esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model
### Network Model  
We consider a three-tier SEC network architecture as illustrated in Figure 1, comprising \(K\) IoTDs, \(M\) UAVs, and \(N\) LEO satellites, denoted by the sets \(\mathcal{K}=1,2,\ldots,K\), \(\mathcal{M}=1,2,\ldots,M\), and \(\mathcal{N}=1,2,\ldots,N\), respectively. In this MLLM-enabled SEC framework, resource-constrained IoTDs are not capable of processing the MLLM task locally; thus, the MLLM task requests are offloaded to UAVs and LEO satellites. UAVs deploy smaller MLLMs, offering accuracy \(A_{m}\), for processing tasks, while LEO satellites, equipped with high-performance computing capability, run larger MLLMs, achieving a higher inference accuracy \(A_{n}\). For clarity, MLLMs deployed on UAVs will be referred to as MLMM \(m\), while those deployed on LEO satellites will be referred to as MLMM \(n\).  
The system time is discrete into \(T\) time slots; within each time slot \(t\), IoTDs can offload MLLM inference tasks to either UAVs through direct wireless links or to LEO satellites  
Figure 1: The architecture of MLLM-enabled UAV-assisted SEC network.  
via ground-to-satellite communication. This hierarchical architecture enables flexible task distribution based on accuracy requirements, latency constraints, and resource availability.  
While some IoTDs may have the ability to perform partial processing of smaller MLLM tasks, this work assumes that all MLLM tasks are offloaded to UAVs or LEO satellites. This assumption is grounded in the resource constraints of typical IoTDs with limited computational power, memory, and energy, which make local processing of large-scale MLLM or quantized MLLM tasks impractical, especially in remote or challenging environments like offshore monitoring or disaster response. By assuming full offloading, we simplify the system model and focus on optimizing task allocation across UAVs and LEO satellites, though extending the framework to include partial local processing remains an interesting avenue for future work.
## 2 Related Work
### GPS-Based Contact Tracing  
In GPS-based methods, contact tracing is performed based on the user's location information obtained through GPS. [PERSON] et al. [15] proposed a contact tracing method that utilized recent GPS location histories and a private set intersection protocol. [PERSON] et al. [5] conducted contact tracing through a spatial transmission trajectory generated using GPS data from contact tracing app users. However, accuracy and privacy issues have been raised for location-based contact tracing methods. Moreover, GPS does not generally operate properly in indoor spaces, and its performance is poor in outdoor spaces, such as central business districts with high-rise buildings [2]. Recognizing the limitations of traditional GPS-based contact tracing that prioritizes distance, our research emphasizes the importance of shared movement paths. Our approach aims to provide a more comprehensive reflection of the transmission characteristics of the viruses, enhancing the predictive capability of contact tracing.
# PSO-Based Voltage Control Strategy for Loadability Enhancement in Smart Power Grids
## 6 Numerical Examples and Results  
The proposed PSO-based voltage control algorithm is applied to two sample test systems: IEEE 9-bus and IEEE 118-bus. The grid data involving branch parameters and bus information are given in [37]. In this research, the PSO settings are selected as below:  
(1) population size = 100; (2) maximum iteration = 200; (3) learning coefficients: \(c_{1}=2.05\) and \(c_{2}=2.05\); (4) \(\psi\) in Equation (21) is set to 0.729.  
Suppose that there are \(n\) load buses in the test system, the performance of the control strategy is measured through the root-mean-square (RMS) defined as  
\[y^{RMS}=\sqrt{\frac{\sum\limits_{i=1}^{m}\left(y_{i}^{a}-y_{i}^{b}\right)^{2}} {n}} \tag{22}\]  
where \(y_{i}^{b}\) and \(y_{i}^{a}\) for each \(i=1,\,2,\cdots,n\) are the load bus voltage before and after control, respectively. Note that smaller \(y^{RMS}\) gives better performance. In order to illustrate the system loadability enhancement after the proposed control strategy, the open-source software PSAT [38] is used to generate the P-V curves for calculating the _VSM_.
