esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Result Analysis  
To facilitate a more comprehensive analysis of the experimental results, we applied pseudo-color mapping to Figure 14a, enabling more intuitive visual comparisons (see Figure 14b). In the intensity maps, the interference of atmospheric light with the target radiance under foggy conditions is clearly visible. Using the proposed dehazing algorithm, a significant improvement in image contrast is observed, and the target radiance obscured by atmospheric light is effectively recovered. To objectively evaluate image quality, we employ four metrics: information entropy, average gradient, image standard deviation, and SSIM (Structural Similarity Index). Information entropy quantifies the amount of information in an image. Higher values indicate richer information content. Average gradient reflects the rate of fine-detail contrast variation, serving as an indicator of image sharpness [31]. Image standard deviation measures the dispersion of pixel values from the mean. Larger values suggest clearer edges and higher contrast [32]. SSIM assesses similarity between two images across three dimensions:  
Figure 14: Pseudo color images of light intensity and final pseudo color images in different scenarios: (**a**) Intensity pseudo color image; (**b**) Final pseudo color image. Figure 13: Intensity images and dehazed intensity images under different scenarios: (**a**) Original intensity image; (**b**) Dehazed intensity image. luminance, contrast, and structure [33]. As shown in Table 1, the proposed algorithm achieves high information entropy and average gradient while maintaining maximum structural similarity (SSIM). These results validate the effectiveness of our dehazing method in enhancing image clarity and preserving critical details. A comparative analysis of the proposed algorithm with histogram equalization and traditional dehazing algorithms is shown in Figure 15. Conventional polarization-based dehazing algorithms, which assume all polarization information originates from atmospheric light, exhibit significant limitations. In regions with high polarization degrees, this assumption leads to substantial errors, causing pixel value overflow and resulting in dark artifacts in the output images. Moreover, these methods demonstrate limited effectiveness in heavy fog conditions and yield images with suboptimal contrast. Histogram equalization is typically used to enhance the global contrast of many images, especially when the contrast of regions of interest is relatively low. This method allows brightness to be distributed more evenly across the histogram. Consequently, it can improve local contrast without affecting the overall contrast. However, though histogram equalization enhances details of objects in foggy conditions, the visual improvement is often not significant. In contrast, the proposed algorithm achieves significant enhancement in target contrast while avoiding the pixel overflow issues inherent to traditional polarization-based dehazing methods. It effectively removes haze while preserving structural similarity to the greatest extent possible, ensuring natural and artifact-free results.
## 2 Materials and Methods  
### Study Site  
This research was conducted in Song Kon commune, located in the North Central region of Dong Giang district with a total area of 7870 hectares (Figure 1). As a mountainous and remote region, it is home to nearly 2500 people, over 90% of whom are ethnic minority groups. Of these upland dwellers, with a population of 2090 people, the \"Co Tu\" people are the largest ethnic minority group living in and around the remote hills and uplands of the district [36]. These populations are predominantly small-scale farmers who grow rice, and maize as their staple crop, and cultivate acacia in the mid-land hills as their main source of income. Official poverty rates are high, standing at an average of 57.15% (379 out of 663 total households) in this upland region, compared with 9.8% countrywide [37]. Some previous reports [38; 39] pointed out several reasons that contribute to the disadvantage that these people are faced with. Those mainly being the varied and challenging topography, the difference in cultural norms, inherent generational lack of economic opportunities, and the remoteness of their locations. These factors have thus caused these ethnic minority groups to be particularly vulnerable to natural hazards and extreme weather variability [20].  
This region subject to a humid tropical monsoon climate, which has created a complex terrain that is characterized by steep slopes. 30% of the total area is at an incline of more than 30 degrees. It has a range in elevation from 293 m to 1070 m above sea level, which increases from the Southern to the Northern parts [40]. The area is characterized by two distinct seasons the rainy season (from September to January) and the dry season (from February to August). The yearly precipitation is around 2350 mm. There are two-to-three tropical storms that hit this area from September to November [36]. Because of such complex terrain and climate, agricultural activities are often affected by natural disasters. The variability and extremity of local weather events include flooding in the mid-low land areas, while soil erosion is most predominant in the upland areas. But despite these conditions, the \"Co Tu\" people, who are indigenous to this complex region, have survived throughout generation by developing agricultural systems and native customs that blend with their environment and result in a symbiotic relationship with their use of the land. The traditional knowledge of the \"Co Tu\" people in relation to their local ecological systems remains a core strength in coping with, and adapting to the modern effects of CC. Also, the IK that the \"Co Tu\" people have developed over a significant period of time and has helped them to negotiate through the socio-cultural incongruities which exist between themselves and the surrounding regions [21]. They can rely on natural phenomena such as the appearance of animals or changes in the growth of bamboo trees to forecast weather over a long period of time in the future [39].
