esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
This paper used high-level semantic information and spatial distribution information to assist the visual location recognition task and used the semantic structure information to realize the global orientation estimation method without drift interference. In this paper, a visual semantic perception system based on the synthesized surround view image was proposed for the multi-eye surround vision system, which was used to obtain the visual semantic information required for SLAM tasks. Different from the traditional method of obtaining feature information from the original multi-eye image, the original multi-eye image was transformed and synthesized by reverse-perspective projection to obtain a synthesized surround view image that could describe the scene in an all-round way, to improve the computational efficiency of the system while retaining most of the effective information. To retain the effective information in the synthesized surround view image and remove the features that had been distorted during the synthesizing process or located on the dynamic object, this paper extracted the passable space in the image with the help of the semantic segmentation network model as a mask for feature extraction. Since the boundary of the passable area contained geometric information about the environment, it could also be converted into a point cloud representation by LiDAR, which effectively complements the visual feature point information. In addition, since the passable space contained rich pavement marking edges, by further using the distortion characteristics of the reverse perspective projection process, the distortion edges could be effectively eliminated, and the hybrid edge information could be used for mapping and localization tasks. The experiments based on the indoor and outdoor automated valet parking verify that the proposed scheme can achieve more precise edge segmentation results, much smaller orientation estimation error, and better trajectory estimation. In the future, we hope to investigate the generalization of our algorithm in more diverse scenarios, such as light changes and more dynamic tasks.  
Conceptualization, G.X. and S.D.; methodology, writing, visualization, G.X. and N.Z.; investigation, N.Z. and G.W.; validation, G.W.; supervision, S.D. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the Natural Science Foundation of Sichuan Province under Grants 2023 NSFSC0475 and 2023 NSFSC1441, the Fundamental Research Funds for the Central Universities under Grant 2022 SCU12004, and the Funds for National Key Laboratory of Special Vehicle Design and Manufacturing Integration Technology under Grant GZ2022 KF007.  
Not applicable.  
The authors declare no conflict of interest.
# A Novel Reconfigurable Vector-Processed Interleaving Algorithm for a DVB-RCS2 Turbo Encoder
## 3 Serial Implementation  
The serial implementation serves as a reference for speedup and relies on Look Up Tables (LUTs). For every input vector, given its length and matching parameters (taken from Table 1), the program (i) creates a LUT by deterministic calculations matching the permutation indexes of each input bit, (ii) loads each bit and its designated new index, and (iii) stores the bit in a new output vector. This process is ineffective for two main reasons:  
1. The LUT calculations are determined by the DVB-RCS2 standard.The modulo-based calculations are complex for any processor and are a changing parameter, disabling simple hardware solutions for these dynamic calculations. Given a finite collection of sets of vector length and parameters, saving all pre-calculated LUT is an option. However, real-time processors run on finite and usually small-sized program memory space (MS), bounding the MS that can be reserved for LUTs.
2. Saving the pre-calculated LUT in memory doesn't solve the following issue. Once the LUT is calculated, the program loads bit-by-bit with its matching new index from the LUT, then the bit is stored to its designated index in the output vector. Loading and storing bit-by-bit (and index-by-index) results in excessive memory access.  
The presented vector algorithm solves these problems by processing the data in max-sized streams (loading and storing more than one bit at a time) and by replacing the modulo-based calculations of the LUT with data and memory manipulations, resulting in lower run-time and memory access. The asymptotic tight bound computational complexity of the serial algorithm is \(O(N2)\).
## 2 Materials and Methods
### Method  
An overview of NSBR-Net is shown in Figure 3. Upon inputting an ultrasound image, we initially extracted four levels of feature maps of various scales sequentially utilizing the pyramid vision transformer (PVT) block [22]. We input the feature maps from the last three stages into NSM individually for the suppression of speckle noise, followed by utilizing a parallel partial decoder (PD) [23] to generate high-level semantic global maps. Lastly, a set of reverse axial attention mechanisms was employed to refine the tumor boundaries progressively. Detailed expositions of NSM and BRM are presented as follows. The overall pseudocode of our method is presented in Algorithm 1.  
Figure 3: The framework of our proposed NSBR-Net primarily comprises the pyramid vision transformer, partial decoder (PD) [23], Noise Suppression Module, and Boundary Refinement Module.
