esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
### Part-Based Re-ID  
Part-based Re-ID is a pedestrian re-identification strategy grounded in part-based analysis, which segments pedestrian images into distinct sections through techniques such as image cropping, pedestrian segmentation, and keypoint localization. Extracting and fusing features from these different parts enhances feature expressiveness, thereby boosting the performance of pedestrian re-identification. These methodologies effectively address the limitations of global features and exhibit heightened robustness against variations in pose and illumination. Ref. [6] introduces a Part-based Convolutional Baseline (PCB), partitioning images into six segments and employing Refined Part Pooling (RPP) to mitigate anomalies arising from uniform segmentation, a practice widely referenced in subsequent studies. [PERSON]. [23] integrates this concept into Graph Convolutional Networks, proposing the Part-Guided Graph Convolution Network (PGCN), which extracts local features from pedestrian parts, constructs adjacency matrices based on similarity to explore relationships among local features of identical parts across different images and diverse parts within the same image, enriching pedestrian feature representation. Another study, [24], presents a multi-branch network that uniformly divides feature maps horizontally to extract fine-grained part features, followed by fusion with global and attribute features via a multitask learning strategy, yielding a more comprehensive pedestrian feature profile. [PERSON]. [25] applies part features to unsupervised pedestrian re-identification, devising a Part-based Pseudo Label Refinement (PPLR) framework. This approach refines global feature pseudo labels using ensemble predictions of part features, mitigating noise in pedestrian feature clustering and reducing the impact of noisy labels. Lastly, [26] proposes a Part-based Representation Enhancement (PRE) network to tackle pedestrian re-identification under occlusion. This network partitions pedestrian images into seven sections, leveraging a Part Relation Aggregation module for autonomous exploration of the subject's visibility. It reinforces the uniqueness of local features through feature completion and reverse fusion strategies. At the testing stage, concatenating global features with reconstructed local features yields high-performance identification results.
# Extraction and Detection of Surface Defects in Particleboards by Tracking Moving Targets
## 2 Algorithm Theory
### The Median Flow  
The median flow is derived from the tracking module in the tracking-learning-detection (TLD) algorithm [24; 25; 26; 27]. According to the principle that a good tracking algorithm should have forward-backward consistency, i.e., in a chronological or antichronological order, the tracking results should be the same. We can define the forward-backward (FB) error of an arbitrary tracker as follows:  
\[FB(\,T_{f}^{k}\Big{|}S)=distance(T_{f}^{k},T_{b}^{k})=\|X_{t}-\hat{X}_{t}\| \tag{9}\]  
where, \(S=(I_{t},I_{t+1},\ldots,I_{t+k})\) denotes the processed image sequence; \(X_{i}\) is the position of the feature point \(P\) at time \(t\); \(T_{f}^{k}=(X_{t},X_{t+1},\ldots,X_{t+k})\) and \(T_{b}^{k}=(\hat{X}_{t},\hat{X}_{t+1},\ldots,\hat{X}_{t+k})\) denote the chronological order and the antichronological order tracking results, respectively; and \(k\) is the number of the current image frame.  
Equation (9) shows that the FB error of a tracker is essentially the Euclidean distance between the initial position \(X_{i}\) and the predicted position \(\hat{X}_{t}\) of the feature point \(P\). The calculation diagram of FB error is shown in Figure 1.  
In the process of tracking, the FB errors of each feature point are calculated, and the point whose FB error is less than the median value of the sum of the total FB errors is taken as the effective tracking feature point. Finally, according to the coordinate changes of these points, the position of the target boundary box in the image at time \(t+1\) can be calculated.  
\[r_{i}=\frac{\|X_{i}^{t+1}-X_{j}^{t+1}\|}{\|X_{i}^{t}-X_{j}^{t}\|} \tag{10}\]  
\[\overline{R}=\frac{\sum_{i=1}^{n}r_{i}}{n} \tag{11}\]  
\[S_{t-1}=S_{t}\times r_{i} \tag{12}\]  
where \(r_{i}\) denotes the change rate of the distance between point \(X_{i}^{t}\) and point \(X_{j}^{t}\) in the two adjacent frames; \(\overline{R}\) denotes the average change rate of \(r_{i}\); and \(S_{t}\) denotes the size of the target boundary box in image \(I_{t}\), which is the number t frame image in the video sequence.  
