esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Network Approaches to Optimize 360-Degree Video Streaming
### Network Caching  
One of the fundamental elements that allow VR applications to run is the cache. Caching presented at the mobile network's edge is designed to optimize the bandwidth and latency required by VR 360-degree video streaming. [PERSON], [PERSON] [64] demonstrated a mobile network edge solution for optimizing the bandwidth and latency required for VR 360-degree video streaming. [PERSON], [PERSON] [65] offer L4C2, in-network caching with low latency, low loss streaming technique for low delay-tolerance streaming with improved quality in real-time. [PERSON] [66] created an optimization framework that enables base stations to choose cooperative caching/rendering/streaming techniques that optimize the accumulated reward they receive while serving customers. In order to maximize the overall performance of 360-degree videos provided to users, [PERSON] and [PERSON] [60] proposed a 360 Video Caching approach using Deep Reinforcement Learning, which is a reactive caching strategy that utilizes the Markov Decision Process (MDP) to determine the location of 360 video content in edge cache networks and then uses the Deep Q-Network (DQN) algorithm, a variant of Q-learning, to determine the optimal caching placement and cache the most popular 360 videos at base quality along with a virtual viewport in high quality.
# DEU-Net: A Multi-Scale Fusion Staged Network for Magnetic Tile Defect Detection
## 4 Experimental Setup  
### Dataset Description  
We evaluated our proposed method on the industrial magnetic tile defect dataset [34], as shown in Figure 6. This study focuses on five defect types in the dataset: Blowhole, Fray, Crack, Break, Uneven, and Free (defect-free) conditions. The dataset comprises 1344 samples, addressing the practical challenge of difficulty in acquiring samples for industrial defect detection. The samples exhibit strong diversity, are influenced by varying lighting conditions, and pose a relatively high detection difficulty. To enhance the dataset and prevent overfitting, we applied various image augmentation techniques, including image rotation, flipping, cropping, and transposition. The Table 1 below shows the number of various types of defects in the images.  
Figure 5: Architecture diagram of the decision network.  
The dataset is partitioned into three subsets: 60% of the dataset is allocated to the training set, 20% to the validation set, and the remaining 20% to the test set. We adopted the Monte Carlo cross-validation method and conducted three random splits of the dataset. Instances featuring defects are denoted as positive examples, while instances without defects are labeled as negative examples.
# A Vehicle-Infrastructure Cooperative Perception Network Based on Multi-Scale Dynamic Feature Fusion
## 3 Method
### Multi-Scale Dynamic Feature Fusion
#### 3.3.1 Multi-Scale Feature Construction  
The first step in the Multi-Scale Dynamic Feature Fusion Module is Multi-Scale Feature Construction, which involves downsampling the vehicle features, \(F^{V}\in\mathbb{R}^{H\times W\times C}\), and the decompressed infrastructure features, \(F^{ID}\in\mathbb{R}^{H\times W\times C}\), to various scales using convolution operations. This process ensures that information across different scales is effectively utilized.  
Large-scale features capture broader contextual information within a scene and exhibit lower sensitivity to local changes. They emphasize the overall layout rather than precise positional details, providing stable and reliable semantic information less affected by minor environmental variations. Conversely, small-scale features focus on fine-grained attributes, such as specific vehicle parts or pedestrian movements, delivering detailed information that enhances perception accuracy. By integrating both large-scale and small-scale features, the system maintains robustness while remaining attentive to critical details.  
To generate feature maps at multiple scales, we employ a step-wise downsampling strategy. Vehicle and decompressed infrastructure features are processed through a series of convolutional and pooling layers to obtain features at three different scales. The multi-scale vehicle and infrastructure features are denoted as \(F^{V}_{i}\) and \(F^{ID}_{i}\in\mathbb{R}^{H_{i}\times W_{i}\times C_{i}}\), respectively, where \(H_{i}=\frac{H}{2^{i}}\), \(W_{i}=\frac{W}{2^{i}}\), and \(C_{i}=2^{i-1}C\) represent the dimensions (height, width, and channels) of the features, with \(i\in\{1,2,3\}\) indicating different scale levels.
# Train Me If You Can: Decentralized Learning on the Deep Edge
## 2 Background
### Federated Learning  
The main goal of FL is to port the inference and at least part of the training process to the data source [69]. Edge devices are considered part of a network and are directly connected or connected through a central server [70]. Nevertheless, each edge device is considered autonomous as it can infer about the surrounding environment without any interaction with third parties. Furthermore, they can autonomously adapt to the surrounding environment by performing model re-training. Edge devices are periodically asked to share their new models or model parameters. This can be performed in a centralized [42] or decentralized design [71].  