# Atom Probe Tomography for Catalysis Applications: A Review
## 3 Case Studies
### Catalysts as Unsupported Nanoparticles
#### 3.3.1 Imaging Core-Shell Ag@Pd Nanoparticles--Electrophoresis Method
(**a**) Schematic principle of the deposition method; (**b**) Transmission Electron Microscopy (TEM) image of a Pt specimen before deposition and; (**c**) TEM image of the same Pt specimen after deposition of Ag@Pd nanoparticles. Panel (**a**): Reprinted with permission from [121]. Copyright (2014) American Chemical Society. Panels (**b**,**c**): Reprinted by permission from Nature: [120], Copyright (2011). Figure 7: Analysis of Ag@Pd nanoparticles prepared by the electrophoresis method. (**a**) Atom map of Ag@Pd nanoparticles with 1:1 ratio showing two distinct particles (highlighted by applying the isosurface method in the bottom part); (**b**) local reconstruction of one single particle; and (**c**) slice through an individual nanoparticle showing the presence of a Ag core and a one to two atomic layer Pd shell. Reprinted by permission from Nature: [120], Copyright (2011). and APT analysis reveals a bimodal distribution of large Cu particles (4-8 nm) together with very small copper clusters (0.4-0.8 nm) stabilized on a defective ZnGa\({}_{2}\)O\({}_{4}\) spinel oxide surface. These small clusters are believed to be responsible for the high catalytic activity. Finally, Pt-Ru nanoparticles deposited on a Pt-Rh tip sample were successfully prepared with the electrophoresis method and show a partitioning between Pt-rich and Ru-rich regions [123]. Electrophoresis thus shows itself to be a straightforward route to APT analysis of nanoparticles; however, due to roughness of substrate needle's surface, aberrations can occur during the analysis and the data is not always of sufficient quality to get a detailed quantitative analysis in terms of 3D localization of the atoms on the atomic scale [124]: embedding methods can be used as an alternative.
# Mining Sequential Patterns with VC-Dimension and Rademacher Complexity
## 2 Preliminaries
### Rademacher Complexity  
The Rademacher complexity [3; 23; 26] is a tool to measure the complexity of a family of real-valued functions. Bounds based on the Rademacher complexity depend on the distribution of the dataset, differently from the ones based on VC-dimension that are distribution independent.  
Let \(\mathcal{D}\) be a dataset of \(n\) transactions \(\mathcal{D}=\{t_{1},\ldots,t_{n}\}\). For each \(i\in\{1,\ldots,n\}\), let \(\sigma_{i}\) be an independent Rademacher random variable (r.v.) that takes value \(1\) or \(-1\), each with probability \(1/2\). Let \(\mathcal{G}\) be a set of real-valued functions. The empirical Rademacher complexity \(R_{\mathcal{D}}\) on \(\mathcal{D}\) is defined as follows:  
\[R_{\mathcal{D}}=\mathbb{E}_{\sigma}\left[\sup_{g\in\mathcal{G}}\frac{1}{n} \sum_{i=1}^{n}\sigma_{i}g(t_{i})\right], \tag{12}\]  
where the expectation is taken w.r.t. the Rademacher r.v. \(\sigma_{i}\)'s.  
The Rademacher complexity is a measure of the expressiveness of the set \(\mathcal{G}\). A specific combination of \(\sigma\)'s represents a splitting of \(\mathcal{D}\) into two random sub-samples \(\mathcal{D}_{1}\) and \(\mathcal{D}_{-1}\). For a function \(g\in\mathcal{G}\), \(\sum_{i=1}^{n}g(t_{i})/n\) represents a good approximation of \(\mathbb{E}[g]\) over \(\mathcal{D}\) if \(n\) is sufficiently large. \(\sum_{i=1}^{n}\sigma_{i}g(t_{i})/n\) represents instead the difference between \(\mathbb{E}[g]\) over the two random sub-samples \(\mathcal{D}_{1}\) and \(\mathcal{D}_{-1}\). By considering the expected value of the supremum of this difference over the set \(\mathcal{G}\), we get the empirical Rademacher complexity. _Therefore, the intuition_ is that if \(R_{\mathcal{D}}\) is small, the dataset \(\mathcal{D}\) is sufficiently large to ensure a good estimate of \(\mathbb{E}[g]\) for every \(g\in\mathcal{G}\). In this work, we study the Rademacher complexity of sequential patterns, which has not been explored before.
