esa-sceva/satcom-chunk-collection huggingface dataset:
# mmHSE: A Two-Stage Framework for Human Skeleton Estimation Using mmWave FMCW Radar Signals
## 4 Methodology
### Data Augmentation  
To improve robustness against sensor noise and motion variability, we apply two augmentation strategies: temporal frame shuffling and 3D keypoint rotation. These are applied to the radar heatmap sequences and corresponding 3D annotations, respectively.  
* Temporal Augmentation: Given a sequence of \(T\) radar frames (with each sample containing 5 frames), we randomly shuffle their temporal order with a probability of 0.5. This introduces temporal variation and encourages the model to learn motion-invariant representations.
* Three-dimensional Keypoint Rotation: To simulate varying orientations, we apply a random rotation around the vertical (y) axis to the three-dimensional keypoints. With probability 0.5, a rotation angle \(\alpha\in[-30^{\circ},30^{\circ}]\) is sampled, and the keypoints \(\mathbf{Y}\in\mathbb{R}^{N\times 3}\) are transformed as \(\mathbf{Y}^{\prime}=\mathbf{Y}\cdot\mathbf{R}\), where \(N=21\) is the number of keypoints and \(\mathbf{R}\) denotes the rotation matrix.  
The augmented heatmaps \(\mathbf{H}^{\prime}_{\text{AZ}}\), \(\mathbf{H}^{\prime}_{\text{EL}}\in\mathbb{R}^{B\times T\times C\times H\times W}\) and rotated keypoints \(\mathbf{Y}^{\prime}\in\mathbb{R}^{B\times N\times 3}\) are then fed into the feature extraction module, where \(B\) is the batch size, \(C\) the number of channels, and \(H\times W\) the spatial resolution.
# A Technique of Recursive Reliability-Based Missing Data Imputation for Collaborative Filtering
## 5 Performance Evaluation
### Experimental Setup  
We used the MovieLens 1M Dataset provided by the University of Minnesota, which is widely used to test the performance of recommender systems. This dataset contains one million ratings from 6000 users on 4000 items. For the purpose of performance testing, we extracted 500 users from the MovieLens 1M Dataset, generated a training dataset, and labeled it \(M_{500}\). The test dataset was generated by the test dataset users were provided in limited numbers of 10, 20, and 30, which are labeled as \(Givet_{10}\), \(Givet_{20}\), and \(Givet_{30}\). This is an experimental setup mostly used to test the performance of recommender systems.  
The performance of an imputation algorithm should be evaluated by the final ratings predicted by the algorithm rather than its interim products. To maintain consistency with other studies, we applied the Mean Absolute Error (\(MAE\)) as a metric to compare the algorithms [31; 32]. Equation (16) is an equation for calculating \(MAE\), where \(r_{as}\) and \(\hat{r}_{as}\) are the real and predicted ratings, respectively, by user \(u_{a}\) on item \(t_{s}\), \(X\) is the test dataset, and \(|X|\) is the size of \(X\). The lower the value of \(MAE\), the higher the prediction accuracy. Its value can be any real number ranging from 0 to 5 as the MovieLens 1M Dataset uses ratings from 1 to 5.  
\[MA\epsilon=\frac{\sum_{(\mathbf{a},\ \mathbf{s})\in\mathbf{X}}\lvert\mathbf{r_{as}}-\hat{\mathbf{r} }_{\mathbf{as}}\rvert}{|\mathbf{X}|}. \tag{16}\]  
Prior to the implementation of the imputation algorithm, the values of the parameters provided by each imputation algorithm must be defined. To avoid research bias problems, we employed the values used in other studies [12]. More specifically, the parameters of EMDP and k-RRI were set at \(\lambda=0.7\), \(\gamma=30\), \(\delta=25\), and \(\eta=\theta=0.4\), and those of AutAI were set at \(\lambda=0.4\) and \(k=10\).
# Behavior and Damage Characterization of Impulsively Loaded Cross-Laminated Timber Panels
## 3 Results
### Damage Characterization  
Varying degrees of damage were observed following impulsive loading. Greater degrees of damage were a function of the impact energy and impulse applied. All specimens were tested within a range of 1.0 kJ to 8.0 kJ of impact energy and exhibited slight damage to complete panel failure. In this research, slight damage is characterized as little to no damage in the form of visible shear or flexural cracks, zero or low residual displacement, and a residual load capacity that is within 10% to 20% of the average quasi-static load capacity. Heavy damage is characterized by partial rupturing of the extreme tensile and compressive laminae, presence of shear cracks, high residual displacement, and significant loss in residual capacity (\(\geq\)50%) of quasi-static capacity. Complete failure is characterized by failure of all lamellas in the tensile face lamina oriented in the major strength direction of the CLT panel.
