esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Compaction Characteristics and Compaction Process of an Unsaturated Silt Based on PFC3D
## 3 Discrete Element Simulation of an Unsaturated Silt Static Triaxial Test
### Rationality Verification of Discrete Element Simulation  
Based on the microscopic parameters of the Hill contact in Table 2, the discrete element model of the static triaxial test is established. By changing the circumferential confining pressure, the discrete element simulation results are compared with the indoor test results to verify the rationality of using Hill contact to simulate an unsaturated silt. Figure 6 is the comparison of the stress-strain curves between discrete element simulation and laboratory test results under different confining pressure conditions, and Figure 7 is the comparison of the failure modes of samples.  
From Figure 6, it can be seen that at the initial stage of loading, there is a certain gap between the indoor test and the discrete element simulation stress-strain curve, but the overall trend is consistent, the similarity between the two is high, and the peak stress that the two can reach is maintained at the same level. Therefore, it can be considered that it is reasonable and feasible to simulate the unsaturated silt with the Hill contact model. It can be seen from Figure 7 that under the action of an axial load, the failure modes of the two are also similar, showing the form of bulging failure, which also confirms the accuracy of using the Hill contact model to simulate an unsaturated soil.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Contact Stiffness/Pa** & **Suction/KPa** & **Damping Coefficient** & **Friction Coefficient** \\ \hline \(2.5\times 10^{8}\) & 130 & 0.25 & 0.25 \\ \hline \end{tabular}
\end{table}
Table 2: Hill contact microscopic parameters.  
Figure 5: Effect of meso-parameter changes on the stress-strain curve. (**a**) Matrix suction. (**b**) Contact stiffness. (**c**) Coefficient of friction. (**d**) Damping coefficient.
## 3 Results
### Agglomerative Hierarchical Clustering (AHC)  
The results for AHC are only concisely discussed here, as the data are more exhaustively explained by the MFA in the next paragraph. The dendrograms representing the AHC based on the dissimilarities using [PERSON]'s method on all the datasets (both chemical and sensory) used for the MFA are presented in Figure 3 for Pinot Gris wine samples and in Figure 4 for Pinot Noir wine samples.  
Figure 4: Dendrogram representing the agglomerative hierarchical clustering (AHC) on all the datasets of Pinot Noir wines from different origins. The Euclidean distance was used for the dissimilarity scale and [PERSON]’s method for agglomeration. The dotted line represents the automatic truncation based on entropy.  
Figure 3: Dendrogram representing the agglomerative hierarchical clustering (AHC) on all the datasets (both chemical and sensory) of Pinot Gris wines from different origins. The Euclidean distance was used for the dissimilarity scale and [PERSON]’s method for agglomeration. The dotted line represents the automatic truncation based on entropy.  
AHC was able to successfully group all the observations for Pinot Gris wines (Figure 3). The first cluster grouped the analyzed wines from Argentina and from Italy (Sicily/Puglia) while the second cluster grouped the rest of the wines (from South Africa, New Zealand, and Veneto/Friuli--Italy).  
In the case of Pinot Noir, AHC also formed two clusters (Figure 4). The first cluster grouped the analyzed wines from Argentina and analyzed wines from Italy (Sicily/Puglia), like for Pinot Gris. However, the analyzed wines from Chile were also grouped in this cluster, while the second cluster grouped the rest of the wines. The wine replicates from South Africa (Western Cape, dry land) formed different clusters, where one of the replicates showed more similarity with the wines from Chile in the first cluster, while the other replicate showed similarity with the wines from New Zealand (Hawkes Bay).  
Multiple factor analysis and multivariate modelling were also used (see the following paragraphs) to determine which chemical variables were responsible for these observed separations.
## 4 Results
### Feature-Based Separation  
As a first approach to the analysis using statistical features, the 15 statistical indicators and the four fractal dimensions were calculated for the three vibration signals (x, y, and z axes) and the current signal, and their distribution was observed using boxplots, with the outliers indicated with red plus signs, to identify if any of the features allowed for the discrimination of the classes on their own. Figure 7 shows the box plots generated for the use of RMS; in these graphs, it is possible to observe that the feature can separate the HLT class from the faulty ones with vibration signals from the y and z axes (Figure 7b,c) and current signals (Figure 7d), while not being able to discriminate between the damaged conditions adequately on any of the sources.  
