esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of Intersection Left Turn Guide Lines Configuration on Novice Drivers' Behavior
## 3 Results
### Impact Analysis Based on Steering Angle and Speed Difference
#### 3.2.2 Medium Intersections  
**Steering angle.** Figure 11 depicts steering angle variations during left turns by novice drivers in different guide line scenarios at medium intersections. In Scenario 2, steering angles are generally similar across different guide line scenarios: in the straight section, the steering angle variation is smaller; in the circular section, the steering angle increases. However, in Scenarios 2-B and 2-C, the fluctuation range of steering angles is relatively small, with maximum ranges of 0.26 and 0.25, respectively. In contrast, in Scenario 2-A, the  
Figure 10: Speed difference distribution during left turn in Scenario 1.  
Figure 9: Steering angle distribution during left turn in Scenario 1.  
maximum fluctuation range in the circular section is 0.43, indicating a better guiding effect on left-turn stability for novice drivers in Scenario 2-A. Comparatively, the fluctuation range of steering angles is significantly larger in Scenario 3, suggesting that the larger the intersection area and the more left-turn lanes, the greater the steering angle fluctuation during left turns.  
**Speed difference**. Figure 12 depicts the speed difference distribution during left turns in Scenario 2 and Scenario 3. In Scenario 2 without guide lines, speed differences transition from negative to positive values. Conversely, when guide lines are present, the transition is from positive to negative, with smaller speed differences in the full guide line scenario compared to the single guide line scenario. This underscores the significant impact of guideline configuration on novice drivers, providing a reference during the circular section, resulting in higher speeds during the circular section and decreased speeds when leaving the intersection. Analyzing speed difference errors, both single and full guide line scenarios exhibit smaller fluctuation ranges compared to the scenario without guide lines, emphasizing the influential role of guide lines on driving behavior. In Scenario 3, under different guide line scenarios, the speed difference follows similar trends, gradually transitioning from positive to negative values. Considering the fluctuation range, all three scenarios show comparable ranges in the intersection mid-section, with maximum fluctuation values of 1.53, 1.56, and 2.02. This suggests that in medium intersections with dual left-turn lanes, the impact of guide line configuration on left-turning vehicle speed is minimal.  
Figure 11: Steering angle distribution during left turn in Scenarios: (**a**) Scenario 2; (**b**) Scenario 3.  
Figure 12: Speed difference distribution during left turn in scenarios: (**a**) Scenario 2; (**b**) Scenario 3.
This cross-sensor comparison allows us to understand the agreement of standard aquatic products derived from hyperspectral and multispectral observations from space. This serves as a quality assessment approach for the first experience in mapping lake water quality parameters using PRISMA data. Moreover, this cross-sensor comparison provides insight into the continuity between multispectral Sentinel-2 and hyperspectral PRISMA data (and forthcoming hyperspectral missions), as well as the synergic use of multi-sensor products. Given the importance of high-quality surface reflectance data for reliable retrieval of constituents based on the physics-based modeling, comparing the results of inversion from two independent sources of data can serve as an indication of the quality of PRISMA level 2D images for aquatic applications. On the other hand, direct comparisons of \(R_{rs}\) data from the two sensors provide more evidence on the quality of the PRISMA level 2D data.
## 4 Our Proposed Framework
### Phase 2: Selecting a Subset of the Encoded Training Data Using Instance Reduction Techniques  
Next, each client applies an instance reduction technique on the encodings produced by the RBM to obtain a reduced subset, \(R\), from the encodings set \(E\) where \(R\subset E\), such that \(R\) does not contain irrelevant (noisy or redundant) instances. The goal of using IR is to select a subset of the encoded data points to send to the central server, as shown in Figure 4.  
Furthermore, removing irrelevant instances tends to improve memory usage and reduce training time and communication overhead. Employing instance reduction strategies, mitigates the impact of outliers, reducing the training time and chances of overfitting the training data [27] and the susceptibility to privacy attacks that may exploit such outliers [66].  
Despite the existence of numerous instance reduction techniques proposed in the literature, with several investigated by [27] for their efficacy in creating reduced datasets for multilayer neural network training, our work exclusively employs the Edited Nearest Neighbor method to produce reduced datasets. This study should primarily be viewed as proof of concept; the focus is not on employing the best instance reduction method Instead, we aim to verify whether a central server can build a practical model using reduced subsets of encoded training data from multiple sources, without having to decode this training data. Another question we address is the server's ability to make accurate predictions using RBM-encoded data points rather than the data points themselves.  
