esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 IoT-Defined Application Protocols
### Messaging Queue Telemetry Transport (MQTT)  
As the name indicates, MQTT is a queuing-based protocol that requires minimum bandwidth because of its design [48]. Its low computational cost and open-source code make it versatile amongst constrained IoT applications. It is a client-server communication protocol that is based on publish/subscribe protocol. Unlike the request/response mechanism in CoAP [49], the MQTT focuses on onetime subscription or one session registration and then unidirectional data flow from the server side to its client(s). Most of the IoT nodes use this protocol for machine-to-machine (M2M) communication. The protocol serves the TCP/IP layer for its communication. Data integrity and session are monitored in MQTT, thus, the data received are reliable and error free.  
MQTT supports unicast, multicast, and broadcast messaging. That is, it can flood the network with the desired message, or it can securely transfer the message to any node/server on demand. A timely update feature is used for subscriptions, which reduces network overheads. Notification mechanisms are also employed for abnormal activity in the network or emergency situations.  
The quality-of-service level is achieved by considering an MQTT protocol such as TCP/IP. The server node, when subscribed to for a specific task, publishes messages based on the specifications of the subscription. These subscriptions can be of three kinds, each explained below.
## 2 Signal Analysis and Neural Network
### Hilbert Transform (HT)  
The Hilbert transform (HT) method changes the previous analysis of nonlinear and nonsteady state. For the combination of IMF, HT is used to obtain the instantaneous amplitude and instantaneous frequency between signals.  
Through the equation, the instantaneous amplitude \(a_{i}(t)\) and instantaneous phase angle \(\phi_{i}(t)\) can be obtained, which can be converted into (1) and (2). By differentiating the time with the instantaneous phase \(\phi_{i}(t)\), the instantaneous frequency \(\omega_{i}(t)\) can be obtained, as shown in (3).  
Figure 1: Flowchart of EMD.  
\[a_{i}(t)=\sqrt{C_{i}^{2}(t)+H_{i}^{2}(t)} \tag{1}\]  
\[\phi_{i}(t)=\textit{arctan}\bigg{[}\frac{H_{i}(t)}{C_{i}(t)}\bigg{]} \tag{2}\]  
\[\omega_{i}(t)=\frac{d\phi_{i}(t)}{dt} \tag{3}\]  
Then, through these calculations, the distribution including frequency, time, and energy can be obtained using the instantaneous amplitude \(a_{i}(t)\) and the instantaneous frequency \(\omega_{i}(t)\). This result is called the Hilbert spectrum.
## 2 Identification Method
### Identification of Gross Vehicle Weight  
Assuming that a vehicle with \(n\) axles drives across the bridge at speed \(v\) in transverse position \(i\), the strain response at measurement point \(j\) can be expressed as follows:  
\[\varepsilon_{ij}(t)=\sum_{i=1}^{n}A_{i}I(t-\frac{L_{i}}{v}), \tag{5}\]  
where \(A_{i}\) is the \(i\)th axle's weight and \(I(t-\frac{L_{i}}{v})\) is the strain influence line. \(L_{i}\) is the wheelbase. Here, it is defined as the distance between the \(i\)th axle and the first axle.  
The strain response area of this measurement point can be expressed as follows:  
\[S_{ij}=\int_{t_{1}}^{t_{2}}\varepsilon_{ij}(t)dt=\sum_{i=1}^{n}A_{i}\int_{t_{1 }}^{t_{2}}I(t-\frac{L_{i}}{v})dt, \tag{6}\]  
where \(t_{1}\) and \(t_{2}\) are the influence line's start point and end point, respectively, which are the same as those listed in Equation (3).  
Since the area of the strain influence line for each axle is equal, the following equation can be formulated:  
\[\int_{t_{1}}^{t_{2}}I(t-\frac{L_{1}}{v})dt=\int_{t_{1}}^{t_{2}}I(t-\frac{L_{2 }}{v})dt=\cdots\cdots=\int_{t_{1}}^{t_{2}}I(t-\frac{L_{n}}{v})dt=\int_{t_{1}} ^{t_{2}}I(t)dt, \tag{7}\]  
We can substitute the above equation into Equation (6) to obtain the following:  
\[S_{ij}=\int_{t_{1}}^{t_{2}}\varepsilon_{ij}(t)dt=\sum_{i=1}^{n}A_{i}\int_{t_{1 }}^{t_{2}}I(t)dt, \tag{8}\]  
Thus, the total weight of the vehicle \(A_{G}\) can be calculated, as follows:  
\[A_{G}=\sum_{i=1}^{n}A_{i}=\frac{S_{ij}}{\int_{t_{1}}^{t_{2}}I(t)dt}, \tag{9}\]  
As seen from the above equation, the total weight of the vehicle load is only related to the strain response area of the measurement point under the vehicle load and the influence line's area.  
