esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Scale Factor Image Super-Resolution Algorithm with Information Distillation Network
## 3 Results
### Comparison of Model Parameters  
Compare the traditional algorithms and the IDMF-SR on the Urban100 test dataset Under \(\times\)4, the relationship between the average PSNR of the model and the parameter, as shown in Figure 10. The IDMF-SR proposed in this section changes the feature learning module based on Meta-SR [21], adopts an information distillation structure, progressively extracts image features, and cascades features. The feature does not fully participate in the next stage of the feature learning task. Therefore, only a few parameters can be used to achieve fast and accurate image super-resolution reconstruction, preventing parameter redundancy. It can be seen from Figure 10 that IDMF-SR has a 69.8% reduction in parameter quantity than Meta-SR [21] and a 2% increase in PSNR value. The algorithm in this section makes a trade-off between the number of model parameters and the PSNR value, which  
Figure 9: The visual effect of each algorithm under \(\times\)2 upscale factor. (**a**) VDSR; (**b**) Lap-SRN; (**c**) Meta-SR; (**d**) RCAN; (**e**) IDMF-SR (Ours); (**f**) HR (Original); (**g**) Set14 img_005 (2\(\times\)); (**h**) VDSR; (**i**) Lap-SRN; (**j**) Meta-SR; (**k**) RCAN; (**l**) IDMF-SR (Ours); (**m**) HR (Original); (**n**) Set5 img_005 (2\(\times\)).  
not only ensures the improvement of the SISR performance but also reduces the number of parameters.
# Targeted Delivery Strategies for Hydrophilic Phytochemicals
## 3 Hydrophilic Phytochemicalemicals
### Phenolic Acids
#### 3.5.3 Carrier-Based Delivery Strategies  
To further optimize its bioavailability and skin penetration, ferulic acid has been effectively encapsulated in several carrier systems, including solid lipid nanoparticles (SLN), nanostructured lipid carriers (NLC), liposomes, and nanoemulsions [135]. Liposomal formulations enhance its solubility and facilitate deeper penetration through the stratum corneum by mimicking the phospholipid layers of cell membranes, allowing ferulic acid to integrate seamlessly into the lipid matrix of the skin [148; 149; 150]. Nanoemulsions, characterized by their ultra-small droplet size, further improve the diffusion of ferulic acid through the skin barrier, enhancing its absorption into deeper layers [149; 150]. Lipid-based carriers like SLN and NLC not only stabilize ferulic acid but also protect it from oxidative degradation and UV-induced damage, extending its therapeutic effects. These nanoparticles reorganize the lipid structure of the stratum corneum, reducing its barrier properties and facilitating improved transdermal delivery [148; 149; 150]. The biphasic release profile of SLN and NLC ensures both immediate and controlled release, enhancing the compound's retention in skin tissues and minimizing irritation [149]. Moreover, hydrogels have been explored as carriers for ferulic acid, providing sustained release and prolonged skin retention, which enhances its antioxidant and protective effects [149]. Although detailed studies on ferulic acid's individual transdermal mechanism are limited [148], its encapsulation in carriers clearly optimizes its permeation and stability. The incorporation into solid lipid nanoparticles, liposomes, nanoemulsions, and hydrogels not only enhances its solubility  
Figure 4: Chemical structure of high hydrophilic phenolic acids; **(a)** Ferulic acid **(b)** Caffeic acid **(c)** Chlorogenic acid.  
but also improves its bioavailability and protective effects against oxidative stress, making it a valuable component in advanced dermatological formulations [135; 148; 149; 150].
## 4 Discussion
### Variability of the SAR Signal with the Spatial Scale
Indeed, the signals extracted with poor radiometric resolution (high impact of speckle effects and low number of pixels used to calculate the median value of backscattering coefficients) give the worst simulation performance. Conversely, signals extracted with better radiometric resolutions offer better quality simulations. Figure 8: Comparison between the values of backscattering coefficients extracted using different sizes of buffers for the X- (**a**) and C-bands with the 3 polarization states: (**b**) HH, (**c**) VV, and (**d**) VH. Whatever the considered configuration, the buffer with a radius of 30 m is used as a reference for the comparison.
