esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Nanocellulose
### Cellulose Nanocrystals (CNCs)  
CNCs are also known as nanocrystalline cellulose, crystallites, whiskers, rod-like cellulose microcrystals. CNCs contain 100% cellulose which is organized in a crystalline fashion and is highly crystalline (54-88%). The diameter of CNCs is not dependent on the source of cellulose and mostly falls in the range of 5-70 nm. However, the length of CNCs is source-dependent, i.e., for CNCs extracted from cellulose of plant biomass, it lies between 100 and 250 nm, and 100 nm to several micrometers if the cellulose sources are tunicates, algae or bacteria. The inconsistency in the length of CNCs from different cellulose sources is attributed to the amount of crystalline cellulose present in the source, as the cellulose from tunicates and bacteria is highly crystalline and contains less amorphous regions, the length of CNCs produced from these sources is greater. Additionally, the length-to-width ratio typically falls between 10 and 100 [28; 31; 33]. Acid hydrolysis is the most common and efficient method for creating nanocrystalline cellulose (Figure 2). During the hydrolysis process, the amorphous region is highly disrupted owing to its high susceptibility toward acids as compared to crystalline regions. This results in the production of rod-shaped cellulose nanocrystals with variable geometrical dimensions, large surface area (\(\sim\)400 m\({}^{2}\)/g) and high crystallinity [39; 40]. In comparison to other varieties of nanocellulose, the CNCs stand out because of their inherent qualities, which include high tensile strength (7500 MPa), rigid structure, amphiphilic nature, optical properties, redispersion from dried powder, chemical purity and a high aspect ratio (\(\sim\)70) [7; 36].
# SPARQL2 Flink: Evaluation of SPARQL Queries on Apache Flink
## 5 Evaluation and Result
### Carry out Scalability Tests on a Local Cluster Environment
Figure 3 depicts the query execution times of nine queries after running the second scalability test. Each SPARQL query transformed into a Flink program generates a plan with several tasks. A task in Apache Flink is the basic unit of execution. It is the place where each parallel instance of an operator is executed. In terms of tasks, Q1 query generates 14 tasks, Q2 generates 31 tasks, Q3 generates 17 tasks, Q4 generates 28 tasks, Q5 generates 18 tasks, Q7 generates 29 tasks, Q8 generates 23 tasks, Q10 generates 18 tasks, and Q11 generates 6 tasks. All Flink program plans are available at [37]. In particular, the first task of the Flink program plan is associated with the dataset loading, the last task with the file creation which contains the query results, and the intermediate tasks are related to query execution. Table 4 describes the times of the _dataset loading time_, _query execution time_, and the sum of both. Figure 3: Execution times of nine queries after running the second scalability test. The x-axis represents the number of nodes on cluster C2, C4, and C5. The y-axis represents the time in seconds, which includes the dataset load time (dlt), the query execution time (qet), and the creation file time with query results. Additionally, on the top, the number of dataset triples is shown.
## 2 Background
### Conditional Generative Adversarial Networks  
Original GAN suffers a key problem, which is mode-collapse. Mode collapse occurs when the generator generates a set of samples that successfully fool the discriminator. Thus, the generator will keep generating the same set of outputs, and the discriminator will not provide a useful gradient to the generator [6]. Therefore, Wasserstein GAN (WGAN) was proposed to force the discriminator to provide an informative gradient. WGAN uses Wasserstein distance with 1-Lipschitz constraint instead of the JSD in the minimax loss. In WGAN, weight clipping is used to force the 1-Lipschitz constraint on the discriminator function. Weight clipping can be defined as clipping the discriminator weights to lie in the range \([-c,c]\), where c is a given hyperparameter. Weight clipping is applied to all layers of the discriminator network to enforce 1-Lipschitz. In WGAN, the discriminator does not classify the samples as real or fake. Instead, it returns a score that implies the distance between real and generated samples. Therefore, the discriminator in WGAN is called critic [6]. Equation (2) presents the Wasserstein loss function.  
