esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Analyses
### Subjective Evaluation  
The subjective evaluation of the multi-exposure fusion methods is shown in Figures 7-10. In Figure 7a, the results of Mertens10 showed nice global contrast and color information, but the details in the brighter regions are lost in the clouds around the sun. Halo artifacts are produced at the boundary between the balloon and the blue sky. Vonikakis11 can preserve the proper details in the brighter regions, but the details of the darker regions are lost at the lawn in the bottom of the balloon in Figure 7b, and halo artifacts are present. The best global contrast can be obtained with Li13 and Ma18, but the halo artifacts in those methods are more prominent. There are almost no halo artifacts in Liu15, but shadow can be observed around the sun. The fused image of the proposed method presented better visual perception and preserved the proper global contrast and color information (e.g., clouds around the sun and the lawn below the balloon), while simultaneously removing the halo artifacts.  
Figure 8 gives the results of an indoor scene. The results of Mertens10 showed nice global contrast, but the details, such as colors in the brighter regions, are lost (e.g., the details of the loudspeaker box on the left side). There are halo artifacts on the right side of the flowerpot. Additionally, the color is distorted, as shown in Figure 8e. Vonikakis11 also preserved the rich details in the brighter regions, but lost details in the darker regions (e.g., details of the outdoor scene in Figure 8b). Halo artifacts are also observed on the right side of the flowerpot. Halo artifacts in Li13, Liu15 and Ma18 are evident too. The proposed method had better global contrast and color information (e.g., vivid color in the  
Figure 7: Performance comparison of different methods on the “balloons” image sequence. (**a**) Mertens10 [15]; (**b**) Vonikakis11 [16]; (**c**) Li13 [18]; (**d**) Liu15 [12]; (**e**) Ma18 [14]; and (**f**) the proposed method.  
loudspeaker box in Figure 8f), and overcame the halo artifacts present in the images produced via other methods.  
The dynamic range of the scene in Figure 9 is not as wide as those in Figures 7 and 8. However, it can also be seen that, in the Vonikakis11, Li13 and Ma18 method images, halo artifacts are observed near the doorframes in the middle of the fused images. There is also nice global contrast  
Figure 8: Performance comparison of different methods on the “recorder” image sequence. (**a**) [PERSON] [15]; (**b**) [PERSON] [16]; (**c**) [PERSON] [18]; (**d**) [PERSON] [12]; (**e**) Ma18 [14]; and (**f**) the proposed method.  
Figure 9: Performance comparison of different methods on the “Belgium House” image sequence. (**a**) [PERSON] [15]; (**b**) [PERSON] [16]; (**c**) [PERSON] [18]; (**d**) [PERSON] [12]; (**e**) [PERSON] [14]; and (**f**) the proposed method.
## 4 Experimental Validation  
Following the optimization study detailed above, a CDPR prototype, presented in Figure 10(a), was developed. The actuators' positions and the end-effector size were adjusted according to the optimal design vector resulting in Table 2. It was composed of a rigid frame with four actuated pulleys controlling the end-effector poses by means of four cables. The patient sits near to the robot and grabs the mobile platform with his hand. Figure 10(b) shows the prototype representation using QTM (Qualisys Track Manager) software recording the passive markers' locations along the end-effector movement.  
The prototype was actuated using the Dynamixel MX-106T and equipped with a feedforward, a PID controller and a gear reducer. The pulleys and the end-effector were produced using 3D printing. The cables were made of Dacron.  
The extended position control mode was used for the actuator commands. The desired angular positions of the motors were computed using the inverse kinematic model. Figure 12 presents the block diagram of the robot control.  
The angular positions of the pulleys were controlled so that the robot end-effector could replicate the prescribed exercise. A motion capture system was also used in this phase to evaluate the performance of the robot by tracking the mobile platform poses along its movement so that a comparison, illustrated in Figure 13, between the target and the performed trajectories could be made. This external and global approach allowed  
Figure 11: (**a**) Robot prototype and (**b**) its representation using QTM (Qualisys Track Manager) software. Markers are represented in green.  
