esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Source Data-Driven Terrestrial Multi-Algorithm Fusion Path Planning Technology
## 4 Experimental Design
### Limitations Analysis  
Although this experiment was designed to closely approximate real-world scenarios, several limitations remain, as detailed below: 1. OSM data inaccuracies: The road network data derived from OpenStreetMap (OSM) may contain errors such as the incorrect classification of road hierarchies or missing annotations of traffic signals, which can undermine the reliability of path planning outcomes. 2. Idealized assumption of positioning information: The experimental setup employs a simulated environment for path planning tests, assuming perfectly accurate positioning information. It does not account for real-world phenomena such as GPS signal drift, occlusion, or measurement errors. Consequently, the deep Q-network (DQN) module's training and decision-making are based on an ideal state estimate, and its robustness under perceptual uncertainty has not been evaluated. 3. Lack of dynamic traffic modeling: The road data used are offline OSM maps, where path costs consider only static attributes such as road types and traffic signal locations. Realistic traffic dynamics--including variable traffic flow, incidents, or road closures--are not simulated, and thus the effectiveness of the A* algorithm's planned routes under highly dynamic conditions remains unverified. 4. Static traffic signal states: The experiment assumes fixed-traffic signal layouts and timing plans, without accounting for adaptive signal control  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Experiment ID** & **Method Type** & **Description** \\ \hline Exp-1 & Global A* & \\ \hline Exp-2 & Global DQN & \\ \hline Exp-3 & Global A* + local optimization via DQN & DQN performs local adjustment upon encountering an obstruction. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental design.  
strategies that vary signal phases in response to traffic conditions. This simplification limits the temporal accuracy of path cost assessments related to traffic signal delays.
# Design and Simulation of Low-Orbit Satellite Broadcast Signal Receiving and Processing Terminal
## 3 Key Technologies
### FFT Fast Acquisition Method Introducing Bit Synchronization Loop
The state transition diagram of the capture and tracking module is shown in Figure 10.  
After the signal is captured and tracked stably, the signal needs to be decoded, which requires steps such as (2, 1, 7) Viterbi decoding, differential decoding, parallel-to-serial conversion, frame header search, descrambling, deinterleaving to form four 255-byte data block, RS decoding, and uploading to the host computer [44]. The FPGA design of the  
Figure 10: Capture and tracking module state transition diagram. Figure 9: FPGA design of data tracking loop. decoding module is shown in Figure 11. Among them, the design of the VTB relay code, descrambling, and deinterleaving are all using IP cores.
## 2 Materials and Methods
### Spectral Observations  
The spectral data were measured using the FluoCSpec2 system, which consists of two subsystems: a hyperspectral observational system used for calculating vegetation indices and a directional-hemispherical system used for SIF retrieval [18; 41]. The hyperspectral observational system has an HR2000+ spectrometer with a wavelength ranging from 350 to 1100 nm, which was used to calculate various vegetation indices. The directional-hemispherical system has a QEPRO spectrometer (Ocean Optics, Dunedin, FL, USA) with a wavelength ranging from 730 to 780 nm, which was used to retrieve canopy SIF data at the near-infrared band. A temperature-controlled box contained the spectrometer and shutter, and the ends of the fibers were placed in a tower 5 m above the ground within a 2.2-meter-diameter sample area on the ground. Some vegetation indices and the fPAR calculated from the red-edge normalized difference vegetation index (NDVI\({}_{\text{rededge}}\)) were also provided alongside these open-access data. A more detailed description of the open-access dataset can be found in [PERSON] et al. [37].  
Upwelling radiance includes solar-induced chlorophyll fluorescence in addition to radiance reflected by foliage and the soil background. Because of the specific absorption by atmospheric molecules, the SIF emitted by leaves contributes significantly more to reflected radiance in the atmospheric absorption regions than the atmospheric window does [42]. Based on the presumption that fluorescence and reflectance in the retrieval bands obey a specific law of change, numerous methods for SIF retrieval have been proposed [43]. In this study, we used the SIF results retrieved using the spectral fitting approach (SFM), which assumes that fluorescence and reflectance exhibit a nonlinear pattern with the wavelength, which can better utilize the features in the fitting window [44]. In addition, according to [PERSON] et al. [45], the observed spectral and flux data were processed into half-hourly and daily averaged data.
