esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Result and Discussion

Table 1: Summary of electrochemical properties of three _TM_Te\({}_{2}\)equivalent Randles circuit models to interpret the mass transfer at the low frequency region (inset in Figure 2d). The Nyquist plots show semicircles in the kinetic control region at the high frequencies with the diameter of R\({}_{\text{CT}}\), which represents the charge transfer resistance. The values of R\({}_{\text{CT}}\) were 5.8, 13.0, and 21.6 k\(\Omega\), which increased in order of VTe\({}_{2}\), MoTe\({}_{2}\), and WTe\({}_{2}\) and the lowest R\({}_{\text{CT}}\) was found in VTe\({}_{2}\). We also estimated the double layer capacitance (C\({}_{\text{DL}}\)) between the _TM_Te\({}_{2}\) electrode and the electrolyte. Cyclic voltammetry (CV) was measured with different scan rates. Figure 3a shows the hysteresis of the CV curves taken in non-Faradaic voltage regions without any additional electrochemical reactions. We plotted the curves of the capacitive current density as shown in the CV curves in Figure 3a as a function of scan rate to obtain C\({}_{\text{DL}}\) of three _TM_Te\({}_{2}\) electrodes. Figure 3b shows similar C\({}_{\text{DL}}\) values due to the similar sample area, the same dielectric constant of the electrolyte, and the same distance of the double layer capacitance. These C\({}_{\text{DL}}\) values in _TM_Te\({}_{2}\) single crystals were almost 100 times smaller than the reported C\({}_{\text{DL}}\) in micro-sized flakes on the glassy carbon working electrode due to the difference of the electrochemical surface area [23]. Figure 2: (**a**) Experimental set-up for electrochemical measurements of three _TM_Te\({}_{2}\) single crystals. (**b**) Linear sweep voltammetry and (**c**) Tafel plots and (**d**) the electrochemical impedance spectroscopy of three _TM_Te\({}_{2}\) with the Randles circuit, which include the series resistance (R\({}_{\text{S}}\)), the charge transfer resistance (R\({}_{\text{CT}}\)), and the constant phase element (CPE). We summarized our experimental results of the HER performances observed in the three \(T\)MTe\({}_{2}\) in Table 1. Among the three \(T\)MTe\({}_{2}\), VTe\({}_{2}\) had the best HER performance with a small overpotential and Tafel slope. Even though VTe\({}_{2}\) had the smallest \(\alpha\) site density and TOF, the smallest R\({}_{\rm CT}\) was found in VTe\({}_{2}\). It seems that the HER process of \(T\)MTe\({}_{2}\) is significantly influenced by the charge transfer process. Therefore, to enhance the inherent catalytic activity of \(T\)MTe\({}_{2}\), we can further enhance the charge transfer for a small R\({}_{\rm CT}\) or to increase the electrochemical active surface area for large C\({}_{\rm DL}\). Considering the Gibbs free energies of \(\alpha\) sites in \(T\)MTe\({}_{2}\), the defect creation can help to enhance the hydrogen adsorption on basal plane of \(T\)MTe\({}_{2}\). Figure 4 shows the energies of hydrogen adsorption of \(\alpha\) and \(\beta\) sites in MoTe\({}_{2}\) with or without Te defects. We can decrease the energy of hydrogen adsorption at \(\alpha\) sites from 0.84 eV to 0.21 eV with Te defects, which can be a considerable value for better HER.
# On the Performance of Interleavers for Quantum Turbo Codes
## 1 Introduction
Simulations also show reduction in memory consumption, while the performance is comparable to or better than that of QTCs with random interleavers. The remainder of this paper is organized as follows: Section 2 presents the QTC system model and presents the classical interleavers considered in the quantum codes; Section 3 presents Monte Carlo simulations showing that interleaver construction is beneficial to lower the error floor of the considered QTCs; finally, Section 4 provides the conclusions reached in this paper.