## 1 Introduction  
In 2018, almost 100,000 arthroscopic procedures on joint cartilage and meniscus and 117,189 total knee replacements (TKR) were performed in Germany. In addition, 14,804 revision procedures were performed [1]. The number of people suffering from osteoarthritis (OA) is increasing every year, with the knee joint being one of the most frequently affected joints [2]. It is assumed that OA will be one of the most frequent causes of restriction of mobility in 2030 [3]. In everyday life, the knee joint is exposed to daily changing mechanical stress. Non-physiological stress can lead to degeneration of the articular cartilage and menisci and, thus, to arthritis. The main function of the menisci is to transmit force to the tibiofemoral joint by increasing the contact surface of the condyles with the joint to almost double the size [4]. The risk of arthritis increases many times over with a meniscectomy [5]. For this reason, attempts are made today to preserve as much of the meniscus as possible, and a meniscectomy is only recommended as a last resort [6].  
So far, menisci from animals have been studied, including bovine [7; 8; 9; 10], canine [7], baboon [10] and porcine [7; 10]. The biomechanical testing of human menisci was mainly done under confined [7; 11] and unconfined conditions [12; 13] without differentiating between arthritic and arthritis-free menisci. For these tests, cylinders were punched out. This type of examination of the meniscus does not allow examination of the entire meniscus. In addition, it leads to tissue damage, which makes a later histological examination at the same place of the biomechanical examination impossible. One study has tried to establish a correlation between biomechanics by means of an indentation test and the structural composition of the tissue [14; 15]. [PERSON] et al. [16] showed in their review the predominant biomechanical testing methods for cartilage. These testing methods include ramp mechanical tests, stress relaxation tests, creep tests and dynamic tests. Most of these tests are performed under confined and unconfined conditions, which leads to tissue damage. Therefore, biomechanical testing methods, which allow biomechanical testing of the tissue without damaging it for later histological examination, are essential for a better understanding of the biomechanical properties of the meniscus. Mechanical characterization of the viscoelasticity of arthritic menisci compared to arthritis-free menisci, based on a complete indentation mapping test over the entire meniscus with subsequent correlation of the proteoglycan content, has not been published so far.  
The aim of this study is to investigate the difference in the biomechanical behavior and proteoglycan content between arthritic and arthritis-free menisci. In addition, the relationship between the biomechanical behavior and proteoglycan content should be investigated. A novel indentation-based method was used, which allows the mapping of the entire meniscus, without damaging it for histological examinations.
## 3 Iterative Scheduling Algorithm for Throughput Optimization
### Algorithm Architecture of ISATR
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Definition** \\ \hline m & Mass of UAV \\ \(u_{n}\) & UAV location at \(n\)th time slot \\ \(a_{n}\) & Flight direction of UAV at \(n\)th time slot \\ \(v_{n}\) & Flight velocity of UAV at \(n\)th time slot \\ \(v_{max}\) & Maximum flight speed of UAV \\ \(e_{i,n}\) & \(i\)th user’s location at \(n\)th time slot \\ \(P_{u,n}\) & Transmission power of UAV at \(n\)th time slot \\ \(p_{max}\) & Upper bound of transmission power \\ \(B_{i,n}\) & Bandwidth allocated to \(i\)th user at \(n\)th time slot \\ \(B_{i}\) & Total bandwidth for A2G communication at time slot \(i\) \\ \(E_{n}^{C}\) & UAV communication power consumption with \(i\)th user at \(n\)th time slot \\ \(E_{n}^{F}\) & UAV flight energy consumption in \(n\)th time slot \\ \(E_{max}\) & Upper bound of total energy consumption \\ \(R_{i}\) & Throughput for communication with user \(i\) \\ \(R\) & Total throughput of A2G communication \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Abbreviation definitions. block is O(NK). Similar to the UAV positions optimization, the time complexity of the other two blocks is also O(NK). Therefore, the complexity of our method can be derived as O(NK), in which K represents user number, and N represents time slots, which can be regarded as O(\(n^{2}\)).
