esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results
Finally, there is Decision Tree [30], because the use of models based on trees provides a solution with a straightforward interpretation by the medical staff. The development of understandable solutions for medical doctors is one of the main objectives of our research. Understanding why a model makes a particular prediction can be as crucial as prediction accuracy in the medical field. In some cases, the best results are obtained with complex models that are difficult to interpret. Thanks to the SHAP library [31], it is possible to obtain from each feature a value of importance for a particular predictor. The SHAP algorithm aims to explain the outcome of machine-learning models, representing the results by means of graphs. It is based on the Shapley values of game theory. In particular, in this paper we will focus on the ones that allow us to show the impact of the different variables in the model. To understand these graphs, two factorsmust be taken into account: the position of the points on the horizontal axis and the color. Let us take Figure 2 as an example. * The color of the dots denotes the numerical value of the variable. In the case of age, the redder the dot the higher the age and the bluer the dot the lower the age. In the case of sex. The red color represents the female sex and the blue color represents the male sex. In the case of education, the red color represents higher levels of education while the blue color represents people with low levels of education or no education. * The position of the points on the horizontal axis in our study indicates the probability of overweight/obesity. The further to the right the point is (positive values), the greater the probability that the person suffers from this problem. The further to the left (negative values), the lower the probability. Thus, we can see as an example that Figure 2 provides us with the following information:
* Age is an important factor for the probability of being overweight/obese. We can clearly see that higher ages (red dots) have a higher probability than lower ages (blue dots). * Gender is an unequivocal factor, with male gender (blue dots) being related to a higher probability of being overweight/obese while female gender (red dots) is similarly distributed but in the negative range. * Education is another important factor to take into account. Low levels of education (blue dots) have a higher probability while high levels (red dots) are related to a lower probability. * In the case of other variables, such as job, for example, we can see that the color of the points is intermixed, indicating no correlation with the probability of being overweight/obese. Figure 2: Impact of variables with SHAP, Random Forest with pathologies, and no FS.
# Smart Cities of the Future as Cyber Physical Systems: Challenges and Enabling Technologies
## 7 Use Cases
### A Skeleton for a Smart City Enabled by Cooperating CPSs  
The framework presented in Section 6 is intended to realize a modular system that is able to interact with generic IoT devices; in this way, it may be easily adopted by various typologies of CPSs. To verify the validity of the solution, it was applied in real city environments as part of the #SmartME [10] initiative, where the S4T framework was used to manage environmental stations distributed in an urban area. This experience was identified and considered as a \"good practice\" by the Italian Ministry and further exploited in a project called _Toolsmart_[24], in which the framework was configured into a template to make its adoption in other cities easier.  
The template was configured in the form of a skeleton composed of three parts: the _Infrastructure_, _Management_, and _Presentation_ layers. Figure 11 shows the architecture of the template. At the base of this template, there is the Infrastructure Layer where the Edge devices (IoT-based) are located, enabling the interaction with physical components of a CPS. These devices are managed by the Management Layer that, thanks to the S4T facilities, is able to manage their life-cycles in an agnostic way.  
Indeed, the Controller entity has to set up the networking facilities enabling the edge to be monitored and controlled by the CPS's administrator, thus, allowing differentiation of the device behavior on the basis of the plugin that is injected and executed. Due to the plugin's logic running on the IoTs, the perceived data are sent to the Application layer (the Open data repository) where they are cataloged and pulled by the application logic defined through a graphical development platform, such as NodeRED.  
The realization of this _smart city skeleton_ is a fundamental step to enable the cities in their transformation into a _\"Smart City of the Future\"_. Indeed, thanks to the intrinsic  
Figure 11: Easy to replicate architectural template of a smart city released in an Italian project called ToolSmart.  
characteristics (such as extensibility, interoperability, self-consistency, and replicability), the template developed allows a reduction in the amount of work to create and manage the CPS and to deal with the management of the physical aspects related to the city life (e.g., weather monitoring, infrastructure control, and social engagement). Figure 12 is a screenshot from the _Toolsmart_[24] project.