# Creation, Analysis and Evaluation of AnnoMI, a Dataset of Expert-Annotated Counselling Dialogues +
## 5 Inter-Annotator Agreement (IAA)
### Supplementary IAA Measure: Intraclass Correlation  
Following MITI, we also use intraclass correlation (ICC) to analyse (Main) Behaviour and Talk Type at the label level to gain more insights and facilitate comparison with other studies. For each label, we count the number of occurrences of utterances annotated with the label in each session by each annotator. Thus, each of the 10 annotators has 7 label counts corresponding to the 7 IAA transcripts. Then, ICC is computed to describe how much of the total variation in the label counts is due to differences among annotators. Moreover, following MITI, the ICC scores are obtained using a two-way mixed model with absolute agreement and average measures.  
As Table 10 presents, all the (Main) Behaviour and Talk Type labels have excellent (0.75-1) [34] agreement scores, which shows the reliability of these annotations. Nevertheless, Change Talk and Sustain Talk have slightly lower ICCs--around 0.9--compared to the other ICCs that are almost 1.0, which somewhat echoes the lower Fleiss'-kappa-based IAA of Talk Type compared to that of (Main) Behaviour.
# A Crowd Cooperative Spectrum Sensing Algorithm Using a Non-Ideal Channel
## 5 Conclusions  
We apply the crowd sensing incentive mechanism to cooperative spectrum sensing under non-ideal channel, and propose a cooperative spectrum sensing algorithm based on crowd sensing incentive mechanism under non-ideal channel with the optimal SUs' utilities. First, the utility expectation function of the SUs is set up, and the maximum utility expectation is obtained by optimizing the sensing time and signal transmission power. The base station compares the received SUs judgment results with the fusion decision results, if the same, the secondary users can get a higher reward, otherwise the reward is low, so as to stimulate the SUs' sensing initiative and the performance of spectrum sensing is improved. Finally, the simulation shows that while the utility of the SU is getting the optimal value, the accuracy of spectrum sensing is also improved. The key insight of our algorithm is that spectrum sensing and crowd sensing incentive mechanism can be combined to stimulate the SUs and improve the sensing performance.  
Figure 4: The change of the SUs average utility with the transmission power of PU.  
Figure 5: The change of the correct probability with the transmission of PU.  
**Acknowledgments:** This work is supported by National Natural Science Foundation of China (61571234, 61631020).  
**Author Contributions:** The manuscript was written by [PERSON] under the guidance of [PERSON]. All authors reviewed the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Comparison of Single-Arm and Dual-Arm Collaborative Robots in Precision Assembly
###### Abstract  
The aim of the study is a multi-criteria comparative evaluation of robots cooperating with humans in single- and dual-arm variants used for the process of precise assembly of complex parts. RobotStudio simulation software with the Signal Analyzer add-on was used for comparative analyses. These studies were conducted as case studies. A robotic station was designed for the assembly of a computer motherboard and two robot variants were programmed to perform the assembly task while maintaining the same motion parameters and functions for both. Then, the TCP motion trajectories associated with the robot were analyzed, as well as monitoring signals from the robot controller during simulation, such as time, speed, acceleration and energy consumption. The costs and profitability of the robot variants were also calculated. The percentage share of tasks performed in the process was also analyzed, divided into assembly tasks and free movements. The differences between the robots in this process include time, 21 s single-arm versus 14 s dual-arm robots. The main influence on achieving the programmed speed was the length of the robot's TCP motion path. In most cases, the maximum programmed speed of 200 mm/s was achieved. The single-arm robot proved to be more energy-efficient, but the dual-arm robot proved to be 20% faster, which in the long run proved to be a more profitable robot. The profitability of the dual-arm robot paid off after eight months of operation. The case study presented in this paper, assembling a computer motherboard using single- and dual-arm collaborative robots, provides a guide for conducting similar comparative analyses of different robotic stations. Simulations enabled reliable verification of collaborative robots in technological processes, supporting the design of production processes and the analysis of several variants of robotic solutions.  
Keywords:industrial robots; collaborative robots; assembly; precision engineering +
Footnote †: journal: Applied sciences
# Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey
## 4 Research on Machine Learning-Based IDSs
### Session-Based Attack Detection  
A session is the interaction process between two terminal applications and can represent high-level semantics. A session is usually divided on the basis of a 5-tuple (client IP, client port, server IP, server port, and protocol). There are two advantages of detection using sessions. (1) Sessions are suitable for detecting an attack between specific IP addresses, such as tunnel and Trojan attacks. (2) Sessions contain detailed communications between the attacker and the victim, which can help localize attack sources. However, session duration can vary dramatically. As a result, a session analysis sometimes needs to cache many packets, which may increase lag. The session-based detection methods primarily include statistics-based features and sequence-based features.