# Amharic _Adhoc_ Information Retrieval System Based on Morphological Features
## 4 Amharic IR System Design and Text Representation
### Text Preprocessing
#### 4.2.1 Stem-Based Morphological Analysis  
We created the stem-based index using the basic stems of words. Several words can be formed by attaching affixes to stems. Therefore, we performed morphological analysis for extracting stems from the rest of morphemes. We conflated variants of words to their basic stems. For example, the morphological analysis used to extract the stems of the words _AbatromMa_ /_Jaltimalosum_ 'they have not returned' and _AbatromMa_ /_kabetasabot/u_ 'from his families'/ are shown as follows (see Notations at the end of this article).  
Although basic stems are better to conflate more variants than derived stems, more than one type of basic stems exist for variants (see Table 3). Therefore, in case of verbs, it is impossible to conflate Amharic variants even using basic stems. However, variants of primary nouns, adjectives, and adverbs have one common basic stem. Therefore, verbs and words derived from verbs need further morphological analysis to be represented by a common form. The morphological analysis of some words requires palatalization to extract basic stems. This has been achieved after separating -A, and -A from a stem. For example, the morphological analyses of the words _AbatromMa_ /_jagodziwoot/i_ 'of harmful [_p_]'/. _AbatromMa_ /_jagodziwoot/i_ 'killers'/ and _AbatromMa_ /_mat/_'ora/a_ 'end'/ are presented as follows.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Variant** & **Derived Stem** & **Basic Stem** \\ \hline htPABe/_kotagodzi/_ & _tPABe/_ /_togadali-/_ & -PBAe/_/_gadali-/_ \\ _AbatromMa/_sslagadda/_ & _AbatromMa_ /_jagadali-/_ & -PBAe/_/_gadali-/_ \\ _AbatromMa/_kasigadda/_ & _AbatromMa_ /_jagadali-/_ & -PBAe/_/_gadali-/_ \\ _AbatromMa/_kagadadda/_ & _AbatromMa_ /_jagadali-/_ & -PBAe/_/_gadali-/_ \\ _AbatromMa/_kagadadda/_ & _AbatromMa_ /_jagadali-/_ & -PBAe/_/_gadali-/_ \\ \hline \hline \end{tabular}
\end{table}
Table 3: Derived and basic stems in Amharic.
## 5 Fatigue Life Calculation of Jiangyin Bridge Based on the Stress Integration Approach
### Grinding Treatment
#### 6.2.1 Influence of Grinding Types  
The two grinding types and their FEMs are shown in Figure 22. In Figure 22, the parameters \(r\), \(d\) and \(\varphi\) are the grinding radius, grinding depth, and the angle between the axial direction of the grinder head and the deck, respectively. These parameters for two grinding types are shown in Table 8. The grinding radius \(r\) and depth of grinding type 1 (GT1) are 3 mm and 0.5 mm, respectively, and the grinding arc intersects with the deck [34]. The grinding type 2 (GT2) is a new grinding type proposed in the present study. The grinding radius \(r\) is also 3 mm and the grinding depth \(d\) is 0. that is, the grinding arc is tangent to the deck without weakening the deck. In the grinding treatment process, the main difference between GT2 and GT1 is the angle \(\varphi\) between the axial direction of the grinder head and the deck. Nonetheless, how to accurately control the angle is a key issue to be considered in the experiment design. A comparison of different grinding types is shown in Table 8.  
Table 8 shows the comparison of different grinding types. For GT1, the equivalent structural stress \(S_{s}\) increases by 5.75% and the fatigue life \(N_{a}\) decreases by 25% compared with the non-grinding type. For GT2, the equivalent structural stress \(S_{s}\) decreases by 1.88% and the fatigue life \(N_{a}\) increases by 10% compared with the non-grinding type. Since GT2 can reduce the stress concentration at the weld toe without weakening the deck section, it is more conducive to improving the fatigue life of the weld.