## 3 Materials and Research Methods
### Assessment of User \(QoE_{i}\) with a Mathematical Model  
The subjective approach to assessing the specific value of \(QoE_{i}\), as the first step, implies the identification of KQI that directly affect the quality of user experience [11]. The following expression represents the generic relation between the \(QoE\) value and identified indicators:  
\[QoE=f(I_{E}^{1}\dots,I_{E}^{K}) \tag{1}\]  
where \(I_{E}^{j}\) denotes the \(j\)th indicator (\(j=1\dots\ K\)). In practice, a linear relationship or function \(f\) is often assumed between \(QoE\) and \(I_{E}^{j}\), which is also the case in this research. In order to determine the specific value of \(QoE_{i}\), it is necessary to assign certain weighting factors to the identified indicators. Thus, most often, \(QoE\) can be expressed by a linear combination of weight indicators, which is shown by the following expression [11]:  
\[QoE=\sum_{j=1}^{K}w_{j}\cdot I_{E}^{j} \tag{2}\]  
where \(w_{j}\) represents the weighting factor assigned to the indicator \(I_{E}^{j}\). In this research, both satisfaction indicators and dissatisfaction indicators were taken into account, and memorized in the subjective user experience for which each \(QoE_{i}\) was determined when creating a linear mathematical model.
###### Abstract  
Accurate measurement of running parameters, including the step length (\(SL\)), step frequency (\(SF\)), and _velocity_, is essential for optimizing sprint performance. Traditional methods, such as 2D video analysis and inertial measurement units (IMUs), face limitations in precision and practicality. This study introduces and evaluates two methods for estimating running parameters using real-time kinematic global navigation satellite systems (RTK GNSS) with 100 Hz sampling. Method 1 identifies mid-stance phases via vertical position minima, while Method 2 aligns with the initial contact (IC) events through vertical velocity minima. Two collegiate sprinters completed a 400 m sprint under controlled conditions, with RTK GNSS measurements validated against 3D video analysis and IMU data. Both methods estimated the \(SF\), \(SL\), and _velocity_, but Method 2 demonstrated superior accuracy, achieving a lower RMSE (\(SF\): 0.205 Hz versus 0.291 Hz; \(SL\): 0.143 m versus 0.190 m) and higher correlation with the reference data. Method 2 also exhibited improved performance in curved sections and detected stride asymmetries with higher consistency than Method 1. These findings highlight RTK GNSS, particularly the velocity minima approach, as a robust, drift-free, single-sensor solution for detailed per-step sprint analysis in outdoor conditions. This approach offers a practical alternative to IMU-based methods and enables training optimization and performance evaluation.  
s 2025251073  
2  
Academic Editor: [PERSON]  
Received: 28 December 2024  
Revised: 24 January 2025  
Accepted: 9 February 2025  
Published: 11 February 2025  
**Citation:**: [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] Parameter Analysis in 400 m Sprint Using Real-Time Kinematic Global Navigation Satellite Systems.  
_Sms_**2025**, 25, 1073. [[https://doi.org/10.3390/s20541073](https://doi.org/10.3390/s20541073)]([https://doi.org/10.3390/s20541073](https://doi.org/10.3390/s20541073))  
**Correction Statement:**: This article has been republished with a minor change. The change does not affect the scientific content of the article and further details are available within the backchanter of the website version of this article.  
**Copyright:**: 62 2025 by the authors.  
License MDPI, Basel, Switzerland.  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license  
([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# An On-Orbit Task-Offloading Strategy Based on Satellite Edge Computing
## 3 Model Introduction and Problem Analysis
### Mapping Analysis  
#### 3.4.1 Node Mapping  
We define \(m\big{(}o_{i},v_{j}^{n}\big{)}=1\) to mean that the subtask \(o_{i}\) is assigned to the \(n\)th time slot \(j\)th satellite node for computational processing. When \(m\Big{(}o_{i},v_{j}^{n}\Big{)}=0\), it indicates that the variable is not allocated. For all subtasks and satellite nodes of the complete time slot, the mapping connection can then be written as a decision matrix \(M\)  
\[M=\left[\begin{array}{cccc}m\big{(}o_{1},v_{1}^{1}\big{)}&\cdots&m\big{(}o_{ 1},v_{d}^{1}\big{)}&\cdots&m\big{(}o_{1},v_{d}^{N}\big{)}\\ m\big{(}o_{2},v_{1}^{1}\big{)}&\cdots&m\big{(}o_{2},v_{d}^{1}\big{)}&\cdots&m \big{(}o_{2},v_{d}^{N}\big{)}\\ \vdots&\vdots&\ddots&\vdots&\vdots\\ m\big{(}o_{p-1},v_{1}^{1}\big{)}&\cdots&m\big{(}o_{p-1},v_{d}^{1}\big{)}&\cdots &m\big{(}o_{p-1},v_{d}^{N}\big{)}\\ m\big{(}o_{p},v_{1}^{1}\big{)}&\cdots&m\big{(}o_{p},v_{d}^{1}\big{)}&\cdots&m \big{(}o_{p},v_{d}^{N}\big{)}\end{array}\right]. \tag{2}\]  
Any node \(m\Big{(}o_{i},v_{j}^{n}\Big{)}\in M\) needs to satisfy  
\[m\Big{(}o_{i},v_{j}^{n}\Big{)}\in\{0,1\},\forall o_{i}\in O,\forall v_{j}^{n }\in V, \tag{3}\]  
Figure 3: DAG model.  
