esa-sceva/satcom-chunk-collection huggingface dataset:
# Polarimetry for Photonic Integrated Circuits
## 3 Results  
### Calibration Validation  
With the measurement methodology in place, the experimental results are presented in this section. The wavelength dependent values of the 18 parameters fitting Equations (4) and (5) are plotted in Appendix C. To verify the fits, we compare the measured free space powers \(P_{FS}\) and SOPs \(\overrightarrow{s}_{meas}\) with the predicted values by the fitted model. For this validation, 37 steps per element (LP, QWP and HWP) and wavelength are measured. This is much more than during calibration, where between three and five steps per element and wavelength are recorded. Therefore, we validate if the fit holds for settings that have not been measured during calibration. The results for \(P_{FS}\) are summarized in Figure 4 and the results for \(\overrightarrow{s}_{meas}\) are shown in Figure 5. For the latter, the setup is brought into its back-to-back configuration, i.e., no DUT.  
Figure 3: Illustration of the measurement and calculation of the device under test (DUT) Müller matrix \(\underline{M}_{DUT}\). TE-, +45\({}^{\circ}\) linearly-, right-hand circularly, and TM SOPs are launched into the DUT, giving four respective SOPs \(\overrightarrow{s}_{out,i}\) at the DUT output. The polarimetric measurement equation (PME) as in Equation (8) is used to calculate \(\underline{M}_{DUT}\) from these four measured SOPs.  
error and it stays below \(3^{\circ}\) for all wavelengths and over the entire Poincare sphere, with an average of \(1.5^{\circ}\).
## 3 Analysis Method and Assumptions
### Computational Model  
As shown in Figure 5, for tunnel-type lined rock caverns, an axisymmetric 2D model of the plane strain problem is used, while for silo-type lined rock caverns, a 3D model that is symmetric about the _xoz_ and _yoz_ planes is adopted.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Class**} & **Unit Weight** & **Internal** & **Cohesion \(c\)** & **Deformation** & **Poisson’s** \\  & \(\gamma\) **(kN/m3)** & **Friction** & **Angel \(\phi\) (\({}^{\circ}\))** & **(MPa)** & **(GPa)** & **Ratio (-)** \\ \hline I & \(>\)26.5 & \(>\)60 & \(>\)2.1 & \(>\)33 & \(<\)0.20 \\ II & \(>\)26.5 & 60\(\sim\)50 & 2.1\(\sim\)1.5 & 33\(\sim\)16 & 0.20\(\sim\)0.25 \\ III & 26.5\(\sim\)24.5 & 50\(\sim\)39 & 1.5\(\sim\)0.7 & 16\(\sim\) 6 & \(<\)0.25\(\sim\)0.30 \\ IV & 24.5\(\sim\)22.5 & 39\(\sim\)27 & 0.7\(\sim\)0.2 &
## 7 Conclusions  
The present work proposed a hybrid cryptosystem based on the Diffie-Hellman protocol. For this reason, it can distribute its keys and sign. As part of the hybrid cryptosystem, using the shared key of the Diffie-Hellman protocol and Lorenz Equations was possible to build a fourteen-round private key cryptosystem. Its substitution boxes, permutation, and round keys are dynamic; they are all different in each encryption process. The most relevant reason for choosing a symmetric system is that this type of development will be less affected in the quantum era, with a less drastic impact. This hybrid algorithm is robust because it resists differential, linear, algebraic, and brute-force attacks. The encryption quality evaluation considered: entropy, correlation, DTF, goodness-of-fit test, NPCR, UACI, AC, homogeneity, energy, and contrast, and the results were satisfactory in all, which is why the AICLDH is safe. To conclude this work, comparing the encrypted images with noise, it can be observed in Figures 4 and 5 that AICLDH is superior to AES-CBC. Furthermore, we say that future work intends to distribute the seed using post-quantum algorithms.  
Conceptualization, methodology, formal analysis, investigation, visualization, writing--review and editing, data curation, software, validation, writing--original draft preparation V.M.S.-G., R.F.-C. and M.A.C.-L.; resources, supervision, project administration, funding acquisition, R.F.-C. All authors have read and agreed to the published version of the manuscript.  
