esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theory and Analysis
### The Classification and Identification Method
#### 2.2.1 Similarity Comparison of Radiation Emission Components
In order to make the discussion more general, the Euclidean distance is used to measure the similarity between the \(i\)th broadband components, expressed as  
\[ED\_B^{i}=\sqrt{\left(amp_{11}^{i}-amp_{10}^{i}\right)^{2}+\left(amp_{21}^{i }-amp_{20}^{i}\right)^{2}+\ldots+\left(amp_{n1}^{i}-amp_{n0}^{i}\right)^{2}} \tag{10}\]  
Considering that the spectrum of equipment generally includes 3-5 significant broad-band components, the similarity measured by broadband components can be expressed by \(S_{2}\), where \(k\) is the total number of broadband components contained in the spectrum:  
\[D_{accard}(Fre_{1},Fre_{0})=1-jaccard(Fre_{1},Fre_{0})=1-\frac{|Fre_{1} \cap Fre_{0}|}{|Fre_{1}\cup Fre_{0}|} \tag{3}\]0  
(3) Similarity comparison based on trend components  
Excluding the components extracted in the previous two steps, all other less significant parts of the spectrum are regarded as trend components. The whole spectrum is cut into ten parts with equal length, with the amplitude information of the frequency points corresponding to the \(j\)th trend component (where \(j\) = 1, 2,..., 10, \(m\) is the length of the trend components). \[D_{accard}(Fre_{1},Fre_{0})=1-jaccard(Fre_{1},Fre_{0})=1-\frac{|Fre_{1} \cap Fre_{0}|}{|Fre_{1}\cup Fre_{0}|} \tag{3}\]1Furthermore, the \(j\)th trend component of standard equipment and the equipment to be identified can be expressed as \(T_{0}^{j}\) and \(T_{1}^{j}\), where  
\[D_{accard}(Fre_{1},Fre_{0})=1-jaccard(Fre_{1},Fre_{0})=1-\frac{|Fre_{1} \cap Fre_{0}|}{|Fre_{1}\cup Fre_{0}|} \tag{3}\]2  
\[D_{accard}(Fre_{1},Fre_{0})=1-jaccard(Fre_{1},Fre_{0})=1-\frac{|Fre_{1} \cap Fre_{0}|}{|Fre_{1}\cup Fre_{0}|} \tag{3}\]3  
Then, the similarity of the corresponding \(j\)th segments between the equipment to be identified and standard equipment is evaluated using the Euclidean distance, as follows:  
\[D_{accard}(Fre_{1},Fre_{0})=1-jaccard(Fre_{1},Fre_{0})=1-\frac{|Fre_{1} \cap Fre_{0}|}{|Fre_{1}\cup Fre_{0}|} \tag{3}\]4  
Considering that the spectrum of the equipment has been divided into 10 segments of trend components, the similarity measured based on trend components can be expressed by \(S_{3}\):  
\[D_{accard}(Fre_{1},Fre_{0})=1-jaccard(Fre_{1},Fre_{0})=1-\frac{|Fre_{1} \cap Fre_{0}|}{|Fre_{1}\cup Fre_{0}|} \tag{3}\]5  
When the similarity of three types of radiation emission components are obtained, the classification and identification of different spectra will be transformed into the evaluation of the similarity of these radiation emission components.
## 2 Background Theories
### Dispersion Entropy  
Similar to PerEn, DisEn is also a symbolic dynamic (i.e., dispersion pattern) entropy to make a fast and robust measure of time-series complexity. That is, a time series is transformed into a new signal with only a few different elements, and thus, study of the nonlinear dynamics of a time series is simplified to the corresponding distribution of symbol sequences. Although some of detailed information may be lost, some of the invariant and robust dynamic properties can be kept. For the given time series \(\mathbf{d}=[d_{1},d_{2},\ldots,d_{L}]^{T}\), calculation of its DisEn follows 4 steps.  
