esa-sceva/satcom-chunk-collection huggingface dataset:
# Augmented Data-Driven Approach towards 3D Printed Concrete Mix Prediction
## 2 Methodology
### Models
#### 2.4.3 Support Vector Regression (SVM)  
A Support Vector Machine (SVM) is a supervised machine learning algorithm used primarily for classification and sometimes for regression tasks [137]. It works by finding the optimal hyperplane that separates data points of different classes with the maximum margin, enhancing the model's ability to generalize to new data [138]. The hyperplane is influenced by support vectors, which are the nearest data points to the decision boundary. SVM aims to maximize the margin and the distance between the hyperplane and these support vectors to ensure robust classification [139]. For non-linearly separable data, SVM uses the kernel trick to transform the data into a higher-dimensional space where a hyperplane can effectively separate the classes, making it a versatile tool for handling complex datasets. Training an SVM model involves solving the following equations:  
\[\text{Minimize}\ \frac{||\omega||^{2}}{2}+C{\sum}_{i=1}^{N}\left(\xi i+\xi i^{*}\right)\] \[\text{subject to}\ |y_{i}{-}<\omega,x_{i}>-b|\leq\epsilon\]  
where \(\omega\) represents the vector perpendicular to the hyperplane. The term \(C\) is associated with a quadratic function. The variable \(b\) indicates the distance from the origin, while \(\xi i\) stands for the slack variable. The parameter Calso refers to the width of the hyperplane. Each training sample is denoted as \(x_{i}\) with its corresponding target value \(y_{i}\) The symbol \(\epsilon\) denotes a free parameter, and \(i\) ranges over the set {1, 2, 3..., \(N\)}. The tuned hyperparameters used for this study are listed in Table 5.
# Predicting Road Traffic Collisions Using a Two-Layer Ensemble Machine Learning Algorithm
## 3 Results
### Results of the Proposed Ensemble Model  
Accuracy is a measure of the effectiveness of a single algorithm, but relying solely on accuracy as a measure of performance index can lead to erroneous conclusions, as the model may be biased toward specific collision classes [44]. To solve this limitation in our study, other performance measurement metrics, such as recall, F1 score, and precision, were evaluated. These performance indicators demonstrate the performance of individual collisions and allow better insights for the model. The outcomes of the \"no collisions\" and \"with collisions\" performance measurements are shown in Tables 5 and 6, respectively.  
performed poorly for collisions. The two-layer ensemble and NB performed well for collisions, as shown in Tables 5 and 6.  
After evaluating the model using the stacking ensemble method with reduced features, there was a significant improvement in the predictive performance of the models. Table 7 shows the classification accuracy of each model. The two-layer ensemble achieved the highest accuracy of 0.88%, while NB had 0.81%, DT 0.81%, and AdaBoost 0.79%. k-NN achieved the lowest score of 0.65%.  
Among the base models, NB had the highest F1 score performance, while k-NN had the lowest. Overall, the best F1 score was achieved by the two-layer ensemble model. Similarly, the proposed two-layer ensemble model had the best recall, while NB had the best recall among the base models, AdaBoost and DT had similar scores, and k-NN had the lowest recall score. The two-layer ensemble model had superior precision when compared with the other models, as shown in Table 7. The objective of the ensemble method is to predict road collisions by utilizing a minimal feature set, which may be acquired within a short period from the collision scene. Based on this prediction, policy makers, road constructors, and health facilities would be able to predict road traffic collisions at any given site and thus take all the measures required to avert collisions and save lives. The improved two-layer ensemble model demonstrates that it is the most effective method for predicting road collisions.
