esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Neural Network-Based Guidance Law Using Supervised Learning
## 5 Results
### Additional Experiment  
In this section, we assess the behavior of the DNNG-driven trajectory when the input variables of the guidance module are reduced to one. This is called the DNN-based only LOS rate input guidance (DNNLG) law because there is only one input variable entering the guidance loop. The DNNLG law learns state-action relationships using state variable \(\Omega\) and action variable \(a_{m}\) from the training data generated by the PNG law. Figure 9 shows the schematic diagram of the missile system with the DNNLG law and the guidance law operates with only the LOS rate.  
The data collection, DNNG architecture, DNNG training and evaluation to create DNNLG are carried out via the same method as in Section 4. Figures 10 and 11 show the simulation results for three guidance laws. The initial conditions are as follows: \(R_{0}~{}=~{}26,252~{}(\mathrm{m})\), \(\lambda_{0}~{}=~{}0.5564~{}(\mathrm{rad})\), \(\psi_{0}~{}=~{}-0.2497~{}(\mathrm{rad})\), \(V_{m}~{}=~{}268~{}(\mathrm{m/s})\) The energy values of the missile for PNG, DNNG and DNNLG are \(3.0942,~{}3.0935\) and \(3.4034\), respectively.  
The average values of the DNNLG were obtained using 100 random items from the validation data in Section 4. These results are given in Table 3. Comparing Table 2 to Table 3, DNNG produces simulation results that are roughly the same as PNG, but DNNLG shows inferior performance to the other two guidance systems in terms of optimality. However, in terms of hitting the targets, DNNLG indicates that it can be used to replace PNG. This experiment tells us that the missile can maneuver while only knowing the minimum input variable, the value for the LOS rate.  
Figure 8: Comparison of the trajectory of PNG and DNNG laws.  
Figure 9: Missile system with DNNLG law.  
This simulation result shows the limitation of SL. SL produces completely identical results for complete input-output pairs, however, changing the input-output variables results in a difference from the training data. Therefore, it is very important to construct training data in supervised learning.
## 7 Applications and Future Research Directions of RIS
### RF Sensing and Localization  
RIS-assisted sensing and localization of radio frequency (RF) is another promising direction. The RIS's wide aperture size and its ability to form the propagation environment will boost RF sensing capabilities significantly. To provide favorable conditions for RF sensing, the channel can be altered and then controlled with high precision. Encouraging findings for potential applications in energy-efficient surveillance assisted living and remote health monitoring has been studied in the literature. The question of optimizing the RIS configurations to improve RF-sensing, however, remains to be investigated. Most of the current research on RIS-enabled SREs has relied on communications-related applications. RISs, on the other hand, provide opportunities for research that extends beyond communications. Electrically massive RISs, for example, can be used for high-precision radio localization and mapping because of their excellent focusing capability (i.e., the construction of a model or map of the environment). The potential of RISs for various applications and anticipated performance as a function of their size, sub-wavelength structure, and near-field vs. far-field operation forms an open research question. Furthermore,radio mapping and localization might be considered key enablers for realizing important communication-related activities [120; 121; 122].
## Appendix A  
_Appendix A.1 K-Nearest Neighborhood_  
A KNN [28] is a simple machine learning method for classification and regression. The output value of the test data is predicted by the output value of the nearest k training data from the test data. Figure A1 provides one example of a KNN: 1-NN and 5-NN.  
In the 1-NN case, test data are predicted as a red circle because the nearest data point is the red circle; however, the 5-NN case test data are as classified as blue circles because more than half of the five closest data points are blue circles. Another important parameter in K-NN is the distance that determines which data are the closest. The most popular distance is measured between two data \(\mathbf{X}=\left(\begin{array}{ccccc}x_{1}&x_{2}&\cdots&x_{n}\end{array}\right)\) and \(\mathbf{Y}=\left(\begin{array}{ccccc}x_{1}&x_{2}&\cdots&x_{n}\end{array}\right)\), where \(*_{i}\) are features of the data as follows:  
\[\text{Euclidean}:\ d_{Euclidean}=\sqrt{\sum(x_{i}-y_{i})^{2}}\] (A1)  
\[\text{Malanobis}:\ \ \ \ \ \ \ d_{Mahalanobis}=\sqrt{(\mathbf{X}-\mathbf{Y})^{T} \Sigma^{-1}(\mathbf{X}-\mathbf{Y})}\] (A2)  
_where \(\Sigma\) is covariance matrix between \(\mathbf{X}\) and \(\mathbf{Y}\)_  
\[\text{Manhattan}:\ d_{Manhattan}=\sum\lvert x_{i}-y_{i}\rvert\] (A3)  
In this paper, we used 5-NN with the Euclidean distance to choose data that predict hepatic enhancement.
