esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Discussion  
In this subsection, the effect of fault impedance on the protection blinding areas for phase-to-phase faults is studied. In fact, protection blinding causes non-operation or delay in the OC relay. The priority of the research is to avoid blind areas, and the secondary goal is to improve the reliability of the power system.  
Figures 4 and 5 show the operating times of overcurrent recloser and DG relays with different fault impedances at points 1 and 2. The short-circuit current value decreases by increasing the fault impedance that, in turn, causes an increase in the operating time of the protective relay. The length of the feeder (line impedance) is also effective. Line length at point 2 is more than the line length at point 1. Therefore, considering a fault with the same fault impedance, the relay operating time at point 2 is more than that at point 1. The main feeder relay cannot operate in case of a fault impedance of more than 40 \(\Omega\) at point 1 and 20 \(\Omega\) at point 2. The relay of DG detects faults just with Z = 0 ohms at point 1 and cannot detect a fault at point 2. The recloser relay can detect any fault with various impedances.  
The operating time of the main feeder relay gradually increases with an increase in the fault impedance at point 1. The main feeder relay operating time is around 6 s for a fault impedance of 40 \(\Omega\) (Figure 4). The main feeder relay cannot detect a fault with an impedance of 45 ohms. The operating time of the DG relay is around 1 s with zero fault impedance, while it does not operate when the fault has impedance. In addition, the recloser relay can detect faults with 45 ohms impedance in less than 1 s.  
The DG relay does not operate with a fault at point 2. The operating time of the main feeder relay is about 4 s at Z = 20 ohms.  
Figure 4: Operating times of overcurrent, recloser and DG relays with different fault impedances at point 1.  
Figure 5: Operating times of overcurrent, recloser and DG relays with different fault impedances at point 2.  
Table 4 lists the operating times of the main feeder, DG and recloser relays with different fault impedances. In the table, the blind areas in the case study are indicated with Non (non-operation). The relay operating times are obtained by changing the fault impedance at points 1 and 2. The locations of the relays are shown in Figure 2. The feeder and DGs relays cannot detect the fault by increasing the fault impedance, while the recloser relay can detect.  
Figures 6 and 7 show the main feeder and recloser relays with a fault impedance of 25 \(\Omega\) at points 1 and 2, respectively. The operating time of the main feeder relay is around twice that of the recloser relay for a fault at point 1. The recloser and OC relays operate around 530 ms and 1.25 s, respectively.  
The main feeder relay does not operate with a fault impedance of 25 ohms at point 2, while the operating time of the recloser is 860 ms.
## 4 Discussion
### The WUE Changes in Drought and Post-Drought Periods  
The stability of ecosystems is related to not only the resistance of plants to water stress, but also the rate of recovery in the post-drought period, which is also referred to as ecosystem resilience. Ecosystem resilience is affected by a number of factors, including the severity and duration of water deficiencies, vegetation types [61], types of drought damage [62], plant growth rate and competition among plant species [63], and the differences in drought observational scale. In this study, the WUE declined during drought periods, revealing a weak resistance to drought in arid ecosystems, which is also likely to be a survival strategy for plants under dry conditions. After the drought event, the ecosystem is highly sensitive to abrupt changes in the environment, and the increase in WUE in the various vegetation types also indicated the resilience of the arid ecosystem, which is consistent with previous studies [64]. Ecosystem WUE is a common connection between the capabilities of biomes and ecosystems, which adapt to changing hydrologic climate conditions by regulating their WUE. In arid areas, ecosystems can self-regulate to restore themselves to pre-drought conditions after severe and persistent drought once the water conditions become appropriate. This study comprehensively analyzes the rapid recovery capacity of arid ecosystems and further confirms the survival strategies of arid ecosystems to cope with drought.  
Some results of this study have also been found in previous studies, such as the spatial patterns of the WUE across the different drought levels and biome types; for example, the ecosystem WUE exhibited a rapid increase after the drought event. However, there are also some differences with previous studies. For example, the drought legacy effect shows that severe drought will increase the impact on ecosystems. The main reason for this difference may be the divergence of drought intensity in different studies. The relationship between WUE and drought found in this study is different from that in previous studies, which may be because the study period in this study was divided into two stages, namely: the drought period (2000-2010) and the post-drought period (2011-2014). The response of the WUE to drought can provide a theoretical basis for understanding ecosystem responses in arid regions under future climate change. Thus, in future studies, the research time should be lengthened and field data should be added to improve the accuracy of remote sensing data in the future.