Figure 12: Control block diagram.  
consideration of all error sources inside and outside the control loop, such as anchor point errors and the cable's behavior.  
In order to assess the robot prototype performance, the mean absolute, the root-mean-square and the standard deviation position and orientation errors were computed as given in Table 4.  
Where \(\mathbf{X_{t}}(i)\) and \(\mathbf{X_{m}}(i)\) are the true and the measured values corresponding to the \(i^{th}\) position, \(\boldsymbol{\epsilon}(i)=\mathbf{X_{t}}(i)-\mathbf{X_{m}}(i)\) is the measured error at the \(i^{th}\) position, \(\overline{\boldsymbol{\epsilon}}\) is the mean of \(\boldsymbol{\epsilon}\) and \(n\) is the number of points forming the trajectory.
## 3 Theoretical Analysis
### The Tiles  
The sum of the number of moves required to move tile \((0,0)\) to position \((0,0)\) from all of the \(n^{2}\) possible sources is (see the leftmost entry of Figure 3, and Figure 4):  
\[2\left(5\sum_{i=1}^{n-1}i^{2}+\sum_{i=1}^{n-2}\sum_{j=1}^{i}j\right)+6\sum_{i=1} ^{n-1}i=\tfrac{11}{3}n^{3}-3n^{2}-\tfrac{2}{3}n \tag{5}\]  
Similarly, for position \((0,k)\), \(0\leq k<n/2\), the sum is (see Figure 3):  
\[\left(\tfrac{11}{3}n^{3}-3n^{2}-\tfrac{2}{3}n\right)-\left(3n^{2}-4n-4\right)k +(6n+4)\sum_{i=1}^{k-1}i=\left(\tfrac{11}{3}n^{3}-3n^{2}-\tfrac{2}{3}n\right)- \left(3n^{2}-7n-6\right)k+(3n+2)k^{2} \tag{6}\]  
Therefore, the total number of moves for moving tiles \(\{(0,k)|0\leq k<n/2)\}\) into place in the first half of the first row is:  
\[\frac{n}{2}\left(\tfrac{11}{3}n^{3}-3n^{2}-\tfrac{2}{3}n\right)-\left(3n^{2}- n-2\right)\sum_{k=1}^{n/2-1}k+(3n+2)\sum_{k=1}^{n/2-1}k^{2}=\tfrac{19}{12}n^{4}- \tfrac{11}{12}n^{3}-\tfrac{1}{3}n^{2}-\tfrac{1}{3}n \tag{7}\]  
and the total number of moves for moving the first row and column tiles into place is, by Equations (5) and (7), at most:  
\[4\left(\tfrac{19}{12}n^{4}-\tfrac{11}{12}n^{3}-\tfrac{1}{3}n^{2}-\tfrac{1}{3} n\right)-\left(\tfrac{11}{3}n^{3}-3n^{2}-\tfrac{2}{3}n\right)=\tfrac{1}{3} \left(19n^{4}-22n^{3}+5n^{2}-2n\right) \tag{8}\]  
The astute reader will have noticed that we have under-counted by \(O(n)\) to bring the blank into position at the start, and \(O(1)\) for the last tile in the row and column. This is more than compensated for by the fact that we have over-counted by \(O(n^{3})\) when moving the blank in Section 3.1 since there is never any need to move the blank to the last row.  
Figure 4: Decomposing the leftmost entry of Figure 3 to show the structure of Equation (5).  
Figure 3: Number of moves required to move a tile from each position to the first half of the first row of the 63-puzzle.
## 5 Discussion  
Male gender, N-positive stage and tumor length greater than five cm are recognized as ASCC clinical prognostic factors, associated with worse clinical outcomes [37; 38]. A few other prognostic factors have been suggested throughout the years, like the baseline neutrophil to lymphocyte ratio for LR recurrence [39], or an age greater than 55, increased circumferential tumor spread, skin ulceration, inguinal node development and a total RT dose of more than 60 Gy for worse colostomy-free survival [40]. Nomograms have even been created with promising results in predicting cancer specific survival and overall survival as well as risk-stratifying patients, but were only based on clinical data [41].  
