esa-sceva/satcom-chunk-collection huggingface dataset:
# HSVNet: Reconstructing HDR Image from a Single Exposure LDR Image with CNN
## 4 Analytical Study
### Loss Function Variations  
We ran variations on different loss functions.  
1. Cone space distance: Since HSV color space can be represented as a cone model, the most intuitive difference between two HSV colors will be a Euclidean distance in a cone space. In this method, we convert HSV colors of \(H\), \(S\), and \(V\) to points in a cone space and calculate a Euclidean distance between the prediction and the target (Figure 11 left).
2. Wrap-around distance (\(d_{h}\)): In this method, we use mean absolute error (MAE) for each channel difference. Since \(H\) values are angles, we compute wrap-around distance, \(d_{h}\), for \(H\) loss as in Equation (2), which is the angle of the shortest arc between two \(H\) values (Figure 4).
3. Square root of wrap-around distance (\(\sqrt{d_{h}}\)): We tried using the squared root of the wrap-around distance, \(\sqrt{d_{h}}\), for \(H\) loss because we believe the penalty should be similar for big errors as described in Section 3.2. MAE was applied to \(S\) and \(V\) loss.
4. Circle plane distance: We converted \(H\) values to the points on a unit circle in polar coordinates and calculated the Euclidean distance between two points for \(H\) loss as shown in the right image of Figure 11. This function also shows steeper slopes for smaller distances as the square root function. MAE was applied to \(S\) and \(V\) loss.
5. Modified ramp: We cropped wrap-around distance \(d_{h}\) and set the maximum value 1 when \(d_{h}\) is over \(60^{\circ}\), as described in Section 3.2 (Figure 5b). MAE was applied to \(S\) and \(V\) loss. We tested the network with various degrees for the penalty threshold, and \(60^{\circ}\) performs better than others, as shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network Variations** & **L1** & **HDR-VDP-2.2** & **PU21-SSIM** & **PU21-PSNR** \\ \hline network (1) & 0.0602 & 51.8483 & 0.7416 & 15.4565 \\ network (2) & 0.0605 & 51.8748 & 0.7288 & **15.7509** \\ network (3) & **0.5770** & **52.1077** & **0.7330** & 15.1419 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The test results for networks (1), (2), and (3) shown in Figure 10. The values in bold indicate the best scores for each metric.  
Figure 10: Block variations of the network. (**a**) Convolution layer followed by a pooling layer, (**b**) one non-pooling convolution layer and one convolution layer followed by a pooling layer (ours), and (**c**) two non-pooling convolution layers and one convolution layer followed by a pooling.  
Table 4 presents test results for different loss functions with the same mask parameters. Although there is no single method which outperforms others in all factors, the last method of modified ramp function, \(Min(6d_{h},1)+MAE_{s,v}\), produces visually plausible results, as well as the satisfactory test results in general.
###### Abstract  
Manual assignment of Universal Decimal Classification (UDC) codes is time-consuming and inconsistent as digital library collections expand. This study evaluates 17 large language models (LLMs) as UDC classification recommender systems, including ChatGPT variants (GPT-3.5, GPT-4o, and o1-mini), Claude models (3-Haiku and 3.5-Haiku), Gemini series (1.0-Pro, 1.5-Flash, and 2.0-Flash), and Llama, Gemma, Miktral, and DeepSeek architectures. Models were evaluated zero-shot on 900 English and Slovenian academic theses manually classified by professional librarians. Classification prompts utilized the RISEN framework, with evaluation using Levenshtein and Jaro-Winkler similarity, and a novel adjusted hierarchical similarity metric capturing UDC's faceted structure. Proprietary systems consistently outperformed open-weight alternatives by 5-10% across metrics. GPT-4o achieved the highest hierarchical alignment, while open-weight models showed progressive improvements but remained behind commercial systems. Performance was comparable between languages, demonstrating robust multilingual capabilities. The results indicate that LLM-powered recommender systems can enhance library classification workflows. Future research incorporating fine-tuning and retrieval-augmented approaches may enable fully automated, high-precision UDC assignment systems.  
