esa-sceva/satcom-chunk-collection huggingface dataset:
# A Risk-Sensitive Intelligent Control Algorithm for Servo Motor Based on Value Distribution
## 3 Problem Description
### State Information of Servo Motor  
Three commonly used servo motors are considered in this work: an externally excited DC motor (ExtEx), a series DC motor (Series), and a three-phase permanent magnet synchronous motor (PMSM). To describe the operational state of a servo motor, it is necessary to use different physical quantities such as the motor's angular velocity, actual current, actual voltage, and motor torque.  
For the ExtEx motor, its state \(O_{ExtEx}\) can be described by the angular velocity \(\omega\), torque \(T\), armature current \(i_{A}\), excitation circuit current \(i_{E}\), armature voltage \(u_{A}\), excitation circuit voltage \(u_{E}\), and service voltage \(u_{ser}\)[2], as shown in Equation (3):  
\[O_{ExtEx}=[\omega,T,i_{A},i_{E},u_{A},u_{E},u_{ser}]. \tag{3}\]  
In the series DC motor, the armature and the excitation circuit are connected in series, so their armature values are the same, i.e., \(i_{A}=i_{E}\), and both of them are denoted as \(i\). According to the basic principles of a series circuit, the input voltage is the sum of the armature voltage and excitation circuit voltage, which can be denoted as \(u=u_{A}+u_{E}\)[2]. Therefore, the state of the series DC motor \(O_{Series}\) is denoted as Equation (4):  
\[O_{Series}=[\omega,T,i,u,u_{ser}]. \tag{4}\]  
A PMSM motor has three phases, phase A, phase B, and phase C, and each of them has a phase voltage and phase current, that are denoted as \(u_{a}\), \(u_{b}\), \(u_{c}\), \(i_{a}\), \(i_{b}\), and \(i_{c}\), respectively. To simplify the dynamic mathematical model of a permanent magnet synchronous motor in a three-dimensional static coordinate system, it is common to convert it into a DQ two-phase rotor coordinate system and simplify the inductance matrix to a constant. In DQ coordinates, the voltage components and current components in the \(d\) and \(q\) direction are denoted as \(u_{sd}\) and \(u_{sq}\), \(i_{sd}\) and \(i_{sq}\), respectively [26]. Moreover, the rotor flux of a PMSM motor is denoted by \(\varepsilon\). Therefore, the state of a PMSM is denoted as \(O_{\text{PMSM}}\), which is shown in Equation (5).  
\[O_{\text{PMSM}}=[\omega,T,i_{a},i_{b},i_{c},i_{sq},i_{sd},u_{a},u_{b},u_{c},u_ {sq},u_{sd},u_{ser},\varepsilon]O_{\text{PMSM}} \tag{5}\]
## 4 Experimental Results and Discussion
### Experiment Design  
Table 1 shows the KDD '99 data sets. For our experiment, we have selected a number of random samples from the original KDD '99 dataset as used in [19]. An extracted 1,774,985 instances as a training set listed in Table 4 and 67,688 instances of the independent testing set. Table 4 also shows the percentage of each class. The most of the important parameter for the performance evaluation of IDS is detection rate (DR). This parameter measures the number of correctly detected attacks from a total number of attacks as defined in Equation (10). Then, the false alarm rate (FAR) measures the ratio between the numbers of normal connections that are incorrectly misclassified as attacks and a total number of normal connections defined in Equation (11).  
Furthermore, it should be noted that the parameter \(\lambda\) in the SPLR determines the degree of sparsity. An increase in \(\lambda\) corresponds to a decrease in non-zero coefficients (selected feature variables) while the degree of sparsity can be measured by Equation (12).  
\[DR=\frac{Number\ of\ correctly\ Attacks\ Detected}{\#\ Number\ of\ Attacks} \tag{10}\]  
\[FAR=\frac{False\ Positive}{\#Number\ of\ Normal\ connections} \tag{11}\]  
\[Sparsity=\frac{Number\ of\ discarded\ features}{\#number\ of\ features} \tag{12}\]  
Here, the proposed technique (SPLR) is assessed with the similar training and testing datasets (1,074,985) as were used in [19; 30; 31]. The proposed SPLR feature selection technique is compared against the KDD '99 in order to show the potential in classification with better performance than [19] as well as other classifier models. However in the experimental results, we provide the discriminative feature selection and classification accuracy by SPLR as shown in Figure 5a,b respectively. Furthermore, Figure 5a presented the OCA and along with degree of sparsity at various \(\lambda\) settings, if \(\lambda_{1}=0.1\) then the degree of sparsity is high (few features are selected as shown in Figure 5b, however at the same parameter values the detection accuracy by SPLR is high (0.9786).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Training Samples** & **\% of Occurrence** \\ \hline Normal & 812,814 & 75.6 \\ Denial of Service & 947,267 & 22.9 \\ Probe & 13,853 & 1.29 \\ R2L & 997 & 0.089 \\ U2R & 54 & 0.0047 \\ Total Samples & 1,774,985 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Dataset descriptions.