### Enzymes Involved in Nucleotide Metabolism
#### 1.2.1 Enzymes Involved in the Synthesis of Natural and Modified 5'-NMPs
Two types of bacterial nonspecific acid phosphatases, also known as acid phosphatase/phosphotransferase, have the ability not only to dephosphorylate but also to synthesize 5 \({}^{\prime}\)-NMPs at optimal pH values of 5 or below [39; 42]. Unlike transferases, these hydrolases do not require organic cofactors as phosphate donors. For example, [PERSON]'s group demonstrated the conversion of natural ribonucleosides into their corresponding 5\({}^{\prime}\)-NMPs using inorganic pyrophosphate as the phosphate donor [43]. However, due to the concurrent phosphatase activity of nonspecific acid phosphatases, NMP yields were moderate, reaching a maximum of 41% with the _Morganella morganii_ enzyme [43] and 45.5% with the _Escherichia blatta_ enzyme [44]. Through random mutagenesis of the _Morganella morganii_ enzyme, IMP production from inosine and inorganic pyrophosphate was increased from 49% to 88% due to its reduced phosphatase activity [45]. Later, using whole _Escherichia coli_ cells expressing the modified gene, more than 99% IMP (around 120 mM and 47 g/L) was synthesized [46]. Additionally, 5\({}^{\prime}\)-NMPs can be interconverted through the modification of functional groups by various enzymes, such as AMP deaminase (AMP to IMP), IMP dehydrogenase (IMP to xanthine-5\({}^{\prime}\)-monophosphate), and GMP synthase (xanthine-5\({}^{\prime}\)-monophosphate to GMP) (Figure 1). Interconversions also occur at the nucleobase or nucleoside level (Figure 1). Adenosine deaminase is commonly employed in nucleoside synthesis, enabling the production of soluble adenosine derivatives via transglycosylation, which can then be converted into less soluble inosine or guanosine compounds [47]. Another interesting interconversion at the nucleoside level is catalyzed by the pyrimidine nucleoside 2\({}^{\prime}\)-hydroxylase (PDN2\({}^{\prime}\)H) from _Neurospora crassa_[48]. It naturally hydroxylates thymidine at the \(\alpha\)-2\({}^{\prime}\)-position to give 5-methyluridine as a product. The enzyme is an \(\alpha\)-ketoglutarate/Fe(II)-dependent dioxygenase.
# Fabrication of Multiscale-Structure Wafer-Level Microlens Array Mold
In contrast, a high-precision MLA mold is used in the UV-imprinting process, which is followed by UV-curing the photoresist layer [16], and in the injection-molding process, in which soft optical resin material is injected into the mold to obtain MLAs with the desired optical properties. Since the width to thickness ratio of the wafer MLA is very large, the mold-based UV-imprint process is currently used in industrial wafer MLA manufacturing. Using high-precision MLA molds provides simple-processing, low-cost, and easier mass production of complex aspherical MLAs. Ultraprecision machining is capable of directly producing metal optical surface molds [21; 22; 23; 24; 25] in the different shapes that are required for various wafer-level MLA applications, such as spherical, aspherical, or freeform microlens surfaces. In order to combine moth-eye nanostructures with the wafer-level MLA-mold fabrication method, a multiscale-structure wafer-level MLA mold is proposed, which encompasses hundreds of nanometer-level structured arrays for AR construction, hundreds of micrometer-level MLAs for camera imaging, and finally hundreds of millimeter-level wafer substrates for wafer-level camera manufacture. In this study, three different materials, namely 6061-T6 aluminum alloy, high-purity polycrystalline aluminum, and pure nanocrystalline aluminum were used as substrates to fabricate MLA molds with an aspherical design, using ultraprecision machining. Then, each mold was treated with a self-assembly process based on anodic aluminum oxidation (AAO) to generate nanostructures over the mold surface. Optical and scanning electron microscopy (SEM) measurements were performed on the three types of multiscale molds. The multiscale mold fabricated using pure nanocrystalline aluminum material exhibited uniform nanostructures, and the optical measurements demonstrated that both the profile peak-to-valley (PV) error of the lenslet and the surface roughness met the optical requirements.
