esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Fundamentals and State of the Art
Figure 3 shows an exemplary supply chain network for the parallel operation of an SME and larger enterprise. Figure 2: Scheme of a simplified metal forming value chain with different enterprises (E). Digitization and digitalization of both enterprises were based on the model of the automation pyramid. Layer 0 covers all physical production processes. Layer 1 is the DAQ level and contains sensors, actuators and programmable logic controllers (PLCs). Layer 2 acts as the Supervisory Control and Data Acquisition (SCADA) level including HMIs. Layer 3 contains the MES and layer 4 includes the ERP. The data of the respective enterprises can be integrated into the higher-level system from layer 3 and thus act as a lower layer in the higher-level layer system of the value chain. The network includes several servers, which execute several services. If possible, the option of executing only one service per server should be checked according to the \"One server, one service\" principle. In the event of a server failure, instead of several services, only individual services would be affected, which can be taken over by backup servers. Depending on the available resources, different approaches regarding WBM, GBM, and BBM can be followed. In the case of enterprise 4 (Figure 3, E4), an SME, an initial WBM was chosen using data acquired from a Finite Element Analysis (FEA). Using the external computing resources of a cloud computing service, the data obtained from FEA can be integrated into a WBM, which in turn can be assimilated with further FEA data for refinement, resulting in a WBMLM approach. Another possibility would be to continuously feed process data into the initial WBM, resulting in a GBMLM, as shown in [56]. Enterprise 5 (Figure 3, E5), a large enterprise that already has data that meets the 5 Vs criteria, has a similar structure as E4 (Figure 2). Due to the existing amount and value of data, an initial BBM approach is applied within this example. The BBM can be fed and refined with further process data recorded during production and thus establish BBMLM, as shown in [52]. If required data cannot be obtained from the DAQ of the process and would require a WBM based simulation, e.g., FEA for material models or microstructure models, a GBMLM approach  
Figure 3: Schematic of a sub-step in a metal processing value chain with two enterprises of different sizes according to the principle of the automation pyramid and its layers. would be followed. In any case, supervised ML should be pursued at the beginning of the implementation to support a successful establishment of the system. For this exemplary framework, the 5V criterium of Big Data is fulfilled, as demonstrated in Table 2.
# The Dichotomy of Neural Networks and Cryptography: War and Peace
## 3 War: Neural Computing against Cryptography
### Detecting Malicious Encryption
#### 3.1.1 Software Code  
Cryptography is a tool that can protect the confidentiality of data, while often used to the benefit of users it can be used by attackers to hide malware on a system until it is needed or obfuscate network traffic. It can be difficult to identify malware that is able to encrypt data as it might require reverse engineering the suspicious program and applying a thorough understanding of many encryption methods [37]. It has been found that NNs could simplify the process of detecting encryption in obfuscated programs. This section will discuss some researchers applying NNs to detecting encrypted malicious code.  
[PERSON] and [PERSON] [38] built a NN system trained with error back propagation that analyzes the ratio of certain opcodes that are commonly used by cryptographic algorithms. Trained on functions in OpenBSD it was found this system could identify most typical encryption functions regardless of compiler optimization or specific implementation, but failed to detect methods that operate sufficiently different from typical encryption methods such as elliptical curve cryptography and public key encryption.  
[PERSON] et al. [37] propose solving this issue with a NN model called K-max-CNN-Attention that looks for common instruction patterns rather than relative instruction density. The improvements this model brings is in the move to a convolutional NN (CNN), which interpret blocks of data maintaining the original structure and a better preprocessing scheme which simplifies the input enough to be meaningful to the NN but leaving more information to be interpreted, while these changes improve on performance and accuracy of existing techniques [PERSON] et al. [37] speculate that better accuracy could be achieved by changing the preprocessing and classification models to consider non-sequential execution of code.
## 2 Problem Formulation  
In this section, under the influence of uncertainty factors such as model uncertainty and external disturbance, the establishment of the dynamics model of the manipulator is mainly explained. According to the [13; 21; 22], the dynamics model containing the above uncertainty factors is as follows:  
\[H(q)\ddot{q}+C(q,\dot{q})\dot{q}+G(q)=\tau+F \tag{1}\]  
where, \(q\), \(\dot{q}\), \(\ddot{q}\in R^{n}\) are the position, velocity and acceleration vectors for each joint. \(H(q)\in R^{n\times n}\) is the inertia matrix including extra mass, the matrix \(C(q,\dot{q})\in R^{n\times n}\) groups is the centripetal and Coriolis forces, and \(G(q)\in R^{n\times n}\) is the gravity matrix vector. \(F\) is the external disturbances (including water flow and other external disturbances.). \(\tau\) is the driving torque and \(n\) is the number of degrees of freedom.  