Yet, radiomics features extracted from medical imaging have the potential to bring additional informative data, as showcased above in both PET and MRI, even if used alone. Adding these novel parameters to previously used clinical parameters seems to offer additive performance, paving the way to build more accurate predictive models.  
Given the potentially high number of radiomics features created on top of a rising number of clinical variables, powerful algorithms are needed to encompass and make all available data flourish. This is where ML algorithms thrive and have already shown tremendous results for a number of malignancies [42; 43; 44], but have, for now, barely been explored in ASCC. As an exception, using various ML algorithms including random forest and J48 decision trees, [PERSON] et al. created a model predicting inguinal relapse with respective sensitivity, specificity and accuracy of 86.4%, 50% and 83.1% on the validation dataset (and superior results compared to logistic regression), highlighting the potential of such algorithms for ASCC care [45].  
However, the available data are still sparse, as all published studies remain exploratory and the created models are unfit for extensive clinical use. Indeed, several limitations curb the expansion of radiomics: inter-observer segmentation variability, lack of a harmonized process for image normalization and acquisition (especially for MRI with high signal inconsistencies), as well as the absence of standardized features' extraction methods. As for ML, large ASCC patients' cohorts are needed in order to fulfill their prerequisites and allow complex ML algorithms to prove superiority over simpler predictive models such as standard logistic regression, but are difficult to gather, given the relative rarity of the disease.
## 4 Case Study
### Simulation Results
)** & \(\mathbf{P}_{max}\) **(MW)** & **Case 1 (MW)** & **Case 2 (MW)** & **Case 3 (MW)** \\ \hline
1 & 11.2 & 1.12 & 13.984 & 13.984 & 13.28 & 13.848 \\
2 & 9.3 & 0.93 & 8.006 & 8.006 & 7.605 & 7.677 \\
3 & 8.4 & 0.84 & 5.896 & 5.896 & 5.608 & 5.416 \\
4 & 7.6 & 0.76 & 4.368 & 4.368 & 4.152 & 3.872 \\
5 & 10.0 & 1.00 & 9.952 & 9.952 & 9.448 & 9.280 output and the rotational speed in Case 3, respectively. Since the rotational speed of WF1 and WF5 is limited to 1.2 p.u. by pitch angle controller, it is noted that the rotational speed are appropriately controlled within the available range (0.7-1.2 p.u.). Each wind farm operates in the MPPT control mode after exhaustion of the KE reserve and offers additional power output to participate in frequency control during the support time. Figure 12: _Cont._ \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Wind Farm**} & \multicolumn{2}{c}{**Case 2**} & \multicolumn{2}{c}{**Case 3**} \\ \cline{2-5}  & \(\omega_{del}\) **(p.u.)** & **KE (MWs)** & \(\omega_{del}\) **(p.u.)** & **KE (MWs)** \\ \hline
1 & 1.200 & 8.504 & 1.200 & 8.504 \\
2 & 1.087 & 14.496 & 1.077 & 13.512 \\
3 & 0.983 & 11.920 & 1.027 & 15.944 \\
4 & 0.889 & 9.760 & 0.962 & 15.976 \\
5 & 1.170 & 16.896 & 1.200 & 20.160 \\ Total & - & 61.576 & - & 74.096 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of KE reserves in Case 2 and Case 3. Figure 12: Active power for individual wind farms in Case 3. (a) WF1; (b) WF2; (c) WF3; (d) WF4; (e) WF5. Figure 13: _Cont._  
Figure 14 presents a comparison of the system frequency responses during a disturbance in the three cases. When frequency drops because of the disturbance, the minimum frequency responses of Case 1 and 2 are 59.63 Hz and 59.75 Hz, respectively. Then, the grid minimum frequency of Case 3 is 59.78 Hz. Therefore, Case 3, i.e., the proposed strategy, can provide the greatest support to improve the minimum grid frequency among all the cases. It may also help in enhancing the robustness of the power system by decreasing the frequency fluctuations under a given load. As shown in Figure 14, the frequency recovery of the proposed strategy is much faster than that of Case 1 and 2, and hence, Case 3 can provide more power to enhance the frequency response of power system. Case 3 can also accomplish a greater development of the frequency support capability of PMSG wind turbines using the PSO-based optimization process for the KE reserve. Consequently, it should be noted that the proposed optimal control and operation strategy can considerably enhance the ability of initial and low-frequency responses in the short-term frequency regulation. Figure 13: Rotational speed for individual wind farms in Case 3. (**a**) WF1; (**b**) WF2; (**c**) WF3; (**d**) WF4; (**e**) WF5.
