esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Description of the Control Object
\right] \tag{6}\]  
where  
\[\text{A}=\frac{\text{m}_{\text{pc}}(\text{R}+\text{r})^{2}}{8}\]  
\[\text{B}=\frac{\text{I}_{\text{pc}}(\text{R}+\text{r})^{2}}{16\left(\text{s}_{ \text{s}}+\text{s}_{\text{p}}\right)^{2}}\]  
\[\text{C}=\text{I}_{\text{k}}\]  
\[\text{m}_{\text{pc}}=\text{m}_{\text{p}}+4\text{m}_{\text{k}}\]  
\[\text{I}_{\text{pc}}=\text{I}_{\text{p}_{\text{rp}}}+4\text{I}_{\text{k}_{\text {rp}}}\]  
and \(\text{m}_{\text{p}}\) is the mass of the platform, \(\text{m}_{\text{k}}\) is the mass of the mecanum wheel, \(\text{I}_{\text{k}}\) is the mass moment of inertia of the wheel in relation to the axis of self-rotation, \(\text{I}_{\text{k}_{\text{rp}}}\) is the mass moment of inertia of the mecanum wheel in relation to the axis \(\text{z}_{\text{p}}\) of the movable system \(\text{x}_{\text{p}}\text{y}_{\text{p}}\text{z}_{\text{p}}\), and \(\text{I}_{\text{p}_{\text{rp}}}\) is the mass moment of platform inertia in relation to the \(\text{z}_{\text{p}}\) axis. In order to solve the simple task of dynamics, the relationship of (5) was transformed to the form  
\[\left[ \begin{array}{c}\ddot{\text{q}}_{1}\\ \ddot{\text{q}}_{2}\\ \ddot{\text{q}}_{3}\\ \ddot{\text{q}}_{4}\end{array} \right]=\text{M}^{-1}\left[ \begin{array}{c} \tau_{1}-\text{N}_{1}\text{f}_{1}\text{sgn}(\dot{\text{q}}_{1})\\ \tau_{2}-\text{N}_{2}\text{f}_{2}\text{sgn}(\dot{\text{q}}_{2})\\ \tau_{3}-\text{N}_{3}\text{f}_{3}\text{sgn}(\dot{\text{q}}_{3})\\ \tau_{4}-\text{N}_{4}\text{f}_{4}\text{sgn}(\dot{\text{q}}_{4})\end{array} \right] \tag{8}\]  
The matrix \(\text{M}^{-1}\) is written in the form  
\[\text{M}^{-1}=\left[ \begin{array}{cccc}\text{D}_{1}&\text{D}_{2}&\text{D}_{3 }&\text{D}_{4}\\ \text{D}_{2}&\text{D}_{1}&\text{D}_{4}&\text{D}_{3}\\ \text{D}_{3}&\text{D}_{4}&\text{D}_{1}&\text{D}_{2}\\ \text{D}_{4}&\text{D}_{3}&\text{D}_{2}&\text{D}_{1}\end{array} \right] \tag{9}\]  
where auxiliary variables are described by the following equations:  
\[ \begin{array}{c}\text{D}_{1}=\frac{2\text{AB}+\text{AC}+3\text{BC}+\text{C }^{2}}{(\text{AB}+2\text{AC}+4\text{BC}+\text{C}^{2})\text{C}}\\ \text{D}_{2}=\frac{\text{B}}{(\text{AB}+\text{C})\text{C}}\\ \text{D}_{3}=\frac{(-\text{B})}{(\text{AB}+\text{C})\text{C}}\\ \text{D}_{4}=\frac{-(2\text{AB}+\text{AC}-\text{BC})}{(8\text{AB}+2\text{AC}+ 4\text{BC}+\text{C}^{2})\text{C}}\end{array}  \tag{10}\]  
On the basis of relationships in (5), it was possible to determine the driving moments \(\tau_{\text{i}}\) of each of the mecanum wheels for a desired motion path while Equation (8) determined the generalized accelerations of each of the wheels for the assumed waveforms of driving moments. On the basis of the mathematical model of the robot, a synthesis of the robot's motion control system was conducted, and the motion of the object was simulated.
