esa-sceva/satcom-chunk-collection huggingface dataset:
# Decorrelation-Based Deep Learning for Bias Mitigation
## 6 Conclusions  
The performance of deep learning mainly depends on the quality of data. Failure to account for the quality of data, e.g., biased data in deep learning can lead to erroneous decisions. We propose a new method based on the core idea of reducing the association between features learned by the ANN or CNN models and bias. Additionally, we evaluated proposed models, which we name the DcANN and DcCNN, on five different datasets with different biases such as age, gender, and color. The experimental results demonstrate that features learned by our models are statistically independent of biases or confounds present in the dataset. Our proposed method leverages the ability of the distance correlation function in decorrelation features from data bias without significantly impacting the performance of a network. Furthermore, we observe that our method also performs better than previous approaches to mitigate the bias. Our models are easy, simple, and require  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **Accuracy** \\ \hline Baseline: ERM & 0.1115 \\ IRM & 0.6208 \\ DcCNN & **0.6630** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of accuracies for reversed color-biased MNIST dataset among different methods. Results are calculated on testing dataset.  
fewer hyperparameters to optimize compared to adversarial training. Thus, our models DcCNN and DcANN, despite having numerous methods to achieve bias mitigation, is a promising and effective novel method. Future work will investigate the use of DcDNN in the medical domain to mitigate bias or confounding effects or any irrelevant dependency issues. In addition, we plan to further evaluate the expansion of the proposed method by applying it to pre-trained models and to different types of data variations. Although we did not observe significant change in training times for all our proposed models in comparison with baseline models, we intend to perform a time complexity analysis in the future by measuring the whole training process in terms of training time as the number of dimensions, the complexity of tasks, and the number of layers increases.  
Conceptualization, P.P.; methodology, P.P.; software, P.P.; validation, P.P.; formal analysis, P.P.; investigation, P.P.; resources, P.P.; data curation, P.P.; writing--original draft preparation, P.P.; writing--review and editing, P.P. and K.P.; visualization, P.P.; supervision, K.P.; funding acquisition, P.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Harrisburg University of Science and Technology.  
We thank Harrisburg University of Science and Technology for their support and AWS funding. We also appreciate [PERSON] for his valuable comments for improving the research.  
The authors declare no conflict of interest.
# Evolution of Residual Stress Based on Curvature Coupling in Multi-Roll Levelling
## 2 Residual Stress in Multi-Roll Levelling
Although the distribution of initial residual stress is uneven and irregular along the thickness of the sheet metal, it will be substantially eliminated in the first bending and most of the residual stress after levelling is due to the rolling process. The rolling residual stress symmetrically distributed along the thickness can be modelled and evaluated by analysing the deformation rule of the multi-roll levelling process [8; 14]. As mentioned above, the key factor that affects the elastoplastic deformation is the curvature \(A_{i}\). Based on previous experiments and simulation studies, during the alternative bending process in which the curvature \(A_{i}\) is gradually reduced, the residual stress is also gradually reduced and homogenised [8; 46]. The calculation of \(A_{i}\) is based on the coupled curvature integral model. This model is composed by a geometric model and a bending moment curvature model, where the former describes the relationship between the intermesh and the curvature, while the latter expresses the relationship between the bending moment and the curvature. The modification of this model is to consider the influence of the residual curvature of the previous bending on the subsequent bending, which improves the accuracy of the model. The construction and solution of the model is not the focus of this study and will be described separately.
