esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Algorithms for Sorting Permutations with Brooms
## 3 Sorting Permutations with a Single Broom
### Correctness of \(A_{b}\)
Split the subsequence \(S_{1},S_{2},\ldots,S_{p-1}\) into \(S_{path}\) and \(S_{star}\), where \(S_{path}\) contains the swaps that occur on path edges and \(S_{star}\) contains the swaps that occur on star edges. Note that \(S_{path}\) does not move marker \(n\) from its position and \(S_{star}\) always places \(n\) back to the center. Additionally, as per Property 5 we can rearrange the subsequence containing \(S_{star}\) and \(S_{path}\) as there are no star markers moving towards the path and vice versa. To rearrange this subsequence, perform the swaps in \(S_{path}\), then the swap \(S_{p}\) followed by the swaps in \(S_{star}\). While performing the saps in \(S_{star}\) replace the marker \(n\) with marker \(t\). This results in same placement of markers with no change in the swap count on the star and path edges; hence, \(S_{opt}\) is optimal and is the same as the one generated by our algorithm \(A_{b}\). Case (3): As discussed before, for Step 3 the optimal number of swaps are \(L_{S}+N_{S}\) where \(L_{S}\) be the number of permutations that have a length \(\geq 2\) not involving the center vertex (say \(V_{c}\)) and \(N_{S}\) be the number of markers that are in these permutations. In case of star, consider a sequence \(P\) of length \(\geq 2\) and the star vertices (say \(V_{s}\)). If \(V_{c}\
otin P\), then the total swap count to sort \(P\) is \(V_{s}+1\). If \(V_{c}\in P\), then the total swap count is \(V_{s}\).As the cycles in \(L_{S}\) are not related to each other, \(L_{S}+N_{S}\) gives the optimal swap count.
## 4 Simulation and Analysis of Magnetic Field Distribution  
### Magnetic Field Distribution for Alignment Coils  
In order to analyze the magnetic field distribution around the vehicle when WCS is working, the space is divided into four geometric areas as defined by SAE J2945, namely 1, 2a, 2b, 3, shown in Figure 11. Area 1 is the main area of energy coupling, and it is necessary to focus on the magnetic field distribution of other areas. Four points (A1, A2, A3, A4) in different regions are selected as our test points in Figure 11a. They are located at the axial plane of the coupling device, and strictly follow the guidelines of SAE J2954. Since drivers stay in the cab for a long time, it is necessary to secure the cab's magnetic field in area 3. The SAE J2954 also gives test points for the magnetic field at the driver's seat  
Figure 8: Circuit of the WCS.  
Figure 10: 3D model of the vehicle body: (**a**) Side view; (**b**) Top view.  
Figure 9: Charging parameters: (**a**) Current; (**b**) Voltage.  
in area 3. It is necessary to observe the strength of the head (B1), chest (B2) and cushion (B3) at each seat. The measurement should be repeated for each combination of alignment and offset conditions. The relative position of test points are shown in Fig 11b, and each test point is 10 mm above the surface and is in the axial plane of the seat.  
The electromotive force (EMF) standard and pacemaker/ implanted medical device (IMD) limit for public applications are given in SAE J2954, while meeting the requirements of ICNIRP 2010. The limits are shown in Table 5. Considering that the simulation reflects a transient magnetic field characteristic, we take the peak limits as reference.  
The current excitation of WCS extracted in Simplorer was respectively injected into the primary and secondary coils, and then we obtained the simulation results of magnetic field in Maxwell. When the coil is aligned, the simulation results are shown in Figure 12. The magnitude of the magnetic field at each test point are shown in Table 6.  
From the results, it can be seen that magnetic field strength of A1 can reach 1223 A/m when the coils are perfectly aligned, and the magnetic field strength of all positions does not exceed the limit except for the effective region. The magnetic field strength of test points outside the vehicle is significantly larger than those inside the vehicle. Except for A1 in the working area, the magnetic field strength at A3 on the lower right side of the vehicle can reach 14.15 A/m. Although A3 does not exceed the standard limit, it has a higher risk of exceeding the limits.