## 5 Evaluation
### Performance Evaluation
#### 5.2.1 Encryption Throughput  
The throughput \(T\) is measured in terms of the amount of data \(D\) processed per unit time \(t\) during the encryption and the decryption phases. In particular, given the size of the encrypted data \(size(ED)\) and of the decrypted data \(size(DD)\), the relative throughput can be computed as follows, respectively:  
\[T_{E}=\frac{size(ED)}{t} T_{D}=\frac{size(DD)}{t} \tag{4}\]  
We evaluated the throughput of the proposed solution on a wide range of data sizes from 10 kb to 3000 kb. Figure 2 illustrates that the system's throughput increases as data size increases. Initially, the throughput was around 3.07 kilobits per second (kb/s) for 10 kilobits (kb) of data. However, with larger data sizes, such as 3000 kb, the throughput has increased to over 307.12 kb/s. The throughput decreases for smaller amounts of data because of the significant time taken by the initial encryption or decryption steps and inherent computational efficiencies at larger data scales. However, as the data volume increases, these initial steps become less significant in the overall process. These results demonstrate that AES is scalable and efficient in processing large data sets and suitable for applications dealing with large data volumes, resulting in secure and efficient data encryption and decryption.
# Dynamic Shortest Paths Methods for the Time-Dependent TSP
## 2 Complexity
If the TDTSP instance in question satisfies the FIFO property, \(C(S,v)\) satisfies a similar relationship:_  
\[C(\{s,v\},v) =c_{sv}(\theta=0) \forall\,v\in V,v\
eq s\] \[C(S,v) =\min_{ \begin{subarray}{c}u\in S\\ u\
eq s,v\end{subarray} }C(S\setminus\{v\},u)+c_{uv}(\theta=C(S\setminus\{v\},u)) \forall\,S\subseteq V,|S|\geq 3,v\in S.\]  
_These relations yield an algorithm for the TDTSP with the same running time as its counterpart for the ATSP. Note that the FIFO property is key here, since it ensures that only the shortest path for fixed \((S,v)\) needs to be considered for subsequent computations._  
_Approximation for Special Cases_  
Let \(\mathpzc{x}:A\to\mathds{N}\) be the static underestimator of the time-dependent cost function \(c\), defined as  
\[\check{c}_{a}\coloneqq\min_{\theta\in\Theta}c_{a}(\theta)\quad\forall\,a\in A.\]  
If the time-dependent cost functions are of low variance, the underestimator yields TDTSP-approximations based on an underlying ATSP:  
**Theorem 2**: _Let \(\lambda\geq 1\) such that it holds for all \(a\in A\), \(\theta\in\{0,\ldots,T\}\) that_  
\[c_{a}(\theta)\leq\lambda\cdot\check{c}_{a}.\]  
_Then, any \(\alpha\)-approximation of the ATSP yields an \((\alpha\lambda)\)-approximation of the TDTSP._  
Let \(T_{\mathrm{opt}}\), \(T_{\mathrm{apx}}\) be the optimal and \(\alpha\)-approximate tour with respect to the costs \(\mathpzc{x}\) and \(T_{\mathrm{opt}}^{\mathcal{T}}\) be the optimal TDTSP tour. We have that  
\[c(T_{\mathrm{apx}})\leq\lambda\cdot\check{c}(T_{\mathrm{apx}})\leq(\alpha \lambda)\cdot\check{c}(T_{\mathrm{opt}})\leq(\alpha\lambda)\cdot\check{c}(T_{ \mathrm{opt}}^{\mathcal{T}})=(\alpha\lambda)\cdot c(T_{\mathrm{opt}}^{\mathcal{ T}}).\qed\]  
Since the ATSP is inapproximable in general, further assumptions, such as a metric lower bound \(\check{c}_{uv}\), are necessary to obtain any approximation results. What is more, we show in Appendix A that while it is possible to generalize the well-known one-tree relaxation [31] from the ATSP to the TDTSP, even the computation of the corresponding lower bound is an \(\mathcal{NP}\)-hard problem.