## 5 Performance Evaluation
### Neural Network Convergence  
In this section, we first demonstrate the convergence of the proposed model, as shown in Figure 6. During the first 300 iterations, the average reward fluctuated and gradually increased. After 300 training sessions, it steadily increased and tended to stabilize. This is because at the beginning of each training session, all UAVs took off from a fixed location and users were randomly distributed. This uncertainty made the network unable to select appropriate actions for UAVs to provide high coverage and fairness to users during the early stages of training. As the number of training sessions increased, the model became more mature and UAVs selected appropriate actions through the network to complete tasks, resulting in a steady increase in rewards. The figure clearly shows that the average reward obtained no longer increases significantly after the number of training sessions reaches 2500 and the trained model converged.
###### Abstract  
This study explores the osmotic dehydration (OD) of fresh-cut cherry tomatoes through the application of Pulsed Electric Fields (PEF) and High-Pressure (HP) pre-treatments. Untreated, PEF-treated (1.8 kV/cm, 0-300 pulses), and HP-treated (0-600 MPa, 5 min) tomatoes were subjected to osmotic dehydration at 35 \({}^{\circ}\)C for up to 3 h. The results reveal that a 100-pulse PEF treatment and HP treatment at 600 MPa yielded optimal outcomes in terms of both OD enhancement (with effective moisture diffusion coefficients of \(7.91\cdot 10^{-10}\) m\({}^{2}\)/s for PEF and \(7.40\cdot 10^{-10}\) m\({}^{2}\)/s for HP-treated tomatoes compared to \(5.17\cdot 10^{-10}\) m\({}^{2}\)/s for untreated samples) and product acceptability (achieving overall acceptance scores between 7 and 8). Applying PEF (100 pulses) and HP (600 MPa) pre-treatments reduced the water activity (a\({}_{w}\)) to 0.887 and 0.760, respectively, after 3 h of OD, compared to a\({}_{w}=0.923\) for untreated OD samples. The selection of these pre-treatment conditions enabled effective dehydration and quality retention, extending the shelf life by up to 40 days under chilled storage.  
Laboratory of Food Chemistry and Technology, School of Chemical Engineering, National Technical University of Athens (NTUA), 15780 Athens, Greece; [EMAIL_ADDRESS] (A.K.); [EMAIL_ADDRESS] (G.D.); [EMAIL_ADDRESS] (E.D.)
# Employing Ray-Tracing and Least-Squares Support Vector Machines for Localisation +
###### Abstract  
This article evaluates the use of least-squares support vector machines, with ray-traced data, to solve the problem of localisation in multipath environments. The schemes discussed concern 2-D localisation, but could easily be extended to 3-D. It does not require NLOS identification and mitigation, hence, it can be applied in any environment. Some background details and a detailed experimental setup is provided. Comparisons with schemes that require NLOS identification and mitigation, from earlier work, are also presented. The results demonstrate that the direct localisation scheme using least-squares support vector machine (the Direct method) achieves superior outage to TDOA and TOA/AOA for NLOS environments. TDOA has better outage in LOS environments. TOA/AOA performs better for an accepted outage probability of 20 percent or greater but as the outage probability lowers, the Direct method becomes better.  
Keywords:localisation; positioning; ray-tracing; support vector machine +
Footnote †: This article is an extended version of the conference paper published in [PERSON], [PERSON]; [PERSON], [PERSON]; [PERSON], [PERSON]; [PERSON], [PERSON] Direct Localisation using Ray-tracing and Least-Squares Support Vector Machines. In Proceedings of the 8 th International Conference on Localization and GNSS (ICL-GNSS) in Guimaraes, Portugal, 26-28 June 2018, doi:10.1109/ICLGNSS.2018.8440915.
# Detection of Defects Using Spatial Variances of Guided-Wave Modes in Testing of Pipes
## 2 Background Theory
### Wave Formation  
The scattered wave would consist of the excited axisymmetric and its associated flexural waves. Depending on the feature shapes, the energy distribution of each of the created wave modes would be different. As an example, a perfectly axisymmetric feature in the ideal scenario would result in the generation of a perfect axisymmetric response with no other flexural waves. In these scenarios, if the axisymmetric wave is stronger than the coherent regional noises in the area, an anomaly is reported by the inspectors. However, since it is a visual interpretation, in some scenarios, the resultant mixture of flexurals can easily lead to false alarms due to their coherence to the expected received signal. Furthermore, the axisymmetric wave can be smaller than the local noise level, and therefore would not be detected. These are some of the main challenges faced when inspecting the temporal domain.  
