esa-sceva/satcom-chunk-collection huggingface dataset:
# Forest Fire Detection Using New Routing Protocol
## 4 Concept of Location-Aided Routing Protocol (LAR)  
Location-Aided Routing (LAR) is considered the first protocol that adopts the location information of mobile sensor nodes [33]. The routing process in the LAR protocol consists of two main nodes, which are the source node \(S\) and the destination node \(D\). Generally, the \(S\) node in the LAR protocol has the location information of the \(D\) node by using Global Positioning System (GPS) technology, where the GPS technology presents the possibility of mobile sensor nodes knowing and sharing their physical location [34]. The region that has the \(D\) node is called the expected zone. Therefore, the \(S\) node shall take into account the predicted place of the \(D\) node at the location L\({}_{0}\) (X\({}_{0}\), Y\({}_{0}\)), at the time t\({}_{0}\), and the average speed of the \(D\) node is V [35]. Consequently, the \(S\) node can suppose that the expected zone is a circular area of radius V (t\({}_{1}-\)t\({}_{0}\)), and it is located at L\({}_{0}\). Figure 3 illustrates the circular region of the expected zone.  
Figure 2: The PCE-OLSR diagram.  
When the \(S\) node needs to transmit a packet to the \(D\) node, the \(S\) node must determine a route to the \(D\) node. In this case, the algorithm of the LAR protocol uses flooding by sending the route discovery process. Subsequently, the \(S\) node defines another region called the request zone, where the \(D\) node should be located inside the request zone. Figure 4 shows the request zone where its corners are C, B, S, and A. The \(S\) node can specify 4 corners of the request zone. Furthermore, when the \(S\) node needs a route to this zone, it will send a Route Request Message (RREQM) that includes the coordinates of the request zone.  
When the \(S\) node sends the RREQM in the route discovery process, other nodes receive this route request only if they belong to the rectangle shape (i.e., the request zone). On the other hand, nodes that are located outside of the request zone will discard the RREQM. As shown in Figure 5, the RREQM is generated to form a route from the \(S\) node to the \(D\) node. This route request will be forwarded through \(K\) and \(I\) nodes because these nodes are located inside the request zone, while, when the \(N\) node obtains the route request, it will discard this RREQM because the \(N\) node is located outside the request zone.  
Once the \(D\) node obtains the RREQM, it responds by transmitting the Route Reply Message (RREPM) to the \(S\) node. The RREPM will follow the same path that is obtained from the \(S\) node. In other words, the RREPM follows the reversed path of the RREQM. Continuing this process, the LAR protocol is considered to be energy-efficient with respect  
Figure 4: The request zone.  
Figure 5: The expected and request zones.  
Figure 3: The expected zone.  
to the network lifetime as compared with conventional flooding algorithms, where it can decrease the cost of nodes routing in the network.
# Improved Autocollimator for Roll Angle Measurement with an Enlarged Measuring Range
###### Abstract  
An autocollimator is a goniometer established according to the principle of autocollimation, but it is ineffective for measuring the roll angle. This paper proposes an improved autocollimator available for large-range roll angle measurement, which maintains the optical structure of the classic one while incorporating a wedge prism (WP) working in transmissive mode as a roll angle sensing element to dissociate the collimated beam into two beams. According to the moving paths of the two light spots focused on the photodetector, the roll angle of the WP can be solved. The measuring method is expounded, and the calibration results reveal that the improved autocollimator has an accuracy of \(\pm 13.55\) arcsec over a range of \(360^{\circ}\), confirming its feasibility for roll angle measurement where a large measuring range is required.  