## 6 Results and Discussions
### Results and Discussions of Questionnaires Data
As in our previous research, three coefficients have been introduced to allow designers to adjust the weights of DDA on designer preference and player performance to fit their games. Our results support that players will have a positive gaming experience when \(w_{1}\), \(w_{2}\), and \(w_{3}\) are set to 0.5. In addition, we posit that it will be difficult to generalize the positive gaming experience to all players if the coefficients are set to extremes, e.g., \(w_{3}\) is set to 0, by which only designer preference is considered. In summary, setting all the coefficients to 0.5 is reasonable, but extreme weighting is not advisable. We highly recommend that the coefficients be adjusted accordingly for each game project. In our procedure outlined in Section 5.4, a given instance will be labeled as A, B, or C when multiple human evaluators unanimously select a label of interest, or when this does not happen, multiple rounds of ChatGPT consistently select it. This procedure ensures that only player feedback with such a label is considered in the following analysis (Table 4), increasing the reliability of the findings. The consistency among the responses from our three human evaluators is 81.25%, while that among the three rounds of responses from ChatGPT is 90.28%. Furthermore, ChatGPT's responses have shown a high similarity of 75% to those of its human counterparts. These results demonstrate the potential of ChatGPT in providing consistent and human-like answers. As a result, we recommend applying the procedure in Section 5.4 when using LLMs such as ChatGPT to assist with classification tasks when human evaluators fail to reach a consensus. Table 4 shows the results of our analysis, revealing interesting trends. During phase one, 9 out of 37 final survey respondents reported complaints, accounting for 24.32% (e.g., _\"The absence of enemy movement sound effects makes it easy for players to be unaware of their positions and get killed.\"_). In phase two, 7 out of 29 final survey respondents complained, accounting for 24.13%, slightly less than in phase one (e.g., _\"1 am highly sensitive to loud noises and as instructions stated I played the game with headphones at max volume. It felt really  
Figure 4: Trend of GUESS values in two phases. claustrophic.\"_). However, during phase two, one player explicitly praised the in-game audio cues, which did not occur during phase one (e.g., _\"Besides, the sound of the footsteps is a key mechanic in the game, it's cool.\"_). The results indicate that, in phase two, players had fewer complaints about the enemy audio cue design and expressed more positive feedback. We argue that this is a significant factor in the overall improved player experience in phase two compared to phase one, according to the results from the modified GUESS questionnaire.
## 4 Experiments and Results
### Experiments Environment and Data Description
The trend is depicted in Figure 5a, where the horizontal coordinates represent the statistical dates and the vertical coordinates represent the number of transfer passengers (in persons) in each statistical period, with 48 statistics placed between each pair of adjacent dates. For the selected 14 days and 676 sets (48 sets of data per day) of one-way passenger flow statistics for inter-hub transfer corridors are available. As an illustration, Figure 5b depicts the transfer passengers counted every 30 min on one of the days (10 May). In Figure 5a, line 1, shown as a yellow line, depicts the transfer passenger flow between Beijing South Railway Station (BSRS) and Capital Airport (BCA) every 30 min between May 10 and May 23, 2021, line 2 (the blue line) depicts the 30 min transfer between Beijing South Railway Station (BSRS) and Beijing Capital Airport (BCA) From 10-23 May 2021. Figure 3: The location of Beijing Capital Airport and Beijing South Railway Station in the Beijing-Tianjin-Hebei urban agglomeration. Figure 4: Air-rail transfer line between the Beijing Capital Airport and Beijing Nan railway station. Figure 5: Transfer passenger flow between BCA and BSRS from 10 May 2021 to 23 May 2021 (**a**) Transfer passenger flow trend from 10 May 2021 to 23 May 2021. (**b**) The transfer passengers counted every 30 min on 10 May 2021.
# USD-YOLO: An Enhanced YOLO Algorithm for Small Object Detection in Unmanned Systems Perception
## 5 Experiments
### Visualization of Results
#### 5.4.1 Citypersons Dataset  
Figure 7 shows the detection results of YOLOv8, YOLOv10, and USD-YOLO on three sets of images with small objects from the Citypersons dataset.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **Recall (\%)** & **mAP50 (\%)** & **mAP50-95 (\%)** & **Params (M)** & **GFLOPs** & **FPS** \\ \hline YOLOv8s & 68.2 & 71.7 & 46.4 & 11.2 & 28.8 & 86.4 \\ TPH-YOLO [25] & 67.6 & 71.1 & 43.7 & 11.36 & 29.8 & 57.1 \\ SPD-YOLO [26] & 62.0 & 66.8 & 39.7 & 12.74 & 46.2 & 63.2 \\ YOLOv5 & 61.3 & 64.6 & 40.9 & 7.02 & 15.9 & 72.4 \\ YOLOv7 & 61.0 & 64.3 & 37.8 & 9.32 & 26.1 & 32.1 \\ YOLOv9 & 62.1 & 64.0 & 41.7 & 6.17 & 23.9 & 43.6 \\ YOLOv10 & 69.1 & 71.7 & 47.1 & 8.06 & 24.8 & 68.0 \\ SSD [22] & 22.6 & 29.6 & 14.9 & 24.48 & 62.2 & 40.3 \\ USD-YOLO & 68.6 & 74.0 & 48.8 & 12.35 & 30.5 & 80.2 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of generalization experiments on the DOTA-v1.0 dataset.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **Recall (\%)** & **mAP50 (\%)** & **mAP50-95 (\%)** & **Params (M)** & **GFLOPs** & **FPS** \\ \hline YOLOv8s & 79.7 & 84.9 & 42.6 & 11.2 & 28.8 & 126 \\ TPH-YOLO [25] & 78.7 & 85.6 & 42.5 & 11.36 & 29.8 & 81.5 \\ SPD-YOLO [26] & 73.1 & 80.1 & 37.1 & 12.74 & 46.2 & 88.4 \\ YOLOv5 & 78.9 & 84.9 & 43.6 & 7.02 & 15.9 & 106.3 \\ YOLOv7 & 64.2 & 69.1 & 28.8 & 9.32 & 26.1 & 55.0 \\ YOLOv9 & 74.2 & 79.9 & 38.9 & 6.17 & 23.9 & 41.8 \\ YOLOv10 & 74.5 & 82.4 & 39.9 & 8.06 & 24.8 & 99.1 \\ SSD [22] & 45.2 & 59.2 & 29.8 & 24.48 & 62.2 & 48.8 \\ USD-YOLO & 81.3 & 86.2 & 45.1 & 12.35 & 30.5 & 124.6 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of generalization experiments on the Flow-Img dataset.  