universal decimal classification; large language models; conversational systems; recommender systems; prompt engineering; zero-shot classification; hierarchical similarity Article
# Non-Uniform Clustering Algorithm for UWSNs Based on Energy Equalization
## 2 Related Works
The EULC and UCUBG algorithms consider the influence of the number of neighbor nodes on clustering but do not consider the problem of the distance from the node to the cluster head, as well as the impact of the coverage redundancy of cluster heads. The HAECR algorithm outperforms UCUBG in terms of balancing network energy consumption but only in static scenarios. This algorithm, combined with the number of neighboring nodes and the distance from the actual dynamic environment, constructs the density function to address the impact of uneven node distribution on cluster splitting and also introduces the coverage redundancy index to handle the impact of easy death of cluster heads on the detection area. It can be effective in prolonging the network lifetime and solving the problem of uneven energy consumption of cluster heads under the uneven distribution of sensor nodes. A qualitative comparison of the clustering protocols is presented in Table 1. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
\begin{tabular}{c} Residual energy, node \\ coverage redundancy \\ \end{tabular} & 3D & Dynamic & Single-hop & Multi-hop \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Qualitative comparison of clustering protocols.
#### 3.1.1 Variation of Oxygen Ratio \(n_{\rm O2}\)  
Figure 2 shows the influence of the oxygen ratio \(n_{\rm O2}\) on the permanent gases at constant temperature \(\vartheta\). With increasing oxygen ratio, the H\({}_{2}\) concentration decreased and the CO\({}_{2}\) and H\({}_{2}\)O concentration increased. Also, a slight decrease of CO was observed. This reflects the increased combustion of H\({}_{2}\) and CO to H\({}_{2}\)O and CO\({}_{2}\) due to the higher oxygen supply. The CH\({}_{4}\) and C\({}_{y}\)H\({}_{y}\) concentrations were not influenced by the oxygen ratio in the investigated range. A rather constant value was observed for the gas yield.  
In most experiments found in the literature, other parameters such as temperature also varied when different \(n_{\rm O2}\) were investigated. Therefore, it is difficult to compare the trends [22; 47; 48; 49]. For coal gasification in a steam-oxygen blown slightly pressurized spouting bed at 940 \({}^{\circ}\)C [50], \(n_{\rm O2}\) variation also showed increasing CO\({}_{2}\) concentration whereas H\({}_{2}\) and CO were increasing until \(n_{\rm O2}\) = 0.35 but then decreased with increasing \(n_{\rm O2}\). The reference connects this to increased gas yield and carbon conversion up to \(n_{\rm O2}\) = 0.35 followed by constant values for higher \(n_{\rm O2}\). The trend of higher y\({}_{\rm CO2}\) for higher \(n_{\rm O2}\) in that reference is fitting well to this work. The variation of gas yield and carbon conversion with \(n_{\rm O2}\) is significantly dependent on the fuel reactivity and temperature, which could be the explanation for the differences between both studies.
# Resource-Aware ECG Classification with Heterogeneous Models in Federated Learning
## 3 Materials and Methods
### HeteroFL Framework for ECG Classification
This figure shows the global model \(W_{g}^{0}\) on the central server, connected to five local ECG clients, each assigned one of three computational complexity levels: \(L^{1}\) (full model), \(L^{2}\) (75% of the model), and \(L^{3}\) (50% of the model). where \(W_{g}^{t}\) represents the global model weights for clients at complexity level \(p\), and \(W_{l}^{p}\) denotes the local updates from participating clients. The updated global model is distributed back to clients for further training. The learning rate \(\eta\) and computational settings are adjusted to optimize future training rounds. This iterative process continues until the global model converges to an optimal performance level. After every \(t\) round, the global model will be updated accordingly. \[W_{g}^{t}=A\sum_{i=1}^{t}W_{g}^{t} \tag{4}\]  
where \(W_{g}^{t}\) is the updated global model after each round of local model training and the aggregation of local weights from clients with varying computational complexity levels and \(A\) is the aggregating function. HeteroFL ensures that local models remain stable by maintaining a shared subnetwork structure across all complexity levels. Even low-resource clients contribute meaningfully, as their trained parameters align with the global model's structure. This heterogeneous model strategy ensures that ECG classification benefits from FL without excluding lower-capacity devices.
