esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Calibration Methodologies & Dataset Used
### Sea-Surface Calibration
Subsequently, at each satellite measuring point \(k\) and in open seas, the following quantities are determined: (1) the Sea Surface Height \(SSH(k)\), calculated as the difference between altitude and altimeter range, after corrections are applied, (2) the geoid height \(\mathcal{N}(k)\) above reference ellipsoid, and (3) the mean dynamic topography \(MDT(k)\). Then, the estimated absolute sea-surface height bias for the altimeter can be computed as:  
\[Bias(k)=SLA(k)-SLA(k_{0})\]  
The bias per cycle for each Sentinel-3A Pass No.14 and Pass No.335 is thus determined. Dataset for Sea-Surface Calibration  
The Sentinel-3A Pass No.14 is an ascending pass, with groundtrack west of Gavdos. This means that just a few seconds before reaching from the south the CDN1 transponder Cal/Val site on the mountains, the satellite makes its way up north from the sea, but 10-15 km west of the Gavdos (Figure 12 left). Additionally, the descending Sentinel-3A Pass No.335, coming from the north, crosses at a point on the island where the Gavdos Cal/Val facility is located, at about 08:47:00 UTC every 27 days (Figure 12 right). At least two GNSS receivers, as well as three tide gauges, have been continuously operating at the Gavdos Cal/Val facility during these Sentinel-3A Passes. This helps maintain a high success rate for sea-surface calibrations (Figure 12). The data used for the sea-surface calibration of Sentinel-3A are SAR mode, Level 2 products, WAT data, Non-Time Critical orbits, and with Processing Baseline PB 2.2 and for its cycles 1-31 (June, 2018).
# A Numerical Study of Blast Resistance of Carbon Fiber Reinforced Aluminum Alloy Laminates
## 3 Finite Element Modeling
### Validation of FE Model  
#### 3.5.1 Validation of Carbon Composite Panels  
There is no experimental study of this kind of situation in the existing literature, and the accuracy of the simulation model has been verified by a shock tube impacting a 32-layer carbon fiber composite material [14]. In this study, specimens with a radius of curvature of 305 mm were selected and simulated in ABAQUS/Explicit using the same boundary conditions, material properties, and blast loads.  
The comparison results are shown in the Figure 9. Three different failure modes of carbon fiber are compared: matrix failure, fiber delamination, and fiber fracture failure. The results show that the failure modes predicted by numerical simulation are basically consistent with the experimental results.
## 4 Implementation of the Proposed UniChain System
### Access a Record from Student Node  
A student node sends the university's ID to its SRHC to retrieve the associated RC address. Upon receiving the RC address, the student node then sends the filename of the requested record and Ethereum address of the student to the RC. The RC forwards the request to the ACC to check whether that received Ethereum address has a permission (i.e., \"read/edit\" access level) on the requested record or not. If the student node has a permission, the AAC forwards the student's encrypted symmetric key \(E_{pk_{student}}(SMK)\) to the RC. The RC in turns forwards the received key with the database access information to the student node.  
The Cipher/Decipher Manager in the student node first decrypts the received symmetric key using its private key and then decrypts the access link with that symmetric key. The DB Manager of the student node follows the related access link and retrieves the encrypted EAR from the university's database. Since students can access their nodes via online wallets, records access can be performed by any device with Internet connection, thus improving the interoperability of EARs. Moreover, the DB Manager creates a log indicating the process of reading the record, hashes the log and then forwards the log to the Cipher/Decipher Manager for encryption. The encrypted log will then be forwarded to the LC. The LC adds a new entry with the encrypted received log, a \"new log\" status to indicate that the new log has not been added to the blockchain yet, and a timestamp indicating the last status update.
# Suitability of NB-IoT for Indoor Industrial Environment: A Survey and Insights
## 2 Technical Background of NB-IoT
### The Standardization and Technology of NB-IoT
#### 2.3.2 NB-IoT and Its Features
Lastly, the in-band deployment is also assigned a bandwidth of 180 kHz and utilizes one of the LTE frequency bands.
