esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Underwater Wireless Optical Communication Channel Model and Its Application
## 2 Modeling of the Communication Channel of UWOC
### Analysis of Communication Range in UWOC
#### 2.1.3 Communication Range Model of UWOC  
The entire process of light transmission underwater can be described as a cumulative loss process where multiple factors contribute to the overall attenuation of the emitting power from the UWOC transmitter. The received signal power at the receiver is directly related to the maximum communication range in optical communication. Considering the optical-to-electrical conversion efficiency of the transmitter and receiver, as well as geometric losses, the optical power equation, assuming alignment between the transmitter and receiver, can be represented as  
\[P_{R}=P_{T}\times\eta_{t}\times\eta_{r}\times\eta_{d}\times e^{-K(\lambda) \times d} \tag{6}\]  
where:  
\(P_{T}\) is the initial transmitting power at the transmitting terminal;  
\(P_{R}\) is the optical power received by the light-sensitive component at the receiving terminal;  
\(\eta_{t}\), as explained above, is the electrical-to-optical conversion loss of the transmitter;  
\(\eta_{r}\) reflects the efficiency of the receiver in converting incoming optical signals back into electrical signals;  
\(\eta_{d}\) is geometry loss, which is inversely proportional to the square of the communication range [17];  
\(K(\lambda)\) is the attenuation coefficient of the transmission channels of seawater, which can be expressed as the sum of the absorption and scattering effects, yielding the equation,  
\[K(\lambda)=a(\lambda)+b(\lambda) \tag{7}\]  
where \(d\) is the range between the transmitter and the receiver of optical communication. Upon analyzing the aforementioned equation, it is evident that the range between the transmitter and receiver, as well as the water quality, significantly impacts the received signal power. As the range and water quality attenuation coefficient increase, the power received at the receiving end decreases, affecting the quality of UWOC.
# Development of a Model Care Pathway for Myasthenia Gravis
## 3 Results  
### Literature Review and Assessment  
From the searched databases we identified 1931 records. After the manual screening, we excluded 1842 articles that were not relevant. Therefore, 89 full-text records were assessed for eligibility: 62 did not meet the inclusion criteria and were excluded. The remaining 27 papers included (Figure 2) 13 guidelines (reporting clinical evidences) [25; 26; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52], 11 reviews (presenting process flows, pathways, and algorithms of integrated care) [27; 28; 53; 54; 55; 56; 57; 58; 59; 60; 61], 1 clinical metrics article (with tools to measure outcomes and process indicators) [62], 1 manual reporting MGFA multi-disciplinary practice recommendations [63] and 1 document with benchmarking information (EURO-NMD network) [64].  
The characteristics and the quality assessment of the included guidelines are described in the supplementary materials (Table S3). In brief, 7 guidelines were developed by US scientific societies (AAN, AANN, AAEM, and AANEM); 3 were developed in Europe by EFNS; 2 were developed in the UK by ABN and by the UK-multispeciality working group, and the last guideline was developed in Japan by JSN.  
The characteristics of reviews that are presenting proposed and/or suggested process flows, pathways, and algorithms of integrated care for MG and other included documents are shown in the supplementary materials (Table S4).  
Figure 2: Flow Diagram of article selection process.
# Optimizing Foreign Language Learning in Virtual Reality:
## 3 Theoretical Foundations
### Cognitive Theories in Language Learning
#### 3.1.1 Cognitive Development and Language Acquisition  
The trajectory of cognitive maturation profoundly influences an individual's capacity to assimilate and articulate language. The illustrious cognitive theorist [PERSON] propounded that offspring navigate through specific epochs of cognitive maturation, each delineated by distinct intellectual modalities and learning proficiencies [41]. Within the sensorimotor epoch (birth to 2 years), neonates commence forging connections between auditory stimuli and their corresponding significances, establishing the bedrock for linguistic mastery. The preoperational epoch (2 to 7 years) heralds a prolific expansion in the lexicon and genesis of rudimentary syntactic constructs. As juveniles segue into the concrete operational epoch (7 to 11 years), they cultivate the proficiency to cogitate logically on tangible occurrences, augmenting their linguistic discernment and articulation. Conclusively, during the formal operational epoch (12 years onward), abstract ratiocination paves the way for a sophisticated grasp and deployment of intricate linguistic architectures.  