# Static Evaluation of a Midimew Connected Torus Network for Next Generation Supercomputers
## 4 Comparative Static Network Performance Evaluation
### Diameter  
Considering all of the shortest paths for node-to-node connections, the diameter was the maximum path. It was a main parameter that contributed to the dynamic performance of the network, as it significantly affected network latency despite regular throughput. High diameter resulted in high delays of message transmissions. In addition, higher diameters lowered the message-passing bandwidth and, thus, degraded the overall performance of the network [42]. Conventional networks, such as mesh and torus, as shown in Table 2, suffer from this specific challenge, making them infeasible for next-generation MPCs. This motivated our proposal of this novel HIN to maintain a low diameter while supporting a large number of node connections. The MTN diameter was generated according to the routing protocol described in Section 3. The MTN had a low diameter, although it was larger than that of the TFBN. However, it was considered low as compared to conventional topologies, as their diameter increased as the number of network nodes increased.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **Diameter** & \begin{tabular}{c} **Average** \\ **Distance** \\ \end{tabular} & \begin{tabular}{c} **Node** \\ **Degree** \\ \end{tabular} & \begin{tabular}{c} **Bisection** \\ **Width** \\ \end{tabular} & \
\begin{tabular}{c} **Arc** \\ **Connectivity** \\ \end{tabular} &
\begin{tabular}{c} **Wire** \\ **Complexity** \\ \end{tabular} \\ \hline  & & & \multicolumn{3}{c}{**256**} & & \\  & & & **Node** & & & \\ \hline
16 \(\times\) 16 Mesh & 30 & 10.67 & 4 & 16 & 2 & 480 \\
16 \(\times\) 16 Torus & 16 & 8 & 4 & 32 & 4 & 512 \\ TESH (2,2,0) & 21 & 10.47 & 4 & 8 & 2 & 416 \\ TTN (2,2,0) & 15 & 7.44 & 6 & 8 & 4 & 544 \\ TFBN (2,2,0) & 10 & 5.75 & 8 & 8 & 4 & 800 \\ MMN (2,2,0) & 17 & 9.07 & 4 & 8 & 2 & 416 \\ MTNs (2,2,0) & 17 & 6.93 & 6 & 8 & 4 & 544 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of static network performance parameters.  
Figure 5: Node degree of various networks when increasing the network size.
## 2 Thematic Review and Analysis
### Implementation Strategies
#### 2.6.2 Ownership Border  
The delineation of ownership boundaries for substations in DH systems refers to the point at which responsibility for maintenance and repair shifts from the utility to the consumer. Typically, in DH systems, ownership is segmented: consumers own the building substations, while the DH operator retains ownership of the distribution piping network. The establishment of this ownership boundary is crucial for achieving low return temperatures, as it affects the maintenance of substations.  
[PERSON] [23] underscores the importance of ongoing monitoring of the supply-return temperature at DH substations, and VEKS [132] recommends annual inspections of consumer substations.  
[PERSON] [46] presents a comparison of cooling capacities for substations based on ownership status (see Figures 9 and 13 in this reference). The data indicate that substations owned by the DH operator have an average yearly cooling capacity of 38-40 \({}^{\circ}\)C, whereas consumer-owned substations exhibit poorer cooling performance with a supply-return temperature difference of 30.7 \({}^{\circ}\)C. Notably, the temperature range for company-owned substations spans from 5 \({}^{\circ}\)C to 60 \({}^{\circ}\)C, with lower temperatures achieved in smaller units. [PERSON] [129] highlights the necessity for a low-flow penalty, which is currently absent from heat meters. Additionally, \"group substations\", which are co-owned by consumers and the company, demonstrate cooling capacities ranging from 30 to 50 \({}^{\circ}\)C [46].
