esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
Thus, the peak value of the multi-phonon coupling between the SO and 1 LO modes, denoted as 1 LO+SO, is indicated by a vertical dashed line at 313 cm\({}^{-1}\) in Figure 5. Similarly, a multi-phonon mode identified as 3 LO\(-\)SO was observed at 348 cm\({}^{-1}\) on the high-frequency side of the 2 LO mode. The presence of both these modes in CdTe has been previously documented [27; 28]. The red line in Figure 5 represents the final fitted curve. Notably, there is excellent agreement between the fit and the experimental data. Table 1 presents a comprehensive summary of the fitting parameters used to model the 1 LO and 2 LO peaks. It is important to note that the fitting parameters associated with multi-phonon modes are unaffected by changes in RMS values, indicating their independence from surface roughness. Nevertheless, these peaks are essential for accurately reproducing the line shape around the 1 LO and 2 LO phonon regions. Finally, to explain the correlation between surface roughness and the intensity of the 2 LO phonon mode, it is important to consider its shorter wavelength compared to 1 LO phonons. This inherent characteristic renders 2 LO phonons more susceptible to crystal lattice imperfections induced by surface roughness. As a result, these phonons undergo heightened scattering because of surface irregularities. Moreover, under resonant excitation conditions, 2 LO phonons can interact with other phonons throughout the Brillouin zone. These interactions between phonons often involve energy exchange, resulting in phonon-phonon coupling. When the energy exchanged during phonon-phonon coupling coincides with the energy of 2 LO phonon mode, the intensity of this mode in the spectrum will be enhanced. The augmentation of surface roughness enhances the probability of the 2 LO phonon mode participating in Raman scattering, thereby intensifying this resonance effect. Consequently, increasing surface roughness directly correlates with an elevated peak intensity of the 2 LO phonon mode.
# Effects of Diverging Nozzle Downstream on Flow Field Parameters of Rotating Detonation Combuster
## 2 Numerical Model and Method  
### Physical Model  
Figure 1 shows the three-dimensional geometric model of the premixed RDC analyzed in this study. The premixed stoichiometric methane-air mixture is directly injected into the RDC. Here we simplify the intake nozzles along the inlet of the RDC to the inlet surface [10; 11] (as is shown in Figure 1). According to our previous analysis on a micro gas turbine with an RDC [36], the inner diameter, outer diameter, and axial length of the RDC were 234.8 mm, 274.8 mm, and 200 mm, respectively. In order to investigate the effects of diverging nozzle on wave formation and RDC performance, the outer wall expanded outwards with an angle of \(\theta(0^{\circ},15^{\circ},30^{\circ},45^{\circ})\). The axial length of the diverging nozzle was 50 mm.
## 3 Experimental Results
### Properties of Hardened Concrete
#### 3.2.4 Elastic Modulus  
* Static Modulus  
The experimental results of the static elastic modulus for the concrete are shown in Figure 10. Although the compressive strengths of the natural and the low-quality recycled aggregate concrete were highly similar, the static elastic modulus of the natural aggregate concrete was higher than that of the low-quality recycled aggregate concrete and even higher than those of the high- and medium-quality recycled aggregate concrete, except for the case with a water-to-cement ratio of 0.60. This difference in trend could be due to a lower stiffness of the recycled aggregate resulting from the relatively weak adhesion of the adhered old mortar.  
* Dynamic Modulus  
The dynamic elastic moduli of the concrete measured with the resonance vibration method are shown in Figure 11. The slope of the curve was largely flat when the water-to-cement ratio changed from 0.60 to 0.45. Compared to the natural aggregate concrete, the dynamic modulus of the concrete with a low-quality recycled fine aggregate was 24.3%, 20.5%, 17.5%, and 15.6% lower for the different water-to-cement ratios, indicating that the difference between the dynamic modulus of the natural and low-quality recycled aggregate concrete decreased with a decrease in the water-to-cement ratio or with an increase in the compressive strength. The dynamic modulus of the low-quality recycled coarse aggregate concrete was approximately 8 GPa lower than that of the natural aggregate concrete.  
Figure 11: Dynamic modulus of concrete at 28 days with different water-to-cement ratios.  
Figure 10: Static modulus at 28 days with different water-to-cement ratios.
## 4 Centralized Scheduling Algorithm for the IEEE802.15.4e TSCH Network
### Maximal Matching Algorithm  
The Internet of things based on IEEE802.15.4e TSCH requires a link-scheduling algorithm, because only duplex conflict-free links can be allocated to the network nodes [9]. A link is in a duplex conflict condition if it is used by the node that transmits and receives data simultaneously and/or receives data from several child nodes at once. The link used by nodes involved in duplex conflict is called the duplex conflict link (DCL), while the link used by nodes that are free of duplex conflict is called  
Figure 7: Graph \(G=(V,E)\) modeling a network with tree topology.  
the duplex conflict free link (DCFL). Figure 8 depicts DCLs and DCFLs. In Figure 8a, duplex conflict occurs in the links used by node 0 and node 9, while Figure 8b shows links that are duplex conflict free.  