# ViT-Cap: A Novel Vision Transformer-Based Capsule Network Model for Finger Vein Recognition
###### Abstract  
Finger vein recognition has been widely studied due to its advantages, such as high security, convenience, and living body recognition. At present, the performance of the most advanced finger vein recognition methods largely depends on the quality of finger vein images. However, when collecting finger vein images, due to the possible deviation of finger position, ambient lighting and other factors, the quality of the captured images is often relatively low, which directly affects the performance of finger vein recognition. In this study, we proposed a new model for finger vein recognition that combined the vision transformer architecture with the capsule network (ViT-Cap). The model can explore finger vein image information based on global and local attention and selectively focus on the important finger vein feature information. First, we split-finger vein images into patches and then linearly embedded each of the patches. Second, the resulting vector sequence was fed into a transformer encoder to extract the finger vein features. Third, the feature vectors generated by the vision transformer module were fed into the capsule module for further training. We tested the proposed method on four publicly available finger vein databases. Experimental results showed that the average recognition accuracy of the algorithm based on the proposed model was above 96%, which was better than the original vision transformer, capsule network, and other advanced finger vein recognition algorithms. Moreover, the equal error rate (EER) of our model achieved state-of-the-art performance, especially reaching less than 0.3% under the test of FV-USM datasets which proved the effectiveness and reliability of the proposed model in finger vein recognition.  
finger vein; biometrics; computer vision; deep learning +
Footnote †: journal: Medical Image and Vision Letters  
**Copyright** © 2022 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 4 System Development and Experimental Verification
### System Development
#### 4.1.3 Communication Interface and HMI Interaction Design  
This system uses the Modbus-TCP communication protocol for data transmission between it and the robotic arm control system. Modbus is a communication protocol applied to the seventh layer of the OSI model, used to achieve client/server communication between devices of different types of buses or networks. MODBUS is a request/response protocol and provides services specified by function codes. MODBUS function codes are elements of MODBUS request/response PDUs. In the communication process, the host (this system) sends request information, including address code, function code, data, and CRC check to the slave (robotic arm control system). The address code is used to identify the address of the slave; the function code is used to specify the type of operation requested, and the data represents the registered address and quantity to be read or written. CRC check ensures the correctness of data transmission. After the slave receives the request, it parses and processes it and executes the corresponding operation according to the function code. After the operation is completed, the slave returns the result to the host. The returned data includes address code, function code, data, and CRC check information. After the host receives the response data, it parses the data. During system operation, access to external networks is prohibited, and communication within the local area network is only allowed with the robotic arm system, thereby achieving secure communication with the robotic arm control system.  
As shown in Figure 12, the HMI interaction design includes camera imaging modules, radar imaging modules, equipment status modules, obstacle warning modules, accessory control modules, and data processing modules. Users can independently configure UI  
Figure 11: Software Flowchart.  
display modes and point cloud display parameters, manually start the camera and LiDAR devices, and control data processing functions.
# BDCOA: Wavefront Aberration Compensation Using Improved Swarm Intelligence for FSO Communication
## 3 Simulation Results
### Bit Error Rate  
The BER of BDCOA is analyzed with different population sizes, as shown in Figure 12. The evaluation of BER with different optimization methods is shown in Figure 13. From the figures, it is confirmed that the BER of BDCOA is lesser when compared to MOA, POA, and COA. An appropriate control signal from BDCOA with DM is used to compensate for the wavefront distortions, thereby improving the quality of the received signal. The enhanced quality of the received signal minimizes the probability of bit errors, therefore also minimizing the BER.  
Figure 11: SR for different optimization approaches.  
Figure 12: BER for different population sizes.
