing hardware attacks.
# FuGeF: A Resource Bound Secure Forwarding Protocol for Wireless Sensor Networks
## 4 The Proposed FuGeF Protocol  
This section presents the proposed FuGeF routing protocol. It is a modification of the DWSIGF [3] that improves on security as well as ensures efficient utilization of constrained resources (maintains base performance). The FuGeF introduces three parameters comprising remaining energy, connectivity cost, and progressive distance, as well as a Fuzzy Logic System (FLS) to enable appropriate node selection in a network, which is presented in Sections 4.1 and 4.2, respectively.  
In contrast to DWSIGF, the proposed FuGeF uses individual CTS reply received by the ORTS sender S, which contains location information and remaining energy. The sender S employs the location information to compute a node's progressive distance and connectivity cost. The computed values, together with the remaining energy, are passed as inputs to the FLS for processing as shown in Figure 2. The processing is in line with the FLS semantics explained in Section 4.2. The node with the highest output value (chance value) is regarded as the most appropriate forwarding candidate and is selected as the next hop node. The use of these parameters together with FLS causes spontaneity in both the forwarding node selection and the routing pattern, thus making it difficult to perform spatio-temporal predictions on the network. This lowers the chances of selecting malicious nodes despite their strategic placement.  
Figure 2: Forwarding process.  
Additionally, FuGeF employs a pseudorandom based dynamic collection window to maintain the dynamism of DWSIGF. However, FuGeF's pseudorandom based window ranges between two fixed intervals. These intervals ensure a slight shift in the protocol semantics such that at least a single CTS response is collected for processing. This dynamic limit-based feature, together with FLS consensus, increases unpredictability in node selection during the routing process. The Algorithm for the proposed FuGeF is described in Algorithm 1.  
```
Definitions  Fwd Area: Sextant Forwarding Area with range C: Set of forwarding candidates within Fwd Area N: set of Neighbors within range \(F_{Parameters}\): Progressive distance, Link cost and remaining energy \(\text{CWT}=\text{Collection Window Time}\) K: Chosen candidate node among set C \(\text{Chance}\) (v): Chance value of nodes in set C Main /* To initialize Packet Transmission by Sender */ whileChannel is clear do \(\text{ORTS}\leftarrow(S,S_{location},D,D_{location},FwdArea)\); \(\text{Broadcast}\) ORTS;  Set ORTS wait-timer ; /* N receives ORTS message. if receiver is in FwdArea, add to C and set CTS response-timer*/ \(\text{CTS}\gets F_{Parameters}\);  Set CWT; /*Send CTS upon timer expiry*/; whileCollection window is open do  Receive CTS from set C; /*every received CTS is checked to determine the appropriate node*/ \(\text{Chance}(v)\gets\text{Fuzzylogic}(\text{CTS})\); ifChance(v) < Max Chance(v); then \(\text{Chance}(v)\gets\text{MaxChance}(v)\); \(\text{K}\gets\text{Chance}(v)\) else \(\text{K}\gets\text{Chance}(v)\)
```  
**Algorithm 1**FuGeF Forwarding Process
# A Novel AHRS Inertial Sensor-Based Algorithm for Wheelchair Propulsion Performance Analysis
## 2 Materials and Methods
### Algorithm  
The implemented algorithm can be visualized in the schematic below with the algorithm's intention to track wheel distance and trajectory.  
As evidenced by Figure 1, the algorithm inputs calibrated inertial sensor data, formatted within the data structure defined by [PERSON] and [PERSON] (2013). The AHRS algorithm of [PERSON] et al. (2013) is then utilised to calculate the sensor orientation, giving the output in a quaternion representation, which is transformed into Euler angles. The wheel sensors were attached so the rotation axis of interest occurred around the yaw axis. Thus, the yaw angle of the wheel sensor derived by AHRS at each time point (termed \(\theta_{WT}\)) was used to evaluate the angular change. This angular change of the wheel sensor over each time sample (termed \(\theta_{WT}\)) compared to the previous time sample was then used to evaluate the distance (d\({}_{T}\)) moved in that sample:  
\[d_{T}=\frac{(\theta w_{T+1}-\theta w_{T})}{360}\times 2\pi r_{wheel} \tag{1}\]  
The chair sensor is attached so that z axis rotation represents the heading angles, so a yaw angle of the chair sensor is regarded as chair heading angle \(\theta_{H}\). The distance at each time series, from Equation (1), was vectorised to create an \(x\) and \(y\) position at each time series. Using the principle of Dead-reckoning ([PERSON], 2013), the current location is determined from the previous calculated coordinates, using Equations (2) and (3), to map the wheelchair trajectory.  
\[x_{pos\ T}=x_{pos\ T-1}+d_{T}cos\theta_{HT} \tag{2}\]\[y_{pos\ T}=y_{pos\ T-1}+d_{T}sin\theta_{HT} \tag{3}\]  
To find orientations (\(\theta_{W}\)_and_\(\theta_{H}\)) the proposed algorithm utilized [PERSON] et al.'s AHRS 6 DOF IMU open-sourced orientation filter [14], which encompasses sensor fusion of the gyroscope and the accelerometer to accurately find an attitude orientation which is referenced to gravity. The filter is tuned by the gain value, \(\beta\). This value represents all mean zero gyroscope measurement errors which encompass sensor noise, aliasing, quantization error, calibration error, sensor misalignment, sensor axis non-orthogonality, and frequency response characteristics [14]. The mean zero gyroscope measurement errors were calculated to establish a beta value for the wheel sensors of \(\beta\) = 0.03 [14]. An iterative approach of incrementing beta from this value in steps of 0.01, and inspecting the unwrapped Euler angle plots, found that a chair beta value of \(\beta\) = 0.2 was the most appropriate to adequately weight the accelerometer signal for accurate heading tracking.