According to the [23], the Equation (1) is transformed into a state space equation form for description:  
\[\ddot{\eta}(t)=F(\eta,t)+G(\eta,t)U+D(t) \tag{2}\]  
where, \(U=[\tau_{1},\,\tau_{2}\,\dots\,\,\tau_{n}]^{\rm T}\) is the control vector for each joint. \(D(t)\) is the external disturbances after conversion. \(F(\eta,\,t)\) and \(G(\eta,t)\) are the matrix vectors. In the Equation (2), \(F(\eta,\,t)\), \(G(\eta,\,t)\), and \(D(t)\) are represented by the Equation (3):  
\[F(\eta,t)=-H(q)^{-1}[C(q,\dot{q})\dot{q}+G(q)] \tag{3a}\] \[G(\eta,t)=-\lambda H(q)^{-1}\] (3b) \[D(t)=-H(q)^{-1}F \tag{3c}\]  
In practice work, there exists uncertainty factors in the dynamics model, and the control of manipulator is affected by external disturbances. Therefore, the nonlinear matrix vectors \(F(\eta,\,t)\), \(G(\eta,\,t)\), and \(D(t)\) in Equation (2), which contains uncertainties, cannot be obtained exactly. So, we convert Equation (2) to Equation (4).  
\[U=\,\ddot{\eta}(t)+H(\eta,t) \tag{4}\]  
\[H(\eta,t)=\left[G(\eta,t)^{-1}-I\right]\ddot{\eta}(t)-G(\eta,t)^{-1}[F(\eta,t) +D(t)] \tag{5}\]  
where, the nonlinear term \(H(\eta,\,t)\) contains all uncertainty factors, and cannot accurately be described. For this reason, we will approximate \(H(\eta,\,t)\) online.  
In summary, the control objective of this paper is to design an adaptive controller \(U\) of the joints control of underwater manipulator, which can effectively overcome the influence of uncertainties such as model uncertainty, external disturbance. It aims to ensure the boundedness of the closed-loop system, make the tracking error uniformly asymptotic converge.
# Xanthone Derivatives in the Fight against Glioblastoma and Other Cancers
## 2 Antineoplastic Activity of Xanthone Derivatives
### Cudraxanthone and Its Derivatives  
Cudraxanthone (**13**, Figure 7A), 8-Hydroxycudraxanthone (**14**, Figure 7B), and Morusignin I (**15**, Figure 7C) [68] are examples of naturally occurring xanthone derivatives. These compounds were tested in **reszaurin reduction assay** on various cancer cell lines. Cudraxanthone I (**13**) was active with IC\({}_{50}\) values ranging from **2.78** (against **breast cancer MDA-MB231 BCRP cells**) to **22.49 \(\upmu\)M** (against **human glioblastoma U87 MG cells**), 8-hydroxycudraxanthone with the IC\({}_{50}\) values ranging from **7.15** (against **leukemia CCRFEM cells**) to **53.85 \(\upmu\)M** (against **human glioblastoma U87 MG.\(\Delta\)EGFR cells**), while Morusignin I (**15**, Figure 7C) with the IC\({}_{50}\) values ranging from **16.65** (against **leukemia CCRFEM cells**) to **70.38 \(\upmu\)M** (against **hepaticocarcinoma HepG2 cells**).
## 3 Proposed System
### Results and Discussion  
As shown in Figure 5, which shows the FFT analysis of the sensed signals at the smartphone side, the intended signals (18.0-20.0 kHz) were successfully captured in the receiver device. Airborne transmission of the signals was barely captured (see the right-hand side parts of Figure 5), meaning that signal transmission through the air is negligible. In an effort to assess the feasibility of the proposed method, we also measured the bit error rate (BER) with a modulation scheme of binary frequency-shift keying (BFSK). A fixed space frequency of 18 kHz and two different mark frequencies  
Figure 4: **(a)** Schematic of the measurement condition. A piezoelectric, electrically insulated using insulation tape, was attached to the front side of the smartphone for capturing the incoming signal from the user’s index finger; **(b)** System architecture of the proof-of-concept device.  
of 25 Hz and 50 Hz were employed in this setup. The measured BERs from the transmission of 1024 bit were 0.008 for the pair of (18,000 Hz and 18,025 Hz) and 0.003 for the pair of (18,000 Hz and 18,050 Hz.)  