autocollimator; roll angle measurement; measuring range; wedge prism Article
# S2p: Provenance Research for Stream Processing System
## 1 Introduction
\begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline  & **Provenance Systems** & **Provenance Support for** & **Provenance Storage** & **Provenance Tracking Granularity** & **Modifying Application** & **Intermediate Data** & **Third-Party Systems** \\ \hline \multirow{6}{*}{Batch -processing  Oriented  Provenance} & RAMP [17] & Hadoop & HDFS & Map/Reduce Level & N & N & N \\ \cline{2-9}  & \multirow{2}{*}{Newt [29]} & Hadoop & \multirow{2}{*}{MySQL} & \multirow{2}{*}{Multiple Levels} & \multirow{2}{*}{Y} & \multirow{2}{*}{N} & \multirow{2}{*}{Y} & \multirow{2}{*}{N} & \multirow{2}{*}{Y} \\  & & & & & & & \\ \cline{2-9}  & \multirow{2}{*}{HadoopProv [18]} & Hadoop & File & Map/Reduce Level & N & N & N \\ \cline{2-9}  & \multirow{2}{*}{Lipstick [30]} & Lipstick [30] & Pig(Hadoop) & File & Operator Level & Y & N & N \\ \cline{2-9}  & \multirow{2}{*}{Trian [19]} & Spark & Native & Spark Stage Level & N & Y & N \\ \cline{2-9}  & \multirow{2}{*}{Arthur [31]} & Hadoop & \multirow{2}{*}{File} & \multirow{2}{*}{Operator Level} & \multirow{2}{*}{Y} & \multirow{2}{*}{Y} & \multirow{2}{*}{N} \\  & & /Pyards & & & & & \\ \hline \multirow{6}{*}{Stream -processing  Oriented  Provenance} & Tracing & \multirow{2}{*}{Spark} & \multirow{2}{*}{External} & \multirow{2}{*}{Operator Level} & \multirow{2}{*}{N} & \multirow{2}{*}{Y} 1  & \multirow{2}{*}{Y} & \multirow{2}{*}{Y} & \multirow{2}{*}{N} \\  & Framework [32, 33] & Spark & & & & & \\ \cline{2-9}  & \multirow{2}{*}{Ariadne [22, 23]} & \multirow{2}{*}{Borealis} & File & Operator Level & Y & Y & N \\ \cline{2-9}  & \multirow{2}{*}{GeneLog [4, 24]} & \multirow{2}{*}{Flink /Liebre} & \multirow{2}{*}{Memory} & \multirow{2}{*}{Operator Level} & \multirow{2}{*}{Y} & \multirow{2}{*}{N} & \multirow{2}{*}{N} \\ \cline{2-9}  & \multirow{2}{*}{On-the-fly  provenance  tracking [34]} & \multirow{2}{*}{Not specific} & \multirow{2}{*}{External} & \multirow{2}{*}{Operator Level} & \multirow{2}{*}{Y} & \multirow{2}{*}{N} & \multirow{2}{*}{Y} \\ \cline{2-9}  & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of features in general-purpose data processing systems with provenance support. The remainder of the paper is organized as follows. Section 2 contains a brief overview of DSP, the DSP provenance problem definition, and a short introduction to Apache Flink. Section 3 provides a framework for how s2p works. Techniques involved during online provenance are introduced in Section 4, and offline provenance-related ones are introduced in Section 5. Section 6 describes how we manage and query provenance data. The experimental evaluation is presented in Section 7. The case study is included in Section 8. Related works about solving big data provenance problems are presented in Section 9. We conclude with a summary and discussion of future work in Section 10.
# An Adaptive Data Rate Algorithm for Power-Constrained End Devices in Long Range Networks
## 5 Adaptive Data Rate Algorithm Design
### Adaptive Data Rate Control Algorithm Based on Network Performance Optimization  
The goal of network performance optimization is to allocate a data rate to each ED to maximize the network performance score and achieve optimal network performance. During the optimization process, it is essential to ensure the communication link quality between each ED and the GW. The optimal data rate must fall within the range of selectable data rates and other communication parameters determined by Algorithm 1. Additionally, the characteristics of the receiver and other relevant constraints must also be considered.
# Face Stability Analysis for Tunnels under Steady Unsaturated Seepage and Inhomogeneity Conditions
## 5 Results and Discussion  
### Parameter Selection  
To analyze the face stability for a tunnel under conditions of steady unsaturated seepage and soil inhomogeneity, certain parameters need to be clarified. The parameters for the case of steady unsaturated seepage include the following: soil pore parameter \(n\), soil entry pressure parameter \(\alpha(\text{kPa}^{-1})\), saturated hydraulic conductivity \(k_{s}(\text{m}/\text{s})\), flow rates \(q(\text{m}/\text{s})\). Different soil types have varying parameters as follows: clay: \(n=2\), \(\alpha=0.005\), \(k_{s}=5\times 10^{-8}\); silt: \(n=3\), \(\alpha=0.01\), \(k_{s}=5\times 10^{-7}\); loess: \(n=4\), \(\alpha=0.025\), \(k_{s}=1\times 10^{-6}\); and sand: \(n=5\), \(\alpha=0.1\), \(k_{s}=3\times 10^{-5}\). The above values come from [PERSON] et al. (2015) [15]. The range of flow rates \(q\) varies from \(-3.14\times 10^{-8}\) to \(1.15\times 10^{-8}\), high evaporation corresponds to \(1.15\times 10^{-8}\), high infiltration corresponds to \(-3.14\times 10^{-8}\) and \(q=0\) corresponds to a no-flow condition ([PERSON] et al. 2019) [20]. The soil inhomogeneity parameter was chosen based on the premise that the effective cohesion of the soil at a depth of \(H\), i.e., \(c^{\prime}\) in Equation (9),  
Figure 2: Iterative flow diagram for calculating the safety factor.  