These epochs elucidate the intricate tapestry interweaving cognitive evolution and linguistic assimilation [42]. Discerning a learner's cognitive epoch empowers pedagogues to fine-tune VR engagements, harmonizing with their developmental prerequisites and championing the zenith of linguistic educational outcomes.
# Cornering Stiffness Prediction Based on Geometric Method
## 3 Results
### Method Applied to Tire Model
#### 3.2.2 UniTire  
By parameterizing side slip data with different loads through Formula (28), we can obtain \(s_{11}\), \(s_{12}\) and \(s_{13}\). The parameterizing result is shown in Figure 10.  
The cornering stiffness dependence of load and camber angle for the UniTire is shown in Formula (31). The variables \(s_{11}\), \(s_{12}\), \(s_{13}\) and \(s_{14}\) are model parameters. Figure 11 shows the comparisons.  
\[K_{y}=s_{11}\cdot F_{z}\cdot\text{sech}(s_{12}\cdot\left(\frac{F_{z}}{F_{z0}} \right)^{2}+s_{13}\cdot\frac{F_{z}}{F_{z0}})\cdot(1-s_{14}\cdot(\gamma)^{2}) \tag{31}\]  
Figure 8: Side slip data with different loads parameterizing result through Formula (26).  
Figure 10: Side slip data with different loads parameterizing result through Formula (28).  
Figure 9: Result comparisons. (**a**) Tire 1\(\#\); (**b**) Tire 2\(\#\).  
From the above result, we can see that the method expresses the influence tendency of camber on cornering stiffness and produces accurate results compared to the full parameterization. Most cases along the curve have accuracies higher than 95%. Neglecting the camber effect values has lower accuracy and fails in expressing the decreasing cornering stiffness. In the MF or UniTire parameterizing expression, the proposed method could be a replacement with high accuracy and low consumption.
# Research on the Construction of Malware Variant Datasets and Their Detection Method
## 6 Experimental Evaluation and Discussion
### Comparison of Malware Detection Results with Obfuscation  
Table 3 shows the classification effect of the obfuscated dataset. It can be seen that BERT still performed relatively well in various classification methods. By comparing the effects of various classification schemes in Tables 3 and 4 on the unobfuscated dataset and obfuscated dataset, it can be seen that the obfuscated dataset could indeed affect  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Unobfuscated Dataset** & **Obfuscated Dataset** \\ \hline normal & 820 & 350 \\ downloader & 1500 & 1000 \\ backdoor & 1060 & 1000 \\ keylogger & 768 & 835 \\ grayware & 1500 & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of API sequence samples of various malware in the dataset.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Normal** & **Downloader** & **Backdoor** & **Keylogger** & **Grayware** & **Average** \\ \hline Random forest & 0.92 & 0.71 & 0.89 & 0.90 & 0.82 & 0.848 \\ CNN & 0.95 & 0.74 & 0.89 & 0.91 & 0.80 & 0.858 \\ LSTM & 0.91 & 0.74 & 0.89 & 0.89 & 0.81 & 0.848 \\ BERT & 0.94 & 0.81 & 0.87 & 0.90 & 0.84 & 0.872 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classification effect of the unobfuscated dataset.  
the classification effect of malware families to a certain extent. Specifically, for decision tree-based random forest methods, the effect of adding obfuscation was relatively low. The classification effect is not strongly related to semantics, so adding confounding factors in semantics has relatively little effect on them. For CNN, the features extracted in the same size window may be biased after adding obfuscation because the window for extracting features was not large. For LSTM and BERT, the classification effect was closely related to the semantics. After adding obfuscation, the semantic dependency became longer, leading to the reduction of classification effectiveness.  