Step 1: Map each element \(d_{j}\) of the signal \(\mathbf{d}\) to one of the \(c\) classes. To do so, first, employ the normal cumulative distribution function to map \(d_{j}\) into \(y_{j}\). Next, \(y_{j}\) is assigned to an integer from 1 to \(c\) by a linear algorithm \(\mathbf{z}_{j}{}^{c}=\) round \((c^{\star}y_{j}+0.5)\).  
Step 2: Map embedding vectors to dispersion patterns. For each vector \(\mathbf{z}_{i}{}^{m,c}=[\mathbf{z}_{i}{}^{c},\,\mathbf{z}_{i+\tau}{}^{c},\ldots, \,\mathbf{z}_{i}\)\({}^{+}(m-1)\tau{}^{c}]^{T}\) (\(i=1,2,\ldots,L-(m-1)\tau\)) with embedding dimension \(m\) and time delay \(\tau\), it is mapped to a dispersion pattern \(\pi_{v0v1\ldots\,\pi m-1}\), where \(v_{0}=\mathbf{z}_{i}{}^{c}\), \(v_{1}=\mathbf{z}_{i+\tau}{}^{c},\ldots,\,v_{m-1}=\mathbf{z}_{i+(m-1)\tau}{}^{c}\).  
Step 3: Calculate the relative frequency of potential dispersion patterns  
\[p(_{v0v1\ldots\,m-1})=\frac{\#(\{j|i\leq L-(m-1)\tau,\mathbf{z}_{i}^{m,c}\text {haspattern}\pi_{v0v1\ldots\,vn-1}\})}{L-(m-1)\tau} \tag{1}\]  
Step 4: Obtain the DisEn in the normalized form as  
\[DisEn(\mathbf{d},c,m,\tau)=-\sum p(\pi_{v0v1\ldots\,vn-1})\ln p(\pi_{v0v1 \ldots\,vn-1})/\ln c^{m} \tag{2}\]  
When all the elements of a signal are assigned to one class (i.e., \(c=1\)), the time series \(\mathbf{d}\) is perfectly predictable and then its DisEn value is equal to zero. In contrast, if all the possible dispersion patterns have equal probability, all values of the \(\mathbf{d}\) are independent and randomly distributed, thus leading to the maximum value of DisEn.
# A Comprehensive Evaluation of Possible RNSS Signals in the S-Band for the KPS
## 3 Navigation Signal Design Considerations
### Spectral Efficiency  
The RF system can only use the frequency band allocated by ITU and should not cause harmful interference to services that operate in adjacent bands. In order to suppress out-of-band emission (OOBE), frequency components outside the allocated frequency band must be filtered at the transmitter. Therefore, we should concentrate signal power within the transmission bandwidth to minimize signal distortion due to filtering and effectively use limited transmit power.  
In order to maintain the signal power before the filtering, the signal should be amplified by the factor \(\lambda_{OOBE}\) to compensate for the filtering loss. The \(\lambda_{OOBE}\) value refers to a ratio of signal power distributed to the out-of-band emission. The factor is expressed as [38]:  
\[\lambda_{OOBE}=\frac{1}{\int_{-B/2}^{B/2}G_{s}(f)df}=\frac{1}{\eta} \tag{6}\]where \(B\) is the allocated bandwidth (or transmit bandwidth) and \(G_{s}(f)\) is the PSD normalized in an infinite bandwidth. The reciprocal of \(\lambda_{\text{OOBE}}\) is the ratio of signal power distributed within the allocated frequency band and it refers to spectral efficiency \(\eta\).
# Experimental and Empirical Studies to Evaluate Liquefaction Resistance of Partially Saturated Sands
## 3 Materials and Methods  
### Testing Device and Test Material  
The dynamic triaxial experiments were conducted at Mus Alparslan University using a fully automated test system (Dynatriax EmS). The triaxial apparatus was manufactured by Wykeham Farrance--Controls Groups (Milan, Italy); its main components are shown in Figure 1.  