# Connected Vehicles: Technology Review, State of the Art, Challenges and Opportunities
## 3 Applications
### Non-Safety Applications
#### 3.2.1. Infotainment Applications  
In the connected vehicles era, drivers expect more from vehicles beyond safety. The increasing demand towards an _\"always connected\"_ paradigm and the growing need for in-vehicle entertainment applications along with the capability of vehicles to connect to the Internet have paved the way for many innovative infotainment and multimedia applications including high-quality music and video streaming, interactive gaming, and file-downloading [10; 12]. These applications require Internet connectivity. Since automakers integrate their own infotainment systems into their own car production lines, there exists a major difference between automakers as to whether these systems should have a built-in Internet connectivity or should use the drivers' smartphones for Internet access. However, most automakers are in favour of in-vehicle Internet, and, hence, great efforts have been conducted to include a wireless modem within vehicles to provide Internet and device connectivity within the vehicle's dashboard. This will allow the integration of in-vehicle sensor data with web information, which provides a convenient, safer, and more secure driving experience [12; 96].
# The Effects of Acidic, Alkaline, and Neutral Anolytes on Electrochemical Seawater Deoxygenation
## 3 Results and Discussion
### Alkaline Anolyte  
The effect of different applied potentials on the oxygen removal in Cell A is shown in Table 2 with 0.1 M NaOH solution as an anolyte and 0.5 M NaCl solution as a catholyte. An increase in potential resulted in a higher oxygen removal efficiency of the electrolyte. At a flow rate of  
Figure 3: Theoretical currents calculated for different flow rates of catholyte.  
10 mL-min\({}^{-1}\) with 1.9 V applied voltage, the oxygen concentration was reduced from 8.44 ppm to 1.31 ppm, and, at the same rate with 2.3 V applied voltage, the oxygen concentration was lowered to 0.55 ppm. A lower conductivity was observed for the anolyte with an increase in the potential while the catholyte conductivity was not altered significantly. In a deoxygenation cell, the hydronium ions are produced in the anodic compartment and are transferred to cathodic compartment. With the increase in voltage, the rate of transfer and oxidation/reduction increase. The conductivity in the catholyte is dominated by sodium chloride, while the migration of hydronium or hydroxide from acidic or alkaline anolyte influences the pH slightly. For the experiments in alkaline anolyte, the produced hydronium neutralized the hydroxide and the conductivity decreased from 15.6 to 14.1 mS-cm\({}^{-1}\). For the experiments in acidic anolyte, the produced hydronium accumulated, and the conductivity increased only slightly, also due to a lower current. The transfer of cations to the cathode compartment with the increase in potential is expected to decrease the conductivity at the anode. The pH of catholyte increased from 7 to 9 during the stabilization period (2 h, see Section 2 for details), and further increased to 10 during oxygen removal operation.  
The test run in Cell A with an applied voltage of 2.2 V resulted in the oxygen concentration being reduced from 8.44 ppm to 0.82 ppm. Over a 300-min experiment, the oxygen removal was steady with a stable current output of 27 mA. The potentials at the anode and cathode were studied using RHE in Cell B at 2.2 V and at a flow rate of 10 mL-min\({}^{-1}\), and a voltage of 2.2 V between the anode and cathode was measured to result in potentials of 1.670 V and \(-\)0.342 V (RHE), respectively. The use of a copper-wire connection at the anode is affected by a positive potential, and some corrosion activities on the wire were observed after each experiment. The ohmic resistance in the solution was considered as the reason for deviation of the applied voltage from observed voltage differences in the cell. There was no observation of hydrogen peroxide formation in the alkaline solution during the potentiostatic test. However, the hydrogen production was calculated to be 25% at 2.2 V and <5% at 2.1 V.
## 7 Modelling and Simulation Assumptions  
Although MSK models provide the opportunity to investigate various pathologies and _in vivo_ parameters, just as important as their strengths, limitations are present and must be considered. These limitations relate to both modelling and simulation assumptions and implementations.  
A commonly criticised assumption within MSK models is their simplified representation of MTU parameters, and the activation/force estimations. This stems from two origins, first the generic MTU parameters being estimated from cadavers. To partially overcome this, numerous calibration and optimisation techniques have been proposed [41; 113; 114; 115; 116; 117; 118] and implemented to produce a set of MTU parameters which are likely more accurate than those in generic estimates. Second, the previously mentioned optimisation methods, although providing a set of forces and activations which balance the MSK system, may not accurately represent various features including antagonistic muscle contraction, and muscle spasticity in specific pathologies such as cerebral palsy. With respect to muscle activations, estimates can be improved by using experimentally acquired EMG [113; 119].  