In this study, the median flow algorithm was used to amend the tracking results of the KCF to make the tracking results more accurate and ensure the reliability of subsequent calculation results of the target defect parameters.
## 4 Simulation Results
### Number of Rounds  
We first explore the number of rounds under different percentages of node death. GB-PEDAP, TBEEP, GSTEB, and PEDAP are simulated, respectively, to observe the number of rounds executed when the rate of node death reaches 1%, 20%, 40%, 60%, 80%, and 100%. In Figure 3, the number of rounds executed by the proposed GB-PEDAP is better than that of TBEEP, GSTEB, and PEDAP. The number of rounds executed by GB-PEDAP is approximately 1.2 rounds of TBEEP, 1.3 rounds of GSTEB, and 1.5 rounds of PEDAP. After TBEEP is executed for a period of time, the selection of the head is progressively farther away from the BS, increasing the energy consumption of the cell head, and leading to a quicker cell death. The path of GSTEB is mainly decided by the distance; thus, the energy consumption of the sensor node is not considered. PEDAP does not consider the remaining energy of current sensor nodes when constructing the tree structure, leading to  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Network area & 100 m \(\times\) 100 m \\ Location of BS & (50, 150) \\ Initial energy & 0.25 J/node, 0.5 J/node \\ Number of cells & \(5\times 5\) \\ Number of sensor nodes & 100–400 \\ Packet length & 2000 bits \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 2: Flowchart of GB-PEDAP tree construction algorithm.  
the rapid death of sensor nodes with low energy, thus reducing the lifetime of the network. GB-PEDAP mainly builds a grid structure in the sensing area and collects the data sensed by the sensors in the cell through the cell head. Since the transmission distance between the sensors is short, the energy consumption can be dispersed effectively.
## 4 Finite Element Analysis
### Discussion of Simulations and Tests
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Specimen Number** & **EX/kN** & **FEM/kN** & **FEM/EX** \\ \hline ZU\({}_{0}\)N\({}_{1}\)W & 1373.51 & 1421.14 & 0.97 \\ ZU\({}_{0.25}\)N\({}_{0.75}\)Y & 2145.77 & 2243.36 & 0.96 \\ ZU\({}_{0.25}\)N\({}_{0.75}\)E & 2857.48 & 2916.52 & 0.98 \\ ZU\({}_{0.5}\)N\({}_{0.5}\)Y & 2154.99 & 2384.71 & 0.90 \\ ZU\({}_{0.5}\)N\({}_{0.5}\)E & 2900.62 & 2960.57 & 0.98 \\ ZU\({}_{1}\)N\({}_{0}\)W & 1665.81 & 1699.04 & 0.98 \\ JU\({}_{1}\)N\({}_{0}\)W & 1586.89 & 1642.71 & 0.97 \\ JU\({}_{0.5}\)N\({}_{0.5}\)E & 2713.33 & 2905.3 & 0.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of finite element calculated values and experimental values. Figure 9: Concrete equivalent stress cloud. In conclusion, the two-stage cohesion model simplified according to the test in this paper shows good accuracy in the simulation of steel tube-concrete specimens constructed with internal welded rebar reinforcement rings, but due to the scarcity of related studies, the relevant parameters of the cohesion model cannot be determined, and additional experiments and numerical simulations with extended parameters must be carried out to facilitate in-depth validation of the cohesion model's applicability.
## 3 ML-Based Routing Algorithms in WSNs  
ML is a technical science that studies and develops theories, methods, technologies, and application systems for simulating, extending, and expanding human intelligence,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Scheme** & **Energy Consumption** \\ \hline EBRP [59] & Delay, residual energy, energy density \\ EESPPEED [60] & Delay, residual energy, forwarding rate \\ GMRP [61] & Delay \\ RSML [62] & Routing quality, supporting time \\ \hline \hline \end{tabular}
\end{table}
Table 3: The comparisons of different optimizing data flow-based routing algorithms.  
and it adopts different learning algorithms to analyze data, continue learning, and make judgments and predictions. ML techniques have been widely used for classification, regression, and density estimation in various fields, such as bioinformatics, natural language processing, computer vision, and graphics processing. The algorithms and techniques used also come from many different fields, including statistics, probability, neuroscience, bionics, and computer science.  
