esa-sceva/satcom-chunk-collection huggingface dataset:
# BeiDou Geostationary Satellite Code Bias Modeling Using Fengyun-3C Onboard Measurements
###### Abstract  
This study validated and investigated elevation- and frequency-dependent systematic biases observed in ground-based code measurements of the Chinese BeiDou navigation satellite system, using the onboard BeiDou code measurement data from the Chinese meteorological satellite Fengyun-3C. Particularly for geostationary earth orbit satellites, sky-view coverage can be achieved over the entire elevation and azimuth angle ranges with the available onboard tracking data, which is more favorable to modeling code biases. Apart from the BeiDou-satellite-induced biases, the onboard BeiDou code multipath effects also indicate pronounced near-field systematic biases that depend only on signal frequency and the line-of-sight directions. To correct these biases, we developed a proposed code correction model by estimating the BeiDou-satellite-induced biases as linear piece-wise functions in different satellite groups and the near-field systematic biases in a grid approach. To validate the code bias model, we carried out orbit determination using single-frequency BeiDou data with and without code bias corrections applied. Orbit precision statistics indicate that those code biases can seriously degrade single-frequency orbit determination. After the correction model was applied, the orbit position errors, 3D root mean square, were reduced from 150.6 to 56.3 cm.  
BeiDou code biases; Fengyun-3C; onboard BeiDou; single-frequency orbit determination 2017
## 4 System modeling
### Unsupervised learning methods  
Unsupervised learning is used to examine how the data are organized. In contrast to supervised learning processes, unsupervised learning processes are only given unlabeled samples. In other words, learners only have independent information \(\mathbf{X}\) but no corresponding information \(\mathbf{Y}\). Cluster analysis is a major topic for unsupervised learning methods. For sensor signal analysis, unsupervised learning methods can not only display the distribution and the grouping of the data but also provide concentrated information for supervised learning processes.  
Figure 5: Support vectors based classification system.  
PCA can be considered as the simplest unsupervised learning method, providing a visual data grouping result. Another simple but effective unsupervised learning method is K-means algorithm. The basic concept of this algorithm is to minimize the sum of the distances between all the samples to their cluster centers (i.e., total intra-cluster variance) through an iteration process. K-means algorithm has been widely used as a tool for system resolution and feature extraction. For example, K-means is a practical method to determine radial basis vectors for RBFNN and center vectors for PNN. The major drawbacks of K-means are that (1) the global convergence of this algorithm largely depends on the initial sample order and (2) the algorithm may have a skewed clustering result, if the cluster number estimate is incorrect. Therefore in this section, we will review a couple of adaptive unsupervised learning methods.
## 4 Attack Taxonomy
### Attacks Based on Adversary Location  
**External Attack:** An attacking device is located anywhere on the Internet. Figure 5 shows the locations of the adversaries in the external networks. The threat model for external attacks can be as follows. A malicious application can be published in the application store, and later this application can be installed on a smartphone that is used to control IoT devices. Similarly, a malicious application can be installed on the cloud servers where sensitive sensor information is stored. The malicious applications can leak sensor data and users' personal information, such as a patient's health conditions received from their medical devices, to the Internet. Moreover, the malware can exploit vulnerabilities in the Gateway device [88; 89] of an IoT network to gain unauthorized access to smart devices and networks remotely. The malware can perform various web service attacks, such as SQL injection and cross-cite scripting, using the compromised Gateway device.  
**Internal Attack:** An attacking device and target devices are located in the same IoT network. The attacking device can be a member of the Destination-Oriented Directed Acyclic Graph (DODAG) [90] formed by smart devices, or it can be deployed to a location in the IoT network such that target devices and the attacking device share the same radio signal used in communications. Figure 6 shows the positions of an attacking device in an IoT network. In the internal attack, an attacking device is used for gathering information on communications protocols and network vulnerabilities. For instance, the attacking device can be used to overhear communications between IoT devices to determine whether messages are exchanged in plain text or are encrypted. If message are encrypted, then it can also be determined whether or not a vulnerable security method is used for encryption, such as heartbleed vulnerability in TLS 1.1 and 1.2 [91]. The attacking device can also be used to identity vulnerabilities in the software used by IoT devices, such as open ports. An adversary can use this information to gain unauthorized access to IoT devices or to perform various network attacks, such as wormhole [92], sinkhole [93], and botnet [94].