As mentioned in Section 2.1.1, kurtosis is another indicator commonly used for condition monitoring; Figure 8 presents the boxplots for this indicator on each source captured in the experiments. The results show no separation between conditions in current signals (Figure 8d) and some trends in the change in value for the kurtosis as the severity increases when analyzing vibration signals, but no clear separation that could lead to a correct diagnosis, as in Figure 8b,c, where the results from the vibration signals show some increment in the value with the severity of the faults but not all conditions follow the trend, like the C1 OR condition in both figures.  
Figure 6: Acquired signals: (**a**) pre-normalization and -segmentation HLT signals; (**b**) processed CONT signals; (**c**) C1 OR condition signals; (**d**) signals corresponding to the C3 OR class; and (**e**) pre-pared C5 OR signals.  
Figure 8: Boxplots for the analysis of the conditions using kurtosis feature: (**a**) plot for the vibrations on the \(x\)-axis; (**b**) results for the \(y\)-axis vibrations; (**c**) boxplot for the \(z\)-axis vibration signals; and (**d**) current signal boxplots.  
Figure 7: Boxplots for the RMS feature of the measured signals: (**a**) results for the \(x\)-axis vibration signal; (**b**) plot showing the distribution for the \(y\)-axis vibrations; (**c**) distribution of the \(z\)-axis vibration signal; and (**d**) distribution of the RMS for the current signal.  
Similarly, the HFD boxplots are shown in Figure 9, displaying that each class is more compact than for RMS and kurtosis. When using this indicator, a clear separation between the HLT condition and the rest of the classes is appreciated across the vibration and current signals. In contrast, the classes corresponding to the faults are closer in value, and no trend in their distribution is shown, making it difficult to differentiate between the faults.
# Raw Material Flow Rate Measurement on Belt Conveyor System Using Visual Data
## 3 Methodology
### Extracting Region of Interest  
The region of interest (ROI) for our problem is the conveyor belt on which the raw material is carried. Different algorithms can be used for localization by treating conveyor belt edges as straight lines and using algorithms such as Hough transform (HT) [23] to detect these lines. For example, [PERSON] [21] proposed an algorithm based on the Canny edge operator, morphology processing, and Hough line detection to localize the belt-positioning under a complex background environment. Similarly, [PERSON] [28] also used Hough transform to localize the belt region for automatic conveyor belt maintenance using inspection robots. In our scenario, the camera's position with respect to the conveyor belt is permanently fixed, making it not only easy for us to extract the region of interest but also computationally very efficient as it does not involve the complex operations like Hough transform, etc. We drew two lines at the boundary of the conveyor belt using OpenCV and extracted the region within those boundaries, which gave us the image mask shown in Figure 4c. Using the image mask, we performed an AND operation of the original image with its prepared masked image, which returns us the region of interest or the surface region of the conveyor belt. Figure 4a shows the original image of the conveyor belt and the lines used to mark the ROI are shown in Figure 4b. Figure 4c shows the masked region and the extracted ROI is shown in Figure 4d.
# FLINT: Flows for the Internet of Things
## 5 Evaluation
### Performance Evaluation
#### 5.3.2 Analysis of Platform Performance  
This section analyzes the different selected platforms under different loads. The responsiveness of every platform is measured using the goodput. This has been defined as the total number of successfully received packets divided by the total number of sent packets. Figure 10 shows the average time and goodput for Node-RED (v.1.3.5), Eclipse Hono (v 1.9) and FLINT.  
Excluded from the Figure are the very large delays for a single Node-RED instance. For completeness, these values are provided in Table 2. These high delays are due to the single-threaded design of the underlying Node.js instance [30]. Furthermore, the goodput for a Node-RED (single instance) application dropped significantly for data rates above 7500 messages per second, which can be seen in Figure 10b. To take advantage of multi-core systems, a cluster of Node.js processes can be deployed to handle the load. Therefore, six Node-RED instances were deployed and is referenced as _Node-RED multiple_. Every instance  
Figure 10: The latency (in seconds) and goodput measured for six MQTT adapters in different platforms. Multiple instances of Node-RED perform best compared to Hono and FLINT. Node-RED single had too large delays for the Figure, but can be retrieved from Table 2.  