Figure 1 shows the spatial arrival of the signals received from two rings of 32 source points. These signals are generated by the finite element model (FEM) [30]. In this setup, ideal source points are assumed, which leads to the generation of a perfect T\((0,1)\) wave mode in all of the figures except (b) and (i), where the frequency response of each transducer is variable.  
Figure 1: Spatial signal reception from two rings of 32 source points from various features where the red, blue, and black dotted lines are showing the first ring, second ring, and the reference offset.  
The red and blue signals are showing the first and second ring, respectively, and the reference is illustrated by the black dotted line. Thus, the values inside the reference would mean a negative phase, and values higher than the circle would mean a positive amplitude of the individual transducer.
## 3 Results and Discussion  
### Optimal UV Wavelength for the Detection of the AI-2 Derivative  
DPD was reacted with DAN to produce the AI-2 derivative (1-(3-methyl-benzo[g] quinoxaline-2-yl)-ethane-1,2-diol; Figure 1).  
The reaction mixture was eluted using an HPLC with a UV detector at a wavelength of 225 nm (Figure 2a). The second peak, appearing at a retention time of 1.5 min, corresponded to DAN. The third peak at a retention time of approximately 3 min corresponded to the derivative of DPD and DAN. The structure of the derivative shown in Figure 2a was confirmed by mass spectrometry (Figure S2). The eluted sample corresponding to the derivative was further scanned using a UV spectrometer in the range of 200-380 nm. The derivative showed specific absorption peaks at 225, 268, and 365 nm (Figure 2b). As the peak at 268 nm gave the highest intensity, it was adopted for the subsequent analysis of the derivative.  
Figure 1: Product of the reaction of (S)-4,5-dihydroxy-2,3-pentandione (DPD) with 2,3-diaminonaphthalene (DAN).
###### Abstract  
The library of new smart contrast agents based on Gd\({}^{3+}\), Eu\({}^{3+}\), and Tb\({}^{3+}\) used as biomarkers is in continuous development due to its applications in diagnostic imaging. The search for safer and more efficient contrast agents has focused on the design of compounds that exhibit high relaxivity. Herein, we present alternative synthetic strategies for the development of theoretically high-relaxivity synthons based on lanthanides using the Solomon-Bloembergen-Morgan equations through click chemistry and direct addition. Special attention has been devoted to the analysis of the different aspects interfering with the successful acquisition of these complexes and their troubleshooting during their synthesis. Our preliminary results showed that not only the mathematical background needs to be considered, but also the synthetic strategy and the use of procedures free of metallic ions favor the total synthesis of these challenging complexes.  
c 2022129969 202212996  
conception of complexes with high chemical stability and low reactivity, and hence high thermodynamic and kinetic stability, is imperative [9].  
Various strategies have been developed to decrease the adverse effects of CA, including increasing solubility to improve the excretion rate, using less-toxic ions such as iron oxide or other transition metal oxides, and, finally, increasing the relaxivity of these compounds [10; 11; 12; 13; 14]. In this paper, we focused on the latter, because in this way the dose concentration will be reduced, and the technique resolution will increase, diminishing the risk of collateral effects on human health.  
The design of these complexes follows two trends: linear agents such as Magnevist(r) and Omniscan(r), which do not fully surround the lanthanide ion, and macrocyclic agents such as Dotarem(r) that do this using a cage-like ligand [15]. These latter compounds have lower metal bleeding and therefore fewer side effects, making them preferred for the synthesis of highly stable CA. Once their thermodynamic and kinetic stability are covered, it is possible to focus on the strategies to increase their relaxivity without affecting these requirements [16; 17].  
Herein, we present new pathways for the development of CA with potentially high relaxivity by applying the Solomon-Bloembergen-Morgan equations within the Redfield limit as an activity predictor for the synthetic strategy [18; 19]. We proposed the following two synthetic pathways to obtain highly active MRI contrast agents based on trivalent lanthanides: Huisgen cycloadditions to build organometallic tetramers with cyclen (1,4,7,10-Tetrazacyclododecane) as a building block and 1,4-phenylenediamine functioning like a molecular scaffold; and direct coupling reactions with DOTA as a starting material to produce macromolecular systems active in MRI. We established different parameters impeding the acquisition of the final products, and finally, we proposed different routes to overcome the challenges found during synthesis for future experiments.