## 2 Principle
### Generation of LFM Signal  
In order to generate LFM signal, the Y-DPMZM is driven by a base-band single-chirp signal \(\cos(kt^{2})\), which has a time duration of 256 ns and a bandwidth of 1 GHz. The eight wavebands LFM signal is shown in Figure 6a; its waveform has stable amplitude cause of flat power. A baseband signal with a bandwidth of 4 GHz, and eight signals with a bandwidth of 8 GHz can be observed in Figure 6b, which agree well with the result of Equation (9). Figure 6c exhibits the time-frequency diagram, which is obtained by short-time Fourier transform. The instantaneous frequency curves present a clear and highlighted track, since little interference is generated simultaneously.  
Figure 7a,c show the recovered instantaneous frequency of the 10 GHz and 80 GHz LFM signals. It is noticed that every chirping signal is composed of two opposite linearly chirped microwave waveforms with a bandwidth of 4 GHz. The instantaneous frequency diagrams confirm that the two opposite chirped waveforms agree well with \(\cos(m\omega_{s}t+4 kt^{2})+\cos(m\omega_{s}t-4 kt^{2})\) term of Equation (9). A LFM signal with two opposite chirped waveforms may increase the range-Doppler resolution of radar [24]. Autocorrelations have also been calculated to evaluate the pulse compression capability in Figure 7b,d. As illustrated in diagrams, the PSRs are 11.1349 dB and 11.2149 dB, and the FWHM of the autocorrelation peaks are 0.14844 ns and 0.15078 ns, corresponding to PCRs of 1724.6 and 1697.8, which is close to the theoretical PCR of 2048 (duration time \(\times\) bandwidth). That is to say, the results reveal a good pulse compression capability. In addition, the PCR is mainly limited by the duration time and bandwidth of the injected chirped signal, which has been highly increased through the implementation of octuple bandwidth in this scheme, and it can also be further increased by extending the duration time.  
Figure 6: (**a**) The waveform, (**b**) spectrum, and (**c**) time-frequency diagram of the chirped signal.  
Figure 7: Time-frequency diagram and autocorrelations of the separate dual-chirp signal with frequency of 10 GHz (**a,b**) and 80 GHz (**c,d**).  
Table 1 presents the performance comparison of proposed approach with other published work. The comparison result clearly shows that our proposed approach demonstrates an improvement in the generation of more wavebands and sorts of signals, as well as a frequency range.
## 4 Methodology for the Performance Analysis of the Physical Layer Proposals
### Test Method and Setup Based on ETSI TS 103 909 v1.1.1 Integrated into a Virtual PLC Lab  
ETSI TS 103 909 v1.1.1 [15] defines a standard and reproducible environment to determine the performance of narrowband PLC technologies under realistic channel conditions. It defines a test setup where a transmitter and receiver are connected independently, through a channel characterized by a nearly flat in-band frequency response, with controlled attenuation and additive noise sources and waveforms. This setup, described in detail in [15], is defined for a controlled alternating current (AC) mains environment, where the transmitter and receiver are connected through two isolated mains branches.  
These standardized setup and environment are integrated into the Virtual PLC Lab [22], which is a fully automated, cost effective, and repeatable test environment. This is a system that replicates all the analogue elements of a PLC laboratory in digital technology. This tool runs multiple virtual PLC modems connected through a virtual digital medium with configurable characteristics, such as attenuation, noise patterns, and transfer function models. Each modem and the medium are software processes running in a computer. These processes interchange the digital representation of the PLC signals through UNIX sockets.
