esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Purpose
This is very important because of the following reasons:  
* It can seamlessly integrate logic implemented in arbitrary devices (including FPGAs), and therefore it is possible to implement a hardware IDM router with PIN capabilities; \begin{table}
\begin{tabular}{l c c c c} \hline  & **Active networks** & **Overlay networks** & **i-NP** & **PIN** \\ \hline Real-time changes & Yes & Rarely & No & Yes \\ Performance enhancement & Limited & Yes & Limited & Yes \\ Functionality enhancement & No & Yes & No & Yes \\ Information simplification & No & Rarely & Yes & Yes \\ Information enrichment & No & Rarely & Rarely & Yes \\ Less load at the destination nodes & No & Rarely & No & Yes \\ Network-aware & Yes & Rarely & Rarely & Yes \\ QoS-aware & No & Rarely & Rarely & Yes \\ Application-aware & No & Rarely & No & Yes \\ Type of nodes & Physical & Virtual/Physical & Physical & Virtual/Physical \\ Range of application & Limited & Wide & Limited & Wide \\ Volume of information & High & High & Limited & High \\ Range of services & Wide & Wide & Limited & Wide \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison of PIN with previous approaches. * It allows deployment of upgrades, migration of routers, versions, fault tolerance, and all services that are available when using an object-oriented middleware (such as ZeroC ICE);
* It is possible to control network processing (execution of partial phases in the routers) using different levels of granularity. It is possible to control which flows, clients, connections, or even object methods PIN is applied to. This could be done by configuring the components that are deployed in the routers, both dynamically and remotely because they are also remote objects. The overall objective of this work is to design and develop a comprehensive PIN mechanism with a wide range of applications and a broad functionality. The processing functions of the network proposed here require the development of specific techniques in various areas of network engineering, such as routing algorithms, deployment, QoS, and the application of statistical or soft computing techniques. Based on these possibilities, the following specific objectives arise:  
* Provide support for a wide variety of equipment, protocols and technologies (heterogeneous networks) for both structured networks (traditional networks) and for environments with no structure (_ad-hoc_ networks), limiting the ability to manage the links (in terms of the capacity, delay, cost, or any other factor or combination of these);
* Provide the ability to handle large volumes of information and a wide range of services (text, audio, image, video), considering at all times the inherent complexity in the treatment of each of them;
* Consider all the time QoS aspects and provide the means necessary for the participation of users with different QoS requirements and / or quality of experience (\(\mathrm{QoE}\));
* Implement elements of networking (with the end-user application, with routing algorithms, or with any other element that is necessary), to conduct efficiently and effectively the processing functions of the network.
# TrustShare: Secure and Trusted Blockchain Framework for Threat Intelligence Sharing
## 5 Security Analysis
### Anonymity Considerations  
Due to the incorporation of the membership service provider (MSP) entity for identity management, Hyperledger Fabric inherently ensures transaction anonymity. The MSP is adaptable, allowing each organization to establish its own, contributing to pluggable data transaction anonymity. Transaction data anonymity is achieved through encryption usingthe client's private key, endorsed by a trusted third party via the Transport Layer Security (TLS) protocol, ensuring encrypted communication between participants.  
Authorized users, acknowledged in the chaincode, must obtain adequate endorsements to validate transactions, adhering and subject to endorser rules. The study by [80] proposes an Anonymous Endorsement System with a threshold endorsement policy. This work introduces a novel ring signature scheme, Hyperledger Fabric's Constant-Sized Linkable Ring Signature (FCsLRS), ensuring transaction-oriented linkability for endorser identity concealment. The alteration of the RSA modulus size, implemented in Golang, undergoes rigorous examination for security and performance, with empirical studies supporting its viability. Notably, the rapid production of signatures and tags, coupled with a constant RSA modulus value, irrespective of message length or endorsement set size, underscores the collaborative creation of signatures by all endorsers.