## Materials and Methods
### Reliability and Validity
#### 2.2.2 Data Collection  
Data to assess the reliability and validity of the DIMS was collected in the special education schools of the five participants, in a standardized set-up using their own head/foot steering wheelchair. Participants performed five powered mobility tasks, which were video-recorded for future scoring of the DIMS. The videos were recorded using a commercially available Sony Handycam HDR-CX405 (Sony Corporation, Tokyo, Japan) placed on a tripod on both sides of the powered mobility tasks set-up. The video recording was started manually by the researchers at the beginning of the data collection and was stopped at the end of it. The sample rate of the videos was 29 frames per second. This video-recording procedure was repeated for the powered mobility performance of each participant.  
\begin{table}
\begin{tabular}{c c} \hline
**Duration** & **Amplitude** \\ \hline
**Score 0** & D/CA is absent & D/CA is absent \\
**Score 1 (\textless{}10\%)** & [PERSON]/CA is occasionally present & D/CA is present in small ROM \\
**Score 2 (\textgreater{}10\textless{}50\%)** & D/CA is frequently present & D/CA is present in moderate ROM \\
**Score 3 (\textgreater{}50\textless{}90\%)** & D/CA is mostly present & [PERSON]/CA is present in submaximal ROM \\
**Score 4 (\textgreater{}90\%)** & D/CA is always present & [PERSON]/CA is present in maximal ROM \\ \hline \end{tabular}
\end{table}
Table 1: Scoring protocol of the Dyskinesia Impairment Mobility Scale (DIMS) duration and amplitude during powered mobility.  
Figure 1: Diagram of the Dyskinesia Impairment Mobility Scale (DIMS) protocol.  
The included tasks [18] were independent driving (i.e., T1), driving through a created corridor (i.e., T2), a 360 degree turn to the left (i.e., T3), a 360 degree turn to the right (i.e., T4) and a slalom (i.e., T5) (Figure A2). The set-up of T1, T2 and T5 was ten meters in length. The width of the corridor (T2) and the distance between cones (T5) were calculated separately per participant by measuring the length of their wheelchair and adding 50% of this length to ensure fairness in set-up, as well as comfort and safety. To meet the aims of this study, each participant was recorded twice, (i.e., Week 1 (W1) and Week 2 (W2) with two weeks' break in-between). To assess the concurrent validity of the DIMS, all five participants were additionally recorded following the standardized protocol of the existing DIS.
## 2 Related Work
### GPU-Accelerated Physics Simulation  
In XR environments, achieving physically plausible real-time interactions with the physical world requires high computational performance [21]. Particularly, in complex nonlinear systems such as cloth, soft bodies, and fluids, it is necessary to compute the physical states of thousands of particles or vertices within a single frame, making it difficult to achieve real-time performance with CPU-based approaches alone [22; 23].  
As a result, research on accelerating physics simulations using the massive parallel processing capabilities of GPUs has been actively conducted [24]. GPUs are capable of simultaneously executing thousands of threads, effectively handling the high parallelism and computational load inherent in particle-based simulations. Technologies such as Compute Shader, CUDA, and OpenCL have been utilized to support these high-performance computations.  
In the context of cloth simulation, GPU acceleration is typically applied in the following ways:  
* Parallel computation of particle data (position, velocity, external forces);
* Parallel enforcement of constraints, including distance, bending, and fixed-point constraints;
* Parallelized collision detection with meshes or colliders;
* Dynamic optimization of simulation iterations to balance quality and real-time performance. Previous studies have explored various GPU-based cloth simulation methods. [PERSON] et al. [4] accelerated mass-spring-based cloth simulation on the GPU for character clothing with interactive performance, while [PERSON] et al. implemented a cloth simulation capable of handling thousands of particles using a PBD-based approach with minimal computational delay.  
However, most existing works have focused on simulations within closed virtual environments, with relatively few addressing direct physical interactions between scanned real-world meshes and deformable cloth objects in dynamic settings.  