# Techno-Economic Analysis of Waste Heat Recovery in Automotive Manufacturing Plants
## 4 Results and Discussion
### Monthly Power Production and Consumption  
Figure 5 illustrates the heat pump's monthly energy production (indicated by positive values) from ORC and PV, alongside the energy consumption (indicated by negative values). In this study, the total net energy generation was calculated by subtracting the energy consumed by the heat pump from the combined energy produced by the ORC and PV system. The results show that the operational schedule of the ORC and heat pump adheres to working days, running for 21-23 days each month rather than a full 29-31 days. This scheduling contributes to variations in monthly energy generation and consumption.  
Figure 4: The result of the estimated PV energy based on solar radiation.  
Figure 5: Monthly energy production (positive values) from ORC and PV systems, energy consumption (negative values) by the heat pump, and total net energy generation.  
A significant reduction in operating days was observed in April due to an annual maintenance and improvement project, leading to lower energy production from both the ORC and PV systems and, correspondingly, reduced energy consumption by the heat pump. Therefore, April recorded the lowest total net energy production at 229.79 MWh, while August, with full operational days, showed the highest net production, reaching 408.86 MWh. Furthermore, the monthly energy production from the ORC varied between 137.05 MWh and 318.5 MWh, with the highest output in July (318.49 MWh) and the lowest in April. Meanwhile, the PV system's monthly energy production ranged from 75.35 MWh to 145.92 MWh. The heat pump's energy consumption varied between 24 MWh and 55.2 MWh, following fluctuations in operational days and system performance requirements. The lowest energy consumption of the heat pump was also observed in April. It is important to note that, while both the heat pump and ORC systems showed the lowest energy production and consumption during non-operational periods, such as annual maintenance, the PV system continued to produce electricity. This ongoing PV production can help support essential power needs during these maintenance downtimes. Also, it is worth noting that the total annual net generation from the system with PV was 4307.29 MWh.
# Accurate Estimation of Air Pollution in Outdoor Routes for Citizens and Decision Making
## 5 Results
### Evolution over 24 h in 2 h Steps  
In this test, we evaluate the behaviour of PM2.5 and NO\({}_{2}\) over 24 h in 2 h steps. In this case, we have selected a large walking route and have analysed how these pollutants change over time. This test was carried out during a working day in summer (19 July 2023, Wednesday). The selected route goes from the south-west to the north-east of the city, from one end to the other end of town.  
As we can see in Figure 5, at 8:00 am NO\({}_{2}\) is near the WHO's limit and PM2.5 is above it. This is due to intensive traffic because of the movement of people travelling to their jobs. In particular, we can see this effect on the outskirts and at the harbour, as well as on main avenues. These hours are peak hours. After that, as seen in Figure 6, pollution starts decreasing at noon until 16:00, which shows the lowest value during the day, when people start going home after work. Until this time, pollution is evenly distributed and decreases more in the zones close to the sea, shown on the left side in the figures, due to the breeze. In Figure 7 at 18:00, pollution increases again but in a slower and more homogeneous, more staggered way, although PM2.5 remains stable. In Figure 8 at 22:00, pollution starts decreasing since road traffic dicreases too. In Table 3, we summarise the average concentration of NO\({}_{2}\) and PM2.5 during this working day. Notice that this pollutant distribution also depends on the orography and weather conditions, but the representations are accurate since they are based on valid and official measurements taken from the AQ surveillance monitoring stations shown in Sections 3.2 and 3.3.  
Figure 6: NO\({}_{2}\) and PM2.5 mapping at 12:00 p.m., 19 July 2023, Wednesday.  
Figure 7: NO\({}_{2}\) and PM2.5 mapping at 18:00 p.m., 19 July 2023, Wednesday.  