Although the experimental results show the feasibility of the proposed method, we have remaining issues. First, because we employed a commercial bone-conducting speaker whose structure is optimized for delivering audible sounds (up to approximately 20 kHz) as an actuation unit, high-frequency signals were not efficiently actuated. This issue needs to be considered due to the unavoidable signal attenuation in the medium between the transmitter and receiver. In case of the touched condition (see Figure 5, left-hand side), power differences measured at the actuator and the sensor were 21.29 dB for 18.0-kHz signal, 21.32 dB for 18.5-kHz signal, 21.17 dB for 19.0-kHz signal, 21.40 dB for 19.5-kHz signal, and 21.41 dB for 20.0-kHz signal, respectively. Second, undesirable low frequency signals, which may generate audible noise sounds, need to be removed  
Figure 5: FFT analysis of the measured signals transferred from the smartwatch.  
although these do not harm the communication quality. Both of these issues can be fundamentally mitigated by developing a high-frequency dedicated bone-conducting transducer.
## Materials and Methods
### Validation of FAPROTAX
#### 2.3.4 Additional Literature on Soil Functions  
Bacterial taxa that were identified as a driver on phosphate solubilization, potassium solubilization and nitrogen fixation were provided (Table 1). These taxa and functions were overlooked from FAPROTAX database (file named \"FAPROTAX.txt\" available at FAPROTAX webpage). Subsequently, the advantage of the literature added was tested using soil samples from agricultural bulk soil and rhizosphere soil of _T. pratense_ datasets. In detail, the nitrogen fixation function was manually assigned to a particular OTU in the datasets when that OTU was identified as one of the taxa in Table 1 (nitrogen fixation). Then, the number of manually assigned OTUs was counted and compared with that of OTUs assigned to nitrogen fixation by FAPROTAX.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Functions** & **Taxa** & **References** \\ \hline \multirow{7}{*}{Nitrogen fixation} & _Allorhizobium, Azorhizobium, Bradyrhizobium, Ensifer,_ & \\  & _Mesorhizobium, Rhizobium, Aminobacter, Devosia,_ & \\  & _Methylobacterium, Microvíga, Ochoractrum,_ & \\  & _Phylbolacterium, Shinella, Burkholderia,_ & \\  & _Paraburkholderia, Cupriariós, Bacillus safencis, Bacillus_ & [46, 47] \\  & _licheniformis, Bacillus cereus, Bacillus megaterium,_ & \\  & _Bacillus aerophilus, Bacillus flexus, Bacillus_ & \\  & _ocannisediminis, Bacillus circulans, Bacillus aerophilus, Bacillus subtilis_ & \\  & _Bacillus subtilis_ & \\ \hline \multirow{7}{*}{Potassium solubilization} & _Bacillus mucilaginous, Bacillus circulanscan, Bacillus_ & \\  & _edaphicus, Bacillus megaterium, Acidithiobacillus_ & \\  & _ferrooxidants, Enterobacter hormachei, Paenibacillus_ & \\ \cline{1-1}  & _mucilaginous, Paenibacillus glucanolyticus_ & \\ \hline \hline \end{tabular}
\end{table}
Table 1: A list of bacterial taxa capable of nitrogen fixation, phosphate and potassium solubilization.
## 2 Materials and Methods
### Study on Adsorption-Desorption Performance and Isotherm Models
#### 2.3.2 Adsorption Isotherm Models  
In order to evaluate the adsorption capacity, 0.1 g L\({}^{-1}\), 0.25 g L\({}^{-1}\), 0.5 g L\({}^{-1}\), 1.0 g L\({}^{-1}\), 0.2 g L\({}^{-1}\), and 4.0 g L\({}^{-1}\) of Iron-Z-A were taken separately in 6 beakers containing 500 mL of 6 mg L\({}^{-1}\) of PO\({}_{4}\)\({}^{3-}\) solution. Sampling was done at 0 min, 5 min, 10 min, 15 min, 30 min, 45 min, 60 min, 90 min, and 120 min (Supplementary Figure S5). [PERSON] and [PERSON]'s adsorption isotherm models were applied to explain the equilibrium studies. To fit the equilibrium data, these two models were employed, which helped in comprehending the mechanisms behind the adsorption-desorption of phosphate by zeolite.