## 3 Methodology
### CADyUNet Architecture  
The proposed CADyUNet consists of three separate components: the encoder, the decoder, and the skip connections. The framework of CADyUNet is displayed in Figure 5.  
The CADyUNet encoder, which contains four encryption blocks, is used to capture image features. Every encryption block contains a dy-block and a conv-block to increase the representational capacity of the proposed CADyUNet. Each encryption block is followed by a downsampling layer, which uses the soft pool method to preserve critical information, with the exception of the last encryption block.  
The decoder of CADyUNet is used to recover image pixels, including three decryption blocks, each consisting of a conv-block and a dy-block. The images processed by a decryption block are transmitted to an upsampling layer to restore the image pixels. Then, through the skip connection structure, the recovered images are concatenated with images of corresponding sizes coded by the encoder stage.  
The skip-connection structure of CADyUNet is combined with an improved CA mechanism. The shallow features captured by the encoder are recoded through the improved CA block before being transmitted to the decoder in the skip connection. The improved CA mechanism recodes the data and sets different location pixels to different weights. The pixels at the location of the hippocampus are set to high weights, and the other background location pixels are set to low weights. The improved CA mechanism proposed in this work can significantly increase hippocampus segmentation accuracy.  
Figure 4: Conv-block and dy-block.  
Figure 5: The proposed CADyUNet.  
The last layer of CADyUNet is a convolutional operation with a kernel size of \(1\times 1\times 1\), which restores the image's number of channels to 1. Additionally, to conserve computing resources, the number of channels in CADyUNet is decreased by four times compared to the number of channels in 3D U-Net in this study.  
The designed CADyUNet is an automatic hippocampus segmentation network similar in architecture to 3D U-Net. In the structure of CADyUNet, dynamic convolution operations with stronger feature extraction capabilities are introduced in the encoding and decoding steps. The introduction of dynamic convolution greatly increases the network's performance without increasing its depth or width. In addition, enhanced CA mechanisms are introduced in each skip connection so that shallow features are recoded with different weights. Finally, soft pooling methods are used in each downsampling layer of CADyUNet, which can greatly reduce the loss of important information.
###### Abstract  
We describe, for the first time, a successful strategy for synthesizing chiral periodic mesoporous organosilica nanoparticles (PMO NPs). The chiral PMO nanoparticles were synthesized in a sol-gel process under mild conditions; their preparation was mediated by hydrolysis and condensation of chiral-bridged organo-alkoxysilane precursor compounds, (OR')\({}_{3}\)Si-R-Si(OR')\({}_{3}\), in the presence of cetyltrimethylammonium bromide (CTAB) surfactant. The resulting nanoparticles were composed merely from a chiral- bridged organo-alkoxysilane monomer. These systems were prepared by applying different surfactants and ligands that finally afforded monodispersed chiral PMO NPs consisting of 100% bridged-organosilane precursor. In addition, the major advancement that was achieved here was, for the first time, success in preparing magnetic chiral PMO NPs. These nanoparticles were synthesized by the co-polymerization of 1,1'-((1,2,2)-1,2-diphenylethane-1,2-diyl)bis(3-(3-(triethoxysilyl) propyl) urea) chiral monomer by an oil in water (o/w) emulsion process, to afford magnetic chiral PMO NPs with magnetite NPs in their cores. The obtained materials were characterized with high-resolution scanning electron microscopy (HR-SEM), high-resolution transmission electron microscopy (HR-TEM), energy-dispersive X-ray (EDX) spectroscopy, powder X-ray diffraction (XRD), solid-state NMR analysis, circular dichroism (CD) analysis, and nitrogen sorption analysis (N\({}_{2}\)-BET).  
magnetic nanoparticles; chiral materials; periodic mesoporous organosilica; nanoparticles 1
Footnote 1: _Appl. Sci._**2020**, _10_, 5960; doi:10.3390/app10175960
## 3 Results
### Phenology Estimated from In Situ Reflectance-Based Indices and SIF  
In this section, we compare the phenology estimated from three in situ observations including GCC90, NDVI\({}_{\text{Ground}}\) and ground-based SIF data (SIF\({}_{\text{Ground}}\)). Since all these measurements were collected only a few meters above the canopy, and because their high temporal frequencies, the effects of forest type heterogeneity and temporal composite are less pronounced.  
