esa-sceva/satcom-chunk-collection huggingface dataset:
# Secrecy Enhancing Scheme for Spatial Modulation Using Antenna Selection and Artificial Noise
## 4 Secrecy Rate Analysis
 \tag{27}\]  
then the normalized value of \(y_{e}\) with \(\eta_{e}=E[\tilde{n}_{e}(\tilde{n}_{e})^{H}]\) can be approximated by:  
\[\hat{g}_{e}\simeq\sqrt{\frac{\theta}{\eta_{e}}}\hat{g}_{j}^{\prime}x_{m}+ \hat{n}_{e}, \tag{28}\]  
where \(\hat{n}_{e}=\tilde{n}_{e}/\sqrt{\eta_{e}}\). Then, the ergodic rate of [PERSON] is expressed as:  
\[ \begin{split} R_{e}&=E_{\hat{\mathbf{g}}}\left[ \int\sum_{j=1}^{N_{t}}\sum_{m=1}^{M}p(\hat{y}_{e},\hat{g}_{j}^{\prime},x_{m}) \log_{2}\frac{p(\hat{y}_{e},\hat{g}_{j}^{\prime},x_{m})}{p(\hat{y}_{e})p(\hat{ g}_{j}^{\prime},x_{m})}d\hat{y}_{e}\right]\\ &=\log_{2}N_{t}M-\frac{1}{N_{t}M}\sum_{j=1}^{N_{t}}\sum_{m=1}^{M} E_{\hat{\mathbf{g}}}\left[E_{\hat{n}_{e}}\left[\log_{2}\sum_{j^{\prime}=1}^{N_{t}} \sum_{m^{\prime}=1}^{M}\right.\right.\\ &\times\left.\left.\exp\left(-\left(\mid\sqrt{\frac{\theta}{\eta _{e}}}\alpha_{j,m}^{j^{\prime},m^{\prime}}+\hat{n}_{e}\mid^{2}-\mid\hat{n}_{e} \mid^{2}\right)\right)\right]\right],\end{split}  \tag{29}\]  
in which \(\alpha_{j,m}^{j^{\prime},m^{\prime}}\) is given by:  
\[\alpha_{j,m}^{j^{\prime},m^{\prime}}=\hat{g}_{j}^{\prime}x_{m}-\hat{g}_{j^{ \prime}}^{\prime}x_{m^{\prime}}. \tag{30}\]  
Using [PERSON]'s inequality, the lower bound of \(R_{e}\) is obtained as:  
\[ \begin{split} R_{e}^{low}&=\log_{2}N_{t}M+1-\frac{ 1}{\ln 2}\\ &-\frac{1}{N_{t}M}\sum_{j=1}^{N_{t}}\sum_{m=1}^{M}\log_{2}\sum_{j^ {\prime}=1}^{N_{t}}\sum_{m^{\prime}=1}^{M}E_{\hat{\mathbf{g}}}\left[\exp\left( -\frac{\theta\mid\alpha_{j,m}^{j^{\prime},m^{\prime}}\mid^{2}}{2\eta_{e}} \right)\right].\end{split}  \tag{31}\]  
Based on (23) and (29), the ergodic secrecy rate can be written as:  
\[ \begin{split}\tilde{R}_{s}=\max\{0,R_{b}-R_{e}\}.\end{split}  \tag{32}\]
# Design of a Polarization-Insensitive and Wide-Angle Triple-Band Metamaterial Absorber
## 3 Analysis and Discussion
For obliquely incident TE and TM waves, the absorption efficiency can be maintained above 90% when the incident angle is less than 50 degrees and 70 degrees, respectively. The authors believe that the proposed metasurface absorber can be applied in the fields of television, broadcasting, and satellite communication.
## 5 Conclusions  
1. The highest decrease in energy cost influenced by a year's training directed at optimization of individual movement technique was found at a level above 8% in a group of competitors moving at racing speed. However, it is important to consider that the study's conclusions are drawn from a small but highly specialized group of athletes, which may not fully represent broader populations. Acknowledging this, the results highlight significant benefits of targeted technical adjustments and tailored training regimens, but further studies with diverse samples are needed. For other gait speeds, EE was lower by approx. 4-6%.