### DBD-PA System  
Figure 4 shows a schematic of the DBD-PA system, which consisted of a DBD-PA electrode and a power supply unit. For the DBD-PA electrode (Asahi Rubber Co., Ltd., Saitama, Japan), a silicone rubber dielectric (thickness = 0.4 mm) was sandwiched between a pair of titanium-film electrodes (thickness = 0.05 mm), i.e., a high-voltage electrode and grounded electrode. A high-voltage electrode was set on the pressure side of the wing model, and its upstream edge was along the leading edge. A grounded electrode was implanted into the dielectric and placed on the suction side. The dielectric was flush-mounted on the surface of the wing, and a high-voltage electrode was attached to the dielectric surface like a trip wire. The widths of the former and latter electrodes were 6 and  
Figure 4: Schematic of the DBD-PA installed on the leading edge of the wing model and power supply system.  
Figure 3: Schematic of the airfoil shape and pressure port locations.  
10 mm, respectively, and the electrodes overlapped by \(\sim\)1 mm. In this configuration, the plasma-induced flow blew from the leading edge toward the suction side of the wing model.  
The power supply system consisted of a DC power unit, an AC inverter, and a high-voltage transformer. The output of the DC power unit was converted into a high-frequency AC waveform by the AC inverter, which was subsequently boosted by the transformer. The transformer output was connected to each titanium-film electrode. The amplitude of the applied voltage was 9 kV\({}_{\text{PP}}\) with a base frequency of 99 kHz. In addition, the applied voltage was modulated using a normalized burst frequency, \(F^{+}\), as illustrated in Figure 5. The values of \(F^{+}\) in these experimental conditions were 0.1, 0.5, 1, 5, 10, 50, and 100. The function generator provided a control signal to determine the burst frequency and duty ratio of the inverter. The duty ratio is defined as a fraction of the duration of plasma activation in one period of the modulation waveform. This value was fixed at 34% throughout the experiments. The time-averaged input power was the same under all conditions because the input power was determined using the base frequency, the voltage applied to the electrodes, and the duty ratio. The waveform applied to the electrode was monitored using an oscilloscope, a high-voltage probe, and a current transformer; it was adjusted to the experimental conditions by controlling the output voltage of the DC power unit and signal of the function generator.
# Construction Progress and Aviation Flight Test of BDSBAS
## 4 BDSBAS Aviation Flight Test
### Flight Test-I  
On 17 September 2020, the take-off and landing operation test was conducted to simulate the flight status of civil aviation aircraft during the approach phase. The flight lasts 2 h and 40 min, and the trajectory is shown in Figure 9.  
Figure 8: The figure presents the flight test environment. The top left panel is the aerial view of Shenyang Caihu Airport. The top right panel is the B-10 QS general aviation aircraft which was used in the test. The bottom left panel is the RTK reference station installed at the airport. The bottom right panel is a photo of the B-10 QS aircraft taken by another companion aircraft during the flight.  
Figure 9: Flight trajectory of test-I. The green line is the trajectory, while the flight distance and azimuth are marked by the red line and the yellow text.  
Table 2 shows the statistics of the performance indicators. It can be seen from Figures 12 and 13, and Table 2 that for the flight test-I conducted on 17 September 2020, the BDSABS SF augmentation positioning accuracy meets the requirements of the civil aviation APV-I accuracy index, and the PL can completely cover the PE. No integrity risk events occurred during the test, and the availability and continuity of the service reached 100%.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Indictors** & **Statistical Results** \\ \hline Horizontal accuracy/m (95\%) & 1.62 \\ Vertical accuracy/m (95\%) & 4.38 \\ Minimum horizontal safety index & 7.160 \\ Minimum vertical safety index & 4.594 \\ NPA availability & 100.00\% \\ NPA continuity & 100.00\% \\ NPA HMI events & 0 \\ APV-I availability & 100.00\% \\ APV-I continuity & 100.00\% \\ APV-I HMI events & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: BDSAS SF performance statistics of flight test-I.  
Figure 13: Stanford triangle charts for flight test-I. The top panel and the bottom panel are the horizontal Stanford chart and the vertical Stanford chart, respectively.
## 4 Result Analysis and Discussion
### Deduction of Concrete Cover Thickness  
The last step, and also the essential one in the proposed methodology, is the use of linear law. In our case, the application of the linear law to the non-linear function (X, Y) in order to obtain the linear function (x, y) is illustrated in Figure 9.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\theta_{i}\) (\({}^{\circ}\)) & \(\sin(\theta_{i})\) & \(\Delta_{i}\) (mm) \\ \hline
0 & 0 & 0 \\
15 & 0.259 & 5 \\
30 & 0.500 & 10 \\
45 & 0.707 & 14.5 \\
60 & 0.866 & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Deduced values of the shifts (\(\Delta_{i}\)) from the fitted temperature profiles.  
