esa-sceva/satcom-chunk-collection huggingface dataset:
# Early Diagnosis of Dementia from Clinical Data by Machine Learning Techniques
## 4 Results and Discussion
### Phase 2
Figure 5: Comparison of the Classification Based on the Precision, Recall, and F-measure criteria for the Diagnosis of Dementia (Phase 2). As the results in Table 12 and Figure 7 clearly demonstrate, SVM has the most accuracy for the diagnosis of dementia (Phase 2) and the value of this algorithm is equal to 74.03%. Moreover, the classification accuracy of logistic regression and random forest were equal to 73.71% and 72.98%, respectively. According to Figure 7, MLP had the lowest correct classification accuracy in the diagnosis of dementia (Phase 2). The accuracy of bagging and Naive Bayes were 72.49% and 71.44%, respectively. The classification accuracy of Bayes Network was 70.95%. To sum up, in Phase 1, MLP showed the highest accuracy with 97.2%, followed by random forest and bagging. The lowest accuracy was Naive Bayes at 81.3%. In Phase 2, SVM was tops among other classifiers for MCI and dementia cases with 74.03%, followed by logistic regression and random forest. Whereas MLP was the best in Phase 1 for predicting normal, SVM was best in Phase 2 for predicting dementia. The results of this study are consistent with findings from several researchers (e.g., [4,18]) \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithms** & **Correctly Classified Instances (\%)** & **Incorrectly Classified Instances (\%)** \\ \hline SVM & 74.03 & 25.97 \\ Logistic Regression & 73.71 & 26.29 \\ Random Forest & 72.98 & 27.02 \\ Bagging & 72.49 & 27.51 \\ Naive Bayes & 71.44 & 28.56 \\ Bayes Net & 70.95 & 29.05 \\ MLP & 68.12 & 31.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Data Classification Accuracy for Dementia (Phase 2). Figure 6: Comparison of the Classification Algorithms Based on MAE, RMSE, RAE and RRSE Criteria for Diagnosis of Dementia (Phase 2). Figure 7: The Comparison of Classification Accuracy by Phase 2.  
showing that the machine learning approaches can be used to diagnose dementia. Our efforts in the diagnosis of dementia may be similar to those mentioned with respect to the employed machine learning approaches. However, inspired by the method used in dementia support centers for early diagnosis, not only can our proposed model diagnose dementia with a data from simple tests from patients, but also we can achieve higher accuracy in early diagnosis of dementia.
## 2 Materials and Methods
### ML Prediction Models  
This study employs a prediction model based on the well-documented CatBoost ML algorithm [34]. This is a state-of-the-art ML classification algorithm, and it is widely used by the research community, especially on medical datasets, with proven efficacy [35; 36; 37; 38; 39]. It is arguably one of the most accurate and modern algorithms in this field of research. Hence, an explainability approach for it could form a step towards shedding light on the black-box mechanisms for its other applications as well.  
Moreover, a data preselection function and hyper-parameter selection were run. For this particular scenario, implementing the forwards sequential feature selection [40] algorithm gave the best-performing feature subset. Thus, the current prediction model uses a dedicated feature subset to achieve maximum prediction accuracy. As far as training and testing splitting is concerned, a 10-fold stratified cross validation [41] was selected. By implementing stratified sampling, the training subsets had the same percentage of CAD-positive inputs as the original dataset (i.e., 43.43%). On this matter, the dataset was split in a 90/10 analogy (training/testing set) and the prediction results were validated across multiple runs. The model achieved the best performance with specific values for its main initialization parameters; specifically, the n_estimators parameter was tuned to '79' and the learning_rate parameter to '0.1'. Eventually, the performance of the deployed prediction model was evaluated using five common metric scores: accuracy, sensitivity, specificity, Jaccard score, F1-score and a confusion matrix.  
Overall, the complete methodology of this work, as depicted in Figure 1, can be summed up in 4 steps:  
1. Feature selection/model optimization;
2. Training/testing of ML model;
3. Plotting SHAP on the prediction results;
4. Explainability of prediction results.
# Design Procedure of a Frequency Reconfigurable Metasurface Antenna at mmWave Band
## 2 Related Works  
Several researchers have reported various RMSs in the last decade [37, 38, 39, 40, 41, 42, 43, 44, 45]. Works in [42] proposed a reconfigurable metasurface antenna based on a PIN diode for beam switching applications. The authors proposed a metasurface consisting of periodic double-SRR, whose fundamental resonant frequency could easily be controlled by their respective dimensions. Based on their simulations and experiments, the proposed metasurface antenna had good impedance matching and moderate gain performance of about 6 dBi across the band of interest. In Ref. [46], the authors presented a dynamically reconfigurable metasurface antenna for applications in near-field beam focusing. Through the use of the proposed metasurface antenna, the authors achieved beam-focusing without requiring phase shifters and power amplifiers. This concluded that the proposed metasurface antenna was suitable for applications that require dynamic reconfigurability, such as imaging and wireless power transfer.  