## Nomenclature  
\begin{tabular}{l l l l} \(A\) & area (m\({}^{2}\)) & \(V\) & volume (m\({}^{3}\)) \\ \(A_{c}\) & the outer surface area of absorber tube (m\({}^{2}\)) & \(W\) & compressor work (kW) \\ \(COP\) & coefficient of performance & \(\Delta T\) & temperature difference (\({}^{\circ}\)C) \\ \(C_{b}\) & synthetic conductance W/(mK) & \(\Delta\Delta t\) & double difference of temperatures (\({}^{\circ}\)C) \\ \(C_{g}\) & bond conductance W/(mK) & & _Greek symbols_ \\ \(c_{p}\) & specific heat (kJ/kgK) & \(\alpha\) & absorptivity, distribution UA parameter \\ \(d\) & diameter of the U-tube (m) & \(\eta\) & efficiency \\ \(D_{ext}\) & external diameter of insulation (m) & \(\lambda_{ins}\) & header pipe insulation thermal \\ \(D_{\text{int}}\) & internal diameter of insulation (m) & \(\pi\) & p! \\ \(E_{\text{surv}}\) & energy saving (kWh) & \(\rho\) & density (kg/m\({}^{3}\)) \\ \(F\) & fin efficiency of straight fin collector efficient factor & \(\tau\) & transmissivity \\ \(F^{\prime}\) & heat removal factor & & _Subscripts_ \\ \(F_{R}\) & heat transfer coefficient (W/m\({}^{2}\)K) & \(a\) & ambient \\ \(h\) & specific enthalpy (kJ/kg) & \(as\) & absorption subsystem \\ \(h_{f}\) & convection heat transfer coefficient between the fluid & \(avg\) & average \\  & and the U-tube W/(m\({}^{2}\)K) & \(cs\) & compression subsystem \\ \(h_{g-a}\) & convection heat transfer coefficient W/(m\({}^{2}\)K) & \(dis\) & discharge \\ \(h_{p-g,c}\) & heat transfer coefficient due to the conduction W/(m\({}^{2}\)K) & \(e\) & evaporator \\ \(I\) & solar irradiance (W/m\({}^{2}\)) & \(etc\) & evacuated tube collector \\ \(L\) & the circumferential distance between the U-tubes (m) & \(g\) & generator \\ \(LMTD\) & logarithmic mean temperature difference (\({}^{\circ}\)C) & \(i\) & inlet, current time \\ \(L_{T}\) & length of the header pipe per one U-tube (m) & \(loss\) & loss \\ \(m\) & mass flow rate (kg/s) & \(min\) & minimum \\ \(Q\) & heat load (kW) & \(o\) & outlet \\ \(t\) & time & _rated_ & rated \\ \(T\) & temperature (\({}^{\circ}\)C) & _ref_ & reference \\ \(UA\) & multiplication of heat transfer coefficient and area (W/K) & \(s\) & isentropic \\ \(U_{e}\) & the heat loss coefficient from absorber tube to & \(suc\) & suction \\  & ambient W/(m\({}^{2}\)K) & \(st\) & storage tank \\ \(U_{L}\) & overall loss coefficient W/(m\({}^{2}\)K) & \(st\) & storage tank \\ \(U_{t}\) & the edge loss coefficient of the header tube W/(m\({}^{2}\)K) & & \\ \end{tabular}
## 4 WBANs: The Key M2M Area Network Technology for mHealth Applications
### WBAN Channel Modeling  
Accurate and reliable channel models are crucial for the design and evaluation of transmission and reception techniques employed at the PHY layer. Hence, considerable efforts have been made to define the characteristics of the body area propagation environment through extensive measurements and simulations. In this section, we present the most important results related to the channel modeling for on-body, in-body and HBC communications in WBANs, along with the different factors that affect the propagation characteristics.