## 2 Proposed Routing Scheme
### Proposed Network Model  
The network model as depicted in Figure 1 is defined as follows. It is a network \(G(BS,\ GW,\ Ho,\ He,L)\), where \(BS\) is the network base station, \(GW\) is the network gateway, \(Ho\) is the set of homogeneous nodes, \(He\) is the set of heterogeneous nodes, and \(L\) is the set of links connecting any of the defined nodes (any node in the union \(BS\cup GW\cup Ho\cup He\)).  
The following are the network properties.  
* The network can be subdivided in four subnetworks. located in Regions 1, 2, 3, and 4 as shown in Figure 1.
* There exists at least one node in Region 4 connected with a node in Region 3.
* The gateway is in Region 2 and is connected to the BS.
* The gateway is connected with the base station.
* Each node in Region 1 is connected to the base station.
* All nodes in Regions 3 and 4 are not connected with the base station.
## 3 Methods
### Characterization of Change in Productivity Using TSS-RESTEND  
#### 3.1.1 NDVI and Precipitation Relationships  
The Bimonthly GIMMS NDVI data were filtered using a quality control (QC) procedure to remove non-reliable values based on a quality flag [49,50]. Pixels with at least 75% reliable values were used. Non-vegetated pixels were excluded based on a threshold of a median NDVI of less than 0.1 [50,51]. Out of 4474 pixels, a total of 4454 pixel were used in the analysis. Complete mean monthly timeseries of NDVI (ctsNDVI) was assessed over each pixel. The PRISM precipitation data was resampled using a bilinear method to match the spatial resolution of the GIMMS NDVI and assessed at monthly time scale.  
An ordinary Least Square regression (OLS) was used to develop the relationships between the ctsNDVI and the complete timeseries of optimal accumulated precipitation for each pixel for the 1982-2015 period (referred to as ctsVPR) [8]. The optimal accumulated precipitation was determined by identifying the pairs of ctsNDVI and accumulated precipitation that provide the highest correlation coefficient. The correlation coefficients of all possible OLS relationships between ctsNDVI and a matrix of precipitation accumulation period (1-12 months) with offset period (0-3 months) were evaluated following [8]. The relationships with the highest correlation coefficients were used to calculate the residual between the observed and predicted ctsNDVI (referred to as ctsVPR-residual).
# Knowledge Distillation-Based GPS Spoofing Detection for Small UAV
## 6 Evaluation  
With compact and efficient GPS spoofing algorithms, UAVs can avoid attacks remotely, and the threats from the manipulated UAVs can be reduced. With KD and LSTM, our proposed approach enables UAVs to detect GPS spoofing attacks efficiently and automatically. In this section, we will evaluate the performance of LSTM-based GPS spoofing and the efficiency of KD-enabled lightweight detection. We utilize the open-source dataset derived from IEEE DataPort [39]. The dataset contains a comprehensive log of a benign flight and one where the UAV experiences GPS spoofing attacks. In the dataset, a Great Scott Gadgets SDR with GPS-SDR-SIM tools is utilized to enact GPS spoofing attacks. The evaluation is executed on Pycharm 2022.3.2 and the configuration of the workstation is as the following. CPU: Intel(R) Core(TM) i9-10900X CPU @ 3.70 GHz; GPU: NVIDIA RTX A4000; OS: Ubuntu 18.04 LTS.
## 4 Limitations and Future Directions
Using all the temporal signals and spatial imaging data, a large number of modelling parameters can be estimated [59] using sophisticated methods such as steepest gradient algorithms and particle swarm techniques [60]. Multi-electrode noninvasive electrocardiogram may act as a confirmative test for existing ischemia, i.e., the presence of downstream sub-perfused myocardium [61]. Autoregulatory processes can be personalized using a combination of ultrasound, transcranial doppler and similar noninvasive routine clinical measurements [62]. Although the model is theoretical in nature, the presented results will guide our future work. As such, the findings of the study remain informative for deeper lumped parameter modelling and will inform our spatially extended modelling.
