esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
To the best of our knowledge, it is the largest available soccer-player-trajectory dataset. Table 2 reports the full statistics of the collected videos, showing that NPSPT is significantly suitable for evaluating an MOT method in soccer scenarios. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Name**} & \multirow{2}{*}{**Scenes**} & \multirow{2}{*}{
\begin{tabular}{c} **Field** \\ **of View** \\ \end{tabular} } & \multirow{2}{*}{**Seat Type**} & \multirow{2}{*}{**Resolution**} & \multirow{2}{*}{**Clips**} & \multicolumn{2}{c}{**Avg.clip**} & \multirow{2}{*}{**Players**} & \multicolumn{2}{c}{**Avg.target**} & \multicolumn{1}{c}{**Open Sources**} \\  & & & & & & & & & Dur(\$) & 1 \\ \hline Hockey [12] & hockey & dynamic & broadcast & 1280 \(\times\) 720 & 84 & 36 & - & - & No \\ \hline SoccerNet-V2 [13] & soccer & dynamic & broadcast & 398 \(\times\) 224 \(\sim\) & 200 & 30 & - & - & Yes \\ \hline Yukun Yang [14] & soccer and basketball & part of the field & multi-static & 1280 \(\times\) 720 \(\sim\) & 5 & 393 & 56 & - & No \\ \hline Basketball-V1 [15] & basketball & part of the field & single static & 960 \(\times\) 540 \(\sim\) & 15 & 120 & 124 & 120 & No \\ \hline Ours & soccer scenes & whole field & single static & 2816 \(\times\) 1408 \(\sim\) & 17 & 120 & 310+ & 110+ & Yes \\ \hline \hline \end{tabular}  
* Open source indicates whether research can obtain data from public sources.  
\end{table} 
Table 1: Statistics of datasets in Sports Scenes. Figure 1: Pannels (**a**–**d**) correspond to the camera images of different venues respectively, illustrating how we installed the cameras. The above pictures only capture the effective area in the camera picture, which can increase the proportion of the target in the picture, thereby reducing the computational overhead and improving the operating efficiency and quality. (**a**) 5-a-side: Set up a high-resolution camera 3 m away and 5 m high from the corner point. (**b**) 7-a-side: Set up a high-resolution camera 5 m away and 18 m high from the post line. (**c**) 8-a-side: Set up a 180\({}^{\circ}\) high-resolution camera 1 m away and 6 m high from the middle-side-line. (**d**) 11-a-side: Set up a 180\({}^{\circ}\) high-resolution camera 15 m away and 15 m high from the middle-side-line. The main contributions of this work are summarized as follows. * We designed and installed single-view video recording systems for several common fields and game types in amateur soccer matches for continuous data collection. * Taking into account all the special cases when implementing automatic tracking in soccer scenarios, we presented a new benchmark dataset for both non-professional soccer players detection and MOT evaluation. * We thoroughly evaluated several state-of-the-art MOT algorithms, and analyzed the conditions when the existing algorithms failed. Comparisons demonstrate that our dataset provides valuable but challenging data for multi-player-tracking.
## 3 Post-Processing Methodologies
### Frequency-Domain Analysis  
The short heat pulse induced by the flash excitation stimulates heat waves over a wide range of frequencies. In this regard, the fast Fourier transform decomposes the thermal dataset into its individual frequency components for enhanced defect detection and quantification. It is well known  
Figure 4: Graphical representation of the considered analysis techniques in the (**a**,**b**) time domain (i.e., dynamic thermal tomography (DTT) and thermal signal area (TSA)) and (**c**) frequency domain (i.e., frequency-domain tomography (FDT) and adaptive spectral band integration (ASBI)).  
that the diffusion length of a heat wave, which also relates to the defect detectability, is frequency dependent [18; 26]:  
\[\mu(f)=\sqrt{\frac{\alpha_{Z}}{\pi\cdot f}} \tag{3}\]  
where \(\alpha_{Z}\) (m\({}^{2}\)/s) is the through-thickness diffusivity. Hence, a lower evaluation frequency \(f\) allows one to detect deeper features in the sample since the thermal diffusion length \(\mu\) increases. In the frequency domain, the phase has the advantage that it is an emissivity-normalized quantity, meaning that undesirable effects of non-uniform heating and non-uniform surface emissivity are significantly reduced [18]. In other words, the calculation of the phase has a similar effect as the standardization procedure in time domain (see Equation (1)).
