esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion and Future Research Directions
### Signal Processing and Data Preparation
#### 5.2.1 Fourier-Based Methods  
Fast Fourier Transform (FFT) remains a fundamental tool for identifying dominant frequency components in (quasi-)stationary signals, such as rotating machinery operating at a fixed speed. Early-stage or repetitive fault signatures often manifest as characteristic frequency peaks or sidebands in the FFT spectrum. However, the effectiveness of FFT-based approaches can degrade under non-stationary conditions or varying load profiles. Short-Time Fourier Transform (STFT) alleviates this by introducing a time window for localized analysis, but optimal window sizing can be challenging, leading to trade-offs between time and frequency resolution.
## 2 Methodology
### Classification on Log-Mel Features  
Apart from extracting feature maps from the raw waveforms using 1D convolutional layers, there are several more popular transforms to convert the raw audio signals into feature representations for classifiers. The most common choice in audio signal processing is the 2D time-frequency representation, for example, based on short-time Fourier transform, usually scaled on the basis of a log-like frequency, i.e., mel frequency. In this study, log-mel filter bank features (log-mel) of the whale-call audios are employed as feature representations to be fed into the CNN classifiers. Based on the results of comparative studies [19; 27], log-mel features are considered to be the best time-frequency feature representations for audio signals to be used for deep-learning methods. As demonstrated in Figure 1, we compute the log-mel features (by 128 filters with 2800-point sliding window and a 220-point hop size) and the corresponding delta and delta-delta features from the raw audio signals at the same time. To produce a 3-channel image-type input for CNN classifiers, these feature maps are stacked to a 3-channel form. Due to the fact that the whale-call clips normally have different time lengths, we apply a random selection strategy to stochastically capture a fixed-length segment in the time axis (150) from the 3-channel feature maps to generate the inputs for classifiers. It should be noted that the selected segments inherit the clip labels for analysis and the classification result for each audio clip is achieved through the majority voting on all the selected segments.
# Effect of Pseudomonas Fluorescens on Isofetamid Dissipation and Soil Microbial Activity
## 1 Introduction
Among PGPR, the most common and numerous are _Pseudomonas_ spp. (_Pseudomonadaceae_), which also include _Pseudomonas fluorescens_. This strain has the ability to produce hydrolytic enzymes, synthesize antibiotics, fix nitrogen, produce phytohormones (i.e., IAA--Indole-3-acetic acid), dissolve hard-to-reach phosphates (phosphorus solubilization) and bind iron by producing siderophores [16; 17]. In addition, there are studies confirming the ability of the genus _Pseudomonas_ to degrade pesticides [18; 19]. The biodegradation of pollutants using microorganisms is a safe alternative to soil remediation, in which microorganisms use toxic compounds as a source of nutrients, i.e., carbon, nitrogen and phosphorus [20]. The problem associated with the use of microorganisms for environmental purification is finding strains with high specificity and effectiveness for selected pollutants. Therefore, introducing new xenobiotics into the environment is always associated with conducting detailed studies on the effectiveness of biodegradation/biotransformation carried out using different strains of microorganisms and searching for the most effective strains [20; 21]. In studies of soils exposed to various pollutants, knowledge of the number of microorganisms is of great importance. Most often, the number of bacteria is determined, and increasing attention is being paid to the presence of archaea. Molecular studies (the sequencing of small rRNA subunits) have shown a large phylogenetic diversity of archaea not only in extreme environments [22]. In addition, these microorganisms play an important role in global biogeochemical cycles. At least two types of metabolic cycles are performed exclusively by archaea: methanogenesis and anaerobic sulfate-dependent methane oxidation. A complete understanding of the contribution of archaea to the biogeochemistry of the Earth is still a distant prospect [23]. One of the indicators that informs about changes occurring in the soil environment is the soil enzymatic activity. Enzymes take part in the transformation of organic matter and the circulation of nutrients such as carbon, nitrogen and phosphorus. Monitoring the activity of soil enzymes therefore allows for the assessment of the activity of microorganisms and, consequently, the health of the soil [7]. Taking into account the above, the aim of this study was to assess the effect of the application of the _P. fluorescens_ strain on the decomposition of the fungicide isofetamid and the biological activity of soils expressed by the number of genes specific to archaea (16S), bacteria (16S) and ammonia-oxidizing bacteria (amoA gene) and the activity of selected soil enzymes responsible for the transformation of carbon (\(\beta\)-Glucosidase), phosphorus (phosphomoesterase), nitrogen (N-acetyl-glucosamineaminidasedase) and sulfur (arylsulfatase). The obtained results will be used to detect the relationship between the biological properties of soils and the disappearance of isofetamid in samples treated and untreated with _P. fluorescens_.