## 3 The Proposed Model
### Feature Extraction and Embedding
#### 3.1.2 Text Feature Extraction and Embedding  
The objective of this phase is to transform raw textual data into high-dimensional dense vectors by employing a pre-trained language model [28]. In this context, we developed a text feature extractor based on the principles of transfer learning, utilizing the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model, which is grounded in the Transformer architecture. The raw textual information utilized in this experiment comprises fashion item titles and detailed descriptions. Given that the IQON3000 dataset was scraped from web sources, it contains certain special characters and advertising terminology. Upon extracting vectors directly from the raw text and computing the cosine similarity between various fashion items, we observed that the presence of similar advertising terminology significantly inflated the overall similarity scores [29]. To achieve a more precise representation of fashion item texts and to more accurately and independently describe the attribute characteristics of fashion items, this experiment employed regular expression matching for data cleansing. This process involved removing special characters used to denote spaces in web data and filtering out high-similarity advertising terms across product text representations. The cleansed text data were subsequently fine-tuned using the BERT model.  
BERT, a model based on the Transformer architecture, is typically utilized in two primary modes: unsupervised pre-training and supervised fine-tuning [30]. For our pre-trained BERT model, we employed BERT_base_japanese. This model architecture comprises 12 layers, a hidden layer dimension of 768, and 12 multi-head self-attention heads. The pre-training data were derived from the Japanese Wikipedia corpus, and fine-tuning was conducted using the supervised fine-tuning (SFT) approach. Given the unique characteristics of Japanese text, we initially processed the input text using the MeCab morphological analyzer with the IPA dictionary for token-level segmentation. Subsequently, the text was subdivided into subwords via the WordPiece algorithm. Considering the average length of the text data and the computational efficiency of the model, we truncated the input text length to 256 tokens. Notably, to effectively capture the textual vector representations of fashion items, we applied average pooling to both the first and last layers of the BERT output.  
\[T=\frac{1}{2}\Big{(}Pool(BERT_{first})+Pool(BERT_{last})\Big{)}\]  
where Pool\((\cdot)\) denotes average pooling. The first layer retains surface-level textual features, while the last layer preserves semantic information [31]. The intermediate vectors obtainedfrom these two layers were concatenated and then subjected to average pooling, resulting in a 768-dimensional raw text vector for each fashion item. Finally, a fully connected layer followed by a ReLU layer was employed to reduce the output dimensionality to \(d_{\text{n}}=300\), yielding the ultimate textual feature vector representation.  
\[E_{2}=ReLU(W_{t}\cdot\text{T}+b_{t})\]
## 3 Result and Discussion
### SERS Specificity of the Positively Charged Au NPs-Embedded MIHs  
To evaluate the specificity of MIHs for the detection of New Red, New Red and its structural analogues (Amaranth, Erythrosine B sodium, and Allura Red) were chosen, and 5.0 mg of the MIHs were incubated with 1.0 mL of aqueous solution of each colorant at a concentration of \(1.64\times 10^{-5}\) M for 2 h. Then, the MIHs were used as substrates to measure SERS spectrum of each colorant.  
Figure 5a-d demonstrates the SERS spectra using the MIHs as substrates after the MIHs were, respectively, incubated with the solutions of New Red, Erythrosine B sodium, Amaranth, and Allure Red at the concentration of \(1.64\times 10^{-5}\) M. Obviously, the four colorants can be readily distinguished according to their corresponding spectrum fingerprint. Strong characteristic signals of New Red at 1572 cm\({}^{-1}\), 1513 cm\({}^{-1}\), and 1439 cm\({}^{-1}\) were observed in Figure 5a. Fairly weak signals of Erythrosine B sodium were detected in Figure 5b. Both Amaranth (Figure 5c) and Allura red (Figure 5d) show no signals at 1572 cm\({}^{-1}\), 1513 cm\({}^{-1}\), and 1439 cm\({}^{-1}\). The results indicate that the three structural analogues did not interfere the detection of New Red when using the positively charged Au NPs-embedded MIHs as substrates and the MIHs indeed possessed imprinted cavities. MIHs were prepared using New Red as a template and, thus, MIHs contained a lot of specific cavities after New Red molecules were removed by washing. These three-dimensional cavities were complementary to New Red molecules in terms of shape recognition, hydrogen bonding, and electrostatic interactions. As a result, Erythrosine B sodium, Amaranth, and Allura Red molecules were very difficult to diffuse into the cavities, and accordingly, cause no interferences to the detection of New Red.