was taken as the default value, in which the soil inhomogeneity was varied by changing \(n_{0}\). In addition, the ratio of the tunnel depth to the tunnel excavation diameter is included as a geometric parameter in the discussion.
## 4 The Importance of Variables
### Coastland  
Studies in coastal areas have highlighted the significant role of vegetation indices in soil salinity inversion within agricultural regions. In contrast, in beach areas, factors such as distance from the sea and the digital elevation model (DEM) are critical considerations. In the Yellow River Delta region, [PERSON] et al. [57] utilized GF-2 satellite imagery to accurately estimate soil salinity by integrating spectral and textural features. Their study indicated that the salinity index-BRI is more crucial than both the vegetation index and GLCM texture features, primarily because BRI can more precisely reflect subtle changes in spectral characteristics and effectively capture surface information alterations due to soil salinity. [PERSON] et al. [35] conducted an in-depth investigation into the influence of vegetation type on soil salinity inversion. They discovered that EEVI emerged as the most significant inversion variable, followed by ENDVI when vegetation type was disregarded. However, when vegetation type was accounted for, while ENDVI and EEVI retained their pivotal roles, NDVI_max supplanted EEVI as the most critical inversion variable.  
The research by [PERSON] et al. [22] demonstrated that surface Fresnel reflectance and soil moisture are the primary factors influencing soil EC inversion. An intertidal mudflat in Yancheng, Jiangsu, China, [PERSON] et al. [128] identified distance from the ocean and vegetation type as key determinants for soil salinity mapping. For the RF model, significant parameters that remained relatively stable from 2015 to 2019 included distance from the sea, vegetation type, and distance from the tidal channel. In the Mekong Delta, Vietnam, [PERSON] et al. [87] assessed the importance of 31 independent variables using an RF algorithm, revealing that the thermal infrared sensor B11 (Landsat) was the most influential variable, followed by DEM and B6. [PERSON] et al. [96] evaluated the efficacy of Sentinel-1 SAR images alongside five advanced ML algorithms for mapping soil salinity intrusion in Penang Province, demonstrating that texture features in both VV and VH bands were highly effective for soil salinity modeling. In Lisbon, Portugal, within the estuary of two rivers influenced by tidal movements, [PERSON] and [PERSON] [129] tested nine vegetation indices derived from Sentinel-2 imagery between 2017 and 2019. Their study found that the Canopy Response Salinity Index (CRSI) exhibited the strongest correlation with soil salinity in agricultural fields, indicating its high application value in addressing soil salinization issues.
# An Innovative and Efficient Method for Reverse Design of Wheel-Rail Profiles
## 5 Application Example  
The new designed wheel profile aims to guarantee the kinematic characteristics of the original matching formed by LMa wheel profile and 60 N rail profile, also with the new designed wheel profile-CHN60 rail profile. The original matching has been chosen because it is characterized by good performances in kinematic behavior and CHN60 rail profile is widely common in China railways.  
A wheel profile matching a CHN60 rail is reverse designed by taking the rolling radius difference between the LMa wheel profile and 60 N rail as the target. The rail gauge is 1435 mm, the nominal rolling radius is 430 mm and the rail can is 1/40.  
1. The CHN60 rail profile shape is given;
2. The initial contact point is given;
3. The LMa wheel profile shape on the right of the initial contact point is given.  
Figure 15a shows the comparison between the optimized wheel profile and LMa profile. The rolling radius difference is compared with the original one in Figure 15b; it shows that the two plots are almost coincident and that the error (see Figure 16) is about zero. Figure 17 shows the contact pairs of the optimized wheel profile. The lateral displacement is within \(-9.8\) mm-\(9.8\) mm, the lateral displacement space is 0.2 mm. The contact for the optimized wheel profile is more intensive.  