# Topp-Leone Modified Weibull Model: Theory and Applications to Medical and Engineering Data
## 3 Some Statistical Features
### Incomplete Moments  
In terms of lifespan models, It is interesting to know the s\({}_{th}\) lower and upper INMs of \(X\) defined by \(v_{s}(t)=E(X^{s}\mid X<t)=\int_{0}^{t}x^{s}f(x,\phi)dx\) and \(\eta_{s}(t)=E(X^{s}\mid X>t)=\int_{t}^{\infty}x^{s}f(x,\phi)dx\) respectively, for any real \(s>0\). The s\({}_{th}\) lower INMs of \(TLMW\) distribution is  
\[v_{s}(t) = \int_{0}^{t}x^{s}f(x)dx=2\lambda\sum_{i,j=0}^{\infty}\pi(i,j)\int _{0}^{t}\left(\alpha x^{s+\theta j}+\theta\beta x^{s+\theta(j+1)-1}\right)e^{-2 \alpha(i+1)x}dx \tag{19}\] \[= 2\lambda\sum_{i,j=0}^{\infty}\pi(i,j)\Bigg{[}\frac{\alpha\gamma( s+\theta j+1,2\alpha(i+1)t)}{[2\alpha(i+1)t)]^{s+\theta j+1}}+\theta\beta\frac{ \gamma(s+\theta(j+1),2\alpha(i+1)t)}{[2\alpha(i+1)t)]^{s+\theta(j+1)}}\Bigg{]}.\]  
Likewise, the s\({}_{th}\) upper INMs of \(TLMW\) distribution is  
\[\eta_{s}(t) = \int_{t}^{\infty}x^{s}f(x)dx=2\lambda\sum_{i,j=0}^{\infty}\pi(i, j)\int_{t}^{\infty}\left(\alpha x^{s+\theta j}+\theta\beta x^{s+\theta(j+1)-1} \right)e^{-2\alpha(i+1)x}dx \tag{20}\] \[= 2\lambda\sum_{i,j=0}^{\infty}\pi(i,j)\Bigg{[}\frac{\alpha\Gamma (s+\theta j+1,2\alpha(i+1)t)}{[2\alpha(i+1)t)]^{s+\theta j+1}}+\theta\beta \frac{\Gamma(s+\theta(j+1),2\alpha(i+1)t)}{[2\alpha(i+1)t)]^{s+\theta(j+1)}} \Bigg{]},\]  
where \(\gamma(s,t)=\int_{0}^{t}x^{s-1}e^{-x}dx\) and \(\Gamma(s,t)=\int_{t}^{\infty}x^{s-1}e^{-x}dx\) is the lower and upper incomplete gamma functions.  