To reduce the impact caused by the obfuscation of malware variants, in this paper, we improved the detection effect in the obfuscated dataset of malware variants by adding adversarial training. In Section 5.2, the adversarial training model of BERT-TextCNN was constructed. Table 4 shows the classification effect of this model on various types of malware on the obfuscated dataset. It can be seen that in the classification effect, the malicious family classification of backdoor, keylogger and grayware had improved, and by the average value of classification effect, it can be seen that it had a certain effect on the obfuscated dataset. The classification accuracy of malware is close to that of unobfuscated dataset.
# Discrete-Time Position Control for Autonomous Underwater Vehicle under Noisy Conditions
###### Abstract  
This paper deals with the discrete-time position control problem for an autonomous underwater vehicle (AUV) under noisy conditions. Due to underwater noise, the velocity measurements returned by the AUV's on-board sensors afford low accuracy, downgrading its control quality. Additionally, most of the hydrodynamic parameters of the AUV model are uncertain, further degrading the AUV control accuracy. Based on these findings, a discrete-time control law that improves the position control for the AUV trajectory tracking is presented to reduce the impact of these two factors. The proposed control law extends the Ensemble Kalman Filter and solves the problem of the traditional Ensemble Kalman Filter that underperforms when the hydrodynamic parameters of the AUV model are uncertain. The effectiveness of the proposed discrete-time controller is tested on various simulated scenarios and the results demonstrate that the proposed controller has appealing precision for AUV position tracking under noisy conditions and hydrodynamic parameter uncertainty. The proposed controller outperforms the conventional time-delay controller in root-mean-square error by a percentage range of approximately 72.1-97.4% and requires at least 89.5% less average calculation time than the conventional model predictive control.  
discrete-time position control; uncertain hydrodynamics; the process and measurement noises; trajectory tracking 2021115790. [[https://doi.org/10.3390/app11135790](https://doi.org/10.3390/app11135790)]([https://doi.org/10.3390/app11135790](https://doi.org/10.3390/app11135790))
# A Systematic Literature Review of Various Control Techniques for Active Seat Suspension Systems
## 8 Control Methods Used in the Active Seating Suspension System
### Hybrid Controller  
In some researches, a hybrid control algorithm consisting of two control strategies was proposed for obtaining robust performances to ensure the safety and ride comfort of heavy trucks' drivers [103]. The core idea of using the hybrid structure is to combine the advantages of each controller to increase the overall performance of the plant. The hybrid fuzzy logic controller with PID controller was proposed to reduce the vertical vibration of the active seat suspension [15]. Coupled rules (CR), which are an improved stage of hybrid fuzzy PID (HFPID) controller along with PI and PD have been applied to improve the controller results [104]. The active seat suspension system integrated with active quarter suspension using HFPIDCR was tested using bump and sinusoidal type road disturbances. The simulation results have proved that HFPIDCR has provided a high level of ride comfort.  
The active seat suspension was controlled using ANFIS and super twisting sliding mode controller (ASTSMC) [63]. The ASTSMC has two control signals. The first signal was designed to deal with the dynamic behavior of the active quarter car suspension while the second signal was called a discontinuous control signal which is primarily used to the keep the plant on the sliding surface. The output of ANFIS was used as the slope value for the SMC, which is essentially required to create a sliding surface. The control structure scheme is shown in Figure 17. The active seat suspension system is evaluated using ISO 2631-1 standard and the proposed ASTSMC has successfully reduced the vibration effects on the human body.  
Figure 16: ANFIS structure [99].
## Materials and Methods
### Description of Power Levels Collection Method  
For the acquisition of the CMLs' power level, we made use of an adjusted version of the Python software pySNMPdaq [22]. The SNMP V2 protocol has been used to remotely access the different CMLs of the Telecel Faso's network. Each CML link is accessible by the acquisition system via its IP address. An SNMP V2 request is defined by the IP address of the target base station and the SNMP object identifiers (OID) which are defined in the Management Information Base (MIB). Each OID identifies the object that can be recovered from a given CML, e.g., the transmitted power level denoted Tx, the received power level denoted Rx, the signal to noise ratio, or the hardware temperature. The OIDs for retrieving Tx and Rx typically differ, depending on the manufacturer and specific hardware model [22]. Two kinds of Aviat series IDUs, CTR 8312 and CTR 8540, are those interconnected to the acquisition system.  