# Thangka Image--Text Matching Based on Adaptive Pooling Layer and Improved Transformer
###### Abstract
[PERSON] [8] proposed to use of object labels as anchors to align image and language modalities in a shared semantic space. The model was pre-trained on a public corpus of 6.5 million image-text pairs, including graphs. It has achieved good results on multiple tasks, such as text matching. The multi-modal matching method based on pre-trained models can effectively solve the limitations of manual feature extractor design and the inability to handle complex image-text associations. However, it also has some shortcomings, such as the large training dataset required for the model. As well as problems such as long training cycles, low model efficiency, and lack of domain knowledge. Thangka is an ancient Tibetan painting art form with rich color and complex backgrounds, which has high aesthetic value and artistic quality. In addition, the Thangka is known as the encyclopedia of Tibetan culture, and each Thangka image symbolizes deep religious and cultural connotations. Thangka image and text matching are crucial for bridging the semantic gap between images and text as well as achieving data intelligence tasks such as multimodal fusion, relational reasoning, and content generation. Meanwhile, due to the small amount of data and strong text-image correlation in the Thangka itself, the existing image-matching methods are unable to solve the problem of image matching in the Thangka domain better; so, a key unsolved problem is how to effectively extract image and text features from Thangka and capture fine-grained global and local correlations for image matching. In order to solve the above challenges, this paper focuses on 30 kinds of Thangka with different themes, and proposes a new image-text matching method based on the VSE model, which combines adaptive feature aggregation and an improved Transformer [9]. This approach introduces three main improvements:  
1. The integration of adaptive pooling techniques in the model framework enhances the feature extraction capability for semantic correlations between Thangka images and texts. 2. The traditional Transformer architecture is improved by incorporating bidirectional residual networks and masked attention mechanisms, increasing the stability of the matching process and the representation capability of semantic information. 3. A multi-granularity label modal alignment module is designed to map global and local features of images and texts into a shared encoding space, fully exploring the semantic correlations between modalities and within modalities, thereby enhancing the precision of image-text matching.
# Weed Detection Method Based on Lightweight and Contextual Information Fusion
## 3 Experimental Validation and Results Analysis
### Ablation Experiments  
To validate the effectiveness of the four different improvement methods on model detection performance, experiments were conducted by individually adding C3-Faster, context aggregation, CARAFE, and Soft-NMS-EIoU to YOLOv5s. For clarity, in this paper, the use of the abbreviations, C3F, CA, CAR, and SNE, represent C3-Faster, context aggregation, CARAFE, and Soft-NMS-EIoU, respectively. The experimental results are presented in Table 4.  
As indicated by Table 4, it is evident that when the C3F module in the neck network was solely improved, the network's parameters and computational load decreased correspondingly, achieving a lightweight design. Furthermore, [EMAIL_ADDRESS] increased by 1.4%, accuracy improved by 1%, and recall increased by 1.3%. When the \(1\times 1\) convolution was replaced with context aggregation only in the head, enabling multiscale contextual  
Figure 8: The training results for CCCS-YOLO.  
Figure 7: (a) presents the Precision–Recall (PR) curve for YOLOv5s, while (b) depicts the PR curve for CCCS-YOLO.  
information fusion, [EMAIL_ADDRESS] improved from 76.3% to 77.9%, showing a 1.6% increase. Accuracy increased from 76.9% to 77.8%, a gain of 0.9%.  