To obtain the area of the influence line, we can measure the strain response \(\varepsilon_{ij}^{C}\) under a calibration vehicle with known weight \(A_{G}^{C}\) passing through the bridge from different transverse positions \(i\), and the influence line's area can be calculated based on the strain response area \(S_{ij}^{C}\):  
\[\int_{t_{1}}^{t_{2}}I(t)dt=\frac{S_{ij}^{C}}{A_{G}^{C}}, \tag{10}\]  
In the following, the strain response under an unknown vehicle load is collected, and the strain response area vector \(\textbf{B}_{T}=[S_{T1},S_{T2},\cdots,S_{T6}]\) is calculated. Based on the proposed cosine similarity \(Cl\) index, the transverse position \(k\) of the vehicle can be determined. Finally, the unknown vehicle's gross weight \(A_{G}^{T}\) can be identified as follows:  
\[A_{G}^{T}=\frac{S_{Tj}}{S_{kj}^{C}}.A_{G}^{C}, \tag{11}\]  
It can be seen from the above theoretical that one axle sensor measurement point is enough to identify the vehicle's gross weight. However, the response of the measurement point is sensitive to the transverse position of the vehicle. The strain response is small and the signal-to-noise ratio is low when the vehicle is far away from the position of the measurement point. Thus, multiple sensors were arranged in this study, and all of the identified results were averaged to decrease the influence of the noise.
## 5 Discussion
### Routing  
Routing is a crucial component of IoT communication, determining the pathways through which data packets are transmitted from source nodes to their intended destinations. In IoT networks, where devices are often dispersed over wide areas and operate under limited power and processing constraints, efficient routing becomes essential for achieving optimal network performance and energy conservation. Given the heterogeneous and often dynamic nature of IoT deployments--ranging from urban smart city applications to rural agricultural monitoring--identifying the shortest and most efficient routing paths is necessary to meet Quality of Service (QoS) requirements, including latency, reliability, and throughput.  
Longer or suboptimal routing paths can lead to increased energy consumption, as data packets must pass through multiple intermediate nodes, each consuming energy during packet forwarding. These longer paths can also result in processing delays, impacting the real-time responsiveness required in applications such as healthcare monitoring or industrial automation. Consequently, route optimisation is vital for reducing the network's overall energy demand and ensuring timely data delivery [47]. Energy-efficient routing protocols help extend device lifetimes, reduce operational costs, and minimise the need for frequent battery replacements, which is especially valuable in remote or inaccessible areas.
## 7 Conclusions  
In this paper, we have studied a special application of data collection in widely distributed sensor networks. The scenario is that a mobile sink cruises on the ground to collect data gathered from Unmanned Ground Vehicles. The practice case could be a mobile sink based data collection in a duty-cycled sensor network, a hard accessible sensor network, or a surface vessel based under water data collection in the deep sea. In these networks, there is only a short time window for each sensor node appearing to meet the mobile sink. Once the window is missed, the data collection latency increases a lot. We address the unique challenge and propose two algorithms to schedule the path of the mobile sink, targeted at visiting the maximum number of UGVs in a timely manner with the shortest path, according to the timing constraints bound by the cycles of UGVs. Then, it is extended to the case that the cycle timing of UGVs cannot be precisely predicted or measured. The path decision is then made on the k-hop knowledge of the environmental parameters. Furthermore, we develop two algorithms to optimize the number of UGVs deployed in the ideal and practice cases, respectively.  
Figure 11: Performance comparison of different number of UGVs. (**a**) distribution in the ideal case; (**b**) distribution in the practice case.  
Figure 12: Experimental results of mobile sinks optimization in the ideal case and practice case. (**a**) number of mobile sinks in the ideal case; (**b**) number of mobile sinks in the practice case.  
Simulations are conducted with a random algorithm and the deadline driven algorithm to evaluate the performance of the proposed algorithm. In some cases, more mobile sinks should be introduced to collect the data in time since even the optimal algorithm could not gather them all. In the future, we will further improve the performance of our path scheduling algorithms with the same or less order of the computational cost.  
This work is supported by the Natural Science Foundation of China (61601157), the Scientific Research Foundation for the Returned Overseas Chinese Scholars (State Education Ministry, China); and the Chinese Scholarship Council (201208330096).  