Figure 9: The test setup. Every test consists of six MQTT clients, subscribed to six MQTT brokers. The clients forward their data to the IoT platform that is being tested. A UDP client fetches data from the platform and forwards the packet to a UDP server.  
subscribes to a MQTT broker, processes the data and forwards it to the UDP server. This resulted in near-zero latency and a 100% goodput.  
For this experiment, Hono used the default configuration. However, as Hono seemed to struggle with data rates above 5000 messages/second (which was also reported in [31]), we deployed a Vert.x MQTT adapter for every MQTT client. This resulted in slightly worse results in terms of latency and goodput, compared to Node-RED.  
Finally, FLINT consisted of six _direct_ MQTT adapters that published their data directly over the message bus to the UDP I/O adapter. Even though FLINT requires at least twice the payload size of the other platforms, the experiment showed slightly better results compared to Hono and slightly worse results than Node-RED running multiple instances. We noticed CPU spikes to 100% for some threads running the _sink_ implementation, which likely causes the 1% packet loss.
## 3 Results and Discussion
### Effect of STP Effluents on SMF Concentrations in the Han River  
Figure 4 shows the STP discharge volumes, distance to the nearest sampling site, and SMF concentration at each point. As shown in Figure 4, of the 17 tributaries, a total of 7 sites were affected by the STPs and had high SMF concentrations (Figure 4). The sites with extremely high concentrations (T5, T13, T4, and T16) were located in the region affected by the STP effluents. We found that the SMF concentrations at T13 and H2 increased due to the effluent from S1, and the SMF concentration suddenly increased at point T4 due to the effluent from S2. At T5, a high HHCB concentration was observed due to the presence of two STPs upstream, but the concentration gradually decreased toward T7, which was probably a dilution effect, and then rapidly increased due to the discharge from S1. The results shown in Figure 4 imply that the SMF concentrations in the tributaries were generally higher at sampling sites affected by STP effluents released from upstream locations.  
Finally, to examine the effect of sewage effluent discharge on the SMF concentration in the receiving water, two factors, i.e., the ratio of sewage discharge to the river flow rate (Qw/Qr) and the distance from the sewer outlet to the sampling point (\(\Delta\)L), were developed. While there was no correlation between SMF concentration and Qw/Qr, we found that there was a statistically strong relationship (r = 0.850, \(p<0.05\)) between the \(1/\Delta\)L value and the SMF concentration in the river. This result implies that the closer the sampling point to the sewage outlet, the higher the SMF concentration in the tributaries or Han River water samples.  
Figure 3: The concentration distribution of HHCB in the samples of STP influent, effluent, tributary, and mainstream of the Han River.
###### Abstract  
Various laws and guidelines on designing for safety have been developed world-wide, and these were used to identify risks at construction sites in advance through qualitative and quantitative safe management. In other words, attention is being paid to safe management based on design drawings and models rather than field visits. It was predicted that the introduction of the concept of design-for-safety would minimize risk at construction sites through safe management in the design phase. This is because the efficiency and reliability of such a strategy has been demonstrated in various cases abroad. However, domestic design-for-safety suggests a very limited range of evaluation items, so there is a limit to using such strategies in construction sites. Building information modeling (BIM) technology is attracting attention in situations when safe management must also be based on design proposals. Although previous studies on the identification of falling disasters and automatic identification of hazards have been conducted, these studies only deal with a limited range of items for identifying risk items. For example, BIM-based risk extraction, which is limited to falling disasters, is only at the level of risk recognition that can be derived based on the process table, and evaluation is not made accordingly. It is difficult to assess the overall disaster risk in many of the items required for risk rating estimation. In this paper, we solve the above problems by deriving a BIM-based risk rating estimation scenario based on the disaster scenario for automating BIM-based risk rating estimation, and we developed an evaluation system using this method. The BIM-based risk rating estimation methodology was presented through BIM-based hazard extraction, evaluation of requirement information, evaluation item selection, and using the evaluation system in a design-for-safety review.  