## 2 Detection Method of Microseisms in Coal Mines
### Extraction of Temporal and Spatial Characteristics of Coal Mine Microseisms Based on Markov Chain
#### 2.1.4 Spatial Feature Extraction of Microseismic Signal of Target Node Coal Mine  
The state sequence of the target node and the state sequence of the neighbor node after the state mapping are respectively constructed into Markov chain models, and the probabilities of the target node's various state values transferring to the neighbor node at the next time under the condition of the current various state values are the cross-state transition probabilities. The spatial dependence of the target node and the neighbor node is captured by calculating the cross-state transition probability matrix, extracting the spatial characteristics of the coal mine microseisms at the target node.  
Set \(A\) as the target node, \(B\) as node \(A\)'s neighbor node; the \(A\), \(B\) nodes' Markov chain model established by the node's state sequence is \(T^{A}=\left\{T^{A}_{n},n\in T\right\}\); \(T^{B}=\left\{T^{B}_{n},n\in T\right\}\), the state space is \(S^{A}\), \(S^{B}\). Order \(s^{A}_{i}\) is model \(T^{A^{\prime}}\)'s state at \(t-1\)'s moment; \(s^{B}_{j}\) is model \(T^{B}\)'s state at \(t\)'s moment; then, \(p^{AB}_{j}\) is when \(s^{A}_{i}\)'s state transition occurs; \(s^{B}_{j}\)'s probability of occurrence is called the cross-state transition probability; the calculation formula of \(p^{AB}_{ij}\) is as follows:  
\[p^{AB}_{ij}=P\Big{(}s^{B}_{i}\Big{|}s^{A}_{i}\Big{)}=\frac{P\Big{(}s^{A}_{0}s^{ A}_{1}\cdots s^{A}_{i}s^{B}_{j}\Big{)}}{P\Big{(}s^{A}_{0}s^{A}_{1}\cdots s^{A}_{i} \Big{)}}=\frac{P\Big{(}s^{B}_{j},s^{A}_{i}\Big{)}}{P\Big{(}s^{A}_{i}\Big{)}} \Rightarrow p^{AB}_{ij}=\frac{N\Big{(}s^{A}_{0}s^{A}_{1}\cdots s^{A}_{i}s^{B} _{j}\Big{)}}{N\Big{(}s^{A}_{0}s^{A}_{1}\cdots s^{A}_{i}\Big{)}}=\frac{N\Big{(} s^{A}_{i},s^{B}_{j}\Big{)}}{N\Big{(}s^{A}_{i}\Big{)}} \tag{6}\]  
where \(N\) indicates the total number of times the status occurs.  
Due to the fact that the state space of both the target node state sequence and adjacent node state sequence is \(S=\left\{a,b,c,d,e,f,g,h,i\right\}\), the size of the cross state transition probability matrix \(P^{AB}_{ij}\) of the target node state sequence and adjacent node state sequence is also \(9\times 9\). The size of the cross state transition probability matrix \(P^{AB}\) is also \(9\times 9\). Matrix \(P^{AB}\) is:  
\[P^{AB}=\left(\begin{array}{cccc}p^{AB}_{ss}&p^{AB}_{ib}&\cdots&p^{AB}_{ii}&p ^{AB}_{ii}\\ p^{AB}_{ib}&p^{AB}_{ib}&\cdots&p^{AB}_{ii}&p^{AB}_{ii}\\ \vdots&\vdots&\vdots&\vdots\\ p^{AB}_{ii}&p^{AB}_{ib}&\cdots&p^{AB}_{ii}&p^{AB}_{ii}\\ \end{array}\right) \tag{7}\]  
where \(p^{AB}_{ij}\geq 0\) and \(\sum\limits_{s^{A}_{i}\in S^{A},s^{B}_{j}\in S^{B}}P^{AB}_{ij}=1\).