Among these three in situ data, SIF\({}_{\text{Ground}}\) shows the best synchronicity with the tower-based GPP (Table 2) with a MAE value of 0.08 and 0.07 in in the spring and autumn seasons, respectively. In contrast, NDVI\({}_{\text{Ground}}\) demonstrates the worst synchronicity with GPP with a MAE value of larger than 0.3 in the both spring and autumn seasons (Table 2). Compared to the other two in situ reflectance-based data, SIF\({}_{\text{Ground}}\) exhibits faster changes in both spring development and autumn senescence (Figure 2d). Among these three, SIF\({}_{\text{Ground}}\) demonstrates the best capacity to detect the GPP-based phenological events in both the spring and fall seasons: its S2 occurs only three days earlier than the GPP time series, while the other two time series yield earlier dates of S2 by longer than one week (Table 1). Similarly, the estimated A2 date from SIF\({}_{\text{Ground}}\) is biased by only of six days later compared to GPP, while these dates in both GCC90 and SIF\({}_{\text{Ground}}\) are more than 20 days later (Table 1). We also found that SIF\({}_{\text{Ground}}\) shows a relatively better performance in estimating the rest of phenological metrics (S1, S3, A1 and A3), but discrepancies still exist (Table 1). The two-year comparison using measurements in 2014 and 2015 also shows that SIF\({}_{\text{Ground}}\) provides similar S2 and A2 as GPP (Tables S4 and S6). Especially, SIF\({}_{\text{Ground}}\) provides a clearly better estimation of A2 compared to GCC90 and NDVI\({}_{\text{Ground}}\).
# DNS over HTTPS Tunneling Detection System Based on Selected Features via Ant Colony Optimization
## 4 Experimental Results and Discussion
### Feature Analysis and Discussion
The stark underperformance of KNN and CNNs, particularly in Non-Doh specificity, highlights the limitations of distance-based and deep learning models in multiclass scenarios with imbalanced or semantically ambiguous categories. These results advocate for ensemble methods (XGBoost, RF) in operational environments requiring high precision across heterogeneous network traffic while cautioning against deploying less interpretable models (CNNs) without targeted architectural optimization. To further validate the effectiveness of the proposed approach, approximately 1000 records of live network traffic were collected and structured to match exactly the feature set and format of the CIRA-CIC-DoHBrw-2020 dataset. After undergoing the same preprocessing steps, the data were utilized to train the classifiers using the previously optimized feature subsets. The classification results, in both binary and multiclass scenarios, demonstrate that all models maintained performance levels comparable to those achieved on the original dataset, with only slight variations observed. These findings confirm the robustness and generalizability of the selected features and classification models across different classification tasks. A detailed comparison of the results is presented in Table 8.
## 2 Materials and Methods
### Experimental Procedures
#### 2.3.2 Experimental Sessions  
Assuming inter-sessional standardisation, the participants attended all experimental sessions after having fasted (12 h) and after having abstained from exercise (24 h). Upon arrival, a refractometer (Sper Scientific, Ltd., Scottsdale, AZ, USA) was used to measure urine specific gravity (USG) to ensure each participant was in a dehydrated state, reading >1.050 [33], as dehydration may increase the likelihood of cramp onset [34].  
To minimise effects between trials, testing sessions were performed on the dominant leg at least one week apart [17]. The leg was prepared as per the familiarisation session, and electrode positions were marked with permanent marker [30] and were photographed to enable replication in subsequent trials. The participants were instructed to re-mark the sites if fading was observed [30]. During the initial testing session, prior to the first volitional contraction, baseline FHB activation was measured for 15 s while the participant relaxed the muscle. Before cramp induction, the participant was instructed to refrain from stretching the upcoming cramp, allowing maximum duration. At their pre-established CTF, a cramp was induced using a 200 mA stimulation for 2 s. Immediately following cramp detection and verification (10 s after electrical stimulus initiation), the participant was signaled to administer the intervention (PJMR, PJI, or water ingestion). Every 10 s during the cramp, the participant was instructed to rate the severity of discomfort experienced on a 100 mm visual analogue scale (VAS), where 0 = no discomfort and 10 = maximal discomfort. A cramp was considered alleviated when the EMG activity returned to \(\leq\)5% of the baseline contraction for 3 s.  