The ML-based routing algorithm is an important routing design in WSNs for the following reasons:  
1. WSNs typically monitor dynamic environments that change rapidly over time, and the locations of nodes may change, thus requiring the development of routing algorithms with rapid adaptation and decision making.
2. WSNs can be used for information collection in hazardous locations that are inaccessible to humans (e.g., volcanic eruptions, sewage treatment, and nuclear energy leaks). The various unanticipated influences that may occur in such situations can analyze some nodes and affect routing decisions, which makes systems require strong learning and self-adaptive capabilities to ensure accurate information collection and transmission. Therefore, ML is very suitable for routing protocol designs with WSNs.
3. Designing routing algorithms in WSNs should consider the challenges posed by multiple influencing factors, such as energy consumption, fault tolerance, scalability, and data coverage. ML enables nodes and sinks to learn from past experience and chooses an optimized routing path to adapt to dynamically changing environments.  
The advantages of using ML to design routing algorithms can be summarized as follows. ML learns the best routing path to reduce energy consumption and extends the lifetime of WSNs. It can reduce computational complexity by dividing the typical routing problem into simpler sub-routing problems. In each subproblem, nodes develop graph structures by considering only their local neighbors, resulting in low-cost, efficient, and real-time routing. Additionally, the quality of service (QoS) requirements of routing are satisfied by relatively simple calculation methods and classifiers, which reduce cost and save power efficiently.
## 5 Conclusions  
Based on TPF theory, this paper establishes a cutting force prediction model for EVC cutting AMCM. The friction components are evaluated at the tool-chip interface (TCI), tool-particle interface (TSI), and tool-matrix (TMI), respectively. In addition, SiCp/Al materials were used to predict and simulate cutting force. The experimental results are consistent with the model. Conclusions as below:  
1. EVC cutting AMCM can effectively reduce cutting force and improve surface processing quality. Based on the proposed new cutting force prediction model, when the cutting speed is greater than 650 m/min, the cutting force is reduced by 22%, and the TPF stress distribution at the TCI is significantly reduced.
2. The cutting speed is in the range of [200, 650] m/min, and the average error of the cutting force prediction model is less than 11.4%, which means that the model is suitable for calculating the cutting force of aluminum matrix composites assisted by elliptical vibration.
3. TCI has two-body sliding friction and three-body rolling friction. The intermittent machining characteristics of EVC can effectively reduce the three-body rolling friction and two-body sliding friction of particles. In the model, the total contact length between cutter and chip decreases from 0.57 mm to 0.36 mm, and the stress change reflects the friction characteristics of the cutter chip.  
Investigation, software, writing--original draft, Y.L.; Funding acquisition, project administration, resources, C.W.; Supervision, X.Z.; Conceptualization, writing--review and editing, Y.L., X.Z. and C.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Science and Technology Project of Jilin Provincial Department of Education (JKH20200310 KJ, JJKH20200960 KJ).  
Not applicable.  
All data included in this study are available upon request by contact with the author [PERSON].
# Rapid Characterization Method for SMC Materials for a Preliminary Selection
###### Abstract  
In electrical machines, laminated steels are commonly adopted as soft magnetic materials, while for permanent magnets, sintered ferrites and NdFeB are the most common solutions. On the other hand, the growing demand for volume reduction with the increment of efficiency leads to the necessity of exploring other magnetic materials able to face the challenge better than the traditional ones. Bonded magnets have been used to replace sintered magnets, obtaining a better use of space and particular magnetic properties. Instead, for the magnetic circuit, Soft Magnetic Composites (SMC) allow realizing very complex magnetic design (3D path for flux) with iron loss reduction at medium-high frequencies, especially for the eddy currents loss contribution. On the other hand, SMC materials have such drawbacks as low mechanical properties and high hysteresis losses. For this reason, in this work, different studies considering several variables have been carried out. SMCs were produced through a moulding process; inorganic and organic layers to cover ferromagnetic particles were used, adopting different coating processes. Particular tests have been performed for a quicker and more indicative overview of the materials obtained. The single sheet tester (SST) is easier than traditional toroidal methods; on the other hand, the multiplicity of variables affects the SMC materials and their process. For this reason, coercivity and conductivity tests permit rapid measurement and provide a direct classification of the produced SMCs, providing the main information needed to select suitable materials. Results highlighted that choosing the more appropriate SMC material is possible after using these simple preliminary tests. After these tests, it was possible to argue that with 0.2 wt% of phenolic resin as the organic layer (and compaction pressure of 800 MPa), it is possible to produce a good SMC. On the other hand, the SMC with 0.2 wt% of epoxy resin (and compaction pressure of 800 MPa) gives a minor coercivity value. Additionally, despite the SMC with the inorganic layer, 0.2 wt% of nano-ferrites showing the best coercivity values (specifically for vacuum treatment at 600 \({}^{\circ}\)C), their resistivity was unsatisfactory.  