Works in Refs. [43, 47] presented a reconfigurable metasurface antenna that manipulated scattering fields by liquid metal. The reflected element phase responses underwent distinct phase differences when the metal shape of the metasurface element changed along the polarization direction. It was concluded that the liquid-metal metasurface antenna presented had less system complexity, lower cost, and much lower power consumption. In Ref. [48], a frequency reconfigurable dielectric resonator antenna using metasurfaces was proposed. The attained bandwidth and gain of the proposed metasurface antenna was 1.1 GHz and 7.8 dBi, respectively. The authors in Ref. [49] designed and evaluated rectangular and circular SRR structures to create a metamaterial superstructure with improved antenna characteristics such as gain, bandwidth, and radiation pattern. However,  
Figure 3: Creating DNG metamaterials by combining wires and SRR.  
from literature, there has not been an explicit method on how to calculate each geometrical parameter of SRRs.
## 4 Discussion
This approach has broad applicability and can be effectively extended to diverse rock mass engineering contexts, including highway construction and water conservancy projects, underscoring its substantial practical value. However, this study has several limitations that merit discussion. For example, our analysis is static and dry; it excludes transient groundwater and seismic/dynamic loading. Infiltration and groundwater rise increase the pore pressure and reduce the effective normal stress, lowering the shear strength via Mohr-Coulomb [31]. Spatially variable hydraulic conductivity and perched water conditions can create localized low Fs patches not captured here. Likewise, seismic actions introduce inertial body forces (pseudo-static acceleration and cyclic degradation); a time-history excitation can transiently reduce [32] the Fs below unity even when the static Fs \(>1\). Neglecting these mechanisms likely biases the Fs upward and underpredicts the extent/frequency of instability during intense rainfall or earthquakes. Consequently, the reported Fs values should be interpreted as the upper-bound indicators under dry, static conditions. Future research should address these limitations through several improvements. Parameter uncertainties could be incorporated using probabilistic methods like Monte Carlo simulations, while groundwater effects could be integrated via a coupled seepage-stability analysis [33]. Field monitoring data could be employed for model calibration [34]. Furthermore, combining alternative stability analysis methods (e.g., energy-based approaches or probabilistic analyses) with spatial variability modeling may enhance evaluation accuracy [35; 36]. Applying machine learning techniques to analyze multi-source engineering data also represents a promising investigative direction [37; 38; 39].
## 2 Materials and Methods
### Biomass Analysis  
#### 2.3.1 Biomass Concentration and Cell Size  
Algal dry weight concentration \(C_{X}\) (kg.m\({}^{-3}\)) was determined by filtering a given volume (V\({}_{\text{samp}}\)) through a pre-dried and pre-weighed glass-fiber filter (Whatman GF/F). The filters were then washed with 1.19 M NH4 HCO2 and MiliQ water to remove residual salts from the culture medium. The filters were dried again at 105 \({}^{\circ}\)C for 24 h, cooled in a desiccator for 10 mins and then weighed again to achieve a constant weight.  
Volumetric biomass productivity \(P_{X}\) (kg.m\({}^{-3}\).d\({}^{-1}\)) was calculated from the biomass concentration and dilution rate \(D\) (\(P_{X}\) = \(C_{X}\).\(D\)), and areal biomass productivity \(S_{X}\) (g m\({}^{-2}\) d\({}^{-1}\)) was deduced from the volumetric productivity and the specific illumination area of the PBR (\(S_{X}\) = \(P_{X}\)/\(a_{light}\)).  
Cell counting was done by analyzing (in triplicate) a 10 \(\upmu\)L aliquot of the culture. A Malassez chamber and an optical microscope fitted with a camera (Axio MRC Cam at Axio Scope A1 microscope, Carl Zeiss, Oberkochen, Germany) were used to take pictures of each sample. The cell diameters were measured manually using image processing software (Axio VisionRoutine).