In centralized design, the data flow is asymmetric. A central server is responsible for aggregating the ML models or parameters, returned by each edge device, and sending back the training results and the updated ML model [42]. In a centralized communication architecture, the data transfer between the manager and the edge can be (i) synchronous or (ii) asynchronous. In a synchronous setting, the central server is responsible for signalizing the beginning of the training pass to the edge [48]. The central server waits until every edge device or at least a portion of them sends the updated parameters. In an asynchronous setting, an edge device can start a new training pass at any time [72]. When the central server wants to deploy a new global ML model, it aggregates the most recent parameters available from each edge device.  
In decentralized design, communication performs among edge devices, where each device can update the global parameters directly [73]. This paradigm shift avoids the integration of a central server but requires each edge device to collect information from all other ones. The communication overhead is proportional to the total number of devices.  
The data partitioning on edge devices is categorized as horizontal, vertical, and hybrid, depending on how data distribute over the sample and feature spaces [74]. Horizontal distribution occurs when different devices share the same feature space but low intersection on the sample space. In vertical FL, data from different devices have the same, or at least similar, sample space; however, they differ in the feature space. There are some scenarios where datasets differ not only in the sample but also in the feature space. In this case, parties are a hybrid of horizontal and vertical partitions.  
Horizontal is the most common data partitioning scheme in Federated Learning Systems (FLSs) due to the cross-device scenario. The first concept of FL, called FedAvg [48], was developed for this data partitioning scheme. The main goal of FedAvg is to train a shared model across clients, minimizing the global loss by performing a weighted average of the local weights and biases. Parameters are weighted by the size of the client's dataset.
## 6 Discussion and Perspectives
### Considerations for Analytical Approaches for C-CTX Detection and Quantification
Moreover, HRMS is usually less sensitive than targeted LC-MS/MS and is insufficient to quantify CTX levels in the sub-ppb range. In \begin{table}
\begin{tabular}{c c c c c} \hline
**Review** & **Organic** & **Eleunt** & **Goal (MS Mode)** & **Precursor Ions** & **References** \\
**Section** & **Eleunt** & **Modifier** & & **(SRM)** & **[M+Na]\({}^{+}\)** \\ \hline
5.2.2 & Methanol & Formic acid and ammonium formate & C-CTX-1 confirmation (MRM) & [M+H–H\({}_{2}\)O]\({}^{+}\) & [175; 176] \\ \hline
5.2.3 & Acetonitrile & Formic acid or ammonium formate & C-CTX-1 analogues (MRM) & [M+H–H\({}_{2}\)O]\({}^{+}\) & [37; 39; 108; 174] \\  & & or ammonium acetate & C-CTXs analogues & [M+Na]\({}^{+}\); [M+H–H\({}_{2}\)O]\({}^{+}\) & [110; 175; 176] \\  & & identification (MRM) & [M+H]\({}^{+}\), [M+H-CH\({}_{3}\)-H\({}_{2}\)O]\({}^{+}\) & [110; 175; 176] \\ \hline
5.2.4 & Methanol and acetonitrile (3:1) & Formic acid and ammonium acetate & 4 CTXs confirmation (MRM) & [M+Na]\({}^{+}\) & [34; 180] \\  & & & (MRM) & [M+NH\({}_{4}\)]\({}^{+}\) & \\ \hline \end{tabular}
\end{table} 
Table 6: Summary of the main analytical conditions used for the confirmation and quantification of C-CTX-1 and the screening of new/putative CTX analogues using (U)HPLC-MS/MS in multiple reaction monitoring, as detailed in Part 5.  
the study of [PERSON] et al., HRMS sensitivity was 2- to 12-fold lower for P-CTXs, but with an increased mass accuracy, the presence of more concentrated P-CTX analogues was confirmed, and interferences determined by co-eluting compounds was also identified [211]. Some LC-HRMS studies have confirmed the presence of targeted C-CTX-1 when reference materials are available [113; 120; 131; 204; 205] and have provided structural information on new C-CTX analogues [113; 120; 131; 205]. LC-HRMS remains a complementary technique. Due to its cost and complexity, utilizing this method requires significant investments and technical skills, making it inaccessible to many Caribbean countries where the control of cigarette fishes is necessary.