The MRL has many applications (see [35; 36]). The MRL function is defined as  
\[\mu(t)=E(X\mid X>t)=\frac{\eta_{1}(t)}{\bar{F}(t)}-t,\]  
where \(\eta_{1}(t)\) is the first INM of \(X\) and by putting \(s=1\) in Equation (20), then  
\[\mu(t)=\frac{2\lambda}{\bar{F}(t)}\sum_{i,j=0}^{\infty}\pi(i,j)\Bigg{[}\frac{ \alpha\Gamma(1+\theta j+1,2\alpha(i+1)t)}{[2\alpha(i+1)t)]^{1+\theta j+1}}+ \theta\beta\frac{\Gamma(1+\theta(j+1),2\alpha(i+1)t)}{[2\alpha(i+1)t)]^{1+ \theta(j+1)}}\Bigg{]}-t.\]  
Furthermore, the mean inactive time of \(X\) is determined as  
\[m(t) = E(X\mid X<t)=t-\frac{v_{1}(t)}{F(t)}\] \[= t-\frac{2\lambda}{F(t)}\sum_{i,j=0}^{\infty}\pi(i,j)\Bigg{[} \frac{\alpha\gamma(1+\theta j+1,2\alpha(i+1)t)}{[2\alpha(i+1)t)]^{1+\theta j +1}}+\theta\beta\frac{\gamma(1+\theta(j+1),2\alpha(i+1)t)}{[2\alpha(i+1)t)]^{ 1+\theta(j+1)}}\Bigg{]},t>0.\]
## 5 Discussion
### Relationship between Drivers and NPP Changes at Each Stage
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Time**} & \multicolumn{3}{c}{**2001–2005**} & \multicolumn{3}{c}{**2005–2010**} & \multicolumn{3}{c}{**2010–2015**} & \multicolumn{3}{c}{**2015–2020**} \\ \multicolumn{1}{c}{**Periods**} & \multicolumn{3}{c}{} & \multicolumn{3}{c}{} & \multicolumn{3}{c}{} & \multicolumn{3}{c}{} & \multicolumn{3}{c}{} & \multicolumn{3}{c}{} & \multicolumn{3}{c}{} & \multicolumn{3}{c}{} & \multicolumn{3}{c}{} & \multicolumn{3}{c}{} \\ \hline
**Construct** & **VIF** & **Loading** & **CR** & **AVE** & **VIF** & **Loading** & **CR** & **AVE** & **VIF** & **Loading** & **CR** & **AVE** & **VIF** & **Loading** & **CR** & **AVE** \\ \hline Soil & & & & & & & & & & & & & & & & & \\ PI & 1.030 & 0.978 & & & & 1.030 & 0.925 & & & - & * & & 1.028 & 1.000 & & & \\ Clay & * & * & & & * & * & & 1.729 & 0.958 & & & * & * & & * & & \\ Slit & 1.030 & 0.373 & & & 1.030 & 0.533 & & 1.729 & 0.839 & & & * & * & & & \\ Climate & & & & & & & & & & & & & & & & \\ Evaporation & 1.008 & 0.897 & & & 1.335 & 0.877 & & & 1.820 & 0.880 & & 1.027 & 0.933 & & \\ Temperature & * & * & & & 1.335 & 0.855 & & & 1.820 & 0.943 & & & * & & \\ Precipitation & 1.008 & 0.521 & & & * & * & & * & & * & & * & & & \\ Radiation & * & & & & * & & & * & & * & & 1.027 & 0.504 & & & \\ Human & & & & & & & & & & & & & & & & \\ activity & & & & & & & & & & & & & & & & & \\ GDP & 1.000 & 1.000 & & & & 1.107 & 0.976 & & & 1.042 & 0.948 & & & 3.222 & 0.976 & & \\ Population & * & & & & 1.107 & 0.521 & & & 1.042 & 0.504 & & & 3.222 & 0.931 & & \\ Terrain & & & & & & & 0.824 & 0.704 & & & 0.814 & 0.692 & & & 0.827 & 0.707 \\ DEM & 1.238 & 0.925 & & & & 1.241 & 0.936 & & & 1.236 & 0.952 & & & 1.235 & 0.922 & & \\ Slope & 1.238 & 0.746 & & & & 1.241 & 0.729 & & & 1.236 & 0.691 & & & 1.235 & 0.751 & & \\ \hline \hline \end{tabular} CR \(>\) 0.65; AVE \(>\) 0.5; values that did not meet the conditional hypothesis or were not significant were removed and are denoted by *.  
\end{table} 
Table 5: PLS-SEM reliability and validity assessment. Figure 20: _Cont._  
The amount of fertilizer used has an effect on vegetation NPP changes; unfortunately, raster data on fertilizer inputs such as N and P were lacking in this study. In a forthcoming follow-up study, we intend to obtain raster data on various types of fertilizer inputs to enhance the accuracy and reliability of the influence factors of vegetation NPP.