## 3 Results and Discussion
### Consequence of Climate Change on Precipitation
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**SSP Scenario**} & \multirow{2}{*}{**Period**} & \multirow{2}{*}{
\begin{tabular}{c} **Representing** \\ **Scenario** \\ \end{tabular} } & \multicolumn{2}{c}{**\% Change in Precipitation**} \\ \cline{3-5}  & & & **NRB** & **VRB** \\ \hline \multirow{8}{*}{SSP245} & \multirow{8}{*}{Near future (2022–2060)} & Warm-Wet & \(-\)4.51 & \(-\)2.06 \\ \cline{3-5}  & & Cold-Wet & +7.51 & +5.35 \\ \cline{3-5}  & & Average & +5.66 & +4.47 \\ \cline{3-5}  & & Cold-Dry & \(-\)1.33 & \(-\)4.60 \\ \cline{3-5}  & & Warm-Dry & \(-\)0.17 & +0.96 \\ \cline{3-5}  & & Warm-Wet & +25.52 & +24.58 \\ \cline{3-5}  & & Cold-Wet & +19.91 & +18.04 \\ \cline{3-5}  & & Average & +13.27 & +11.74 \\ \cline{3-5}  & & Cold-Dry & +10.29 & +7.80 \\ \cline{3-5}  & & Warm-Dry & +7.89 & +6.49 \\ \hline \multirow{8}{*}{SSP370} & \multirow{8}{*}{Near future (2022–2060)} & Warm-Wet & +17.79 & +19.30 \\ \cline{3-5}  & & Cold-Wet & +8.30 & +7.06 \\ \cline{3-5}  & & Average & +4.29 & +2.54 \\ \cline{3-5}  & & Cold-Dry & \(-\)2.68 & \(-\)3.55 \\ \cline{3-5}  & & Warm-Dry & \(-\)0.51 & +0.45 \\ \cline{3-5}  & & Warm-Wet & +36.41 & +35.93 \\ \cline{3-5}  & & Cold-Wet & +34.73 & +34.60 \\ \cline{3-5}  & & Average & +19.45 & +13.53 \\ \cline{3-5}  & & Cold-Dry & +7.57 & +4.34 \\ \cline{3-5}  & & Warm-Dry & +1.65 & \(-\)0.24 \\ \hline \multirow{8}{*}{SSP585} & \multirow{8}{*}{Near future (2022–2060)} & Warm-Wet & +23.88 & +22.87 \\ \cline{3-5}  & & Cold-Wet & +9.27 & +7.69 \\ \cline{1-1} \cline{2-5}  & & Average & +14.32 & +10.60 \\ \cline{1-1} \cline{2-5}  & & Cold-Dry & \(-\)1.09 & \(-\)2.50 \\ \cline{1-1} \cline{2-5}  & & Warm-Dry & \(-\)1.57 & +0.44 \\ \cline{1-1} \cline{2-5}  & & Warm-Wet & +60.51 & +59.09 \\ \cline{1-1} \cline{2-5}  & & Cold-Wet & +35.10 & +31.38 \\ \cline{1-1} \cline{2-5}  & & Average & +22.11 & +16.35 \\ \cline{1-1} \cline{2-5}  & & Cold-Dry & +25.52 & +19.46 \\ \cline{1-1} \cline{2-5}  & & Warm-Dry & +8.48 & +7.51 \\ \hline \hline \end{tabular} Note: “\(\star\)” sign indicates increasing in future, “\(\star\)” sign indicates decreasing in future, NRB—Nagavali River Basin, VRB—Vamssdhara River Basin.  
\end{table} 
Table 4: Percentage change in precipitation compared to historical data. Figure 4: Mean monthly precipitation, streamflow, and sediment yield for the Nagavali watershed. (a) and the Vamsadhara watershed (b) based on historical data from 1975 to 2014.
# NTT and Inverse NTT Quantum Circuits in CRYSTALS-Kyber for Post-Quantum Security Evaluation
## 1 Introduction
### Contributions  
1. First Quantum Circuit Implementation for NTT and inverse NTT: To the best of our knowledge, this is the first implementation of Kyber NTT and InvNTT in quantum circuits. We designed a structure for operating NTT and inverse NTT in quantum circuits.
2. Quantum Circuit for Optimal Utilization of Quantum Resources: We propose NTT and inverse NTT quantum circuits to maximize the utilization of quantum resources. The design of an efficient quantum circuit aims to curtail the consumption of quantum resources, including qubits and quantum gates, throughout the operational process.