## 2 General Description of the Technology
### Injection Methodology  
The methodology and method procedure are based on drilling holes less than 30 mm (typically between 12 and 26 mm), spaced between 50 and 150 cm apart. Figure 3a. Metal pipes are then inserted into these holes down to the established depths, Figure 3b, and the expanding resin is injected into the soil through the pipes. The injection is controlled following specific protocol. The two basic points of this injection protocol include the injection head of the resin, Figure 3c and the laser level that controls the lifting of the structure, Figure 3d.  
The resin is injected into the soil in a liquid state [11]. Almost instantly, a chemical reaction is triggered causing an increase in volume and the resin changes from a liquid to solid state. The expansion pressure of the resin can reach 10.000 KPa. The reaction begins and ends very quickly. The resin reaches its final physical-chemical characteristics in a few seconds.  
When the resin penetrates the soil to be treated and increases in volume, it compresses the soil in all directions. This radial expansion is favored by the pathways that offer the least resistance. The resin continues to expand until the soil prevents any further radial compression. At that point, the only possibility for expansion is an upward displacement of the soil [12].  
When this initial lifting is observed, it means that the consolidating action is being directed vertically upward and that this is the direction that offers the least resistance, while the surrounding soil offers greater resistance in respect of the decrease in the structural load, which means that the  
Figure 2: Tension status under the foundation after treatment.  
foundation soil has been compacted enough to withstand not only the increase in static loads, but also the loads created by the lifting [5,6].  
This initial rising of the structure (tenths of mm), is what makes it possible to confirm the effectiveness of the method in real time. When the resin is injected at different depths, the injection usually begins with the top level, proceeding to the next level once the resin mixture hardens.  
During the injection, the amount of mixture used is measures at each injection point and compared with the nominal consumption for the project. After the injection, a laser level is used to detect any vertical movement of the treated structure. This is the only way to check the effectiveness of soil treatment in real time.
# Generative Adversarial Networks in Histological Image Segmentation: A Systematic Literature Review
## 4 Review Methodology
### Data Extraction and Synthesis  
To ensure consistency, reproducibility, and reliability in the data extraction process, a structured form was developed using Google Forms. This instrument was carefully designed to encompass all elements necessary to answer the research questions, comprehensively characterize the selected studies, and identify gaps and promising trends for future investigations. Each article was read in full by the reviewers, and the relevant data were systematically and consistently extracted. Subsequently, Microsoft Excel (version 2504, Build 16327.20264) was employed as a supporting tool for organizing and systematizing the extracted data. The information collected from each article included in this systematic review covered key aspects essential for a detailed comparative analysis of GAN-based approaches applied to histological image segmentation. Among the extracted data were the year of publication, the organ analyzed in the study, the specific segmentation domain, the nature of the dataset used (public or private), the dataset name, the GAN technique employed, the metrics used to evaluate GAN performance along with their respective results, the specific task assigned to the GAN, the segmentation technique applied, the evaluation metrics for segmentation and their results, the experimental setting or environment,  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Inclusion Criteria (IC)** & **Exclusion Criteria (EC)** \\ \hline (IC1) Studies using GANs for segmentation of histological images. & (EC1) Studies using GANs only to synthesize images, without a focus on segmentation. \\ \hline (IC2) Studies with experimental validation (quantitative or qualitative). & (EC2) Works that do not involve histological or microscopic images. \\ \hline (IC3) Published between 2014 and 2025. & (EC3) Studies using GANs only for classification or detection, not segmentation. \\ \hline (IC4) Articles in English. & (EC4) Articles in a language other than English. \\ \hline (IC5) Full articles. & (EC5) Review, surveys, Case studies, reports, short papers, conference, abstracts, communications, theses, and dissertations. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Inclusion and exclusion criteria for studies.  
the availability of source code or algorithm URLs, the type of learning employed (supervised, unsupervised, or semi-supervised), and the overall objective of the study as reported by the authors. The synthesized results are presented in Supplementary File S1.
## 3 Experimental Procedure
### Coreflooding Test  
The epoxy-molded Berea sandstone was prepared for a core size of 15 cm in length and 3.81 cm of diameter. The epoxy was hardened for 20 h, and a leak test was performed. One core was prepared for each coreflooding test (proposed and comparison formulas). The consolidated core sample was connected to the pump, the pressure transducer, and the fraction collector (Figure 2). The absolute permeability was determined in the brine-saturated sample. The brine had the same salinity as the formation water (0.64 wt% NaCl). The crude oil flowed downward into the brine-saturated sample until reaching 1% of the watercut, and then the initial oil and water saturations were determined.  