## 3 Results and Discussion
### Influence of Fluid Salinity
#### 3.1.2 Influence of Borehole Fluid Salinity
Since the variation of the borehole electromagnetic wave number cannot change the element value of the coefficient matrix, the electromagnetic field response coefficient \(A_{H}\) is not influenced by the salinity of the borehole fluid. According to Equation (5), in addition to the response coefficient \(A_{H}\), the remaining terms can be expressed in the form \(i\omega\mu\frac{I_{1}(\eta_{e}a)}{\eta_{e}}\), and its value hardly influenced by \(\eta_{e}\). In summary, although the change in borehole fluid salinity can make the change of the fluid conductivity and thus influence the wave number of borehole electromagnetic waves, the variation of the wave number in the logging frequency band hardly influences the borehole electric field. As a result, the salinity level in the borehole hardly influences the borehole electric field. This is consistent with the results presented in Figure 4. However, for the conventional acoustic sources, the influence of the salinity level of the borehole fluid on the seismoelectric response is more complicated [48]. Both the salinity values of the borehole fluid and the formation affect the electric field. In addition, the ratio between the two also affects the normalized amplitude of the electric field [5]. Figure 6 compares the seismoelectric wavefields of a centered monopole source for different borehole salinity, with a source center frequency of 6 kHz. \(E_{z}\) is the axial electric field. Figure 5: Diagram of the first kind modified Bessel function values. The left one is the first-kind zero-th order modified Bessel function \(I_{0}(\eta_{e}a)\), the middle one is the first-kind first-order modified Bessel function \(I_{1}(\eta_{e}a)\), and the right one is the ratio of \(I_{1}(\eta_{e}a)\) to the independent variable. When fluid salinity \(C_{b}\) varies, the composition of \(E_{z}\) does not change. As shown in Figure 6, the first component is the electromagnetic interface response. The following components are the accompanying electric fields. The first to arrive is the accompanying electric field of P-wave, and then the accompanying electric fields of S-wave and Stoneley wave. When the salinity in the borehole varies, the arrival time of each component is almost unchanged but the amplitude changes. Obviously, the greater the salinity, the smaller the amplitude. According to the earlier discussion, this amplitude change is mainly caused by the influence of the fluid salinity on the borehole fluid conductivity. That is to say, borehole fluid salinity has an obvious influence on the electric field, which is very different from the response law of the borehole SH-TE waves to borehole salinity. According to the above discussion, the response law of the coupling electric field of the borehole pure SH wave to borehole salinity is a special case. This is mainly due to the low frequency source and the special shape of the acoustic source.
# Blockchain and Smart Contracts for Digital Copyright Protection
## 5 Proposed Protocol
### Identification and Arbitration Protocol  
The protocol is shown in Table 3 and is employed by \(\mathcal{CP}\) to recognize, with statistical certainty, who was the legitimate copyright owner of a pirated copy of \(\bar{X}\), denoted as \(X^{\prime}\), illegally distributed on the Internet [9].  
The protocol starts with extracting the watermark \(W^{\prime}\) from \(X^{\prime}\). Since \(W^{\prime}\) can be expressed as \(W^{\prime}_{\mathcal{CP}}\|N^{\prime}\), \(\mathcal{CP}\) can use \(W^{\prime}_{\mathcal{CP}}\) to search its databases for a match. If a possible match is found [3], \(\mathcal{CP}\) can access the corresponding entry and obtain the tokens \(X_{d}\), \(T_{X}\), \(pk^{X}\), \(\mathbb{E}_{pk^{X}}(N)\), and \(SBlock\) previously saved during the purchase transaction of \(\bar{X}\). These tokens, together with \(N^{\prime}\), are included in the message \(m_{1}\) and sent by \(\mathcal{CP}\) to \(\mathcal{J}\). \(\mathcal{J}\) then forwards the received tokens, except \(N^{\prime}\), to \(\mathcal{BC}\) in the message \(m_{2}\).  
When \(m_{2}\) is received, the smart contract \(sm_{v}\) can be automatically executed. It searches the blockchain \(\mathcal{BC}\) for a block publishing \(X_{d}\), \(T_{X}\), \(pk^{X}\), \(\mathbb{E}_{pk^{X}}(N)\), and \(SBlock\). If a block is found, \(sm_{v}\) decrypts \(SBlock\) and compares the retrieved tokens with those received in \(m_{2}\). If all the tokens match, \(\mathcal{BC}\) sends \(B_{id}\) and \(N\) to \(\mathcal{J}\) in the message \(m_{3}\). This condition ends \(sm_{v}\).  
