esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Soil Treatment and Experimental Design  
Contaminated soils of 10 kg were first sieved using a 2.0 mm diameter sifter, then evenly divided into five samples, each of which was placed in a \(\varphi\)15 cm \(\times\) 20 cm cylindrical container to form a 15 cm-depth microcosm. The sample in Container 1 was sterilized and used in the treatment of natural volatilization, marked as \"S\". The sample in Container 2 was used in the treatment of natural attenuation, marked as \"C\", in which no microorganisms or nutrients were applied. The sample in Container 3 was treated by adding (NH4)2 SO4 and K2 HPO4 as the nutrients and marked as \"N,\" i.e., biostimulation. The proportion for carbon, nitrogen, and phosphorus was C:N:P = 100:1.25:1, according to [PERSON] et al. [46]. According to the proportion and the diesel content, 38.22 g (NH4)2 SO4 and 80.64 g K2 HPO4 were added into the container with 2000 g soil. The sample in Container 4 was inoculated with 50 mL of liquid culture of _Pseudomonas_ sp. and marked as \"B\" i.e., bioaugmentation. The strain was isolated from the contaminated soil and contained an _alkB_ gene but no _nah_ gene (More information about the isolated strain was shown in Figure 11 (Appendix C). The sample in Container 5 was treated by adding the nutrients mentioned above and microbes, i.e., a biostimulation-bioaugmentation joint process, and thus marked as \"N-B.\" At the end of 3, 6, 9, 12, 15, 18, 21, 24, and 27 days, 5.0 g of soil samples were collected from 10 points at the superstratum and substratum from each treatment. We divided each sample into two parts, one of 2.5 g was stored at \(-20\)\({}^{\circ}\)C for DNA extraction, and the other of 2.5 g was stored at 4.0 \({}^{\circ}\)C for diesel analysis. All treatments mentioned above were undertaken in triplicate.
# Water Quality Monitoring: A Water Quality Dataset from an On-Site Study in Macao
## 1 Introduction
In response, our system employs a machine-learning-based triple-classification model (normal, borderline, and abnormal), improving the anomaly detection accuracy and enabling adaptive responses. Another limitation of conventional IoT systems is their dependence on cloud computing, which introduces latency and data transmission delays. Our system mitigates this by processing data locally, significantly reducing response time and enabling real-time decision-making. Moreover, our system meets 60% of the water quality monitoring standards set by the World Health Organization (WHO) and the US Environmental Protection Agency (EPA), making it a more reliable alternative for urban water management. A key advantage of our system lies in its enhanced data availability. Traditional systems typically operate with long sampling intervals, failing to capture rapid fluctuations in water quality. In contrast, our system performs continuous data collection with a sampling interval of 5 s, providing a more comprehensive representation of water quality trends. Lastly, regarding deployment and cost, conventional systems often depend on external cloud servers, increasing operational costs. Our system, built on open-source technology and low-cost hardware, significantly reduces deployment costs and enhances scalability, making it a cost-effective solution for large-scale water monitoring. So, our key contributions in this study include the following:  
* **Real-time and cost-effective monitoring--**By using low-cost sensors and a modular Raspberry Pi-based architecture, the system enables continuous data collection at significantly lower costs than commercial systems [4]. * **Comprehensive anomaly detection--**A boundary-condition-based triple-classification rule categorizes water quality into _normal_, _borderline_, _and abnormal states_, allowing for early warning and adaptive responses. * **Compliance with international standards--**Our system meets 60% of the WHO and EPA physicochemical water quality monitoring criteria, making it a robust alternative for urban water management. * **Macao water quality dataset--**We have established a high-quality water quality dataset for Macao, which provides a database for future water management in Macao.
# IoT Helper: A Lightweight and Extensible Framework for Fast-Prototyping IoT Architectures
## 4 Use Cases
### Quality Control Scenario  
We also tested the effectiveness of the approach in an Industry 4.0 scenario. The experiments were conducted with Centro Ricerche FIAT (CRF) within the activities of the \"Integrated Collaborative System for Smart Factory (ICOSAF)\" Project.  