In the first set of images, USD-YOLO accurately identified and bounded extremely small and occluded pedestrian objects, while YOLOv8 failed to detect them, and YOLOv10 detected only one target. This indicates that the addition of the CGAM attention mechanism in USD-YOLO enhanced the perception of important features in the image, improving the detection accuracy of small objects. In the second set of images, YOLOv8 and YOLOv10 incorrectly detected two densely packed and occluded tiny pedestrian objects as one. In contrast, USD-YOLO accurately detected and precisely bounded both targets. In the third set of images, YOLOv8 and YOLOv10 falsely detected non-target regions as pedestrian objects, leading to false positives. Conversely, USD-YOLO accurately detected all targets without any false positives.  
These visualization results demonstrate that the use of the APE, CGAM, and SCConv modules in the USD-YOLO algorithm significantly improved candidate box capabilities and enhanced the detection accuracy of small objects.
## 2 Results and Discussion
### Validating the Effectiveness of Genic SSR Markers among 22 Allium Accessions  
A sample of 38 primers (Table S4) was randomly selected from 91 polymorphic primer pairs to validate the effectiveness of genic SSR markers and to differentiate a set of 22 _Allium_ accessions (Table 3). Of these 38 primers, six genic SSR markers could not be effectively amplified, and 10 genic SSR markers generated unified or poor polymorphic bands, which could have been due to the use of inappropriate materials or defective SSR markers. A total of 22 genic SSR markers exhibited a high level of polymorphism (Table 4), and 116 alleles were detected in total. The PCR amplification bands generated by the genic SSR markers MCL32, MCL40, and TO249 in 22 _Allium_ accessions are displayed in Figure 1.  
Figure 1: PCR amplification bands generated by the Welsh onion genic SSR markers MCL32 (**a**), MCL40 (**b**), and TO249 (**c**) in 22 _Allium_ accessions.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**No.** & **Primer** & **Repeat Motif** & **Primer Sequence (5\({}^{\circ}\)–3\({}^{\circ}\))** & **Size Range (bp)** & **A\({}^{*}\)** & **Ng\({}^{\circ}\)** & **Na\({}^{*}\)** & **PIC\({}^{*}\)** \\ \hline
1 & MCL32 & (TG)9 & F:gatttccetgatcgtcat & 111–117 & 0.4545 & 4 & 4 & 0.5388 \\  & & & R:getaacatcgetcgetcatca & & & & & \\
2 & MCL37 & (AAG)5 & F:aaggatcgetcagagag & 411–461 & 0.4333 & 8 & 8 & 0.7195 \\  & & & R:ctagatttccgetggtctc & & & & & \\
3 & MCL40 & (AGA)5 & F:cggagagatccggatcat & 189–226 & 0.4250 & 6 & 6 & 0.6882 \\  & & & R:agctcgetcgtgtgtctct & & & & & \\
4 & MCL42 & (GCA)7 & F:cggggacagagagag & 355–497 & 0.2273 & 12 & 9 & 0.8465 \\  & & & R:acagaccacaacaagctctct & & & & & \\
5 & MCL47 & (GAT)5 & F:agg  
The number of alleles (Na) discovered at these loci varied from three to nine with an average of 5.27 alleles per locus. The PIC value illustrated the high degree of polymorphism at each locus, which ranged from 0.44 to 0.85 with a mean of 0.63. According to the previously-described criterion, the three categories were defined as high (PIC \(>\) 0.5), moderate (0.25 \(<\) PIC \(<\) 0.5), and low (PIC \(<\) 0.25) [34, 35], and a total of 16 (72.73%) primers displayed a high level of PIC. In contrast to other plants, the Na and PIC values for the Welsh onion were lower than those reported in pear buds [36], 9.42 _vs._ 0.75, respectively. However, the values were higher than those for sweet potato (2.94 and 0.35, respectively) [25], cowpea (3.06 and 0.53, respectively) [37], and mung bean (3 and 0.34, respectively) [16].