2. Clear, statistically significant lowering of the total external work level was found when competitors walked at racing speed, compared to the results of the first study.
3. Changes of potential energy were characterized by a slight growth influenced by increasing velocity of gait in both tests. The lower level for racing speed was recorded in the second test.
4. Changes of kinetic energy turned out to be of key importance for the level of total external work. Its values increased by approx. 30% at body acceleration from technical to racing speeds. A clear, statistically significant decrease of this component energy value in the second test during walking at racing speed was registered, which indicates a distinct improvement of contact phase efficiency.
5. Year-long training directed at optimization of individual gait technique resulted in notable benefits for elite race walkers, both in lowering energy cost and in total external work, particularly when moving at racing speed.
## 2 Methods
### Study Selection  
The process described involves a systematic approach to selecting studies for a scoping review. Initially, search results were collected and refined using EndNote, with duplicates removed. The screening involved two levels: title and abstract review, followed by full-text assessment, both conducted independently by two authors, with discrepancies resolved by a third. The selection adhered to the PRISMA 2020 guidelines, ensuring transparency and reliability. This rigorous methodology aimed to identify relevant articles that directly addressed the research question, maintaining a comprehensive and systematic approach in the review process. To refine the selection from the initial 27 records to the final 4 studies, we applied specific inclusion and exclusion criteria focused on study design, population, and outcomes. Only peer-reviewed studies that assessed the biomechanical impact of lacing techniques on plantar pressure distribution, Achilles tendon strain, or foot stability were included. Eligible studies involved human participants engaged in physical activities, with a focus on athletes or individuals with foot health concerns. Studies with a sample size below 10, case reports, or those solely evaluating orthotics without specific reference to lacing techniques were excluded to maintain relevance and robustness in outcomes.
## Appendix A
### Direct Chebyshev Filter  
The transfer function of a normalized direct Chebyshev filter is written as follows [38]:  
\[\mathrm{H(s)}=\prod_{\mathrm{k=1}}^{n}\frac{\mathrm{K}}{s-s_{\mathrm{k}}} \tag{16}\]  
where:  
\[s_{\mathrm{k}}=\sigma_{\mathrm{k}}+j\omega_{\mathrm{k}} \tag{17}\]\[\sigma_{\rm k}=-\rm sh\left[\frac{1}{n}\cdot\rm arcsh\left(\frac{1}{ \varepsilon}\right)\right]\cdot\rm sin\left(\frac{2\cdot k-1}{2\cdot n}\cdot \pi\right) \tag{10}\] \[\omega_{\rm k}=\rm ch\left[\frac{1}{n}\cdot\rm arcsh\left(\frac{1 }{\varepsilon}\right)\right]\cdot\rm cos\left(\frac{2\cdot k-1}{2\cdot n}\cdot \pi\right) \tag{11}\]  
In Equations (10) and (11), parameter \(\varepsilon\) is an oscillation parameter that characterizes the maximum relative attenuation Amax in the passband and is determined in accordance with the expression:  
\[\varepsilon=\sqrt{10^{\rm(A_{max}/10)}-1} \tag{12}\]  
Taking \(\rm A_{max}=0.1\) dB, we obtained the oscillation parameter \(\varepsilon=0.15262\). The results of calculating the transfer function parameters of the normalized direct Chebyshev filter are summarized in Table 1.  
The filter gain was determined as follows:  
\[\rm K=\prod_{k=1}^{n}\left(\sigma_{k}^{2}+\omega_{k}^{2}\right)=0.41 \tag{13}\]  
After substituting the obtained values of the transfer function parameters of the normalized direct Chebyshev filter (Table 1) into Equation (10), we obtain:  
\[\rm H(s)=\frac{0.41}{s^{5}+1.745\cdot s^{4}+2.772\cdot s^{3}+2.398\cdot s^{2} +1.423\cdot s+0.41} \tag{14}\]  
Filter scaling is performed by substituting Equation (11) into Equation (14). The expression for calculating the frequency response of the direct Chebyshev filter of high frequencies is obtained in accordance with Equation (10). The AFR of the direct Chebyshev high-pass filter is shown in Figure 11b. As can be seen in Figure 11b, the AFR of the direct high-pass Chebyshev filter has a smooth character in the stopband, and there are oscillations in the passband.