# A Review of Double-Walled and Triple-Walled Carbon Nanotube Synthesis and Applications
## 2 Synthesis and Characterization of DWNTs
### The Arc Discharge Method
[PERSON]'s group has addressed the observation that sulfur forms a coating layer that involves the metal nanoparticles, and this coating limits carbon diffusion and segregation in the metal nanoparticle surface [78]. [PERSON] _et al_. [82] demonstrated that the addition of potassium chloride improved the yield of CNTs and the selectivity of DWNTs. The yield of the CNT amount increased by five-fold, and the selectivity of DWNTs increased from 10% to over 50% after the optimization of the KCl content. While both KBr and NaCl also worked similarly as KCl, halogens seemed to play a key role in increasing the yield of CNT growth, as well as the selectivity of DWNTs during the synthesis reaction [83]. It was proposed that chloride (or halogens generally) lead to the formation of aromatic carbon species, which are easily transformed into CNT walls or cap structures.
# Vulnerable Road User Detection for Roadside-Assisted Safety Protection: A Comprehensive Survey
## 4 Datasets
### Multi-Sensor Datasets
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Datasets** & & **Data Source** & **Synchronization** & **Object Class** & **Content** \\ \hline \multirow{8}{*}{DAIR-V2X-C [107]} & \(\bullet\) & Infrastructure & & \(\bullet\) & **Pedestrian** & \\  & \(-\) & \(1\times\) 300- & & \(\bullet\) & **Cyclist** & \\  & & layer & & \(\bullet\) & **Tricyclist** & \\  & & LiDAR & & \(\bullet\) & **Barrow** & \\  & & \(-\) & \(1\times\) camera & & \(\bullet\) & Motorcyclist & \\  & & \(\bullet\) & Vehicle & & \(\bullet\) & **Sus** & \\  & & \(-\) & \(1\times\) 40-layer & & \(\bullet\) & **Van** & \\  & & LiDAR & & \(\bullet\) & Truck & \\  & & \(-\) & \(1\times\) camera & & \(\bullet\) & **Traffic cone** & \\ \hline \multirow{8}{*}{TUMTraf V2X Cooperative Perception Dataset [147]} & \(\bullet\) & Infrastructure & & \(\bullet\) & **Pedestrian** & \(\bullet\) & 2000 frames of LiDAR data, \\  & \(-\) & \(1\times\) 64-layer & & \(\bullet\) & **Cyclist** & \\  & & LiDAR & & \(\bullet\) & **Motorcyclist** & \\  & & \(-\) & \(4\times\) cameras & & \(\bullet\) & Car \\  & & Vehicle & & \(\bullet\) & **Van** & \\  & & \(-\) & \(1\times\) 32-layer & & \(\bullet\) & Bus & \\  & & LiDAR & & \(\bullet\) & Truck & \\  & & \(-\) & \(1\times\) camera & & \(\bullet\) & **Trailer** & \\ \hline \multirow{8}{*}{V2X-Real [148]} & \(\bullet\) & Infrastructure & & & & \\  & \(-\) & \(1\times\) 64/128- & & \(\bullet\) & **Pedestrian** & \\  & & layer & & \(\bullet\) & **Sooter** & \\  & & LiDAR & & \(\bullet\) & **Cyclist** & \\  & & \(-\) & \(2\times\) cameras & & \(\bullet\) & Motorcyclist & \\  & \(\bullet\) & Vehicle & & \(\bullet\) & **Car** & \\  & & \(-\) & \(1\times\) 128- & & \(\bullet\) & **Bus** & \\  & & layer & & \(\bullet\) & **Truck** & \\  & & LiDAR & & \(\bullet\) & **Box truck** & \\  & & \(-\) & \(4\times\) cameras & & \(\bullet\) & Barrier & \\ \hline \multirow{8}{*}{V2X-SIM [149]} & \(\bullet\) & Infrastructure & & & & \\  & \(-\) & \(1\times\) LiDAR & & & & \\  & \(-\) & \(4\times\) cameras & & & & \\  & \(-\) & \(1\times\) BEV & & & & \\  & & semantic & & & \\  & & segmentation camera & & & \\  & & & & & \\  & & Vehicle & & & & \\  & & \(-\) & \(1\times\) LiDAR & Yes & \\  & & \(-\) & \(1\times\) semanan- & & & \\  & & \(-\) & \(1\times\) semanan- & & & \\  & & \(-\) & \(6\times\) & & \\  & & RGB/depth/semantic & & & \\  & & segment- & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & \(-\) & \(1\times\) BEV & & \\  & &Additionally, the simulated dataset V2X-SIM is also mentioned [149]. However, its limitation is that it only includes one type of VRU: pedestrians. Simulated datasets are particularly useful for generating corner cases, such as accidents and occlusions involving VRUs, which are difficult and hazardous to capture in real-world settings. In conclusion, a comprehensive dataset that covers a wide range of VRU categories and includes diverse scenarios, including corner cases, is still lacking.