###### Abstract  
Power consumption in interconnects is a critical constraint on performance improvements in integrated circuits. This paper proposes a novel design methodology to minimize loss in interconnects and address this limitation. The approach incorporates air cavities within the substrate to lower the equivalent loss tangent, thereby reducing dielectric losses. Additionally, the inner conductor is engineered with a periodically nonuniform width to maintain stable effective characteristic impedance. To validate the effectiveness of the methodology, it is applied to both a substrate integrated coaxial line (SICL) and a stripline. Simulation results reveal a 9.76% reduction in loss for the SICL and a 19.40% reduction in loss for the stripline, demonstrating significant improvements with wide tolerance. Furthermore, this design methodology can be generalized to other interconnect types, offering the potential for additional power savings.  
air cavity; interconnect; low loss; nonuniform; periodically +
Footnote †: journal: Applied sciences  
0
Footnote 0: _Applied Sciences_
a measured per-unit insertion loss of 0.13 dB/mm in the D-band (110 to 170 GHz) [21]. A hollow rectangular dielectric waveguide, composed of three dielectric layers with a hollow structure created by cutting an air-filled slot in the middle layer, exhibits an average attenuation constant of 0.028 dB/mm across the 220-294 GHz frequency range, as supported by measurement results [22]. For integrated circuits, a self-aligned air-gap interconnect process is proposed in [23]. A misalignment-free multilevel air-gap interconnect with a via-base structure is fabricated using self-aligned gap formation and etch-back processes [24]. Air-gap transmission lines can also be fabricated on organic substrates for low-loss interconnects [25]. The modeling, optimization, and benchmarking of air-gap interconnects are discussed in [26]. Furthermore, periodically introducing air cavities can also reduce dielectric loss due to the low loss tangent of air. However, this approach may introduce discontinuities and impedance mismatches, leading to increased return losses and signal attenuation.  
This work proposes a design methodology for low-loss interconnects featuring air cavities and periodically nonuniform conductor widths. In this approach, the columnar air cavities are utilized to reduce dielectric losses, while the nonuniform-width conductor is designed to compensate for impedance mismatches introduced by the cavities. To validate the effectiveness of the proposed methodology, it is applied to both an SICL and a stripline. Simulation results demonstrate that the SICL and stripline incorporating the proposed design exhibit reduced losses and improved impedance matching compared to conventional designs.
# Automatic Classification of Foot Thermograms Using Machine Learning Techniques
## 2 Materials and Methods
### Proposed Models  
In this work, two models for classifying the thermographic images are proposed. The LR algorithm and the variants of SVMs, k-NN were applied to both models.  
In the first model, the foot thermograms are classified into four classes: healthy and three categories for diabetics, which represent progressive severity of the disease.  
In the second model, the classification consists of two stages: initially a binary classification is performed and the foot thermogram is cataloged as belonging to a healthy or diabetic individual; then, and only for cases classified as diabetic, a second classifier is used to obtain a refinement of the classification for diabetic cases.
## 4 Results and Discussion  
The NSL-KDD data set was used in this study and had the basic host feature content which included time and traffic. The training data set contained 22 different attacks. To simulate an actual situation, new attacks would appear. The test data set contained 17 attack types that had not appeared in the training dataset. The KDDTest+ and KDDTrain+ datasets, which have 22,544 and 125,973 network data records respectively, were used. The WEKA-ReSample tool was used for the sampling of four sub-datasets A, B, C, and D from the original (KDDTest+ and KDDTrain+) respectively, for use as experimental data set samples, as shown in Figure 1.  
Each NSL-KDD network data record has 38 digital type attribute features, as well as three character type attribute features including protocol type, service, and flag. Furthermore, the protocol type has 3 features, service has 70 features, and flag has 11 features. It was therefore necessary to map and transform the character type features in the original record to digital feature attributes. The WEKA-Nominal to binary encoding method was used to encode the character features, turning each original data record into a 122-dimensional eigenvector. Since the data had significantly different resolutions and ranges, the range of value captured was not uniform. Therefore, standardization or mean removal and variance scaling was needed for each eigenvector. After the transformation, each dimension had a mean of 0, also called the Z-score normalization. Calculation involved subtraction of the mean (M) from feature (X) and division by the standard deviation (S) (calculation equation: Z = (X - M)/S), so the attribute data was scaled within a range of [0, 1]. After standardization of the training data set, the same procedure was used to standardize the test data set.  