# Context-Based Model for Browsing the Web Through Voice
## 5 Model Functionalities for Voice-Based Web Browsing
### Result Delivery and Voice Navigation

Table 8: Intents related to the modification of the user interface. Figure 7: Process to make a request by voice. language. These responses are synthesized and sent to the Multi-modal User Interface for presentation to the user in graphical and audible form. Regarding the graphical and audible responses provided through the Multi-modal User Interface, some examples are presented below. In each case, the graphical user interfaces (GUIs) are shown along with a description of the audible responses generated by the application:  
* The process described in Section 5.1, which is related to the generation of the user context, starts with a welcome screen as shown in Figure 8a. At this stage, an audio file provides instructions to the user. It asks the user to answer a questionnaire and tells the user that they can skip any question by saying the word \"omit\". If the user decides not to continue, the application closes. Otherwise, the questionnaire starts. Figure 8b-d show examples of some screens from the questionnaire. On each screen, there is a specific audio instruction for that question;
* Once the user's context is generated, they can make requests to search for content on the web and explore the results. The contents found by the application are presented according to their type, as illustrated in Figure 9a,b. For example, if the user searches for images or videos, the application informs by audio that the result corresponds to an image or video related to their request, together with the available navigation actions (see Table 8);
* Similarly, other types of results are presented in the corresponding screens. Figure 10a shows an example of the search results for terms and web pages, accompanied by an audio playback that allows the user to select among the available options. Once the user chooses an option, it is displayed on its corresponding screen, as illustrated in Figure 10b,c. In the case of term results, the application plays in audio the relevant information found about the searched term. For web pages, the available navigation options are reproduced, allowing the user to explore the content. Figure 8: Graphical user interfaces for the generation of the user context: (**a**) welcome to the survey screen; (**b**) question screen “how old are you?”; (**c**) question screen “what is your highest completed education level?”; and (**d**) question screen “what are your main interests?”.
# Addressing the Gaps of IoU Loss in 3D Object Detection with IIoU
## 1 Introduction
However, IoU loss in 3D object detection brings additional complexity, such as higher DOF and huge 3D space compared to its 2D counterpart, leading to slow convergence and affecting the network performance [30]. During our earlier study [26], we observed that convergence of existing IoU losses and their variations are affected when the boxes have the same centers and aspect ratios but still have varying shapes and orientations. Additionally, objects in 3D space can have arbitrary orientations, and usage of axis-aligned loss functions results in prediction mismatch [31]. To address these drawbacks, an improved loss function 3D IIOU is proposed in this research. This loss function considers orientation as a parameter in the 4 th dimension and calculates the center distance between x, y, and z centers individually. The proposed loss function addresses the edge cases when the loss value and the guided regression aid in better convergence. The performance of this loss is evaluated on both single-stage and two-stage 3D object detectors on the KITTI and nuScenes datasets. The research article follows the below structure,  
* A background study on various categories of 3D object detectors and their challenges Section 2. * Section 3 analyzes the shortcomings of IoU losses and Section 4 explains the proposed the loss function. * Section 5 demonstrates the performance of the proposed loss function in a synthetic dataset. * Sections 6.1 and 6.2 provide information on the training networks and dataset utilized in this study
* The performance evaluation on KITTI datasets is carried out in Section 7.
###### Abstract  
Regional changes in the flow velocity of Antarctic glaciers can affect the ice sheet mass balance and formation of surface crevasses. The velocity anomaly of a glacier can be detected using the Double-Differential Interferometric Synthetic Aperture Radar (DDInSAR) technique that removes the constant displacement in two Differential Interferometric SAR (DInSAR) images at different times and shows only the temporally variable displacement. In this study, two circular-shaped ice-velocity anomalies in Campbell Glacier, East Antarctica, were analyzed by using 13 DDInSAR images generated from COSMO-SkyMED one-day tandem DInSAR images in 2010-2011. The topography of the ice surface and ice bed were obtained from the helicopter-borne Ice Penetrating Radar (IPR) surveys in 2016-2017. Denoted as A and B, the velocity anomalies were in circular shapes with radii of \(\sim\)800 m, located 14.7 km (A) and 11.3 km (B) upstream from the grounding line of the Campbell Glacier. Velocity anomalies were up to \(\sim\)1 cm/day for A and \(\sim\)5 cm/day for B. To investigate the cause of the two velocity anomalies, the ice surface and bed profiles derived from the IPR survey crossing the anomalies were analyzed. The two anomalies lay over a bed hill along the glacial valley where stick-slip and pressure melting can occur, resulting in temporal variation of ice velocity. The bright radar reflection and flat hydraulic head at the ice bed of A observed in the IPR-derived radargram strongly suggested the existence of basal water in a form of reservoir or film, which caused smaller friction and the reduced variation of stick-slip motion compared to B. Crevasses began to appear at B due to tensile stress at the top of the hill and the fast flow downstream. The sporadic shift of the location of anomalies suggests complex pressure melting and transportation of the basal water over the bed hill.  