## 3 Candidate Universal Decoders for URLLC
### Automorphism Ensemble Decoding (AED)  
An automorphism decoder is a decoder that operates on a signal that has been received and permuted in accordance with a code automorphism. The original codeword is then recovered by rearranging the result estimation [24]. Codewords are permuted using automorphisms. A permutation of N elements that maps each codeword \(x\in C\) onto another codeword \(\pi(x)\in C\) is called an automorphism of a code C. Different permutations are applied to the channel output Log-likelihood ratio (LLR) vector in an AE decoder. To recover the initial codeword estimate, an automorphism decoder scrambles a signal that has been received and applies an automorphism. The outcome is then scrambled again. AED executes many low-complexity decodings, like successive cancellation (SC) in parallel, and can be applied to any code that has automorphisms. [PERSON] et al. [25] proposed AED as a universal decoding technique to enhance error rate performance through the execution of an ensemble of M-independent SC decoders with high implementation efficiency. Each decoder \(j\) operates on a permuted version \(\pi_{j}(y)\) of the received sequence, and each decoding result is then unpermuted (applying \(\pi_{j}^{-1}\) on decoder) as  
\[\hat{x}_{j}=\pi_{j}^{-1}(SC(\pi_{j}(y)) \tag{2}\]  
where \(\hat{x}_{j}\) is estimated codeword output. The permuted received sequences correlate to different noise patterns that are easier to decode. Therefore, choosing the most likely option from \(\hat{x}_{j}\) increases the probability of finding the correct codeword. Algorithm 1 is developed with reference to [25].  
```
1 Varaiable declaration
2y: received vector of channel LLRs
3\(A\): set of \(M\) automorphisms
4\(\pi_{j}\): automophism \(\in A\), \(j\in\{1,2,\ldots,M\}\)
5 Step I Permutation (Automorphism application)
6 Input:y
7 Output:\(y^{\prime}_{j}\)
8\(\pi_{j}\): \(y\to y^{\prime}_{j}\) for \(j\in\{1,2,\ldots,M\}\)
9 Step II Decoding permuted words
10 Input:\(y^{\prime}_{j}\)
11 Output:\(\hat{x}^{\prime}_{j}\)
121. Decode \(y^{\prime}_{j}\) using a polar decoder
132. Obtain \(\hat{x}^{\prime}_{j}\)\(\forall j\in\{1,2,\ldots,M\}\)
14 Step III Inverse permutation
15 Input:\(\hat{x}^{\prime}_{j}\)
16 Output:\(\hat{x}_{j}\)
171. Apply \(\pi_{j}^{-1}\) to \(\hat{x}^{\prime}_{j}\)
182. Obtain \(\hat{x}_{j}\) for \(j\in\{1,2,\ldots,M\}\)
```  
**Algorithm 1**Automorphism ensemble decoding (AED)
## 2 Materials and Methods
### Data Sets
#### 2.2.2 Drought Index and Climate Variables  
The monthly gridded, self-calibrated Palmer Drought Severity Index (PDSI) data set with a spatial resolution of 4 km was downloaded from the WestWide Drought Tracker website ([64]; WWDT, [[https://wrcc.dri.edu/wwdt](https://wrcc.dri.edu/wwdt)]([https://wrcc.dri.edu/wwdt](https://wrcc.dri.edu/wwdt))). PDSI is the most prominent index of meteorological drought in the United States [65], which incorporates antecedent precipitation, moisture supply, and moisture demand into a hydrologic accounting system [66]. Negative (positive) PDSI values indicate a drought (wet) period, and PDSI \(<\)-2, \(<\)-3, and \(<\)-4 refer to moderate, severe, and extreme drought, respectively [67]. Parameter-elevation Regression on Independent Slopes Model (PRISM, [[http://prism.oregonstate.edu](http://prism.oregonstate.edu)]([http://prism.oregonstate.edu](http://prism.oregonstate.edu)); [68]) climate data serves as the foundational data set for computing the WWDT drought index. United States Historical Climatology Network (USHCN) data