esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results  
### Dataset  
Our dataset comprises 626 malicious HWP files and 216 benign files; benign files were sourced from websites, usually originating from government agencies. Malicious files were provided by a Korean cybersecurity company. From 842 files, we collected 6782 streams, (benign: 5470; malicious: 1312). Subsequently, the data were split into training and testing sets, with a ratio of 85% and 15%, respectively. The statistical distribution of benign and malicious streams was 4657:1107 for training and 813:205 for testing.  
The streams were categorized as either malicious or benign, and further categorized into a specific malware family. Given that a single file comprises several streams, as mentioned in Section 3.1, the classification process begins with per-stream prediction. Each stream is evaluated for maliciousness. Subsequently, the file-level detection is obtained based on the aggregation of the stream-level results.
## 2 Centrifugal Pump Structure Design and Testing Experiments
### Testing Experiment  
#### 2.2.1 Test Rig  
Figure 3 illustrates a closed test rig established to investigate the hydraulic and dynamic pump performance. During testing, the refrigerant tank provides the liquid fluid for the investigated pump. An air-cooled chiller with a water loop system maintains the system's refrigerant temperature. The vortex flowmeter measures the flow rate (the range is 0.417\(\sim\)4.17 L/s, and the error is \(\pm\)0.2 FS%). The drifted refrigerant reservoir provides different net positive suction heads (NPSHA) by lifting. The pressure transmitters are positioned at both the inlet and outlet of the prototype pump. The pressure measurement range and error are 0-1.6 MPa and \(\pm\)0.25 FS%, respectively. The calibrated T-type thermocouple measures the refrigerant temperature, and the error is \(\pm\)0.15 K.  
The pump head can be calculated by the following equation:  
\[\frac{P_{1}}{\rho g}+\frac{V_{1}^{2}}{2g}+Z_{1}+H_{1}=\frac{P_{2}}{\rho g}+\frac {V_{2}^{2}}{2g}+Z_{2}+H_{2} \tag{1}\]  
where \(H_{2}\) is the head loss along the internal flow passage and the outlet pipe. The pipeline pump features an inlet and outlet with identical heights (\(Z_{in}=Z_{out}\)) and inner diameters (\(V_{in}=V_{out}\)), the total head of the test pump can be calculated using Equation (2).  
\[H_{t}=H_{2}-H_{1}=\frac{P_{out}-P_{in}}{\rho g} \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Parameter** & **Symbol** & **Value** \\ \hline \multirow{4}{*}{Inducer} & Number of blades & \(Z_{\text{{l}}_{\text{{l}},\text{{ind}}}}\) & 3 \\  & Inlet diameter & \(d_{i1}\) & 27.2 mm \\  & Inlet blade angle & \(\alpha_{i1}\) & 43\({}^{\circ}\) \\  & Wrap angle & \(\Delta\varphi\) & 160.3\({}^{\circ}\) \\  & Outlet diameter & \(d_{i2}\) & 27.2 mm \\ \hline \multirow{4}{*}{Impeller} & Number of blades & \(Z_{\text{{l}}_{\text{{l}},\text{{imp}}}}\) & 6 \\  & Inlet diameter & \(d_{3}\) & 27.2 mm \\  & Blade outlet width & \(b_{3}\) & 3.8 mm \\  & Outlet blade angle & \(\beta_{2}\) & 14.8\({}^{\circ}\) \\  & Outlet diameter & \(d_{4}\) & 64.0 mm \\ \hline \multirow{4}{*}{Vaned diffuser} & Number of blades & \(Z_{3}\) & 8 \\  & Inlet diameter & \(d_{5}\) & 65.0 mm \\ \cline{1-1}  & Outlet angle & \(\beta_{3}\) & 5.8\({}^{\circ}\) \\ \cline{1-1}  & Outlet diameter & \(d_{6}\) & 82.0 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main parameters of the prototype.  
Figure 3: Test rig for refrigerant pump.  
The following equation calculates the uncertainties based on the experimental data.  
\[\omega_{A}=\left[\sum_{i=1}^{j}\left(\frac{\partial A}{\partial Z_{i}}\omega_{Z_{i }}\right)^{2}\right]^{0.5} \tag{3}\]  
where \(Z_{i}\) is the independent variable that could influence the dependent variable \(A\), \(\omega_{Z_{i}}\) is the uncertainty associated with an independent variable \(Z_{i}\), and \(\omega_{A}\) is the total uncertainty. The flow rate, pressure, and head uncertainties are approximately 0.2%, 0.25%, and 0.35%, respectively.