Figure 16: Absolute value of the error \(e\) in rolling radii difference distribution.  
Figure 15: Wheel profile and rolling radius difference. (**a**) Wheel profile; (**b**) Rolling radius difference.
# Thrust and Pressure Control in a Solid Propulsion System via Reinforcement Learning
## 2 Model and Algorithm
### Training Environment
#### 2.3.3 Valve Servo Motion  
Achieving the closed-loop control of nozzle opening by regulating the valve core position is crucial. Modeling the dynamic behavior of the valve accurately is challenging due to the presence of various mechanical components and frictional forces within the gas valve. To address this, we employ system identification techniques to tailor the servo system.  
We establish a fourth-order state space model, achieving a system similarity of 92.1%. The identified observable state space is represented in Equation (8).  
\[\begin{split}\dot{x}(t)&=Ax(t)+B\omega_{cmd}\\ y(t)&=Cx(t)+D\omega_{cmd}\end{split} \tag{8}\]  
Here, \(x(t)\) denotes an n-dimensional state vector, \(A\) is the system matrix, \(B\) represents the control matrix, \(\omega_{cmd}\) denotes the speed command, \(C\) stands for the output matrix, \(D\) indicates the direct transfer matrix, and \(y(t)\) symbolizes the motor speed. Figure 6 illustrates the system output obtained by applying the same swept frequency signal input to both the actual and identification systems, as referenced in prior work [30].
# An Aspect-Oriented Approach to Time-Constrained Strategies in Smart City IoT Applications
## 2 Existing Approaches
### Time-Constrained Data in Smart Cities  
Due to their resource constraints, IoT applications in smart cities contexts often operate such that their data are time-constrained into a window of validity (WoV) [11; 16; 17; 18]. In many smart city applications, such as smart transportation, the correctness and applicability of the data may be strongly impacted by the ability to communicate it upstream within the WoV [19].  
In [16], we proposed to model the WoV of IoT device data according to two parameters: whether the data are relevant to the decision-making algorithm at a particular time, and whether an acceptable version of the data is cached locally. When the decision-making algorithm which runs on the IoT data attempts to access data which is not in its WoV, the supporting software infrastructure is engaged either to fetch the data for the first time or to update an invalidated, locally cached copy [16] (Figure 1).  
There are various practical approaches used to resolve data in IoT systems when they are outside their WoV. In [19], the authors propose an algorithmic framework to adjust, ex post facto, for temporal and spatial discrepancies caused by communications losses in smart transportation data to improve the quality and precision of the data. This approach is suitable where the data is collected into a dataset for later analysis but is less useful where decisions must be made based on the real-time values of IoT sensors.  
Others have proposed offloading the data from less capable edge devices to more capable mobile devices such as unmanned aerial vehicles (UAV) [11; 20]. In theory, this approach would improve the real-time data gathering and processing in the IoT network; however, in practice, the deployment of swarms of autonomous vehicles may not be feasible or available with sufficient coverage at scale in a smart city. Other potential obstacles to this approach include the high level of heterogeneity typical of IoT devices and their limited capacity to communicate with complex, moving platforms such as UAVs.  
Figure 1: Example execution flow in our model for managing time validity of data in IoT systems according to their window of validity (WoV).