\[\min_{G}\max_{D}\mathbb{E}_{\mathbf{x}p_{\text{data}}}\left[D(\mathbf{x})\right]- \mathbb{E}_{\mathbf{z}\sim p_{z}}[D(G(\mathbf{z}))] \tag{2}\]  
WGAN with weight clipping still suffers from two problems: vanishing gradient and exploding gradient [6]. To overcome the problems of WGAN, a gradient penalty can be used instead of weight clipping. The critic function is 1-Lischitz if and only if its gradient is at most 1 [6]. In general, the gradient penalty is the squared difference between the linear interpolations and 1. Equation (3) presents the Wasserstein loss with gradient penalty. Conditional WGANGP (CWGANGP) combines conditional GAN (CGAN) with WGANGP to enable WGANGP to produce conditioned data. CGAN is a simple variant of GAN that adds a conditional vector to the generator and discriminator to generate data with specified class labels [6].  
\[\min_{G}\max_{D}\mathbb{E}_{\mathbf{x}\sim p_{\text{data}}}\left[D(\mathbf{x})\right] -\mathbb{E}_{\mathbf{z}\sim p_{z}}[D(G(\mathbf{z}))]+\lambda\mathbb{E}_{\mathbf{x}\sim p_{ \mathbf{x}}}\big{[}(\|\
abla_{\mathbf{x}}D(\mathbf{\hat{x}})\|_{2}-1)^{2}\big{]} \tag{3}\]  
where:  
* \(\lambda\) is the gradient coefficient.
* \(\hat{x}\) is the linear interpolations between the generated and real samples.
## 2 Experimental Methodology
### Experimental Equipment and Instrumentation  
#### 2.2.1 Flow Velocimetry  
Instantaneous flow records were taken using a 3D Acoustic Doppler Velocimeter (Vectrino I \(\otimes\) ADV, Nortek Inc., Providence, RI, USA), recording time series of local flow measurements at 25 Hz. Records of the local flow velocity along the water column were obtained to acquire mean flow velocity and turbulent intensity profiles. These allowed for the derivation of mean flow velocity and shear stress \(u\)- (assuming logarithmic velocity profiles [54; 55]) estimates for each of the flow rates trialed herein. Shields shear stresses \(\tau_{*}\) are found using the bed shear stresses (\(\tau_{\rm o}=\rho u_{*}^{2}\)), estimated from the shear velocity \(u\)-:  
\[\tau_{*}=\frac{\tau_{\rm o}}{(\rho_{\rm s}-\rho_{\rm w})gD} \tag{1}\]  
where \(\rho_{\rm s}\) and \(\rho_{\rm w}\) are the densities of solid particle and water, respectively, g is the gravitational acceleration constant (g = 9.81 m/s\({}^{2}\)), while the flow (\(Re\)) and particle (\(Re_{*}\)) Reynolds numbers are found using the particle's diameter (D) as a lengthscale and the mean flow \(U\) or shear velocity \(u\)-, respectively:  
\[Re=\frac{\rho UD}{\mu} \tag{2}\]  
\[Re_{*}=\frac{\rho u_{*}D}{\mu} \tag{3}\]  
Results of the above flow parameters are given in the sections below, including in a tabulated form in Appendix A. The flow rates are fully developed hydraulically rough, turbulent flows, referring to above-threshold flow conditions, for the range of solid particle densities used here.
## 2 Related Work
### Multi-Object Tracking  
The current multi-object tracking (MOT) methods predominantly follow the Tracking-By-Detection (TbD) paradigm, wherein targets are first localized using an object detector and subsequently associated across frames via a tracking algorithm [27, 28]. Within this framework, two mainstream strategies have emerged: Joint Detection and Embedding (JDE) and Separate Detection and Embedding (SDE). JDE methods [29, 30] simultaneously perform object detection and feature extraction within a single deep network, offering improved computational efficiency. However, because detection and embedding share the same feature extraction backbone, JDE methods are prone to feature confusion in high-density scenes--particularly in unmanned drilling operations, where debris particles are densely packed, small in size, and weak in texture--leading to frequent mismatches and ID switches. Additionally, JDE methods face accuracy bottlenecks when handling small objects, compromising tracking stability.  