## 4 Evaluation
### Results  
Of all possible combinations of the surfaces depicted in Figure 5b and all the algorithms, ten were chosen and analyzed. These have been selected to cover all algorithms in several scenarios, the only exception being Ortheo and Geo-Spiral as the performance is evident from one test. The results from this evaluation are shown in Table 1 and a selection of the chosen combinations can be seen in Figure 7a-e. What can be observed is that the Geo-Contour and Geo-FISO algorithms show a low curvature while maintaining a level of fiber area fraction and load adequacy. The direction parallel algorithms excluding Ortheo show higher curvature because of the sharper angles when changing directions but have a consistently high fiber area fraction. They can be observed to incorporate discontinuities for parts with holes or complex and large surfaces. Ortheo yields especially high load adequacy and the lowest curvature while introducing a lot of discontinuities as expected. Geo-Spiral performs poorly in every aspect with even curvature being high, which is suspected to have its cause in the rerouting, which could be improved by smoother transitions. In total, the results show the trade-offs and differences supporting the claim that having multiple algorithms is necessary to adapt to the varying conditions when planning the reinforcement patches.  
Figure 7: **(a)** FExA on surface A **(b)** Geo-FISO on surface B **(c)** Ortheo on surface C with surface D as source with \(d_{iso}=1\) mm **(d)** FlnA on surface D **(e)** FlnA twice with orthogonal planes on surface E. For all paths \(d=2\) mm for illustration purposes, planes chosen to align with the vector field. Filament thickness displayed does not correspond to thickness when printed.  
To the end of analyzing the variety of applications, a qualitative feature-based matching is made in the following. All algorithms are numerically stable and deliver results in edge cases, as well as highly curved surfaces. Additionally, computation times were observed to be in the lower minute range. The primitive cartesian smoothing with the Savitzky-Golay filter can lead to collisions with the surface for highly curved surfaces if its degree is set too high.
## 1 Introduction
Furthermore, the majority of the algorithms do not consider the decomposition of the AOI with relation to the camera/sensor footprints which can significantly increase the number of turns in the path. To overcome the aforementioned limitations, this study proposes a new coverage flight path planning algorithm that fulfil the multiple objectives of CPP. The rest of the paper is organized as follows; Section 2 explains the background and related work regarding well-known coverage path planning algorithms. Section 3 presents the proposed coverage flight path planning algorithm and explains its principal steps. Section 4 discusses the experiments and simulation results. Finally, conclusions and future directions are offered in Section 5.
## 2 Development and Validation of the Scaled-Down PCS Model
### Design and Testing of the Scaled-Down PCS  
Drawing from the actual engineering structure depicted in Figure 1c, a 1:7.5 scaled-down model of the PCS was designed for both experimental testing and finite element (FE) simulation. As shown in Figure 2a, the scaled structure adopts a three-beam, two-span configuration. The geometric dimensions of the scaled model are presented in Figure 2b. The columns stand at a height of 1250 mm, with a rectangular cross-section measuring 200 mm \(\times\) 200 mm. The cantilever beams feature a variable cross-section and have a total length of 1700 mm, with the smallest cross-section measuring 130 mm \(\times\) 100 mm and the largest measuring 130 mm \(\times\) 200 mm. The cantilever beams are spaced 600 mm apart. The connection between the cantilever beams and the columns is made using 12 bolts, each with a diameter of 16 mm, which are secured with double nuts to prevent loosening. To replicate the anchoring effect of the mountainous terrain on the structure, ground anchor beams were incorporated. These anchor beams are connected to ground anchor rods, which are secured with bolts and steel sections, and are inclined at an angle of 60\({}^{\circ}\) relative to the ground. In the constructed test structure, the cantilever beams and columns were made from C50 concrete, while the other components were constructed using C30 concrete. The experimental loading configuration is illustrated in Figure 3. A dual-channel electro-hydraulic servo system with a 100 T capacity was employed for loading. The load was applied to the three cantilever beams via a distribution beam, with the loading points located 45 cm from the column center. During the test, the loading rate was initially set at 0.5 mm/min, which was reduced to 0.2 mm/min once cracks were detected. The loading process was terminated when the structural load capacity fell to 85% of the peak load, after surpassing the peak value.