Using CARAFE to replace the original upsampling in YOLOv5s significantly enhanced the performance of detecting small targets like weeds and sugar beets, increasing the network's receptive field. [EMAIL_ADDRESS] improved to 77.5%, a gain of 1.2%, and recall increased to 72.7%, a 1.2% improvement.  
When only Soft-NMS-EIoU was improved, [EMAIL_ADDRESS] increased by 1.3%, [EMAIL_ADDRESS]:0.95 improved by 4.4%, effectively enhancing small target detection performance, and recall increased by 1.5%.  
Upon integrating all four improvement modules into YOLOv5s, with minimal changes in parameters and GFLOPs, [EMAIL_ADDRESS] increased by 3.2%, [EMAIL_ADDRESS]:0.95 increased by 5.1%, accuracy improved by 4.4%, and recall increased by 3.3%. The test results demonstrate that the performance of the improved model has been effectively enhanced in all aspects.
## 3 Methodology
### Evaluative Metric  
The properties employed for the suggested framework in this investigation include precision, accuracy, and recall, alongside the F1-score.  
Confusion matrix: The confusion matrix is an evaluation parameter that is used for classification models. It is represented as a table that summarizes the prediction by comparing real target values with the predicted values from the model. It comprises TP--true positive, TN--true negative, FP--false positive, and FN--false negative.  
\[\text{Confusion Matrix}=\begin{bmatrix}TP&FN\\ FP&TN\end{bmatrix} \tag{5}\]  
Accuracy: The metric that indicates how frequently an ML approach correctly projects an outcome is known as accuracy. The division of the number of correct predictions by the total estimation quantity is implemented to evaluate the accuracy.  
\[ACCURACY=\frac{TP+TN}{FP+TP+FN+TN} \tag{6}\]  
Recall: The capacity to identify all pertinent instances within the data set is termed recall. It is defined as the ratio of actual positives to the total of true positives and false negatives in the genre of mathematics.  
\[RECALL=\frac{TP}{FN+TP} \tag{7}\]  
Precision: The metric that represents the efficacy of an ML approach is known as precision. It reflects the accuracy of the algorithms' positive predictions. It is the ratio of genuine successes to the total number of positive projections.  
\[PRECISION=\frac{TP}{FP+TP} \tag{8}\]  
F1-score: the balanced average of accuracy and recall is termed the F1-score. Accuracy and recall are aggregated into a single statistic in order to enhance the comprehension of the efficacy of the suggested framework.  
\[F1\text{-}SCORE=\frac{2\times RECALL\times PRECISION}{PRECISION+RECALL} \tag{9}\]  
Loss: A function called a loss function is a formula of algebra that quantifies the variance between the envisioned results derived from the computerized model and the actual target values. The loss function evaluates the degree to which the forecasts of the model align with the factual information.  
\[\text{Loss}=-\sum_{i=1}^{n}\sum_{c=1}^{C}y_{i,c}\log(\hat{y}_{i,c}) \tag{10}\]
## 4 Results  
### LULC Classification and LULC Change Detection  
The results of LULC classification in 2009 and 2019 using the SVM algorithm are presented in Table 6 and Figure 3. As a result, the top three dominant LULC types in 2009 and 2019 were forest land, paddy field, and perennial trees and orchards. Meanwhile, the top three least dominant LULC types were miscellaneous land, para rubber, and rangeland in 2009 and miscellaneous land, wetland, and para rubber in 2019.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model Evaluation**} & \multirow{2}{*}{**Constituent**} & \multicolumn{4}{c}{**Performance Ratings**} \\ \cline{3-6}  & & **Unsatisfactory** & **Satisfactory** & **Good** & **Very Good** \\ \hline \multirow{2}{*}{\(R^{2}\)} & SS & \multirow{2}{*}{\(<\)0.5} & \multirow{2}{*}{0.5–0.6} & \multirow{2}{*}{0.6–0.7} & \multirow{2}{*}{0.7–1} \\  & TP, TN & & & & \\ \hline \multirow{2}{*}{\(PBIAS\)} & SS & \multirow{2}{*}{\(>\)55} & \multirow{2}{*}{30–55} & \multirow{2}{*}{15–30} & \multirow{2}{*}{\(<\)15} \\  & TP, TN & & & & \\ \hline \hline \end{tabular} Note: SS, suspended sediment; TP, total phosphorus, and TN, total nitrogen. Source: [51; 52].  