## 4 Experimental Results and Analysis
### Mask Wearing Classification  
To better understand the model training process, we plotted the variations in model accuracy against the training iteration, and the results are shown in Figure 15. It shows that our proposed model consistently outperformed the original model during the training phase. To be more specific, it reveals that MobileNetv2 attained an accuracy of 95.49%, while ResNet achieved an accuracy of 96.61%. Upon incorporating SLPF into ResNet, the accuracy was improved to 97.50%. Moreover, by adding both SLPF and LA to the ResNet model, the accuracy was further increased to 98.14%. These results suggest that the proposed techniques led to a 1.58% enhancement in accuracy for ResNet. It can be seen from Figure 15 that the accuracy of the model with added SLPF and LA is almost always higher than that of the model with only added SLPF, and the accuracy of the model with only added SLPF is always higher than the original model. Unlike Figure 13, the accuracy of the model with SLPF and LA added at the beginning is already higher than that of the model with only SLPF added. This is mainly because the model is easy to learn the rules for identifying the wearing status of masks. Therefore, in the early stages of training, LA effectively enhances the feature layers that have a significant impact on the prediction results.
# Urban Free-Space Optical Network Optimization
## 4 Results
### Genetic Algorithm Experiments
#### 4.2.1 Comparison with Other Heuristics
This graph presents the run time of the 2-factor approximation method (**green**) and the GA (**blue**).
# Calibration and Analysis of Seeding Parameters of Soaked _Cyperus esculentus_ L. Seeds
## 4 Discussion
### Seed Soaking Pre-Treatment Facilitates Germination  
_C. esculentus_ is typically propagated using its tubers as seeds. When dry tubers are used for sowing, the hard outer skin and slow water absorption often result in low germination rates, prolonged germination duration, and weak seedling vigor. These factorssubsequently affect the subsequent growth and development of the plants, as well as the final yield establishment. Therefore, soaking _C. esculentus_ tuber seeds before sowing as a pre-treatment can effectively ensure seed germination and improve seedling emergence rate and uniformity [33].
# Performance Evaluation of Track Curvature Sensor for
## 2 Development of the Track Curvature Sensor  
### Curvature Measurement Method  
The algorithm for measuring the curvature for the curved section is derived from the geometric relationship located on the curve of the vehicle car body and the bogie when running in the curved section of the railway vehicle. In the case of running on a straight track, as shown in Figure 1, the bogies are arranged in line with the vehicle car body, and the bogie angle, which is the rotating angle of the bogie with respect to the vehicle car body, does not occur. However, when running on a curved track, as shown in Figure 2, the bogie located on the curved line rotates according to the curvature of the curve and a bogie angle (\(\theta_{1}\) and \(\theta_{2}\)) is generated between the vehicle car body and the bogie, and relative displacement (\(\Delta_{1}\) and \(\Delta_{2}\)) occurs between the vehicle car body and the bogie in the running direction, as shown in Figure 3.  
Figure 1: Position of car body and bogies in a straight line.  
From the geometric relationship formed between the car body and the front bogie and the rear bogie on the curved track above, the following approximate relationship can be established to derive the radius of the curve (\(R\)).  
\[\theta_{1}=\frac{\Delta_{1}}{x},\ \theta_{2}=\frac{\Delta_{2}}{x},\ \Delta=\Delta_{1}+ \Delta_{2},\psi=\frac{L}{R}2\psi=\theta_{1}+\theta_{2}=\frac{\Delta_{1}}{x}+ \frac{\Delta_{2}}{x}=\frac{\Delta_{1}+\Delta_{2}}{x}=\frac{\Delta}{x}=\frac{2L} {R}R=\frac{2 Lx}{\Delta} \tag{1}\]  
Therefore, the curvature \(C\), which is the reciprocal of the curve radius, is expressed as follows:  
\[C=\frac{1}{R}=\frac{2 Lx}{\Delta} \tag{2}\]  
where one-half of the distance between the center of the front bogie and the rear bogie (\(L\)), and lateral spacing between the curvature sensor and the bogie center (\(x\)) are a constant value. Therefore, the curvature of the curve can be estimated by measuring the relative displacement (\(\Delta_{1}\)) between the car body and the front bogie, and the relative displacement (\(\Delta_{2}\)) between the car body and the rear bogie in real time.  