Considering the reliable communication, the transmission rate should be less than the AIR of the coded modulation scheme and the AIR depends on the input distribution of the QAM signal, hence the input distribution should be optimized to maximize the AIR. Furthermore, to measure the performance of the PS scheme under a Gamma-Gamma channel, we also simulate the average symbol error rate (SER) and the bit error rate after the FEC decoder (post-FEC BER). Different from [11], we consider the PS scheme under the Gamma-Gamma channel instead of the Rayleigh fading channel. Moreover, in [20], [PERSON] et al. also discussed the capacity of the PS scheme for an FSO channel, which is modeled as a Gamma-Gamma channel. They adopted the pulse amplitude modulation (PAM) signals under the assumption of IM/DD. Distinguishing this with [20], we consider the optimal input distribution of the capacity under the coherent FSO system. The main contributions of this manuscript are twofold. First, under the FSO system, the heuristic algorithm is proposed to optimize the PMF of the constellation signal points and maximize the AIR. Secondly, we theoretically analyze the error performance of the PS scheme for the turbulence fading channel. We derive a closed-form pairwise error probability (PEP) expression for the non-uniform signal sets under the Gamma-Gamma channel for the first time. The structure of this paper is as follows. In Section 2, the system model is described. We present the achievable information rate of bit-metric decoding and optimize the input distributions of the turbulence fading channel in Section 3. The error performance analysis is provided in Section 4. The simulation results are demonstrated in Section 5, and Section 6 gives the conclusion. Notations: Random variables are denoted by capital italic letters and their realizations with small italic letters. Scalars and vectors are distinguished using normal and bold fonts. \(\mathbb{E}[\cdot]\) denotes expectation. The entropy of random variables is denoted by \(\mathbb{H}[\cdot]\).
## 1 Introduction  
Polymers have been used extensively as bitumen modifiers to improve the performance of asphalt pavements [1; 2; 3; 4]. Several studies have shown that such improvements occur in the form of an enhanced resistance to putting, thermal cracking, and fatigue damage [4; 5; 6; 7; 8]. Further benefits, which have been reported in literature, refer to the positive changes that can be obtained in terms of temperature susceptibility, stripping resistance, and ageing behavior [9; 10; 11; 12]. Past experience has been, for the most part, developed through the use of thermoplastic elastomers, among which the most common are styrene-butadiene-styrene (SBS), styrene-butadiene rubber (SBR) and crumb rubber (CR) derived from end-of-life tites [4; 6; 13; 14; 15; 16]. Few studies have focused on the use of polybutadiene rubber (PBR), which is more frequently employed by the tire industry and in the manufacturing of abrasion-resistant products (including footwear, floor tiles, industrial components) [17; 18; 16].  
As a result of the local availability of raw materials, SBR and PBR are among the most widely produced polymers in Iran [17; 19]. However, it has been reported that currently employed production technologies lead to a relevant quantity of off-grade polymers that, in comparison to the so-called premium quality polymers, are characterized by a greater variability of their chemical and physical properties. These products possess a commercial value that is approximately equal to 30% to 50% of the value of the corresponding premium quality polymers. Thus, they are left in a shapeless form and are thereafter employed in secondary manufacturing activities. Ultimately, in the absence of adequate requests from the market, they are considered as waste products.  
In view of the situation described above, an experimental investigation was carried out in order to verify the possibility of using SBR and PBR off-grade waste polymers as bitumen modifiers. Such an application would have the primary advantage of reducing production costs of polymer-modified binders (PMBs), thereby facilitating their use in infrastructures of all categories, including secondary roads in which the available budget for initial construction is usually limited. Further opportunities for use may arise in developing countries, where the introduction of paving technologies needs to be backed up by an appropriate balance between technical and financial factors.  
This paper provides a synthesis of the laboratory studies that were performed on bituminous binders modified by means of different quantities of premium quality and waste SBR and PBR. Employed characterization procedures initially focused on the physical interactions taking place between bitumen and modifiers. Investigations thereafter included the assessment of performance-related rheological properties of bitumen-polymer blends in different ageing conditions.