# Image Resolution Enhancement of Highly Compressively Sensed CT/PET Signals
## 6 Evaluation  
The proposed algorithm outputs were compared with four state-of-the-art algorithms: Non-Rigid Multi-Modal 3D Medical Image Registration Based on Foveated Modality Independent NeighbourhoodDescriptor, Enhanced Deep Residual Networks for Single Image Super-Resolution, Residual dense network for image super-resolution, and Image super-resolution using very deep residual channel attention networks. L1-loss function has been exploited to train the networks of the deep residual nets. Neural net depth is of key element in neural network architectures, but deeper networks are not easy to learn. The residual learning framework simplifies the learning of these kinds of nets, and enables them to be significantly deeper providing enhanced performance in both visual and non-visual tasks. These residual nets are much deeper than their plain counterparts, yet they need a similar number of weighting parameters. To ensure clinical usability of these algorithms, the author has run the networks and extended them to training based on imperfect target images derived from a higher-resolution clinical scanner, thereby utilising the SR framework to define a mapping from an LR scanner's image domain to an HR scanner's image domain. The goal of the trials was to evaluate the performance of the compressively sensed super-resolution image reconstruction compared to the native (the unmodified prototype) and naive (the ones that behave in a very simple way) algorithms. The efficiency of the motion estimation (see Table 11), super-resolution (see Tables 4, 7, 9 and the usage of the compressed sensing technology (see Tables 1-3, 5, 8 and 10) is evaluated in terms of image quality and PSNR values. In all the tables, the worst and the best results have been highlighted. The separate aim of the study was to emphasise the reliability of results obtained by processing realistic digital phantoms. Analysing the relevancy of the models such as XCAT phantom considers both the flexibility of numerical phantoms and the truthful nature of voxelized phantoms. The term 'XCAT phantom' refers to the 4D extended cardiac-torso phantom for multimodality imaging research [24].
# Predictive Quantization and Symbolic Dynamics
## 6 Applications
### Recurrent Quantification Analysis  
Another field of application for symbolic dynamics is non-linear time series analysis (NLTSA). When applied to human voices, NLTA characterizes disorders in voices that are depicted in their temporal dynamics rather then in their instantaneous acoustic properties. Numerous studies try to characterize non-linear phenomena in vocal signals with evidence of non-linear dynamical behavior in speech signals and pathological voices. In a series of works [23; 24], we have applied VMO as a pre-processing step for the extraction of non-linear features using so-called recurrent quantification analysis (RQA). RQA extracts features or statistics from a recurrence plot (RP) that is a two-dimensional binary representation of the distances between signal trajectories in phase space over time, where a Heaviside function (0 or 1 mapping) is applied to the distances between points in the phase space at any given pair of time points to determine if it is below or above that distance threshold. Instead of the classical time-delay embedding of the signal, VMO is used for a short-term spectral analysis of the acoustic signal, resulting in optimal symbolic representation that renders itself to a simple recurrent analyzer from its oracle suffix structure. Simply described, a repeated suffix found by the oracle is equivalent to a recurrence diagonal sequence in a RP.The use of symbolization not only automates the process of finding approximate repetition but it  
Figure 9: Motifs found in Bach recording for different features.  
also solves the issue of finding the optimal threshold for generating the RP. The combined VMO-RQA method was applied to various acoustic signals, from the detection of emotions and affect in speech and audio to the detection of COVID-19 in vocalizations of coughs and sustained vowels. Slightly different audio features were extracted in each case (constant Q or CQT transform, and mel frequency cepstral MFCC, in the affect and COVID-19 applications, respectively).
# Low-Resolution Target Detection with High-Frequency Information Preservation
## 3 Method  
In this section, we will introduce the high-frequency information preservation (HFIP) block, which effectively addresses high-frequency information loss in SPD-Conv, providing a superior downsampling alternative. As shown in Figure 2, the HFIP block consists of three core components: the dilated frequency-aware channel correlation component (DFACC), the decoupled high-frequency learning component (DHFL), and the linear fusion component (LF). The DFACC module extracts multifrequency features with enhanced high-frequency representation, enabling effective downsampling while preserving task-relevant high-frequency information. The DHFL module tackles the high-frequency information loss caused by interval sampling in the space-to-depth operation. The LF component combines features from both the spatial and frequency domains to obtain downsampled feature maps with richer high-frequency details.
## 1 Introduction
### State of the Art  
As \"a shift in the use of materials driven by energy and resource scarcity\" occurs, and the benefits of FRPs for lightweight construction are further explored in architecture, there has been significant effort to create viable alternatives in both material usage and the molding process [4].  