## 3 Results and Discussion
### Surface Characterization
#### 3.1.2 Fourier-Transform Infrared Spectroscopy  
The molecular structure of PET material abundantly features ester groups. When exposed to an alkaline environment, hydroxide ions (OH-) proactively engage in nucleophilic addition reactions with the carbonyl carbon (C=O) present in the ester bond. This interaction leads to the hydrolysis of the ester bond, giving rise to carboxyl groups (-COOH) and hydroxyl groups (-OH) [32]. These newly formed polar functional groups offer reactive sites for subsequent chemical modifications on the material, thereby expanding its potential applications in biomedical fields. Referring to Figure 3, we noticed that, in comparison to PET, PET-COO--following the hydrolysis of ester groups--exhibits a prominent hydroxyl characteristic absorption peak at 3430 cm\({}^{-1}\). This finding suggests the successful introduction of hydroxyl groups onto the material's surface. Nevertheless, no appreciable alterations were detected at the characteristic absorption peak corresponding to carboxyl groups in PET-COO. This observation could be attributed to the fact that the emergence of carboxyl groups primarily stems from the internal cleavage of ester groups. Given that the infrared spectra of both ester and carboxyl groups are predominantly influenced by the stretching vibration of C=O, distinguishing them spectroscopically poses a challenge. Consequently, we can indirectly ascertain the occurrence of a hydrolysis reaction on the PET material's surface by scrutinizing the characteristic absorption peak of hydroxyl groups. Referring again to Figure 3, subsequent to EDC/NHS activation and crosslinking with collagen, PET-COL displays distinct absorption peaks corresponding to Amid II, Amid I, and Amid A of type I collagen at 1550 cm\({}^{-1}\), 1665 cm\({}^{-1}\), and 3330 cm\({}^{-1}\), respectively. This provides compelling evidence that collagen has been covalently grafted onto the PET material's surface.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{3}{c}{**Elementary Composition**} \\ \cline{2-4}  & **C (\%)** & **N (\%)** & **O (\%)** \\ \hline PET & 71.34 & 0.47 & 27.91 \\ PET-COO & 71.96 & 0.89 & 26.74 \\ PET-COL & 62.78 & 17.36 & 19.47 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Surface elementary composition of the scaffolds from XPS analysis.  
Figure 2: The XPS scan spectra of PET, PET-COO, and PET-COL scaffolds.
## 3 Problem Formulation
### Constraints  
The solution must adhere to the following constraints at each stage \(h\):  
1. Task completion: The entire workload of each task must be allocated to the UAVs: \[\sum_{i=1}^{N_{U}}W^{h}_{i,j}=L^{h}_{j},\quad\forall D_{j}\in\mathcal{D}_{h}.\] (9)
2. Energy limit: The cumulative energy consumption of UAV \(U_{i}\) along its route \(R^{h}_{i}\) must not exceed its maximum energy capacity \(E_{\text{max}}\), unless the UAV recharges at a station. If recharging is required, the UAV must visit the nearest available station \(S_{k}\) when its energy level drops below a predefined threshold. Upon recharging, the energy is restored to \(E_{\text{max}}\) in a time period of \(\frac{E_{\text{max}}-E_{\text{current}}}{r_{\text{charge}}}\), where \(E_{\text{current}}\) is the remaining energy before recharging. Here, \(E_{\text{current}}\) is the remaining battery level of the UAV immediately before recharging.
3. Station capacity: The number of UAVs simultaneously charging at any station \(S_{k}\) must not exceed its capacity \(C_{\text{max}}\).
4. Dynamic task updates: Between stages, new tasks may be introduced (up to a maximum of \(N_{D_{\text{max}}}\) tasks), or the workloads of existing tasks may increase by a rate \(\delta\), reflecting the dynamic nature of the environment. Here, \(\delta\) is the maximum increment in the workload for existing tasks between stages, while \(N_{D_{\text{max}}}\) is the maximum allowable number of tasks in the system.  