# Joint Interference and Phase Alignment among Data Streams in Multicell MIMO Broadcasting
## 5 MIMO-BC Robust Interference Alignment
### Algorithm Summary  
The joint interference and phase alignment algorithm based on Bayesian estimation and data stream power allocation is summarized as follows in Algorithm 1:  
```
Step 1: By making the Bayesian estimation of Equation (13), the receiving end gets better for accurate CSI. Step 2: Through iterations of Equations (17) and (21) until convergence, a robust precoding \(\hat{V}\) and interference suppression matrix \(\hat{\boldsymbol{U}}\) are obtained. Step 3: Distribute power between user data streams using Equation (32) to optimize system mutual information. Step 4: By aligning the phase of the transmitted symbol and the received symbol in Section 5.2, the received data stream obtains a high diversity gain to enhance the received power of the target data stream.
```  
**Algorithm 1** Joint interference and phase alignment.
## 5 Integrated System Testing
### Scenario 3
#### 5.3.2 Test Results  
The results for Scenario 3 are shown in Figure 18. Data from Sensor 1 (temperature, humidity, pressure and battery power) was successfully transmitted using GSM and the Ethernet. During the one-hour test period, seven data entries were gathered at intervals of ten minutes. After the Ethernet connection failed at 21:15, data collected at 21:20, 21:30, and 21:40 was transmitted via GSM/SMS to phone number 8860914020198. This indicates that the system had already automatically switched to transmitting data using GSM. The Ethernet connection was reestablished at 21:45. Data collected at 21:50 and 22:00 was transmitted using the Ethernet, indicating that the system had already resumed the correct communication settings. These results verified that the smart gateway is capable of automatically activating the back-up communication mechanism. It also shows the real-time and historical tracking of temperature, humidity, pressure, and battery power from Sensor 1 in Scenario 3.
# Anomaly Detection in Network Traffic via Cross-Domain Federated Graph Representation Learning
## 5 Experiments and Results
### Results  
#### 5.2.1 Comparative Performance Results  
To comprehensively evaluate the performance of the proposed model in network traffic anomaly detection, we adopted four key metrics: accuracy, precision, recall, and F1 score. A comparative analysis between AD-FG and several existing methods was conducted, as summarized in Table 3. Overall, AD-FG demonstrated superior performance across all metrics. This improvement is attributed to its dual-channel feature-structure decoupling architecture, which aggregates only the graph structure encoder parameters during federated learning. In particular, AD-FG achieved an F1 score of 79.19, significantly outperforming baseline approaches such as FedAvg (70.66), which relies on simple parameter averaging, and FedProx (69.85), which incorporates regularization during optimization. This highlights the advantage of the structure-sharing strategy in enhancing classification performance. Although FedSage and GCFL, as GNN-based federated methods, exhibit certain capabilities in structural modeling, their performance in this experiment remained suboptimal, indicating limited adaptability to cross-domain heterogeneous traffic patterns. The conventional method, MemAE-EIF, also showed inferior performance in terms of both accuracy and precision, further emphasizing the necessity of integrating graph modeling with federated learning. In contrast, DyGCN and CTGCN, two GNN-based classification methods, demonstrated certain strengths in specific metrics but still fell short of AD-FG in terms of recall and F1 score. These results validate the effectiveness of the proposed approach, particularly the integration of degree- and random walk-based structural encoding with cross-domain structure sharing. This design enables the model to capture richer topological patterns and latent anomalies in network traffic, making it especially suitable for complex, heterogeneous, and cross-domain detection scenarios.