One example, AEROCHAIR, demonstrates a novel moldless forming process to create a lightweight carbon fibre chair (Figure 1a) [15]. It employs form-finding methods based on [PERSON]'s catenary arches, using a series of precisely placed and measured non-elastic strings to \"pull\" the carbon fibres into the desired shape. This creates nodes at each fibre/string intersection, which after curing, allow for subsequent fibres to weave the catenaries together and form a 3D surface. This approach enables fabrication of an FRP structure using a low cost and relatively simple frame and a series of precise tensioning devices. It also demonstrates the potential of a physical form-finding process that use fibre-fibre interactions to create complex 3D surfaces. However, the final prototype was designed as a tension only system, unable to transfer compression and bending forces common in most architectural use cases. Secondly, while the sequence-based curing and forming process allowed for the creation of fibre nodes in space, the multi-stage process is rather complex.  
A successful example of a moldless compression-capable composite using natural fibres is the FlexFix Stool (Figure 1b) [16]. To eliminate the need for a mold and create a performative structure with continuous flax fibres, the research developed a multi-stage fabrication process that employs Tailored Fibre Placement (TFP) to create a preform which becomes an embedded frame for Coreless Filament Winding (CFW). The TFP preform has distinct fibre paths accounting for controlled bending, structural reinforcement, and winding tabs. The preform undergoes vacuum resin infusion and bending prior to winding. The embedded TFP preform increases stiffness without the need for secondary materials common in CFW processes. The final prototype stool weighed only 1080 g and could carry an 80 kg human, demonstrating the capacity for highly customized NFRPs to perform as sustainable alternatives to typical FRPs, but the efficiency benefit of using highly automated CFW and TFP techniques is contrasted with the many manual steps still needed to create the structure.  
Existing work in transferring bobbin lace to architectural applications includes the A Case for Lace project [17]. The project incorporated FEM and embodied effort analysis to create computationally informed, self-supporting artefacts. Carbon fibre was chosen as the main lacing material due to its high performance for architectural use cases, although it leaves much to be desired in sustainability and human-friendliness. The designs investigated in this research are mostly limited to simple, continuous surfaces, instead of complex 3D artefacts formed by multiple surfaces.
# Removal of Cochineal Dye Color through Atmospheric Pressure Plasma Discharge Jet
## 3 Results and Discussion  
### Analysis of Plasma Composition  
The results of the spectral analysis of the APPJ discharge measured using OES revealed that reactive oxygen (ROS) and nitrogen (RNS) species were responsible for the spectral emission lines (Figure 1). The majority of the peaks were located in the near-UV region, ranging from 200 to 400 nm. Reactive nitrogen species (RNS), specifically the second positive system N\({}_{2}\)(C-B), were formed in the wavelength range of 315-356 nm, and the first negative system N\({}_{2}\)+ was observed in the wavelength range of 375-391 nm (see inset Figure 1). Additionally, two excited atomic oxygen peaks were identified at 777 and 844 nm [42], and the emission of OH was observed at 309 nm. Similar results were reported by [PERSON] [27], who used a dielectric barrier discharge (DBD) plasma source and identified singlet emission. These results highlight that APPJ plasma utilizing air as the working gas is an efficient source of reactive oxygen and nitrogen species (RONS) N\({}_{2}\), N\({}_{2}\)+, OH, and O.  
Figure 1: Air plasma emission line spectrum (OES). Inset graph shows RONS.  
Understanding the reactive species present and their potential formation mechanisms can be valuable for optimizing the dye removal process. However, it is important to note that the characterization of the spectral lines generated by the APPJ discharge using optical emission spectroscopy has limitations concerning the spectral resolution and sensitivity of the equipment used. Additionally, it is possible that not all reactive species present in the discharge were identified. Further research should be conducted to delve deeper into the formation mechanisms of reactive species and their relationship with the properties of APPJ discharges. Moreover, exploring other potential applications of this technology in fields such as disinfection, medicine, and agriculture could be worthwhile [17].
