esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[16; 17] used discrete-element analysis to evaluate excavation by both spoke-type and face-plate EPB shield TBMs (diameter, 6-7 m). Unlike the previous studies [10; 12; 14] that used the PFC3D commercial software, they used EDEM commercial software because of its advantage of being able to couple other analysis methods, such as multi-body dynamics and the finite element method. Although coupled analysis is attractive as both the ground and the TBM should be considered concurrently for an accurate simulation of excavation, [PERSON] et al. [16; 17] evaluated the TBM excavation performance using only EDEM, without other coupled workings. However, their simulations were conducted at an introductory level. This study aimed to evaluate the overall excavation performance of a 7.73-m-diameter EPB-shield TBM under various operating conditions, without consideration of soil conditioning or additional support pressure in the chamber. The TBM model comprises six parts--cutterhead, disc cutters, cutter bits, chamber, shield, and screw conveyor. A ground model of size 1.5 m (width) \(\times\) 15.6 m (length) \(\times\) 14.6 m (height) is created from 400,000 spherical particles (diameter, 100 mm) that are introduced once the TBM geometry is in place. The operation conditions are optimized based on these two models, simulating excavation for 360 s at four cutterhead RPMs and three screw conveyor RPMs.
## 4 Hazards Analysis
### Desertification Hazard Assessment
#### 4.4.3 The Vegetation Quality Index (VQI)  
The Vegetation Quality Index (VQI) evaluates how well the vegetation of an area contributes to land stability and resilience against degradation. It is calculated as the geometric mean of four vegetation-related factors: fire risk, erosion protection, drought tolerance, and vegetation cover. The formula used is:  
VQI = (Fire Risk \(\times\) Protection against Erosion \(\times\) Drought Tolerance \(\times\) Vegetation Rate)*(1/4)  
Figure 15: Spatial distribution of factors used to evaluate the Climate Quality Index (CQI).  
\begin{table}
\begin{tabular}{c c} \hline \multicolumn{2}{c}{**Precipitation**} \\ \hline
**Precipitation (mm)** & **Rainfall Index** \\ \hline \(>\)650 & 1 \\ \hline
280–650 & 2 \\ \hline \(<\)280 & 4 \\ \hline \multicolumn{2}{c}{**Slope Orientation**} \\ \hline
**Slope Aspect** & **Orientation Index** \\ \hline NE-NW & 1 \\ \hline SE-SW & 2 \\ \hline \multicolumn{2}{c}{**Aridity**} \\ \hline
**BGI** & **Aridity Index** \\ \hline \(<\)50 & 1 \\ \hline
50–75 & 1.1 \\ \hline
75–100 & 1.2 \\ \hline
100–125 & 1.4 \\ \hline
125–150 & 1.8 \\ \hline \(>\)150 & 2 \\ \hline \end{tabular}
\end{table}
Table 5: Parameter ranking used to obtain the CQI [44].  
Each of these parameters reflects an important role that vegetation plays in maintaining ecological balance and protecting the land from erosion and climate stress. The use of the geometric mean ensures that no single factor dominates the index and helps reduce the effect of any extreme values.  
Based on the VQI score, vegetation quality is categorized as:  
* High quality: \(\text{SQI}\leq 1.13\);
* Medium quality: \(1.13<\text{SQI}<1.45\);
* Low quality: \(\text{SQI}\geq 1.45\).  
For Lefkas Island (Tables 6 and 7; Figure 16), the vegetation rate is generally high, earning a value of 1, which indicates good ground coverage. The drought tolerance of plant species is also favorable, with values between 1.2 and 1.4, suggesting that vegetation can withstand moderately dry conditions. In terms of protection against erosion, values range from 1.3 to 1.6, reflecting the ability of the island's vegetation to reduce soil loss on slopes and disturbed ground.  
Taken together, these indicators show that much of Lefkas Island has moderate vegetation quality, with several areas showing traits of higher resilience, particularly in well-vegetated zones. However, some locations with more exposed or degraded vegetation cover may require closer monitoring and targeted conservation measures.