\[\sum_{n=0}^{N}\sum_{j=0}^{d}m\left(o_{i},v_{j}^{n}\right)=1,\forall o_{i}\in O. \tag{4}\]
## 5 Conclusions  
For millimetre waves, a printed high-gain Yagi antenna was developed. The antenna's straightforward design makes it easy to fabricate. The antenna is lightweight, low-cost, and low-profile. The planned antenna measures 10.8 \(\times\) 10.01 mm\({}^{2}\) in total. The antenna functions according to the conventional Yagi radiation theory. The developed antenna has a maximum return loss of \(-\)30 dB at 70.6 GHz and excellent impedance matching. It was found that as the electrical width of the effective aperture rises, so does the gain. How several of the parameters changed was examined, which had an impact on how gain improved. The heterogeneous substrate is another method that is used and is in charge of further improving gain, directivity, bandwidth, and efficiency. The developed antenna has an impedance bandwidth of 15.17 percent and 10.72 GHz. The antenna has a high gain of 11.8 dB and a high directivity of 12.1 dBi, respectively. The V-Band of mmW frequency spectrum, i.e., 50-75 GHz, is concentrated for the design's band of operation. The 50-75 GHz frequency spectrum of mmW is undoubtedly planned in 5G intended for Device-to-Device (D-2-D) transmission. This is expected to be utilized in ad hoc communication around short distances. Furthermore, this spectrum could be used for high-frequency short-range WLAN, WHDMI, and WPAN.  
Conceptualization, A.A., F.A., K.A. and M.A.; methodology, M.A.S., A.A., F.A., K.A. and M.A.; software, M.A.S., A.A., F.A., K.A. and M.A.; validation, M.A.S., A.A., F.A., K.A. and M.A. formal analysis, M.A.S., A.A., F.A., K.A. and M.A.; investigation, M.A.S., A.A., F.A., K.A. and M.A.; resources, F.A.; data curation, M.A.S., A.A., F.A., K.A. and M.A.; writing--original draft preparation, M.A.; writing--review and editing, M.A.S., A.A., F.A., K.A. and M.A.; visualization M.A.S., A.A., F.A., K.A. and M.A.; supervision, F.A.; project administration M.A.S., A.A., F.A., K.A. and M.A.; funding acquisition, A.A., K.A.; All authors have read and agreed to the published version of the manuscript.  
No funding is received for this paper.  
The data are available as per request.  
The authors declare no conflict of interest.  
Figure 12: Investigation of gain as function of frequency.
# Memory-Efficient Batching for Time Series Transformer Training: A Systematic Evaluation
## 1 Introduction
* **Memory-Optimized Batching Framework:** We present a memory-efficient batching framework for training time series foundation models on GPUs, utilizing a view-based sliding-window technique to eliminate redundant data replications. * **Comprehensive analysis across multiple training dimensions:** We provide an in-depth evaluation of our approach under varying sequence lengths, batch sizes, feature dimensions, and model architectures. This is the first study to systematically examine the end-to-end impact of batching strategies across the time series training stack. * **Scalable Optimization for Distributed Training**: We demonstrate the scalability of our memory optimization techniques in distributed training environments, enabling efficient training of large-scale time series models on GPU clusters. The rest of this paper is organized as follows: Section 2 discusses background and related work. Section 4 provides an overview of our system. Section 5 details a set of performance experiments and results from running our optimizations against the traditional batching baseline, including tests on both transformer-based and linear models. Section 6 analyzes the experimental results and Section 7 describes future works.