Campbell Glacier; DDInSAR; Ice Penetrating Radar; velocity anomaly +
Footnote †: journal: 8 July 2021  
Volocity Anomaly of Campbell Glacier, East Antarctica, Observed by Double-Differential Interferometric SAR and Ice Penetrating Radar  
1,2,691
# Integration of Machine Learning-Based Attack Detectors into Defensive Exercises of a 5G Cyber Range
## 9 Exercises
### DoH Flooding Attack Detection Scenario  
In this section, we describe the DoH flooding attack detection scenario that occurs in the data plane of a 5G network. First, we provide an explanation of the DoH flooding attack scenario. We then introduce the integration of ML/DL models into the training scenario for this particular attack. Finally, we describe the proposed exercise to train cybersecurity operators in the use of traditional and more advanced ML/DL-based methods to detect DoH flooding attacks in a 5G network.
# A Novel Deep Learning Method for Predicting RNA-Protein Binding Sites
## 1 Introduction
It used the distributed representation of the k-mers sequence instead of the traditional one-hot encoding. The distributed representation was used as the input of the CNN and Bi-LSTM to undertake the prediction task. With the development of graph neural networks (GNNs), more and more are being applied to predict RBP binding sites [30; 31]. The developers of RPI-Net [32] used a graph convolution network (GCN) to learn a graphical representation of the RNA secondary structure, making it possible to directly capture RNA structure information. DeepPN [33] is a deep parallel neural network constructed using a CNN and GCN. It can use a two-layer CNN and GCN to extract RNA sequence features, but it only considers sequence information and ignores RNA structure information. Other methods directly compute global structure information while ignoring the complex hierarchical relationship between nodes. Consequently, an HPNet algorithm based on deep learning is introduced in this paper. It can learn specific binding sites and binding preferences from RNA sequences and secondary structures. HPNet employs a CNN and GNN with hierarchical pooling to extract sequence and structure information directly. The main contributions of this research are summarized below. * HPNet uses DiffPool, a hierarchical pooling network, to discover hierarchical features of RNA secondary structure. It divides the substructures of the secondary structure into the same cluster and learns more meaningful graph-level embeddings;
* HPNet recognizes binding sequences and automatically extracts binding motifs using the CNN and DiffPool. It can determine whether binding sites exist and capture binding motifs without domain knowledge. The area under the curve (AUC) for HPNet was found to be significantly better than the state-of-the-art prediction method;
* A context-average debiasing method is proposed. In response to the traditional debiasing method of replacing clip sites with random nucleotides, this paper proposes a debiasing method of replacing clip sites with average-context features of clip sites.
# A Joint Detection and Decoding Scheme for PC-SCMA System Based on Pruning Iteration
## 2 System Model  
Figure 1 shows the transmitter-receiver symmetric PC-SCMA system model, where the number of users in the system is \(J\) and the number of resources is \(K\) (\(J>K\)). The information bits of each user \(j\) are distributed on different resources while each resource is superimposed with different user data to achieve the system overload of \(J/K\). For every user \(j\), its I information bits \(u_{j}=\left\{u_{j,1},u_{j,2},\ldots,u_{j,I}\right\}\) are encoded into polar codes \(c_{j}=\left\{c_{j,1},c_{j,2},\ldots,c_{j,N}\right\}\). In order to reduce the interference caused by the burst errors in the system, the polar codes \(C=\left\{c_{1},c_{2},\ldots,c_{j}\right\}\) are interleaved into \(\left\{b_{1},b_{2},\ldots,b_{I}\right\}\) and then input to the SCMA encoder. Every \(\mathsf{Q}\) bits set \(\left\{b_{j,1},b_{j,2}\ldots,b_{j,Q}\right\}\) is mapped to a \(\mathsf{M}\)-dimension codeword of the SCMA codebook according to the mapping relationship: \(\left\{b_{j,1},b_{j,2}\ldots,b_{j,Q}\right\}\rightarrow\left\{x_{j,1},x_{j,2 }\ldots,x_{j,M}\right\},\)\(Q=\log_{2}M\), \(x_{j,m}\in\mathbb{C}\), and then transmitted to the channel. At the receiver, interleaver and de-interleaver are added between the SCMA detector and polar decoder for joint detection and decoding.  
