esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Simulation Environment and Results
### Connectivity Components of the Simulator  
We have recently integrated two different connectivity research enablers in the environment, since all the data are typically considered perfect and delayless in the simulators, which is not a realistic assumption. There are delays and interference in practice, affecting how safely the ship can operate in different environments. Propagation delays are caused by the physical link distance, e.g., between the remote operator and the ship, and there are also processing delays. Packets can be lost in the network, e.g., due to intentional interference or jamming and also due to congestion in the network. Thus, we have modified the ship simulator to take into account the imperfections in the connectivity and in order to use existing networks in the research and development.  
First, a connectivity component software that enables studying remote control systems, the effects of delays or disturbances in communication systems and optimization of data transmission was implemented. The software takes the national marine electronics association (NMEA) data [55] from the simulator bridge and packetizes it into transmission control protocol (TCP) or user datagram protocol (UDP) data. The data are buffered before sending it to the ship simulator to emulate remote access conditions. The user can select the delay values in seconds and packet loss ratio percentages that can be based on the measurements in a real network. The connectivity component can be configured both in the start time and in the runtime of the simulations to allow flexible research activities.  
Second, we have recently integrated the simulator to our 5G test network [56] using the implemented software. This enables research with multiple different radio technologies and setups, since the data can be transmitted over actual operational networks in different locations in Finland. The available test network interfaces include multiple terrestrial technologies and high-speed satellite access as well. Both commercial radios and software-defined radio (SDR)-based research equipment are used. Furthermore, we can simulate and emulate large-scale networks with various tools. In addition to sending the control data from the bridge over the network, we are also able to route, e.g., SA data  
Figure 8: Track and track limits.  
over the connectivity links to study the effects of missing or modified environmental information. Thus, we measure the performance of the system with varying delays in order to determine limits for safe operation, looking specifically into the ability to avoid collisions with the implemented setup. We evaluate this by measuring the distance between our own ship and the other vessels during evasive actions.
# Research and Application of a Multi-Agent-Based Intelligent Mine Gas State Decision-Making System
## 4 Construction of the Intelligent Mine Gas State Decision-Making System
### Application Layer  
User interaction with the system primarily occurs through an intuitive graphical user interface (GUI). This interface integrates multiple functional modules, including alarm queries, intelligent Q&A, and report generation, allowing users to easily access real-time gas concentration data, historical alarm records, and relevant regulatory information, facilitating in-depth data analysis and decision-making. The system also supports voice recognition functionality, enabling users to perform queries and operations via voice commands, further enhancing usability.  
In practical operation, after logging into the system, users can quickly understand the current gas concentration, alarm status, and historical trend charts through the dashboard. When an alarm occurs, the system immediately triggers an alert and provides recommended measures. Users can document and process alarm events to ensure traceability. Additionally, the system can regularly generate safety reports and store them in the safety management system, supporting management in conducting overall safety assessments.  
To facilitate integration with existing mine management systems, the application layer provides standardized API interfaces. This integration mechanism enables the system to obtain key operational data from the mine in real time and feedback gas monitoring results and alarm information to the mine management program, ensuring bidirectional data flow and enhancing the timeliness and accuracy of decision-making.
## 4 Conclusions  
We have proposed and demonstrated a spatial frequency domain multiplexing method to extend the effective imaging depth range for SDOCT system without any expensive acousto-optic or electro-optic modulation device. The spatial frequency domain multiplexing is realized by using two pivot-offset galvo scanners in the two reference arms. The images of shallow and deep regions of a sample can be multiplexed and acquired simultaneously with no image acquisition rate penalty. The proposed two-reference-arm spatial frequency domain multiplexing SDOCT system provides an approximately 1.9-fold increase in the effective imaging range compared with that of a conventional single-reference-arm full-range SDOCT system. We present key system parameter design criteria for ensuring a high signal/crosstalk ratio imaging. The images of an artificial phantom and the swine adipose tissue reconstructed with the proposed method demonstrate the expected increase in the effective imaging depth range. This simple and low-cost method is also applicable to the SSOCT system, and desirable in the in vivo applications requiring a relatively long imaging range.  