Figure 8: Temperature profiles with fitting curves by using the moving average for different angles of incident waves (\(\theta=\theta_{i}\)).  
Thus, the remaining problem to resolve is to find the equation representing the linear relation (x, y) in order to deduce the value of A and B (Figure 9). For this purpose, the graph (x, y) needs to be drawn by using the values in Table 2.  
Note that the value of x when \(\theta_{\rm i}=60^{\circ}\) is too large compared to the average value. For this reason, this point is not taken for determining the equation of the linear function. It can also allow us to see that this angle may be too large for detection with our microwave antenna due to the constraint related to its dimensions.  
By using the values in Table 2, the graph of the linear function (x, y) is obtained (an approximation with R\({}^{2}\) = 0.965), as shown in Figure 10.  
Figure 10: Graph of the linear function (x, y) for deducing the value of concrete cover thickness.  
\begin{table}
\begin{tabular}{c c c c} \hline \(\theta_{\rm i}\) (\({}^{\circ}\)) & **X = (sin\(\theta_{\rm i}\))\({}^{2}\)** & **y = Y = \(\Delta_{\rm i}\)\({}^{2}\) (mm\({}^{2}\))** & **x = Y/X (mm\({}^{2}\))** \\ \hline
0 & 0 & 0 & 0 \\
15 & 0.067 & 25 & 373.205 \\
30 & 0.250 & 100 & 400 \\
45 & 0.500 & 210.25 & 420.5 \\
60 & 0.750 & 3600 & 4800 1 \\ \hline \end{tabular} \({}^{1}\) Too large compared to the average value of x.  
\end{table}
Table 2: Values for drawing the linear function (x, y).  
Figure 9: Application of linear law (x, y) for non-linear relation (X, Y).  
According to the equation of the fitting line: \(\mathrm{y}=3.861\times\mathrm{x}-1424\), and the Equation (6), the values of \"\(\epsilon_{2}\)\" and \"\(\mathrm{e}\)\" can be deduced as follows:  
\[\epsilon_{2}=3.861\;\approx\;4.\;\mathrm{e}^{2}=1424\Rightarrow\mathrm{e}= \sqrt{1424}=37.74\;\mathrm{mm}\]  
Therefore, the deduced value of the concrete cover thickness by the proposed methodology (37.74 mm) is acceptable compared to the real value (38 mm).
## 1 Introduction
This is compounded by inadequate public records, which is a prevailing issue for many developing countries due to limited resources [26]. Landslide susceptibility modelling has been conducted sporadically in Malaysia over the past decade. Statistical approaches are prevalent in areas where inventories are available such as Cameron Highlands, Penang, Putrajaya, and eastern Selangor [27; 28; 29; 30; 31; 32; 33; 34; 35; 36]. Generally, the inventories are limited, and the best available data are utilised. More recently, a bivariate statistics approach combined with expert inputs was used to improve the susceptibility model [9]. In many cases, model validation is not mentioned [28; 29; 30; 32; 33], while in others only the success rates are reported [9; 34]. Investigations that report on predictive capability have relied on random partitioning and spatial correlation techniques [27; 31; 35; 36]. All the investigations have been conducted for academic purposes and retrospective validation has not been reported. Kuala Lumpur, the capital city of Malaysia, suffers from numerous slope failures especially after a severe rainfall episode, where residential infrastructure and business complexes are most affected [23]. Many of the incidents occur in cut and fill slopes, road embankments, highways, and hill slope development projects [28; 37; 38]. The situation is expected to worsen with climate change and continuous expansion of the city. This paper highlights a new spatial partitioning technique for landslide susceptibility modelling that was conducted in Kuala Lumpur, Malaysia. The technique is introduced for assessing the predictive capability of landslides, to overcome the limitations associated with lithological heterogeneity. The ensuing landslide susceptibility map was then formally submitted to the City Council of Kuala Lumpur (DBKL) to support decision-making. An extreme rainfall event with a recorded precipitation of more than 250 mm occurred in December 2021 exceeding the monthly rainfall in over a day. The unusual phenomenon was caused by the northeast monsoon flow factors and a low-pressure weather system which caused continuous heavy rain over the west coast of peninsula regions besides the usually affected central east coast and northern peninsula regions. The event caused massive flooding of 100-year return period and triggered 122 landslides in the city. This provided an opportunity to conduct retrospective validation to confirm the robustness of the partitioning technique and increase confidence in the landslide susceptibility map.