The main goal of the experiment was to support quality assessment on Resistance Spot Welding (RSW) used to assemble car body parts. A typical car contains more than 5000 welding spots of different materials and thicknesses. Assuring the quality of this process is crucial to guarantee the solidity of the assembled vehicle. Several offline and online tests were proposed to evaluate the quality of the final welded workpieces [27; 28; 29].  
The quality control process starts with an operator that places a welded workpiece on a custom workbench, as shown in Figure 5. This bench is designed so that all the welding spots are reachable by a collaborative robot (cobot) provided with an ultrasound probe that will read the dynamic resistance curves of the spots. Before starting the probe, it is important to verify the correct placement of the workpiece. Since it has a very flexible and uneven shape, it is hard to fasten with clamps. Reading data from a misplaced location will generate dirty data that might negatively impact the quality check algorithm.  
To overcome this problem, we placed several digital position sensors in correspondence with the contact points between the shape and the bench, as shown in Figure 6. These sensors are then wired connected to an Arduino Mega 2560 that runs the generic firmware described in the paper. The operator will check the sensors interacting with a custom controlling software that will communicate with Arduino using our supporting library. Using an HMI, the operator starts the process. The controlling software will publish a READ command for all sensors to the Message Broker. After receiving the sensor states, if all of them are evaluated as _pressed_, the cobot is started. The dynamic resistance curves of the welding spots are then read and stored in order to be processed using quality assessment techniques.  
From the architectural point of view, we adopted a hybrid approach by using edge computing to control the sensors and the cobot while dynamic resistance curves are processed on a cloud architecture.  
Since the company has strict security policies, we cannot use a SaaS solution for the Message Broker. However, thanks to the flexibility of our solution, we were able to deploy a local Message Broker based on the MQTT protocol. The MQTT Broker was installed using a docker image on a Raspberry Pi connected to the same local network of the Arduino. The complete architecture is described in Figure 7.  
Figure 5: Quality control scenario.  
Figure 6: Workbench enriched with position sensors.  
These scenarios prove that our architecture can be applied within a wide range of cases and can not only be adopted to deploy rapid and affordable data collection in the control scenario but also in industrial and commercial cases.
# Internet of Things Applications as Energy Internet in Smart Grids and Smart Environments
## 4 IoT-Enabled Applications for Smart Homes
This layer also covers energy-based management applications required for DSM, DR, and dynamic pricing. There are several studies in the literature related to the security of IoT-based smart home applications. The study in [55] analyzed security and authentication issues of smart home systems. A constrained application protocol (CoAP)-based non-IP SHMS structure is recommended in [56]. Entire devices and applications available in smart home applications can utilize secure IoT protocols, such as a CoAP, a hypertext transfer protocol secure (HTTPS), message queue telemetry transport (MQTT), and an extensible messaging and presence protocol (XMPP).
# A New COVID-19 Detection Method Based on CSK/QAM Visible Light Communication and Machine Learning
## 4 Methodology
### AI-Based Procedures to Replace Human Processing
#### 4.3.2 Naive Bayesian with Gaussian optimisation  
The Naive Bayesian with Gaussian optimisation (GaussianNB) method finds promising parameter values by using a Gaussian process model of the objective function [77]. The Probability of Improvement (PI) is an intuitive strategy that can be calculated analytically by using Gaussian processes to maximise the probability of improvement over the best current value [78]:  
\[a_{PI}(x;\{x_{n},y_{n}\},\theta)=\Phi(\gamma(x))\, \tag{25}\]  
\[\gamma(x)=\frac{f(x_{best})-\mu(x;\{x_{n},y_{n}\},\theta)}{\sigma(x;\{x_{n},y _{n}\},\theta)}\, \tag{26}\]  
where \(f:\chi\rightarrow\mathbb{R}\), \(x_{best}\) is the best current value, \(\mu\) is its predictive mean function and \(\sigma\) is the predictive variance function. In order to maximise the expected improvement over the best current value, the Expected Improvement (EI) could also be calculated using a Gaussian process:  
\[a_{EI}(x;\{x_{n},y_{n}\},\theta)=\sigma(x;\{x_{n},y_{n}\},\theta)(\gamma(x) \Phi(\gamma(x))+\mathcal{N}(\gamma(x);0,1))\, \tag{27}\]  
where \(a_{EI}\) represents the acquisition function with the highest expected improvement, \(\Phi(\cdot)\) represents the cumulative distribution function and \(\mathcal{N}\) represents the normal distribution. The upper confidence limit of Gaussian Processes (GP) seeks to exploit the concept of lowerand upper confidence limits in the maximisation case in order to build acquisition functions that minimise regret as optimisation progresses [79]:  
\[a_{LCB}(x;\{x_{n},y_{n}\},\theta)=\mu(x;\{x_{n},y_{n}\},\theta)-\kappa\sigma(x; \{x_{n},y_{n}\},\theta)\, \tag{28}\]  
where \(a_{LCB}\): \(\chi\rightarrow\mathbb{R}^{+}\) denotes the acquisition function, LCB is the lower confidence bound and \(\kappa\) is tunable to balance exploitation versus exploration.