## 2 Experimental Apparatus and Test Conditions  
The schematic of the experimental apparatus is depicted in Figure 1. The R410A refrigerant was discharged by the plunger pump in a state of liquid single phase, and it flowed into the preheater through the mass flow meter. The refrigerant was heated by the preheater up to the desired enthalpy at the inlet of the test section. Then, the refrigerant returned to the pump through the test section and condenser. The saturation pressure at the outlet of the test section was controlled by cooling the refrigerant in the condenser with temperature-controlled brine.  
The test section for upward flow is shown in Figure 2. The test section for downward flow was used upside down. The test tubes were made of copper; their cross-section shapes are shown in Figure 3. Table 1 shows the specifications of each tube. The inside diameters of circular tubes were 0.5, 0.7, 1.0, 1.5, and 2.0 mm, and the hydraulic diameters of the rectangular and triangular tubes were 1.04 and 0.88 mm, respectively. The corner of the flow channel had a radius of about 0.1 mm of curvature for both the rectangular and triangular tubes. The test tube was arranged vertically, and its length was 440 mm. The pressure drop between the inlet and the outlet pressure taps was measured by the differential pressure gauge.  
Figure 1: Schematic diagram of the experimental apparatus.  
Figure 2: Test section for upward flow.  
The temperature of the test-section-surrounding air was adjusted very close to the test tube wall temperature in order to minimize the heat transfer between the test tube and the surroundings. The measurement was performed under the steady-state condition.  
The frictional pressure drop were measured in the range of mass fluxes of 50 and 100. The qualities were in the range of 0.05 to 0.9 under the adiabatic condition, with 200 and 400 kg\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\) for upward flow and 30, 50, 100, and 200 kg\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\) for downward flow. The saturation temperature was 10 \({}^{\circ}\)C. The measured total pressure drop consists of two components: the frictional pressure drop and the static pressure drop. Therefore, the frictional pressure drop \(\Delta P_{\rm f}\) (Pa) was obtained as  
\[\Delta P_{\rm f}=\Delta P_{\rm a}-\Delta P_{\rm s} \tag{1}\]  
where \(\Delta P_{\rm a}\) (Pa) is the measured total pressure drop, a value obtained from differential pressure gauge, and \(\Delta P_{\rm a}\) (Pa) is the static pressure drop. The measurement error of the frictional pressure drop was \(\pm\)0.2 kPa in general, while the maximum error was estimated to be \(\pm\)0.7 kPa at a quality of about 0.1 due to the large estimation error of the static pressure drop.  
The physical properties of R410A were evaluated using REFPROP Ver.10.0 [12].
# Positioning Performance Limits of GNSS Meta-Signals and HO-BOC Signals
## 2 GNSS Signal Model, Meta-Signals, and HO-BOC Signals
### Other Large Bandwidth GPS, Galileo, and BeiDou Signal Combinations  
Notice that the previous signal combinations considered signals in the L5/E5, E6, and L2 bands. The main goal of such combinations is to have a larger bandwidth and narrower ACF main peak, which in turn may lead to better time-delay estimation performance, and therefore a better position estimation. Since the goal is to have a narrower ACF, one may seek to exploit other signal combinations with the GPS, BeiDou, and Galileo signals in the L1/E1/B1 band. The ACFs for these large bandwidth meta-signals are summarized in Figure 3: GPS L2 + L1, GPS L5 + L1, Galileo E6 + E1, Galileo E5 + E1, BeiDou B3 + B1, and BeiDou B2 + B1.  