## 3 Malfunction Probability Models for Communication Delays and Bit Errors
### False Trip Probability Model for Inconsistent Delays  
Protection would be activated by an inconsistent delay when it exceeds the implied threshold while no fault occurs within the protection range [20,21]. In this paper, we mainly consider the inconsistent delays caused by the uncertainty of the current measurement error.
# Construction of a 3D Model Knowledge Base Based on Feature Description and Common Sense Fusion
## 2 Related Work
### Construction of a 3D Model Knowledge Base  
Many general knowledge domains establish knowledge graphs to represent knowledge. [PERSON] et al. defined a knowledge graph as a multi-relationship graph composed of entities and relationships, where entities are represented by nodes, and relationships are represented by different types of edges [22]. Unlike the common sense knowledge base, the domain knowledge map pays more attention to the depth of knowledge in professional fields and has been widely used in geographic information, medicine, e-commerce, etc. [23]. [PERSON] et al. proposed a semantic integration method for engineering design to realize the knowledge ontology automation of mechanical engineering part design and manufacturing. The knowledge mainly came from the design documents. Its non-literal sketch recorded the starting and ending points of the boundary, extrusion, rotation, and other operations to improve the description of the shape [24]. [PERSON] et al. studied the construction of a knowledge graph of cultural relics, achieving tasks such as entity extraction, relationship mining, knowledge fusion, and inference from the natural language describing cultural relics and assisting in the construction of smart museums [25].  
Ontology is the core of knowledge representation, organized according to public norms to promote knowledge sharing. Ontology does not inherently specify the mechanisms by which the represented knowledge is used in practice [26]. [PERSON] et al. established a broad basic ontology that included different domains [27]. When new concepts were discovered, they could be automatically assigned to different categories through WordNet, and each feature in the machine-readable comprehensive knowledge base was machine interpretable. [PERSON] and others derived the ontology about the metal material field from Yago open knowledge base based on a string matching algorithm and used the existing knowledge base to efficiently establish the required domain knowledge base [28]. [PERSON] proposed a method for constructing multi-relationship and multi-level knowledge graphs based on the clustering of 3D shapes to achieve multi-pattern search combining 3D components and text data obtained from previous design-related tasks [29].  
In summary, the sources of knowledge were mostly extracted from text. Traditional knowledge representation methods have the advantage of accurate expression and easy reasoning, but they require manual construction, which is time-consuming and labor-intensive. In the knowledge description of 3D models, the relevant corpus data are still scarce. Although there are methods for directly extracting knowledge from features in some production parts' knowledge bases, common 3D models do not all have regular shapes and precise production processes. In addition, there are difficulties in describing the 3D model itself in text, requiring different types of data to describe the 3D model.
# GIS-Analysis for Active Tectonics Assessment of Wadi Al-Arish, Egypt
## 4 Results and Discussion
### Morphotectonic Indices
#### 4.1.5 Valley Floor Width to Valley Height Ratio Index (Vf)  
Values of the Vf index vary with many factors, such as catchment size, rock type, and stream discharge [6]. Thus, Vf values should be estimated and calculated for similar geological conditions [7]. Values of this index were extracted along the main rivers of each catchment/sub-catchment over the entire study area, with a distance between \(\sim\)0.1 and 1 km depending on the size of the catchments/sub-catchments examined. The lowest Vf value was recorded for sub-catchment 56 as 0.58 in the northern part, and the highest value was estimated for sub-catchment 86 as 20.33 in the middle part of the study area. Results for this index are shown in Table S4 in the Supplementary data. Both the lowest and highest values were incised in similar geological conditions. The authors of [66] calculated Vf index values in the SE Spain and suggested that valleys with Vf values \(>\)1 indicate tectonic quiescence and major lateral erosion, and values \(<\)1 indicate active tectonic uplifting. The work in [1] was carried out in central Anatolia in Turkey and stated that values \(>\)1.5 indicate high activity, values between 1.5 and 2.5 moderate activity, and values \(>\)2.5 low activity.