# Cross-Entropy Method in Application to the SIRC Model
## 3 Description of the Numerical Results
### Comparison of Results  
This section gathers the results from each version and compares them with each other. At first, the behavior of each group of the SIRC model was looked at and compared with the solution of the uncontrolled model. This is shown in Figures 6-9. The results are presented only for the first situation, where the epidemic begins to spread, because there you can see the results of using control much better.  
In this article [31] weight and impact of functions \(u(t)\) and \(v(t)\) were compared. The chart in the article compares the results obtained for the application of both functions, one at a time and none. It turned out that the main influence on the number of infected has control over susceptible persons, i.e., the function \(u(t)\). Choosing the optimal values for \(v(t)\) is not very important for the solution of the model. Therefore, such discrepancies are possible when choosing optimal values.  
Figure 8: Comparison of results obtained for group R among various methods.  
Figure 6: Comparison of results obtained for group S among various methods.  
Figure 7: Comparison of results obtained for group I among various methods.  
Table 1 contains a comparison of the cost index values and the duration of the algorithms for each method. The first value is when the epidemic started, and the second is when it has already developed. The article does not specify the algorithm time of the method used.  
It can be seen that for each version the cost index values came out very similar. The differences can be seen in time. This is because in the first version of the CE method a smaller sample was simulated because both functions were considered separately. Fortunately, the functions were not closely related and it was possible to consider them separately.
## 6 Future Directions
### New Channel  
Measuring radio propagation and channel modeling represent the foundation of communication systems. A very good understanding and knowledge of the channel makes it possible to carry out communication smoothly and softly. However, since the channel is not sheltered and unpredictable, it is difficult to provide channel optimization. This is one of the main focuses in the communication sector and one of the main topics that needs to be improved. New challenges are inevitable in 6G communication with new spectra, new technologies, user and data explosions, new fields, and new antenna integrations. Exceeding the frequency band beyond 100 GHz increases attenuation and path loss. To compensate for this loss, more advanced beamforming technologies will be needed [37]. New channel models will be needed for scenarios such as the integration of terrestrial and non-terrestrial networks, as well as the use of drones to act as mobile base stations. It may not be possible to respond to such a variety of scenarios with a single-channel model. It may be useful to investigate hybrid channel models according to content usage scenarios [37].  
Recent advancements in non-terrestrial networks have been significantly shaped by large-scale initiatives such as Starlink, OneWeb, and Amazon's Kuiper. These systems demonstrate the feasibility and growing role of LEO constellations in global broadband connectivity [37; 142]. Although primarily designed for current-generation networks, their architecture, deployment models, and inter-satellite communication mechanisms provide valuable insight into the future realization of 6G satellite networks. Lessons learned from these commercial systems--such as phased-array antenna use, laser-based ISLs, and latency challenges--can inform the integration of terrestrial and space-based 6G infrastructures.  
The deployment of 6G networks in satellite domains will require navigating complex international regulatory frameworks involving spectrum allocation, orbital slot coordination, and cross-border data governance. With the growing density of satellite constellations, issues such as orbital congestion, interference management, and space debris mitigation have become critical. Collaborative efforts among global regulatory bodies like the ITU, FCC, and national space agencies will be essential to establish policies that support secure, fair, and sustainable 6G satellite operations.