First, notice that as expected, the main ACF peak is narrower than the narrower Galileo E5A + E6B combination previously considered. However, the price to be paid is larger secondary peaks, which are also closer to the main one. This is an effect that can also be seen in Figure 2c-f, and as already stated, it is a critical point to take carefully into account. Indeed, large secondary peaks must be avoided to minimize the effect of possible false locks. As will be discussed in the rest of the article and being especially clear for the E5A + E6 signal, these possible false locks have a strong impact on the ML behavior (i.e., on the ML threshold, and therefore on the optimal receiver operation point). This is the main reason why these large bandwidth meta-signals are not useful in practice because even if in the asymptotic regime, the time-delay estimation performance may be better, the SNR needed to achieve such a regime is not worth exploiting these signals. In addition, in terms of computational complexity, notice that such combinations need a bandwidth in the order of 400 MHz, exceeding the real-time processing capabilities. Therefore, these combinations were briefly introduced for completeness, but are not taken into account in this contribution.  
Figure 1: PSD for the different GNSS meta-signals.  
Figure 2: **(a)** Main ACF peak comparison for different meta-signals w.r.t. E5: L2C + L5, B2A + B3, E5B + E6 and E5A + E6. **(b)** Main ACF peak comparison for different meta-signals w.r.t. E5 and E5A + E6: Beidou B2 + B3 and Galileo E5 + E6. The comparison of each individual meta-signal ACF with the Galileo E5 is given in the remaining subplots. **(c)** GPS L2C + L5Q, **(d)** Galileo E5B + E6B, **(e)** Beilou B2 AQ + B3Q, **(f)** Galileo E5A + E6B, **(g)** Beilou B2 + B3Q and **(h)** Galileo E5 + E6B. (i.e., in all plots, the vertical axis represents the normalized autocorrelation). The magnitude and position of the first secondary peak are given for completeness.
## 7 Performance Analysis
### Security Properties  
In Table 6, we present the results of protocols related to security comparisons and our proposed protocol based on batch verification. The suggested protocol prevents more attacks than other related previous studies, and also provide privacy-preserving and mutual authentication. Therefore, our proposed protocol is significantly safer than the considered related protocols. The system consumes some energy during implementation, depending on the real time and communication overhead of the system.
## 4 Numerical Application
### Evaluation Metrics
#### 4.1.3 Normalized Cross-Correlation (NCC)  
NCC is used to measure the overall similarity between the inversion results and the true model and is defined as follows:  
\[NCC(x,y)=\frac{\sum\limits_{i=1}^{N}\left(x_{i}-\overline{x}\right)(y_{i}- \overline{y})}{\sqrt{\sum\limits_{i=1}^{N}\left(x_{i}-\overline{x}\right)^{2 }}\sqrt{\sum\limits_{i=1}^{N}\left(y_{i}-\overline{y}\right)^{2}}} \tag{19}\]  
where xi and \(y_{i}\) represent the velocity values at the i-th grid point of the inversion results and the true model, respectively, \(\overline{x}\) and \(\overline{y}\) represent the average velocity values of the inversion results and the true model, respectively, and \(N\) represents the total number of  
Figure 7: The P-wave velocity component of the 2D 2007 BP TTI anisotropic velocity model. Red stars schematically illustrate the positions of the 85 sources deployed along the surface. Black dashed lines show the VSP receiver array within the borehole. The color bar indicates P-wave velocity in m/s.  
grid points. The range of the NCC is [1, 1, 1], where values closer to 1 indicate a higher overall similarity between the inversion results and true model, values closer to \(-1\) indicate a negative correlation between the inversion results and true model, and values closer to 0 indicate no correlation between the inversion results and true model.
# Distortion of Thomson Parabolic-Like Proton Patterns Due to Electromagnetic Interference
## 2 Experimental Arrangement and Measurement  
A 2 TW iodine laser system with a wavelength of 1315 nm was employed to irradiate targets composed of polymethyl methacrylate or silicon wafers doped with boron. The ions accelerated in the backward direction were detected by the TP placed at a 0\({}^{\circ}\) detection angle with respect to the normal target surface and at a distance of 2.17 m from the target (see Figure 2).  