This work was funded in part by the economic support program of the Comision de Operacion y Fomento de Actividades Academicas (COFAA) and the Secretaria de Investigacion y Posgrado (SIP) of the Instituto Politecnico Nacional under grant 20221263.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Image** & **Algorithm** & **Entropy** \\ \hline \multirow{4}{*}{Peppers} & AICLDH & 7.9993 \\  & Ref. [58] & 7.996 \\  & Ref. [59] & 7.9938 \\ \hline \multirow{4}{*}{Lena} & AICLDH & 7.99935 \\  & Ref. [60] & 7.9975 \\ \cline{1-1}  & Ref. [14] & 7.9953 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Entropy comparison between the AICLDH algorithm and other works.
## 3 Zero-Trust Architecture Implementation
### Policy Evaluation Process  
After the policies are set in the zero-trust architecture, the next step is the evaluation process. There are multiple scenarios of the policy evaluation, which depend on the infrastructure of the organization. For an on-premises architecture, where the enterprise resources are hosted within the organization's data center internally, the request for the resource will be evaluated by the policy enforcement point before the request reaches the endpoint. Figure 2 demonstrates such architecture where the SIEM system collects all the analytics and security events from multiple sources to decide upon a risk score before feeding them into the policy decision point which is been queried continuously by the policy enforcement point as and when it gets a request for access. Depending on the decision made by all the policy combinations, the access request is either granted, denied, or granted with lower privileges.  
Another scenario is when the enterprise resources reside on cloud infrastructure managed by a third-party cloud provider. Here, the user accesses the resource directly through the internet because they are on the cloud, and all the resources are configured to contact the policy decision point when there is an access request. The policy decision point evaluates the policies to make a decision before responding to the cloud resource which then allows or denies access to the requester. In a complete cloud environment, the policy decision point and the SIEM system also reside in the cloud reducing the communication time. Figure 3 shows an example of such architecture.  
Figure 2: Policy evaluation process for On-premises infrastructure.  
A public cloud or even a hybrid cloud environment has blurred the physical boundaries of a corporate infrastructure. The applications are no longer confined within the firewall-protected zones and most of them are migrated to the cloud as a SaaS (Software-as-a-Service) based or an organization-hosted application residing on a cloud infrastructure that is owned and managed by third-party cloud providers. These cannot be protected by traditional perimeter-based security anymore and needs an elevated solution such as the zero-trust model to completely secure their accesses.
# Deep Learning-Based Consistent Object Detection in Distance on Highly Perspective Region
###### Abstract  
CCTVs are commonly used for traffic monitoring and accident detection, but their images suffer from severe perspective distortion causing object size reduction with distance. This issue is exacerbated in tunnel CCTVs, positioned low due to space constraints, leading to challenging object detection, especially for distant small objects, due to perspective effects. To address this, this study proposes a solution involving a region of interest setup and an inverse perspective transformation technique. The transformed images, achieved through this technique, enlarge distant objects, maintaining object detection performance and appearance velocity across distances. To validate this, artificial CCTV images were generated in a virtual tunnel environment, creating original and transformed image datasets under identical conditions. Comparisons were made between the appearance velocity and object size of individual vehicles and for deep learning model performance with multiple moving vehicles. The evaluation was conducted across four distance intervals (50 m to 200 m) from the tunnel CCTV location. The results reveal that the model using original images experiences a significant decline in object detection performance beyond 100 m, while the transformed image-based model maintains a consistent performance up to the distance of 200 m.  
inverse perspective transform; tunnel CCTV; perspective effect in distance; appearance velocity; region of interest; deep learning-based object detection +
Footnote †: journal: Applied Sciences
# LVRT and Stability Enhancement of Grid-Tied Wind Farm Using DFIG-Based Wind Turbine
###### Abstract  
According to the grid code specifications, low voltage ride-through (LVRT) is one of the key factors for grid-tied wind farms (WFs). Since fixed-speed wind turbines with squirrel cage induction generators (FSWT-SCIGs) require an adequate quantity of reactive power throughout the transient period, conventional WF consisting of SCIG do not typically have LVRT capabilities that may cause instability in the power system. However, variable-speed wind turbines with doubly fed induction generators (VSWT-DFIGs) have an adequate amount of LVRT enhancement competency, and the active and reactive power transmitted to the grid can also be controlled. Moreover, DFIG is quite expensive because of its partial rating (AC/DC/AC) converter than SCIG. Accordingly, combined installation of both WFs could be an effective solution. Hence, this paper illustrated a new rotor-side converter (RSC) control scheme, which played a significant role in ensuring the LVRT aptitude for a wide range of hybrid WF consisting of both FSWT-SCIGs and VSWT-DFIGs. What is more, the proposed RSC controller of DFIG was configured to deliver an ample quantity of reactive power to the SCIG during the fault state to make the overall system stable. Simulation analyses were performed for both proposed and traditional controllers of RSC of the DFIG in the PSCAD/EMTDC environment to observe the proposed controller response. Overall, the presented control scheme could guarantee the LVRT aptitude of large-scale SCIG.  