In summary, with the predefined objective function (7), the decision variables, and the constraints, given the sets \(\mathcal{U}\) and \(\mathcal{S}\), and the dynamically evolving set \(\mathcal{D}_{h}\) at each stage \(h\), the problem is to determine the task allocation \(W^{h}\) and the routes \(\mathcal{R}^{h}\) that minimize the objective function \(J^{h}\) at each stage, subject to the constraints outlined above. The dynamic arrival of new tasks and the evolving workloads necessitate a replanning strategy, motivating the use of a rolling horizon framework integrated with an adaptive optimization algorithm to ensure timely and efficient mission execution.
## 4 Results
### Micro Destructive Investigations
#### 4.2.3 \(\mu\)-Raman Analysis  
In sample MY33, the occurrence of hematite, Fe\({}_{2}\)O\({}_{3}\), with the Raman bands at 227, 294, 411, 612 and 681 cm\({}^{-1}\)[49] (Figure 7a), vermillion with the Raman signals at 253, 285 and 341 cm\({}^{-1}\)[50] (Figure 7b), quartz (SiO\({}_{2}\)) with the bands at 130, 206, 266, 356, 405 and 466 cm\({}^{-1}\)[51] (Figure 7c) and anatase (TiO\({}_{2}\)) with the Raman bands at 145, 397 and 641 cm\({}^{-1}\)[52](Figure 7d) was attested by \(\mu\)-[PERSON]. With regards to the occurrence of vermillion, since the most intense Raman bands of this pigment have not been blue-shifted about 5-7 cm\({}^{-1}\), one may suggest that cinnabar (\(\alpha\)-HgS) occurs in the paint layer instead of metacinnabar (\(\beta\)-HgS) [53].  
The presence of anatase should not be considered as a modern intervention in the paint layer [54]. As [PERSON] et al. [55] have shown, anatase is a good Raman scatterer and occurs as a minor impurity in clays and silicates, e.g., quartz.  
\(\mu\)-Raman also showed the carbon black was present in the MY44 sample with the two broad and strong bands at about 1360 and 1580 cm\({}^{-1}\) (Figure 8). While the band at 1360 cm\({}^{-1}\) is attributed to stretching vibrations in the planar graphite structure (G band), the band at 1580 cm\({}^{-1}\) appears as a result of defects of graphite structure or disorder band (D band) [56]. The full width at half maximum (FWHM) for the D and G bands (\(\omega\)D and \(\omega\)G, respectively) was about 230 and 115, respectively, and the intensity ratio of D and G bands (ID/IG) was about 0.9. These values show that the carbon black that occurred in the paint layer is reasonably similar to lampblac, whose Raman spectrum has been studied by [PERSON] et al. [57]. The absence of additional Raman bands due to the compound associated with carbon black as impurities [58] may support this idea. One should, however, note that the excitation source used by [PERSON] et al. and [PERSON] et al. (i.e., laser line 514 nm) is different from the one used in this study (i.e., laser line 632.8 nm). Thus, slight to moderate Raman shifts might have occurred in the Raman spectrum of the pigment under study with respect to the standard carbon blacks reported by [PERSON] et al. and [PERSON] et al. [57; 58].
# Machine-Learning-Based Path Loss Prediction
## 1 Introduction
This work is not only one of the few studies examining V2V communication in an H environment but also stands out for its use in various machine learning models, including ANN, RF, LR, GB, SVR, and AB, and their comparisons with traditional path loss models. In summary, the main motivation of this study is to highlight the potential of machine learning models in this domain by evaluating the performance of different machine learning models, particularly regression models, and comparing them with empirical models. This study contributes to the existing literature and aims to suggest a model that can be effectively used in practical applications by comparing the advantages and disadvantages of empirical models with machine learning techniques in path loss prediction. This paper is structured as follows to provide a thorough understanding of how machine learning approaches can affect path loss prediction and how to increase the effectiveness of V2V communication systems. A comprehensive review of the existing literature and the main motivations for this study are presented in Section 1. The theory of traditional and machine-learning-based models for path loss estimation is briefly explained in Section 2. Section 3 discusses the design and implementation of the models and explains the methodology, data-gathering procedures, model selection, and algorithm configurations. The practical findings, numerical performance comparisons, and suggestions for further research are presented in Sections 4 and 5, which also summarize the conclusions of the study.