## 3 Results
### Benchtop Transceiver Testing Results  
After accounting for the losses after the erbium doped fiber exit and added attenuation in the U-bench, the amplifier produced 2.5 ns wide pulses with a peak power of 2.13 W during the amplifier verification testing. When adjusting for a 35.7% loss, which was present in the optical amplifier characterization simulation, the peak power was 1.37 W. The ND filter used for attenuation in the U-bench has a 5% uncertainty and the monitor diode on the pump laser has approximately a 10% uncertainty.  
The BER measurement experiment results can be seen in Table 6. In this test, the higher optical power (176  
Figure 17: (**a**) pulses with 102 nW peak power captured and 20 mV threshold; (**b**) pulses with 176 nW peak power captured and 28 mV threshold. Histograms of the time stamps and pulse position error at the best thresholds for 102 nW and 176 nW. A slot width of 400 ps is shown on each histogram with vertical red lines, where time stamps located outside these lines are bit errors.
# Research on an Index System for the Earthquake Disaster Resistance Capability of Highway Tunnels
## 4 Seismic Capability Index System of Highway Tunnels
### Analysis of the Evaluation Index
#### 4.1.2 Index Analysis of the Tunnel Structural Factors  
Through the identification of the factors influencing the seismic damage of highway tunnels, tunnel structural indicators include portal forms, supporting structures, section conditions and seismic measures.  
By consulting the relevant specifications and engineering experience (see Table 2 for details of the relevant regulations or experience), the portal form indicators can be expressed by the tunnel portal type and tunnel portal size. The supporting structure indicators include the lining material, lining thickness, reinforcement, inverted arch setting and construction joint link strength. The cross-sectional condition indicators can be expressed by the cross-sectional shape and cross-sectional size. The indicators of seismic measures are evaluated by the setting of seismic joints and special seismic measures.
## 5 Conclusions  
This paper aimed to estimate both the instantaneous, and the global pleasantness, of the soundscape during 3 min urban walking trips. For this purpose, two laboratory experiments were conducted, in which controlled and natural sound sequences were presented, and during which participants were asked to continuously assess the sound pleasantness along the sequence, and globally, at its end. The conclusions are:  
* The modeling of the recency effect, through the state-of-the-art SIMPLE model, improves the estimation of the global sound pleasantness over the controlled sound sequences. This effect tends to decline or disappear when the sound sequences are more realistic, including, among other things, some visual information.
* The global sound pleasantness can be estimated by using the median or the arithmetic average of the instantaneous sound pleasantness values.  
* The instantaneous sound pleasantness is mainly impacted by the sound level during the last few seconds. Reaction and integration times are used by participants for estimating the continuous judgment of the pleasantness of the sound environment. The sound level time series can be more accurately taken into account with the SIMPLE model, which then highlights that the last 30 s also influence, although to a lesser extent, the instantaneous sound pleasantness assessments.
* Finally, the Global sound pleasantness can be accurately estimated based on the sound level time series of the 3 min sequences, either by relying on an intermediate estimation of the instantaneous sound pleasantness values, or directly based on the sound level time series, through an arithmetic average or a median value of the \(L_{\text{eq,1s}}\) values. Both approaches are relevant, explaining about 60% of the variance in the global sound pleasantness, with an error inferior to 0.75 points over an 11-points scale.  
The final proposed model enables one to estimate the sound pleasantness of a walking trip along a particular path, based on the sound level time series encountered along that path. The conclusions from this work could thus be helpful for constructing models that select urban walking routes with optimal sound pleasantness.  
This work has been carried out in the framework of the GRAFIC project, supported by the French Environment and Energy Management Agency (ADEME) under contract No. 1317C0028.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON], [PERSON] and C.R. contributed material/analysis tools; [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
# Battery Management System Hardware Concepts: An Overview
## 6 Safety and Reliability
### Insulation Measurement
#### 6.1.2 Minimum Insulation Resistance  
Two standards, _ECE-TRANS2010_ and _ISO6469-1_, differentiate between DC and AC. For a DC system, a minimum insulation resistance of \(100\,\frac{\Omega}{\mathrm{V}}\) is recommended, for an AC system, a resistance of \(500\,\frac{\Omega}{\mathrm{V}}\) before an insulation fault should be reported. _ECE-R100_ demands an insulation resistance of minimum \(500\,\frac{\Omega}{\mathrm{V}}\). In all standards, these minimum resistances have to be measured at the nominal system's voltage. It should be noted that these are the minimum required insulation resistances. In _DIN61557-8_, a minimum requirement for insulation resistance measurement of 15% is demanded. A good rule of thumb for minimum insulation resistances is noted in [84], where a minimum value of \(1\,\frac{\mathrm{M}\Omega}{\mathrm{K}\mathrm{V}}\) and, for systems below 1000 V, a minimum insulation resistance of \(1\,\mathrm{M}\Omega\) is recommended.