low voltage ride-through (LVRT); wind farm (WF); squirrel cage induction generator (SCIG); doubly fed induction generators (DFIG); PI controller +
Footnote †: journal: Applied System
# Genetic Algorithm-Based Optimization of Clustering Algorithms for the Healthy Aging Dataset
## 2 Materials and Methods
### Clustering Algorithms
#### 2.4.2 Density-Based Spatial Clustering of Applications with Noise (DBSCAN)  
Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a clustering algorithm that groups data points together based on their density in the feature space. Density-based clustering algorithms, in contrast to centroid-based clustering algorithms such as K-means/Kmeans++, form clusters based on areas of high density that are separated by areas of low density. The DBSCAN algorithm initiates by randomly selecting a data point from the dataset. A point is defined as a core point if it has a minimum number of other points (MinPts) within a specified radius (\(\varepsilon\)). Points that fall within the \(\varepsilon\)-radius of a core point, including the core point itself, are said to be density-reachable from that core point.  
The process proceeds by iteratively including all the points that can be reached from the core point and have sufficient density, thus expanding the cluster. This process continues until the cluster can no longer accommodate any additional points. Border points are classified as points that are not core points themselves but are located inside the \(\varepsilon\)-radius of a core point. These points are included in the cluster; however, they are not classified as core points. Points that do not fall into the category of core points or border points are categorized as noise points and do not belong to any cluster. DBSCAN is highly efficient at detecting clusters with diverse shapes and is resistant to interference caused by noise.
# An Energy-Efficient Game-Theory-Based Spectrum Decision Scheme for Cognitive Radio Sensor Networks
## 3 Energy-Efficient Game-Theory-Based Spectrum Decision  
We propose an energy-efficient distributed spectrum decision (EGSD) scheme for a CRSN. Firstly, we present the CRSN settings, models, operation modes, and notations that we used. Then, we describe the modules of the EGSD scheme on the coordination mode and data transmission mode.
# An Opportunistic Network Routing Algorithm Based on Cosine Similarity of Data Packets between Nodes
## 3 An Efficient Forwarding Strategy Based on Cosine Similarity between Nodes
### Algorithm Design
Empty(U\({}_{i}\))) do
10.For(Neighbor j : U\({}_{i}\)) do
11.D\({}_{\beta}\)=D\({}_{i}\cap\)D;
12.C\({}_{i}\)'; C\({}_{j}\)'; /*Merge the data packets of the node i and node j, Recalculation of data packet
13.weight vectors of nodes*/
14.SIM\({}_{\beta}\)=cos(C\({}_{i}\)'; C\({}_{j}\)'); /*calculate the cosine similarity between node i and node j*/
15.End for;
16.If (all(SIM\({}_{\beta}\))<\(\alpha\) or all(SIM\({}_{\beta}\))>\(\beta\)) then
17.selectNode(max(SIM\({}_{\beta}\)) or min(SIM\({}_{\beta}\))); T.setChildNode(i,j);
18.Else If (onePart(SIM\({}_{\beta}\))<\(\alpha\) and theRest(SIM\({}_{\beta}\))>\(\beta\)) then
19.selectNode(max(onePart(SIM\({}_{\beta}\))) and min(theRest(SIM\({}_{\beta}\)))); T.setChildNode(i,j);
20.Else SelectNode(SIM\({}_{\beta}\)); T.setChildNode(i,j); /*select the similarity between r and R*/
21.End if
22.For(ChildNode k : T.getChildNode(i)) do
23.setCurrentNode(i, k); continue;
24.end for;
25.Node p=T.getParentNode(i); Up; Ui= Ui- Up; Sort(Ui); Ui.Delete(K);
26.End while;
27.getPath(T); /*Gets the transmission paths based on the tree T*/
28.Return T
```  