This work was supported by the National Natural Science Foundation of China (61205201, 11174147), the Fundamental Research Funds for the Central Universities, Nanjing University of Aeronautics and Astronautics (NZ2015104), Postdoctoral Research Funds of Jiangsu Province (1201034C), the China Postdoctoral Science Foundation (2013M531345). [PERSON] and [PERSON] conceived the ideas; [PERSON] and [PERSON] designed and performed the experiments; [PERSON] and [PERSON] processed the data; [PERSON], [PERSON], and [PERSON] contributed with the materials and samples; [PERSON] wrote the paper.  
The authors declare no conflict of interest.
# Efficient Intersection Management Based on an Adaptive Fuzzy-Logic Traffic Signal
## 1 Introduction
From this approach, the controller is capable of adjusting the signalization long enough to clear the queues formed during red intervals, improving the operational performance of the intersection by reducing waiting times, queue lengths, and densities. The proposed controller is based on a type-1 fuzzy logic, which only demands operators to know the minimum and maximum expected values for arrival flows and cycle lengths to establish if-then mappings. In addition, the algorithmic proposal is based on sequential instructions, such as basic arithmetic and if-then statements. Therefore, the execution time is constant, since it directly depends on the number of fuzzy sets and incoming traffic streams, whose size remains immutable during the execution. This last means the proposal could be implemented over simple microcontrollers becoming an affordable solution for many cities. The controller performance was evaluated using a microsimulation model of a real-world intersection. The simulation scenario was calibrated on data collected from a six-month traffic survey. Using the simulated intersection as a test bench, the proposed controller (Proposed Adaptive FL) was compared against five approaches: (a) the existing pre-timed controller (Pre-Timed), (b) a time-gap-based actuated controller (Time-Gap), (c) a time-delay-based actuated controller (Time-Delay) [43; 44], (d) a fuzzy system for green extension (FL Green-Extension), and (e) an adaptive FL-based method with modified Webster's formula (FL Phase-Adjustment) [34]. Simulation results show that the performance of the proposed controller is competitive with the most sophisticated and effective approaches, even without needing complex or expensive requirements for deployment and setup. The remainder of the document is organized as follows. Section 2 summarizes the state-of-the-art on traffic signal controllers. Section 3 presents some background definitions. In Section 4, the proposed adaptive FL controller is described. In Section 5, the proposedcontroller is evaluated, and the findings are discussed. Finally, the main conclusions are stated in Section 6.
## 2 Waste Glass as Resource for Construction Materials
### Glass Wastes as Cementitious Substitutions  
Cement production is known as a contributor to greenhouse gas, and efforts to reduce its use in construction materials rely on the ability of substitutes that have the chemical potential to undergo pozzolanic reactions [19]. Pozzolanic reactions occur when vitreous siliceous materials in the presence of a binder paste react with excess Ca(OH)2 to form a C-S-H compound that strengthens the concrete. The pozzolanic reaction from silica can generally be represented by the equation [15]:  
\[\text{Ca(OH)}_{2}+\text{SiO}_{2}+\text{H}_{2}\text{O}\rightarrow\text{CaO SiO}_{2}\text{ H}_{2}\text{O}.\]  
CWG powders with particle size less than 140 um are known [20; 21] to exhibit pozzolanic reactions in cement, and they have been investigated as substitutions for general purpose cement in concrete. CWG substitution for pozzolanic activity plays a key role in water absorption of concrete, with a record of higher water absorption at early ages [22]. Another report [5] recorded a lower water absorption for late age where a CWG pozzolans concrete mix suggests slower rates of pozzolanic reaction compared with hydration reaction. Although the siliceous pozzolanic reactions caused increase strength, 100% substitutions of glass powder for cement cannot be achieved, since SiO2 on its own does not undergo the hydration reaction required for the cement binding action. Substitutions of 10 to 40% have been reported [20; 23; 24; 25]. It is found that for substitutions higher than 10%, compressive strengths in early ages decrease with an increasing proportion of substitution of glass powders. However, late ages strength of concretes with siliceous pozzolans either matched or surpassed the mechanical strengths of control mixes. The late development of full strength can largely be explained by the slower rate of the pozzolanic reactions compared with the normal hydration reaction in a fully cementitious paste.  
The potential CWG powders to form ASR in a cementitious mix have been investigated, and it is generally agreed that the high specific surface attending the micron-size powders promotes the formation of pozzolanic reaction ahead of ASR. Therefore, further studies [26; 27; 28] have shown that glass particles with less than 1.0 mm are less susceptible to undergo ASR in concrete mix.