From these figures, it is clearly seen in Figure 5 at 8:00 in the morning that PM2.5 is above the WHO's limit. At that time, in case of respiratory and cardiovascular problems related to the lungs or high blood pressure [5], the user should decide to change the route (avoiding in particular the city center) or delay it at least two hours, because by that time these levels would have dropped, as depicted in Table 3.
## 3 Aloha Protocols  
Aloha-based protocols use a random-access strategy in order to successfully identify the number of tags in an interrogation area [16; 17; 18; 19; 20]. They belong to the group of probabilistic protocols because the tags transmit their own ID in randomly selected slots in a frame in order to reduce the possibility of a collision. However, there is no guarantee that all of the tags will be identified in the interrogation process. These protocols suffer from the well-known tag starvation problem, in the sense that a tag  
Figure 6: Code Division Multiple Access (CDMA) procedure.  
Figure 7: Time Division Multiple Access (TDMA) procedure.  
may not be correctly read during a reading cycle due to an excessive number of collisions with that same tag. Every frame consists of a certain number of slots, and the tags can only respond once per frame [16].  
The main Aloha-based protocols can be divided into four subgroups: Pure Aloha (PA), Slotted Aloha (SA), Frame Slotted Aloha (FSA) and Dynamic Frame Slotted Aloha (DFSA) protocols.
## 4 Structure and Unstructured Lines
### Classification of Structured and Unstructured Lines  
The scene in the Manhattan World hypothesis consists of vertical and horizontal surfaces, and most of the intersecting lines of building surfaces are aligned with the three directions, \(xyz\). Therefore, straight lines can be recognized as structural lines aligned with the three axes of the Manhattan world based on vanishing points in the image, and the rest of the line segments are categorized as non-structural lines.  
After finding a vanishing point in space, its location can be calculated based on three dominant directions of the Manhattan world. The \(x\) axis is calculated as follows:  
\[v_{x}^{\psi_{i}}=KR_{w}^{c}[\cos\psi_{i},-\sin\psi_{i},0]^{T} \tag{11}\]where \(\psi_{i}\in[0,\pi/2)\), \((i=1,2,\ldots,N)\) denotes the heading of the localized Manhattan world, and \(R_{w}^{c}\) is the rotation matrix from the world coordinate system to the camera coordinate system. Similarly, the vanishing point in the \(y\) axis direction is:  
\[v_{y}^{\psi_{i}}=KR_{w}^{c}[\sin\psi_{i},\cos\psi_{i},0]^{T} \tag{12}\]  
The vanishing point in the \(z\) axis direction is independent of the heading of the Manhattan world, which is calculated as:  
\[v_{z}=KR_{w}^{c}[0,0,1]^{T} \tag{13}\]  
The theoretical location of the vanishing point in relation to the structure line is as follows:  
\[\begin{cases}\tau(v_{1})\cdot L_{1}=0\\ \tau(v_{2})\cdot L_{2}=0\\ \tau(v_{3})\cdot L_{3}=0\end{cases} \tag{14}\]  
where \(\tau\) represents the transformation from camera coordinates to image coordinates, and \(L_{1},L_{2},L_{3}\) represents the structure line with the direction of the vanishing point.  
In this paper, the threshold detection method is used to judge the direction of structural lines. First, a ray is drawn from the vanishing point to the midpoint position of the line feature. Then, a distance threshold \(d_{t}\) and an angle threshold \(\theta_{t}\) are set to categorize the line feature into three directions. Specifically, if the distance from the vanishing point to the midpoint is less than \(d_{t}\) and the angle to the line feature is less than \(\theta_{t}\), the line feature is considered a structural line in the corresponding direction. If more than one vanishing point satisfies the line feature detection condition, the one with the best consistency is selected and the corresponding local Manhattan world is constructed on the basis of that vanishing point.  
Line segments that do not meet the threshold are appended to a similar Manhattan world based on angle and distance, and when the number of proximally categorized line features is greater than 50% of the total number of detected lines, the detection of vanishing points in all three directions is continued using the two-line method, which further identifies the line features to new Manhattan worlds based on the threshold. Finally, unclassified line features are recognized as unstructured lines.