building information modeling (BIM); design for safety (DFS) review; hazard prevention; risk rating estimation; safety management +
Footnote †: journal: Applied Sciences
# A Simplified Pointing Model for Alt-Az Telescopes
## 3 The Methodology for Creating the Telescope Pointing Model
### Step 1: Capture Sky Images  
During a clear night, the sky images were captured by inputting telescope azimuth and elevation angles at intervals of 10 degrees in both azimuth and elevation directions. Due to the telescope limitations, the azimuth axis has a rotation range from \(-160\) degrees to \(+160\) degrees (with 0 degrees corresponding to true south), and the elevation axis has a rotation range from 20 degrees to 80 degrees for PAST. We matched the observation images with standard star charts to obtain the equatorial right ascension and declination in the J2000 epoch corresponding to the image center (i.e., the telescope's optical axis). The sky coverage of the pointing measurements for the PAST telescope is illustrated in Figure 3.  
In this work, we used the Astrometry.net system [10], which is widely used in astronomical and planetary communities, to calibrate the sky images. The Astrometry.net system is a reliable and robust system for calibrating astronomical images of arbitrary provenance, which we used to detect the stars in the image at a sub-pixel accuracy. For a successfully calibrated image, the right ascension and declination in the J2000 epoch for the central pixel of the image are returned. The field of view of the PAST is 15 arcminutes, with a wavelength ranging from 280 nm to 680 nm. The exposure time for the sky map in each direction is 0.2 s, which is short enough to avoid star trails, while ensuring that enough stars can be captured in the images. To ensure the reliability of the results, the number of stars in each map is more than 10. Figure 4 shows an example of an original sky map at an elevation angle of 50 degrees and an azimuth angle of 100 degrees.  
The pixel resolution of the PAST telescope is 0.5 arcs, therefore, the accuracy of the right ascension and declination of the image center is better than 0.5 arcs, which is sufficiently high enough to calibrate the pointing model. It is noted that other important error factors in this step include the deviation between the true optical axis and the central pixel of the image and the deviation between the optical axis and the mechanical axes. During the laboratory alignment of the optical system, the optical axis was precisely adjusted to the center of the image. The pointing mechanisms are composed of a high-precision encoder and a stepping motor; however, the mechanical imperfections and sensor inaccuracies could not be eliminated. During laboratory calibration, these mechanical errors were  
Figure 3: The sky coverage of the pointing measurements for the PAST telescope.  
determined with an uncertainty of less than \(\sim\)2 arcs using a high-precision Leica theodolite. We left these as system errors, which will be naturally corrected in the model fitting process.
## 9 Challenges and Future Directions for AI in Power Systems
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Application Area** & **Achievements** & **Challenges** & **Future Directions** \\ \hline \multirow{4}{*}{Predictive Maintenance} & AI algorithms analyze sensor data to predict equipment failures, reducing downtime and maintenance costs. & \multirow{4}{*}{Data quality and availability.} & Improving data collection and preprocessing techniques. \\ \hline \multirow{4}{*}{Load Forecasting} & Machine learning models provide accurate load forecasts, helping better grid management and reducing the risk of blackouts. & Scalability of AI solutions to large power systems. & Developing scalable AI models for large power systems. \\ \hline \multirow{4}{*}{Grid Optimization} & AI optimizes the operation of power grids, improving energy distribution and reducing losses. & \multirow{4}{*}{Interpretability of AI models.} & Enhancing the interpretability of AI models. \\ \hline \multirow{4}{*}{Fault Detection} & Neural networks and expert systems detect and diagnose faults in the grid, enhancing reliability. & Cybersecurity vulnerabilities introduced by AI integration. & Integrating robust cybersecurity measures with AI solutions. \\ \hline \multirow{4}{*}{Renewable Energy Integration} & AI helps manage the variability and uncertainty of renewable energy sources, improving their integration into the grid. & Regulatory and ethical issues related to data privacy and AI decision-making. & Developing regulatory frameworks and addressing ethical concerns in AI deployment. \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Main AI challenges and future directions for some important application areas in power systems. Figure 8: AI challenges in power systems [165]. social considerations. Key aspects for successful AI implementation in power systems include high data availability and quality, privacy, security, and seamless integration with both new and existing setups. High costs and investment requirements can pose barriers, making scalability a significant concern [166]. While factors such as human-AI collaboration, explainability, human involvement, and performance evaluations are given medium to low priority, they still contribute to the overall success of AI strategies. High priority is given to privacy and security, explainability, reliability, regulation, and scalability to ensure that AI approaches are technically sound and adhere to ethical and legal standards. Medium-priority factors like human involvement, ethical concerns, cybersecurity, and transparency help build trust and confidence in AI systems. Although factors like human-AI collaboration, data availability and quality, integration with existing systems, cost and investment, and performance evaluation are considered lower priorities, they still play a role in the overall impact of AI on optimal power system operations. Table 10 summarizes the main AI challenges and future directions for some important application areas in power systems [27; 158; 167; 168].