# Flue Gas Composition and Treatment Potential of a Waste Incineration Plant
## 2 Methods and Materials
### Stoichiometric Air and Oxygen Required for Combustion (Excel Model)  
The ideal combustion process is known as a stoichiometric or theoretical combustion where a fuel is completely burned using the least quantity of air. The probability of fuel element atoms meeting all oxygen atoms (air) is implausible due to the combustion of fuel working with an excess amount of air (for excedentary combustion [lambda > 1]) to increase the chance of complete combustion or to control the temperature of the combustion chamber. The ratio between the real air volume introduced into the burning chamber is Va, and the stoichiometric air volume, where \(V_{a}^{0}\) is the air ratio or excess air coefficient, is denoted by \(\lambda\).  
The following model was used to calculate the required amount of air for the combustible fraction of the MSW. A further amount of air (excess air) was required for the combustion of water content inside the fuel itself (waste). This air requirement can be modeled according to a total heat release concept from the designed loading rate of the fuel. The air fraction required for the combustible materials of the fuel can be found according to the model in [1]. The results of the laboratory analysis of the elementary analysis were found as follows:  
The stoichiometric volume of oxygen required for combustion \(\left(\frac{Nm^{3}of~{}O_{2}}{kg~{}of~{}fuel}\right)\) is:  
\[V_{O_{2}}^{0}=\frac{22.41}{100}\left(\frac{C}{12}+\frac{H}{4}+\frac{S-O}{32}\right) \tag{1}\]  
Oxygen has a volume participation in air of 21%, and for this reason, the stoichiometric volume of dry air required to burn one kilogram of fuel \(\left(\frac{Nm^{3}of~{}air}{kg~{}of~{}fuel}\right)\) is:  
\[V_{a~{}dry}^{0}=V_{O_{2}}^{0}/0.21 \tag{2}\]  
Air flow rate:  
\[\dot{V}_{a~{}dry}^{0}=V_{a~{}dry}^{0}*B \tag{3}\]  
where \(B\) is the fuel flow rate (kg/h).  
To find the dry air mass flow required for the designed MSW mass flow and excess air:  
\[required~{}air=V_{a~{}dry}^{0}*MSW~{}mass~{}flow*\rho_{air}~{}*Excess~{}air~{} ratio \tag{4}\]  
where, \(\rho_{air}\) [1.29 kg/m\({}^{3}\)] at standard conditions was taken. It should be noted that the previous equations are applicable to dry air. These equations can be modified to insert the moisture existing within the air; the stoichiometric volume of wet air is \(\left(\frac{Nm^{3}of~{}air}{kg~{}of~{}fuel}\right)\):  
\[V_{a~{}moist}^{0}=\left(1+1.61*X\right)V_{a}^{0} \tag{5}\]where \(X\) is the absolute humidity [kg H\({}_{2}\)O/kg dry air] of air which depends on its temperature and its relative humidity \(\varphi\). The usual value considered in the calculations was \(X=10\) g H\({}_{2}\)O/kg, and it corresponds to the air parameters: ta = 25 \({}^{\circ}\)C and \(\varphi=50\%\)[1]. Note that the factor 1.61 is the quotient of the density of air under normal conditions (1.29 Kg/Nm\({}^{3}\)) divided by the density of water vapor under normal conditions (0.804 Kg/Nm\({}^{3}\)).
## 2 Methods
### Data Acquisition Procedure  
The participant was first informed about the study. Initially, plantar pressure measurements were taken in the shoe while walking. Participants were asked to walk on an \(\infty\)-shaped path in two familiarization trials, followed by three trials for pressure data collection. Twelve steps were analyzed to calculate the average peak plantar pressure during the stance phase of walking for each foot. The shoe pressure sensor (_Parologg Pressure Measurement System, Paromed, Neu-Beuren, Germany)_ provided data that were processed to obtain the maximum plantar pressure, the average maximum plantar pressure, and the average plantar pressure at each anatomical point.  
After completion of plantar pressure measurements, participants followed a procedure to measure foot temperature using a _Flir One Pro_ thermal imaging camera. The temperature was recorded immediately before and immediately after the insoles, following these steps:  
1. The participant was asked to be barefoot and lie supine on a flat couch, with a cushion placed under the head for comfort. Specific regions of interest on the feet were marked for thermal measurements. These regions included the hallux, first metatarsus, third metatarsus, fifth metatarsus, midfoot (proximal fifth metatarsal head), medial arch (proximal first metatarsal head), and heel, as shown in Figure 3.