## 7 Conclusions  
This paper presents a new single VD-EXCCII-based electronically tunable mixed-mode filter structure. The filter employs only one VD-EXCCII, three resistors and two capacitors. The mixed mode filter enjoys inbuilt tunability and can realize all five filter responses in all four modes of operation (VM, CM, TAM and TIM). A CM SIMO filter can also be derived from the presented minimum component mixed mode MISO filter topology. The detailed theoretical analysis, non-ideal gain analysis and parasitic study are given. The VD-EXCCII is designed in Cadence Virtuoso software and extensive post-layout simulations are carried out to examine and validate the proposed filter in all four modes of operation. The proposed filter has all the advantages mentioned in Section 3. The filter is designed for a frequency of 8.0844 MHz with a \(\pm\)1.25 V supply. The Monte Carlo analysis shows that the frequency deviation is within acceptable limits. Furthermore, the THD is within 5% for a considerable voltage/current input signal range. The power dissipation of the filter is found to be 5.76 mW. The VD-EXCCII and the mixed mode filter are also designed and tested using the models of commercially available ICs the AD844 and LM13700 in PSpice. The simulation results are found consistent with the theoretical predictions.  
Conceptualization, M.F., M.A.A. and J.S.; methodology, M.F., N.H., M.A.A. and J.S.; software, M.F.; validation, M.F. and M.A.A.; formal analysis, M.F., N.H. and J.S.; investigation, M.F., N.H. and J.S.; resources, M.A.A. and J.S.; data curation, M.F., N.H. and M.A.A.; writing--original draft preparation, M.F., N.H, M.A.A. and J.S.; writing--review and editing, M.F., N.H. and J.S.; visualization, N.H.; supervision, J.S.; project administration, M.F. and M.A.A.; funding acquisition, N.H., M.A.A. and J.S. All authors have read and agreed to the published version of the manuscript.  
Part of this work was carried out at the Institute of Microengineering and Nanoelectronics (IMEN), University Kebangsaan Malaysia (UKM). This work is funded by the Ministry of Education Malaysia under grant (FRGS/1/2018/TK04/UKM/02/1) and AKU254:HICoE (Fasa II) 'MEMS for Biomedical Devices (artificial kidney)'.  
We would like to thank the anonymous reviewers for their insightful comments and suggestions.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
Figure 26: Time domain analysis result for the VM-BP response of the filter.
## 3 Results
### Classification Models Built on Individual Spectroscopic Blocks
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{3}{*}{**Method**} & \multirow{3}{*}{**Data Set**} & \multicolumn{4}{c}{**Optimal Number of LVs**} & \multicolumn{4}{c}{**Correct Classification Rate (\%)**} \\ \cline{3-8}  & & & & & & **Training Set** & & **Test Set** \\  & & **Raw** & **SNV** & **1 st Der.** & **2 nd Der.** & **(Cross-Validation)** & **(Prediction)** \\ \cline{5-8}  & & & & & **Senise** & **Adulterated** & **Senise** & **Adulterated** \\ \hline \multirow{3}{*}{PLS-DA} & MIR & 3 & & & & 80.0 & 72.0 & & \\  & & & 4 & & & 80.0 & 76.0 & & \\  & & & & 3 & & 80.0 & 84.0 & & \\  & & & & & 3 & 87.5 & 80.0 & 75.0 & 93.3 \\ \multirow{3}{*}{PLS-DA} & \multirow{3}{*}{NIR} & 2 & & & & 100.0 & 92.0 & & \\  & & & 3 & & & 100.0 & 92.0 & & \\  & & & & 1 & & 100.0 & 100.0 & 86.7 \\ \multirow{3}{*}{SPORT} & \multirow{3}{*}{MIR} & \multirow{3}{*}{1} & \multirow{3}{*}{0} & \multirow{3}{*}{0} & \multirow{3}{*}{4} & \multirow{3}{*}{92.5} & \multirow{3}{*}{92.0} & \multirow{3}{*}{75.0} & \multirow{3}{*}{93.3} \\  & & & & & & 100.0 & 100.0 & 100.0 & 86.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of partial least squares-discriminant analysis (PLS-DA) and sequential preprocessing through orthogonalization (SPORT) classification for the MIR and the NIR data sets.