A pre-flush using brine was carried out until 1% of oilcut, i.e., extensive waterflooding. The pressure was stabilized to determine the flow rate and the oil production rates to calculate the residual oil saturation. The ASP mixture flowed 0.4 pore volume (PV), and then 1.6 PV of polymer followed. Two ASP cases were compared: one adding the nonionic surfactant, and the other adding heavy alcohol into the anionic surfactant pair (LAS and DOSS). The residual oil saturations and oil recovery  
Figure 2: Schematic diagram of the experimental apparatus for the coreflooding test. ‘ASP’ stands for alkaline–surfactant–polymer and ‘PD’ represents the polymer drive.  
were determined by the material balance from the effluent samples. The effluent salinity was measured to track the progress of the varying slugs. The cumulative residual oil recovery (\(f_{op}\)) was defined as:  
\[f_{op}=\frac{\sum V_{o}}{V_{orw}} \tag{3}\]  
In Equation (3), \(V_{o}\) (mL) is the produced-oil volume and \(V_{orw}\) (mL) represents the residual-oil volume after waterflooding. To evaluate the ASP efficiency, the cumulative ASP oil ratio (CAOR) was introduced with Equation (4):  
\[CAOR=\frac{\sum V_{o}}{\sum V_{ASP}} \tag{4}\]  
where \(V_{ASP}\) indicates the volume of the ASP injected into the sandstone core. The injection volumetric velocity was maintained at 0.24 mL/min (approximately 1 ft/day superficial velocity).
# Strength Correlation and Prediction of Engineered Cementitious Composites with Microwave Properties
## 2 Methodologies
### Microwave Near-Field Detecting Technique  
#### 2.2.1 Preparation of ECC Specimens for Microwave Measurement  
For the purpose of obtaining conformity measurement, three ECC (PE1, PE2 and PE3) cubes with 200 mm sides were cast and prepared for daily measurement. The purpose of choosing a relatively large cube size was to make an approximate infinite half space as seen by the open-ended rectangular waveguide (RWG) probe at R-band frequencies. After casting, the cubic specimens were moved inside a laboratory for natural air curing for about 24 h and then demolded. The laboratory has an automatic climate control to maintain a temperature of 24 \(\pm\) 2 \({}^{\circ}\)C and a humidity of 50% \(\pm\) 5%.
## 3 EEG Essentials: Unveiling Standard Protocols for Advanced Clinical Practice
### Post-Procedure  
Post-EEG, the electrodes are removed, the scalp is cleaned, and a neurologist analyzes the data to detect abnormalities. The results are shared and explained to the patient and/or their family, along with clinical recommendations  
The following section deals with the analysis of EEG signals, which is particularly complex due to the artifacts that not only introduce noise but also ambiguity, originating both from physiological sources internal to the patient, such as eye movements, muscular activity, and cardiac rhythms, and from environmental influences external factors such as electromagnetic interference and electrode movements.
# Sparse Representing Denoising of Hyperspectral Data for Water Color Remote Sensing
## 2 Materials and Methods
### In Situ Dataset  
#### 2.2.1 Hyperspectral Data  
The in situ \(R_{\rm rs}\) was measured using an ASD FieldSpec spectroradiometer, which has a spectral range of 350-1050 nm with increments of 1.5 nm. When the boat was anchored, the radiance spectra of the reference panel, water, and sky were collected, using the above water measurement method [44]. To avoid direct solar radiation and influence of the ship, when measuring the water radiance signals, the azimuth difference of ASD and solar is about 45\({}^{\circ}\). The zenith angle of ASD is also about 45\({}^{\circ}\). When measuring the skylight radiance, the spectroradiometer was rotated upwards by 90-120\({}^{\circ}\). The \(R_{\rm rs}\) data for each spectral band were acquired, using the Ocean Optical Protocols:  
\[R_{\rm rs}(\lambda)=\Big{(}L_{\rm t}(\lambda)-rL_{\rm sky}(\lambda)\Big{)}/ \left(L_{\rm p}\pi/\rho_{\rm p}\right) \tag{1}\]  
where \(L_{\rm t}\) is the radiance from water; \(L_{\rm sky}\) is the sky radiance; \(L_{\rm p}\) is the radiance measured from a gray reference panel with a diffuse reflectance of \(\rho_{\rm p}=30\%\); and \(r\) is the surface reflectance of the water depending on the wind speed (2.2% for calm weather, 2.5% for <5 ms\({}^{-1}\) wind, 2.6-2.8% for 10 ms\({}^{-1}\) wind) [16].  