# Diffuse-Field Equalisation of Binaural Ambisonic Rendering
## 4 Evaluation  
DFE of Ambisonics was evaluated in three ways by comparing binaural Ambsonic renders, with and without DFE, to a reference HRIR data set [25] of 16020 HRIRs (89 elevations at 180 azimuths in 2\({}^{\circ}\) increments). The objective change in spectral difference between binaural Ambsonic rendering and standard binaural rendering (HRIR convolution) when implementing DFE was investigated, to measure the influence of DFE over all directions on the sphere. Sagittal plane localisation was assessed through a binaural model, which allows evaluation of many more directions than would be feasible in a listening test. Finally, two listening tests were conducted to measure the perceptual effect of DFE on timbral similarity between binaural Ambsonic rendering and standard binaural rendering.  
For each angle of the reference data set, binaural Ambisonic renders were generated and diffuse-field equalised through convolution of each render with the inverse filters of that order of  
Figure 4: Diffuse-field response, inverse filters and resulting responses of the three tested Ambisonic configurations (left ear): (**a**) \(M=1\), (**b**) \(M=3\) and (**c**) \(M=5\).  
Figure 5: Diffuse-field responses of the 50 pt. Lebedev loudspeaker configuration for \(M=5\) for the 18 human subjects of the SADIE II database, with average response (left ear).  
Ambisonics. The reference, Ambisonic and DFE Ambisonic HRIRs were truncated to 1024 taps with 50 sample in/out half-Hanning windows applied. Linear phase was preserved.
# Three-Phase PV CHB Inverter for a Distributed Power Generation System
## 2 System Topology and Control Strategy
### The Sorting Algorithm  
According to (2), \(v_{imp}\) can be synthesized by adding up the output voltages \(v_{Hi}\) of \(K\)-1 cells (by permanently connecting them) plus one cell in PWM mode, while the others are kept in the zero output voltage state. The new idea is to determine--in every control cycle--the state of each cell depending on the voltage error \(\Delta v_{i,p}=v_{pci,p}^{ref}-v_{pci,p\_f}\) between the reference \(v_{pci,p}^{ref}\) given by the MPPTs and the filtered actual \(v_{pci,p}\).  
The algorithm is sketched in Figure 4.  
The pairs \((v_{pci,p}^{ref};\Delta v_{i,p})\) are sorted in ascending order with respect to the voltage error, thus assigning the last position, \(N\), to the cell with the highest positive error, while the first position, 1, is occupied by the cell with the lowest negative error (it is worth noting that the position in the sorted vector is not related to the position in the physical circuit). Then, according to the new order, the voltages \(v_{pvj,p}^{ref}\) are added until the index \(K\) that makes true the following inequality is found:  
\[\sum_{j=1}^{K-1}v_{pvj,p}^{ref}+v_{pvK,p}\geq\left|v_{inv,p}\right|\geq\sum_{j =1}^{K-1}v_{pvj,p}^{ref} \tag{6}\]  
Figure 4: Sorting algorithm.  
Figure 3: Proposed pulse-width modulation (PWM) technique.  
The index \(K\) is the one already defined in (2); therefore, the first \(K\)-1 cells of the sorted vector (i.e., the cells with the lower voltage errors) have to be kept in the \(\pm 1\) mode (discharging mode), the \(K\)-th cell has to be driven in PWM mode, and the last N-\(K\) cells (i.e., the cells with the higher voltage errors) have to be kept in the 0 mode (charging mode).  
It should be noted that the modulation strategy is specially conceived for the CHB with unequal DC sources, exploiting small differences in the voltage of the individual power cells to reduce switching losses and improve the converter efficiency [19].