Figure 4: Selecting a subset of the encoded training data.  
Phase 3: Sending the Subsets of Encoded Training Data to the Central Server to Train Deep Learning Models  
As illustrated in Figure 5, the reduced sets of encodings are transmitted to a central server, which amalgamates them into a single training set for use in training global deep-learning models. During the prediction phase, clients can dispatch encodings of the data points to the server. The server then operates the deep learning models and returns the label of the predicted output. Notably, there is no need for the server to decode either the training data or the query data.
# Adapative Perturbation Patterns: Realistic Adversarial Learning for Robust Intrusion Detection
## 3 Proposed Method
### Pattern Sequences  
Domains with diverse constraints may require an aggregation of several interval and combination patterns, which can be performed by pattern sequences. Furthermore, the main advantage of applying multiple patterns in a sequential order is that it enables the fulfilment of countless inter-feature constraints of greater complexity. It is pertinent to note that all patterns in a sequence are independently adapted to the original data, to prevent any bias when recording its characteristics. Afterwards, the sequential order is enforced to create cumulative perturbations on that data.  
To exemplify the benefits of using these sequences, a small, but relatively complex, domain will be established. It contains three nominal features, F0, F1 and F2, and two integer features, F3 and F4. For an adversarial example to be realistic within this domain, it must comply with the following constraints:  
* F0 must always keep its original value,
* F1 and F4 can be modified but must have class-specific values,
* F2 and F3 can be modified but must have class-specific values, which are influenced by F0 and F1.  
Figure 3: Combination pattern (business process model and notation).  
The base configuration corresponding to these constraints specifies the feature subsets that each pattern will analyze and perturb:  
1. Combination pattern--Modify {F1};
2. Combination pattern--Modify {F2, F3}, Lock {F0, F1};
3. Interval pattern--Modify {F3, F4}, Integer {F3, F4}.  
A2 PM will then assign each class to its own pattern sequence. For this example, the 'probability to be applied' will be 1.0 for all patterns, to demonstrate all three cumulative perturbations (Figure 4). The first perturbation created for each class is replacing F1 with another valid qualitative value, from 'B' to 'C'. Then, without modifying the original F0 nor the new F1, a valid combination is found for F0, F1, F2 and F3. Since the original F2 and F3 were only suitable for 'A' and 'B', new values are found to match 'A' and 'C'. Finally, the integer features F3 and F4 are perturbed according to their valid intervals. Regarding F3, to ensure it remains coherent with F0 and F1, the perturbation is created on the value of the new combination.
# A Novel Base-Station Selection Strategy for Cellular Vehicle-to-Everything (C-V2X) Communications
###### Abstract  
Cellular vehicle-to-everything (C-V2X) communication facilitates the improved safety, comfort, and efficiency of vehicles and mobility by exchanging information between vehicles and other entities. In general, only the macrocell or only the femtocell is the communication infrastructure for C-V2X. Currently, a macro-femtocell network is used as the new C-V2X networking architecture. However, there are two unresolved problems for C-V2X in macro-femtocell networks. Firstly, vehicle mobility requires the frequent switching of connections between different base stations; invalid switching results in worse communication quality. Secondly, unintelligent base station selections cause network congestion and network-load imbalance. To address the above challenges, this paper proposes a base station selection strategy based on a Markov decision policy for a vehicle in a macro-femtocell system. Firstly, we present a mechanism to predict received signal strength (RSS) for base station selection. Secondly, a comparing Markov decision policy algorithm is presented in C-V2X. To the best of our knowledge, this is the first attempt to achieve predicted RSS based on a Markov decision policy in C-V2X technology. To validate the proposed mechanism, we simulated the traditional base station selection and our proposal when the vehicle moved at different speeds. This demonstrates that the effectiveness of a traditional base station selection policy is obvious only at high speeds, and this weakness can be resolved by our proposal. Then, we compare our solution with the traditional base station selection policy. The simulation results show that our solution is effective at switching connections between base stations, and it can effectively prevent the overloading of network resources.  