## 4 Performance Evaluation
### Result Evaluation
#### 4.3.3 Network Throughput vs. Traffic Load  
Our research indicates a significant improvement in network throughput under different traffic loads when our ML-based MAC protocol is compared to conventional MAC methods, as shown in Figure 8. With its capacity to dynamically change clusters, optimize bit rates, and effectively manage data packet sizes based on learned patterns, the ML-MAC protocol shows impressive versatility. Due to the protocol's ability to adapt, congestion was reduced, collisions were avoided, and the available bandwidth was optimized, particularly in situations when there was a lot of traffic. The results indicate how well our ML method works to maximize network performance and how well it can handle the demands of dynamic and varied traffic loads, outperforming conventional MAC protocols in the process. Moreover, our ML-MAC protocol demonstrated a distinct advantage over conventional protocols when examining the crucial parameters of clusters, bit rate, and data packet size. Our protocol produced well-informed judgments that led to greater bit rates inside clusters, appropriate data packet sizes, and more effective intra-cluster communication by using learned insights from the environment. Throughput was increased using a comprehensive approach, and the protocol's flexibility in various network situations was shown. When we compare our machine-learning MAC protocol to standard MAC protocols, it shows that it is a smart and resilient way to obtains higher network throughput under different traffic loads. This proves that it has the potential to be a cutting-edge solution for how wireless communication networks need to change over time.
# Improved War Strategy Optimization with Extreme Learning Machine for Health Data Classification
###### Abstract  
Classification of diseases is of great importance for early diagnosis and effective treatment processes. However, etiological factors of some common diseases complicate the classification process. Therefore, classification of health datasets by processing them with artificial neural networks can play an important role in the diagnosis and follow-up of diseases. In this study, disease classification performance was examined by using Extreme Learning Machine (ELM), one of the machine learning methods, and an opposition-based WSO algorithm with a random opposite-based learning strategy is proposed. Common health datasets: Breast, Bupa, Dermatology, Diabetes, Hepatitis, Lymphography, Parkinson, SAheart, SPECTF, Vertebral, and WDBC are used in the experimental studies. Performance evaluation was made by accuracy, precision, sensitivity, specificity, and F1 score metrics. The proposed IWSO-based ELM model has demonstrated better classification success compared to the ALO, DA, PSO, GWO, WSO, OWSO metaheuristics, and LightGBM, XGBoost, SVM, Neural Network (MLP), CNN machine and deep learning methods. In the Wilcoxon test, it was determined that IWSO was \(p<0.05\) when compared to other algorithms. In the Friedman test, it was determined that IWSO was first in the ranking of success compared to other algorithms. The results reveal that the IWSO approach developed with ELM is an effective method for the accurate diagnosis of common diseases.  
Keywords:extreme learning machine; health data; classification; random opposition-based learning; war strategy optimization Article
# Programmable Deterministic Zero-Copy DMA Mechanism for FPGA Accelerator
## 3 Programmable Deterministic Multi-Queue Accelerator Model
### UserSpace Zero-Copy Driver  
The zero-copy module consists of a kernel zero-copy driver, a shared memory pool, and a userspace PMD. The zero-copy implementation owes much to a DMA-consistent shared memory pool allocated by the zero-copy driver. This pool consists of a hardware descriptor ring (ring buffer), a software descriptor ring (sw_ring), and contiguous memory region for sending and receiving. Packets moving between the accelerator and the application are implemented via DMA+mmap, which copies most packets and does not involve the CPU.  
Figure 4 shows the process of zero-copy. Take receiving packets as an example: (1) the ZC (zero-copy) driver initializes by allocating a memory pool for rx_ring, sw_ring, and mbuf; (2) requests mbuf; (3) puts the physical address of the new mbuf into the hardware descriptor, setting the status bit DD (descriptor complete) to 0 (indicating available); and (4) assigns the virtual address of the mbuf to the software descriptor (sw_ring) for direct application access by the application. (5) The driver starts from the first descriptor and polls the DD flag bit of the hardware descriptor to see if it is 1. If it is 1, the mbuf corresponding to this descriptor is considered unavailable. If it is 0, it is acquired by DMA to the accelerator. (6) When the packet arrives, the DMA resolves the physical address of the mbuf from the fetched descriptor and then (7) writes the packet from the queue to the specified hardware address (an mbuf element corresponding to a sw_ring descriptor), setting the DD flag bits of the rx_ring and sw_ring descriptors and other information. (8) The application checks for packet arrivals by PMD polling.  