Figure 1: Diagram of electrode and stimulation conductor placement. The recording electrodes were placed over the belly of the FHB muscle on the medial aspect of the foot. The reference electrode was placed over on the ipsilateral lateral malleolus, and the stimulating conductor was positioned inferior to the medial malleolus.
# A Predictive Method for Temperature Based on Ensemble EMD with Linear Regression
## 4 Experimental Data
### Experimental Data Obtained from Real-World  
In our experiment, real-world temperature data were utilized. Within meteorology, temperature data hold significant importance and serve as a representative metric. They directly reflect natural phenomena, remaining unaffected by human interference, and exhibit complex and fluctuating patterns in the meteorological realm.  
These temperature data constitute a portion of weather data released by the US Embassy. Specifically, they encompasse climate records for Beijing during the period spanning from January 2010 to December 2014, with the weather having been recorded on an hourly basis. The dataset comprises various parameters including the date, weather conditions, time, PM2.5 levels, duration over which snowfall accumulated, temperature, wind direction, wind speed, dew point, atmospheric pressure, and additional meteorological details.  
Access to the initial temperature dataset file is provided via the UCI website, which facilitates further analysis and research endeavors.
## 4 Results and Discussion
### Geophysical Logging Response Characteristics of Coal Quality  
#### 4.3.1 Logging Response Characteristics of Moisture Content  
Moisture in coal exists as both free water, which readily evaporates, and endogenous moisture adsorbed onto clay particle surfaces. The moisture content exhibits an initial decrease followed by an increase with rising apparent resistivity and density (Figure 6a,c), a trend governed by moisture occurrence states and coal reservoir physical properties [25]. In the study area, deep burial depths promote water molecule adsorption onto inorganic mineral surfaces, enhancing ion conductivity and reducing apparent resistivity [51; 52]. However, in middle-to-low rank coals, increasing moisture and humic acid content, combined with active tectonic activity fostering pore-fracture systems, elevate apparent resistivity [14]. Density logging values correlate with coal rank, mineral content, lithotype composition, and reservoir properties [53]. For low-rank bituminous coals, advancing metamorphism  
Figure 5: Scanning electron microscopy image of minerals in coal. Note: (**a**) Kongzhuang Coal Mine (KZ-W), No. 7 coal seam; (**b**) Kongzhuang Coal Mine (KZ-W), No. 8 coal seam; (**c**) Kongzhuang Coal Mine (KZ-W), No. 7 coal seam; (**d**) Xuzhuang Coal Mine (XZ-W), No. 7 coal seam; (**d**) Xuzhuang Coal Mine (XZ-W), No. 7 coal seam; (**e**) Xuzhuang Coal Mine (XZ-W), No. 7 coal seam; (**f**) Xuzhuang Coal Mine (XZ-W), No. 7 coal seam; (**g**) Xuzhuang Coal Mine (XZ-W), No. 8 coal seam; (**h**) Xuzhuang Coal Mine (XZ-W), No. 7 coal seam; (**i**) Xuzhuang Coal Mine (XZ-W), No. 8 coal seam; ID, Iron dolomite; Ca, Calcite; Gy, Gypsum; Py, Pyrite, Ka, Kaolinite; Do, Dolomite.  
reduces moisture, humic acid, and inorganic minerals, lowering seam density. Conversely, higher vitrinite content enhances water molecule adsorption capacity, increasing density [54,55].  
Moisture content negatively correlates with lateral resistivity, natural gamma (GR), short-spaced gamma-gamma (CGS), and acoustic transit time (AC) (Figure 6b,d-f). Water molecules enhance coal conductivity, reducing lateral resistivity [14]. Pore and fracture development in coal slows longitudinal acoustic wave propagation, increasing AC. As moisture rises, wave velocity accelerates, decreasing AC [56, 57, 58]. Radioactive elements concentrate in inorganic minerals; however, clustered or striated mineral infillings in pores/fractures reduce water retention, causing moisture to decline with increasing GR [59,60].