3. Offers the Foundation for Quantum Strength Assessment of Kyber: The quantum circuit introduced in this paper serves as a foundational tool for assessing the quantum robustness of the Kyber cryptographic scheme. As we anticipate the emergence of powerful quantum computers in the future, it will become imperative to evaluate post-quantum resistance for post-quantum cryptography (PQC) protocols.
## 3 Problem Formulation and Solutions
### Solution: Hybrid Precoder
#### 3.2.1 Analog Precoder (AP)  
The purpose of the AP is to produce significant array gain through the leverage of a multitude of antennas within the mMIMO system. This is possible to accomplish by aligning the phases of the aggregate DL channel \(\mathbf{H}=[\mathbf{h}_{1},\ldots,\mathbf{h}_{K}]\). Following [3], quantized phase shifters are utilized for the design of the AP. Both FC and SC architectures are taken into account and compared. The pseudo-code for the FC AP is presented in Algorithm 2.  
In the case of the SC structure, step (**11**) can be realized by exploiting \(\partial p_{d}^{sub}(n)=\frac{1}{\sqrt{M}}exp(j\frac{2\pi\theta}{2^{B}})\), where \(n=(d-1)M+1,(d-1)M+2,\ldots,dM\). Subsequently, once the formulation of the AP is completed, the DP design is carried out with the goal of maximizing the achievable sum rate while drastically suppressing noise/interference.
# Green Analytical Chemistry--Recent Innovations
## 3 Innovations in Green Analytical Techniques
### Green Separation Techniques
#### 3.1.1 Green Gas Chromatography (GC) and Liquid Chromatography (LC)
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Aspect**} & **Standard LC Columns** & **Capillary LC Columns** \\  & **(4.6 mm i.d.)** & **(10–100 \(\upmu\)m i.d.)** \\ \hline \multirow{2}{*}{Column Efficiency (N)} & \multirow{2}{*}{\(\sim\)50,000 plates/m} & \multirow{2}{*}{
\begin{tabular}{c} \textgreater{}100,000 plates/m, due to \\ smaller particles \\ \end{tabular} } \\  & & & \\ \hline \multirow{2}{*}{Resolution (Rs)} & Moderate to high (e.g., & Higher (e.g., Rs = 2.5 for \\  & Rs = 1.8 for enantiomers) & enantiomers) \\ \hline \multirow{2}{*}{Solvent Consumption} & \multirow{2}{*}{High (1–1.5 mL/min flow rate)} & Low (e.g., 300 nL/min, \\  & & 100–1000\(\times\) reduction) \\ \hline \multirow{2}{*}{Sample Capacity} & \multirow{2}{*}{High, microgram range} & Low, nanogram range, \\  & & & limits preparative use \\ \hline \multirow{2}{*}{Sensitivity} & Standard, may require larger & Enhanced, due to reduced \\  & injection volumes & dilution, better with MS \\ \hline \multirow{2}{*}{Trade-offs} & \multirow{2}{*}{Lower efficiency, higher waste} & Higher efficiency, but \\  & & lower capacity, higher cost \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of traditional LC and capillary LC based on efficiency, resolution, solvent use, sample capacity, and sensitivity. Capillary LC enhances efficiency and reduces waste but requires specialized instrumentation and has lower sample capacity.
###### Abstract  
Integer ambiguity resolution (IAR) is important for rapid initialization of precise point positioning (PPP). Whereas many studies have been limited to Global Positioning System (GPS) alone, there is a strong need to add Globalnaya Navigatsionnaya Sputnikovaya Sistema (GLONASS) to the PPP-IAR solution. However, the frequency-division multiplexing of GLONASS signals causes inter-frequency code bias (IFCB) in the receiving equipment. The IFCB causes GLONASS wide-lane uncalibrated phase delay (UPD) estimation with heterogeneous receiver types to fail, so GLONASS ambiguity is therefore traditionally estimated as float values in PPP. A two-step method of calibrating GLONASS IFCB is proposed in this paper, such that GLONASS PPP-IAR can be performed with heterogeneous receivers. Experimental results demonstrate that with the proposed method, GLONASS PPP ambiguity resolution can be achieved across a variety of receiver types. For kinematic PPP with mixed receiver types, the fixing percentage within 10 min is only 33.5% for GPS-only. Upon adding GLONASS, the percentage improves substantially, to 84.9%.  