According to [PERSON] study [45], the inland water spectra can be categorized into 13 OWTs. They also offered the average spectral of each class. The 150 spectra were clustered in terms of the L2 norm distance to the 13 centers. The results are shown in Figure 2. Most of our samples belong to the OWTs 5, 11, and 12, which are sediment-laden waters, waters high in CDOM with cyanobacteria presence and high absorption efficiency by NAP, and turbid, which are moderately productive waters with cyanobacteria presence, respectively. Only a few samples were clustered into the OWTs 2, 3, 4, and 9, which represents the common-case waters with diverse reflectance shape and marginal dominance of pigments and CDOM over inorganic suspended particles, clear waters, turbid waters with high organic content, and optically neighboring to the OWT2 waters but with higher \(R_{\rm rs}\) at shorter wavelength. From another aspect, we can see that the samples collected in Hangzhou Bay and Hongze Lake are mainly clustered into the OWTs 5 and 11. The samples from Taihu Lake are mainly belong to the OWTs 11 and 12. Nine out of ten samples collected in Qiandao Lake are categorized to the OWTs 2, 3, and 9. Note that in this research, only the \(R_{\rm rs}\) curves between 400 and 800 nm were discussed. This is because the lack of the inherent optical property curves in the wavelength ranges shorter than 400 nm and longer than 800 nm limits the bio-optical simulation.
## 3 Methodology  
### Feature Selection  
Feature selection (FS) identifies the features that are related within a dataset, and it allows the elimination of irrelevant or unimportant features that contribute little or nothing to the definition of the target variable, providing more accurate models. This technique increases the performance of the developed model, improving its accuracy and reducing its complexity and overfitting, as well as its execution time.  
In this study, to determine the most relevant meteorological variables for the estimation of UVER, a selection of variables was performed based on the Pearson correlation  
Figure 6: Box and whisker plot of the GHUVE/GHI standard sky type (1 to 15).  
Figure 7: Box and whisker plot of the GHUVE/GHI ratio based on 10 min data grouped by sky type group (overcast, intermediate, clear).  
coefficient (r), and the following rules: if GHUVE and one selected variable have a very weak relationship, the Pearson coefficient is 0; the relationship is very strong when r is close to 1 (direct correlation) or \(-1\) (inverse correlation). To facilitate the assessment of correlation strength, the Thumb rule [50] established five r intervals for the correlation: direct (\(1\geq|\mathrm{r(GHUVE,variable)}|\geq 0.9\)), strong (\(0.9>|\mathrm{r(GHUVE,variable)}|\geq 0.7\)), moderate (\(0.7>|\mathrm{r(GHUVE,variable)}|\geq 0.5\)), weak (\(0.5>|\mathrm{r(GHUVE,variable)}|\geq 0.3\)), and negligible (\(|\mathrm{r(GHUVE,variable)}|<0.3\)).  
Table 3 shows the various intervals of Pearson's coefficients calculated for the different meteorological variables. It can be observed that GHI exhibits a very strong and direct influence on GHUVE for all-skies, overcast, and clear skies, while for intermediate skies, GHI is strongly correlated. In clear skies, \(\theta_{z}\) is also very strong and is inversely correlated with GHUVE, while for all-skies, overcast, and intermediate skies, this variable is strongly correlated. Likewise, in the case of all-sky types, k\({}_{\mathrm{t}}\), D, \(\varepsilon\), RH, DNI, and T have a moderate relationship with GHUVE. When analyzing overcast skies, a direct and strong relationship with k\({}_{\mathrm{d}}\) and DHI is observed, and a moderate relation with k\({}_{\mathrm{t}}\), D, \(\Delta\), DNI. For intermediate skies, the relation between GHUVE and k\({}_{\mathrm{t}}\), k\({}_{\mathrm{d}}\), D, DHI, and DNI is moderate. WS, \(\psi\), and CC present a negligible relation with GHUVE, so these meteorological variables were discarded as inputs for modeling GHUVE. These findings agree with the literature, which identifies GHI and \(\theta_{z}\) as the two variables that strongly influence GHUVE measurements [18; 19; 31]. Meteorological variables whose relationship with GHUVE is moderate, strong, or very strong (r \(\geq 0.5\)) have been selected.