and Penn State STATSGO soil water holding capacity data also contributed to the climate data adjustment and the soil water balance calculations respectively [64]. The WWDT drought data has been intensively applied to climatological and hydrological studies in recent years (see [69; 70; 71; 72; 73; 74; 75]), and has been proved with good reliability. To match the spatial resolution of the MODIS NDVI maps, the PDSI dataset was interpolated to a 250 m \(\times\) 250 m resolution using bilinear interpolation. The uncertainties and limitations of the data rescaling are discussed in Section 4.  
The 30-year monthly PRISM Normals of temperature and precipitation were used in this study to demonstrate the normal climate conditions over the space of the study area [55]. This data set covers  
Figure 2: Distribution of the five major land cover types in Southern California: chaparral, coastal sag scrub (CSS), grassland, forest, and developed land. Most of the native vegetation communities are distributed over the coastal ranges in Southern California.  
the period of 1981-2010 and has an initial spatial resolution of 800 m, which was rescaled on 250 m grids using bilinear interpolation. In addition, the monthly Daymet climate data set (version 3) on a 1 km grid was used to elucidate the climate change background for the period 1980-2017 in Southern California ([76]; [[https://daymet.ornl.gov/](https://daymet.ornl.gov/)]([https://daymet.ornl.gov/](https://daymet.ornl.gov/))). The long-term trends of monthly averages of maximum temperature, minimum temperature, as well as monthly total precipitation were analyzed.
## 1 Introduction  
Carbon-based materials fabricated by catalytic chemical vapor deposition (CVD), such as graphene, carbon nanotubes (CNTs), carbon nanofibers (CNFs), diamond and microcrystalline graphite, have good electrical properties [1; 2; 3; 4]. They are considered as ideal candidates for field emitters [5; 6]. Especially, CNTs exhibit superior structural advantages over other allotropes in terms of emitting electrons due to their unique sharp emitting nano-tips that increase local field strength easily [7; 8].  
Recently, carbon nanocomposites have been attracting increasing attention because they can integrate the advantages of various carbon materials, thus obtaining any desired properties and broadening their application field. Recent works have focused on the synthesis of graphene-CNT on a copper catalyst for high-performance field emission (FE), and some progress has been made. [PERSON] et al. [9] introduced the fabrication of graphene-vertical CNTs (GF-CNT) with higher FE stability. However, the GF-CNT shows poor FE properties due to the shield of field enhancement from CNTs, which limits the improvement of the FE current density [10]. Furthermore, our previous work on carbon nanostructures (CNS) has shown that Cu-catalyzed coiled CNS showed an optimized FE property, compared with that of straight CNTs [11]. Therefore, it is believed that the FE performance of CNT field emitters can be further improved by combining the advantages of CNS in special shapes.  
This paper introduces a simple approach to synthesize carbon composites through a copper containing catalytic pyrolysis method. The growth of these reed-leaf-like carbon nanostructures (CNLs) using chemical vapor deposition (CVD) is achieved by controlling the catalyst and gas. We studied the internal relations between the catalysts and reaction carrier gas. Additionally, the effects of surface morphologies of CNS on the performance of FE were also investigated.