# Research on the Path Planning of Unmanned Sweepers Based on a Fusion Algorithm
## 6 Experimental Results  
### Simulation Experiment of Improved Algorithm Based on Key Points  
The simulation experiments of the traditional A* algorithm and improved A* algorithm were carried out using Matlab. The raster map sizes were set to 20 \(\times\) 20 and 30 \(\times\) 30. The path-planning experiments were carried out using two algorithms in two maps, respectively. The path planning of the traditional A* algorithm is shown in Figure 7a,b, and the path planning of the improved A* algorithm is shown in Figure 7c,d. The red number in the figure represents the number of nodes in the path, the blue solid line is the planned route, the black grid is the obstacle, and the white grid is the driving area.  
Figure 7a,b shows the simulation results of the conventional A* algorithm. From the experimental results, it can be seen that although the complete path planning can be completed from the starting point by following the flow of the A* algorithm, there are many intermediate nodes, which affects the planning efficiency. Figure 7c,d shows the experimental results of improving the A* algorithm using key points. Long-distance linear path planning is achieved by connecting two key points by removing unnecessary expansion points in the middle, and then the nodes are searched with the heap sorting optimization algorithm. Simulation results show that the improved A* algorithm improves the efficiency of the planning extension nodes by 71.5%. From Figure 8, it can be seen that the proposed A* algorithm is able to reach the next target point, and the  
Figure 6: Flow chart of fusion algorithm.  
that the number of turning points is reduced by 46.2%. It can be seen that the improved algorithm not only improves the planning efficiency, but also plans fewer path nodes and is more efficient compared with the traditional algorithm.
# A Trusted Federated Incentive Mechanism Based on Blockchain for 6G Network Data Security
## 4 Experimental Results and Analysis
### Training Effectiveness of the Global Model under the Incentive Mechanism  
From Figure 11, it can be observed that when training for 100 rounds simultaneously, the scheme without the incentive mechanism involves edge nodes participating in the training process using a regular training mode, resulting in a slower convergence rate of the global model. On the other hand, the ETFL federated learning scheme with the incentive mechanism, as depicted in the graph, exhibits a faster convergence rate of the global model. The introduction of the incentive mechanism encourages edge nodes to participate in the training process more effectively, leading to better training results uploaded by the nodes. This enhancement improves the overall training performance of the system, and edge nodes also receive relatively better rewards, making them more willing to participate in subsequent training sessions.  
Figure 11: Incentive mechanism’s impact on model training results comparison.  
Figure 10: Comparison of global model accuracy for 5% and 20% malicious node cases.
To avoid the disadvantage that a single type of time-frequency processing method cannot express the rich information in the raw vibration signal, this paper proposes a dual time-frequency image fusion method based on convolutional neural network for compressor manufacturing defect diagnosis. This method realizes the fusion and complementation of different time-frequency information and makes full use of the image feature recognition ability of the deep neural network. The main contributions of the paper can be summarized as follows:  
(1) The proposed method uses CWT and HHT for feature extraction of the same raw vibration signal, and the extracted features are complementary. The proposed method achieves richer feature extraction for a single vibration signal and ensures the accuracy of diagnosis. (2) The proposed method constructs a dual-channel fusion convolutional neural network that can effectively learn manufacturing defect features from two time-frequency images and fuse them to realize an end-to-end diagnosis of compressor manufacturing defect based on the time-frequency image of the vibration signal. (3) A large number of experiments verified the differences between different time-frequency processing methods and different classification models and proved the superiority of the proposed method with higher diagnostic performance and robustness. The rest of this paper is organized as follows: Section 2 introduces the feature extraction method--dual time-frequency images fusion. Section 3 presents a diagnosis method of hermetic refrigeration compressors based on dual time-frequency images fusion. Section 4 introduces the experimental setup and data processing. Section 5 gives the experimental results and comparative study, and Section 6 concludes the paper.