Figure 6: Example of a MSK model implemented in OpenSim which includes a model of a transtibial prosthetic device [102].  
Additionally, further processing and pathology-specific procedures have been developed to better represent known differences in muscle function [118]. The sensitivity of simulations to differences in MTU parameters and optimization method have shown variations in estimated hip joint contact force from 2.5 body weights to 5.4 body weights [111].  
Along with these MTU related assumptions, simplified representations of anatomy may affect simulation result accuracy. For the lower limbs, the ankle-foot is often represented with a single plantar-/dorsi-flexion DOF despite the numerous joints and DOF present _in vivo_. This simplification has implications when calculating model parameters [120]. Additionally, the tiboferomal joint is often represented as a single DOF hinge joint where kinematic constraints are often used for the remaining DOFs. To overcome these joint model simplifications a number of methods have been proposed to more accurately represent _in vivo_ joint motions [121; 122; 123; 124; 125; 126]. These simplifications also exist in the upper body where the torso is typically represented as a single unit without muscles and highly simplified articulation not representative for the complexity of spinal movement [49].
## 4 Validation and Comparison
### Comparative Testing of Baseline Methods
#### 4.4.2 Path Loss Prediction Method Based on CNN  
Convolutional neural networks have shown great potential in handling various data-related tasks and have been used for path loss prediction in several studies [19; 20; 21]. We implemented a CNN model using PyTorch version 2.3.0 with hyperparameters, including the number of convolutional layers, the number of filters per layer, the convolutional kernel size, the pooling kernel size, and the activation function. Table 6 presents the optimal combination of hyperparameters for the convolutional neural network determined through the hyperparameter optimization process.
## 2 General Model  
We consider the IoT scenario that is depicted in Figure 1 with two IoT-SPs deploying their private WSNs in order to provide sensor-data-based services to sensor-data users or simply final users, who pay to the IoT-SPs for this service. The sensor nodes are grouped into clusters. Each cluster has a large number of sensing nodes connected through a multi-hop wireless network [28], and belong to only one IoT-SP. Each cluster has a sink node, which transmits the data collected by all the nodes in the cluster to their IoT-SP server (IoT-SP\({}_{i}\) srv) through a network operator (OP) and Internet. In the IoT-SP servers the data is aggregated in order to provide a service to final users. Our scenario has the following market actors:  
* _Sinks._
* _Network Operator_ (OP).
* _Users._
* _Internet of Things-Service Providers_ (IoT-SPs).
# Capability of Advanced Ultrasonic Inspection Technologies for Hydraulic Turbine Runners
## 3 CIVA Modelling and Analysis
### Simulation of Conventional Ultrasonic Testing (_Utt_)  
A rectangular single element crystal assembled to simulate conventional ultrasonic testing to a flat refracting wedge was selected as the probe. Both beams refracting at 60\({}^{\circ}\) and 70\({}^{\circ}\) were tested, as shown in Figure 8. Two steel alloys with slightly different ultrasonic properties have been used for the T-joint sample model. Spherical voids, whose distribution and size mirror the manufactured T-joint sample, have been inserted into the model, as shown in Figure 9. In the real sample, the flaws were produced by silicon nitride balls whose ultrasonic impedance is very similar to that of steel. However, experimentally,we have noticed that ultrasonic reflection does not come from an impedance mismatch at the steel/ceramic interface but from a lack of wetting between the two. Therefore, for simulation purposes, we prefer to use voids. According to Hydro-Quebec's procedure, the numerical system's sensitivity was calibrated, namely, a side-drilled-hole with a diameter of 1.5 mm at a depth of 20 mm.  
Figure 8: Parameters of the ultrasonic probe.  
Figure 7: Configuring the CIVA model and input parameters.  
Figure 9: Scanning pattern and material properties.  