Upon receiving \(m_{3}\), \(\mathcal{J}\) compares \(N^{\prime}\) with \(N\). If \(N^{\prime}==N\), \(\mathcal{B}\) is found guilty of being a traitor, and the identity associated with \(B_{id}\) is made known. Otherwise, the protocol ends with no result.
# Orthogonal Chirp Division Multiplexing for Underwater Acoustic Communication
###### Abstract  
The objective of this study is to investigate a novel Underwater Acoustic Communication (UWAC) system based on a modulated chirp signal termed as Orthogonal Chirp Division Multiplexing (OCDM). Originating from the Fresnel transform, OCDM uses chirp signals to exploit the multipath diversity of the channel, achieving a good robustness against frequency fading, especially in the underloaded scenario where only a subset of the available waveforms is modulated. The implementation of the OCDM system for the UWAC scenario is described, and the performance results over an experimental water tank and realistic replayed underwater channel are compared against the traditional Orthogonal Frequency Division Multiplexing (OFDM) transmission scheme.  
UWAC; OFDM; OCDM +
Footnote †: journal: Accepted: 4 November 2018; Published: 7 November 2018
## 3 Methodology
### Feature Selection
#### 3.2.4 Forward Feature Selection Using Stochastic Gradient Descent  
Forward Feature Selection (FFS) is a wrapper method for feature selection. FFS is a greedy algorithm for selecting relevant features in machine learning models [38]. It starts with an empty model and iteratively adds features that improve the model's performance the most. This process continues until a desired number of features is reached or a stopping criterion is met. The effectiveness of FFS is contingent upon the classifier used; thus, the choice of classifier can significantly influence the selected features and the overall model performance.  
Stochastic Gradient Descent (SGD) is a popular optimization algorithm used to train various machine learning models, including those with FFS. It iteratively updates the model parameters by taking small steps in the direction of the gradient of the loss function. This allows SGD to efficiently optimize models with a large number of features [39; 40].  
The FFS was implemented using SGDClassifier from scikit-learn. The code iteratively selects features and trains the SGDClassifier for each feature set. The feature selection is based on maximizing the F1 score on the cross-validation set. For each iteration, a new feature is added to the feature set based on the F1 score improvement.  
Figure 6 shows the rankings of the top 100 features selected by FFS with the SGD algorithm. Features on the left were selected earlier, indicating a stronger positive effect on the model's performance.  
Figure 5: Top 100 features selected using the chi-square feature selection method.
## 2 A Rapid Output Matching Optimization Method with Dual-Objective Orientation  
As the final stage of RF power transmission in a PA, the OMN plays a decisive role. The large-signal behavior of a transistor is sensitive to the load impedance, making precise OMN design essential to achieving the expected matching conditions. At mmWave frequencies, \(Z_{\text{opt}}\) shows pronounced frequency dependence due to the device's enhanced parasitic effects, primarily the nonlinear drain-source capacitance (\(C_{\text{ds}}\)), and is denoted as \(Z_{\text{opt}}(f_{\text{i}})\) at the frequency \(f_{\text{i}}\). Additionally, the OMN transforms the standard 50 \(\Omega\) load into the impedance \(Z_{\text{omn}}(f_{\text{i}})\), whose rotational trajectory on the Smith chart opposes that of \(Z_{\text{opt}}(f_{\text{i}})\). These two issues hinder the design of a broadband OMN, making point-by-point matching unfeasible, especially under complexity limitations. Thus, industry practice is to choose an appropriate OMN topology and fine-tune its component values to closely approach \(Z_{\text{opt}}(f_{\text{i}})\) with reasonable compromises in matching accuracy. This section details a rapid and automated parameter optimization method for the OMN, free of artificial intelligence algorithms.
# Encryption of 3D or Higher-Dimensional Images: A Review
## 3 Methodology and Research Analysis
### Research Questions  
The research questions (RQ) are as follows:  
1. Is the encryption predominantly carried out in the spatial or frequency domain?
2. If the encryption is in the frequency domain, which transform is predominantly used?
3. Does the method occur completely in the image or partially?
4. How is the encryption performance assessed?
5. Do the methods frequently use chaotic sequence generation? Which one?  
Figure 5: Examples of 3D point clouds: (**a**) cloud with 8,128,921 points and (**b**) cloud with 150,388 points.