# Performance of the Transport Layer Security Handshake Over 6 TiSCH
## 6 Improvements in (D)TLSv1.3  
In 2013, the IETF started working on a new version of the TLS protocol, called TLSv1.3 [30]. TLSv1.3 entered RFC status in March 2018. The latest iteration of the TLS protocol contains some significant improvements that are also interesting for low-power devices. Not only does the new specification improve the security of the protocol but it also reduces the latency of the handshake phase. It now takes one full RTT less to complete. While TLSv1.2 takes two full RTTs until application data can be exchanged, TLSv1.3 by default only requires one RTT. The client tries to guess the key exchange algorithm the server is going to pick, allowing the client to send its key share during the first RTT. In case the client picks an unsupported algorithm, the server requests a new key share. Since the number of possible key exchange algorithms is drastically reduced in TLSv1.3 compared to TLSv1.2, there is a good chance the client chooses a supported algorithm.  
At the time of writing, the DTLSv1.3 specification [31] is still in draft status at the IETF. In addition to the changes it inherits from the TLSv1.3 specification--faster handshake completion--it also updates some features specific to DTLS. It omits superfluous version numbers and type fields in the headers, it has a novel variable-length record header with support for a connection identifier and uses fewer bits to encode sequence and epoch numbers. However, the most important change concerning constrained devices is the introduction of a new content type called ACK. A DTLSv1.3 endpoint can use acknowledgment messages when it detects disruptions during the reception of a DTLS flight. The draft proposes to arm a timer for 1/4 of the duration of the current retransmission timer timeout after a disruption is detected. When the timer expires, the endpoint generates an acknowledgment for the parts of the flight that were well-received and correctly processed by including a list of the record numbers in the acknowledgment message. Upon reception of an acknowledgment the sender disables the retransmission timer and retransmits only the record fragments that were lost.
## 8 Experimental Results
### Performance Evaluation of Electrode Positions
#### 8.1.3 Confusion Matrix  
The confusion matrix, see Table 9, shows that the NREM stage is the most accurately classified, followed by Wake and REM stages. In binary classification, the NREM has been mis-classified the most by calling it Wake stage; one explanation could be the increased number of Wake stage cases. In the case of multi-class classification, the most mis-classified stage is Wake mislabeled as NREM.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{1}{
# Video Multi-Scale-Based End-to-End Rate Control in Deep Contextual Video Compression
## 2 Background and Related Work
### Video Multi-Scale Super-Resolution  
In recent years, learning-based super-resolution techniques have been widely used. For example, in [23], the synergy of supervised learning and super-resolution technology is exploited to enable low-overhead beam and power allocation. For video coding, super-resolution techniques assume that the relationship between low-resolution and high-resolution frames can be learned from a training set that contains several low-resolution frames and their corresponding high-resolution frames. For the traditional single-framesuper-resolution approach called patch-based or dictionary-based super-resolution [24], a low-resolution input frame is segmented into small patches. Then, each patch is compared against the high-resolution patches in the training set to find its best match. Finally, an input low-resolution patch is replaced with the corresponding high-resolution patch of its best match. Since the residual network structure adopts a skip connection in which the input of the current network is the difference between the input and output of the previous network, it can effectively solve the gradient disappearance problem to improve the learning ability of the network. The residual network is widely used to extract high-level features in super-resolution techniques [25]. To extend the super-resolution application, single-frame super-resolution is developed for videos where a low-resolution video is segmented into spatiotemporal patches [26]. Learning-based multi-frame super-resolution methods are also introduced in videos by leveraging the temporal correlation between video frames to reconstruct an accurate high-resolution video [27; 28].