# Toward an Advanced Human Monitoring System Based on a Smart Body Area Network for Industry Use
## 4 Results and Discussion
### Network Performance  
This subsection describes performance evaluations in the network, such as the average throughput and latency of each sensor node (\(Tp_{i}\), \(L_{i}\)), as shown in Table 4. In this instance, \(T_{D}\) was set to 250 ms, and \(r_{sAP}:r_{CM}\) was set to 9:1. The parameters of the PHY were based on Table 5. In addition, \(T_{IP}\) was assumed to be sufficiently shorter than \(T_{D}\). Tables 14 and 15 list examples of \(Tp_{i}\) and \(L_{i}\). For the low data rate sensors (sensors 1-6), \(Tp_{i}\) and \(L_{i}\) were not significantly changed regardless of \(R_{b}=R_{s}\times\log_{2}M\) and \(R_{10}\). In particular, \(L_{i}\) was basically within \(T_{D}\). In other words, those sensors were reliably able to transmit data frames in the interbeacon interval. On the other hand, a lower \(R_{b}\) decreased \(Tp_{i}\) and increased \(L_{i}\) significantly for the high data rate sensors (sensors 7-10). In particular, sensors 8-10 could not transmit enough data at the \(R_{b}\) of the conventional SmartBAN PHY. However, sufficient \(Tp_{i}\) and \(L_{i}\) values were obtained for sensors 7-9, even though \(R_{10}\) was changed due to the improvement in the number of modulation levels and \(R_{s}\). For sensor 10, sufficient \(Tp_{i}\) and \(L_{i}\) values were achieved even when \(R_{10}\) = 2500 kbps by setting \(R_{b}\) to 6 Mbps. From the above results, it is necessary to increase the number of modulation levels and the symbol rate to improve the PHY/MAC layer throughput to perform audio or video transmission, as well as the vital sign sensor in the SmartBAN.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \(R_{s}\times\)**log\({}_{2}\)M (Mbps)** & **Target \(R_{10}\) (kbps)** & \(Tp_{1}\) & \(Tp_{2}\) & \(Tp_{3}\) & \(Tp_{4}\) & \(Tp_{5}\) & \(Tp_{6}\) & \(Tp_{7}\) & \(Tp_{8}\) & \(Tp_{9}\) & \(Tp_{10}\) \\ \hline \multirow{4}{*}{1.
## 7 Research Gaps and Future Opportunities
### Protection against Computational Attacks  
There are gaps in self-awareness, business process integration, and support for business evolution that need to be filled to improve the protection against computational attacks. The BC-SCM systems must ensure operational compliance with certain policies and directives, while identifying vulnerabilities and their exploitation in various computational parts of the system, e.g., smart contracts and virtual machines.
# A Cloud-Based Distributed Architecture to Accelerate Video Encoders
## 4 Experimental Setup and Results
### Scalability and Resource Usage  
The first experiment evaluates the scalability of the proposed solution. With this purpose, the Sintel video is encoded at a requested bit rate of 1.5 Mb/s with a chunk size of 50 frames with 1, 3, 6, 9, 12 and 15 workers for each codec. The workers are distributed evenly on the compute nodes in order to balance the workload. The master node generates 123 VideoJob messages in this configuration. Each message is processed by a worker, which invokes ffmpeg with appropriate parameters to apply the two encoders.
# Converging Telco-Grade Solutions 5G and beyond to Support Production in Industry 4.0
## 5 The Telco Cloud Enabled 5G Applications
### Telco Cloud Industrial Applications
#### 5.5.4 Robotics  
Due to its reliance on decentralization in processing, Telco Cloud provides high speed in data processing and decision making, where robots can be controlled in real-time using Telco Cloud remotely, obtaining very high accuracy in all industry sectors [206,207]. This is the ability to fully control robots or robotic arms, issue commands, and collect data from sensors connected to them accurately and safely in places where humans cannot reach.