In contrast, SDE methods utilize separate object detectors and re-identification (Re-ID) models, allowing independent optimization of detection and feature extraction. This significantly enhances detection precision and tracking robustness. ByteTrack, an SDE-based approach, further improves tracking stability by retaining low-confidence detections and employing an IoU-based matching strategy; however, the model does not natively support velocity estimation.  
In summary, YOLOv11-Dd as the detection head and ByteTrack as the tracker form an effective solution for unmanned drilling environments, yet challenges remain in further improving detection accuracy and enabling real-time velocity measurement.
## 5 Discussion  
In-situ tests, including SPT, CPT, and SLT, were conducted to estimate the UBC of a single PHC pipe pile embedded in the saturated sandy layer in different depths. The average values of skin friction capacity, end-bearing capacity, and UBC obtained with these in-situ tests were comprehensively compared with the values recommended in the survey report, as shown in Figure 11. All values of UBC based on in-situ tests were over 1.5 times higher than the UBC value recommended in the survey report regardless of pile length, while the tested values were similar. The UBC value recommended in the survey report is calculated by the JGJ94-2008 method with blow count (N) of SPT (SPT) in the survey phase. Hence, the difference between the calculated UBC and recommended UBC might be caused by SPT-N. The SPT-N in the survey phase is compared with that in the in-situ test, as shown in Figure 12. It can be seen that the SPT-N values in the survey report are less than 25, while most of the SPT-N values in the in-situ test are higher than 25 at the position of 10 m below ground surface where coarse sand and medium sand lies. The compactness of the tested soil layers based on blow counts in the survey report varies from slight denseness to medium denseness. In contrast, the compactness of the tested soil layers based on in-situ SPTs mainly varies from medium denseness to heavy denseness, especially for soil layers at the position of 10 m below ground surface, which might be caused by the bentonite mud technology. The bentonite mud technology was not adopted in the survey phase, and the drilling wall might collapse during SPTs, leading to low blow counts (SPT-N). In contrast, the drilling wall did not collapse during SPTs in the in-situ SPTs due to the adoption of bentonite mud technology, reflecting the real density and strength of the tested soil layers, especially for saturated sandy layers with cohesionless characteristics. It indicates that the bentonite mud technology is significant and necessary for accurate estimation of the UBC of pile foundations.  
Figure 11: Comparison of bearing capacity. (**a**) Long piles (L = 21 m); (**b**) short piles (L = 12.5 m).
# A Novel Traffic Classification Approach by Employing Deep Learning on Software-Defined Networking
## 3 Materials and Methods
### Modify
#### 3.3.2 Label Encoding  
Transformation of the target variable labels from letters to integers was carried out to enhance the distribution of the target variables and align with model requirements. This numerical transformation allowed the models to operate more effectively using numerical values instead of letter categories. This process employed two libraries, Keras' to_categorical, and Scikit-learn LabelEncoder, working in tandem to take categorical labels and prepare them for use in deep learning models. First, a LabelEncoder was created to map categorical labels to numerical values. Labels such as \"multimedia\", \"voip\", \"instantmessage\", \"filetransfer\", and \"attack\", were assigned to each traffic class are allocated unique numerical values. Subsequently, one-hot encoding was applied using the to_categorical function. This transformation converts numerical values into binary vectors, where each label is represented by a vector with a single active bit set to 1 and the remainder set to 0. Ultimately, an encoded one-hot matrix was obtained and served as target labels in machine learning models. It is important to emphasize that this process is crucial for enabling models to understand and classify categories in classification problems effectively.
# An Autoencoder-Based Task-Oriented Semantic Communication System for M2M Communication
## 4 Results
### Computational Complexity  
While the proposed autoencoder-based system is computationally expensive compared to the JPEG system during the initial phase, this is due to the extensive training required at the beginning. However, it is important to note that the model is trained only once during the development phase. After this one-time training, the model is deployed and used for inference, which is significantly less computationally demanding. The computational intensity is primarily confined to the training stage, which can be performed using high-performance computing resources. Once deployed, the system only requires forward passes through the trained model for encoding and decoding. This makes it highly feasible for use in resource-constrained environments during the deployment phase.  
Figure 14: Performance comparison of the proposed system versus JPEG (BER vs. PSNR).  
Figure 15: Effect of the LV size on (**a**) PSNR and (**b**) accuracy of prediction.
