esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Analysis Methods
#### 2.3.2 Partial Correlation Analysis  
The partial correlation coefficient measures the degree of linear relationship between two variables while controlling for the effects of other variables. To minimize the influence of other climatic factors on grassland GPP in the arid regions of Xinjiang, we employed  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Slope (R)** & \(p\)**-Value Range** \\ \hline Extremely Significant Decrease & \(R<0\) & \(p<0.01\) \\ Significant Negative & \(R<0\) & \(0.01\leq p<0.05\) \\ No Significant Decrease & \(R<0\) & \(p\geq 0.05\) \\ No Significant Increase & \(R>0\) & \(p\geq 0.05\) \\ Significant Increase & \(R>0\) & \(0.01\leq p<0.05\) \\ Extremely Significant increase & \(R>0\) & \(p<0.01\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classification of significance levels in linear regression.  
partial correlation analysis using MATLAB 2024b (MathWorks, Natick, MA, USA). This method has been widely applied in previous studies to explore the interactions between vegetation and individual meteorological factors [26]. When using three additional meteorological factors as control variables, the partial correlation coefficient between GPP and a single meteorological factor can be calculated using the following formula:  
\[R_{xy|z_{1}z_{2}z_{3}}=\frac{R_{xy}-R_{xz_{1}}\times R_{yz_{1}}-R_{xz_{2}} \times R_{yz_{2}}-R_{xz_{3}}\times R_{yz_{3}}}{\sqrt{(1-{R_{xz_{1}}}^{2})(1-{R_ {xz_{2}}}^{2})(1-{R_{xz_{3}}}^{2})(1-{R_{yz_{1}}}^{2})(1-{R_{yz_{2}}}^{2})(1-{R _{yz_{3}}}^{2})}} \tag{5}\]  
GPP in the arid regions of Xinjiang is denoted as \(x\), and the meteorological factor of interest is represented by \(y\). Three controlling climatic factors are defined as, \(z_{1}\), \(z_{2}\), and \(z_{3}\). Correlation coefficients were computed in MATLAB 2024b as follows: \(R_{xy}\) between \(x\) and \(y\); \(R_{xz_{1}}\),\(R_{xz_{2}}\), and \(R_{xz_{3}}\) between \(x\) and each controlling factor \(z_{1}\), \(z_{2}\), \(z_{3}\); and \(R_{yz_{1}}\), \(R_{yz_{2}}\), and \(R_{yz_{3}}\) between \(y\) and each \(z_{1}\), \(z_{2}\), \(z_{3}\). To quantify the direct association between GPP (\(x\)) and the target factor (\(y\)) independent of the confounding effects of the controlling factors (\(z_{1}\), \(z_{2}\), \(z_{3}\)), the partial correlation coefficient \(R_{xy|z_{1}z_{2}z_{3}}\) was calculated. Statistical significance of the impacts of individual meteorological factors on GPP across spatial pixels was assessed by deriving \(p\)-values from \(t\)-tests (also performed in MATLAB 2024b), enabling subsequent categorization of significance levels (as defined in Table 3) [27]:
# Virtual Fire Evacuation Drills through a Web-Based Serious Game
## 6 Conclusions  
A web-based serious game for virtual fire evacuation drills was developed in this study, and a case study of fire evacuation drills with more than 100 trainees was performed. The following conclusions were drawn:  
1. Through the developed serious game, virtual evacuation drills can be smoothly performed on the web, and the safety evaluation results of evacuation drills can also be provided in a timely manner for trainees.
2. The drill results indicated that the average evacuation time of the trainees decreased from 79.77 to 54.32 s after two drills, a drop of 31.9%. Additionally, the safety scores of the trainees improved from 74.71 to 81.21. The above results demonstrated the effectiveness of the developed serious game.
3. The average evacuation efficiency and safety scores in the virtual drill with signs also improved, which suggests that evacuation signs can facilitate the occupants' evacuation.  
Figure 13: Average evacuation time and safety score of third drill and drill with sign.  