# Effect of Surfactant on Water Content of Phosphogypsum
## 3 Results and Discussion
### Effect of Surfactant on the Water Content  
#### 3.4.1 Effect of Single Surfactant  
The phosphogypsum was dried in an oven at 45 \({}^{\circ}\)C for desorption of free-water; the loss of phosphogypsum mass is shown in Figure 6. The water content was calculated according to Equation (3) by weighing method. The mass of phosphogypsum in the oven was not changed, which indicated that the free-water was exitrpated.  
\[\omega=\frac{\mathrm{m}_{1}-m_{2}}{m_{1}}\times 100\% \tag{3}\]  
The results shown in Figure 6 indicate that the water-loss rate was faster than blank group with addition of SDS, SDBS, PDMS and SR. Table 7 displays the effect of single surfactant on water content of phosphogypsum. The water content of phosphogypsum was 20.3%, and decreased to 18.0%, 19.4% and 18.9%, respectively, with addition of SDBS, SDS and PDMS, while increased to 23.1% with addition of SR. SDS and SDBS are anionic surfactants containing hydrophilic group and oil-based, while the silicon methyl group in PDMS is strongly hydrophobic. Tables 3 and 4 show that the addition of SDS, SDBS or PDMS could decrease the surface tension of phosphogypsum and contact angle and was beneficial for separation. In addition, the size of phosphogypsum grew up with the addition of surfactant, which could increase the filtrate rate and decrease the water content.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**System** & **Filtration Rate** & **Mean Deviation** \\ \hline No Surfactant & 905.80 kg/m\({}^{2}\).h & 2.12 kg/m\({}^{2}\)-h \\ SDBS & 1089.56 kg/m\({}^{2}\)-h & 2.11 kg/m\({}^{2}\)-h \\ SDS & 1031.46 kg/m\({}^{2}\)-h & 2.43 kg/m\({}^{2}\)-h \\ SR & 870.86 kg/m\({}^{2}\).h & 2.31 kg/m\({}^{2}\)-h \\ PDMS & 1056.89 kg/m\({}^{2}\)-h & 2.14 kg/m\({}^{2}\)-h \\ SDBS+SDS & 1094.76 kg/m\({}^{2}\)-h & 2.21 kg/m\({}^{2}\)-h \\ SDBS+SR & 1126.34 kg/m\({}^{2}\)-h & 2.23 kg/m\({}^{2}\)-h \\ SDBS+PDMS & 1063.49 kg/m\({}^{2}\)-h & 2.24 kg/m\({}^{2}\)-h \\ \hline \hline \end{tabular}
\end{table}
Table 6: Of the species of surfactants on the filtration rate of phosphogypsum.  
Figure 6: Of phosphogypsum changes with time.
# The Impact of 6G-IoT Technologies on the Development of Agriculture 5.0: A Review
## 3 6G-IoT: New Technologies and Emerging Services  
Following the revolution brought about by the convergence of IoT applications and 5G network technology, 6G is expected to outperform its predecessor in many aspects, thereby enhancing both our daily lives and business productivity [53]. Figure 3 depicts the evolution of wireless communications over time, beginning with 1G communications developed in the 1980s and ending with 6G communications expected to be completed in the 2030s. As shown in the figure, 6G is expected to provide all of the features offered by previous network generations, as well as some new ones, such as full network coverage, the development of massive IoT and mobile applications using AI, improved satellite communications, and the development of fully autonomous systems [54].  
In this context, 6G networking combines current and emerging technologies from the physical to the application network levels, creating a fully digitized and unified interface among users, services, computers, sensors, and smart objects in response to the new era's challenges [55]. In the areas of mobile broadband, end-to-end latency, geographical coverage, and mass connectivity, new horizons are opening, setting the foundation for the next generation of the Internet of Things.  
The 6 th Generation of the Internet of Things (6G-IoT) is anticipated to address critical issues involving massively reliable and ultra-low-latency communication, efficient use of communication protocols, and extensive IoT network geographic coverage. Moreover, with the advent of AI, devices are becoming more sophisticated and, in conjunction with their increased computing capabilities, are able to interact actively with the network, as opposed to merely transmitting data [1]. Consequently, it is anticipated that the 6G-IoT framework will provide efficient solutions for industrial IoT, smart cities, and remote applications, which will have a significant impact on the next generation of smart agriculture [16]. The essential technologies that will make it possible to meet the 6G-IoT requirements and enable Agriculture 5.0 are depicted in Figure 4 and described in the sections that follow.  
Figure 4: 6G-IoT enabling technologies in Agriculture 5.0.  
Figure 3: The evolution of wireless communications toward future 6G networks and applications.