## 2 Results and Discussion  
General Approach to Estimating Compound \(\times\) Sample Interactions (\(\sigma_{std\ 1,std\ 2}\)) in Plant Populations  
The purpose of this study was to (i) develop and apply a quantitative approach for estimating the extent of Compound \(\times\) Individual interactions during HS-SPME analysis and (ii) examine the appropriateness of using a single surrogate standard for volatile profiling in a given matrix as comparedto more accurate (and more tedious and expensive) methods such as recovery spikes and isotopically labeled standards. In brief, the approach is as follows:  
1. Samples are spiked with a cocktail of internal standards prior to SPME-GC-MS analysis (Figure 1). In the present work, as we were studying plant populations, these standards were either isotopic analogues of plant-derived odorants, or non-labeled surrogate standards previously reported for use in plant volatile profiling.
2. Pairwise matrix error (\(\sigma_{std\ 1,std\ 2}\)) is calculated as described in the Methods section and Figure 1, where the Compound \(\times\) Individual interaction is assessed across a population for each internal standard, and quantified by the \(\sigma_{std\ 1,std\ 2}\) value.  
As proof of principle of the approach, we evaluated two plant populations where genome sequencing has been performed and for which there is interest in understanding the biochemical pathways responsible for regulating plant health, i.e., resistance to biotic and abiotic stress, as well as fruit quality [26]: (i) a _Vitis_ spp. grape population (\"Horizon\" \(\times\) Illinois 547-1) that had recently been genotyped using next-generation sequencing (NGS) approaches [7; 27], and (ii) a tomato RIL population, which was recently genotyped by using the GBS (genotyping by sequencing) method [28] and evaluated using a single internal standard ([PERSON] et al., in preparation). While this approach yielded good results in a melon population [29], it is possible to discover better QTLs with better-matched internal standards.  
Figure 1: Overview of the experimental design. (**a**) Multiple non-native internal standards were added to each plant individual during sample preparation. (**b**) Samples were analyzed by HS-SPME-GC-MS (simulated data shown). In some cases, pairs of standards had similar relative ratios across multiple individuals (Outcome 1), while in other cases there was evidence of Compound \(\times\) Individual matrix effects, in which the relative peak areas for pairs of standards changed among individuals (Outcomes 2 and 3). (**c**) Compound \(\times\) Individual matrix effects were quantitated by comparing the log-transformed integrated peak areas for each standards pair using the pairwise matrix error formula (\(\sigma_{std\ 1,std\ 2}\)) described in the Methods section
###### Abstract  
Expansive soils pose significant challenges to structural integrity, primarily due to volumetric changes that can lead to detrimental consequences and substantial economic losses. This study delves into the intricate dynamics of expansive soils through loaded swelling pressure experiments conducted under diverse conditions, encompassing variations in the sand content, initial dry unit weight, and initial degree of saturation. The findings underscore the pronounced influence of these factors on soil swelling. To address these challenges, a novel method leveraging machine learning prediction models is introduced, offering an efficient and cost-effective framework to mitigate potential hazards associated with expansive soils. Employing advanced algorithms such as decision tree regression (DTR), random forest regression (RFR), gradient boosting regression (GBR), extreme gradient boosting (XGBoost), support vector regression (SVR), and artificial neural networks (ANN) in the Python software 3.11 environment, this study aims to predict the optimal applied stress and dry unit weight required for soil swelling mitigation. Results reveal that XGBoost and ANN stand out for their precision and superior metrics. While both performed well, ANN demonstrated exceptional consistency across training and testing phases, making it the preferred choice. In the tested dataset, ANN achieved the highest R-squared values (0.9917 and 0.9954), lowest RMSE (7.92 and 0.086), and lowest MAE (5.872 and 0.0488) for predicting optimal applied stress and dry unit weight, respectively.  
clay; sand (additives); swelling pressure; loaded swelling pressure; partial saturation; machine learning +
Footnote †: journal: Applied sciences
# Optimization Design of Lattice Structures in Internal Cooling Channel of Turbine Blade
## 4 Results and Analysis
### Sensitivity Analysis
#### 4.1.2 Overall and Single Sensitivity for the Results of Op-II  
Figure 9a shows the Pareto fronts of Op-II. The Pareto fronts distributed at a curved surface, approximately. The values of _freq_1 had a great impact on the other two parameters. At the same time, the optimal solutions of the _freq_1 were all concentrated between 269 Hz and 271 Hz. Therefore, _freq_1 was the highest for the overall sensitivity of Op-II.  