## 3 Results and Discussion
### Effect of Operating Conditions  
In this section, the effect of the operating conditions, such as gasification temperature, pyrolysis-oil moisture content, Rectisol temperature and pressure on syngas composition, SN and methanol yield, as well as methanol synthesis operation conditions are discussed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Component Dry Basis** & **Feed Syngas** & **Purified Syngas Experimental Results** & **Simulated Results** \\ \hline Pressure (MPa) & 7.8 & 7.6 & 7.8 \\ H\({}_{2}\) & 62.5\% & 95.3\% & 95.9\% \\ N\({}_{2}\) + Ar & 0.5\% & 0.8\% & 0.7\% \\ CO + CH\({}_{4}\) & 2.7\% & 4.0\% & 3.4\% \\ CO\({}_{2}\) & 34.1\% & 20 ppm & trace \\ H\({}_{2}\)S + COS & 0.3\% & 0.1 ppm & trace \\ Flow rate (kmol/h) & 6021.1 & 3936.1 & 3890.3 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison between simulated and experimental results of Rectisol.
## Adaptive Filtering Queueing for Improving Fairness  
**[PERSON]**  
Department of Information Technology and Communication, Shih-Chien University,  
Kaohsiung 84550, Taiwan; E-Mail: [EMAIL_ADDRESS]; Tel.: +886-9-7535-7705  
Academic Editor: [PERSON]  
_Received: 20 March 2015 / Accepted: 27 May 2015 / Published: 5 June 2015_  
**Abstract:** In this paper, we propose a scalable and efficient Active Queue Management (AQM) scheme to provide fair bandwidth sharing when traffic is congested dubbed Adaptive Filtering Queueing (AFQ). First, AFQ identifies the filtering level of an arriving packet by comparing it with a flow label selected at random from the first level to an estimated level in the filtering level table. Based on the accepted traffic estimation and the previous fair filtering level, AFQ updates the fair filtering level. Next, AFQ uses a simple packet-dropping algorithm to determine whether arriving packets are accepted or discarded. To enhance AFQ's feasibility in high-speed networks, we propose a two-layer mapping mechanism to effectively simplify the packet comparison operations. Simulation results demonstrate that AFQ achieves optimal fairness when compared with Rotating Preference Queues (RPQ), Core-Stateless Fair Queueing (CSFQ), CHOose and Keep for responsive flows, CHOose and Kill for unresponsive flows (CHOKe) and First-In First-Out (FIFO) schemes under a variety of traffic conditions.  
**Keywords:** adaptive filtering; active queue management; fairness; bandwidth
## 2 System Model
### Relay System: Amplify or Decode?  
In the relay scenario, the intermediate node needs to decide whether to adopt an AF or DF strategy for each incoming bundle. It will base this decision on the channel SNR estimates and the code rate of the bundle. With DF, the system is equivalent to two separate links, which could be independently optimized using the same HARQ protocol as for the single link scenario. With AF, the HARQ problem is slightly more complex. When a bundle arrives, the relay will forward it without any processing, but we assume that it caches the LLR values temporarily. If the end user is successful in decoding the whole bundle, these LLR values can be discarded, but if the end user suffers any decoding failures, the relay reverts to DF. It decodes the bundle using its cached LLR values (employing HARQ if needed) and only after having succeeded it sends IR to the end user.  