# Atom Probe Tomography for Catalysis Applications: A Review
## 3 Case Studies
### Catalysts as Unsupported Nanoparticles  
Performing APT experiments on real-world catalysts remains challenging. There is a broad diversity of catalysts' structures, sizes and morphologies, and thus there is no generic recipe for their preparation. Every sample is different, and a specific sample preparation needs to be developed for every category of sample: supported nanoparticles, unsupported nanoparticles, porous materials such as zeolites, nanoporous materials or agglomeration of catalysts as powders. From an APT point of view, the sample preparation should be optimized to reduce as much as possible the presence of aberrations during the analysis and 3D reconstruction: this can be done by avoiding the presence of high (or low) field precipitates if an embedding method is chosen, and by avoiding the presence of surface heterogeneities leading to enhanced local electric fields. From a catalyst point of view, the sample preparation has to preserve the integrity of the sample and should thus avoid the use of processes inducing chemical or structural changes of the catalysts; for example, oxidation/reduction processes, reaction of the catalytic materials with the embedding medium, or sample preparation requiring a high temperature (relative to the structural stability of the sample). The following examples present two different sample preparation methods applied to the analysis of unsupported nanoparticles: the electrophoresis method and embedding method.
###### Abstract  
Strengthening of concrete structures is applied as a solution for various deterioration problems in civil engineering practice. This also refers to the structures made of self-compacting concrete (SCC), which is increasingly in use, but there is a lack of research in this field. This paper presents an experimental analysis of flexural behavior of reinforced concrete (RC) continuous beams made of SCC, strengthened with fiber reinforced polymer (FRP) materials (glass (GFRP) and carbon (CFRP) bars, CFRP laminates), by the use of near surface mounted (NSM) and externally bonded (EB) methods. Six two-span continuous beams of a total length of 3200 mm, with the span between supports of 1500 mm and 120/200 mm cross section, were subjected to short-term load and tested. The displacements of beams and the strains in concrete, steel reinforcement, FRP bars and tapes were recorded until failure under a monotonically increasing load. The ultimate load capacities of the strengthened beams were enhanced by 22% to 82% compared to the unstrengthened control beam. The ductility of beams strengthened with GFRP bars was satisfactory, while the ductility of beams strengthened with CFRP bars and tapes was very small, so the failure modes of these beams were brittle.  
strengthening; self-compacting concrete; FRP materials; continuous beams; EB method; NSM method +
Footnote †: journal: Applied Sciences  
Article  
Experimental Analysis of Continuous Beams Made of Self-Compacting Concrete (Scc) Strengthened with Fiber  
Reinforced Polymer (Frp) Materials
# Topology Optimisation in Structural Steel Design for Additive Manufacturing
## 6 Recent Advances in Related Fields with Applicability in Structural Steel Design
### Aerospace and Defence Industries  
Aerospace engineering, with its utter need for weight reduction and frequently generous funding, has been a source of inspiration for TO breakthrough innovations. Over the years, sensible contributions to critical issues in TO, such as fastener design and dynamic analyses, now used in structural steel design, came from this field [488].  
Recent advances in TO from this field include optimisation methodologies for additively manufactured parts with enhanced accuracy to strain and displacement [489], experimental analysis of several topologically optimised micro and macro-structural systems for ribs [490], optimisation to cumulative mechanical and thermal loads [491], and advanced materials design for high-quality AM [492]. All these contributions can have a deep impact on the quality of topologically optimised and additively manufactured alloys, leveraging its rapid application also in the construction industry.
## 2 Methodology
### Evaluation Criteria  
To evaluate the effectiveness of our approach, we adopted three metrics, which are EXAM score, NDCG (Normalize Discounted Cumulative Gain) [15], and EIO (the effect of the improved method relative to the original method).  
EXAM is an evaluation index widely used in software fault localization. As shown in Equation (4), it is defined as the number of statements to check the program to find a fault_stat in total statements all_stat, The lower the stat ratio, the better the effect of fault localization.  
\[EXAM=\frac{check\_stat}{all\_stat} \tag{4}\]  
NDCG is a common index for evaluating information retrieval, which considers the recommendation order and the effective results contained in the recommendation. It can be used to evaluate the recommendation sequence in the retrieval of information. The output of software fault localization can be regarded as the recommendation of suspicious fault lines.  
\[DCG(D,t,g,r)=\sum_{d\in D}\frac{g(d,t)}{s(r(d))} \tag{5}\]  
\[NDCG(D,t,g,r)=\frac{DCG(D,t,g,r)}{\max_{l\in Dn}DCG(D,t,g,l)} \tag{6}\]  
where \(D\) is a group of documents, \(t\) is a subject, \(R(d)\) obtains the ranking of document \(D\), and \(G\) is the income function, which depends on the real score of subject \(t\) predicted from document \(D\), and \(S\) is the distance function used for ranking. The score range is [0,1]. The higher the value, the better the effect.  