## 2 Materials and Methods
### Discriminant Analysis  
To discriminate tomatoes of different storage days, the features extracted from the tomato images, the spectrophotometric data, the physicochemical attributes, and the ATR-FTIR spectroscopy were subjected to ML methods. Ten (10) different machine learning classifiers from the scikit-learn Python library ([[https://scikit-learn.org/](https://scikit-learn.org/)]([https://scikit-learn.org/](https://scikit-learn.org/)), accessed on 11 December 2024) were tested on the six (6) categories corresponding to days 3, 7, 10, 14, 17, and 24. The ten machine learning algorithms tested in the study were: Classification and Regression Decision-Tree (CART), K-Nearest Neighbor (KNN), Linear Discriminant Analysis (LDA), Logistic Regression (LogReg), Nearest Centroid, Perceptron, Naive Bayesian, Random Forest (RF), Support Vector Machines (SVM), and ExtraTreesClassifier. According to the detailed explanation provided by [PERSON] et al. (2023), the model with the highest accuracy in correctly assigning the tomatoes to their respective storage days was selected [38]. The CART (Classification and Regression Tree) classifier gave the best performance on our data. The CART classifier was designed using all possible feature combinations up to a maximum of three features (1/3 of the minimum class size). Principal component analysis (PCA) was used to reduce each combination into two components, designated PCA1 and PCA2. Two-dimensional PCA scatterplots were used to demonstrate the discriminant analysis results, which were evaluated using the CART machine learning classifier and the 'K-Fold' cross-validation function from Python's Scikit-Learn module. Class-separation surfaces were used in the plots to highlight the differences between different classes. The dataset was divided into K subsets for K-fold cross-validation. The model was then trained and evaluated five times (K = 5), with a different subset used as the test set each time, while the remaining subsets were used for training.
# Evaluation of IEEE802.15.4g for Environmental Observations
## 4 Analysis
### Smart Agriculture Scenario  
In this scenario, high data rates with high PDR are achieved up to 337 m, and maximum length of the radio link is reached around 615 m away from the TX node.
## 3 Sensor Technologies in Chemical and Elemental Detection  
Detection of chemical compounds and elements in plants often relies on laboratory techniques. While these methods can yield high accuracy and provide quantitative information, they are inherently destructive. They require plant tissue to be sampled, processed, and often destroyed. These techniques limit the possibility of repeated measurements on the same plant and increase labor, time, and cost, making them less feasible for large-scale testing or real-time monitoring of plants [8]. In contrast, non-destructive sensing technolo  
Figure 1: The PRISMA 2020 Flow diagram of screening process.  
gies, such as hyperspectral cameras and spectrometers, have been shown to be powerful alternatives. These tools allow for the detection of plant chemical composition without damaging the plant, enabling repeated measurements of the same plant over time and preserving the plant's integrity. Hyperspectral imaging, in particular, captures a wide range of spatial and spectral data simultaneously [8]. Similarly, spectrometers provide precise spectral information at a specific point on the plant, enabling focused and detailed analyses. These non-destructive methods not only accelerate data acquisition but also support sustainable and efficient monitoring, making them invaluable in modern agricultural research and production systems. The next subsections explore the aforementioned technologies in more detail.  
Spectral measurements indoors and outdoors both introduce challenges. While Indoor measurements are inherently more controlled, they still require a strict illumination setup, usually a combination of LED and fluorescent lighting, which can introduce noise. Outdoor measurements introduce variability in lighting conditions, such as angle of light and cloud cover, requiring an advanced real-time algorithm. It can also be affected by temperature, humidity, and atmospheric interference.