The electromechanical actuator of the Dynatriax EmS system has a 15 kN dynamic load capacity, and the system is capable of producing loading cycles with various wave shapes (e.g., sinusoidal) with frequencies ranging from 0.01 to 10 Hz. The software and  
Figure 1: Dynamic triaxial test system at Mus Alparslan University.  
data-acquisition system of the equipment provides automatic and effective control of the saturation, consolidation, and cyclic-shearing stages of the triaxial tests. The basic instrumentation of the system consists of a submersible load cell (with a 0-25 kN measurement range and accuracy of \(\pm\)0.05% FS); pressure transducers that measure the pore water, cell, and back pressure (0-1000 kPa measurement range and accuracy of \(\pm\)0.25% FS); external displacement transducers (\(\pm\)25 mm measurement range and accuracy of 0.2% FS); and a 100 cc automatic volume-change device (precision of \(\pm\)0.1 cc). The instruments were periodically calibrated to minimize the uncertainties of the measurements made during the test program.  
The triaxial specimens were prepared using Sile sand (AFS 55/60). Figure 2 presents the particle size distribution (PSD) curve of this sand and its comparison with different types of sand that are widely used in geotechnical laboratories around the world. Based on the liquefaction boundaries of Tsuchida [52], it is a highly liquefiable soil. The index properties of Sile sand are given in Table 1.
## 2 Microseismic Monitoring Method under Tunnel Construction
### Installation of Monitoring System  
Figure 2 shows the typical topology of the microseismic monitoring system employed in a tunneling environment. The disturbance of tunnel excavation is monitored continuously for 24 h using a sensor array installed near the working face. The microseismic signal received by the sensor is converted into an analog signal, which is transmitted to the data acquisition system through the signal cable and then converted into a digital signal by the A/D converter built into the data acquisition system (the sampling rate is at least 5 kHz from experience). To ensure a reliable long-distance transmission, a fiber optic cable is used to transmit the signal to the data processing system. Based on the recorded signals, the data processing system solves the information, such as the location and source parameters of the microseisms, and uploads the processed microseismic data to the remote server simultaneously. The analysis and early warning center obtain the precursory information of rockbursts by thoroughly analyzing the spatial distribution law of the microseismic events and the evolution law of the focal parameter information. The stability state of the rock mass is then evaluated. Finally, the analysis and early warning center sends the analysis and early warning results to the engineers' office and transmits them to a mobile terminal (this should be done at least once a day).  
The sensor arrangement significantly influences the monitoring effect and the stable operation of the system. In the tunnel driving environment, the microseismic sensors are typically arranged on the tunnel walls on both sides behind the working face, and the distance along the tunnel axis ranges from 30 m to 50 m. To consider the monitoring accuracy of the sensor and ensure that the transmission line is not damaged by falling rocks near the working face, the distance between the sensor array and the working face  
Figure 2: Topology diagram of a typical microseismic monitoring system in a tunneling environment.  
should be approximately in the range of 50-80 m. To ensure the continuous monitoring of the microfractures in the surrounding rock due to excavation and unloading of the working face, the sensor array is made to move forward once with the advance of 30-50 m. To ensure that the microseismic monitoring system has good positioning accuracy, the sensor should not be installed in the same plane. While laying the signal line of the sensor, breaking should be prevented. To avoid the interaction between microseismic monitoring equipment and the tunnel boring machine (TBM), it is recommended that all microseismic monitoring equipment be installed on the tunnel wall and completely separated from the TBM. This can effectively reduce the damage rate of sensors and cables and improve the stability of the system.