The Weka tool was used to load the sampled experimental data set (sub-data sets A, B, C, and D, each having 5000 records, and the random sampling reflected, as far as possible, the various information expected during the analysis) for data preprocessing, and PCA was used to reduce the number of features to 94. Four kinds of classification algorithms: SVM, naive Bayes, decision tree (J48), and MLP were used in the experimental tests. Data that had not been dimensionally reduced, and data which had been reduced, were both used. In the accuracy experiment for each group, data was compared in three different combinations. Data set A was used to train each group, data set B was used to test the first group, data set C was used for the second group, and data set D was used for the third group.
## 3 Robust Extended Kalman Filter
### Iterative Reweighted Least Squares Algorithm  
An Iterative Reweighted Least Squares Algorithm (IRWLS) is performed to eliminate the outliers. The steps for the robust estimation procedure are defined as follow:  
1. Find an initial estimate \(\hat{X}_{0}\) \[\hat{X}_{0}=\left(G_{0}^{T}G_{0}\right)^{-1}G_{0}^{T}y\] (33)
2. Estimate the vector for initial residuals of the observations \(r_{0}\): \[r_{0}=y-G_{0}\hat{X}_{0}\] (34)
3. Define the initial scale value \(s_{0}\)[33] \[s_{0}=1.4826\,\text{median}\left(|r_{0}|\right)\] (35) where \"\(median\)\" stands for the median function computed on the residuals vector \(r_{0}\).
4. Estimate the initial diagonal weight matrix by MM-Estimation. \[W_{0}(i,i)=\left\{\begin{array}{l}1\\ 0\end{array}\right.\begin{array}{l}\frac{r_{0}(i)}{s_{0}}\leq\alpha\\ \left|\frac{r_{0}(i)}{s_{0}}\right|>\alpha\end{array}\] (36) where \(i=1,\ \ldots,\ n;n\) being the number of observations.
5. While (\(j\) is \(jth\) iteration) 1. Update the value of the matrix \(G_{j}\) at the \(jth\) iteration with \(\hat{X}_{j-1}\) 2. Solve the estimated state \(\hat{X}_{j}\) using the weighted least-squared method \[\hat{X}_{j}=\left(G_{j}^{T}W_{j-1}G_{j}\right)^{-1}G_{j}^{T}W_{j-1}y\] (37) 3. Calculate the HPL (in Section 3.4) 4. If \(\left\|\hat{X}_{j}-\hat{X}_{j-1}\right\|<0.001\); break  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Name** & **Weight Function** \\ \hline Huber & \(w(r_{i})=\left\{\begin{array}{l}1\\ \alpha/|r_{i}|\end{array}\right.\begin{array}{l}|r_{i}|\leq\alpha\\ |r_{i}|>\alpha\end{array}\right.\) \\ Bi-Tukey & \(w(r_{i})=\left\{\begin{array}{l}1\\ 0\end{array}\right.\begin{array}{l}|r_{i}|\leq\alpha\\ |r_{i}|\geq\alpha\\ 0\end{array}\right.\begin{array}{l}|r_{i}|\leq\alpha\\ |r_{i}|>\alpha\end{array}\) \\ Bi-Square & \(w(r_{i})=\left\{\begin{array}{l}[1-(|r_{i}|/\alpha)]^{2}\\ 0\end{array}\right.\begin{array}{l}|r_{i}|\leq\alpha\\ |r_{i}|>\alpha\end{array}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Weighting functions [22].  
1. If \(\left\|\hat{X}_{j}-\hat{X}_{j-1}\right\|>0.001\); continue 2. Update the estimated residuals \[r_{j}=y-G_{j}\hat{X}_{j}\] (38) 3. Calculate the scale value \[s_{j}=1.4826\,\text{median}\left(\left|r_{j}\right|\right)\] (39) 4. Recalculate the diagonal weighted matrix using MM-Estimation: \[W_{j}(i,i)=\left\{\begin{array}{ll}1&\left|\frac{r_{j}(i)}{s_{j}}\right|\leq \alpha\\ 0&\left|\frac{r_{j}(i)}{s_{j}}\right|>\alpha\end{array}\right.\] (40) 5. Go to step (a)
6. End
7. If \(HPL\leq HAL\) then the estimated positions are accepted, (HAL: Horizontal Alert Limit) If not, they are rejected.  