A SCMA factor matrix with \(J=6\) and \(K=4\) can be denoted by:  
\[F\ =\ \left[\begin{array}{cccccc}0&1&1&0&1&0\\ 1&0&1&0&0&1\\ 0&1&0&1&0&1\\ 1&0&0&1&1&0\end{array}\right] \tag{1}\]  
The channel gain matrix for an uplink PC-SCMA system is expressed as:  
\[H_{j}^{l}=\left[\begin{array}{cccc}h_{1,j}^{l}&0&0&0\\ 0&h_{2,j}^{l}&0&0\\ 0&0&\ddots&0\\ 0&0&0&h_{K,j}^{l}\end{array}\right]=\ \text{diag}\left(h_{1,j}^{l},h_{2,j}^{l}, \ldots,h_{K,j}^{l}\right) \tag{2}\]  
where \(h_{k,j}^{l}\) denotes the channel gain of the \(l\)-th transmitted signal between resource \(k\) and user \(j\).  
Assuming that all users are synchronized in time, the \(l\)-th received signal is the superposition of all user signals, which can be denoted as:  
\[y^{l}=\sum_{j=1}^{l}H_{j}^{l}x_{j}^{l}+z^{l} \tag{3}\]  
where \(1\leq l\leq L=\ N/Q\), \(y^{l}=\left[y_{1}^{l},y_{2}^{l}\ldots,y_{M}^{l}\right]^{T}\). \(x_{j}^{l}=\left[x_{j,1}^{l},x_{j,2}^{l},\ldots,x_{j,M}^{l}\right]^{T}\) is the l-th SCMA codes of user \(j\). \(z^{l}=\left[z_{1}^{l},z_{2}^{l}\ldots,z_{M}^{l}\right]^{T}\) represents the additive Gaussian noise vector in the channel and \(z^{l}\sim\)cN (\(0\), \(\delta^{2}\)).
## 3 Convergence-Performance Analysis and Re-Design of PLDPC Channel Codes
### A Re-Design Scheme for PLDPC Channel Codes
The base matrix of \(\mathbf{B}^{\text{IARA2}}\) and its enhanced code \(\mathbf{B}^{\text{IARA2}}_{\text{en}}\) are proposed in (16). Moreover, the optimized channel codes in JSCC systems over AWGN channels are utilized as benchmarks. For instance, the channel code in code pair \((\mathbf{B}^{\text{s4}},\mathbf{B}^{\text{c2}})\) proposed in [9] and the channel code in code pair \(\mathbf{B}^{\text{opt}\_1}_{\text{J}}\) proposed in [11] are redesigned by Algorithm 2. The base matrices \((\mathbf{B}^{\text{c2}}\) and \(\mathbf{B}^{\text{opt}\_1}_{\text{en}})\) and the enhanced ones \((\mathbf{B}^{\text{c2}}_{\text{en}}\) and \(\mathbf{B}^{\text{opt}\_1}_{\text{en}})\) are proposed in (17) and (18). For convenience of description, in all the base matrices below, the last two columns denote the VNs of channel codes connected to the source codes and the fourth column represents the punctured VN. \[\mathbf{B}^{\text{IARA2}}=\left( \begin{array}{ccc}1&2&1&0\\ 0&1&1&2&1\\ 0&1&2&1\end{array} \right)\quad\mathbf{B}^{\text{IARA2}}_{\text{en}}=\left( \begin{array}{ccc}1&0&0&2&1\\ 0&1&0&3&1\\ 0&1&1&1&2\end{array} \right) \tag{16}\]  
\[\mathbf{B}^{\text{c2}}=\left( \begin{array}{ccc}1&0&2&0\\ 0&1&1&1&1\\ 0&1&2&2\end{array} \right)\quad\quad\mathbf{B}^{\text{c2}}_{\text{en}}=\left( \begin{array}{ccc}1&0&0&2&0\\ 0&1&1&1&1\\ 0&1&0&4\end{array} \right) \tag{17}\]  
\[\mathbf{B}^{\text{opt}\_1}_{\text{c}}=\left( \begin{array}{ccc}1&2&2&1&0\\ 0&1&1&1&1\\ 0&0&2&1\end{array} \right)\quad\mathbf{B}^{\text{opt}\_1}_{\text{en}}=\left( \begin{array}{ccc}1&0&1&0&4\\ 0&1&1&1&1\\ 0&1&1&0&2\end{array} \right) \tag{18}\]