soft magnetic composites; magnetic characterization; single sheet tester; resistivity; coercivity; phenolic resin layer; epoxy resin layer; ferrite layer +
Footnote †: journal: Applied Sci. 2021, 11, 12133. [[https://doi.org/10.3390/app112412133](https://doi.org/10.3390/app112412133)]([https://doi.org/10.3390/app112412133](https://doi.org/10.3390/app112412133))
# Machine Learning: Models, Challenges, and Research Directions
## 3 Machine Learning Models
### Supervised Learning
In these techniques, the generalization capacity of an ensemble learner is broadly stronger than an individual learner [68; 69]. Although this theory holds more for weak learners, strong traditional learners give better performance results. Additionally, ensemble models usually provide better results when there is a diversity among the combined base learners [70]. These models provide several benefits, including low variance, high performance, and the removal of noise and biased data. Moreover, these models are robust, which can decrease the spread or dispersion of the predictions. Despite the benefits of these models, they deal with several shortcomings, including the lack of simplicity and explainability, generalization, and high prediction time [71]. Ensemble-based models are classified into three different types, bagging, boosting, and stacking, as shown in Figure 3. The Bagging model, known as the bootstrap aggregation technique, can minimize the variance and thereby improve the prediction [71; 72]. Boosting models use a sequential iterative technique to combine multiple weak individual learners into one strong learner with a better performance. To generate the output of this strong learner, a relationship based on a weighted average or voting is established between the basic learners with different distributions [73]. Finally, stacking, known as stack generalization, is another type of ensemble learning technique that includes heterogeneous weak learners. Stacking models may provide some benefits, such as increasing the robustness and improving the performance [74]. These techniques ensure a higher performance in comparison with any single contributing model. Table 2 provides the characteristics, advantages, and disadvantages of these supervised classification techniques. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classification** & **Characteristics** & & **Advantages** & **Disadvantages** \\
## 5 Experimental Results and Analysis  
### Experimental Setting  
The leave-one-out cross-validation approach has been adopted to evaluate the performance of the proposed EBP method over three publicly available benchmark datasets named avenue dataset, crowd-activity UMN dataset, and ADOC. These datasets cover human crowd-based indoor/outdoor and normal/abnormal scenes.  
The avenue dataset [47] comprises 16 preparation and 21 challenging video clips. The videos were taken in the Chinese University of Hong Kong (CUHK) site avenue with 30652 frames overall. The exercise videos capture usual circumstances. Challenging videos comprise normal and abnormal actions. Three abnormal examples are strange, wrong, and abnormal objects, and the dataset comprerehends a few contests such as minor camera vibration, outliers and normal outlines.  
The second dataset in our designed EBP framework is the activity of the crowd The University of Minnesota UMN dataset [48] is a machine mapping of populated areas.  
Figure 11: The design diagram of the Maximum Entropy Markov Model algorithm.  
Normal crowd behavior is observed until a predefined singular moment, where at the point the behavior shifts to an evacuated scenario, in which each individual hurries out of the camera frame to resemble terror. The collection is made up of 11 video clips that start normal and then move to deviant behavior. The anxiety issue is shot in three different locations, one indoors and two exteriors. All clip had been recorded at a frame rate of 30 frames per second with a resolution of 640 \(\times\) 480 pixels using a static camera.  