## 2 Methods and Data
### Methods
#### 2.1.3 Sensitivity Analysis  
Based on the two types of simulation experiments, the sensitivities of vegetation growth to CO\({}_{2}\) (S\({}_{\text{CO2}}\)), SW (S\({}_{\text{SW}}\)), and VPD (S\({}_{\text{VPD}}\)) at the annual scale can be estimated using the differences between the first simulation results (E\({}_{\text{all}}\)) and the second type of controlling experiment (E\({}_{\text{CO20}}\), E\({}_{\text{SW0}}\), and E\({}_{\text{VPD0}}\)). Specifically, the S\({}_{\text{CO2}}\), S\({}_{\text{SW}}\), and S\({}_{\text{VPD}}\) can be calculated using Equations (3)-(5):  
\[\triangle Y_{(E_{ALL}-E_{\text{CO2}})}=\beta_{0}+S_{\text{CO2}}\cdot\triangle CO _{2(E_{ALL}-E_{\text{CO20}})}+\varepsilon \tag{3}\]  
\[\triangle Y_{(E_{ALL}-E_{\text{SW}})}=\beta_{0}+S_{\text{SW}}\cdot\triangle SW _{(E_{ALL}-E_{\text{SW0}})}+\varepsilon \tag{4}\]  
\[\triangle Y_{(E_{ALL}-E_{VPD})}=\beta_{0}+S_{VPD}\cdot\triangle VPD_{(E_{ALL}- E_{VPD0})}+\varepsilon \tag{5}\]  
where \(\Delta Y\big{(}E_{ALL}-E_{\text{CO20}}/E_{\text{SW0}}/E_{VPD0}\big{)}\), \(\Delta CO_{2(E_{ALL}-E_{\text{CO20}})}\), \(\Delta SM_{(E_{ALL}-E_{\text{SW0}})}\), and \(\Delta VPD_{(E_{ALL}-E_{VPD0})}\) separately represent the differences in LAI, CO\({}_{2}\), SW, and VPD between the two types of simulation experiments. S\({}_{\text{CO2}}\), S\({}_{\text{SW}}\), and S\({}_{\text{VPD}}\) represent the sensitivity of LAI to CO\({}_{2}\), SW, and VPD, respectively. \(\beta_{0}\) and \(\varepsilon\) are the intercept and residuals of these two regression equations.
# Cryogenic System for FTIR Analysis of Hydrocarbon Fuels at Low Temperature and Atmospheric Pressure
## 2 Materials and Methods
### Calibration of Cryogenic Cell by Temperature  
The temperature measurement accuracy of the copper sample holder was verified using a reference platinum resistance thermometer (Class A, PT100) and compared with readings from a type E thermocouple (chromel-constantan) connected to a PID controller. Deviations did not exceed \(\pm\)0.5 K in the range of 80-300 K.
## 4 Grading Model of Traffic Interruption States Based on the Entropy Weight-TOPSIS Method and FCM
### Fcm  
Fuzzy clustering operates through the iterative optimization of an objective function. It initiates with a randomly assigned clustering center and iteratively refines the center points and fuzzy membership degrees of each sample by seeking the minimum point that fulfills the objective function. Finally, sample data are classified into corresponding clusters. The objective function for FCM is as follows.  
\[minJ_{m}(U,V)=\sum_{i=1}^{c}\sum_{j=1}^{n}u_{ij}^{m}\parallel x_{j}-v_{i} \parallel^{2} \tag{12}\]  
where \(U=\left[u_{ij}\right]\) is a fuzzy partition matrix; \(u_{ij}\) is the degree membership of the \(j\)th sample (\(x_{j}\)) belonging to the \(i\)th category in data set \(X=\{x_{1},x_{2},\cdots,x_{n}\}\), and its constraints include \(0\leq u_{ij}\leq 1\), \(0<\sum_{j=1}^{n}u_{ij}<n\), and \(\sum_{i=1}^{c}u_{ij}=1\), \(\forall j=1,2,\cdots,n\); n is the number of objects in dataset X; \(V=\{v_{1},v_{2},\cdots,v_{c}\}\) consists of vectors of c clustering centers; the fuzzy index \(m>1\) is the weighting coefficient of the membership degree.  
A new objective function is formed using the Lagrange multiplier approach and Equation (13).  
\[\mathrm{L}(\mu,\
u,\lambda)=\sum_{i=1}^{N}\sum_{j=1}^{C}\mu_{ij}^{m}\parallel x _{i}-v_{j}\parallel^{2}+\sum_{i=1}^{N}\sum_{j=1}^{C}\left(\lambda_{i}\mu_{ij}- \lambda_{i}\right) \tag{13}\]  
The partial derivatives of \(\mu\) and \(\
u\) in the equation are calculated. The iterative equations of the fuzzy membership degree and clustering center are obtained by combining constraint conditions.  
\[u_{ij}=\frac{1}{\sum_{k=1}^{c}\left(\frac{\parallel x_{i}-v_{j} \parallel}{\parallel v_{i}-v_{k}\parallel}\right)^{\frac{2}{(n-1)}}} \tag{14}\] \[v_{i}=\frac{\sum_{j=1}^{m}u_{ij}^{m}x_{j}}{\sum_{j=1}^{n}u_{ij}^ {m}} \tag{15}\]  
The iterative process of FCM [22] is as follows.  