* The developed serious game for virtual fire evacuation drills can improve the safe evacuation capabilities of trainees in a convenient web-based way, and therefore can play an important role in fire safety education.  
Conceptualization, Z.X.; methodology, Z.X. and Y.Y.; investigation, Y.Y., Y.W. and W.W.; formal analysis, Z.X., Y.Y. and Y.W.; writing--original draft, Y.Y.; writing--review and editing, Z.X. and R.S. funding acquisition, Z.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Beijing Nova Program of Science and Technology (Grant No. Z191100001119115), General Program of the National Natural Science Foundation of China (Grant No. 51978049), and Interdisciplinary Research Project for Young Teachers of USTB (Fundamental Research Funds for the Central Universities) (Grant No. FRF-IDRY-20-030).  
Not applicable.  
The authors declare no conflict of interest.
# Radio Channel Scattering in a 28 GHz Small Cell at a Bus Stop: Characterization and Modelling
## 2 Experimental Set-Up
### Measurement Scenario  
Figure 4 shows a full picture of the set-up. We placed the directional transmitting antenna at a 2.5 m height beside the bus stop pole. The transmitting antenna was pointed toward the receiving one (see Figure 5), positioned on a stool inside the bus stop shelter, at a 0.9 m height. The horizontal distance between both antennas was 7 m. The most meaningful distances among the different elements in the measurement setup are included in Figure 6. Several signal records were obtained as different vehicles, including cars, vans and buses, passed by the bus stop, i.e., in the presence of scattering sources with different sizes and shapes (see Figure 4, Figure 7 and Figure 11).  
Figure 3: Receiver.  
Figure 4: Transmitter and receiver at the bus stop.  
Figure 5: Transmitting antenna by the bus stop pole.  
Figure 6: Environment dimensions.  
Figure 7: A van passing by the bus stop.
# Reduction Potential of Gaseous Emissions in European Ports Using Cold Ironing
## 7 Time Spent in Ports by the Ships  
The next step was the quantification of the time spent by ships in ports. For this purpose, a two-week investigation focused on collecting data from the 10 sampling set ports. It was carried out by a systematic individual (ship-by-ship) analysis of the duration  
\begin{table}
\begin{tabular}{c c} \hline
**Country** & **Port** \\ \hline Belgium & Antwerp \\ France & Le Havre \\ Germany & Hamburg \\ Greece & Piraeus \\ Italy & Genoa \\ Netherlands & Amsterdam \\ Netherlands & Rotterdam \\ Spain & Barcelona \\ Sweden & Gothenburg \\ United Kingdom & London \\ \hline \end{tabular}
\end{table}
Table 1: Sampling set of ports providing cold ironing services.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Fuel in Use** & **NOx** & **SO2** & **VOC** & **PM** \\ \hline
2.7\% Sulphur & 12.47 & 12.30 & 0.40 & 0.80 \\
0.1\% Sulphur & 11.80 & 0.46 & 0.40 & 0.30 \\ \hline \end{tabular}
\end{table}
Table 3: Average emission factors of auxiliary engines operating in ports [g/kWh].  
\begin{table}
\begin{tabular}{c c c c} \hline
**Fuel in Use** & **NOx** & **SO2** & **VOC** & **PM** \\ \hline
2.7\% Sulphur & 12.47 & 12.30 & 0.40 & 0.80 \\
0.1\% Sulphur & 11.80 & 0.46 & 0.40 & 0.30 \\ \hline \end{tabular}
\end{table}
Table 4: Average emission factors from electricity production in the EU [g/kWh].  
of the port's calls. The data source was the MarineTraffic(r) portal. As an example, the graph in Figure 2 depicts the duration of the various ships' typologies at the 10 sampling ports in the first two weeks (336 h) of the year 2023.  
The differentiation among ports is very relevant, as is the variability by ship typologies: the average number of ships in ports is variable between 0.4 in Goteborg and 4.6 in Piraeus. Therefore, the potential effectiveness of the will be accordingly variable, with the highest values in the most crowded ports (e.g., Piraeus) and for the longest-staying ships (e.g., bulk carriers).