## 4 Review of [PERSON] et al.'s Protocol
### Smart Grid Device Registration Process  
The SG device is called \(SD_{i}(i=1,2,\ldots n)\), where \(n\) is the number of \(UC\) to be deployed initially in SG system. The \(SD_{i}\) must register with \(TA\) to receive any services, where \(n\) is the number of the SG devices. A trusted authority \(TA\) chooses a \(ID_{i}\) and calculates \(RID_{i}=h(ID_{i}||x)\) and \(TC_{i}=h(x||RTS_{i})\), where \(RTS_{i}\) is the registration timestamp of the SG device. After that, the \(TA\) pre-loads the data \(\{TC_{i},RID_{i},h(),E_{p}(a,b),G\}\) into memory before deployment in SG system. Figure 3 describes the SG device registration process of [PERSON] et al.'s protocol.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Description** \\ \hline \(TA\) & Trusted authority \\ \(SD_{i}\) & \(SG\) device \\ \(ID_{i}\) & \(SG\)’s identity \\ \(RID_{i}\) & \(SG\)’s pseudo-identity \\ \(UC_{j}\) & Utility center or remote control center \\ \(ID_{j}\) & \(UC\)’s identity \\ \(TC_{i}\) & Temporal credential \\ \(T_{i}\) & Timestamp \\ \(E_{p}(a,b)\) & A nonsingular elliptic curve \(y^{2}=x^{3}+ax+b\) (mod \(p\)) \\ \(G\) & A base point for elliptic curve \\ \(k.G\) & An elliptic curve point multiplication \\ \(U_{i},V_{j}\) & The public key for \(SD_{i}\) and \(UC_{j}\) \\ \(x\) & \(TA\)’s secret key \\ \(K_{s}\) & \(TA\)’s master key \\ \(SK_{ij}\) & Session key \\ \(h(\cdot)\) & Hash function \\ \(\oplus\) & XOR operation \\ \(||\) & Concatenation operation \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations.  
Figure 3: Smart grid device registration process of [PERSON] et al.’s scheme.
# NDVI or PPI: A (Quick) Comparison for Vegetation Dynamics Monitoring in Mountainous Area
## 4 Discussion
### Limitations  
Enhancing the accuracy (i.e., minimizing the effects of atmospheric perturbation) of this study could be achieved by utilizing Level-2A Sentinel-2 images (i.e., surface reflectance) instead of Level-1C (top of atmosphere) imagery. To implement this, we recommend employing the Sen2 cor program [59] to correct atmospheric effects in Level-1C images. The preprocessing of the substantial volume of images utilized in this study (n = 2920) posed challenges in terms of computational time and resources. For future investigations, such as pixel-scaled spatial analysis, considering high-performance computing is advisable. Recent advancements in data cubes for efficiently processing large volumes of satellite analysis-ready data offer a promising avenue for reducing preprocessing time, thereby enabling more comprehensive studies [60; 61; 62; 63].  
It is pertinent to note that our vegetation mapping method does not facilitate the identification of evergreen non-Ericaceae shrublands, leading to an incomplete characterization of this vegetation class and its dynamics. To address this limitation, a time-first approach for land cover mapping could offer significant advantages [32]. By retrieving dense time-series data of Sentinel-2 imagery and calculating various indices to monitor the seasonal spectral signals of different types of vegetation, it becomes possible to discriminate them [64].  
Furthermore, while our study primarily focuses on the behavior of four main vegetation classes across the entire Valais region, there are avenues for further exploration. It would be beneficial to (i) incorporate more detailed vegetation types and (ii) evaluate spatial trends at pixel resolution to gain deeper insights into the vegetation dynamics.  
It would also be highly valuable to analyze more in-depth relationships with ground data, such as eddy covariance GPP derived from FluxTower [65; 66], for several locations across the Alps. [39] demonstrated that PPI is closely related to eddy covariance GPP data throughout Europe. Unfortunately, there are no data specifically available for the Valais. The nearest FluxTower is in Torgnon, in the Val d'Aoste (IT). This underscores the need for more high-quality, spatially distributed ground data in the Alps.