Step 1: The number of clusters and fuzzy index \(m\) are preset to determine the precision conditions and the maximum allowable iterations for algorithm termination.  
Step 2: Relational matrix \(U\) is initialized, and distance \(d_{ij}\) between samples is calculated.  
Step 3: Membership matrix \(U\) is updated based on Equation (14).  
Step 4: Calculated \(u_{ij}\) is substituted into Equation (15) to update the matrix of clustering centers (\(V\)).  
Step 5: The value of the objective function is calculated based on Equation (12) and Step 4.  
Step 6: Steps 3-5 are iterated until \(J(n)-J(n-1)\leq b\), or the maximum number of iterations is reached. Then, the optimal function value of FCM is output.  
The flowchart of FCM is shown in Figure 10:
# Interference Analysis for Vehicle-to-Vehicle Communications at 28 GHz
## 2 System Model
Now, using (4) and \(\lambda_{x}=\frac{P_{k}|h_{x}|^{2}}{N_{0}}\), where \(x\in\{c,i\}\), we get the expression for the PDF of instantaneous SNR of \(x\)  
\[f_{\lambda_{x}}(\lambda) = \frac{\alpha_{x}\lambda^{\left(\frac{\alpha_{x}p_{x}}{2}-1\right) }}{2^{\left(\mu_{x}+1\right)}\Gamma\left(\mu_{x}\right)\left(\bar{\lambda}_{x }\right)^{\left(\frac{\alpha_{x}p_{x}}{2}\right)}}\exp{\left(-\frac{\lambda^{ \frac{\alpha_{x}}{2}}}{2\left(\bar{\lambda}_{x}\right)^{\frac{\alpha_{x}}{2}} }\right)} \tag{6}\] \[\times \sum_{k=0}^{\infty}\frac{ktc_{k,x}}{\left(\mu_{x}\right)_{k}}L_{k }^{\left(\mu_{x}-1\right)}\left(2\left(\frac{\lambda}{\bar{\lambda}_{x}}\right) ^{\frac{\alpha_{x}}{2}}\right).\]  
Similarly, the CDF of SNR is given by using \(\lambda_{x}\) and (5) as  
\[F_{\lambda_{x}}\left(\lambda\right) = \frac{\left(\frac{\lambda}{\bar{\lambda}_{x}}\right)^{\left(\frac {\alpha_{x}p_{x}}{2}\right)}\exp{\left(-\frac{\lambda^{\left(\frac{\alpha_{x}} {2}\right)}}{2\left(\bar{\lambda}_{x}\right)\left(\frac{\alpha_{x}}{2}\right) }\right)}}{2^{\left(\mu_{x}+1\right)}\Gamma\left(\mu_{x}+1\right)}\sum_{k=0}^ {\infty}\frac{k!m_{k,x}}{\left(\mu_{x}+1\right)_{k}} \tag{7}\] \[\times L_{k}^{\mu_{x}}\left\{\frac{2\left(\mu_{x}+1\right)}{\mu_{ x}}\left(\frac{\lambda}{\bar{\lambda}_{x}}\right)^{\frac{\alpha_{x}}{2}} \right\}.\]  
In (6) and (7), \(\bar{\lambda}_{x}=\frac{P_{e}}{N_{0}}\) denotes the mean value of the channel gain of the corresponding link.
# A UAV-Based Visual Inspection Method for Rail Surface Defects
## 3 Experiment Results
### Experiment Setup  
#### 3.1.1 A Brief Introduction of the Equipment for UAV Images Acquisition  
As shown in Figure 6, the DJI Matrice 600 equipped with Zenmuse Z30 (DJI-Innovations, Shenzhen, China) was used to capture rail images. The Matrice 600 is a six-rotor flying platform designed for professional aerial photography and industrial applications. The aircraft uses six Intelligent Flight Batteries to extend the time of flight. The built-in API Control feature, expandable center frame, and maximum takeoff weight of 15.1 kg make the Matrice 600 ideal for connecting other devices to meet the specific needs of different applications. The Zenmuse Z30 enables non-contact distance detection by a high-performance camera system with a zoom lens. This aerial camera offers30\(\times\) optical zoom, 6\(\times\) digital zoom, and HD 1080P video. The UAV adopts an industrial level Zenmuse platform with a precision of 0.01 degrees, so the problem of image blur caused by jitter is effectively solved.