To overcome these limitations, this study proposes a GPU-based PBD cloth simulation system built on the Meta Quest 3 platform and Unity's Compute Shader framework. The proposed system enables real-time physical interactions between scanned real-world spatial meshes and virtual cloth objects, thereby introducing a novel approach for physics-driven XR interaction.
# Secure On-Off Transmission in UAV Relay-Assisted mmWave Networks
## 5 Conclusions  
In this paper, we investigate secure transmission in UAV relay-assisted mmWave networks where the beam alignment is achieved or not. The closed-form expressions for TP and SOP in both the on-off and non-on-off transmission schemes are derived by considering the beam alignment error or not. Analyzing the numerical and simulation results, we find that the SOP indeed can be improved by using the on-off transmission scheme, and the SOP can be further decreased by increasing the threshold of data transmission. In addition, the TP is not a monotonous function of the UAV relays' altitude in the on-off transmission scheme, and there exist an optimal altitude of UAV relay to obtain the best TP. Moreover, the SOP will not deteriorate when the beam alignment error is relatively small, and we can improve the SOP by adopting the antenna with more elements. Nevertheless, in the high alignment error regime, the antenna array with smaller number of elements provides the better SOP.  
[PERSON] and [PERSON] conceived the model; [PERSON] performed the performance analysis and simulation results; [PERSON] and [PERSON] analyzed the numerical and simulation; [PERSON] wrote the paper; and [PERSON], [PERSON] provided some suggestions and revised the paper.  
This work was supported by the National Natural Science Foundation of China (No. 61771487 and No. 61471393).  
The authors declare no conflict of interest.
# Machine Learning-Assisted Secure Random Communication System
## 3 Allied Concepts
### Decision Trees  
DTs enable one to conveniently perform simple and intelligent decision-making within complex processes by dividing them into smaller and manageable components. This enables straightforward interpretation, which represents a powerful yet easy way to analyze several variables. Several approaches are available for DT construction. However, C4.5 [58], the enhanced version of iterative dichotomizer 3 (ID3), and classification and regression trees (CARTs) [59], are considered the most reliable algorithms when decision-making on complex data is required through a single tree. If the two algorithms are compared on the basis of accuracy, the C4.5 algorithm is more suitable for the construction of DTs with datasets that possess negligible or no noise. In contrast, the performance of CART algorithms has been proven to be better for noisy datasets [60]. It has been found to be comparatively successful, particularly in the presence of random noise in a dataset. Moreover, its accuracy has been extensively tested on standardized datasets that have already added random noise as a class variable with varying noise power. It has also proven its applicability to datasets containing label noise during the training phase of supervised classification [61]. Specifically, when conventional communication systems are incorporated, a CART significantly enhances the classification accuracy compared with static methods. It has also shown robustness in detecting and estimating mixed noise signals in challenging communication environments, such as fading channels [62]. This makes the CART approach a potential candidate for the efficient estimation of Sk\(\alpha\)S noise carrier signals for RCSs. Because this is an underlying principle that enables the retrieval of binary information via the LR in the proposed ML-RCS, we proceed by reviewing the basic methodology behind it.