# Biofilm Lifestyle in Recurrent Urinary Tract Infections
## 5 Contribution of Biofilm Formation to Pathogenicity in Urinary Tract Infections
### Resistance of Biofilms to Antibioticstics  
The resilient resistance of biofilms to antibiotics is a serious complication that is commonly implicated in most recurrent UTIs. Biofilms show an increased resistance to antibiotics that is estimated to be 10 to 1000 times greater than that of planktonic cells [112]. The resistant nature of biofilms is attributed to the collective effects of multiple factors: (i) the extracellular polymer matrix which confers a physical protective barrier against antibiotic penetration, hence diminishing the ability of antibiotics to reach hidden bacteria within the biofilm [18; 39; 96; 113]; (ii) secretion of antibiotic inactivating enzymes that are upregulated under quorum sensing control [98]; (iii) phenotypic variations within the biofilm and the formation of persister cells which are inherently resistant to antibiotics as a result of their suppressed metabolic activity; (iv) increased expression of efflux pumps, which are membrane-bound transport proteins involved in the expelling of toxic molecules outside of the bacterial cell, including antimicrobial molecules; and (v) horizontal gene transfer (HGT) of antibiotic resistance genes. The HGT examples and their role in antimicrobial resistance development in biofilms are widely reviewed in many previous studies [108; 109; 110; 114; 115]. Additionally, efflux pumps allow the microorganisms to organize their internal environment by removing wastes, metabolites and quorum sensing signal molecules [116]. Furthermore, the biofilm bacteria share virulence factors' encoding genes that make them reveal physiological and morphological changes [18; 26; 105; 117]. For instance, the depletion of nutrients and oxygen in the biofilms leads to sub-optimal bacterial growth rates and shift in the phenotypic variations towards the formation of the persister cells. Interestingly, biofilms may be formed as a defensive consequence to the presence of antibiotics; for instance, the persistence of antibiotics as aminoglycos, tetracycline, or cephalatine at sub-inhibitory concentrations induce _E. coli_ and _P. aeruginosa_ to form biofilms [113; 118].
# A Review of Fault Diagnosing Methods in Power Transmission Systems
## 4 Fault-Types Classification (FC)
### Artificial Neural Network (ANN) Based FC
#### 4.1.1 Feedforward Neural Network (FNN)  
Feedforward neural network (FNN) is the simplest configuration from all the ANN models, characterized as a single or multi-layer perceptron. Typically, an FNN has an input layer, a hidden layer and an output layer as shown in Figure 2[52]. The neurons in adjacent layers are fully linked, and the weights/parameters yield the output of the network. To make it simple, the learning procedure is accomplished by fine-tuning the parameters/weights of the ANN, in such a way that the output fulfills certain situations [59]. In 1986 [PERSON] et al. proposed the back-propagation (BP) method [60]. FNNs' training process is mainly based on the BP algorithm, the term BPNN is used. Applications of FNN with BP on power systems are found in [61,62]. [PERSON] et al. decomposed the voltage profile into six frequency bands and the energy of each band is determined by creating 18 features for the input layer [63]. This 18-12-3 FNN has the ability to select a faulty line. [PERSON] et al. used discrete FNN units to detect various types of fault, thus each neural network (NN) would have to learn fewer patterns [30].  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**No.** & **Algorithm** & **Input** & **Test System** & **Feature** & **Complexity** & **Result** & **Ref.** \\ \hline \multirow{8}{*}{5} & \multirow{8}{*}{\
\begin{tabular}{c} Superimposed \\ sequence \\ components \\ based \\ integrated \\ impedance \\ (SSCII) \\ \end{tabular} } & \multirow{8}{*}{
\begin{tabular}{c} 300 km, \\ 400 kV, \\ voltage profiles \\ at both ends of \\ TL \\ \end{tabular} } & The sampling frequency is 1 kHz. & & & & & & \\ \cline{1-1}  & & & 300 km, & Reliable for low & & & \\ \cline{1-1}  & & & 400 kV, & and high & & & & \\ \cline{1-1}  & & & 50 Hz with a & & 50 Hz with a & & & \\