###### Abstract
In the case of single chamber MFCs, the electron acceptor is the oxygen, and its abundance in the air is utilized. The separators used in dual chamber (H-type) MFCs are usually membranes (such as proton-exchange membrane), which are expensive materials [19; 20]. The advantage of single chamber MFCs is that the expensive separator can be switched with a cheaper one, acting at the same time as the structural material for the cathode electrode [21]. In recent years, the research has focused on the use of ceramic materials as separators and cathode electrodes in MFCs [22; 23]. The use of these materials lowers the internal resistance of the MFC and allows improved reduction in the oxygen, as they are resistant to fouling [24] and offer mechanical, thermal and chemical stability [25]. In order for the ceramic materials to be used as a structural component of the electrode, a catalyst needs to be deposited on the surface exposed to air. Various techniques have been employed to accomplish this. Assuming the oxygen reduction catalyst is activated carbon, the following ways of deposition are the most notable. A two-step process was employed to create the activated carbon catalyst, with a phytic acid-doped polyaniline coating and subsequent high-temperature pyrolysis, by [26]. The catalyst was then placed on a stainless steel mesh with polyfluorettaethylene (PTFE) using a rolling method technique [26]. Another approach of utilizing activated carbon was presented by [27], placing an activated carbon layer on a gravel bed with a copper plate for the electrical connection. This set-up was used as the air-exposed cathode electrode of a single chamber MFC [27]. Activated carbon mixed with carbon black placed on a stainless steel mesh was used by [28]. The activated carbon mix was prepared by adding carbon black, activated carbon, PTFE and DI water using ultrasonication and afterwards spreading the paste on stainless steel mesh [28]. The aim of this work is to propose a facile way to manufacture tubular ceramic electrodes, coated with the oxygen reduction catalyst, which are then to be used in a single chamber MFC. The various manufacturing processes employed presented promising results and are facile to reproduce. The selected catalyst (activated carbon) is deposited on the inner surface of the tubes by wash-coat technique, brush-coat technique and ultrasound-assisted deposition/infiltration (sono-coat). The cathode electrodes are tested in a single chamber MFC, operating in batch mode and having as feedstock a leachate originating from a landfill unit in Athens, Greece. The performance of the different electrodes is compared and the ability of the MFC to treat the leachate is examined, in all three cases.
# In Vitro Compatibility of Three Native Isolates of _Trichoderma_ with the Insecticide Chlorpyrifos
###### Abstract  
The compatibility between biocontrol agents and pesticides seems to be a sustainable control strategy in agriculture. Therefore, the in vitro compatibility of three native isolates of _Trichoderma_ was evaluated in three concentrations of chlorpyrifos (960, 1200, and 1440 mg/L), by determining the effect on spore germination, mycelial growth, and the antagonistic capacity. The isolates correspond to _Trichoderma aspecellum_ TCA 3, _Trichoderma aspecellum_ TCA 21 and _Trichoderma harzianum_ TCA 22. Both spore germination and mycelial growth were performed using the poisoned medium method, while the antagonistic capacity was evaluated against _Botrytis_ sp. in a dual culture. The results showed that TCA 21 strain had a higher germination percentage (79.46, 59.79, and 37.43%) than the TCA 3 and TCA 23 strains, in the three concentrations of chlorpyrifos. Regarding the mycelial growth of the three native strains in chlorpyrifos are affected when concentration of chlorpyrifos increase (\(p<0.05\)). Finally, the antagonistic capacity of the three strains was not affected by any concentration of chlorpyrifos, where strains TCA 21 and TCA 23 presented a degree of antagonism of one, while TCA 3 presented a degree of two, according to the scale used by [PERSON]. In conclusion, _T. aspecellum_ TCA 21 was the one that presented the best in vitro compatibility with chlorpyrifos at concentrations of 960 and 1200 mg/L, compared to _T. aspecellum_ TCA 3 and TCA 23. These results are favorable for field application since these native strains can also have the ability to degrade the insecticide, representing a sustainable and eco-friendly alternative to the environment.  