When employing AF, we assume unit transmit power at the base station and that the relay amplifies its received signal to invert the attenuation of the first channel. In other words, if the relay receives  
\[y_{1}=g_{1}x+n_{1},\]  
where \(g_{1}\) is the channel gain on the first link, \(x\) is the signal with \(E[x^{2}]=1\) and \(n_{1}\) is Gaussian noise with variance \(\sigma_{1}^{2}\), the relay amplifies \(y_{1}\) by a factor \(1/g_{1}\) before forwarding it. Then, the received signal at the end user is  
\[y_{2} =g_{2}\frac{1}{g_{1}}y_{1}+n_{2} \tag{8}\] \[=g_{2}\Big{(}x+\frac{n_{1}}{g_{1}}+\frac{n_{2}}{g_{2}}\Big{)}, \tag{9}\]  
where \(g_{2}\) is the gain over the second link. Since the noise components \(n_{1}\) and \(n_{2}\) are independent, the SNR at the end user with AF is  
\[\text{SNR}_{AF}=\frac{E[x^{2}]}{\text{Var}[\frac{n_{1}}{g_{1}}+\frac{n_{2}}{g_{ 2}}]}=(\text{SNR}_{1}^{-1}+\text{SNR}_{2}^{-1})^{-1}, \tag{10}\]  
where \(E[\cdot]\) and \(\text{Var}[\cdot]\) denote expectation and variance respectively, and \(\text{SNR}_{j}=g_{j}^{2}/\sigma_{j}^{2}\) (\(j=1,2\)) is the SNR on the \(j\)-th link. Note that \(\text{SNR}_{AF}\) is always lower than the SNR on either link.
# A Depth-Based Hybrid Approach for Safe Flight Corridor Generation in Memoryless Planning
## 4 Free Space Detection
### Supervised Learning and Conservativeness  
It is necessary to provide a deep-learning object detector with adequate training data that fully capture our object's features [33]. For a standard 2D annotation describing the ground truth of an object, the features will be the size of the bounding box and the pattern inside, which will be translated as pure depth values of pixels in our problem. Those features will be determined by specifying how far the bounding boxes, which are projections of ground-truth SFCs in our case, should stay from the closest obstacles and how deep they will be to remain collision-free. Thus, labeling SFCs using these criteria is equal to annotating patterned objects.  
Supervised learning systems work best with fully labeled data points, meaning free space (in the form of an SFC) should be exhaustively extracted from the depth image. However, many SFCs that satisfy the abovementioned criteria would be in an arbitrary depth image of cluttered scenarios, resulting in many bounding boxes overlapping each other. This scenario of annotation is not an ideal training data point for a deep-learning object detector since it confuses generalization [34]. Thus, we label only one largest SFC per depth image. Although this approach is conservative since certain parts of the free space stay unlabeled (weak labeling [35]) in a very cluttered environment, it benefits the generalization of the deep-learning model. We will demonstrate that the result of training this dataset with supervised learning is practical enough for applications. In this paper, the deep-learning model Yolov7 [36] has been selected as the object-detection engine due to its status as the best object-detection model at the time of writing.
# Evaluation of Local Security Event Management System vs. Standard Antivirus Software
## 5 Discussion  
Traditional systems for detection and classification of threats in networks and computer systems are increasingly encountering difficulties for early detection of malicious code. This is because detection systems based on signature and heuristics have become outdated in the battle against novel evasion techniques. Therefore, this paper highlights the importance of implementing alternative detection methods, such as those based on the analysis and classification of events generated within the user's computer. Through this type of analysis, it is possible to detect advanced techniques and establish security checkpoints for events generated in the system that may indicate that the system is being compromised.  
The results presented in this paper correspond to commercial antivirus software and the proposed Event Management System EBDS, all running locally in the end-user computer. In large companies, it is possible to use SIEM (Security Information and Event Management) systems or corporate versions of antivirus software that centralize events, therefore attaining better results than a stand-alone antivirus. However, the work presented here was carried out with the focus on home, home-office, or small businesses, which need to rely on local computer detection.  
The results show that, by applying obfuscation techniques, it is possible to hide malicious code from any detection system based on code signatures. Any encrypted code (malicious or benign) uploaded to VirusTotal will yield \"safe\" results, or maybe a warning related to fact that the file is encrypted. In addition, most advanced attacks manage to extract the obfuscated malicious code directly in the memory, without additional hard drive access, therefore avoiding a possible signature-based detection, because antivirus softwares scan files only while reading or writing to the hard drive.  