# Cognitive Manufacturing in Industry 4.0 toward Cognitive Load Reduction: A Conceptual Framework
## 3 Results
### SRQ1: What Causes Cognitive Overload in Manufacturing Environments?
The numerous attributes that impact product quality can be monitored and understood by utilizing cognitive manufacturing tools. Potential quality issues can be recognized earlier by using analytics, algorithms, automated visual inspections, and machine learning instead of customary methods [6]. The symbiosis between the operator and the cyber-physical system (CPS) allows new margins for controlling and improving picking activities [36]. New strategies were suggested where operator cognition is enhanced by moving between assembly modes [53]. * Resource optimization in cognitive manufacturing can help in guaranteeing laborer safety and health. Equipment with sensors that identify immediately hazardous circumstances ensure laborer safety and improve operations in energy resource optimization. Moreover, the use of IoT, data analytics, and machine learning allows evaluating the factors that contribute to energy consumption and in improving floor planning and scheduling. This can be done to optimize the configuration of a production line to balance the workload between stations, as well as use labor more efficiently, increase the rate of production, and optimize available plant capacity [6]. To perform this, ergonomic aspects must be considered, since they are changing as the world advances to Industry 4.0; thus, the discipline must adapt to this new paradigm and its new methods [54]. * Supply chain optimization in cognitive manufacturing gathers different data from structured and unstructured data sources so as to limit supply chain costs, disruptions, and risks. Alerts that describe the threat and present the information in a proper manner to help in decision making, as well as search for alternative suppliers and recommend solutions, represent some of the solutions that a cognitive manufacturing tool can offer [6]. Most companies poorly integrate technology into their supply chain, whereas an optimized supply chain will develop new value propositions and allow meeting new business needs [55].
## 4 Role of sRNA in Antibiotic Resistance
### Inhibiting Drug Uptake Systems
#### 4.1.3. _EsrA_ and Sr0161  
_Pseudomonas aeruginosa_ is a prominent opportunistic human pathogen responsible for approximately 3000 deaths annually in the United States [185]. Multi-drug-resistant (MDR) _P. aeruginosa_ infections are often treated with carbapenem antibiotics like meropenem; however, 2-3% of clinical isolates produce carbapenemase, rendering them resistant to this antibiotic class. Carbapenem uptake in _P. aeruginosa_ is mediated by the major porins OprD and OpdP, with _oprD_ deletion increasing carbapenem resistance in MDR isolates [186].  
Using Hi-GRIL-seq, Sr0161 and ErSA were identified as sRNAs that negatively regulate _oprD_ expression via interactions with its 5' UTR [187] (Figure 3a). The overexpression of Sr0161 enhances resistance to meropenem [187], while the deletion of _sr0161_ or _ersA_ significantly increases susceptibility to this antibiotic. Both _esrA_ and _sr0161_ are transcribed by stress-induced sigma factor 22. Sigma factor 22 (\(\sigma^{22}\)), also known as AlgT or AlgU, in _Pseudomonas aeruginosa_ is a key regulator of the alginate biosynthesis pathway and plays a central role in the transition to a mucoid phenotype during chronic infections. The RNA chaperone Hfq also contributes to carbapenem resistance by regulating OprD and OpdP porins, as shown by the increased carbapenem susceptibility observed in _lfq_ deletion mutants [188].