## 5 Simulation Results
### Simulating a Whole Day of Traffic  
The final tests simulated a whole day of traffic, from 0H00 to 23H59, with variable traffic density throughout the day. Two different daily profiles were tested: a weekend profile, which has normal traffic during the day, and a weekday profile, which has three periods of high-density traffic (early morning, midday, and late afternoon). The simulation results can be seen in Table 7. The passive routing and the smart routing are identified in the titles by the P and S prefixes, respectively.  
The gains achieved by the smart routing algorithm for the weekday are not as good as those of the normal traffic profile; this can be explained by the influence of the three periods of high traffic density. Not surprisingly, results for the weekend profile are better, almost reaching those of the low traffic profile. As a matter of fact, the CO2 reduction (\(-40\%\)) equals the same result of the \"very low\" profile.  
These simulations for a whole day give an insight on the total daily reduction in CO2 emissions that can be achieved by a smart routing algorithm. In this case, the potential reduction is 381 kg for a weekday and 362 kg for a weekend day.
## 4 Results and Discussion  
### t-SNE Findings  
Figure 6 shows 16 different runs at four perplexity values (40, 70, 100, and 130) and four numbers of steps (100, 300, 500, and 700). Unfortunately, there is no particular number of steps that yields a stable result. Different datasets can require different numbers of iterations to converge [25]. The t-SNE algorithm adapts its notion of \"distance\" to regional variations in data density, and cluster set sizes do not represent actual distances. This is different from using, for instance, k-means to directly visualize the groups by designating a unique identification number to each group [2]. The coloring shows well that the map preserves the similarities within each class.  
As a result, t-SNE naturally expands dense clusters and contracts sparse ones. It is not suited for finding outliers because the sample arrangement does not directly represent distance, as in PCA. However, the useful dimensionality reduction allows us to visualize data in 2D [26]. During t-SNE processing, parameter optimization changed the obtained results dramatically. While changes in the number of steps did not improve performance, increasing the value of perplexity yielded more preferred solutions.  
Vulnerable slope locations, which may be considered as slow-moving landslides, had (in a certain extent) a close distribution pattern to landslides class locations. This is because they tend to share similar mother rock (mainly shale) from the Yeonil Formation of the Gyeongsang Basin formed in the third period of the Cenozoic Era. However, the vulnerable slopes class was clearly isolated from other classes (unlabeled and non-landslide).  
Figure 6: _Cont._  
Some overlapping between the feature classes was observed in some locations, as a result of (1) generalization in the delineation of points by the data provider, and (2) the additional random point (1359 locations) that was created during processing. No-landslide and landslide points occurred together because the no-landslide points are from the same period as the 1998 landslide events (i.e., they include slopes that were prone, but did not slide) and do not indicate absolute no-landslide regions (areas of very low slopes, for instance). We concluded that using other common grouping functions, like k-means or k-medoids, will not be able to group (classify) the inventory classes in a meaningful way, especially in small and complex study areas. t-SNE has significantly better visualization capacity through its expansion of dense clusters and contraction of sparse ones [27]. As a result, unlabeled locations were increasingly clustered around landslide locations, especially when using higher perplexity values.
# Terahertz Rotational Spectroscopy of Greenhouse Gases Using Long Interaction Path-Lengths
## 2 Materials and Methods
### Synchrotron Based FT THz Spectroscopy  
The AILES beamline of the SOLEIL synchrotron was used for high-resolution broadband FT measurements in the 0.6 to 19.5 THz, (20-650 cm\({}^{-1}\)) spectral range on CH\({}_{4}\) and CF\({}_{4}\). The very bright light extracted from the synchrotron bending magnet has the advantage of displaying a lower divergence compared with other sources aiding its use with long path interaction White type cells. The FT IFS125 spectrometer, used in this case, has a 5 m delay stage allowing a resolution of 30 MHz to be achieved [12].