## 3 Results  
### The _eDNA-Container App_  
The _eDNA-container app_ is based on a core _QIIME2_ pipeline, with data reformatting carried out using _python_ scripts [13]. The only software requirement is that the free application _Docker Desktop_ is installed on the host system. Pipeline execution is managed by _Snakemake_ with the entire application packaged in a _Docker_ image so that it is cross-platform and will run reproducibly across different computer frameworks [24]. The image can be obtained freely from the _Docker_ hub using the search tag \"dwheelerau/edna\" or using the _Docker pull_ command from a terminal window. Advanced users with access to Linux can use the pipeline independently of _Docker_ by cloning the _conda_ environment from the supplied environment file and executing the _snakemake_ workflow manually. A _Flask_ app is used as a GUI front-end that can be accessed using a standard web-browser with no data shared across the internet. An extensive user-friendly guide targeted at ecologists and laboratory staff is provided with the package (Supplemental File S2).  
Figure 1: A summary of the _Snakemake_ rules used to execute the _eDNA-container app_ pipeline. Each box represents a _Snakemake_ rule that runs a specific step in the pipeline as indicated by the description. The rules are executed in the order indicated by the arrow.
# Detecting Image Forgery over Social Media Using U-NET with Grasshopper Optimization
## 2 Materials and Methods
### Models and Architecture
#### 2.2.2 Hyperparameter Tuning Using Grasshopper Optimization Algorithm (GOA)  
In this section, the U-NET network with the grasshopper optimization method is designed to detect image forgery.  
Through a series of experiments and evaluations, the effectiveness of the proposed method will be demonstrated. Figure 5 shows the implementation of the grasshopper optimization algorithm to optimize the hyperparameters of the U-Net network. The algorithm involves several steps, including initializing the search agents, calculating the merit of each grasshopper, assigning optimal hyperparameters to U-NET networks, evaluating the U-NET networks using mean squared error (MSE), updating the position of grasshoppers, and checking whether the stop condition is met. If the stop condition is met, the best U-NET network with the minimum MSE is returned, and the results are processed and visually compared. If the stop condition is not met, the merit of each grasshopper is calculated again, and the algorithm continues until the optimal hyperparameters are found. The grasshopper optimization algorithm is used to explore the search space of hyperparameters and find the optimal combination that leads to the best U-NET network.
# A Vulnerability Scanning Method for Web Services in Embedded Firmware
## 5 Evaluation  
### Experiment Setup  
We have developed a prototype system utilizing Python on the foundation provided by the reverse-engineering tool IDA [26]. The front-end keyword extraction module is implemented based on SaTC [12], while the hazardous-function-filtering module utilizes the IDA plugin VulFi [27]. The taint analysis engine is constructed atop angr [24]. We evaluate WFinder in real-world embedded systems and answer the following questions:  
Q1: Can SaTC find real-world vulnerabilities? (Section 5.2.) How effective is it compared to a state-of-the-art tool? (Section 5.2.)  
Q2: Can WFinder accurately detect boundary binary functions and external data introduction functions? (Section 5.3.)  
Q3: Do the detection of external data input points and the filtering of hazard functions provide positive significance? (Section 5.4.)  
Q4: How efficient and accurate is our taint analysis? (Section 5.5.)  
**Dataset.** As shown in Table 3, the dataset for this study is sourced from IoT device firmware provided online by five vendors: Tenda [28], D-Link [29], Netgear [30], ToToLink [31], and Vivotek [32]. These firmware architectures include ARM and MIPS, the mainstream architectures used in embedded devices. As shown in Table 3, we selected ten firmware samples, six of which use the ARM architecture and four of which use the MIPS architecture.  
**Existing Tool.** We compared our tool with SaTC, the state-of-the-art static bug hunter for embedded systems. It searches for input entries in back-end binary files by analyzing keywords shared between the front end and back end and utilizes a taint analysis to track data flow to detect vulnerabilities.  
**Bug Confirmation.** Each alert generated by WFinder contains a call trace from the starting point to the receiving function, as well as corresponding input keywords. We distinguish between true and false positives based on whether the path is reachable. If we can manually generate a proof of crash (PoC) based on alerts and validate it on physical devices, we consider it a real bug.  
Figure 5: Function call graph. The figure shows the possible function call paths and data flow paths from Func-A, which calls the external data import function, to the potentially dangerous function. Finc_B called Func_D before calling Finc_sink1. Func_C and Func_E contain a pair of function calls for system configuration functions.