# DCS-ST for Classification of Breast Cancer Histopathology Images with Limited Annotations
## 4 Experiment
### Ablation Study  
To investigate the effectiveness of the proposed dynamic window prediction and cross-scale attention mechanisms, we conduct ablation experiments on the BreakHis, Mini-DDSM, and ICIAR2018 datasets following the implementation settings described in Sec. IV-A. As shown in Tables 7 and 8, the proposed DCS-ST consistently outperforms the baseline Swin-Transformer across all magnifications and classification tasks. In the BreakHis data set, DCS-ST achieves an AUC-ROC of 0.9528 at 200\(\times\) magnification, improving upon Swin-Transformer by 0.0098 (=0.9528 \(-\) 0.9430). Balanced Accuracy and F1 Score also increase by 0.0149 and 0.0026, respectively. Similar improvements are observed at other magnifications. In the Mini-DDSM dataset, DCS-ST improves the AUC-ROC, the balanced precision, and the F1 score by 0.0514, 0.0542, and 0.0695, respectively, compared to the baseline. Furthermore, on the ICIAR2018 dataset, DCS-ST achieves an AUC-ROC of 0.8781,surpassing Swin-Transformer by 0.0502, with Balanced Accuracy and F1 Score increasing by 0.0396 and 0.0632, respectively. The confusion matrices in Figures 6 and 7 further illustrate these improvements. At 200\(\times\) magnification on the BreakHis dataset, the number of correctly classified benign images by DCS-ST increases from 1411 to 1466, while malignant classifications also increase from 1411 to 1466, substantially reducing misclassification rates. These results demonstrate that the proposed dynamic window predictor and the cross-scale attention module effectively enhance feature extraction and classification robustness under limited annotation scenarios. Figure 5: Magnification-Wise ROC and PR-AUC Trends of DCS-ST on BreakHis. Figure 6: Comparison of the Confusion Matrices of the BreakHis Dataset. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Network Models**} & \multicolumn{4}{c}{**Mini-DDSM Dataset (Three-Class Classification)**} & \multicolumn{4}{c}{**ICIAR2018 Dataset (Four-Class Classification)**} \\ \cline{2-9}  & **AUC-ROC** & **Balanced Accuracy** & **F1 Score** & **Cohen’s Kappa** & **AUC-ROC** & **Balanced Accuracy** & **F1 Score** & **Cohen’s Kappa** \\ \hline Swin-Transformer [5] & 0.7820 \(\pm\) 0.0173 & 0.6999 \(\pm\) 0.0140 & 0.5972 \(\pm\) 0.0137 & 0.3886 \(\pm\) 0.0266 & 0.8279 \(\pm\) 0.0237 & 0.7375 \(\pm\) 0.0325 & 0.6080 \(\pm\) 0.0476 & 0.4750 \(\pm\) 0.0651 \\
**DCS-ST** & **0.8334 \(\pm\) 0.0523** & **0.7541 \(\pm\) 0.0338** & **0.6687 \(\pm\) 0.0857** & **0.6355 \(\pm\) 0.0722** & **0.8781 \(\pm\) 0.0201** & **0.7771 \(\pm\) 0.0207** & **0.6712 \(\pm\) 0.0368** & **0.6542 \(\pm\) 0.0415** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Ablation Experiment: Performance Comparison of Swin-Transformer and DCS-ST Based on Mini-DDSM Dataset (Three-Class Classification) and ICIAR2018 Dataset (Four-Class Classification)
# Reliable and Energy-Efficient LEO Satellite Communication with IR-HARQ via Power Allocation
## 4 Algorithm Implementation and Simulation Results  
In this section, numerical simulation results are presented based on the above theoretical analysis. The simulation experiment mainly includes the calculation results of outage probability using different methods and the energy efficiency of the proposed variable power allocation method.
# Inspiration for Seismic Diffraction Modelling, Separation, and Velocity in Depth Imaging
## Materials and Methods
### Regional Geology  
In the southeast Asian basins, there are several geophysical challenges [23]. Some of them are listed below:  
* Imaging thin sands, which is often beyond seismic resolution;
* Imaging below gas clouds;
* Karst imaging in carbonate fields;
* Fractured basement imaging and its internal architecture;
* Understanding the wave propagation of ineffective media and related anisotropy;
* Velocity analysis and anisotropy;
* Multiple eliminations;
* Diffraction imaging for small-scale events.  
The focus of this research is to image the fractures that are the main challenges faced in the field, including fractured basements, fracture distribution, connectivity, and lateral variation, which cause poor seismic imaging.  
A regional map of the Malaysian basin is shown in Figure 4a--on the left side is the Malay Basin, the right side shows the Sabah Basin, and the below-right side shows the Sarawak Basin.  
Figure 4: (**a**) Geographical map of the Malay Basin. Shown are the provinces of Peninsula in the Malay Basin, Sarawak, and Sabah Basin [24]. The Malay Basin subsurface structure shown with a regional cross-section view. (**b**) South-west to north-south section. The fractured basement can be seen at a depth of 2–5 km and varies with lateral extension. (**c**) North-west to south-east section [5].  
As mentioned, our objectives are to focus on fractures, so in this study the Malay Basin is considered. A regional cross-section of the Malay Basin is shown in Figure 4b,c. As already mentioned, the difference between a good well and a dry well is whether it encounters main fracture corridors, which must be known before drilling. However, the identification of fractures is impossible without imaging the fractured basement, and these complex structures cannot be identified by conventional imaging alone.