The \(EIO\) index is defined in this paper to evaluate the effect of the improved method compared with the original method. A positive score represents the improvement of the effectiveness. A score of 0 represents no change in the effect, and a negative score represents a decrease of effectiveness.  
\[EIO=\frac{org-evo}{org}\times 100\% \tag{7}\]  
where \(org\) represents the number of lines of the original method to find a fault check and \(EVO\) represents the number of lines of the improved method to find a fault check.
# High-Precision Combined Tidal Forecasting Model
## 3 Combined Tidal Forecasting Model Based on Harmonic Analysis and ARIMA-SVR
### Prediction Steps
These four performance measures are respectively calculated as follows:  
\[E_{\text{MA}}=\frac{1}{n}\sum_{i=1}^{n}\lvert(y-Y)\rvert, \tag{13}\]  
\[E_{\text{MS}}=\frac{1}{n}\sum_{i=1}^{n}(y-Y)^{2}, \tag{14}\]  
\[E_{\text{RMS}}=\sqrt{\sum_{i=1}^{n}\left(y-Y\right)^{2}/n}, \tag{15}\]  
\[r=\frac{\text{Cov}(y,Y)}{\sqrt{\text{Var}[y]\text{Var}[Y]}}. \tag{16}\]  
Here, \(y\) and \(Y\) are the measured and predicted tidal heights, respectively; \(n\) is the number of tidal samples; \(\text{Cov}(y,Y)\) is the covariance between \(y\) and \(Y\); and \(\text{Var}[y]\) and \(Var[Y]\) denote the variances in \(y\) and \(Y\), respectively.
## 2 Overview of OM Structure and Functions
### LPS Structure and Function  
LPS is typically organized into three structural domains: lipid A, a core oligosaccharides and a highly variable \(O\)-antigen constituted of repeating oligosaccharidesamide units (Figure 1) [23]. The core is covalently linked to lipid A and can be further divided into inner (lipid A proximal) and outer core. The chemical structure of the outer core is variable, whereas the inner core tends to be quite conserved and in all species so far analyzed, it contains at least one residue of 3-deoxy-d-_manno_-oct-2 ulosonic acid (Kdo) linking the inner core to lipid A. Therefore, Kdo is a chemical hallmark of LPS and amarker of LPS-producing Gram-negative bacteria [24]. The \(O\)-antigen is the distal, surface exposed LPS moiety responsible of the immunogenic properties of this macromolecule [23].  
LPS is essential in most Gram-negative bacteria with the notable exception of _Neisseria meningitidis_[25]. The LPS structural requirements for bacterial viability however, may vary across genera/species. In _E. coli_ the minimal LPS structure required for growth has been defined as Kdo\({}_{2}\)-lipid A [23], although the lethal phenotype of Kdo-deficient mutants may be rescued by several suppressor mutations [26]. In contrast, _Pseudomonas aeruginosa_, in addition to lipid A, requires the full inner core and at least part of the outer core to be viable [27, 28]. The structural complexity of LPS reflects the multiple functions displayed by this macromolecule. While the outer hydrophilic layer of LPS leaflet in the OM represents an effective barrier for passive diffusion of lipophilic compounds, the lipid A moiety, together with the phospholipids of the internal leaflet, forms a hydrophobic barrier. Moreover, LPS appears tightly packed at the outer OM leaflet as strong lateral interaction between LPS molecules occurs by the bridging action of Mg\({}^{2+}\) and Ca\({}^{2+}\) divalent cations, thus, counteracting the negative repulsive charges and stabilizing the structure [12, 24].  
LPS is also a potent activator of the innate immune response and lipid A (also known as endotoxin) represents the conserved pathogen associated molecular pattern (PAMP) recognized by innate immune receptors to signal and activate complex signaling cascades that lead to the release of pro-inflammatory cytokines [29]. Recognition of lipid A requires the TLR4-MD2 complex [30, 31] and the accessory protein CD14 and LPB (LPS binding protein) [32].