## 3 Part I: Localization of Real and Virtual Sources in the Absence of Visual Cues
### Experiment Design
#### 3.1.4 Response Method  
The localization task consisted of providing information about the perceived azimuth, elevation and distance of each real or virtual source. The GUI shown in Figure 4 was used to collect the responses. This GUI was presented on the tablet monitor positioned in front of the subject. For collecting the azimuth responses, the GUI showed the head as seen from above, with a circle around it. To enter in the perceived source azimuth, subjects could click on any point on this circle. For collecting the elevation responses, the GUI showed an equivalent depiction of the head as seen from the side. To give the perceived source distances, subjects were supplied with a real reference sound source (the same loudspeaker type as mounted on the loudspeaker arc), which was positioned at a fixed position in front of the subject outside the acoustically transparent tent (see Figure 3b). By clicking the \"Play\" and \"Reference\" buttons, subjects could switch between the (real or virtual) target source and the reference source, respectively. In TestVR, subjects were asked to take off the headphones while listening to the reference source. Subjects had to judge the perceived distance using an ordinal scale from 0 to 4, corresponding to perception (0) in the head, (1) outside but near the head, (2) outside the head and closer than the reference, (3) outside the head and at the reference distance or (4) outside the head and at a further distance than the reference, inspired by similar scales commonly used in studies investigating externalization of virtual sources [19; 20]. The reference and target sources were adjusted to have the same level at listener position (55 dB SPL). Subjects had no information about the exact position of the reference source and were instructed not to consider this source as a reference for azimuth and elevation, but only for the perceived distance. The \"Reset\" button was used during TestVR to reset the head tracker. For TestReal, this button was omitted from the GUI.  
Figure 4: GUI for collecting the responses on source azimuth, elevation and distance. By clicking the “Reference” button, subjects could switch to the signal coming from the reference source in the room, to give the perceived source distance between 0 (inside the head) and 4 (outside the head and at a further distance than the reference). The “Reset” button was active only during TestVR and was used to reset the head tracker. The frontal direction of azimuth = 0\({}^{\circ}\) and elevation = 0\({}^{\circ}\), corresponding to the forward head orientation, was marked with a colored point on the GUI.
# A Study of MANET Routing Protocols in Heterogeneous Networks: A Review and Performance Comparison
## 1 Introduction
### Heterogeneous MANET Environment  
A heterogeneous MANET (HMANET) is like a regular MANET, except that all the devices/nodes are connected to the network with varying capabilities, including the mobility, data transmission rates, distance coverage, battery life, and power consumption [13; 14; 15; 16]. The heterogeneity of the nodes can be complex if multiple heterogeneous devices are connected to the network for communications. Using the OMNeT++ simulation tool, a heterogeneous MANET model is developed to study the impact of heterogeneous nodes on the performance of various MANET routing protocols.
## 3 Results and Discussion
### Simulation Work  
All drops are allowed to grow by direct condensation at a slow rate depending on their size and \(\Delta T\). At the same time, new drops are generated with a size of 0.2 \(r_{e}\) at every time step to ensure the dominance of coalescence. Condensing surfaces for DWC within the hybrid surface are illustrated in Figure 5 for \(R_{D}\) values of 0.125, 0.25, 0.5, 0.75, and 1 in addition to a full-DWC surface with a \(10\times 10\) mm domain. In all surfaces, larger drops are developed due to successive coalescence that subsequently causes surface renewal. Figure 5 shows areas where surface renewal is caused by sweeping, e.g., within the bold rectangle, and others caused by merging, e.g., within the bold circles.  
The simulation work is a reproductive process of drop growth and surface renewal. A demonstration for DWC within a hybrid surface with \(R_{D}\) equaling (a) 0.5 and (b) 1 is shown in Figure 6. In initial condition phase A, all drops are randomly distributed without overlapping. When the simulation starts, the surface becomes more populated due to the generation of new drops and direct condensation, as indicated in phase B. Larger drops  
Figure 5: Condensing surfaces of DWC within a hybrid surface with different maximum diameter to DWC region width ratios, \(R_{D}\), in comparison with the full-DWC domain. Swept areas are indicated by bold rectangles, and merged areas are indicated by bold circles.  
started to appear due to coalescence, and drop merging became obvious in phase C until the surface occupied by drops was minimal, as shown in phase D. Surface renewal was achieved in both cases, but we observed that it was faster at a higher \(R_{D}\) value. Comparing cases (a) and (b) in Figure 6, the drops' population density is close in each phase. However, the time cycle taken to reach phase D is shorter in case (b). This is because drops merging sideways into filmwise regions are more evident.