# An Effective Wide-Bandwidth Channel Access in Next-Generation WLAN-Based V2X Communications
## 1 Introduction
On the other hand, wireless mesh network of IEEE 802.11s is assumed in reference [9], where the authors propose an implementation method to provide proportional fairness without non-linear and non-concave optimization. However, these works assume static environments where APs or stations rarely move. Since there is no concept of AP in WLAN V2X scenario and all vehicles may change their positions dynamically, channel access methods considering static graphs are not suitable. The fair channel access scheme in WLAN V2X environment is considered in reference [10]. It compromises throughput and fairness considering multi-rate and multi-channel operation defined in IEEE 802.11p and IEEE 1609.4, respectively. It includes the grouping method of service channels (SCHs) regarding the data rate and the distance of transmission. However, in a real V2X environment, since WLAN V2X channels are pre-allocated and cannot be dynamically changed over time, throughput and fairness should be controlled with channel access schemes. Furthermore, 20-MHz transmission operation in the next generation V2X is not considered. In contrast to the related works, in this paper, the proposed channel access scheme jointly optimizes fairness and throughput performance for 20-MHz transmission under realistic WLAN V2X environment. In this paper, a novel and effective wide-bandwidth channel access scheme in next-generation V2X communication systems is proposed. The proposed scheme is carefully designed to provide both enhanced fairness with other stations and reduced channel access delays. The proposed scheme is designed to alleviate interchannel interference problems that exist in OCB environments. The proposed architecture is able to effectively prevent a hidden node problem in the extended channel with the detection capability of exact channel usage time. The performance of the proposed scheme is analyzed in order to show the enhanced fairness. In addition to the proposed scheme, alternative channel-accessmethods are given in the case of hardware limitations. In order to design the best scheme, the proposed scheme and other variant wideband channel access schemes are compared by computer simulations. Through extensive simulations, the best performing scheme is selected. The rest of the paper is composed as follows. Section 2 presents background on conventional WLAN V2X communications and channel access schemes, Section 3 describes the proposed channel access scheme and alternative channel access schemes for the wide bandwidth of 20-MHz transmission in next-generation V2X communication systems, Section 4 provides extensive analysis of the fairness and delay performance of the proposed scheme, Section 5 evaluates the performance of the proposed scheme, Section 6 discusses the simulation results, and Section 7 concludes the paper.
# Enhancing Kazakh Sign Language Recognition with BiLSTM Using YOLO Keypoints and Optical Flow
## 3 Importance of Components
### YOLOv8 for Keypoint Detection  
YOLOv8 is a well-established and stable model in the YOLO family of object detectors and is designed to process entire images in real time. In our framework, YOLOv8 is trained using annotated gesture data collected with MediaPipe. This enables the model to accurately detect hand keypoints and extract bounding boxes from each frame of the video [12]. The use of YOLOv8 allows efficient processing of full images without the need for generating region proposals first, ensuring that spatial information is captured reliably [13].  
Keypoint loss is computed by comparing predicted keypoints (pred) with ground truth keypoints (truth). First, the Euclidean distance loss measures the difference between predicted and ground truth keypoints in Euclidean space. The formula computes the squared Euclidean distance between the predicted and ground truth keypoints.  
\[d=(\text{pred}[:,0]-\text{truth}[:,0])^{2}+(\text{pred}[:,1]-\text{truth}[:,1 ])^{2} \tag{1}\]  
Then, the keypoint loss factor is adjusted using a formula that incorporates the area of the object and a scaling factor \(\sigma^{2}\) representing the uncertainty of the prediction. This equation normalizes the keypoint loss based on the object's size and prediction confidence.  
\[\text{loss\_factor}=\text{area}\times\left(\frac{1}{(\text{area}\times \sigma^{2}+10^{-9})}\right) \tag{2}\]  
The formula adjusts the loss for each keypoint depending on the object's area and the uncertainty of the keypoint prediction. A small constant \(10^{-9}\) is used to avoid division by zero and maintain numerical stability.  