We also verified with a third standard dataset entitled ADOC [49], a 24-hour event that includes 25 different event types and spans 721 occurrences. This is the biggest dataset accessible for intrusion detection with localized bounding box descriptions. The information has been gathered through a security camera that was installed on a huge university campus. It looks out over a corridor that connects many structures and catches the activities of students, educators, and employees on a hectic day. Video is captured at a frequency of 1080p at a frame rate of three frames per second by the camera. The footage has been encoded with the H.264 protocol, a lossy image compression technology that is widely used in the monitoring sector. We generated a dataset using footage that was collected over 24 h. The video depicts a variety of lighting settings as well as congested situations with debris in the backdrop. The data were annotated with a variety of events increasing in occurrence from low to high. Where the bicyclist had a 238 frequency, the individual on the lawn had a 132 frequency, the golf cart on the sidewalk had a 105 frequency, the person on the phone had a 58 frequency, and the individual on a skateboard had a 48 frequency.
# Comparison of the Usefulness of CBCT and MRI in TMD Patients According to Clinical Symptoms and Age
## 2 Materials and Methods
### Imaging Analysis
#### 2.2.1 CBCT Evaluation  
The CBCT images of the TMJ were obtained with an Alphard 3030 device (Asahi Roentgen Ind. Co. Ltd., Kyoto, Japan) using the following parameters: tube voltage, 80 kVp; tube current, 8 mA; exposure time, 17 s; and field of view, \(154\times 154\) mm. Image reconstruction was performed to obtain a true sagittal and coronal TMJ view with a slice thickness of 1.0 mm according to the long axis of the condylar head by a skilled radiographer using OnDemand 3D software (Cybermed, Seoul, Korea).  
The bony structures in the condylar head and articular fossa were evaluated. CBCT findings were defined as the presence of sclerosis (a condition in which the bone density is significantly increased), flattening (a loss of the round contour), erosion (an interruption or absence of the cortical lining), or osteophyte (marginal hypertrophic bone formation) according to the criteria proposed by [PERSON] et al. [12] and [PERSON] et al. [14] (Figure 1).
# Inverse Multiscale Discrete Radon Transform by Filtered Backprojection
## 3 Results  
In Figure 8 a \(256\times 256\) grayscale image is shown, its conventional DRT backprojection and the backprojection extended to a three times bigger domain. If we subtract one backprojection from the other, centered, we would see they are identical, as the method is designed so that the extended backprojection agrees with the conventional one in the original domain.  
The next figure, Figure 9, shows what happens when we deconvolve the extended backprojection in Figure 8 with just one impulse response, incorrectly assuming the direct+backproject transforms can be treated as a LTI system. Artifacts of high frequency can be observed.  
On the other hand, Figure 10 shows the application of the method described on Section 2.3 with \(64\) vertical + \(64\) horizontal impulse responses and two iterations.  
In addition, as \(N/4+N/4\) deconvolutions per iteration, even if there are only two iterations, may seem a lot of computations, we studied how similar are these impulse responses between them. In Figure 11 we show the recovery when using less than the required \(64+64\) impulse responses. In order to reduce the different impulse responses we have applied a k-means clustering algorithm with norm-\(2\) distance, and averaged the impulse responses within the same cluster, see Figure 12.  
In our tests we have found a good balance to be \(N/16\), in this case \(16+16\) impulse responses (top right image), as for several images we have obtained PSNR of around \(30\) dB, independently of \(N\), with that trade-off (\(N/16\) instead of \(N/4\)). The execution times are presented in Figure 13. Notice that both axes are on \(\log 2\) scale.  
Figure 8: **Top row, input image and conventional backprojection. Bottom row, extended backprojection.**  
Figure 11: Images reconstructed with fewer than \(N/4\) impulse responses, \(N=256\). **Top** row, from left to right, reconstructions for four, eight and sixteen impulse responses per dimension. **Bottom** row, from left to right, 32 and 64 (\(N/4\)) impulse responses. Then, the original picture to compare with. The obtained PSNR from lesser to greater number of different impulse responses considered are: [24.97, 27.36, 30.98, 32.96, 33.08] dB.  
Figure 10: **Left,** first iteration. **Right**, second iteration of deconvolution using \(64+64\) discrete impulse responses on a \(256^{2}\) image.  
Figure 9: Extended backprojection from previous figure, filtered assuming there is a unique impulse response.