2. After a 15 min acclimatization period on the couch, baseline foot temperature was recorded using the thermal imaging camera.
3. The participant then wore shoes and walked at a natural pace along the designated pathway.
4. Immediately after completing the walk, the shoes were removed and the participant was asked to lie on the couch.
5. Temperature measurements were taken at specific intervals 30 s, 90 s, 120 s, and 180 s after walking. For each measurement, the participant returned to the couch, removed their footwear, and thermal images were captured.
6. These temperature readings, along with the baseline values recorded after acclimatization, were used to calculate temperature changes.  
This systematic approach ensures that each participant is evaluated under consistent starting conditions, thereby improving the reliability and validity of the study results. The data collection process resulted in a comprehensive dataset that includes thermal and pressure measurements from each participant. Specifically, each dataset comprises baseline foot temperature, thermal images taken at various time points during walking, and corresponding average pressure metrics at specific anatomical points.  
Figure 2: Thermal image capture setup.
# Cutting Path Planning Technology of Shearer Based on Virtual Reality
## 2 An Ide of Cutting Path Planning Technology of Shearer Based on Virtual Reality  
The main technical equipment in the fully mechanized mining face includes shearer, scraper conveyor and hydraulic support. The hydraulic support is mainly used for face advance and supporting the roof of the working face. The shearer and scraper conveyor jointly complete the circular coal cutting. Among them, the scraper conveyor is arranged on the coal seam floor. When the coal seam floor is undulating, the scraper conveyor section by section adapts to the terrain to produce slight bending. The shearer is meshed in the chute of the scraper conveyor through the traveling wheel, which is connected with the scraper conveyor to move forward smoothly, and the shearer sends the drum to the designated position by adjusting the rocker arm to complete the cutting work.  
In the selection and design stage of comprehensive mining projects, geological conditions, coal storage, equipment selection, production capacity, and other factors are often considered. Therefore, the selection and design cycle of comprehensive mining face is long and difficult, and coal mining enterprises need to select quickly, and previewed the pre-selected schemes as much as possible, so as to reduce the problems that may occur in the future.  
Using the virtual reality engine Unity3D, we studied the cutting path planning method of the shearer drum and obtained the operation simulation of the virtual shearer on the 3D terrain. The overall research framework is shown in Figure 1, which was realized through the following steps.  
* The Unity3D engine was used to build the model for the coal seam roof and floor (3D coal seam), and to obtain the orientation of the shearer and scraper conveyor; based on the geometry model, movement model, and rule model of real equipment, the virtual model of the shearer operation was developed, which is completely consistent with reality.
* The virtual Shearer was obtained to move and adjust the drum automatically on the 3D terrain;
* The prototype system was developed, different schemes simulated, and the simulation results processed and evaluated.  
Figure 1: Overall framework.
## 2 Literature Review
### Plane Segmentation with Panoplane360  
Plane segmentation focuses on identifying and representing major geometric structures within an environment, such as walls, floors, and ceilings [25; 26; 27; 28]. Panoramic image-based plane segmentation is widely utilized in various indoor construction site applications, including room layout estimation [29], construction progress monitoring and site management [30], and site layout planning [31]. However, due to the structural characteristics of panoramic images--such as a wide field of view (FoV), projection distortions, and non-centralized object distributions--specialized methods are required to process them effectively [32].  
Panoplane360, a CNN-based deep learning model developed by [PERSON] et al. in 2022 [19], was designed specifically for plane segmentation in panoramic images. The model adopts a divide-and-conquer approach to group pixels based on planar orientation. It then applies pixel embedding clustering to identify individual plane instances and predicts geometric parameters to generate labeled masks. Additionally, it incorporates a yaw-invariant V-plane reparameterization technique, allowing the model to infer vertical plane orientations without prior knowledge of the 360\({}^{\circ}\) camera's yaw rotation. By leveraging these techniques, Panoplane360 effectively extracts vertical and horizontal plane masks from panoramic images.  
However, as Panoplane360 is trained on static and fully constructed indoor environments, it lacks an understanding of construction site conditions, leading to reduced segmentation quality when applied to such environments. Recognizing the need for precise segmentation in indoor construction sites, this study selects plane segmentation as the target task and adopts Panoplane360 as the target model for evaluating the effectiveness of the proposed method.