Next, the final loss is calculated by applying a weight based on the two main components--the object's area and keypoint uncertainty. The sensitivity factor \(e\) is computed using the following equation:  
\[e=\frac{1}{(\text{area}\times\sigma^{2}+10^{-9})} \tag{3}\]  
This term \(e\) determines how strongly the penalty is applied to each keypoint, with larger \(e\) values corresponding to smaller areas or higher uncertainty, increasing the penalty for inaccuracies in those keypoints.  
Finally, the keypoint loss is weighted by a binary mask kpt_mask, which indicates whether the keypoint is visible (1) or occluded (0). This is defined as follows:  
\[\text{kpt\_mask}\in\left\{0,1\right\}^{B\times N}\]  
where \(B\) is the batch size and \(N\) is the number of keypoints per sample. Only visible keypoints contribute to the loss. The final loss formula, incorporating all of these components, is as follows:  
\[\text{final\_loss}=\text{loss\_factor}\times\exp(-1)\times(1-\exp(-e)\times \text{kpt\_mask}) \tag{4}\]  
This equation applies the loss factor and penalty based on visibility and uncertainty, ensuring that only relevant keypoints are considered during training.
## 2 Materials and Methods
### Tikhonov Regularization  
Tikhonov regularization, also known as ridge regression, is a common strategy for solving ill-posed problems, which is the case for the weighting function matrix used here. This method improves the conditioning of the matrix by adding positive elements along its diagonal.  
\[\mathbf{C}_{n,est}^{2}=(\mathbf{M}^{T}\mathbf{M}+\mathbf{\Gamma}^{T}\mathbf{\Gamma})^{-1}\mathbf{M}^{T }\mathbf{V}. \tag{4}\]  
\(\mathbf{\Gamma}=\alpha\mathbf{L}\) is the Tikhonov matrix, where \(\alpha\) is the ridge parameter and \(\mathbf{L}\) is the identity matrix. Regularization is used as an improvement over the pseudo-inverse to calculate the \(C_{n}^{2}\) profile. The ill-conditioned nature of the weighting function matrix makes it highly susceptible to measurement noise, which appears as dropouts in the profile (negative and therefore unphysical values of \(C_{n}^{2}\)). The pseudo-inverse is only able to eliminate these dropouts using higher thresholds, which removes the contribution of select weighting functions. Tikhonov regularization can instead be applied to solve the ill-conditioned  
Figure 7: The influence function at 50 m, 125 m, 200 m, and 275 m. The function breaks down past the final weighting function notch near 350 m.  
problem by minimizing the solution and residual norms. The regularized solution, \(\mathbf{X}\), is determined by  
\[\mathbf{C}_{n,\mathbf{est}}^{2}=argmin\big{\{}||\mathbf{MX}-\mathbf{V}||_{2}+\alpha||\mathbf{XL}||_{ 2}\big{\}}. \tag{5}\]  
The MATLAB constrained nonlinear optimizer function _fmincon_ was used to find the minimum of Equation (5). Ridge parameter selection is informed using the L-curve method, which seeks the knee in a log-log plot of the regularized solution against the norm of its residual norm. The parameter nearest to the knee in the L-curve was used as the initial guess, which was then adjusted to eliminate \(C_{n}^{2}\) dropouts from the profile while not over-regularizing the solution [18]. An example of the ridge parameter's influence on the turbulence profile is shown in Figure 8.  
Figure 8: _Cont._
## 2 System and Channel Model
### Scenario Overview
These data may include the status information of charging stations, charging plans, traffic information, etc. If RIS-assisted communication is available, the base station can improve the channel quality and enhance the strength and reliability of signals reaching the vehicles through RISs. RIS Processing of Downlink Signals: An RIS receives downlink signals sent by the base station and reflects the signals towards the direction of the vehicles based on pre-designed reflection coefficients and phase adjustments to enhance signal reception. Vehicles Receive Signals: Vehicles receive downlink signals from both the base station and the RIS and utilize the received information to perform corresponding operations, such as adjusting charging behavior, updating charging plans, etc. Information Interaction:  
During the communication process in both uplink and downlink transmissions, there is interaction and processing of information among the base station, vehicles, and RISs. The base station is responsible for scheduling charging stations, processing information uploaded by vehicles, issuing charging plans, etc. Vehicles are responsible for uploading their own status, location, and other information, as well as receiving charging plans issued by the base station. RISs serve as an intermediary node, responsible for optimizing the channel, enhancing communication reliability and performance, and reflecting signals from the base station to vehicles or from vehicles to the base station. Through the above communication process and information interaction, the electric vehicle charging communication network needs to achieve efficient communication between charging stations and vehicles, providing support and optimization for the charging behavior of electric vehicles.