# Precise Orbit Solution for Swarm Using Space-Borne GPS Data and Optimized Pseudo-Stochastic Pulses
## 2 Reduced Dynamic Orbit Determination  
Space-borne GPS for Low Earth Orbiter (LEO) POD is the most effective technology that offers continuous space-borne GPS data. There are three main approaches to LEO precise orbit solutions making use of space-borne GPS observation. The first approach is pure dynamic OD [18; 19], which depends on highly precise force models and corrects a few dynamic parameters as part of the LEO POD. The second approach is kinematic OD [20], which is different from satellite dynamics because the kinematic orbit solution process demands only pure geometric information offered by the GPS data. The third approach is reduced-dynamic OD [21; 22; 23; 24; 25; 26], which makes the best of the dynamic and geometric information. The outstanding reduced-dynamic approach estimates the pseudo-stochastic parameters [27] (e.g., pseudo-stochastic pulses and empirical accelerations) to absorb force model errors.  
The outstanding reduced-dynamic approach is chosen for Swarm POD. Swarm orbit strategy is presented in detail in the following section. The orbit quality of Swarm satellite in the ZD reduced-dynamic POD depends on the quality of force models, the POD strategies (i.e., pseudo-stochastic pulses optimization), and the quality of GPS observations applied to the ZD reduced-dynamic solutions. Proper force models should be selected for precise orbit determination of Swarm satellites. In this paper, some considered force models include gravity field model EIGEN-684, solid tide model IERS2010, and tide model FES2004. Satellite attitude control file comes from the ESA. Solar pressure and other unmodeled force errors are replaced by pseudo-stochastic pulse parameters. During the LEO orbit determination, it is not necessary to set stochastic velocity pulses in each observation epoch, but rather to set one or a set of stochastic pulses at a certain time interval, so it is called pseudo-stochastic pulses. The velocity variations can be constrained to suitably small values by means of pseudo-observed values of the velocity change introduced in advance [22]. Therefore, pseudo-stochastic parameters are expressed by pseudo-stochastic pulses in R, T and N directions with a time interval and a priori standard deviation (STD). Table 1 shows the model standards adopted for Swarm ZD reduced-dynamic POD.
# Effective Fabric Defect Detection Model for High-Resolution Images
## 4 Conclusions  
In this study, we proposed a defect detection model for high-resolution fabric images with complex backgrounds based on Cascade R-CNN. The main findings of our work are discussed below.  
The high resolution of fabric images and the large span of defect size lead to extremely poor detection results. Thus, a block recognition algorithm was proposed in this study that yielded a mAP of 50.33%. To solve the problem of a small number of defects resulting in poor detection results, we proposed a multi-morphology data augmentation method to effectively generate defects. This data augmentation method achieved excellent improvement and increased the mAP by 20%. To enhance the feature extraction ability of the backbone network, we replaced the 3 \(\times\) 3 convolutional layer in Stage 2 with the SAC module, thus increasing the mAP by 1.45%. Finally, we optimized the up-sampling algorithm and network structure of the FPN module to solve the problem of missing some small defects. The experimental results demonstrated that the detection results for small defects improved, and the mAP increased by 3.55%.  
In the actual production process of the fabric, the detection time of a single image is required to be within 0.5 s. The improved model could detect defects in a high-resolution image in 0.37 s, the time for Faster-RCNN to detect a single image is 0.25 s, and the time for YOLO V3 to detect a single image is 0.12 s. Although the detection speed of the improved model is slower than that of the other two mainstream models, it has met the requirements of detection speed. Furthermore, we introduced a series of improvements on the baseline model to perform deep learning-based fabric defect detection. Table 1 summarizes the detection results of different experiments, and the final test image is shown in Figure 22; The mAP value has increased from 49.44% to 75.33%, which is an increase of 52.37%.  
Figure 21: Detection results of comparison algorithm.  
In future work, we plan to focus on constructing a fabric defect dataset while attempting to decrease model parameters to increase the detection speed. Moreover, we plan to detect fabric images with a pattern not included in the training dataset.  
Figure 22: Detection results.  