Figure 3: Relative displacement between car body and bogie.  
Figure 2: Position of car body and bogies in a curved section.
## 7 Food Safety and Transportation  
The World Resources Institute's (WRI) Comprehensive Report on Future Food Demand, released in 2018, highlights the food industry's need to feed ten billion people before 2050. The statement aims for a five-course menu of solutions to decrease food waste and losses in the future [(167)]. Furthermore, the assumption is that reducing waste and loss of food by 25% by 2050 can decrease the food gap by 12%, the land gap by 27%, and the difference by which greenhouse gas emissions can be reduced by 15% [(167)]. For a better understanding, losses of food occurring along the food supply chain (from food production to consumption) at different geographical regions worldwide are depicted in Figure 5. The highest percentage of calories lost and wastage (61%) in food consumption was observed in North America and Oceania, whereas the lowest (5%) was observed in sub-Saharan Africa. In contrast, the lowest percentage of calories lost and wastage (23%) in food production and handling was observed in North America and Oceania, whereas the highest (76%) was observed in sub-Saharan Africa.  
Figure 5: Losses of food occur along the food supply chain [(5; 167)].  
In addition to the WRI, the information released by the FAO of the UN is also shocking. It is projected that one-third of all food produced for humans globally wasted each year is worth USD 1 trillion [168; 169]. Food waste in the US alone accounts for 1.3% of its gross domestic product [170]. Based on these numbers, it can be assumed that food wastage is critical in food industries, and such loss does not exist in non-food industries [171]. Even though food wastage worth USD 1 trillion has its economic value, more significantly, the impact of these losses on the environment is critical. For example, the water wasted on producing food that has never been consumed is the same as the water that can meet the water demand of all of Africa [167; 172; 173]. Today, all over the world, only 10% of perishable foods are properly well-preserved [174]. There is a massive opportunity to reduce food losses and better distribute food simply via realizing temperature-regulated transportation systems. Based on these facts, it can be concluded that enhancing food production is not enough to attain food safety. However, many useful actions are needed to obtain a better-skilled way of distributing existing food effectively. As shown in Figure 5, the IoT can monitor and maintain food quality at all stages of the entire supply chain from yield to consumption.
# Wire and Cable Quality Traceability System Based on Industrial Internet of Things and Blockchain
## 3 Identity Resolution System and Blockchain Technology Integration Method
### Blockchain-Based Identity Data Storage  
In terms of data processing, the IIoT supports the interconnection and sharing of data elements, but it is difficult to ensure data security and confirm data rights. Especially for discrete enterprises, the use of blockchain technology to solve data storage, sharing, security, and other problems in the manufacturing process of industrial products has become a consensus in the industry.  
Figure 5: Middleware for blockchain and IIoT architecture model diagram.  
The enterprise node uniquely identifies and encodes the information of the entire production elements such as personnel, machines, goods, raw materials, methods, environment, and testing in the cable production process through the IIoT identity resolution system, and the identification code runs through the cable supply chain. With the help of the blockchain to store the identification data, a system with the characteristics of decentralization, de-trust, and open traceability is formed [24]. Blockchain system user nodes utilize the consensus mechanism for common maintenance. Each transaction makes the blockchain generate new data records, and the endorsers peer is responsible for simulating the execution of the transaction. When a transaction request is received, it simulates the execution of this transaction request; no matter whether it is agreed to or rejected, it encrypts the result of the execution and return the signature to the client. Based on the pow (proof of workload) mechanism, the committing peers are selected to carry out bookkeeping and broadcast to synchronize with the whole network. When other user nodes receive the broadcast, they generate the corresponding hash value from the local identification coding data in piecewise records and then send them to the blockchain for encrypting and storing the data after being packaged into a block. The generated block structure consists of a block header, transaction number, and data list. The identity resolution data storage scheme based on blockchain technology is shown in Figure 6.  
The management of transaction data is carried out through the quality traceability system. When the user applies for access resolution, the blockchain smart contract is executed to verify the user's access rights, the data corresponding to the resolved identification are analyzed, and the data are finally shared and traced.