# Spaceborne Sparse SAR Imaging Mode Design: From Theory to Implementation
## 2 Traditional SAR Imaging Mode
### Signal Characteristics
Therefore, the AASR is  
\[\text{AASR}_{\text{Sc}}\ =\ \frac{\sum_{m=-\infty}^{m=+\infty}\int_{-B_{D}/2-K_{d }t_{0}}^{B_{D}/2-K_{d}t_{0}}G(f_{a}+m\cdot\text{PRF})^{2}df_{a}}{\int_{-B_{D}/ 2-K_{d}t_{0}}^{B_{D}/2-K_{d}t_{0}}G(f_{a})^{2}df_{a}}. \tag{8}\]  
For TOPS, Sliding Spotlight, and Mosaic modes, the AASR can be written as  
\[\text{AASR}_{\text{T}\text{S},\text{M}}\ =\ \frac{\sum_{m=-\infty}^{m=+\infty} \int_{-B_{D}/2+f_{ac}}^{B_{D}/2+f_{ac}}G\left(f_{a}\,^{\prime}+m\cdot\text{PRF }\right)^{2}df_{a}}{\int_{-B_{D}/2+f_{ac}}^{B_{D}/2+f_{ac}}G\left(f_{a}\,^{ \prime}\right)^{2}df_{a}} \tag{9}\]  
with  
\[f_{a}\,^{\prime}\ =\ (1-\frac{K_{c}}{K_{a}})(f_{a}-f_{ac}). \tag{10}\]  
The expressions of AASR are identical for these three modes. The difference lies in the sign of the steering angle rate. TOPS has a positive rate. Sliding Spotlight and Mosaic have a negative one. In summary, it can be concluded that the differences in AASR among various modes arise from variations in the AAP, which result from different antenna scanning mechanisms employed in each mode. The causes of RASR in each imaging mode are similar, and its expression can be given by  
\[\text{RASR = \frac{\sum_{m\
eq 0}\frac{G_{r}{}^{2}(\theta_{r}(r+m</\text{PRF}/2) )\,\text{ord}\cos[\theta_{r}(r+m</\text{PRF}/2)]}{(r+m</\text{PRF}/2)^{3}\sin[ \theta_{r}(r+m</\text{PRF}/2)]}}}{\frac{G_{r}{}^{2}(\theta_{r},\mu)\cos[\theta_{ r}(r)]}{\eta^{3}\sin[\theta_{r}(r)]}} \tag{11}\]  
where \(\theta_{i}(r)\) is the incidence angle of \(r\), and \(G_{r}(\cdot)\) is the range AAP. It is noted that in imaging modes with multiple subswaths, i.e., Scan, TOPS, and Mosaic, each subswath has a different PRF and incidence angle. Therefore, the RASR is different for each subswath.
# Integrated Double-Sided Random Microlens Array Used for Laser Beam Homogenization
## 5 Conclusions  
This paper has proposed an integrated D-rMLA fabricated by double-sided exposure technique for laser beam homogenization. The experimental results show that the uniformity of the homogenized spot for the laser beams with wavelengths of 650 nm (R), 532 nm (G), and 405 nm (B) are 91%, 89%, and 90%, respectively. The energy utilization rate is 89%, 87%, and 86%, respectively. It is verified that the integrated D-rMLA can break the interference lattice phenomenon brought by the periodic MLA and improve the homogenization quality. Compared with the traditional double MLAs in series, the homogenizer greatly improves the system integration and can be used in laser projection, laser backlighting, and other fields.  
Conceptualization, L.X. and A.C.; Data curation, L.X.; Formal analysis, C.X.; Funding acquisition, A.C.; Investigation, L.X.; Supervision, Y.F. and Q.D.; Validation, W.Y.; Visualization, L.X. and H.P.; Writing--original draft, W.Y.; Writing--review and editing, A.C. All authors have read and agreed to the published version of the manuscript.  
This research was supported by National Natural Science Foundation of China (Nos.61605211, 61905251); The authors thank their colleagues for their discussions and suggestions to this research.  
Figure 8: Homogenized spots of the laser with different wavelengths: (a) 650 nm; (b) 532 nm; (c) 405 nm.
## 5 Water Treatment Employing Bioadsorbents
### Bioadsorbents Based on Herbage and Forage
#### 5.3.3 Nutrients and Pharmaceuticals  
To resolve the imbalance caused by excess nutrients in water sources, [PERSON] [107] explored date palm wastes as an effective adsorbent for phosphate elimination. The removal percentages obtained at the initial PO\({}_{4}\)3- concentration of 50 mg were 87% for granular date stones and 85% for palm surface fibers. Regarding removal of pharmaceuticals, [PERSON] et al. [85] developed a sustainable method to adsorb clofibric acid and carbamazepine from aqueous solutions by employing rice straw at different pH conditions and biomaterial concentrations. The adsorption of both drugs depended on the adsorbent dosage and the pH. In the case of clofibric acid, 30 and 60 g/L of straw at pH 3.1 yielded the highest removal percentages (42.5 and 75.3%, respectively). The adsorption isotherms confirmed that the bioadsorbents had a heterogeneous surface structure, as both pharmaceuticals better fit the Freundlich isotherm model (Table 5).