# Acoustic Rocket Signatures Collected by Smartphones
## 3 Results
### Atlas V  
ULA's Atlas V is a medium-lift launch vehicle with a first stage powered by one NPO Energomash RD-180 liquid-fuel engine and up to five SRBs. It is the second most represented rocket type in the dataset, with 77 signals originating from 19 launches. Due in part to the variable number of SRBs, the characteristics of Atlas V signals can vary significantly from launch to launch. For configurations with four to five SRBs, the waveforms are similar to those shown in Figure 17, which are from a launch using five SRBs. At a less than 30 km range, the waveforms are characterized by impulsive onsets similar to those seen in the Artemis I data, followed shortly thereafter by a more emergent onset, longer duration signal. The expected propagation effects are observed, as is a less extreme version of the peak separation seen in the Artemis I data.  
In the time-frequency domain, transient signals near the aligned-start estimate of arrival time are present at all ranges, with a characteristic frequency near 6 Hz, while the power of the main launch signal is concentrated near 10, 20, and 45 Hz, with the 10 Hz band dominating at ranges greater than 30 km, as seen in Figure 18.  
Figure 17: Normalized, start-aligned waveforms of all signals collected from Atlas V launch AV-093. Labels on the left-hand \(y\)-axis indicate the estimated propagation distance of each signal.  
Figure 18: CWT power of start-aligned signals collected from launch AV-093 at 20.5 km (**a**), 34.1 km (**b**), and 51.9 km (**c**) from the launch pad.  
Alternatively, Atlas V configurations without SRBs show fewer similarities with Artemis I data and more with data from Falcon 9 launches. Figures 19 and 20 show data from Atlas V launch AV-104, which used a configuration without SRBs. The 6 Hz transient leading the main launch signal in the previous example does not appear.
## 4 Discretization  
The partial derivative equation (PDE) presented in Equation (6) in continuous space was reduced to a second-order ordinary differential equation (ODE), Equation (7), in the discretization of the  
Figure 6: Step response for a dose of 1 ppm of chlorine for different values of \(k_{R}\) (0.0001, 0.0005, 0.0009, 0.0013 s\({}^{-1}\)).  
Figure 7: Comsol simulation for decay constant of \(k_{R}=9.0\times 10^{-4}\) (s\({}^{-1}\)).  
simulator. It was considered that the type of simulator flow belongs to the \"Plug Flow\" pattern where the fluid circulates through the tank evenly and along parallel paths from the entrance to the exit of the tank.  
\[0=D\frac{d^{2}C}{dx^{2}}-U\frac{dC}{dx}-k_{R}c(f)-k_{r}c^{2}(1-f)-k_{s}c(1-f) \tag{7}\]  
Among the different methods of discretization for the type of equations that govern the behavior of the simulated system process (Quick, Upwind, etc.), we chose a central finite differences equation, which was adapted in an acceptable way for the values of flow, diffusive characteristics, and Peclet numbers of the present problem [24]. In [25], different discretization methods were presented and discussed to address advection, diffusion, and reaction problems in contact tanks; in [26], a discretization scheme was presented for storage tanks in transport processes and distribution of the same type of water treated by the DWTP of this article.  
When applying central finite differences by replacing the first and second derivative in Equation (7), then Equation (8) is obtained:  
\[0=D\frac{C_{i+1}-2C_{i}+C_{i-1}}{x^{2}}-U\frac{C_{i+1}-C_{i-1}}{2x}-k_{R}c_{i} (f)-k_{r}c_{i}^{2}(1-f)-k_{s}c_{i}(1-f) \tag{8}\]  
for a diffusion coefficient D and a chlorine concentration c at different instants of time.  
Figure 8 shows a diagram of the diffusion effect in chlorine concentration. It supposes a tank of length \(L\) with constant increments of space on the mesh (\(\Delta\)x) at different periods of time. An increment of \(\Delta\)x = \(L/(n-1)\) was considered, with \(n\) being the number of mesh divisions. To avoid the effects of dynamic instability, the increment values of x fulfilling \(x\leq\frac{2D}{v}\) were restricted, and the stability criterium \(t\leq\frac{(x)^{2}}{2D+k_{R}x^{2}}\) was applied according to [27]. Applying the stability restriction on the spacing \(\Delta\)x in the most unfavorable case, a value of \(\Delta\)x \(\leq\) 0.2 m was obtained and \(\Delta\)t \(\leq\) 2.16 s. In the simulation, knowing that the actual dosage analyzer 2 (An2) is located at 83 m from the tank 1 entrance, a value \(\Delta\)x = 0.127 m and \(\Delta\)t = 1 s with a mesh of \(n\) = 685 slots was used, in accordance with a trade-off to minimize the error with a computing time that would allow the simulation in real time.