In this paper, we use the bi-Tukey weight function in Equations (36) and (40). The HPL is calculated using the RAIM algorithm presented in Section 3.4.
# Antioxidative and Photoprotective Activity of _Pinus nigra_, _Pinus strobus_ and _Pinus mugo_
###### Abstract  
Substances that delay the skin aging process have become very popular lately. Undoubtedly, this is influenced by all kinds of efforts to maintain a youthful appearance for as long as possible. Plant-derived antioxidants are a group of compounds that exhibit protective properties against the degenerative effects of oxidative stress on skin aging. Another important factor that protects skin against aging is photoprotective agents. The comparison of antioxidant and photoprotective activities seems to be interesting. The aim of this study was to evaluate the antioxidant properties of _Pinus strobus_, _Pinus nigra_, and _Pinus mugo_ extracts using two frequently applied methods, i.e., DPPH and ABTS. Moreover, the polyphenol content was evaluated using Folin-Ciocalteu method. The correlation between the polyphenol content, antioxidant potential of the extracts, and sun protection factor in vitro was evaluated. Extracts were prepared using methanol, ethanol, isopropanol, and n-propanol in three concentrations: 40% (\(\
icefrac{{v}}{{v}}\)), 70% (\(\
icefrac{{v}}{{v}}\)), and undiluted. Ultrasound-assisted extraction, which is a type of green extraction technique, was applied for 15, 30, or 60 min. The highest antioxidant activity determined by the DPPH and ABTS methods was observed for _Pinus mugo_ extracts in 40% ethanol and 40% methanol, respectively, both after 15 min extraction. The highest total polyphenol content was also found in _Pinus mugo_ extracts. These activities were significantly higher than those of _Pinus strobus_ and _Pinus nigra_. Similarly, the highest SPF values were also found for _Pinus mugo_ extracts. Moreover, a strong correlation was observed between the antioxidant potential and SPF--the highest values were found for the correlation between the SPF and antioxidant activity determined using the ABTS method. Based on the obtained results, _Pinus mugo_ could be suggested as a possible component for use in cosmetics.  
Keywords:white pine; black pine; mountain pine; antioxidant potential; in vitro sun protection factor +
Footnote †: journal: Applied sciences
## 4 Case Study and Analysis of Vibration Control Effect
### Design of Periodic Row Piles
Firstly, according to the reference [63], when the tunnel is buried at a depth of 0.8\(\lambda_{\rm sv}\)-1.4\(\lambda_{\rm sv}\), the length of the pile is better if longer than 1.6\(\lambda_{\rm sv}\) to achieve a good vibration control effect, where the \(\lambda_{\rm sv}\) represents the shear wavelength in the first layer of soil corresponding to the centre-frequency of the bandgap, and the calculated pile length was 24 m. Therefore, the pile length should be around 24 m. Secondly, the FE model was applied to calculate the ground vibration acceleration response under several conditions of pile lengths of 16 m, 20 m, 24 m and 28 m, respectively, shown in Figure 13.  
It can be seen from this figure that, when the pile length is 24 m and 28 m, the difference in ground vibration acceleration is small. But when the pile length decreases to 20 m (shorter than the distance from the ground to the bottom of the tunnel), the ground vibration significantly increases, which means that the vibration reduction effect of the row piles is significantly reduced. Finally, the length of the pile was determined as 24 m.  
Figure 11: Schematic of structural form and basic unit of local resonance low-frequency vibration isolation row piles. Based on the calculated design parameters of the periodic row piles, a periodic row pile model was inserted into the track bed-tunnel-soil FE analysis model, as shown in Figure 14. The piles were simulated using solid units, and the pile-structure interaction was simulated using a common node force. The grid size of the periodic row piles was 0.6 m. Next, the ground vibration acceleration response at the detection point under SFST conditions was calculated using this model.