# QiOi: Performance Isolation for Hyperledger Fabric
## 7 Related Work  
### Performance Evaluation and Characterization  
Since Hyperledger Fabric was introduced in 2015 [7; 8], many studies have characterized the performance of Fabric. Most studies aim to evaluate throughput and latency with different parameters. For example, [PERSON] et al. [44] analyzed the performance of two versions of Hyperledger Fabric (v0.6 and v1.0). They showed that Fabric v1.0 outperforms Fabric v0.6. Furthermore, Fabric v1.0 provides stable performance independent of the number of peers. In [1; 45; 46], the authors demonstrated the impact of different parameters such as batch sizes, transaction types, different endorsement policies, and the number of peers on Fabric performance. They showed that small batch sizes reduce performance, while reducing the number of endorsements in the endorsement policy improves performance. Furthermore, performance decreases as the number of peers increases. In addition, the experimental results in [1] indicate that the complex endorsement policy leads to performance degradation and the increase of the CPU and network utilization.  
[PERSON] et al. [16] found that different types of databases affect Fabric performance. For example, when the block size increases in GoLevelDB and CouchDB, latency of the validate phase in CouchDB is approximately four times higher than that in GoLevelDB. [PERSON] et al. [47] analyzed the performance of blockchain networks depending on the parameters such as transaction type, number of transactions, transaction sending rate, and concurrent execution of several chaincodes. When the number of concurrently executing transactions increases, throughput also increases. However, when throughput reaches its maximum value, latency increases without further throughput increase.  
In [19; 48], the authors compared different consensus protocols such as Solo, Kafka, and Raft. [PERSON] and [PERSON] [19] demonstrated that Raft is superior to Kafka. [PERSON] and [PERSON] [48] investigated the performance characterization of each phase, including execute, order, and validate phase. The authors found that the validate phase can become a bottleneck due to the low validation speed of chaincodes.  
In short, previous studies have carried out experiments to achieve a maximum performance in environments where sufficient computing resources were provided, using individual VMs with a large number of virtual CPUs (e.g., 16 or 32). However, they did not analyze the characteristics of each component in terms of CPU scheduling and did not consider performance interference when Fabric components run with other services concurrently. Thus, QiQi is complementary to these studies. By considering the different characteristics of Fabric components, QiQi prevents performance interference with other services.
# Two-Level-Composite-Hashing Facilitating Highly Efficient Anonymous IoT and D2D Authentication
## 3 The Proposed Schemes
### The Device-to-Server (D2S) Anonymous Authentication  
Now we are ready to introduce the D2S anonymous authentication. Figure 3 shows the authentication flow. Basically, this scheme is my anonymous version of the classic challenge-and-response authentication; \(D_{i}\) sends its current pseudonym \(PN=h_{2}(N_{i})\) for \(S\) to identify the device, and they use the secret key \(K_{i}\) to calculate the responses. The details are described as follows.  
Step 1. \(D_{i}\to S\): \(PN\), \(ID_{S}\), \(X\), \(Req\)  
\(D_{i}\) executes PN_Seed_Update(\(N_{i}\)) to generate the pseudonym \(PN\) and updates \(N_{i}\); \(D_{i}\) chooses \(x\in_{R}Z_{q}^{*}\), and calculates \(X=xP\) and \(Req=Mac(K_{i},PN,X)\) for \(S\) to validate the request.  
Step 2. \(S\to D_{i}\): \(ID_{S}\), \(PN\), \(Y\), \(Res1\)  
\(S\) executes PV_Update(\(PN\), \(Req\)), which uses \(PN\) to search a matched entry in \(D_{i}\)'s pseudonym vectors, and to update \(D_{i}^{\prime}s\) pseudonym vectors. \(S\) chooses \(y\in_{R}Z_{q}^{*}\), and calculates \(Y=yP\) and its response \(Res1=Mac(K_{i},PN,ID_{S},X)\) (which is a response to \(D_{i}^{\prime}s\) challenge \(PN\)). \(S\) sends the messages specified in Step 2.  