C-V2X; macro-femtocell system; Markov decision policy; load-balance control Article
# Intelligent Packaging for Real-Time Monitoring of Food-Quality: Current and Future Developments
## 2 Food Quality Indicators
### pH Changes  
During food storage, certain types of both aerobic and anaerobic microorganisms can proliferate, generating organic acids (e.g., lactic acid and acetic acid) that can decrease the pH of food. Additionally, carbon dioxide, which is a product of microbial growth, may dissolve in food products and produce carbonic acid, which further decreases the pH value [47; 48]. Hence, pH-sensitive sensors have been widely explored to evaluate food freshness and safety [49; 50; 51].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sensing** & **Working** & **Main** \\
**Indicator** & **Principle** & **Applications** \\ \hline \multirow{2}{*}{O\({}_{2}\)} & Luminescence-based & \\  & Redox-based & Modified atmosphere packages \\  & Colorimetric assay-based & \\ \hline \multirow{2}{*}{CO\({}_{2}\)} & Luminescence-based & \\  & Colorimetric assay-based & Modified atmosphere packages \\ \hline \multirow{3}{*}{Humidity} & Inductor and capacitor-based & \\  & Colorimetric assay-based & \\  & Photonic crystal-based & Dry food products \\ \hline \multirow{2}{*}{pH} & Colorimetric assay-based & \\  & Electrochemical assay-based & Meat, fish, and dairy products \\  & Photonic crystal-based & \\ \hline Temperature & Colorimetric assay-based & Frozen food products \\ \hline \multirow{2}{*}{Nitrogen} & Antibodies-based & \\  & Colorimetric assay-based & Meat and fish products \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the main food quality and safety indicators and their main applications in food packages.
# MUSIC: Cardiac Imaging, Modelling and Visualisation Software for Diagnosis and Therapy
## 3 Data Management  
### Data Format  
The data management system in the MUSIC project includes local databases per user managing the data imported or generated by the MUSICradio application, access from the application to several data sources including PACS systems, and our online web portal that allows hospitals in the VT MUSIC consortium to send their data to our institute and to receive output data from us.  
Within the application, users can import and export data in various formats, including VTK, ITK, DICOM, Nnrd, GIS, Nifti, Gipl, OBJ, STL, Medit, etc. MUSICradio can export data to electrophysiology mapping systems such as BioSense Webster, Johnson & Johnson (New Brunswick, NJ, USA) \"CARTO\", Boston Scientific (Natick, MA, USA) \"RHYTHMIA\", and Saint-Jude Medical (Saint Paul, MN, USA) \"EnSite NavX\". Within the VT MUSIC consortium, these compatible data are sent to VT centres around the world where clinicians can visualise their catheters in real time inside a virtual patient-specific heart created in the application through their navigation systems.  
The application also allows us to export cardiac structures in DICOM RT-Struct format. These data are compatible with any radiation therapy (RT) planning software. Depending on the strategy and RT technology available onsite, these cardiac targets and associated segmentations may be registered onto a 4D planning CT and expanded to take into account the cardiac/respiratory motion and margins of errors for misregistration. Dose simulations are then performed to validate the final strategy, which is often the result of a trade-off between safety and efficacy of radiation delivery. Once validated, the actual SBRT (stereotactic body radiation therapy) treatment is delivered.
# Optimizing Geospatial Data for ML/CV Applications:
## 2 Materials
### Coordinate Reference System  
Handling spatial data from different sources required using appropriate Coordinate Reference Systems (CRS) and transformations.  
EPSG:3857 (Web Mercator Projection) is a projected CRS commonly used in web mapping applications. It preserves angles on the Earth's surface but distorts distances and areas near the poles [54]. LP-Portal uses the EPSG:3857 CRS.  
EPSG:2180 (Polish CS92 / PUWG 1992) is a national projected CRS designed for Poland. It provides accurate spatial representations for the country [55]. ULDK and Geoportal utilize EPSG:2180.  
To ensure spatial consistency, transformations between EPSG:3857 and EPSG:2180 were performed when necessary. This allowed an exact overlay of road data, parcel data, and imagery.