**Algorithm 1** Opportunistic Network Routing Algorithm Based on Cosine Similarity of Data Packets
## 4 Discussion
In bilateral training tasks, the recognition of movement patterns from the non-paretic limb, paired with focused attention on the hemiplegic side, may improve the precision of FES and optimize the operation of exoskeleton-assisted rehabilitation devices [2]. Moreover, the ability to localize temporal intervals of EMG activity during such interventions could enhance rehabilitation outcomesby supporting session planning and promoting patient engagement (which should always be considered a central priority) through real-time visualization of motor gains [7]. This approach also offers the potential for accurate diagnostics and remote monitoring of muscular weakness, facilitating early spasticity detection and enabling the design of advanced, individualized rehabilitation protocols [3, 8]. While the classification accuracy achieved in this study is considered acceptable given the degree of motor impairment in the patient population, further improvements are possible. Future research should explore alternative DNNs, such as bidirectional LSTM networks, which may better capture the temporal dependencies in EMG data [25]. Additional efforts will focus on incorporating time-frequency domain feature extraction techniques to enhance model performance, expanding the dataset to include a broader cohort of stroke patients, and validating the model and bilateral data fusion in real-time clinical applications with new participants beyond the current study population [17, 20].
## 3 Our Approach
### Tensorized Quantized Models  
To decompose the models, as shown in Figure 7 we employ three distinct approaches. The first employs variational Bayes matrix factorization (VBMF), which necessitates the transformation of tensors into a two-dimensional format. This is achieved by unfolding the convolutional layers along modes 0 and 1, followed by the application of VBMF to the resultant tensor. The rank is determined by the first dimension of the diagonal matrix computed by the VBMF algorithm [75; 76].  
The second approach utilizes heuristic methods, leveraging the sensitivity analysis of the layers for decomposition. Here, six distinct fixed ranks are predetermined, and the model's layers are evaluated sequentially, as detailed in the preceding section.  
The third strategy involves a stochastic method, wherein the decomposition is guided by random numbers that are aligned with each layer's dimensions. For convolutional layers, the selection range is based on the kernel size (low range) and the dimensions of the tensor when unfolded into a 2D matrix (high range). Conversely, for fully connected layers, the low range is derived from a minimal matrix shape value, with the high range utilizing the matrix's larger dimension to guide the decomposition.  
Figure 6: Comparison of layer connections in convolutional layers. (**a**) Conventional FP-32 convolutional layer, showing typical connections in a full-precision network. (**b**) BNN convolutional layer, illustrating the structure when using binary weights and activations.  
Tensor Train decomposition is applied with rank R, and the convolutional layers are formulated as matrix-by-matrix multiplication, in which the four-way tensor is reshaped into a matrix (K) of size \(D^{2}S\times T\). Then, TT-Format is applied, in which \(G\) is TT-cores, as discussed in [53]. We obtain the following decomposition of convolutional kernels.  
\[\mathcal{K}_{t,s,j,i}=\mathbf{G}_{0}[i,j]\mathbf{G}_{1}[t,s_{1}]\dots\mathbf{G}_{d}[t_{d},s _{d}]. \tag{15}\]  
The same substitution as the previous methods are used to map tensor \(\mathcal{X}\) to a tensor (\(\mathcal{Y}\)) by convolving \(\mathcal{X}\) with kernel \(\mathcal{K}\) as follows:  
\[\mathcal{Y}_{t,\omega^{\prime},h^{\prime}}=\sum_{j=1}^{D}\sum_{i=1}^{D}\sum_{s _{1},\dots,s_{d}}\mathcal{X}_{s,u_{j},h_{i}}\mathbf{G}_{0}[i,j]\mathbf{G}_{1}[t_{1},s_{ 1}]\dots\mathbf{G}_{d}[t_{d},s_{d}]. \tag{16}\]
## 4 Parametric Study
### Diameter of the SFT  
In this section, the diameter of the SFT was assumed to 17 m, 20 m and 23 m to analyze its effect on the behavior of the SFT. The common variables of the analytical model include a wave condition of 6 m (8 s), a BWR of 1.2, an inclination angle of the main cables of 12 degrees and an installation depth of 70 m.  