Trichoderma aspecellum; Trichoderma harzianum; biocontrol agent; pesticides; Botrytis sp. Article
## 4 Experimental Section
### Comparison with Other Methods
To provide a clearer understanding of these metrics, it is important to note that PSNR and SSIM are primarily focused on pixel-level accuracy and structural similarity, respectively, and may not fully capture the perceptual aspects of image quality as perceived by humans. This is where LPIPS becomes significant, as it evaluates perceptual similarity based on learned visual features, making it more relevant for assessing the quality of remote sensing images under varied conditions such as lighting or atmospheric effects. Additionally, CIEDE 2000 is particularly useful for assessing precise color differences, which is crucial when evaluating color correction algorithms. These metrics together provide a comprehensive assessment, balancing objective accuracy with perceptual quality. In Table 1, the proposed method and other methods are evaluated using the specified performance metrics. The proposed method, particularly the mid-stage model of the WGAN-GP-based color correction model, demonstrates the best performance, showing optimal results across all metrics. This indicates that the proposed model can reproduce the colors and textures of the original image more accurately than existing methods. Table 1 highlights that while the early-stage and fully trained models show variations in performance, the mid-stage model achieves the most balanced and optimal outcomes. This suggests that the mid-stage training phase effectively captures both color accuracy and texture details, demonstrating superior performance in high-quality image reproduction.
# Investigation of the Influence of Splitter Blades on the Resonance Conditions of Impellers
## 5 Conclusions
\begin{tabular}{l l} \(h\) & Harmonic component \\ \(I_{4}\) & \(4\times 4\) unit matrix \\ \(K\) & Stiffness matrix of the system \\ \(k\) & Any integer \\ \(k_{b}\) & Blade stiffness \\ \(k_{c}\) & Coupling stiffness \\ \(k_{d}\) & Disk stiffness \\ \(M\) & Mass matrix of the system \\ \(m_{b}\) & Blade mass \\ \(m_{d}\) & Disk mass \\ \(N\) & Number of duplicated sectors \\ \(n\) & Engine order of excitation \\ \(n_{ND}\) & Nodal diameter index \\ \(Q_{Bi}^{(p)}\) & DFT of \(q_{Bi}^{(p)}\) \\ \(q\) & Physical displacement vector of the system \\ \(q_{1}^{j}\) & Displacement of the main blade of the \(i\)th sector \\ \(q_{2}^{j}\) & Displacement of the splitter blade of the \(i\)th sector \\ \(q_{3}^{j}\) & Displacement of the disk part corresponding to the main blade of the \(i\)th sector \\ \(q_{4}^{j}\) & Displacement of the disk part corresponding to the splitter blade of the \(i\)th sector \\ \(q_{Bi}^{(p)}\) & Blade mode shape for the \(p\)th mode of ND \(i-1\) \\ \(U_{Bi}^{(p)}\) & Blade part of modal displacement vector \(u_{i}^{(p)}\) \\ \(u\) & Modal displacement vector of the system \\ \(u_{i}^{(p)}\) & Modal displacement vector of the \(p\)-th mode of ND \(i-1\) \\ \(V_{j}\) & Speed of the \(j\)th blade \\ \(W\) & Work done to the whole impeller \\ \(W_{i}\) & Work done to the \(i\)th sector \\ \(W_{N}\) & \(N\times N\) DFT sinusoid matrix \\ \(w_{N}\) & Primitive \(N\)th roots of unity \\ \(X_{j}\) & Displacement of the \(j\)th blade \\ Greek Symbols \\ \(\alpha\) & Mass coefficient for splitter blade \\ \(\beta\) & Stiffness coefficient for splitter blade \\ \(\delta_{j}^{i}\) & Relative deviation of the \(j\)th blade of the \(i\)th sector \\ \(\Delta\theta\) & Angular spacing of the blades \\ \(\mu\) & Mean value of the uniform distribution \\ \(\sigma\) & Standard Deviation of the uniform distribution \\ \(\varphi_{h}\) & Generic phase for the angle variable \\ \(\varphi_{j}^{\prime}\) & Generic phase angle \\ \(\varphi_{t}\) & Generic phase for the time variable \\ \(\Omega\) & Rotating speed of the impeller \\ \(\omega_{t}\) & Excitation frequency \\ \(\omega_{n}\) & Natural frequency of the impeller \\ Abbreviations \\ EO & Engine Order \\ EoM & Equation of Motion \\ DF & Disk Dominated Mode Family \\ DFT & Discrete Fourier Transform \\ HCF & High Cycle Fatigue \\ MBF & Main Blade Dominated Mode Family \\ ND & Nodal Diameter \\ RMV & Representative Modal Vector \\ SAFE & Singh’s Advanced Frequency Evaluation \\ SBF & Splitter Blade Dominated Mode Family \\ \end{tabular}