## 5 Evaluation Results and Discussion
### HUD Ease of Use  
The post-questionnaire findings regarding the ease of use and the familiarisation time required for the prototype HUD system are confirming the aforementioned observations (Figures 12 and 13) and subjective feedback. To this end, a promising response from 80% of the participants found it either extremely easy or very easy to get accustomed to the HUD interface and interactivity as shown in Figure 17.
## 1 Introduction
However, the aforementioned composite PCMs are white powders, which present poor absorptive performance in the optical light, resulting in a weak photothermal conversion property and a low solar energy utilization ratio. Therefore, the carbon materials are good additives for improving the optical absorption performance of PCMs because of their high visible light absorptivity. Graphene nanoplates (GNPs) are thin flat particles that consist of single and few layer graphene mixed with thicker graphite, thus, structurally they are in between graphene and graphite [22]. Compared to other carbon-based materials, GNPs possess many outstanding functional performance, such as unique photonic/optical transportation, light weight, high thermal/electrical conductivity, excellent mechanical property [23; 24]. Furthermore, in the optical regime, the GNPs have only very limited loss at Dirac point because of their outstanding optical characteristics, including ballistic transport and saturable absorption [25; 26; 27; 28]. Therefore, GNPs can be easily and successfully incorporated with polymeric matrices to enhance the comprehensive performance of polymers. As for PCMs, it has already been demonstrated that the addition of even a small mass fraction of GNPs can significantly improve the thermal conductivity of different PCMs such as palmitic acid [23; 29], beeswax [12], eicosane [30], 1-octadecanol [31], decosane [32]. In addition, the rheological behavior of PCMs can be significantly influenced by the addition of nanoplates, which determines the transportablilty in pump systems for PCMs nanofluids applications. However, in previous literatures, the comprehensive effects of GNPs on the thermal conductivity, rheological behavior, and optical absorption performance of PCMs are hardly explored together, hence, this work will focus on analyzing the three effects. In this paper, a series of PEG/GNPs composites were prepared through a temperature-assisted solution blending method in order to develop new PCMs for utilizing solar energy. The good compatibility between PEG and GNPs conduce to excellent thermal conductivity and high latent heat at low filler mass fraction. Furthermore, the composites exhibit a better performance in absorbing and conversing solar energy in comparison with the traditional organic PCMs. Therefore, the outstanding thermal conductivity and photothermal performance make the PEG/GNPs composites a promising candidate for solar thermal energy storage application.
## 3 Modeling Framework
### Energy Modeling Based on the Fundamental Principle of Dynamics
#### 3.2.1 Forces Acting on the UAV  
The UAV is subjected to three main forces during flight:  
* Gravitational force \(F_{w}\), due to the total weight of the drone and payload: \[F_{w}=(m_{d}+m_{p})\cdot g\] (1) where \(m_{d}\) is the drone mass, \(m_{p}\) is the payload mass, and \(g\) is the gravitational acceleration.
* Aerodynamic drag force \(F_{D}\), which includes both vertical and horizontal components. For a constant-speed flight, it is expressed as: \[F_{D}=\frac{1}{2}\cdot\rho_{air}\cdot C_{x}\cdot S\cdot v^{2}\] (2) where \(\rho_{air}\) is the air density, \(C_{x}\) the drag coefficient, \(S\) the reference surface area, and \(v\) the UAV speed.
* Thrust force \(F_{T}\), required to counteract both weight and drag: \[F_{T}=\sqrt{F_{w}^{2}+F_{D}^{2}}\] (3) Power and Energy Computation The power required by the propulsion system is calculated as: \[P=F_{T}\cdot vs.+P_{axionics}\] (4) where \(P_{axionics}\) accounts for the constant power drawn by onboard electronics. The total energy consumed during a flight segment of duration \(t\) is given by: \[E=\frac{P\cdot t}{\eta}\] (5) where \(\eta\) is the efficiency of the powertrain.