[PERSON] and [PERSON] contributed to the main idea of this paper; [PERSON] wrote the paper; [PERSON] designed the algorithms and experiments; [PERSON] wrote the code and performed the experiments; [PERSON] analyzed the data and drew the figures.  
The authors declare no conflict of interest.
# Removal of Cochineal Dye Color through Atmospheric Pressure Plasma Discharge Jet
## 3 Results and Discussion
### Measurement of the Concentration of Nitrites, Nitrates, and Their Role in the Dye Bleaching Process  
Table 1 shows the quantification of nitrites and nitrates in the samples before and after treatment with the APPJ plasma. A 10 min treatment results in an approximately 200% increase in the concentration of the reactive species NO\({}_{2}\)\({}^{-}\) and NO\({}_{3}\)\({}^{-}\). These species belong to the group of tertiary reactive species of RONS, including O\({}_{3}\), H\({}_{2}\)O\({}_{2}\), NO\({}_{2}\)\({}^{-}\), and NO\({}_{3}\)\({}^{-}\), which are generated in the liquid phase because of the interaction of the secondary reactive species with the dye solution [18]. As noted by [PERSON] et al. [18], NO\({}_{2}\) and NO\({}_{3}\) are soluble in water and promptly transform into NO\({}_{2}\)\({}^{-}\) and NO\({}_{3}\)\({}^{-}\) through the following reaction patterns:  
\[\text{NO}_{2}+\text{OH} \rightarrow \text{H}^{+}+\text{NO}_{3}^{-}\] \[\text{NO}_{2}+\text{NO}_{3}^{-}+\text{HO}_{2} \rightarrow \text{NO}_{2}^{-}+\text{NO}_{3}^{-}+2\text{H}^{+}\] \[\text{NO}+\text{NO}_{2}+\text{HO}_{2} \rightarrow 2\text{NO}_{2}^{-}+2\text{H}^{+}\]  
This mechanism could explain why a liquid solution with a strong acidic character is responsible for the oxidative color removal of the cochineal dye [13,35].  
The findings indicate that subjecting cochineal dyes to an atmospheric-pressure plasma jet (APPJ) for a period of 10 min results in a significant increase in the concentration of reactive species, including NO\({}_{2}\)\({}^{-}\) and NO\({}_{3}\)\({}^{-}\). These reactive species, including O\({}_{3}\), H\({}_{2}\)O\({}_{2}\), and NO\({}_{2}\)\({}^{-}\)/NO\({}_{3}\)\({}^{-}\), are produced in the liquid phase as a result of secondary reactive species interacting with the cochineal dye solution The solubility of nitrogen oxides (NO\({}_{2}\) and NO\({}_{3}\)) in water facilitates their rapid conversion into nitrate and nitrite ions (NO\({}_{2}\)\({}^{-}\) and NO\({}_{3}\)\({}^{-}\)) through a variety of reactions involving the presence of hydroxyl radicals and other reactive species. This transformation results in the formation of a highly acidic liquid solution that is responsible for the oxidative degradation of the dye. By understanding the mechanisms involved in the generation of these reactive species, it is possible to control and optimize the dye degradation process and develop alternative methods for removing the dye. Future studies should investigate the effects of varying plasma treatment parameters, such as duration and intensity, as well as the utility of alternative reactive species like O\({}_{3}\) and H\({}_{2}\)O\({}_{2}\), to gain further insights and uncover potential industrial applications. Additionally, it is crucial to perform a total organic carbon (TOC) test to evaluate the efficacy of dye degradation treatment using APPJ plasma and identify other organic pollutants [43].
## 2 Material and Methods
### Laboratory Analysis of Trace Metals  
All materials that came into contact with the samples were previously washed thoroughly, soaked in dilute HNO\({}_{3}\) (Merck, Darmstadt, Germany), and rinsed with ultrapure water of 18.2 M\(\Omega\) cm (Millipore, Bedford, MA, USA). For the preparation of all required solutions, class A volumetric glassware was used. For the determination of As, Ba, Cd, Cr, Cu, Mn, Ni, Pb, Sr, V, the samples were digested with HNO\({}_{3}\) 65% supra pure (Merck) with the subsequent addition of H\({}_{2}\)O\({}_{2}\) 30% (Merck), according to the procedure described by [22]. For Hg and Se, samples were acid digested with HNO\({}_{3}\) in Teflon vials closed and left overnight at room temperature. The following day, the vials were thermostated at 80 \({}^{\circ}\)C for 3 h. The samples were allowed to cool at room temperature before their dilution with Milli-Q water. The digested samples were analyzed through inductively coupled plasma mass spectrometry (ICP-MS) by a Thermo Scientific ICAP Qc (Waltham, MA, USA). Measurements were carried out in a single collision cell mode, with kinetic energy discrimination (KED) using pure He. Matrix induced signal suppressions and instrumental drift were corrected by internal standardization (\({}^{45}\)Sc, \({}^{103}\)Rh). Analyses of Al, Fe, and Zn were carried out by flame atomic absorption spectrometry (FAAS) (SpectrAA 200; Varian, Mulgrave, Vic, Australia), following digestion of the samples with a mixture of HNO\({}_{3}\) (65%), HCl (30%) and HF (40%). The calibration curves matched the matrix (acidity) of the samples.