## 3 Application of MOFs as Sorbents in Selected Techniques of Preparing Liquid Samples for Analysis
### Solid-Phase Microextraction (SPME)
Selected compounds were detected with LOD ranging from 0.11 to 1.40 ng/kg, with a relative standard deviation of <8.6% and recovery ranging from 80.2 to 101.0%. Figure 10: A SPME arrow-like system presented by [PERSON] et al. [52]. Figure 9: The preparation sketch of MOF-monolith composite-based capillary microextraction column (MM-CMC) by [PERSON] et al. [60]. Some MOFs, despite their high specific surface area, are also characterized by low water resistance. One of the ways to maintain a high specific surface area of the sorbent and increase its resistance to hydrolysis is the carbonization of MOF [54]. The authors first in-situ synthesized MOF-74 on the surface of the SPME fiber, then subjected it to the carbonization process. The obtained sorption fiber MOF-74-C was characterized by good thermal and chemical stability. It has been successfully used to analyze selected fragrance compounds. The developed method was characterized by a wide linearity range (more than two orders of magnitude), a relative standard deviation below 8.7%, with recovery ranging from 83.6 to 115.5%. In another study, the rare CIM-80(Al) was used as a sorbent in the HS-SPME analysis of selected six methylsilicanes and seven fragrances in aqueous samples. In combination with the GC-MS technique, the MOF coating used was particularly effective in the determination of volatile methylsilicanes, showing moderately lower LOD (0.2-0.5 \(\upmu\)g/L) and slightly better precision (RSD \(<\) 17%) compared to commercial DVB/PDMS fiber, with LOD = 0.6 \(\upmu\)g/L and RDS value \(<\) 22%. CIM-80(Al) was applied to the surface of a fiber made of nitinol using a cross-linking agent, APTES. Initially, a silica layer was applied to the surface of nitinol using APTES; then, on the surface of the fiber prepared in this way, the MOF was synthesized in-situ. A summary of MOF materials used in SPME is presented in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
# 5G Security Threat Assessment in Real Networks
## 2 Background
### 5G NSA Arthitecture
#### 2.2.2 5G NSA EPS Session Establishment Procedure  
5G NSA EPS session establishment procedure consists largely of three procedures: (1) S5 GTP-U tunnel creation, (2) data radio bearer (DRB) creation between the terminal and eNB, and S1 GTP-U tunnel creation between eNB and SGW, (3) DRB creation between the terminal and gNB, and S1 GTP-U tunnel creation between gNB and SGW [17]. The reason why DRB and S1 GTP-U tunnels are created repeatedly is that NSA is configured with E-UTRA new radio-dual connectivity (EN-DC). The purpose of this is to form eNB and bearer first for master base-station, followed by gNB and bearer for secondary base-station.  
1. (S5 GTP-U tunnel creation) Figure 4 shows the procedure to create S5 GTP-U tunnel represented in a sequence diagram. UE IP is allocated in PGW first, followed by S5 GTP-U tunnel creation between SGW and PGW.
2. (eNB DRB and S1 GTP-U tunnel creation) Figure 5 shows the procedure to create eNB DRB and S1 GTP-U tunnels represented in a sequence diagram. Where, DRB is formed between a terminal and eNB, and then S1 bearer is formed between eNB and SGW.
3. (gNB DRB and S1 GTP-U tunnel creation) Figure 6 shows the procedure to create gNB DRB and S1 GTP-U tunnels represented in a sequence diagram. Finally, DRB is formed between the terminal and gNB, and then S1 bearer is created between gNB and SGW [18].  
Figure 4: S5 GTP-U tunnel creation procedure.