Obfuscation techniques have no limits, there are multiple techniques and combination of techniques that can be applied. Since these techniques use a randomly generated key, every sample of obfuscated malware is different, and the creation of a general file signature is not possible. In addition, completely new malware, even without obfuscation, cannot be detected through signatures.
## 2 Materials and Methods
### Application of the Pd-Based Photocatalyst in the Degradation Process of Phenol  
The obtained Pd-based photocatalyst was used in the degradation process of phenol from aqueous solutions, using a photoreactor PhotoLAB B400-700 Basic Batch-L (provided by Peschl-Ultraviolet, Mainz, Germany). The photocatalytic effect of the obtained Pd-based photocatalyst containing different amount of Pd loaded (F-IL-Pd1--impregnated Florisil with IL and 1 mg Pd/g and F-IL-Pd2--impregnated Florisil with IL and 2 mg Pd/g) was compared with the effect of raw Florisil (F) and impregnated Florisil with Cyphos IL-101 (F-IL). A well-defined quantity of the photocatalysts (0.7 g) was treated with 700 mL of phenol solution containing 20 mg/L phenol. Initially, the phenol solution was stirred for 30 min with the photocatalyst in the dark in order to achieve the adsorption-desorption equilibrium between the phenol and the surface of the photocatalyst. After this time, the sample was irradiated for 3 h with a UV-vis lamp type TQ 150. The samples were collected at different time intervals (5, 15, 30, 45, 60, 120 and 180 min), and the photodegradation profiles of phenol weremonitoring by realizing the UV-vis absorption spectra using a Varian Cary 100 spectrophotometer (Agilent Technologies, Santa Clara, CA, USA). The degradation degree of phenol was determined from the absorbance recorded at \(\lambda\) = 270 nm, which is one of the two maximum local excitation bands of hydroxyl-monosubstituted phenyl:  
\[\text{Degradation degree }(\%)\ =\ \frac{A_{i}^{270}-A_{t}^{270}}{A_{i}^{270}}100\]  
where \(A_{i}^{270}\) is the initial absorbance of the phenol solution, recorded at 270 nm and \(A_{t}^{270}\) is the absorbance of the solution after time t, recorded at 270 nm.  
To determine both the degree of phenol degradation and the degree of mineralization, total organic carbon (TOC) was measured by using a TOC analyzer (provided by Shimadzu, Kyoto, Japan).  
Experiments were triplicate, and the results are presented as a mean.  
The phenol degradation efficiency was determined as a function of the nature of the used photocatalyst and the S:L (solid:liquid) ratio.
# The Development of an Optimal Control Strategy for a Series Hydraulic Hybrid Vehicle
## 2 System Configuration and Modeling
### Forward Simulation Model  
Using the Physical Network approach of the Simscape modeling environment, SimHydraulics software is a modeling environment within Simulink and MATLAB 2014a (MathWorks, Massachusetts, MA, USA) for engineering design and for the simulation of hydraulic power and control systems. It contains a comprehensive library of hydraulic blocks that extend the Simscape libraries of basic hydraulic, electrical and one-dimensional translational and rotational mechanical elements and utility blocks. This toolbox allows a transient analysis of hydro-mechanical systems, specifically when modeling scenarios with hydraulic actuators as part of a control system. It is also appropriate for systems that allow lumped parameters.  
In this work, a model of conventional vehicle with dual clutch transmission (DCT) [16] was used as a foundation for modeling the SHHV system. The model of the SHHV is extended from the conventional vehicle model. The dual clutch transmission is removed. The high-pressure accumulator, the low-pressure reservoir, the variable displacement pump and the variable displacement pump/motor are connected to establish a series hydraulic hybrid powertrain, as shown in Figure 2.  