# BPET: A Unified Blockchain-Based Framework for Peer-to-Peer Energy Trading
## 3 A Unified Framework
### Markets Analysis  
From the perspective of energy trading business models, there are three typical market models: bilateral market, pool market, and balancing market [10]. A bilateral market requires a pair of buyers and sellers to negotiate with each other to agree on the final energy price and amount periodically. Buyers and sellers usually negotiate price and quantity off-chain and then make transactions on-chain. A pool market requires buyers and sellers to put bids in a pool, which determines the price and trading volume based on the demand and supply curves. The merit order effect trading model is usually used as a pricing strategy in a pool market, which can be easily implemented in a smart contract [10] and executed on-chain. A balancing market exists to supplement bilateral and pool markets and ensure total power demand and supply are balanced at all times in physical energy networks.  
From the perspective of market structures, all P2P-ET markets can be divided into three types: full decentralized market, community-based market, and composite market [28]. A full decentralized market provides a platform for participating prosumers to independently and directly negotiate with each other to decide on the energy trading parameters without centralized supervision. A community-based market [29; 30; 31] relies on a community manager to enable members to trade their energy within the community. A composite market is a combination of fully decentralized and community-based markets in which each community and each consumer can interact with each other while maintaining their market properties.
## 2 Measurement Model
### Analysis of Measurement Requirement  
According to the sensitivity matrix analysis method of optical systems [19; 20; 21; 22], optical misalignment is defined as the deviation of the ideal position of the optical element from the actual position. For the optical system shown in Figure 1, M1 is used as the test mount reference, and the sensitivity matrix A is obtained from M2 and M3 to form the out-of-tune quantities as shown in Table 1. Dx, Dy, and Dz represent the wave image difference in \(\lambda\)/mm for a 1 mm variation in the X, Y, and Z directions, respectively. Tx and Ty represent the wave image difference in \(\lambda/^{\circ}\) for a 1\({}^{\circ}\) variation in the X and Y directions, respectively.  
[PERSON] et al. have analyzed the sensitivity curves of M1, M2, and M3 in the optical machine structure of off-axis reflecting telescopes [20]. According to the sensitivity matrix A and the sensitivity curve, M1 is most sensitive in Dx, Tx, and Ty. M2 is most sensitive in Dz. As for Dy, M1 and M2 are equally sensitive. As for Dx, Dy, and Dz, M2 is much more sensitive than M3. As for Tx and Ty, M2 is about 3-5 times more sensitive than M3. In summary, the variation in sensitivity between M1 and M2 is much greater than the variation in sensitivity between M1 and M3. As both M1 and M2 are mounted on the M1&Instruments holder (as in Figure 1), the above model can be simplified to a common reference for M1 and M3. Using M1 as a reference, the wave aberration assignments for each dimension of M2 and the mounting tolerance requirements are shown in Table 2.  
As can be seen from Table 2, the M2 mounting tolerance requirements are very demanding in terms of measurement accuracy, and it is difficult to meet the tolerance requirements for measurement accuracy based on the existing measurement equipment and sampling density. Therefore, it is necessary to combine the existing equipment to improve the measurement and mounting accuracy as much as possible and to leave a sufficient margin for the wavefront correction method to eliminate the deformation [23; 24; 25].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reflector** & **Dx** & **Dy** & **Dz** & **Tx** & **Ty** \\ \hline M1 & 0.5761 & 1.337 & 4.381 & 53.61 & 92.55 \\ M2 & 0.3955 & 1.324 & 8.982 & 27.35 & 7.531 \\ M3 & 0.0233 & \(-\)0.0145 & \(-\)0.0908 & 11.78 & 1.474 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Off-axis triple-reverse optical system sensitivity matrix A.  
Figure 1: A typical model of an off-axis space telescope truss structure.