When PMD reads the packet, the result of determining the DD bit is opposite to that of the accelerator using the hardware descriptor. It first checks if the DD bit is 1. If it is not 1, there is no packet to read. If it is 1, it means the accelerator has DMAed the data to memory and can read it. After reading, the application sets the DD bit to 0. Finally, the mbuf is released and can be used again. The above cooperation between the driver, shared memory, and DMA eliminates the resource consumption caused by CPU copy in traditional data processing and realizes the zero-copy in userspace.
## 1 Introduction
[30] developed a 3D convolutional neural network (3D CNN) coupled with random finite element modeling (RFEM) to assess slope stability under spatially variable soil conditions, achieving a 94.7% correlation with field-monitored displacement data. This framework leverages multi-scale shear strength reduction strategies to enhance computational efficiency while maintaining accuracy in volumetric strain predictions. Several studies applied VGG-16 architecture to automatically monitor and classify freshness (e.g., fish and eggs) [31; 32]. A CNN classifier was developed to classify the froth images captured from an industrial coal flotation column operated under various process conditions [33]. Moreover, to aid CNNs to better deal with variations in input distributions, [PERSON] and [PERSON] [34] extended Coral to incorporate it directly into deep networks (CNN) by constructing a differentiable loss function that minimizes the difference between source and target correlations. Despite widespread applications of deep learning for recognizing objects in images, few reports exist that use deep learning algorithms to analyze borehole images. To address this shortcoming, a program was developed in the present work that automatically recognizes structural planes in borehole images using deep learning. To address this shortcoming, in the present work we developed a program that automatically recognizes structural planes in borehole images using deep learning. Application cases are also presented to demonstrate the recognizing procedure and confirm the accuracy of this approach.
# Autonomous Load Regulation Based Energy Balanced Routing in Reshargeable Wireless Sensor Networks
## 3 System Model
### Energy Transfer Model  
Each node in the same charging cell can be charged by the MCV simultaneously. Taking node \(v_{i}\) as an example, its energy transfer efficiency \(\eta_{i}\) is calculated as follows [24]:  
\[\eta_{i}=\frac{G_{S}G_{R}}{L_{P}}\Bigg{(}\frac{\lambda}{4\pi(d_{i_{c_{m}}}+ \beta)}\Bigg{)}^{2},i\in c_{m} \tag{1}\]  
where \(G_{S}\) is the source antenna gain and \(G_{R}\) is the receiving antenna gain. \(\lambda\) is the wavelength and \(L_{P}\) is polarization loss. \(\beta\) is a parameter to adjust the Friis free space equation for short-distance transmission. \(d_{i_{c_{m}}}\) is the distance between node \(v_{i}\) and its cell center. The efficiency decreases dramatically with the increase of distance.
# A Simulation Study on Pressure Control in Oil Well Drilling Using Gain-Scheduled PID Controllers
## 2 Wellbore Pressure Model  
This section presents mathematical models based on fluid mechanics, using the equation of state and Reynolds transport equations (continuity and momentum). These models allow the derivation of differential equations that describe the bottom-hole pressure and flow through the control valve. The model, originally proposed by [PERSON] (2006) and used by [12; 13; 14; 15; 16], serves as the basis for further analysis. Figure 1 shows a typical schematic of a well drilling system.  