Step 3. \(D_{i}\to S\): \(PN\), \(ID_{S}\), \(Res2\)  
Figure 2: \(D_{i}\)’s methods and S’s methods for the Level-1 D2S authentication.  
\(D_{i}\) verifies the received \(Res1\), calculates \(Res2=Mac(K_{i},PN,ID_{S},Y)\), and computes the session key \(K_{sess}=h_{1}(^{*}KEY^{\prime*},ID_{S},PN,K_{i},xyP)\). It sends the data specified in Step 3.  
Upon receiving \(D_{i}\)'s response, \(S\) verifies \(Res2\) and computes the session key.
## 3 Materials and Methods
### Basic Concept  
The basic concept of our framework is to validate and measure how data quality may change due to latency. A system collects raw data from sensors that go through various data processing components and aggregation steps. The data structure and content may change at each step. If the raw data is incomplete or corrupted, it has immediate effects at the first level of aggregation, but can ultimately have more serious consequences if the entire set of data processing components operate on corrupted data.  
In Figure 1, we present an architectural representation of the framework concept. We propose using a readily configurable emulation environment to obtain actual execution times for algorithms and arbitrary processes employing real data. Due to the environment's configurability, the entire process can be measured with devices of varying strength. We can generate a histogram from the execution times and provide an approximation technique for determining the distribution function that best describes the environment's behavior. Using a formal specification of the observed system, we could perform a satisfying evaluation by examining the entire state space and all possible states, but this could be extremely large for even a small system. Therefore, based on the Probability Distribution Function (PDF), we are able to generate execution times and conduct simulations in a more targeted manner. This process can be repeated thousands of times to achieve more precise results, and the emulation and simulation processes can be chained if a more complex system is being analyzed. The simulation yields a list of values that cannot be processed in time due to the system's real-time behavior. The final phase of the framework demonstrates how data quality changes if data is corrupted due to latency. The most significant variables, such as network conditions, can also be approximated with distribution functions, allowing for a comprehensive examination of the entire real-time system. The concept can be generally applied to other types of real-time systems; however, for demonstration purposes, we have chosen a specific telemedicine use case in which all components can be presented in detail and where poor data quality can lead to critical issues and wrong decisions.
# Evaluating GNSS Receiver Resilience: A Study on Simulation Environment Repeatability
## Appendix A  
To better illustrate the unpredictable nature of the jamming effect on GNSS modules, it is crucial to assess the location deviation statistically. This appendix provides the formulation of the necessary steps to achieve the visualization and assessment goals set forth in this study, quantifying the mean location deviation and its temporal variability while accounting for missing data points caused by signal interference.
## 3 Analysis of Roll Modulation Signals
### SNR Analysis of the Roll Modulation Signals
The overall SNR of roll modulation signals can be represented as:  
\[\text{SNR}_{roll}=\text{SNR}_{static}-P_{rotation}-\Delta\Big{|}\text{sinc}(f_{ flu})\Big{|}-L_{SQ} \tag{21}\]  
where \(\text{SNR}_{static}\) is the SNR of correlation results in static situation, \(P_{rotation}\) is the power attenuation caused by rotation, equals to 3 dB. Using this formula, we can compute the SNR of roll modulation signals and estimate the tracking errors of PLL, which will be introduced in the following sections.
## 4 Results and Discussion  
### Pressure Drop in a Smooth Tube  
Figure 3 shows the relationship for R1234 ze(E) between the frictional pressure drop \(\Delta P_{\rm F}/\Delta Z\) and the wetness \(1-x\) for the mass velocities of 400, 200, and 100 kg m\({}^{-2}\)s\({}^{-1}\). As shown in the figure, the frictional pressure drop decreases as mass velocity decreases, and it decreases as vapor velocity and wall shear stress decrease, as condensation proceeds. The comparison of results for different refrigerants shows that the frictional pressure drop of R410A is lower than that of other refrigerants, under most conditions. The second lowest is that of R32, and the highest is that of R152a. The frictional pressure drop of R1234 ze(E) was shown to be lower than that of R152a, and higher than that of R32.  