The data acquisition (DAQ) server requests the power levels of the transmitted and received signals of each link, and instantly stores these data in a file (Figure 5). The data acquisition program runs in a background loop, as a daemon program, and collects the Tx and Rx power data from the various CMLs at the rate of one minute.  
To ensure constant synchronization of simultaneous SNMP V2 requests, the program is subdivided into three sub-processes (Figure 5). First, there is the subprocess that continuously triggers data request events with a time resolution of one minute. This task continues in the background. Next, a data acquisition sub-process is started for each CML. This sub-process manages the SNMP V2 communication with the CMLs. Finally, it follows the process that receives all data from all links via a queue and stores them in files. The architecture thus adopted is illustrated in Figure 5.  
The SNMP requests are launched from the acquisition server via the microwave link that interconnects the LA.ME's local network to Telecel Faso's network. These requests are launched at the same time for all the CMLs the system has access to.  
For better management of the data, a folder is created for each CML as soon as it is integrated into the system. Inside this folder, one data file is created per hour for the considered link. This later data file records the Tx and Rx power levels of the CML, with a temporal resolution of one minute. This storage method allows for efficient data management. Indeed, it is possible to easily access and process the data of a given CML over a given time range. It is also possible to load and process all the data of one or several CMLs.  
One may note that this acquisition system guarantees the integrity of all data of the mobile telephony operator Telecel Faso.
## 5 Discussion
### Ablation Experiment  
An ablation experiment is conducted, with the results to verify the effectiveness of each module shown in Table 3. Scheme 1 is the original UNet; Scheme 2 is the semantic segmentation GAN network; Scheme 3 is UNet with the addition of the Transformer; Scheme 4 is GAN using UNet as the generator; Scheme 5 is GAN using UNet with the Transformer as the generator; Scheme 6 is UNet with the Transformer and improved MHSA; and Scheme 7 is GAN using the improved generator. Due to the limited generator capability, Scheme 2 has a poor segmentation performance, but it is greatly improved after introducing UNet as the generator (Scheme 4). Scheme 5 uses a GAN generator with a Transformer, and its IoU is improved by 1.36% compared to Scheme 4 without a Transformer. Scheme 7  
Figure 10: Experimental Effects on the Nucleus Dataset.  
Figure 9: Model Performance Comparison on the Rice Leaf Dataset.  
uses the improved MHSA with minor computational complexity and two-dimensional position encoding in the Transformer, and its IoU is improved by 1.91% compared to Scheme 5 without the improved MHSA, with only 43.9% of the latter's model parameters. Comparing Schemes 6 and 7, Scheme 7 has an IoU improvement of 0.71% over Scheme 6, indicating that introducing the discriminator and discrimination loss can optimize the generated image quality and improve segmentation performance.
# Project Robust Scheduling Based on the Scattered Buffer Technology
## 5 Experimental Analysis
### Experimental Design
#### 5.1.2 Evaluation Index  
The stability probability (\(SP\)) of the schedule and the stability cost (\(SC\)) are taken as the evaluation index of the solution robustness, the timely project completion probability (\(TPCP\)) and the average project length (\(APL\)) as the evaluation index of the quality robustness.  
The stability probability (\(SP\)) of the schedule refers to the percentage of the number of activities whose predicted start time is not less than the start time in the simulation schedule and the total number of activities. The larger this value, the better the solution robustness of the schedule is.  
In the simulation of the robust schedule, the stability cost refers to the additional cost that happens in the moment when the realized start time of activity deviates from the start time of activity in the schedule. The smaller this value, the better the solution robustness of the schedule is.  
In the simulation of the robust schedule, the timely project completion probability (\(TPCP\)) of project refers to the percentage of the number of projects whose realized completion duration is less than the predicated deadline and the total number of projects in the simulation schedule.  
The average project length (\(APL\)) of the project refers to the ratio of the sum of the realized completion time in the simulation schedule to the total number of the simulation schedules. The smaller this value, the better the quality robustness of the schedule is.