## 2 Results
### Effects of HHM, HHE-1/1 on Hematological Parameters  
At the end of the study, the mean value of red blood cells in the CCl\({}_{4}\) group was 8.71 \(\pm\) 1.15 T L\({}^{-1}\). This figure was significantly lower compared with the silymarin group (10.05 \(\pm\) 0.91 T L\({}^{-1}\)). The mean value of HBG in the CCl\({}_{4}\) group was 122 \(\pm\) 17.91 g L\({}^{-1}\). This value was significantly lower compared with the control group, silymarin-treated and extracts-treated groups (\(p\) < 0.01 and \(p\) < 0.001). The mean value of PLT in the CCl\({}_{4}\) group (415.3 \(\pm\) 137.57 G L\({}^{-1}\)) was also lower than the control group, HHM group and HHE-1/1 group (604.78 \(\pm\) 82.11 G L\({}^{-1}\); 577.30 \(\pm\) 79.84 G L\({}^{-1}\) and 591.10 \(\pm\) 106.77 G L\({}^{-1}\) respectively) (\(p\) < 0.01). The mean value of WBC in the control group was lower than in other groups (\(p\) < 0.05 and \(p\) < 0.001). The highest WBC mean figure belonged to the CCl\({}_{4}\)-treated group, which was significantly higher than HHM and HHE-1/1 groups (\(p\) < 0.001 and \(p\) < 0.05) (Table 1).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Group (_n = 10_)** & **RBC (T L\({}^{\textbf{-1}}\))** & **HGB (g L\({}^{\textbf{-1}}\))** & **PLT (G L\({}^{\textbf{-1}}\))** & **WBC (G L\({}^{\textbf{-1}}\))** \\ \hline Control & 9.19 \(\pm\) 1.38 & 157.21 \(\pm\) 13.48 & 604.78 \(\pm\) 82.11 & 14.67 \(\pm\) 3.13 \\ CCl\({}_{4}\) & 8.71 \(\pm\) 1.15 & 122.00 \(\pm\) 17.91 *** & 415.30 \(\pm\) 137.57 ** & 26.69 \(\pm\) 3.04 *** \\ Silymarin & 10.05 \(\pm\) 0.91 *** 9.32 \(\pm\) 0.84 & 146.00 \(\pm\) 9.78 *** & 542.20 \(\pm\) 167.71 & 23.82 \(\pm\) 6.11 \\ HHM & 9.66 \(\pm\) 1.25 *** & 153.50 \(\pm\) 10.6 *** & 577.30 \(\pm\) 79.84 *** & 20.52 \(\pm\) 2.72 *** \\ HHE-1/1 & & 156.40 \(\pm\) 11.6 *** & 591.10 \(\pm\) 106.77 *** & 22.19 \(\pm\) 6 *** \\ \hline \hline \end{tabular}  
* \(p\) \(<\) 0.01 and **
* \(p\) \(<\) 0.001 compared with the control group.
* \(p\) \(<\) 0.05, **_p_ \(<\) 0.01 and **
* \(p\) \(<\) 0.001 compared with the CCl\({}_{4}\) group.  
\end{table}
Table 1: HHM and HHE-1/1 effect on red blood cells, hemoglobin, platelets of CCl\({}_{4}\)-induced liver fibrosis in rats.
## 1 Introduction
However, these methods fail to address issues with certificate verifiability and raise concerns about the existence of privacy-sensitive information. A hash network links the blockchain in chronological order. For each node, it has a copy of a ledger, and few mutually distrusting nodes often maintain this hash network. This system provides better confidentiality and non-repudiation over the Ethereum networks, but the cost requirement is high and the system's feasibility low. To overcome these challenges, a novel blockchain-based privacy preservation model is implemented in the Ethereum network to provide better privacy preservation. The contributions of the designed Ethereum-based privacy preservation model are listed below. * To design a blockchain-based data privacy preservation model with a hybrid meta-heuristic algorithm over the supply chain network to secure information exchange and guarantee the privacy of data access in the Ethereum platform. Here, the performance improvement of the proposed model is applicable to different applications regarding cryptocurrency, food supply chains, and sealed-bid auctions. * To generate the key with the help of developed ABC-ROA for exchanging the secured data in the supply chain framework using data restoration and data sanitization procedures in the Ethereum environment. The developed ABC-ROA algorithm is utilized to restore the data from the receiver side. Consequently, it helps to access the original data that can be generated from the original key. * To implement the hybrid meta-heuristic algorithm known as ABC-ROA for choosing the best optimal key to maximize the performance of the developed blockchain-basedprivacy preservation model. Here, the designed ABC-ROA algorithm improves the system's robustness. It is also used to solve complex issues. * To compare the developed ABC-ROA-based privacy preservation system with existing meta-heuristic algorithms using a variety of metrics to verify the performance of the developed model. This paper is split into the following sections. The merits and demerits of recent privacy preservation in the Ethereum network model based on blockchain are described in Section 2. The suggested dataset for the blockchain-based privacy preservation system in Ethereum and the model explanation offered are covered in Section 3. The procedures used to create the privacy preservation model, such as data restoration and sanitization in the supply chain method, are shown in Section 4. The Ethereum privacy preservation model with the ABC-ROA algorithm, optimal key details, and objective function details are described in Section 5. The acquired outcomes of the recommended method are summarized in Section 6. The paper is concluded in Section 7.