## 3 Background
### Coap Architecture  
The Constrained Application Protocol (CoAP) is a specialized web transfer protocol, specially designed for constrained applications. The CoAP's interactive client/server model is similar to HTTP model. It has similar features and principles as in HTTP. Due to higher communication overhead of HTTP and HTTPS, such protocols are not suitable for IoT communication. Therefore, CoAP is designed to interface with HTTP, to easily integrate with existing web-browsers, while ensuring constrained networks and M2M [38; 39] specialized requirements i.e., multicast support, simplicity, and low message overhead. The IETF  
Figure 2: IoT protocol stack for CSFSA.  
The CoAP architecture has message layer and request/response layer as shown in Figure 4. The message layer handles the message exchange over UDP between end points. It deals with reliability and asynchronous mechanism. While request/response layer deals with request and response messages and their mapping. CoAP has four types of messages i.e., Confirmable (requires an acknowledgement), Non-Confirmable (needed no acknowledgement), Acknowledgement (receipt of confirmable message), Reset (confirmable or non-confirmable message not processed properly), Piggy-backed (sends Acknowledgement with confirmable message). The CoAP used same methods i.e., GET, POST, PUT, DELETE for generating, restoring, creating or updating and deleting processes as like HTTP. The CoAP protocol lacks trusted standards for secure architecture and its messages are encrypted by using DTLS, which was not designed for resource constrained devices and hence is not suited for CoAP [40].
# A Novel Protocol Using Captive Portals for FIDO2 Network Authentication +
## 1 Introduction
### FIDO2: A New Standard for Web Authentication  
Considering that information systems protected by passwords are heavily threatened, some relevant technology companies such as Google, Microsoft, Meta, or Yubico have started to develop a new authentication method. Back in 2014, these companies created the FIDO Alliance [2], which has published two versions of the FIDO Client To Authenticator Protocol (CTAP) standard during the last few years: CTAP1, previously known as Universal Second Factor (U2F), and CTAP2. Since then, some security keys compliant with these standards have been developed, such as the Yubikey, the Solokey, or the Google Titan Security Keys.  
In this context, in 2019, the W3C Consortium developed WebAuthn, a new standard compatible with these hardware security keys [3] and other software-based authenticators. WebAuthn is a new W3C standard that aims to complement or even replace passwords as a web authentication method. Relying on the FIDO standards, WebAuthn defines a browser API that has already been implemented in famous browsers such as Firefox and Chrome [4] (see Figure 1). In addition, it describes a protocol that allows its usage as an authentication method in web application servers. Consequently, some relevant web applications have already added WebAuthn security keys as an authentication method to be used together with passwords or even to replace them [5].  
The adoption of WebAuthn has evolved during these few years. \"Passkeys\" are the new commercial name of WebAuthn credentials for passwordless authentication. During the last year, we have seen a great increase in passkeys, which increased by 50% from 2019 to 2022 according to a Duo report [6], for instance, Google [7], Microsoft and Apple accounts, some cloud provider portals like Amazon AWS, but also in social networks like Facebook and Twitter.  
FIDO CTAP standards and the W3C WebAuthn standard form together a new authentication paradigm under the name of FIDO2 authentication, also known as the \"passwordless\" paradigm. It opens new possibilities for authentication mechanisms in multiple systems. Although the efforts were directed to web application user authentication, there are other computer systems where it could be integrated. One of them is authentication in computer networks, where access to the network or its resources are controlled.