# A Novel Index for Detecting Bare Coal in Open-Pit Mining Areas Based on Landsat Imagery
## 5 Conclusions  
Determining the extent of many open-pit coal mines on a large scale is challenging, especially without a spatial location database. This paper proposes a novel BCI by analyzing the spectral features of bare coal using the GEE cloud computing platform and free Landsat satellite images. The BCI enhances the spectral information of the NIR, SWIR1, and SWIR2 bands, highlighting the spectral values of bare coal areas so they can be distinguished from other features, including bare soil, vegetation, and man-made surfaces. This enables the fast and accurate identification of bare coal areas over large spatial ranges. The results show that the BCI can identify bare coal areas quickly and accurately, overcoming the limitations of existing methods constrained by complex environments. When applied to Chenqi Coalfield, Shengli Coalfield, and Dongsheng Coalfield in Inner Mongolia, China, the BCI accurately identified surface mining areas with an average overall accuracy of 97% and a kappa coefficient of 0.87. Secondly, through an analysis of the spectral characteristics of bare coal across different months, this paper finds out the relationship between the reflectance of various bands and the monthly variations. A mathematical model linking the BCI to the months is then constructed, offering enhanced possibilities for selecting data sources in the continuous monitoring of long-term series. In conclusion, the BCI proposed in this study demonstrates significant advantages in the rapid and accurate digital mapping of large-scale open-pit coal mining areas. This method provides robust technical support for monitoring and regulation in the coal industry, with broad application prospects and practical value.  
Conceptualization, Y.Z. and H.R.; methodology, Z.L.; software, Z.L.; validation, Z.L.; formal analysis, Z.L.; investigation, Y.S.; resources, Y.Z.; writing--original draft preparation, Z.L.; writing--review and editing, H.R.; visualization, Z.L.; supervision, Y.Z. and H.R.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 420701250).  
Publicly available datasets were analyzed in this study. The data sources and access links are indicated in the text.  
We are immensely grateful to the editor and anonymous reviewers for their comments on the manuscript.  
The authors declare no conflicts of interest.
## 3 Results and Discussion
### Gas Recovery Efficiency and Interlayer Compatibility
#### 3.3.1 Gas Production Coefficient  
The results of single-layer depletion experiments for six layers are presented in Table 3. Figure 8a,b illustrates the relationship between the gas production coefficient and outlet pressure under single-layer depletion conditions for layer Q5 and layer H8. When the outlet pressure decreases, the gas production coefficient experiences an initial rapid increase, followed by a gradual decline during the stable production stage until reaching the terminal pressure. The gas production coefficient of layer H8 exhibits an initial upward trend, followed by a subsequent decline; however, the overall pattern is characterized by a more gradual change. During the single-layer depletion experiments, the gas recoveries of the layers Q5 and H8 are measured to be 81.80% and 93.26%, respectively, while the gas recovery in all other layers also exceeds 81.80%.  
During commingled multilayer production, the gas production coefficients of either a high-pressure layer or a low-pressure layer have undergone significant changes. Such fluctuations in single-layer depletion and commingled two-layer production experiments of the Q5 + H8 layer are depicted in Figure 8a,b. During commingled production, the outlet pressure of the layer Q5 initially increases from its original formation pressure of 7.30 MPa to 12.48 MPa, characterized by a suppressed gas output due to interlayer interference until pressure equilibrium between the two layers is achieved and joint gas production is initiated. Compared to single-layer depletion, commingled production experiences a  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Layer**} & **Permability** & **Porosity** & **Pressure** & **Long Core Length** & **Gas Production Rate** & **Gas Recovery Efficiency** \\  & **(\(\times\)103 \(\mu\)m\({}^{2}\))** & **(\%)** & **(MPa)** & **(cm)** & **(mL)** & **(\%)** \\ \hline Q5 & 0.76 & 8.17 & 7.30 & 47.40 & 2020.38 & 81.80 \\ \hline H8 & 0.66 & 7.80 & 22.00 & 42.40 & 6216.33 & 93.26 \\ \hline S1 & 0.45 & 6.05 & 22.60 & 42.40 & 6910.33 & 92.59 \\ \hline TY & 0.56 & 8.70 & 23.40 & 43.40 & 4940.75 & 92.47 \\ \hline BX & 0.85 & 7.08 & 25.00 & 22.00 & 3214.55 & 95.47 \\ \hline S2 & 0.54 & 6.90 & 22.80 & 42.40 & 7679.36 & 92.66 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of gas production from single-layer depletion experiments.  
more rapid decrease in the gas production coefficient, with an increase in pressure and a decrease in gas rate at a later stage. Although the layer H8 exhibits a higher gas discharge during commingled production, most of the gas is backflowed into the Q5 layer.