## 2 Model, Concept, Theory and Methods  
This section is divided into three parts. In the first subsection, we introduce the model and present some general aspects of the theory, together with the necessary details of the theory for symmetry breaking by a laser pulse. This part mostly builds upon and is quite similar to the theory published in our previous work (see Section 1)--we call it the \"basic\" theory. It is included here for self-containment of this article. The second subsection describes the new strategy for breaking the symmetry of the electronic structure of the electronic ground state and for restoring it in an excited state with different \(IRREP_{e}\
eq IRREP_{g}\). The third subsection presents the theory for the new strategy--we call it the \"extended\" theory.
# Jointly Production and Correlated Maintenance Optimization for Parallel Leased Machines
## 4 Optimization Approach
### Case 1: Low Standard Deviation  
We consider the following input data and present the corresponding results.  
Temporal data: \(\Delta k=1\); \(H=20\); \(t_{r}=1\);  
Cost coefficients: \(c_{1}=500\); \(c_{2}=10\); \(c_{3}=50\); \(c_{4}=2,5\); \(c_{h}=15\); \(c_{n}=20\); \(c_{o}=30\); \(M_{p}=100\); \(M_{c}=50000\);  
Bounders: \(m=4\); \(M=6\); \(u=40\); \(X_{\max}^{N}=8\); \(X_{\max}^{S}=4\);  
Service rate: \(\beta=0.95\) and initial inventory level: \(I_{0}=\)300;  
Random Demand: \(\sigma_{d}=25\) and \(\mu_{k}^{d}=1000\);  
To compute the degradation function of each machine, we assume that the nominal degradation follows a Weibull distribution given by:  
\[\lambda_{n}(t)=\frac{\gamma}{\mu}.\left(\frac{t}{\mu}\right)^{\gamma-1}\]  
This quantity corresponds to a nominal processing rate when machines perform at their maximum level.  
with: \(\gamma=2\); \(\mu=100\);
## 2 Background and Related Work  
The rapid development of the IIoT has provided many opportunities for industries such as industrial manufacturing, agriculture, environmental monitoring, and safety supervision [7]. The use of the blockchain as a solution to realize data security in the industrial Internet of Things is that due to the characteristics of distributed connection of the blockchain, a huge P2P transmission network can be formed. By restricting resource utilization and peer-to-peer connections, the IIoT networking architecture can be optimized and decentralized storage can ensure information security and user privacy [8]. Although blockchain has significant benefits, it is difficult for the traditional blockchain to provide better scalability to meet the high transaction throughput requirements of the IIoT. For example, Bitcoin can only confirm 3-4 transactions per second on average, although Ethereum has increased the throughput to an average of 14 transactions per second, but this still cannot meet the transaction processing needs of the IIoT [9].  
[PERSON] et al. [10] designed a secure data-sharing architecture with blockchain authorization for distributed multi-parties. The shortcoming of this scheme is that the data demand needs to be broadcast to all blockchain nodes in the whole network, data needs to be collected from various parties, and the process of federated learning needs to be implemented. This consumes a large amount of computing power and it takes a long time to retrieve and train models, affecting the efficiency of data sharing. [PERSON] et al. [11] constructed a data-sharing system composed of a two-layer blockchain. This solution needs to broadcast data demands to the whole network in the blockchain. Excessive broadcast demands may lead to network congestion and demand cannot be responded to in a timely manner. Although the above work is some blockchain-based data-sharing-related research work, it only considers the security of data sharing, not the efficiency of data sharing. This cannot shorten the time of data sharing or increase the value density of shared data.  
Based on the above information and with a focus on data security issues in the industrial Internet of Things and the need to ensure data authenticity, this paper proposes a data-privacy security mechanism of the industrial Internet of Things based on blockchain, with the main contributions as follows:  
1. A data-privacy security mechanism for the industrial Internet of Things based on blockchain with a confidential and accurate data-sharing process that takes advantage of the untamability of data stored on the blockchain and the automatic execution of smart contracts is proposed.
2. An improved algorithm of the PBFT mechanism is proposed, with an added reward mechanism based on node behavior and improved the design of the algorithm process to improve the efficiency of the algorithm.