# Adaptation of Object Detection Algorithms for Road Indicator Lights in Complex Scenes
## 1 Introduction
It possesses the innate ability to discern and prioritize the most pertinent image regions pertinent to indicator light detection, thereby amplifying the algorithm's focus and enhancing detection accuracy. Moreover, the algorithm introduces an innovative loss function, Focaler-IOU loss, which ingeniously addresses the long-standing issue of positive-negative sample imbalance. This loss function not only maintains high detection performance under normal conditions but also excels in challenging scenarios involving occlusion, blurring, and background interference, further solidifying its practical applicability. Extensive experiments conducted on diverse datasets, including the custom-built headlight dataset and renowned traffic signal datasets such as S2 TLD and BSTLD, under varying and complex environmental conditions, have yielded remarkable results. These experiments underscore the algorithm's prowess in achieving exceptional detection accuracy, broad generalization capabilities, and efficient detection speeds. Consequently, this road indicator object detection algorithm represents a significant advancement, offering both profound research insights and immense practical value. The rest of this paper is organized as follows. Section 2 presents the framework of the deep learning network used and the methods proposed in this work. In Section 3, the experimental results are provided and analyzed. Section 4 provides further discussion on the proposed methods. Finally, the paper is concluded in Section 5.
# Structural Optimization of Slotting Nozzle Used to Improve Coal-Seam Permeability
## 3 Materials and Methods
### Experimental System and Equipment  
The experimental system consisted of two parts: a flow-coefficient test system and a hydraulic slotting system. A diagram of the system is shown in Figure 6. Real-time monitoring of pressure and flow were achieved with an accuracy of 0.1 MPa and 0.1 mL/min, respectively, using a data-processing system. The water-injection system comprised a plunger pump with a maximum pressure of 31.5 MPa and a maximum flow of 200 L/min.  
In the slotting experiment, the slotted material was concrete. The mass ratio of cement, river sand, and stone in the concrete was 1:4:4. Its average uniaxial compressive strength was 6.13 MPa, as determined by uniaxial testing. Specimens are shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Slotting Nozzle** & \begin{tabular}{c} **Stable Segment Length** \\ \(L\) **(mm)** \\ \end{tabular} & \
\begin{tabular}{c} **Convergent Angle** \\ _\(\theta\)_**(°)** \\ \end{tabular} &
\begin{tabular}{c} **Straight Segment** \\ _Length I_ **(mm)** \\ \end{tabular} \\ \hline Original slotting nozzle & 5 & 13 & 6 \\ Optimized slotting nozzle & 0 & 30 & 9 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Structural parameters of original and optimized slotting nozzles.  
Figure 5: Relationships between dynamic pressure and **(a)** length of the stable section \(L\), **(b)** convergent angle \(\theta\), and **(c)** straight segment length \(l\).  
Figure 6: Diagram of experimental system device. 1: Water tank; 2: emulsion pump; 3: relief valve; 4: treading cutoff value; 5: pressure gauge; 6: flow meter; 7: high-pressure water pipe; 8: sealing drill pipe; 9: drilling rig; 10: sealing drilling rod; 11: slotting device; 12: experimental box.  
The flow coefficient of a slotting nozzle represents its energy-transfer efficiency: a higher value indicates better performance. In this experiment, a seismic pressure gauge was used, and the influence of density on the flow coefficient was considered. The formula for calculating the flow coefficient is as follows:  
\[\mu=\frac{Q}{\text{A}_{0}}\sqrt{\frac{\rho}{2\Delta P}} \tag{1}\]  
where \(Q\) is flow rate, m\({}^{3}\)/s; A\({}_{0}\) is slotting nozzle section area, m\({}^{2}\); \(\rho\) is water density, kg/m\({}^{3}\); \(\Delta P\) is the pressure difference between two sections of slotting nozzle, MPa.  
To minimize reading errors, three flowmeter readings were repeatedly recorded once the pressure and flow of the system were stable. Test data recording and flow-coefficient calculations are shown in Table 5. The results show that the flow coefficient of the optimized slotting nozzle was higher than that of the original slotting nozzle. The optimized structure was, therefore, more conducive to the passage of fluid.