The following conclusions were obtained from the simulation results: Sample alloys appear to have little to no impact on the system's detection rate and could probably be attributed to the similarity between the two alloys' ultrasonic properties. Further work will consider the impact of steel microstructure on the ultrasonic beam's scattering, increasing its attenuation and producing a higher background noise level. Beam refracted at 70\({}^{\circ}\) gave a better detection rate than beam refracted at 60\({}^{\circ}\). This result agrees with our experimental measurements and tends to prove our hypothesis; namely, this performance is due to the beam's larger divergence. However, we observe a significant difference between the simulated detection rate (Figure 10) compared to that obtained experimentally (Figure 6). The reasons for this difference are that the ceramic beads do not behave exactly like a void, and the simulation does not capture the operator's judgment.
# Survivability Prediction of Open Source Software with Polynomial Regression
## 2 Background
### Polynomial Regression
#### 2.2.2 Model Optimization Algorithm  
The regression model is optimized using methods such as OLS (Ordinary Least Squares) and gradient descent. The OLS method enhances the model's prediction accuracy by minimizing the sum of squares of the residuals. The process of optimizing the parameters of a multiple quadratic polynomial regression model with \(n\) data points through OLS can be summarized as follows:  
1. **Matrix (Vector) Transformation:** The model is expressed in matrix form to encapsulate the linear, quadratic terms, and interaction terms for five features (\(p=5\)) based on Equation (2). The regression coefficients are organized in a vector \(W\) (\([21\times 1]\)) as: \[W=\begin{bmatrix}w_{0}\\ w_{1}\\ \vdots\\ w_{5}\\ w_{1,1}\\ \vdots\\ w_{2,5}\\ \vdots\\ w_{5,5}\end{bmatrix}\] For all \(n\) observations, the matrix \(X\) that includes five independent variables, their squares  
The model in matrix notation is then expressed as:  
\[Y=X\cdot W\]
2. **Calculation of Least Squares Estimator:** The optimal regression coefficient matrix \(W\), known as the least squares estimator, is calculated by solving: \[\hat{W}=(X^{T}\cdot X)^{-1}\cdot X^{T}\cdot Y\] where \((X^{T}\cdot X)^{-1}\) is the inverse of the matrix product \(X^{T}\cdot X\), and \(X^{T}\) is the transpose of \(X\). This yields the least squares estimates for the regression coefficients.
# Experimental Evaluation of Graph Databases: JanusGraph, Nebula Graph, Neo4j, and TigerGraph
## 4 LDBC SNB Benchmark  
The experimental evaluation uses the Linked Data Benchmark Social Network Benchmark (LDBC SNB) from the Linked Data Benchmark Council. This is a well-known benchmark in the scientific community, and it is used to test the performance of various graph databases. Its datasets are an ideal example for graph databases because they represent real social networks where people are connected and interact with each other. This data is very relevant to graph databases because the connections between people and their interactions in social networks are naturally represented by graphs, which is the underlying data structure of graph databases. This means that the results from this benchmark are highly relevant to real-world use cases. These datasets have several scaling factors associated with their size. The larger the scale factor, the larger the number of nodes and the relationships between them. We can use this data to evaluate the behavior of databases when dealing with large amounts of data.  
In addition, the LDBC SNB queries are also designed to evaluate the performance of graph databases in realistic usage scenarios, making the data even more representative and relevant to the scientific community. The benchmark includes 29 queries, some of which are more complex with many nodes, others simpler, and there are some that perform updates on the data, allowing the researcher to evaluate how graph databases handle different types of queries and data loading, as well as how they handle real-time updates.  
All of these ways to evaluate graph database performance provided by the LDBC SNB allow for a fair comparison between different graph databases. The community involved in this benchmark is active and collaborative, which means that new datasets and queries are added regularly to make the LDBC SNB increasingly representative and challenging.  
Figure 5 shows the graphical scheme of the benchmark. It consists of people who live in a particular city belonging to a certain country and continent, who are users who belong to a forum where it is possible to post comments, and people who know others and send messages to each other.  
Figure 4: TigerGraph Studio.  
The schema is divided into two parts: Static, in which the number of nodes is maintained across all scale factors, and Dynamic, where the number of nodes and relationships increases as the scale factors also increase.  
The entity with the most records is Comment which has 203,354 nodes and 1,016,770 properties at a scale factor of 0.1 and 24,271,888 nodes and 121,359,440 properties at a scale factor of 10.