## 4 Results and Discussion
### Estimation of Defect Depth and Width
The MRE of Isomap+SVR is approximately 3.64% when estimating the depth of defects and 1.96% when estimating the width. This accuracy can greatly help polarizer and LCD panel manufacturers identify defects. Regardless of the dimensionality reduction technique and regression method chosen, there is always considerable estimation error for the two following reasons. Firstly, the labeling of the sample is completed manually. The labeling results for the defect shape attributes are slightly subjective. As shown in Figure 7, determination of the defect depth is more difficult than that of the width. This is also why the MRE of the depth estimation is generally larger than that of the width estimation, as shown in Table 1. Secondly, it is difficult to collect defective samples. A few outliers have significant influences, which is a typical problem of small samples. Therefore, the estimation performance has room for improvement.
## 4 Results and Discussion
### Demonstration of Texture Discrimination
If this is not the case (x\({}_{\text{j}}\langle\text{m}_{\text{j}}-\sigma_{\text{j}},\ x_{\text{j}} \rangle\)m\({}_{\text{j}}+\sigma_{\text{j}}\)), \(w_{\text{j}}\) is calculated by ranking in ascending order the difference between the measured value with the average values of each sample using \(\text{argmax}\big{(}\big{|}\text{m}_{\text{j}}-\text{x}_{\text{j}}\big{|} \big{)}\). Here, the maximum value is 16. In this manner, the sum of the weight values (\(\sum w_{\text{j}}\)) is obtained by adding all calculated \(w_{\text{j}}\) values of each sample for the six indicators. Using this result, the following equation is used to calculate the degree of similarity (Y\({}_{\text{k}}\)) with each sample. \[Y_{\text{k}}=\frac{p\times q-\sum w_{\text{j}}}{p\times q}\times 100(\%) \tag{11}\]  
where k is the tactile sample number ranging from 1 to 16, \(p\) is the number of tactile samples accumulated in the database, and \(q\) is the number of indicators. As a result of performing the texture discrimination experiment using our method with Sample No. 2, for example, the probability of Sample No. 2 was the highest (77.15%), as shown in Figure 8. Ninety-eight percent accuracy was obtained after repeating the texture discrimination experiment 50 times.
# Dynamic Voltage Optimization Based on In-Band Sensors and Machine Learning
## 6 Machine Learning in Voltage Optimization
### Supervised Learning  
Supervised Learning is the most common used ML method due the basic workflow to achieve learning at a high accuracy performance. Main goal of the supervised learning is to train the model with a set of inputs and known outcomes known as the training dataset. The model will then be validated and/or tested with another dataset with the input values only and check the outcomes with the desired outputs. Hyperparameters of the model can be adjusted to achieve higher accuracy levels as the design circles the training and test phases before implementation for its intended application. Classification and regression applications are common under the supervised learning method for ML algorithm models.
## 4 Discussion
### Synchronism Phenomenon for a Ship Sailing in Regular Beam Waves in a Non-Resistant Environment  
In the previous sections, the behaviour of a ship rolling freely was analysed considering a wave slope most common, of about 6\({}^{\circ}\) (0.1046 rad). However, as previously mentioned, according to the literature, the maximum wave slope can reach up to 10\({}^{\circ}\) (0.1745 rad). For this reason, Figure 20 now simulates and compares the influence of different wave slopes on a ship's behaviour from a theoretical point of view.  
Figure 19: Comparison of initial and limited to 0.20 rad rolling angles.  
For a different ship natural roll period (Td = 9 s) from previous simulations, it is observed that the moments of reaching the maximum ship rolling angles are the same, as was concluded in the previous section. However, relevant differences are revealed when different maximum wave slopes (\(\theta_{\rm MW}\)) are simulated. For instance, in Figure 20a, where \(\theta_{\rm MW}\) = 0.1046 rad (\(6^{\circ}\)) when t = 9 s, the relative maximum rolling angle is 0.3286 rad, while for \(\theta_{\rm MW}\) = 0.1745 rad (\(10^{\circ}\)), it is 0.5482 rad, i.e., about 66% higher. Therefore, it is evident that, from a theoretical point of view, the differences between the wave slopes can be decisive in the time available before the capsizing. However, in any case, the time available for ship operators to alter, for example, the ship's loading conditions (Td) is minimal. Once again, the importance of predicting this undesirable situation well in advance is concluded, regardless of a certain wave slope.