\end{table}
Table 5: Model performance scale.  
According to a simple comparison of LULC change in the area, the annual change rate and percentage of change between 2009 and 2019 are reported in Table 7. The dominant increasing areas of LULC types were perennial trees and orchards, para rubber, and rangeland, with annual change rates of 1.95, 1.64, and 1.05 km\({}^{2}\), respectively. Contrarily, the major decreasing areas of LULC classes in the same period were forest land and paddy fields, with annual change rates of 3.98 and 2.04 km\({}^{2}\), respectively. The primary cause of change areas was the conversion of paddy fields into perennial trees and orchards and the expansion of agricultural areas into forest land.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No**} & \multirow{2}{*}{**LULC Type**} & \multicolumn{2}{c}{**2009**} & \multicolumn{2}{c}{**2019**} \\ \cline{3-7}  & & \(\mathbf{km^{2}}\) & **Percentage** & \(\mathbf{km^{2}}\) & **Percentage** \\ \hline
1 & Urban and built-up area & 29.78 & 3.34 & 33.10 & 3.71 \\
2 & Paddy field & 241.14 & 27.05 & 220.74 & 24.76 \\
3 & Field crop & 17.49 & 1.96 & 21.84 & 2.45 \\
4 & Para rubber & 3.35 & 0.38 & 19.78 & 2.22 \\
5 & Perennial trees and orchards & 59.71 & 6.70 & 79.22 & 8.89 \\
6 & Forest land & 476.73 & 53.48 & 436.91 & 49.02 \\
7 & Water body & 26.06 & 2.92 & 33.37 & 3.74 \\
8 & Rangeland & 16.75 & 1.88 & 27.26 & 3.06 \\
9 & Wetland & 19.47 & 2.18 & 16.41 & 1.84 \\
10 & Miscellaneous land & 0.87 & 0.10 & 2.71 & 0.30 \\ \hline  & \multicolumn{2}{c}{**Total**} & \multicolumn{2}{c}{**891.35**} & \multicolumn{2}{c}{**100**} & \multicolumn{2}{c}{**891.35**} & \multicolumn{2}{c}{**100**} \\ \hline \hline \end{tabular}
\end{table}
Table 6: Area and percentage of LULC data in 2009 and 2019.  
Figure 3: Spatial distribution of LULC classification in: (**a**) 2009 and (**b**) 2019.
# A Survey of of Side-Channel Attacks and Mitigation for Processor Interconnects
## 7 Future Directions for Interconnect Side-Channel Attacks
### Development of Interconnect Side Channels  
Examining the history of on-chip interconnects and their security research, it is evident that the increasing complexity of interconnect structures with the expansion of SoC or CPUs has provided new opportunities for side-channel attacks. During the era of dedicated signal line buses, interconnects were generally considered immune to side-channel exploitation. However, with the adoption of Network-on-Chip (NoC) concepts to accommodate complex SoC, contention-based timing side channels began to emerge. As processors continued to scale, the increased distance between cores introduced observable timing differences and distance-based interconnect side channels. The appearance of contention-based side-channel attacks from ring-to-mesh interconnects underscores the longstanding neglect of interconnect side-channel issues.  
The advent of higher computational power chips is an irreversible trend, necessitating further optimization of NoC Quality of Service (QoS), arbitration mechanisms, and data flows to support larger processors. This will lead to more complex interconnects with new structures or characteristics, which are highly susceptible to side-channel exploitation. Examples include novel physical side channels introduced by emerging technologies such as wireless NoC [86; 87] and optical NoC [88; 89; 90]. Even existing interconnect structures harbor potential side-channel attack opportunities, such as routing table blocking in mesh interconnects.