## 2 Materials and Methods
### Spatial Data Presentation  
The geoportal is a type of web portal (web service platform) used to find and access geographic information and associated geographic services (display, editing, analysis, etc.) via the internet, in a service-oriented architecture. This kind of portal can dramatically expand the availability of location-based data to users for review, editing, and analysis. Modern geoportals include direct access to data (in any formats or forms), metadata, visualization tools, automated provenance linkages across users, review tools for data interpretation, and sharing results in files, papers, or directly on social networks. Many use case examples, not only government healthcare (disease outbreak tracking) but also retail (trade area analysis) or marketing (location-based marketing effectiveness), which proves that it is possible to use the inherent advantages of such solutions [13].  
COVID-19 spread monitoring models were selected according to the availability of epidemic data sets. The growth dynamics of the disease was modeled using the SIR (S--susceptible; I--infectious; R--recovered) model applied to two possible scenarios: pessimistic and optimistic. Also, epi-curves and the reproduction number Rt are used to monitor the effectiveness of control measures to suppress the spread of infection.  
These models with parameters related to the dynamics and prediction of disease spread were part of reports generated and published via the ELIS geoportal.
## 4 Results and Discussion
### Subjective Evaluations
Thus, considering the average MOS of each approach, it is obvious that listeners prefer the samples obtained by our proposed method due to its effectiveness in improving the intelligibility and naturalness of ES. 2. ABX preference test: this is a test to identify the similarity between enhanced and target sequences. In this evaluation, we presented to fifteen listeners a series of three speech samples: A, B, and X, respectively, as the source, target, and enhanced speech sample. We asked each listener to judge by a score the degree of closeness of enhanced sample X to the two other samples A and B. No preference (NP) could be selected in the event that the listener could not distinguish between two types of voice. Thus, we carried out four series of experiments: GMM with our method (Seq2 Seq+Attention mechanism), DNN with our method, BiLSTM+Attention with our method, and Seq2 Seq without attention with our method. Figure 7 summarizes the results of the ABX test. The first two bars indicate that our model behaves much better than GMM and DNN. The third bar shows that our model works at similar levels with a BiLSTM+Attention. From the fourth bar, we can see that  
Figure 5: MOS on intelligibility test results with 95% confidence interval. our method performs much better when attention is applied and the speech generated by our approach is of better quality than that obtained by the other three methods. It is therefore clear that the inclusion of the attention mechanism increases the robustness of the model. Some samples obtained from this work are depicted at the following demo link: [[https://techtech-solution.com/Kadria/seq2](https://techtech-solution.com/Kadria/seq2) seq-ES-enhancement.php]([https://techtech-solution.com/Kadria/seq2](https://techtech-solution.com/Kadria/seq2) seq-ES-enhancement.php) (accessed on 10 June 2022).
## Materials and Methods
### Equipment  
A Trigno(tm) wireless IMU sensor (Delsys Inc., Boston, MA, USA) was used in this study. The IMU sensor comprises a triaxial accelerometer (sampling rate 148 Hz; range \(\pm\)16 g), a gyroscope (sampling rate 148 Hz; range \(\pm\)2000\({}^{\circ}\)/s), and a magnetometer gyroscope (sampling rate 74 Hz). All the three sensors were coupled with a 16-bit analogue-to-digital converter. However, in this study, we used only the data from the accelerometer and gyroscope.  
The wireless IMU sensor was synchronized with a three-dimensional (3D) motion-capture system comprising seven cameras (sampling rate 200 Hz) (Qualisys AB, Goteborg, Sweden) and three force platform devices, namely two AMTI (Advanced Mechanical Technology, Inc., Watertown, MA, USA) force platforms (sampling rate 2000 Hz) (Advanced Mechanical Technology, Inc., Watertown, MA, USA) and one Kitsler force platform (sampling rate 2000 Hz) (Kitsler Instruments, Winterthur, Switzerland). All the equipment, including the camera, force plates, and IMU sensors, were synchronized via a trigger line and controlled with _Qualisys Track Manager QTM_ (Vers. 2.12; Qualisys AB, Goteborg, Sweden). However, because of the different sampling rates of the motion-capture system and wireless wearable devices, the IMU sensor sampling rate was up-sampled to 2000 Hz, matching that of the force platform.
# Development of a Coordinate Measuring Machine--Based Inspection Planning System for Industry 4.0
## 5 Optimization Models
### Optimization of Initial Measuring Path  
As aforementioned, this planning system considered a model for optimizing the MP based on ants colony. The application of ACO in a coordinate metrology is based on the defining collision zones (Figure 5a,c) and solution of travelling salesmen problem (TSP), where the set of cities corresponds to the set of points of a minimal MP length.  