# Comparison of Hysteresis Based PWM Schemes \(\Delta\Sigma\)-PWM and Direct Torque Control
## 3 Hysteresis Based Pulse Width Modulation Schemes
### Direct Torque Control  
The basic idea of DTC is to focus on the stator flux linkage [11]. This can be realized by the control structure presented in Figure 4. First of all, a two-step as well as a three-step controller generate the voltage vector. A switching table [12] is considered. Secondly, a machine model calculates all state variables of the PMSM. This includes the torque and the magnitude as well as the sector of the stator flux space vector [12; 29].  
Figure 5a shows the reference trajectory of DTC. It is described by the motion of the stator flux space vector [29]. The two-step controller keeps the magnitude of the stator flux within a defined tolerance band. Each time the trajectory reaches the lower boundary the control changes its switching state [12]. The new applied switching state increases the magnitude of the stator flux until the upper boundary is reached [34]. Additionally, the effect of voltage space vectors depends on the actual position of the stator flux space vector. The coordinate system of the flux vector is divided into six different sectors [30]. The definition of those sectors is shown in Figure 5b and described in [29].  
In [35], it is explained that the torque can be expressed depending on the angle between rotor and stator flux linkage. Hence, the DTC regulates the torque by accelerating or decelerating the stator flux space vector. In this context, three different discrete output values are possible [29]. The first is expressed by \"1\" which results in an acceleration. Thus, the angle towards the rotor flux space vector as well as the generated torque will increase. Secondly, an output of \"\(-1\)\" effects an acceleration of the stator flux space vector against the direction of rotation. This reduces the torque. When the output of the controller is \"0\", the space vector of the stator caused flux components is stopped immediately [12]. Hence the angle towards the rotor caused flux components decreases and the torque reduces. Due  
Figure 4: Speed control loop of DTC.  
Figure 5: (**a**) Trajectory of stator flux space vector and (**b**) sectors of DTC.  
to the choice of the hysteresis parameters the control only uses the states \"1\" and \"0\" under normal conditions [29].  
The information of both controllers and the sector of the flux space vector is passed to a switching table [12]. This element defines the switching state of the inverter. Table 1 shows the complete switching table which is derived in [12].  
Because DTC already implements a torque control within the inner cascade, only an outer speed control has to be realized [29]. For comparability, to \(\Delta\Sigma\)-PWM a PI controller is implemented. Figure 4 shows the holistic DTC model. Due to the asymmetrical structure of the torque controller, the information of the direction of rotation is needed [30].
# Performance Evaluation of Ground AR Anchor with WebXR Device API
## 4 Results and Discussion
### 3D Model Rendering FPS Evaluation in WebXR  
FPS represents the rate at which frames change in seconds. In general, 15 frames are supported for animation, 24 frames for movies, 30 frames for television, and 60 frames for broadcasting such as sports. The human eye can process a variable frame rate and recognizes more than 8 continuous FPS as natural motion; it can be said that real-time performance is guaranteed when the number of FPS is more than 30. If real-time performance is guaranteed, users can use it naturally while providing services. Therefore, FPS measurement was performed to check whether XR guarantees real-time performance in a web environment. The FPS experiment was conducted after loading a 3D model in a web environment.  
Figure 10 shows the result of measuring FPS after the 3D model loading in the WebXR environment while moving in each direction from 1 to 2 and 3 as shown in Figure 4a,b. The experiment was conducted for 174 s. The X-axis is the time (s), and the Y-axis is the frame count rendered per second. The results confirmed that an average of 29.7 FPS were processed.
## 2 Materials and Methods
### Statistics  
Data distribution for each muscle's activation difference between static and dynamic conditions was first evaluated for normality using the Shapiro-Wilk test. The significance of differences in muscle activation between static and dynamic simulations was tested for each muscle using either a paired Student's \(t\)-test or a non-parametric Wilcoxon signed-rank test (paired, two-tailed).