In this system, the main pump extracts fluid (\(q_{p}\)) from the sludge tank. The drilling fluid (mud) then travels through the pipes (\(P_{p}\)) to the drill string and the bit, ascending through the annular region while carrying rock debris from the drilling to the well's exit (\(P_{e}\)). The drill string is assembled by adding pipes as the drilling depth increases. Subsequently, the fluid passes through the choke valve (\(u\)), where effective control measures optimally regulate the pressure at the drill bit. Finally, the fluid returns to the initial sludge tank (\(q_{e}\)), facilitating fluid mixing. In situations requiring higher pressure, a backpressure pump (\(q_{c}\)) can be added [17; 18; 19; 16].
# Security of IoT Application Layer Protocols: Challenges and Findings
## 4 Messaging Protocols
### Amqp  
Advanced Message Queuing Protocol (AMQP) is an open protocol for business messaging (OASIS Standard). The protocol offers sophisticated functionalities and is widely used presently in many scenarios where a reliable asynchronous communication between endpoints is needed.  
Concerning security, AMQP supports the Simple Authentication and Security Layer (SASL) framework [56] for client authentication and TLS for ensuring integrity and confidentiality of communication. Let us remark that unlike MQTT and CoAP, these security services are generally enabled by default, thus reducing the potential security risks. Nevertheless, according to the NVD database, a wide variety of vulnerabilities have been discovered in the past six years in products and services based on AMQP. These vulnerabilities mainly involve the broker component and affect processes, such as access control, message and identity validation, message queue management. The effects of these vulnerabilities include privilege escalation, information disclosure, Denial of Service attacks, authentication and authorization bypass, remote code execution, traffic hijacking. More specifically, several vulnerabilities refer to the lack of hostname and certificate validation whose exploitation allows attackers to spoof identities and intercept traffic for MiTM attacks (e.g., CVE-2018-11087, CVE-2018-8119, CVE-2016-4467). Similarly, the lack of access control in the message queues reported by CVE-2019-3845 allows attackers to execute privileged commands. In addition, several CVEs suggest that the use of specifically crafted AMQP messages and of exposed shutdown commands makes it possible to achieve a Denial of Service attack (e.g., CVE-2015-7559, CVE-2017-15699, CVE-2015-0224, CVE-2015-1499).  
Other security risks affecting AMQP environments are related to broker configuration. In fact, AMQP brokers are very complex and despite the presence of a web user interface their setup can be very challenging. Incorrect choices in the setup of message queues, exchanges, producers and consumers might lead to serious vulnerabilities. Moreover, the user interfaces might be affected by vulnerabilities typically encountered in the web domain (e.g., CVE-2015-0862, CVE-2016-0734, CVE-2017-4965). We finally outline that a simple--although very common--misconfiguration refers to the use of default login credentials that can be abused by an attacker to take control of a publicly exposed broker administrator interface and of the entire AMQP environment.
## 4 Discussion: Mechanistic Studies of the CL Detection System
### Reaction Mechanism  
Batch experimental results in the present study, UV absorption spectra, CL spectra, and ESR spectra of the proposed CL detection system were conducted to investigate the CL reaction mechanism between Ni(IV) complex and luminol/salbutamol. According to these results, a CL reaction mechanism (Scheme 1) was proposed.  
It is speculated that free radical is formation in the reaction of Ni(IV) complex with luminol/salbutamol, respectively. The produced luminol radical reacts with Ni(IV) to yield an  
Figure 5: CL spectra of the Lu-Ni(IV)-SAL/TEB system. Reaction conditions: [Ni] = 3.0 \(\times\) 10\({}^{-7}\) M; [Lu] = 1.0 \(\times\) 10\({}^{-4}\) M; [OH\({}^{-}\)] = 0.006 M; [TEB] = 1.0 \(\times\) 10\({}^{-5}\) M; [SAL] = 1.0 \(\times\) 10\({}^{-5}\) M.  
unstable endoperoxide and electronically excited 3-aminophthalate anion to create chemiluminescence. A portion radicals came from salbutamol-Ni(IV) reaction give to luminol rapidly in the course, which leads to more luminol radicals that increase CL enhancement (Scheme 1).