According to [30], ACO is based on Equation (18) for calculation of the point-to-point measuring probe path during measurement on N points:  
\[\min\{D_{tot}\}=K+\left\{\begin{matrix}\mathrm{N}_{1}-1&(\min\left\{\left|P_{ i1}\overset{\rightarrow}{P}_{(i+1)2}\right|\right\}\vee\min\left\{\left|P_{i1} \overset{\rightarrow}{P}_{(i+1)1}\right|\right\}\vee\\ &\vee\min\left\{\left|P_{i(i+1)}\overset{\rightarrow}{P}_{(i+1)2}\right|\right\} \vee\min\left\{\left|P_{i2}\overset{\rightarrow}{P}_{(i+1)1}\right|\right\} \right\})\end{matrix}\right\} \tag{18}\]  
where \(K=N\cdot(2\cdot d_{1}+d_{2})\) represents a constant lengths of MP, \(d_{1}=\left|P_{i1}^{\overset{\rightarrow}{\rightarrow}{P}_{i}}\right|\) and \(d_{2}=\left|P_{i2}^{\overset{\rightarrow}{\rightarrow}{P}_{i1}}\right|\) as presented in the fourth chapter.  
According to [30; 31; 32], \"TSP can be represented by a complete weighted graph \(G=(N,A)\) with \(N\) being the set of nodes representing the cities, and A being the set of arcs. Each arc \((i,j)\in A\) is assigned a value (length) \(d_{ij}\), which is the distance between cities i and j, with  
Figure 5: Collision zone and simulation paths: (**a**) truncated sphere collision zone, (**b**) simulation path for truncated sphere, (**c**) plane collision zone, (**d**) simulation path for plane. In order to solve Equation (18) and thus obtain the optimal path, it is necessary to solve TSP.  
\(\mathrm{i},\mathrm{j}\in\mathrm{N}\). \"The goal in TSP is to find a minimum length of Hamiltonian circuit of the graph where a Hamiltonian circuit is a closed path visiting each of the \(\mathrm{n}=\)\(\mathrm{|N|}\) nodes of \(G\) exactly once\" [30; 31], so that an optimal solution to the TSP is a permutation \(\pi\) of the node indices \(\{1,2,\ldots,\mathrm{n}\}\).  
The decision on which path is the shortest is obtained based on the criteria of the maximum amount of ants colony evaporated pheromones. For that it use the values of the weight coefficients \(\mathrm{\tau_{ij}}\) and \(\mathrm{\eta_{ij}}\). Coefficient \(\mathrm{\eta_{ij}}\) represents the influence of the distance between two nodes of a graph, while \(\mathrm{\tau_{ij}}\) is the probability that an ant leaving the node \(\mathrm{i}\) visits the node \(\mathrm{j}\) as the next one.  
The results of comparison based on this optimization model for three paths and two GFs (Figure 5b,d) is given in Table 2 (Section 6.4). The value of optimal path saving is shown in rows 4 (I\({}_{\mathrm{C}}\)--impact) and 5 (I\({}_{\mathrm{S}}\)--impact). D\({}_{\mathrm{C}}\) is distance PTC Creo MP and D\({}_{\mathrm{S}}\)--distance STEP-NC Machine generated MP.
###### Abstract
Metal organic frameworks (MOFs) are of broad research interest due to their porous, flexible nature which allows the entrapment of different types of guest molecules, thereby increasing their electrical conductivity and stability; they hold immense potential in diverse industrial applications, such as drug delivery, gas storage, separation, heterogeneous catalysis and chemical sensing [31; 32; 33; 34; 35; 36; 37]. In the present study, silver quantum dots immobilized on Zn-MOF-8 (Zinc-metal organic framework-8) were sonochemically synthesized and characterized using TEM, FTIR and XRD. The encapsulated Zn-MOF-8@AgQDs (silver quantum dots) were applied over the gold electrode to create a modified Zn-MOF-8@Ag/Au electrode for the electrochemical reduction of 2,4-DNT. The performance of this electrode was examined using impedance spectroscopy, cyclic voltammetry and linear sweep voltammetry wherein the electrode exhibited noteworthy features in terms of high surface area, better electron transfer, good sensitivity, stability and detection limit. This is an unprecedented finding for the electrochemical sensing of 2,4-DNT.