The spectrometer used a microchannel plate coupled to a phosphor screen and to the CCD. The strength of the TP's magnetic field was increased up to 0.12 T. A potential difference of 2.9 kV was applied across the 21.5 mm wide gap between the spectrometer electrical plates in order to reach a separation of the ion tracks on the spectrometer's detection system. The parabolic tracks on the image plane (within Cartesian coordinate  
Figure 1: Typical Thomson parabola (TP) snapshots showing distorted ion tracks. Parabola parts corresponding to high-energy particles partially overlapped with each other.  
system \((x,y)\) and under nonrelativistic approximation) are described by the following equation [9]:  
\[y=\frac{q}{m_{0}}\frac{EL_{F}\Big{(}\frac{L_{F}}{2}+L_{rE}\Big{)}}{BL_{/B}\Big{(} \frac{L_{fB}}{2}+L_{rB}\Big{)}}x^{2}, \tag{1}\]  
where \(q\) is the charge of ion, \(m_{0}\) is its invariant mass, \(E\) is the strength of the spectrometer's electric field, \(B\) is the strength of its magnetic field, \(L_{fE}\) (\(L_{fB}\)) is the length of the electric (magnetic) field, and \(L_{rE}\) (\(L_{rB}\)) stands for the distance between the end of the electric (magnetic) field and the recording system, respectively. The \(x\) axis was oriented in the direction of ions deflection in the magnetic field, and the \(y\) axis was oriented in the direction of their deflection in the electric field.  
Figure 2 also shows a typical TP snapshot with the Cartesian coordinate system, where the parabolic track corresponding to protons is distorted due to EMP interference. While the protons were detected by the TP, the EMP generated in the target chamber and propagated outside from it was simultaneously recorded by a plane antenna positioned directly on the TP electrodes, as can be seen in Figure 2.
# Smart Platform for Monitoring and Control of Discrete Event System in Industry 4.0 Concept
## 2 Preliminaries and Problem Formulation
### Development of a Mixed Reality Application
#### 3.4.3 Vuforia  
Vuforia [37] is an augmented reality (AR) software platform that enables developers to integrate digital elements into the physical world using mobile devices. The implementation of Vuforia in the Unity project allowed us to recognize 3D objects and track them in real time. But this was preceded by the creation of 3D models of production lines, based on which Vuforia was able to recognize a real object.  
We tested several applications for creating 3D objects of production lines, such as Polycam, Scandy Pro, Metascan, Qlone, and MafiScan. Polycam was the most effective, providing us with the most authentic 3D models at no cost, with object size data that could be exported in common 3D model file types, e.g., in an.obj or.fbx format.  
Using the Polycam application, we scanned 3D objects using a mobile device. In our case, it was an iOS mobile device, the iPhone 11 Pro Max. The device had a sufficiently high-quality camera, with which we captured more than 100 photos of the laboratory model of the production line. These photos were used by the application to create a 3D model of the corresponding production line and export it to an.fbx file. In this way, we created 3D models of the production lines. In Figures 15 and 16, we can see scanned 3D models created using the Polycam application.  
Figure 14: Communication within the entire project.  
Figure 15: Scanned 3D model of the production line (model 2).  
To detect real world objects, it was necessary to train the recognition of real models based on the obtained 3D models. This was enabled by the Vuforia Model Target Generator application. Based on the obtained 3D models, we created the SustavyPro database, which contained the data of the 3D models.  
It was necessary to import the Vuforia Engine AR library into the Unity project. We implemented the trained database into the Unity project and created all the necessary objects for recognizing real objects. Here, all Vuforia objects had to be put into an empty object called VuforiaContent. If we skipped this step, the Vuforia library would not record objects at all. Objects System1 and System2 represent 3D objects, based on which real objects are recognized. Both objects required a connection to the database and the target model in the database. After connecting the target model, the dimensions of the object were automatically delivered.  
Another necessary configuration was to set what should happen when objects are recognized and lost. We use the RecognitionObjects object for this, which provides functions that are performed in these events. When the object is recognized, the Unity project connects to all MQTT sessions, requests the local server for the JSON UI, and, after rendering the user environment, requests all the initialization data of the PLC device of the given system. When the object is lost, it is disconnected from all MQTT sessions and the currently generated user environment is deleted from the scene.