# Physical-Layer Security in Power-Domain NOMA Based on Different Chaotic Maps
###### Abstract  
Nonorthogonal multiple access (NOMA) is a relevant technology for realizing the primary goals of next-generation wireless networks, such as high connectivity and stability. Because a rising number of users are becoming connected, user data security has become a critical issue. Many chaotic communication systems have been established to address this important issue via exhibition of affordable physical-layer-security solutions. In this study, we propose a chaotic downlink NOMA (C-DL-NOMA) system over the additive white Gaussian noise and Rayleigh-fading channels to enhance the security of the DL-NOMA system. The proposed algorithm is based on a coherent analog modulation technique that combines various chaotic maps for chaotic masking of encrypted data. On the transmitter, chaotic encryption was used for transmitted data with fixed power-allocation-level control, whereas on the receiver, successive interference-cancellation demodulation was utilized to detect multiple users, after which chaotic decryption was performed. Simulation results were evaluated based on security analyses, such as statistical analysis (histogram and correlation analyses and information entropy), bit-error-rate performance, and achievable-data-rate performance. According to these security analyses and numerical results, the proposed C-DL-NOMA system outperformed traditional unencrypted NOMA systems.  
n ontorhogonal multiple access; physical-layer security; power domain; downlink; encryption; chaos; logistic map; Arnold's cat map; decryption; hyperchaotic DNA; Henon map 2023
# Solution to the Inverse Problem of the Angular Manipulator Kinematics with Six Degrees of Freedom
## 2 Methods and Algorithms
### Numerical Solution of Inverse Problem of Manipulator Kinematics
In this study, we propose to use the normalization of the resulting angle values based on their representations in the unit circle. This method was used above in order to derive Equation (4). Figure 8 shows a unit circle, the points on the rim of which correspond to certain angles, and the cosines and sines of these angles are plotted along the axes. Figure 8: Diagram towards the normalization of angles and their differences. Whatever the value of any angle, the representing point will always be on the circumference of the unit circle, for which it is always possible to calculate the value of the sine and cosine of the angle in the range of one revolution. Using the function \(\text{atan2}(y,x)\), where \(x\) and \(y\) are the cosine and sine of the desired angle, it is possible to compute the angle value to the range \((-\pi,+\pi]\), which is the range of values of the atan2 function. For example, for angles \(\alpha\) and \(\beta\) (Figure 8), we have  
\[\alpha=\text{atan2}(\sin\bar{x},\cos\bar{x}),\beta=\text{atan2}\big{(}\sin\bar {\beta},\cos\bar{\beta}\big{)}, \tag{19}\]  
where \(\bar{x}\), \(\bar{\beta}\) are the angles calculated in some computational process (not normalized at all) and for which any arbitrarily large values are possible; \(\alpha\), \(\beta\) are the same angles normalized to the range \((-\pi,+\pi]\). More problematic is the calculation of angle differences in various problems when both angles can take any value. For example, the difference between an angle of \(1^{\circ}\) and \(359^{\circ}\) could be equal to \(-358^{\circ}\) or even \(718^{\circ}\), although in fact it is \(2^{\circ}\). In this case, it is also proposed to calculate the sine and cosine of the angle difference through the sines and cosines of these angles, and then, using the atan2 function, to find the angle difference normalized to the range \((-\pi,+\pi)\) as follows:  
\[ \begin{split}\sin(\beta-\alpha)&=\sin\bar{\beta} \cos\bar{x}-\sin\bar{x}\cos\bar{\beta}\\ \cos(\beta-\alpha)&=\cos\bar{\beta}\cos\bar{x}+ \sin\bar{\beta}\sin\bar{x}\\ \beta-\alpha&=\text{atan2}(\sin(\beta-\alpha), \cos(\beta-\alpha))\end{split}  \tag{20}\]  
Thus, if the points on the unit circle representing angles are close, then the difference in angles, whatever these angles are and whatever ranges they are considered, will be small as a result of normalization according to Equation (19) or Equation (20). To sum up, we can conclude that the angles at the joints of the manipulator calculated with the Newton method must be normalized according to Equation (19), and the discrepancies of the calculated and specified angles used in the equations for the Newton method must be calculated according to Equation (20).