# A Novel Traffic Classification Approach by Employing Deep Learning on Software-Defined Networking
## 4 Results and Discussion
### Discussion
#### 4.3.3 LSTM Model  
Figure 9 shows that the accuracy achieved with LSTM at 20 epochs in the training set is (98.86%), while in the validation set it is (99.63%). Although there is a difference between the accuracy of the training set and the validation set (0.77%), this gap is minimal, indicating effective generalization of the model. The overall trend shows a steady increase in accuracy and a decrease in loss across epochs, supporting the effectiveness of the training and the ability of the model to learn and generalize to unseen data. On the other hand, loss shows a steady downward trend, indicating a continuous improvement in the model's ability to fit the data. These results suggest successful training, with the model achieving high accuracy and low loss on both datasets.  
The F1-score showed high performance in all classes, with values ranging from (94.00%) to (100%). Specifically, classes 1, 3, and 4 achieved an F1-score of (99.00%) and (100%), respectively, indicating high precision and recall in classifying those classes. The precision per class was (100%), suggesting no false positives for any class. However, recall varied between classes, with values ranging from (88.00%) to (99.00%), indicating that the model had some difficulty recovering all instances of class 2 compared to the other classes. Overall, the model performed well in most classes, with high accuracies, F1-score, and recall, suggesting a robust ability to classify the different classes correctly.  
The total training time of the model was 389 s. During this process, (33.00%) of the graphics processing unit (GPU) was utilized, indicating a moderate use of available hardware resources to accelerate model training. This percentage of GPU utilization suggests efficient resource allocation and an appropriate workload for the training task.
# On the Use of Deep Learning for Video Classification
## 3 Video Classification
### Summary of Some Notable Deep Learning Frameworks Developments  
A summary of some deep learnings architectures for video classification is provided in Table 3. These studies are summarized based on the architecture, the datasets, the evaluation metrics, the fusion strategy, and the notable findings. The most common architectures for video classification are fundamentally based on the RNN and CNN architectures; classification accuracy is one of the most common evaluation metrics; UCF-101 and Sports-1M datasets are the choice for validation in most cases, multi-class classification problem is considered in almost all cases, SMART blocks outperform 3D convolutions in terms of spatiotemporal feature learning, and average fusion, kernel average fusion, weighted fusion, logistic regression fusion, and MKL fusion are all proven to be inferior compared to the multi-stream multi-class fusion technique. Moreover, a more applied form of classification in videos is to identify/recommend tags or thumbnails in videos, and this specific task is successfully caried out in [75; 76; 77; 78; 79].
# Experimental Study on Chromaticity Control in Visible Light Communication Systems
###### Abstract  
To apply visible light communication systems in different scenarios, this article utilizes an excellent temperature-adjustable light source mixed with RGB LEDs and applies it in a visible light communication system. It uses color division multiplexing technology to achieve three-channel communication, thereby improving the communication bandwidth of the system. The communication system adopts three constant current driving circuits to control the duty cycle of Pulse Width Modulation (PWM) of each channel, thereby changing the proportion of RGB LEDs, and obtaining different color temperatures to achieve the purpose of color control for mixed-color LEDs. The experimental results show that when adjusting the color temperature, the change in luminous flux is very small, with fluctuations of less than 2.24%. When adjusting the brightness, the color temperature fluctuation is within 40 K, which is less than the 50 K color temperature limit that the human eye can distinguish, and the average color temperature error is 0.609%. Color tolerance less than \(5.5\times 10^{-3}\) indicates good dimming effect, and the communication performance of the system is better in the high color temperature range, which is significantly superior to the low color temperature range. When the error rate is below \(3.8\times 10^{-3}\), the total modulation bandwidth of the three channels reaches 11.7 MHz.  
Keywords:adjustable color temperature; RGB LEDs; PWM; visible light communication _et al._ Article