# Pollen-Food Allergy Syndrome: Allergens, Clinical Insights, Diagnostic and Therapeutic Challenges
## 7 PFAS Diagnosis
### Basophil Activation Test (BAT)  
The Basophil Activation Test (BAT) is an advanced in vitro diagnostic tool used to assess allergic reactions mediated by IgE. BAT measures the activation of basophils in response to allergen exposure [206; 207]. It involves exposing the patient's basophils, isolated from whole blood, to specific allergens under controlled laboratory conditions. Upon recognition of an allergen by IgE bound to the basophil surface, the cells become
## 2 System Definition
### Wildfire Detection Methods
#### 2.1.2 Overhead Remote Detection Methods  
Satellite-based wildfire detection can reach low false alarm rates in near real time. However, geostationary satellites may have limited spatial resolution, while polar orbiting satellites have limited temporal resolution, which can be significant for rapidly-evolving wildfires [36, 37]. The accuracy of the automated methods based on satellite monitoring can be further reduced by image acquisition problems and image navigation errors, which can compromise the monitoring of individual fire events that would otherwise be correctly identified by analysts [38].  
Unmanned aerial vehicles (UAV) can efficiently monitor for wildfires over large areas [39, 40]. Equipped with various fire detection techniques, they can survey the area continuously and cost effectively from a diversity of vantage points, much closer than the satellites. UAVs operation can be affected by local weather conditions (fog, strong wind, low altitude clouds), wind being a well-known risk factor during their takeoff and landing [41].  
These methods also share most operating limitations of the ground-based remote detection techniques mentioned in Section 2.1.1, like unfavorable weather, obstructions of the direct line-of-sight, ambient light quality and incidence angle.
## 2 Materials and Methods
### Effects of Modulation Indices Mismatch
\begin{array}{c}\cos\left(M^{\prime}\right)\approx 1-\frac{(M^{\prime})^{2}}{ 2}\\ \cos(M)\cos(M\sigma)\approx 1-\left(1+\sigma^{2}\right)\frac{(M)^{2}}{2}\\ \sin(M)\sin(M\sigma)\approx\sigma M^{2}\end{array}  \tag{7}\]Therefore, the Equation (4) is altered as:  
\[\left(M^{\prime}\right)^{2}=M^{2}\left(1+2\sigma\cos\left(\phi_{A}-\phi_{B} \right)+\sigma^{2}\right) \tag{8}\]  
Since \(\frac{M}{m}=\frac{M^{\prime}}{m^{\prime}}\) (from Equation (2)) the above equation can also be written as follows:  
\[\left(m^{\prime}\right)^{2}=m^{2}\left(1+2\sigma\cos\left(\phi_{A}-\phi_{B} \right)+\sigma^{2}\right) \tag{9}\]  
Secondly, for the SCW QKD the approximation of a small modulation index, i.e., \(m<1\) is valid. Then the mean photon number can be estimated as:  
\[\mu=\mu_{0}\eta\left(1-\left|d_{00}^{S}\left(M^{\prime}\right)\right|^{2} \right)\approx\mu_{0}\left(1-j_{0}^{2}(m)\right)\approx\frac{\mu_{0}m^{2}}{2} \tag{10}\]  
As a consequence we express the optical power dependent on the difference between phases and amplitudes of modulation radio signals (defining mismatch of modulation indices) as follows:  
\[P_{\text{det}}\left(\phi_{A},\phi_{B}\right)=\mu\eta(L)\left(1+2\sigma\cos \left(\phi_{A}-\phi_{B}\right)+\sigma^{2}\right) \tag{11}\]