# Nature-Based Management of Laws--Enhancing Biodiversity in Urban Green Infrastructure
## 2 Materials and Methods
### Methods of Vegetation Assessment  
Vegetation was assessed using a remote sensor method. The study used the unmanned aerial vehicle DJI Matrice 600 with the multispectral camera Parrot Sequoia+. The camera allows images to be recorded in four spectral channels: green (GRE), red (RED), near-infrared (NIR), redge (REG), and RGB images in visible lights. The data collected allowed a visual assessment of the plants and an evaluation of plant health based on the chlorophyll index. The chlorophyll index allows the amount of chlorophyll in the green parts of plants to be determined. The Normalized Difference Vegetation Index (NDVI), which is the most commonly used index due to its ease of calculation, was used in the study. The NDVI index is based on the ratio of the NIR and RED channels and is expressed by the Formula (1) [46]:  
\[\text{NDVI}=(\text{NIR}-\text{RED})/(\text{NIR}+\text{RED}) \tag{1}\]  
where:  
NIR--near-infrared band  
RED--red band  
Figure 2: View of the Water Center of SGGW with the water park.  
Vegetation was assessed using phytocoenological releves with as size of 2 m\({}^{2}\). Four plots were surveyed for each lawn variant, on which vegetation was assessed in July 2020 and 2021. First, all taxa of plants that occurred on the phytocoenological releves were identified, and then the aboveground biomass coverage was estimated for the respective taxa. The scientific names of the individual plant species were obtained from the Pladias database of flora and vegetation [47]. Based on the data from the Pladias database, plant species were classified into several groups according to the selected characteristics. The indicator value for moisture was expressed on an ordinal scale from 1 to 12, as defined by [PERSON] [48]. The values for the individual taxa were modified and extended by [PERSON] [49].  
The coverage values of individual plant species observed at each site were analyzed using a multidimensional analysis of ecological data. To determine the appropriate analytical method, the gradient length was assessed using segment Detrended Correspondence Analysis (DCA). Canonical Correspondence Analysis (CCA) was also performed. To assess statistical significance, a Monte Carlo test was employed, and 999 permutations were calculated. Data analysis was performed using Canoco 4.0, a computer program widely used for ecological data analysis [50].
## 1 Introduction
For DEMO divertor plasma-facing components, the IPFL is not considered as a potential failure mechanism for inelastic assessment because it is argued that the flow localization helps in strain redistribution and the ultimate failure will be captured by the ILFED criteria [21]. However, since the irradiated Eurofer retains considerable durability after necking, IPFL is considered the main concern for Eurofer instead of ILFED [23; 25]. For BB's first wall, IPFL was the criteria used for the inelastic assessment in [17]. It is explained in [25] that the IPFL is related to the uniform elongation of the material and the ILFED to its total elongation. The uniform elongation indicates the limit of the force that the structure can handle, and the total elongation is the limit of the strain/displacement  
Figure 1: HCPB BB concept within the DEMO tokamak. that can be developed in a structure. Therefore, it can be realized that the IPFL is the relevant criteria for force-limited failures (due to primary loads) that can occur in the structure, whereas ILFED is the criteria for displacement-limited (due to secondary loads) failures. It is thought that the assessment with respect to any one of these criteria alone may not give a complete picture of the reliability of the design. For example, if the plastic instability is reached at any point in the structure, a slight further increase in primary loads can cause failure by rapidly consuming the ductility. Likewise, even if the primary loads are not causing the plastic instability, the secondary loading like thermal expansion can cause the structure to fail, by straining the structure beyond its total elongation point. Hence, in this paper, the inelastic assessment is carried out against both IPFL and ILFED criteria.