Tables 5 and 6 show that the set of Pareto solution of XLS was mainly concentrated in the region with higher \(Nu\). The optimal solutions with \(Nu\) between 140 and 172 were 95.3% of the total set of solutions of \(Nu\). The optimal solutions with \(\overline{p}\) between 3 and  
It can be found from Figure 9b,c that the optimal solutions of \(D\) concentrated between 2.3 mm and 2.9 mm, were generally distributed at a curved surface, and were relatively uniform. As shown in Figure 9d,e, the optimal solution of \(\omega\) was between 52\({}^{\circ}\) and 66\({}^{\circ}\). Figure 9f reveals that \(D\) and \(\omega\) had negative correlation with _freq1_. \(Nu\) was more sensitive to \(\omega\) than to \(D\), while \(\overline{p}\) was more sensitive to \(D\) than to \(\omega\). However, _freq1_ was not sensitive to \(D\) and \(\omega\), which means that the change in \(D\) and \(\omega\) has little effect on _freq1_. _freq1_ may be most sensitive to \(H\) (height of cooling channel), while \(H\) herein remains unchanged.
## 2 Materials and Methods
### Correlating the Laboratory Results of Plates and Rotor Blade Sections  
In order to validate the procedure for use on rotorcraft tail blade profiles, it was determined that scaling upward from a plate generated correlated results. This in turn validated the procedure for use on rotorcraft tail blades. It is important to note that the goal was to determine if the military blade, painted in matte, and the commercial blade, painted in gloss (both salvaged from an operational aircraft) presented in Figure 13, would generate similar results to the aluminum baseline test plate surfaces.  
Figure 12: Multi-tool analysis under partially melted and refrozen snow–simulation of dry snow on a warm soaked surface. Note: Numbers correspond to the tool used and number of passages during de-icing. Where “20 to 16” represent the broom, “15 to 11” represent the soft polymer blade, “10 to 6” represent the squeege, and “5 to 1” represent the hard polymer blade.  
All tools previously described in Sections 2.4 and 3.1 were used in the same order with the same numbers of passages. When considering the condition of HZR, for all samples tested, ice remained after all tools were used with five passes. The same results were obtained with the matte, gloss profiles, and under the condition of LZR. When tested using snow as the contamination, the snow was removed after only one brush stroke for all trials. It is therefore reasonable to conclude that, under identical icing conditions, the aluminum plates behave the same way as the tail blade profiles. Additional testing was conducted with profiles coated with ski wax and Nusil and then compared with the aluminum reference plates. Under the precipitation conditions studied, the Swix wax obtained comparable results, including de-icing with the same tools and the same number of passes. A considerable difference, however, was noticed with Nusil. The latter coating applied to the profile required the use of a harder tool. It did not facilitate the de-icing process in certain concave areas or on the leading edge.  
The majority of the tests conducted demonstrated that testing on flat plates can reproduce the de-icing process done on a rotorcraft tail profile. However, they also demonstrated the importance of testing in actual conditions on a full-sized aircraft before considering use due to the possible complexities associated with geometries.  
Although the results obtained with this procedure are repeatable and provides relevant information regarding the behavior of a material under winter decontamination conditions, some improvements could be made. To ensure a more reliable and repeatable process, the tools could be instrumented to measure the shear stress applied as a function of time for the purpose of classification. However, at the present moment, the proposed procedure is highly representative of the operational techniques currently used.