# A Fast Dynamic Dim Target Tracking Approach for UAVs Using Improved Lightweight Siamese Networks
## 2 Related Work
### Deep Learning Algorithms
[28] proposed Target-aware deep tracking (TADT), which applies the channel attention mechanism to the target tracking algorithm, arguing that the high response channel of the same target should be kept constant by reducing the response to highlight the target. During training, Deep Attention Tracking (DAT) [29] improves the tracking performance of the network by introducing an attention paradigm in the loss function. Although the above Siamese network tracking algorithm research has achieved promising results, the following problems still exist in the tracking tasks facing weak targets:  
* In the presence of background information interference and similar targets, the tracking accuracy decreases. It is prone to target loss and false tracking. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref** & **Model** & **Datasets** & **Accuracy** & **Pros and Cons** \\ \hline
[17] & KCF & Mnist & - & High complexity \\
[21] & MCNN & NSL-KDD & 86.90\% & High speed \\
[24] & SiamRPN & NSL-KDD & 96.35\% & Low power \\
[25] & DaSiamRPN & SCADA data & 95.84\% & High time cost \\
[26] & SiamRPN++ & IoT datasets & 96.20\% & High complexity \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Algorithm comparison. * Most Siamese network target tracking algorithms use a shallow deep feature extraction backbone network, failing to take full advantage of the feature extraction capabilities of deep neural networks. * Using features from the last layer of the feature extraction network for similarity measures ignores the accurate localization information in the shallow network. We expect to improve the tracking efficiency and accuracy by optimizing the design of the feature network and combining the similarity metric with the attention mechanism.
# Post-Processing Partitions to Identify Domains of Modularity Optimization
## 2 The CHAMP Algorithm (Convex Hull of Admissible Modularity Partitions)
We leverage these intersections to efficiently identify the upper envelope of modularity for a given set of partitions, and the corresponding dominant partitions (relative to the set) for all \(\gamma\geq 0\) as follows. Starting at \(\gamma_{0}=0\), the partition with maximum \(\hat{A}_{\sigma}\) is optimal. For networks with a single connected component, this partition is a single community containing all nodes; for multiple disconnected components, any union of connected components gives the same \(\hat{A}_{\sigma}\), but we select the partition wherein each separate component defines a community. Denoting the optimal partition at \(\gamma_{0}\) by \(\sigma_{0}^{*}\), we calculate the intersection points \(\{\gamma_{\sigma\sigma}^{*}\}\) with the other partitions \(\{\sigma\}\) where \(Q_{\sigma_{0}^{*}}(\gamma)=Q_{\sigma}(\gamma)\). Substituting Equation (2) into this constraint yields\[\gamma_{\sigma_{p}^{*}\sigma}=\frac{\hat{A}_{\sigma_{p}^{*}}-\hat{A}_{\sigma}}{P_{ \sigma_{p}^{*}}-\hat{P}_{\sigma}}, \tag{3}\]  
where \(p\geq 0\) for generality. Starting with partition \(\sigma_{p}^{*}\) for \(p=0\), we identify the smallest intersection point \(\gamma_{\sigma_{p}^{*}\sigma}>\gamma_{p}\), which we define as \(\gamma_{p+1}\). We denote the associated partition by \(\sigma_{p+1}^{*}\). That is, partition \(\sigma_{p}^{*}\) is optimal for the \(\gamma\)-domain \(\gamma\in[\gamma_{p},\gamma_{p+1})\), above which partition \(\sigma_{p+1}^{*}\) becomes optimal. In the unlikely event that multiple partitions are associated with the \(\gamma_{p+1}\) intersection point, the one with smallest \(\hat{P}_{\sigma}\) becomes \(\sigma_{p+1}^{*}\). Setting \(p\) to \(p+1\), we iteratively repeat this process until there are no intersections points satisfying \(\gamma_{\sigma_{p}^{*}\sigma}>\gamma_{p}\). We thus obtain an ordered sequences of optimal partitions, \(\{\sigma_{p}^{*}\}\), and intersection points \(\{\gamma_{p}\}\) for \(p=0,1,\dots\). The optimal modularity curve for \(\gamma>0\), given by the upper envelope of the set \(\{Q_{\sigma}(\gamma)\}\), is then given by the piecewise linear function  
\[\tilde{Q}(\gamma)=Q_{\sigma_{p}^{*}}(\gamma)\,,\quad\gamma\in[\gamma_{p}, \gamma_{p+1}). \tag{4}\]  
Of course, this procedure can be started at any selected \(\gamma\) of interest, and the analogous procedure for identifying intersections for decreasing \(\gamma\) could be used to obtain the upper envelope for \(\gamma<0\); but in practice here we restrict our attention to \(\gamma\geq 0\).