# AWEncoder: Adversarial Watermarking Pre-Trained Encoders in Contrastive Learning
## 3 Proposed Method
### Watermark Generation  
The first step is to generate an adversarial perturbation \(\mathbf{w}_{\text{adv}}\) as the watermark using the pre-trained encoder \(E_{\theta}\). Since our task is encoder-oriented, the traditional perturbation optimization strategy based on classification labels is inapplicable. To deal with this problem, we use the embedding of a randomly selected image \(\mathbf{x}_{\text{tar}}\) (called _key image_) extracted by \(E_{\theta}\), that is, \(E_{\theta}(\mathbf{x}_{\text{tar}})\), to generate \(\mathbf{w}_{\text{adv}}\) with a clean dataset \(\mathcal{D}\). This enables the embedding of a perturbed image denoted by \(E_{\theta}(\mathbf{x}_{i}+\mathbf{w}_{\text{adv}})\) (where \(\mathbf{x}_{i}\in\mathcal{D},i\in\{1,2,...,|\mathcal{D}|\}\)) cluster around \(E_{\theta}(\mathbf{x}_{\text{tar}})\). In other words, the distance between \(E_{\theta}(\mathbf{x}_{\text{tar}})\) and \(E_{\theta}(\mathbf{x}_{i}+\mathbf{w}_{\text{adv}})\) is expected to be as low as possible. To achieve this goal, we minimize the following loss during perturbation optimization:  
\[\mathcal{L}_{\text{adv}}=\mathbb{E}_{\mathbf{x}_{i}\sim\mathcal{D}}[1-\text{ sim}(E_{\theta}(\mathbf{x}_{i}+\mathbf{w}_{\text{adv}}),E_{\theta}(\mathbf{x}_{ \text{tar}}))]. \tag{4}\]  
By back-propagation, \(\mathbf{w}_{\text{adv}}\) can be generated without changing the parameters of \(E_{\theta}\). It is obvious that different \(\mathbf{x}_{\text{tar}}\) results in different \(\mathbf{w}_{\text{adv}}\). By using \(\mathbf{x}_{\text{tar}}\) as a key, it is difficult for the adversary to forge the watermark. Some watermark generation methods by linearly superimposed fixed patterns [28] may also perturb the embedding of an image, but their effect of changing the behavior of the encoder is weaker than our method.
# A Real-Time Network Traffic Classifier for Online Applications Using Machine Learning
## 4 Implementation
### ML Models  
Both the RF and ANN models are implemented using Keras development environment [16]. Keras is an open-source neural network library written in Python, which uses TensorFlow [17] as a back-end engine. Keras libraries running on top of TensorFlow make it relatively easy for developers to build and test deep learning models written in Python. The KNN model is implemented using Python programming language.  
We set the batch size and number of epochs to be 150 k packet flows and 10 epochs, respectively. The model training was carried out using a server computer equipped with a 4.50 GHz Intel Core(tm) i7-16 MB CPU processor, 16 GB of RAM, and CUDA GPU capability. The training phase took approximately 2 days to run 10 epochs. We took a snapshot of the trained weights every 2 epochs to monitor the progress.  
The training error and loss of the ML models are calculated as follows:  
\[M=\frac{1}{n}\sum_{i=1}^{n}(y_{i}-x_{i})^{2} \tag{12}\]  
where \(M\) is the mean square error of the model, \(y\) is the value calculated by the model, and \(x\) is the actual value. \(M\) represents the error in class detection.  
Figure 4 illustrates the calculated training error and loss of the ANN model graphically. As shown in the figure, the mean squared error loss decreases over the ten training epochs, while the accuracy increases consistently. We can see that our ANN model converged after the 8 th epoch, which means that our dataset and the fine-tuned parameters were a good fit for the model.
## 3 Results  
### Scattering in 2-D Plane Strain Models  
#### 3.1.1 Symmetric Smooth Defect  
The first case study examines the accuracy of the Mindlin plate theory for defects that closely align with the theoretical assumptions, specifically, those with a defect profile symmetric to the plate's midplane (Figure 1b). Figure 5 compares the reflection coefficients between the Mindlin model (FDM) and the FEM model (FEM) for various defect lengths and depths for the \(A_{0}\) mode at 50 kHz. The reflection amplitude for these defects is generally low, peaking at approximately 0.14 for the deeper defects. The agreement between the two models is strongest for shallower, longer defects, while the relative difference increases as the defects become shorter and deeper, with a maximum difference of about 3.5%.  
Figure 6 shows that for smooth defects, most of the energy is transmitted, with the transmission coefficient close to 1. The consistency between the models in Figure 5c mirrors that in Figure 6c, indicating that transmission accuracy decreases for short, deep defects. However, the relative difference between the models remains minimal, at less than 1%.  
Figure 4: (**a**) Schematic of complex defect in 3-D modeling; (**b**) Cross-section of the FEM plate mesh along the dashed line.  
Figure 5: Reflection from symmetric smooth defect: (**a**) reflection coefficients from FEM model (FEM), (**b**) reflection coefficients from Mindlin model (FDM), (**c**) relative difference between the reflection coefficients.