Figure 4 shows the present data of R1234 ze(E) as the relationship between \(\phi_{\rm V}\) and \(X_{\rm tt}\). The experimental results of R1234 ze(E) can be correlated to approximately \(\pm 30\%\) by Equation (11), proposed by [PERSON] et al. [12], with a two-phase pressure drop multiplier \(\phi_{\rm V}\) based on the obtained R32, R410A, and R152a of smooth and microfin tubes.  
\[\phi_{\rm V}=1+1.55X_{\rm tt}^{0.4}, \tag{9}\]  
where the Lockhart-Martinelli parameter, \(X_{\rm tt}\), and the two-phase pressure drop multiplier, \(\phi_{\rm V}\), are respectively defined as  
\[X_{\rm tt}=\{(1-x)/x\}^{0.9}(\rho_{\rm V}/\rho_{\rm L})^{0.5}(\mu_{\rm L}/\mu_ {\rm V})^{0.1}, \tag{10}\]  
\[\phi_{\rm V}=\sqrt{(\Delta P_{\rm F}/\Delta Z)/(\Delta P_{\rm V}/\Delta Z)}, \tag{11}\]  
where \(\Delta P_{\rm F}/\Delta Z\) is the frictional pressure drop of the vapor/liquid two-phase flow, \((\Delta P_{\rm V}/\Delta Z)\) is the frictional pressure drop when the vapor component flows alone inside a tube, and can be found by the following equation:  
\[(\Delta P_{\rm V}/\Delta Z)=2f_{\rm V}G^{2}x^{2}/(\rho_{\rm V}d_{\rm i}), \tag{12}\]  
where the friction factors \(f_{V}\) were provided by [PERSON] and [PERSON] [11], for a single-phase flow inside a 4 mm outside smooth tube, as follows:  
\[f_{\rm V}=0.26 Re_{\rm V}^{-0.38}, \tag{13}\]  
where \(Re_{\rm V}\) is the vapor Reynolds number of the refrigerant, and is defined as follows:  
\[Re_{\rm V}=Gxd_{\rm i}/\mu_{\rm V}. \tag{14}\]  
Figure 3: Relationship between pressure drop and wetness for the small-diameter smooth tube.
# Spinel Magnetic Iron Oxide Nanoparticles: Properties, Synthesis and Washing Methods
## 5 Stabilization of Magnetic and Maghemite Nanoparticles in Solution
### Main Stabilization Methods  
The surface functionalization of magnetic iron oxide nanoparticles can have two separate objectives: on the one hand, the stabilization of nanoparticles to avoid their aggregation in a given solution, on the other hand, the desire to make these nanoparticles functional for a given application. In this part, we will only deal with surface functionalization of nanoparticles. Generally, the stabilization of nanoparticles in solution goes through the creation of repulsions between particles. The two main types of repulsions which can be induced by the surface functionalization of nanoparticles are electrostatic repulsions and the repulsions by steric hindered.  
The electrostatic repulsions can be induced by grafting of small charged molecules, the encapsulation in liposomes constituted of a double layer of surfactants having a charged head or the use of other inorganic materials such as silica or gold. The repulsion by steric hindered can be induced by grafting in surface of polymers nanoparticles either by simple adsorption or by grafting of the polymer via a terminal function.  
The main types of functionalization (Figure 7) described in the literature are [84]:  
* The functionalization by adsorbed polymers such as dextran [85, 86], PVA [87, 88] or chitosan [89, 90].
* The functionalization by small charged molecules such as carboxylates [91, 92], phosphates [93], or sulfonate [94].
* The functionalization by inorganic shell most of the time noble metals such as silver and gold [95] or silica [96, 97].
* The functionalization by grafted polymers by the terminal functions such as PEG-COOH [98, 99] or polyethylenimine [100].
* The encapsulation in liposomes in the case of a double layer of fatty acids (oleic acid for example) [101].