# Linewidth Measurement of a Narrow-Linewidth Laser: Principles, Methods, and Systems
## 3 Research Progress
### Delayed Self-Heterodyne Method
#### 3.3.1 MZI-Type Delayed Self-Heterodyne Method
The laser linewidth is calculated by extracting the power difference between the first-order envelope (between the maximum and minimum points) and the frequency difference between the zero-order minimum point and the center frequency from the power spectrum. The device uses delay optical fibers of km length to reduce the impact of \(1/f\) noise and achieve ultra-narrow-linewidth measurements of 100 Hz. In 2022, [PERSON] et al. proposed an iterative algorithm to demodulate coherent envelope spectra for short-delay optical-fiber self-heterodyne devices [97], which can analyze the beat frequency power spectrum linetype and linewidth. The method obtained the same Lorentz line shape as the long-fiber self-heterodyne method. In the same year, the team proposed a short-delay coherent envelope spectrum demodulation scheme to address the issue of computational speed in iterative algorithms [98]. The relationship between the amplitude difference of a pair of extreme points (maximum and minimum) in the envelope spectrum and the linewidth and fiber length was calculated. The characteristics of coherent envelopes were simulated and experimentally studied, and the influence of background noise on short-delay self-heterodyne/self-homodyne difference linewidth measurements was analyzed [99]. The short-delay fiber self-heterodyne method can solve the problems of the \(1/f\) noise broadening spectrum and phase noise jitter caused by fiber length, but there are also shortcomings. On the one hand, it is necessary to estimate the linewidth of the tested laser to select the appropriate length of fiber. On the other hand, the conversion between the power spectrum amplitude difference and the linewidth is not a constant numerical relationship but is related to the setting of the fiber length and needs to be continuously adjusted to avoid the influence of background noise.
* There is an urgent need for comprehensive reference documents that provide detailed real-world accounts of experiences from the design of custom-built multihop-based SNs to their deployment and performance assessment under indoor and outdoor conditions. Investigations are needed to evaluate how the benefits of cluster-based multihop architectures materialize in custom-built MCA-IoT networks. * The impact of outdoor environmental conditions (such as humidity, temperature, dust concentration, rainstorms, and crop obstructions) on radio communication (including the effective range and link quality of BLE and LoRa) in typical sub-Saharan African (SSA) settings requires further study. Figure 2: GeneralizedAgri-IoT framework: field layout overview of Agri-IoT framework (**a**), a sample of Agri-IoT in state-of-the-art applications (**b**), and key components of an SN or a BS (**c**) [10]. This paper presents a detailed examination of a real-world application and evaluation of an MCA-IoT network, utilizing freely available wireless communication technologies such as LoRa and BLE. This evaluation is conducted through a realistic multihop architecture, as discussed in [4; 8; 10; 17], and is validated under indoor and outdoor environmental conditions. Specifically, this study introduces a WSN-specific MCA-IoT testbed that encompasses the following elements:  
* A custom-designed, low-power, robust, contextually relevant, and task-scalable SN and MCA-IoT network based on LoRa and BLE 4.2 wireless communication technologies. This configuration ensures that the network is location-independent, user-friendly, easily deployable, and capable of being wirelessly managed by non-experts in IoT without costly fixed support systems. * A comprehensive account of the MCA-IoT network framework, detailing the successes and challenges encountered during the design, deployment, and operation of SNs, as well as cloud data storage. This documentation aims to serve as a valuable reference for the Agri-IoT community. * An empirical assessment of the practical realization of the theoretical benefits associated with the cluster-based architecture. * An observation of the effects of climatic extremes, physical obstructions, and variations in SN power on radio connectivity and packet loss. The structure of this paper is as follows. Sections 2 and 3 provide a systematic review of the relevant literature and a theoretical overview of the proposed approach, respectively. Section 4 details the design of the testbed. Section 5 discusses the validation experiments and their results, and Section 6 presents a discussion of the findings and suggestions for future work.