With common analysis variables, the SFT model with the tube diameter has an almost similar motion as that shown in Figure 16a. The heave motion of Type 1 became smaller as the diameter increased. It can be seen that the BWR is set to 1.2, but it gained weight as the tube diameter increased. On the other hand, in the case of Type 2, it was seen that the motion was almost the same, the heave motion was 1.1 m, and the sway value was 0.4 m.  
Figure 16b illustrates the absolute maximum acceleration of the SFT, according to the tube diameter. For Type 2, the greatest acceleration value was in the buoyancy acting direction (Az) in the case of the SFT with a 23 m diameter (D23). For Type 1, it can be seen that the acceleration was relatively small, compared to that of Type 2, because the fluctuation of the motion was relatively small.  
Figure 16c shows the maximum reaction force at the foundation of the tether, and the maximum reaction force was increased by the diameter. This means that a large diameter causes the SFT to have a large weight. This creates a large inertial force and has a significant influence on the section force and moment. Figure 16d,e show the section force and section moment of the SFT under the external boundary conditions. These have a linear change tendency, compared with the motion of the SFT, according to the diameter of the SFT.  
Figure 15: Behavior of the SFT, according to the inclination angle of the main cable: (**a**) maximum motion of the SFT; (**b**) maximum acceleration of the SFT; (**c**) maximum reaction force on the foundation of the SFT; (**d**) maximum section force of the SFT; (**e**) maximum section moment of the SFT.  
Figure 16: _Cont._
# Energy-Saving Routing Protocols for Smart Cities
## 2 Materials and Methods
### Methodology
#### 2.6.3 Evaluation Metrics  
To evaluate the performance of the protocols implemented in Cupcarbon, several quantitative metrics calculated by the gateway during the reception of data packets were considered.  
* Packet Delivery Rate (PDR): this metric is the ratio between the number of packets sent by a source node and the number of packets received by the destination, as provided by [9]: \[PDR=\frac{\sum{N_{RX_{i}}}}{\sum{N_{TX_{i}}}}\times 100\%,\] (2) where \(N_{RX_{i}}\) represents the number of data packets successfully received by a destination node \(i\) and \(N_{TX_{i}}\) is the number of packets sent to this device.
* Throughput (THR): the number of data packets successfully transmitted to their final destination on a given communication channel per unit of time [11]; THR can be calculated as follows: \[THR=\frac{N_{RX_{i}}}{\textit{Total simulation time}}.\] (3)
* End-to-End Delay (E2 ED): the average time required for a number of data packets to be successfully transmitted over the network from a source node to a specific destination [9]; it can be defined mathematically by \[E2 ED=\frac{\sum{T_{RX_{i}}}-{T_{TX_{i}}}}{N_{RX_{i}}},\] (4) where \(N_{RX_{i}}\) is the number of data packets received by node \(i\), \(T_{RX_{i}}\) represents the time the data packet was received, and \(T_{TX_{i}}\) is the time when the package was sent.
* Average Jitter (JIT): this represents the variation of the average end-to-end delay in the delivery of data packets in a network, which may cause a situation of non-regularity in the reception of data packets; the formula is provided by [36; 37]: \[Jitter=\frac{\sum{E2 ED_{i_{k}}}-{E2 ED_{i_{k-1}}}}{N_{RX_{i}}},\] (5) where \(E2 ED_{i_{k}}\) is the average delay of a given instant \(k\), \(E2 ED_{i_{k-1}}\) is the average delay of the previous instant, and \(N_{RX_{i}}\) is the number of data packets received by node \(i\).  
\begin{table}
\begin{tabular}{c c} \hline Total simulation time & 3600 s (1 h) \\ \hline Pause time & 90 s \\ \hline Radius of each node & 1.56 km \\ \hline Radio propagation model & Egli \\ \hline Number of nodes & 25 \\ \hline Wireless communication technology & LoRa \\ \hline Transmission frequency & 915 MHz \\ \hline Spreading Factor (SF) & 7 \\ \hline Code Rate (CR) & 4/5 \\ \hline Bandwidth (BW) & 125 kHz \\ \hline \end{tabular}
\end{table}
Table 1: General parameters used in the simulations.