## 4 Case Study
### Impact of the Linearization on the Centralized VOP
Nevertheless, the reported results give evidence that the differences between the nodal voltages obtained by the _LinearDistFlow VOP_ with respect to those obtained by the _Benchmark VOP_ are small and, consequently, the _LinearDistFlow VOP_ provides a good approximation of the benchmark optimization. Obviously, to reduce such approximations, it is advisable to evaluate various offline \(\Gamma\) matrices for different operating conditions of the distribution system; in these applications, the worst condition for the proposed method has been assumed. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Case**} & **Benchmark VOP** & **LinearDistFlow VOP** \\  & **(10\({}^{-3}\) p.u.)** & **(10\({}^{-3}\) p.u.)** \\ \hline \(1\) & 1.9 & 2.2 \\ \(2\) & 1.5 & 2.4 \\ \(3\) & 2.4 & 5.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Voltage performance index for _Benchmark VOP_ and _LinearDistFlow VOP_. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multicolumn{2}{c}{\(V\) (p.u)} & \multicolumn{2}{c}{\(\sigma\) (p.u)} & \multicolumn{2}{c}{\(V_{M}\) (p.u)} & \multicolumn{2}{c}{\(V_{m}\) (p.u)} \\  & **Benchmark** & **LinearDistFlow** & **Benchmark** & **LinearDistFlow** & **Benchmark** & **LinearDistFlow** \\  & **VOP** & **VOP** & **VOP** & **VOP** & **VOP** & **VOP** & **VOP** & **VOP** \\ \hline \(1\) & 1.0001 & 0.9994 & 0.0019 & 0.0020 & 1.0055 & 1.0051 & 0.9957 & 0.9949 \\ \(2\) & 1.0001 & 0.9988 & 0.0017 & 0.0018 & 1.0059 & 1.0044 & 0.9971 & 0.9958 \\ \(3\) & 1.0001 & 0.9976 & 0.0021 & 0.0023 & 1.
# A Combined Anomaly and Trend Detection System for Industrial Robot Gear Condition Monitoring
## 1 Introduction
### State of the Art
#### 1.1.1 Industrial Robot Condition Monitoring
Classification performances over 94 percent for recall and precision values were achieved [15]. Time domain and frequency domain features derived from a residual signal were used in combination with thresholding for gear fault detection for different test trajectories [16]. A one-class generative adversarial autoencoder was used for the detection of artificially introduced faults in a robot gear in [17]. Classification accuracies of 97 percent were achieved for the identification of different faults.
# Efficiency Analysis of Manufacturing Line with Industrial Robots and Human Operators
## 2 Problem Definition--Manufacturing Process of Engine Heads  
We take into consideration an example from the automotive industry. The company will build a new manufacturing line for the production of engine heads for motor cars. These parts are made from castings made of aluminium alloy. The manufacturing process of these parts requires many machining operations including precise milling, drilling and deburring. Very high quality is required in order to meet the requirements of the recipient.  
The simplified schema of the engine head manufacturing process from the automotive industry is presented in Figure 2.  
Production systems can consist of a different number of specialized machines and human operators or robots needed for materials handling.  
The schema of the main machining cell is presented in Figure 3. The cell consists of four semi-automatic CNC (Computer Numerical Control) milling machines and needs human operators or robots for material handling. The Air Blow Station is used to remove chips from the part. The operators are also required for product testing and final inspection.  
One operator is required for tending four semi-automatic milling machines that are working in parallel in order to obtain greater production volume. Parts from a part family (engine heads) are large and heavy and, therefore, the usage of robots is preferred.  
In the stage of conceptual design, some variants of the manufacturing line were taken into consideration, including the manual operation of machines or the robotic operating system. What happens when we replace human operators with industrial robots? What productivity growth is possible to achieve? These questions require consideration of many human and robot factors.
## 5 Performance Evaluation
### Data Transmission Reliability
provide broadband communication services, which typically means that the power and bandwidth allocated for navigation are less than those for communication. To ensure communication capacity when allocating bandwidth, it is necessary to allocate most subcarriers to communication signals. Based on the above results, it is appropriate to allocate 200 and 800 subcarriers for navigation and communication signals, respectively. In contrast to ZC-NOMA and PN-OTFS, we can allocate more power to the navigation signals of DDBDM to further improve the navigation performance, as the BER performance of DDBDM does not significantly degrade with a decrease in CNPR. Therefore, DDBDM not only achieves the lowest BER in broadband communication, but also provides fast and reliable augmentation information for PNT applications.