GLONASS; precise point positioning; ambiguity resolution; uncalibrated phase delay; inter-frequency code bias 2018 10.399/rs10030399
## 2 Motivating Applications and Use Cases
### Smart Home  
The smart home application provides home and health management services. It uses surveillance cameras, audio sensors, smart grid sensors, and bio-sensors for collecting environment data and the health data of individuals at home. Then, the application uses computationally intensive machine learning models and audio-video processing algorithms for activity and situation recognition, early threat detection, emotion detection, and abnormal health condition detection. On the basis of the analysis, the application takes necessary actions, such as sending situation information to an emergency or security service provider or asking a mobile robot for help.
# Influence of Cutting Parameters and MQL on Surface Finish and Work Hardening of Inconel 617
## 2 Related Works
### Gaps and Contribution  
From the literature review, multiple gaps in this field have been identified and addressed in this paper. Overall, there is a scarce amount of published knowledge on the topic of machining Inconel 617, with the existing literature focused on improvements of other sister materials such as Inconel 718. Below are some of the identified gaps that were found in the literature that were used to build the studies for this paper:  
* It was determined in the literature that the effectiveness of MQL deposition depended on the workpiece material that was being machined. However, there is a lack of evidence to support its use in the machining of Inconel 617. Therefore, there is a need to understand the effectiveness of MQL machining on Inconel 617, and, in particular, how it compares to machining in a conventional flood coolant.
* The existing literature draws focus to the surface finish as the primary metric of surface quality in turning Inconel 617. However, in high-stress, high-temperature environments, it is also important to understand the subsurface post machining to evaluate the change in material properties that may affect the candidacy of the components.
* While tool coating applications are widely studied for Inconel 718, the efficacy of these coatings to increase tool life are not thoroughly studied for Inconel 617. This is pertinent knowledge as the large-scale manufacturing of Inconel 617 components is imminent due to the encroaching deployment of Gen IV SMRs.  
The main contribution of this paper is to provide a comprehensive assessment on the machinability of Inconel 617 to further support its candidacy in the nuclear sector. Furthermore, the cutting parameters used in machining are analyzed alongside MQL deposition for their impact on the surface and subsurface of the machined material. Through identifying the gaps, the following experimental studies were conducted:  
1. A comparison of various tool coatings to the uncoated tungsten carbide turning tool: This test was performed to assess the efficacy of tool coatings in machining of Inconel 617 to improve the tool life. Conclusions from this experiment create a baseline for improving tool life in machining and improve the efficiency of mass manufacturing Inconel 617 components.
2. Cutting parameter variation using [PERSON]'s L9 orthogonal array to measure the influence of three parameters on the surface finish: A statistical analysis was performed to identify what, if any, cutting parameters have the largest impact on the surface finish. The results from this experiment were used to design the final experiment.
3. Machining in MQL compared to conventional flood coolant for Inconel 617: The cutting parameters that generated the best surfaces and lowest tool wear in the second experiment were used. Samples were then measured for surface roughness and the level of work hardening in the subsurface in order to better understand the impact of both the coolant and cutting parameters on the surface quality of Inconel 617 after machining.
# Recent Progress on Underwater Wireless Communication Methods and Applications
## 2 Main Methods of Underwater Wireless Communication  
Presently, underwater wireless communication predominantly hinges on three major technologies: acoustic communication, electromagnetic wave communication, and optical wireless communication. Each of these approaches possesses unique strengths and weaknesses, thereby rendering them appropriate for various application scenarios in underwater environments. Acoustic communication, for instance, offers significant advantages in terms of long-range transmission capabilities, making it particularly suitable for deep-sea exploration and large-scale underwater sensor networks. However, it suffers from relatively low data rates and high latency. On the other hand, electromagnetic wave communication, though limited in underwater propagation due to high attenuation, can achieve high data rates over short distances and is thus ideal for applications near the water surface or in shallow water. Optical wireless communication stands out for its extremely high data transmission rates and resistance to interference, but its performance is highly susceptible to water turbidity and particulate matter, confining its application to short-range, clear-water environments such as underwater data centers or high-definition video transmission systems. The diverse characteristics of these technologies drive the ongoing research into hybrid communication architectures and adaptive protocol designs, aiming to maximize the advantages of each method while mitigating their limitations in complex and dynamic underwater settings. Figure 1 shows the structure of underwater wireless communication system.