# PET/CT Radiomics in Lung Cancer: An Overview
## 3 Applications
### Discrimination between Benign and Malignant Pulmonary Nodules  
Solitary pulmonary nodules (SPN) are relatively common findings, although the available data about the estimated prevalence at CT examination vary significantly [57; 58]. Clinical management of SPN poses significant challenges to the clinician, for a non-negligible fraction of them (estimated between 3.7% and 5.5% [59]) may actually be malignant. Traditionally, the evaluation involved manual assessment of some key image characteristics at CT that are considered strong indicators of benignity or malignancy [60]. In this scenario, recent studies have shown that prediction models based on quantitative imaging features can help differentiate between benign, malignant, and inflammatory pulmonary nodules [37; 50; 61; 62; 63; 64].  
Positron emission tomography has also proved effective in the assessment of suspicious SPN. The standard strategy in this case consists of comparing the uptake value against some absolute or relative threshold: uptake in the nodule higher than background mediastinal activity [65] and/or SUV\({}_{\text{max}}>2.5\) are typically considered indicative of malignancy [66]. This approach has demonstrated good sensitivity but rather low specificity (pooled values in a recent meta-analysis [67], respectively, of 89% and 78%). Quantitative assessment of radiotracer uptake by texture analysis has shown to improve the diagnostic accuracy--in particular specificity--compared with SUV\({}_{\text{max}}\) alone [68; 69].
## 4 Features and Issues of Existing Applications
### Data Collection
Moreover, markers can be dropped during the data collection procedure. These limitations should be carefully considered when applying optical MoCap systems. In addition, four research papers utilized other data collection technologies, such as indoor positioning [13], non-wearable sensors [28; 37], and optical cameras [36]. In panel (c) of Figure 9, the types of data collected by existing applications are classified into four categories: posture, location, movement, and biometric. Posture data are typically collected in terms of joint angles obtained from the skeleton of a human body and are utilized in most relevant research papers. The reason why posture data are widely used is that assessing postural risks that can cause musculoskeletal disorder and injury is useful. Moreover, posture data can be used as input parameters for observational methods widely used for ergonomic assessment, such as the Rapid Upper Limb Assessment (RULA), Rapid Entire Body Assessment (REBA), Ovako Working posture Analysis System (OWAS), etc. [64]. Consequently, many existing applications focus on postural risks accompanied by manual work, as shown in Figure 9.  
Location data are also collected and utilized in a significant number of existing applications, including both absolute location in the workplace and relative location, which provide the proximity to specific objects such as robots. In particular, safety distance, the distance between a human worker and a robot, is a good indicator of collision risk, and some applications are designed to stop or slow down the robot when safety distance is not maintained [4; 28; 29; 37; 39]. Additionally, location data can be used to analyze the motion trajectory of human workers [14; 34; 38] or to monitor their status [13; 36]. Movement data include information about the changes in a human worker's location, such as speed, acceleration, and direction of movement. Movement data can be used to assess the magnitude of risk [30], to predict motion trajectories [28], to measure joint torque [33], and to detect accidents [13]. Sometimes, biometric information, including blood pressure, heart rate, breathing rate, and skin temperature, is used to assess fatigue and to monitor the status of human workers [40].
# Data Extraction Method for Industrial Data Matrix Codes Based on Local Adjacent Modules Structure
## 3 The Graph Structure of the DM Symbol  
A DM symbol is a square array composed of black and white modules. The structure of the symbol comprises Finder Pattern, Timing Pattern, and data region, as shown in Figure 2. The Finder Pattern is composed of two solid adjacent borders in an \"L\" shape, and the Timing Pattern consists of alternating black and white modules along the right and top edges. The Finder Pattern can determine the barcode position and orientation, and the Timing Pattern provides a count of the number of rows and columns in the symbol. The data region contains encoded text or numeric data. As more data is encoded in a barcode, the number of modules increases. Barcode symbol sizes vary from \(10\times 10\) to \(144\times 144\) in the new version ECC200. By adding error correction codes, the symbols can be read even if they are partially damaged.  
Figure 2: The structure of the DM symbol.