The model is implemented in Simulink, using corresponding hydraulic, mechanical and other fundamental blocks from the Simscape toolbox. The key component blocks are a diesel engine, hydraulic variable pumps and gas-charged accumulators. The hydraulic valve subsystem comprises several pipes, and hydraulic directional valves. The vehicle model block has the longitudinal dynamics and tire models for a rear-wheel-drive (RWD) vehicle. The important parameters of the system are listed in Table 1. Details of system modeling and model validation can be found in the work of [17].  
Figure 1: Schematic and Control Signal Paths of Series Hydraulic Hybrid Vehicle (SHHV).
# Measurement and Analysis of Industrial Forklifts Vibration Levels for Unit Load Testing Purposes
## 3 Results and Discussion
### The Effect of Payload on the PSD  
To determine the payload weight's effect on the vibration levels, PSD profiles were collected and analyzed for the three forklifts (Figure 8). The comparison was conducted using vibration data measured at the fork line tip while traveling at a speed of 4.82 km/h, because this speed created the highest intensity vibration. The results showed that for the investigated forklifts, more payload probably suppressed the vibration to lower levels, predominately at lower speeds. It was also found that the 3-4 Hz peak, which is generally attributed to the suspension system of the vehicles, shifted to a lower frequency as a consequence of the increase in weight. It was observed that the peaks for overloaded trucks shifted to a lower frequency in comparison to normally loaded trucks. This observed behavior is expected because the natural frequency of an object tends to decrease with the increasing weight of the spring mass system. In addition, increases in the high frequency region were observed for the electric forklift and the reach truck, but not for the gas forklift.  
Figure 6: The highest intensity power spectral density (PSD) plot for each of the studies forklift types compared to the International Safe Transport Association (ISTA) truck test vibration level PSD (payload: 680 kg, speed: 4.82 km/h).  
Figure 8: PSD of the three investigated forklifts as a function of payload (680 kg and 1134 kg).  
Figure 7: PSD plots of the three investigated forklifts as a function of speed (3.22 km/h, 4.82 km/h).
# CUDA-Optimized GPU Acceleration of 3 GPP 3D Channel Model Simulations for 5G Network Planning
## 4 GPU-Based Acceleration Using NVIDIA CUDA
### Global Memory  
Global memory is the off-chip dynamic RAM (DRAM) available on the GPU board, and it is typically separate from the CPU memory. It is used as a communication buffer for large amounts of data between the CPU and the GPU. It has high latency and relatively low bandwidth, similar to a CPU, compared with lower levels of the hierarchy. The host code is in charge of transferring data between the host memory space and the global memory. Arrays (less frequently scalars) allocated in global memory must be tagged as __device__ in CUDA.
## 2 DC Fault Current Analysis
### Simulation Verification  
The analyses of the arm and DC line currents have been verified via a simulation. The monopolar MMC-HVDC system is adopted in this study, and the model parameters are listed in Table 1. The rectifier side takes DC voltage and active power control, and the inverter side adopts active and reactive power controls. The protection action, including blocking IGBTs, is disregarded in the simulation to investigate the natural response to a fault. A completely discharged SM capacitor can be equivalent to blocking IGBTs at \(t_{dis\_peak}\). The comparison between the calculation and the simulation in terms of DC line and arm currents (Phase-A upper arm) is depicted in Figures 8 and 9, respectively. Figure 8 demonstrated that the DC line current increases to a high level in a few milliseconds. The IGBTs must be blocked rapidly due to the electrical pressure on the DC line. The DC line current decays after completely discharging the SM capacitor. Similarly, the arm current accelerates during the discharging period (Figure 9) but involves the AC feeding component after completely discharging the SM capacitor at 1.213 s. The difference between the simulation and calculation values in the arm current before 1.213 s may be attributed to the imbalance shunt current among the converter units. In addition, the initial AC current at fault time (1.2 s) is ignored in the calculation, thereby leading to more or less error. However, the overall accuracy of the calculation analysis can be accepted for fault study on the basis of the general consistency between the simulation and calculation curves in Figures 8 and 9.