## 4 Discussion
Presently, the rapidly expanding integration of AI and computed CBCT evaluation holds promise for the future of next-generation orthodontic and orthognathic surgery treatments. However, there is still much work to be carried out, primarily because further improvements are necessary [6]. The most significant clinical observation is that contemporary orthognathic surgery relies heavily on 3D-CBCT patient evaluation, complemented by facial, bite, and occlusal photographs, which are integrated and amalgamated to estimate, measure, and predict the most effective treatment plan for each patient case. Without the aid of new technologies, such as CBCT and AI-automatic software tracings, achieving this level of precision would not be feasible. However, it is important to note that a few exceptionally complex cases of severe skeletal malocclusion may still necessitate manual-based evaluations, particularly those associated with significant skeletal jawbone disproportionsand similar factors [71; 72]. While the key conclusions drawn by the authors align partially with those of other relevant papers, it is crucial to acknowledge the variability in results across different studies, attributable to the diverse capabilities and resources of each tool and software program for cephalometric tracings. The following limitations were encountered during the study: an insufficient number of papers comparing natural head position (NHP) and the usage of AI-driven software, leading to gaps in understanding; significant variability in the software and computer-enhanced programs utilized, along with their 3D/measurement capabilities, complicating comparisons and standardization; a limited number of studies on patients discussing and describing the preparation, positioning, and evaluation process before and during the study, which hinders reproducibility and generalizability; diverse cephalometric measurements utilized across various orthodontic protocols, making it challenging to establish consensus and comparability; limitations in the size, resolution, and accuracy of CBCT scans, traditional radiographs, scanners, and software utilized, affecting the quality and reliability of the data; and a plethora of radiographic software, programs, and companies involved in the dental market, contributing to heterogeneity and potential inconsistencies in methodologies and results. Significant heterogeneity among the included studies does not allow for performing a meta-analysis. However, further research should be conducted to enable proceeding with a meta-analysis.
## 5 Experiments
### Introduction to Simulation Hardware  
In order to verify the trajectory tracking performance of the improved terminal sliding mode controller designed in this paper, simulation experiments were carried out using MATLAB 2019b, Carsim2019 and Matlab-Carsim HIL simulation platforms. Matlab-Carsim HIL simulation platform is shown in Figure 9. It includes a servo motor driver, display, industrial computer, rear wheel drive motor, etc.
## 2 Materials and Methods
### Statistical Analyses  
Descriptive statistics, including means and standard deviations (SDs) with 95% confidence intervals, were calculated for anthropometrics and trunk endurance measures. Comparisons were made through the Bayesian ANOVA with the factors CA, MAT, and sex used as between-subject comparators. Post hoc tests were computed for multiple comparisons to determine outcomes according to MAT and CA. The BF\({}_{10}\) was interpreted using the following evidence categories suggested by [PERSON] and [PERSON] [40]: \(<\)1/100 = extreme evidence for H\({}_{0}\); from 1/100 to 1/30 = very strong evidence for H\({}_{0}\); from 1/30 to 1/10 = strong evidence for H\({}_{0}\); from 1/10 to 1/3 = moderate evidence for H\({}_{0}\); from 1/3 to 1 anecdotal evidence for H\({}_{0}\); from 1 to 3 = anecdotal evidence for H\({}_{1}\); from 3 to 10 = moderate evidence for H\({}_{1}\); from 10 to 30 = strong evidence for H\({}_{1}\); from 30 to 100 = very strong evidence for H\({}_{1}\); \(>\)100 extreme evidence for H\({}_{1}\). The median and the 95% central credible interval (CI) of the posterior distribution of the standardized effect size (8) (i.e., the population version of Cohen's d) were also calculated for each of the comparisons carried out. Magnitudes of the posterior distribution of the standardized effect size were classified as follows: trivial (\(<\)0.2), small (0.2-0.6), moderate (0.6-1.2), large (1.2-2.0), and very large (2.0-4.0) [41]. Only those comparisons that showed at least strong evidence for supporting the alternative hypothesis (BF\({}_{10}\)\(>\) 10), an error percentage \(<\) 10 (which indicates great stability of the numerical algorithm that was used to obtain the result), and \(\delta\)\(>\) 0.6 (at least moderate) were considered robust to describe significant differences. Statistical analysis was performed using the JASP computer software Version 0.11.1 (JASP Team, Amsterdam, the Netherlands).
# Review of Physical Layer Security in Integrated Satellite-Terrestrial Networks
## 4 Review
### Cooperative Communication
Despite these challenges, cooperative communication presents a valuable tool for boosting PLS in ISTNs and creating a more secure and reliable communication environment for future 6G applications. The disparity between satellite and terrestrial networks can lead to significant differences in signal strength, delay, and interference levels, making it difficult to coordinate cooperative transmissions effectively. Additionally, the dynamic nature of satellite orbits and terrestrial network topologies further complicates the design of cooperative protocols. Another significant limitation is the high latency associated with satellite links. This can delay the exchange of information between cooperating nodes, impacting the effectiveness of cooperative schemes. Furthermore, the limited bandwidth of satellite links can constrain the amount of data that can be exchanged for cooperative purposes. Finally, the imperfect channel state information available at the nodes can hinder the optimal design of cooperative protocols.