**Author Contributions:** Conceptualization, L.L. and Q.L.; methodology, L.L., Q.L. and Z.L.; software, Q.L.; validation, Z.L.; formal analysis, Z.L.; investigation, Z.L.; resources, L.L. data curation, Q.L.; writing--original draft preparation, L.L.; writing--review and editing, L.L. and Q.L.; visualization, Z.L.; supervision, L.X.; project administration, L.X. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available upon request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Validation and Verification Aspects
### Cost and Limitations of V&V Techniques  
Verification and validation (V&V) techniques are crucial in ensuring the quality and reliability of software systems for IoT and cloud computing systems. However, these systems come with unique challenges and limitations that must be considered when implementing V&V techniques.  
One cost of V&V techniques for IoT and cloud systems is the sheer scale of these systems. These systems can involve thousands or even millions of interconnected devices, making it difficult to perform comprehensive testing and validation. Additionally, the het erogeneity of these systems, with devices from different manufacturers and with different capabilities, can complicate the verification and validation process.  
Another cost of V&V techniques for IoT and cloud systems is the need for specialized testing environments and tools. These systems require highly specialized tools and environments for testing and validation, such as simulators, emulators, and testbeds. These tools can be expensive to implement and maintain and may require highly skilled personnel to operate effectively.  
Furthermore, there are limitations to the effectiveness of V&V techniques for IoT and cloud systems. One limitation is the difficulty of testing for security and privacy vulnerabilities. These systems often involve sensitive data and critical infrastructure, making security and privacy a top priority. However, it is challenging to test for all possible security and privacy vulnerabilities, especially as new threats emerge constantly.  
Another limitation is the challenge of testing for real-time and low-latency requirements. IoT and cloud systems often require real-time performance and low-latency communication, which can be difficult to test and validate. These systems may involve complex interactions between devices and services, making it challenging to ensure that they meet these requirements.  
In conclusion, V&V techniques are crucial for ensuring the quality and reliability of software systems for IoT and cloud computing systems. However, these systems have unique challenges and limitations that must be considered when implementing V&V techniques. By understanding these costs and limitations, software developers can implement V&V techniques more effectively and efficiently, ultimately leading to higher-quality software systems.
# Target Detection Technology of Mechanical, Electrical, and Plumbing Components Based on CV
## 4 Results
### Contour Extraction  
After successfully obtaining the pipeline segmentation results from the construction site photographs and the BIM projections, the contours are extracted from both sets of results. This process is critical for comparing the differences between the two segmentation outcomes. It aims to validate whether the on-site pipelines align with the BIMs designed during planning. The performance of the edge detection algorithms is evaluated using the F-score metric, which is formulated as \(F=2\times\frac{P\times R}{P+R}\). Precision (\(P\)) is the proportion of correctly detected edges among all predicted edges, and recall (\(R\)) is the proportion of correctly detected edges among all actual edges. The F-score effectively balances precision and recall, comprehensively measuring the edge detection performance (Table 1). Notably, this study further validates the effectiveness of the proposed method by challenging it against [13; 17; 32; 33]. Figure 6 visualizes examples of contour extraction, where each subfigure presents the extraction results of different methods. This visual comparison demonstrates that the edge maps obtained using the proposed method exhibit superior accuracy and clarity while significantly reducing noise interference.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **Canny** & **SE** & **OEF** & **Pidinet** & **Ours** \\ \hline P & 0.801 & 0.751 & 0.851 & 0.902 & 0.92 \\ R & 0.59 & 0.684 & 0.616 & 0.616 & 0.616 \\ F-score & 0.691 & 0.713 & 0.725 & 0.769 & 0.795 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative results on the our dataset.  
Figure 5: Segmentation results. (**a**) Construction mask; (**b**) Projected mask.
## 1 Introduction
Consequentially, the multiple viewing angles provided by multiple HAPs improve the availability and rate of the HAP-based network. If properly designed, additional HAPs can also improve the total system capacity of the network [14]. Multiple-input multiple-output (MIMO) techniques, which make use of the spatial dimension by utilizing multiple antennas at the transmitter and receiver, have been proved to be efficient solutions for providing higher data throughput and/or link reliability in WSNs. Due to the very close distance between the antennas in a single HAP, the use of traditional MIMO techniques cannot overcome large scale fading. Additionally, distributed antenna techniques can be introduced to a HAP constellation to form a distributed virtual multiple-input single-output (V-MISO) or virtual MIMO (V-MIMO) HAP network, which will further improve diversity performance to overcome obstruction by buildings and other tall objects, and strengthen the spectrum utilization and reliability of the user data transmission [15].