# Recent Advances in Efficient Dynamic Graph Processing
## 6 Future Work
### Optimizations for High Efficiency  
While numerous algorithms currently exist for handling dynamic graphs, improving these algorithms' time and space efficiency remains a critical objective as graph data scales and complexity increases. Future algorithms should aim to minimize resource requirements of update processing. Investigate which types of graph computations can be performed incrementally, allowing for partial updates without recalculating from scratch.
# Blockchain Technology and Its Potential to Benefit Public Services Provision: A Short Survey
## 3 Related Works
Regarding the method of analysis, the majority of reviews adopt a theoretical/conceptual approach, with only [14; 18; 21; 23; 24] providing empirical examples of real-world implementations and pilot projects. Taking into account already existing literature reviews' distinctive elements, ours stands out for considering not only blockchain technology and smart contracts in a narrow sense but also other blockchain-enabled technologies such as SSI (self-sovereign identity), smart cities, the Metaverse (and its foundational technologies), NFTs and DAOs, and their respective impact on public services provision. Our survey comprises works published up to the end of 2023, contributing to enriching the existing body of knowledge, particularly for smart contracts and the Metaverse. From a content perspective, our work has adopted a mixed conceptual/empirical approach, explaining first how application areas are likely to benefit from blockchain and blockchain-related technology adoption and then providing examples of real-world implementations for each of them, contributing to addressing the lack of studies that focus on cases that are already implemented, as pointed out in [17; 19]. Finally, when selecting the pool of relevant references to conduct the literature review, the vast majority of existing works focus only on peer-reviewed articles that are made available through reliable sources like Scopus, Google Scholar, IEEE, ACM, SpringerLink, ScienceDirect, and Web of Science, neglecting what is often referred to as \"gray literature\". Our work represents a step forward in this sense insofar as it also considers reports, working papers, and government documents to provide a more detailed overview of blockchain adoption for public service delivery. To summarize, our survey aims to answer the following research questions (RQ):  
**RQ1:**: What is the current state-of-the-art in research on blockchain technology and its application towards public services? **RQ2:**: What are the main application areas in government and public sectors that are likely to benefit from blockchain technology? **RQ3:**: Are there any real-world implementations and case studies, or are blockchain and public services just research topics? **RQ4:**: Are there any challenges that may prevent the widespread adoption of blockchain technology in public services? We anticipate that RQ1-3 will be answered in Section 5, while RQ4 will be addressed in Section 6.
# Detection of Defects Using Spatial Variances of Guided-Wave Modes in Testing of Pipes
## 5 Results
### Second Set of Tests
#### 5.3.1 Method One  
Figure 17 shows the limits required from method one to detect Defect 2. The only excitation frequency that allowed the detection of defects without any outliers was 37 kHz, while 34 kHz resulted in the detection of defects with at least three outliers in both cases, and in 30 kHz, the defect could not be detected. With 37 kHz as the excitation frequency, it can be seen that when the defect energy decreases, the threshold is increased to 0.009. As this value is close to the maximum allowable threshold (0.01), no smaller defects could be detected even using this frequency. This is unlike the first set of tests, where it was easier to detect the defect using lower frequencies, since Defect 2 is located in a region with higher energies of flexural noises. Consider the case of the 34-kHz frequency, as reported in Figure 11: the maximum limit of detection of Defect 1 without any outliers is approximately 0.013. However, in the case of Defect 2, the minimum level is 0.019 for the 5% CSA defect, which is higher than the previously reported limit. This outcome indicates the detection of outliers in the test. The same can also be said regarding 37 kHz, where previously, the maximum limit was approximately 0.012, and now, the minimum required for the detection of a 4% CSA defect is 0.01, which is below the level where outliers will be detected. It can be concluded that method one is highly affected by the existing energy of flexurals in the same iteration.