To get DCFLs, the scheduling algorithm in this research uses the graph maximal matching method, which is described as follows:  
Maximal matching (G; V; E):  
1. M = \(\O\);  
2. While (more edges can be added):  
2.1. Starting from the edge that connected to the highest rank node, select an edge, e, which has no vertex in common with an edge in M;  
2.2. \(M=M\cup e\).  
3. Return M.  
G is a directed graph with tree topology, V and E are sets of vertex and edge in graph G. M is a set of matching edges on graph G, where M is a subset of E.
## 2 Materials and Methods
### Off-Line Signal Processing and Synchronization  
The recordings are processed off-line in order to achieve synchronization. The spikes in the audio signal and the square signal registered by the SEMG (with both events generated by the same switch) are employed to synchronize the signals of both systems, as shown in Figure 3. The switch was pressed three times at the beginning of the simultaneous recording. Three square pulses of 5 V of amplitude can be observed in the SEMG signal and six peeks can be observed in the audio recording. Both signals are synchronized when the spikes coincide with the flanks of the square pulses.
# Integrated Design of Moon-to-Earth Transfer Trajectory Considering Re-Entry Constraints
## 3 Opportunity Determination with Re-Entry Constraints
### Transfer Opportunity Determination  
Given that the mission search date interval is \([D_{2},D_{3}](D_{3}-D_{2}>1)\), the optimal transfer trajectory and its corresponding perilune altitude \(H\) on each day can be solved with the above calculation process.  
The fuel consumption required to achieve Moon capture after trajectory correction is highly correlated with the altitude of the perilune before correction. If the perilune altitude of the transfer trajectory is too large, the fuel consumption for correction will be too large, which will be beyond the capability of the probe. Therefore, the existence of the daily transfer opportunity can be judged according to the daily minimum perilune altitude of the Moon.  
The altitude deviation limit of the perilune is set as \(\chi\). If the daily minimum perilune altitude of the Moon-to-Earth transfer trajectory at date \(\tau\) satisfies \(H<\chi\), the date \(\tau\) is judged to be included in the transfer opportunity. Then, the whole transfer opportunity \(\Gamma\) within the date interval \([D_{2},D_{3}]\) is obtained.  
Module M3 in Figure 2 reveals the transfer opportunity selecting process.
# Incorporating Drone and AI to Empower Smart Journalism via Optimizing a Propagation Model
## 3 Proposed Drone-Enabled AI Framework
### The Proposed Conceptual Architecture
#### 3.1.1 ITU-R P.529-3 Model  
The PL of this model can be calculated in the case of an urban area as:  
\[\mathrm{a(h_{r})} =1.11\ \mathrm{log}\ (\mathrm{f})-0.7(\mathrm{h_{r}})-[1.56\ \mathrm{log}\ (\mathrm{f})-0.8]\mathrm{a(h_{r})} =1.11\ \mathrm{log}\ (\mathrm{f})-0.7(\mathrm{h_{r}})-[1.56\ \mathrm{log}\ (\mathrm{f})-0.8] \tag{1}\] \[\mathrm{a(h_{r})} =1.11\ \mathrm{log}\ (\mathrm{f})-0.7(\mathrm{h_{r}})-[1.56\ \mathrm{log}\ ( \mathrm{f})-0.8]\] (2) \[\mathrm{b} =1\ \mathrm{for}\ \mathrm{d}\leq 20\ \mathrm{km}\]  
\[\mathrm{b} =1+\left(0.14+1.87\times 10^{-4}\ (\mathrm{f})+1.07\times 10^{-3}\ ( \mathrm{h_{t}}^{\prime})\ \left(\mathrm{log}\left(\frac{\mathrm{d}}{20}\right)\right)^{0.8}\ \mathrm{for}\ 20\ \mathrm{km}< \mathrm{d}<100\ \mathrm{k} \tag{3}\]  
Figure 2: The proposed conceptual architecture.  
\[{\rm h_{t}}^{\prime}=\frac{{\rm h_{t}}}{{[1+(7\times 10^{-6})\times(\ {\rm h_{t}})^{2}]^{ \frac{1}{2}}}} \tag{4}\]  
The PL of this model can be calculated in the case of a suburban area as:  
\[{\rm P_{L}=P_{L(urban)}-2[log\left(\frac{f}{28}\right)]^{2}-5.4} \tag{5}\]  
The PL of this model can be calculated in the case of a rural area as:  
\[{\rm P_{L}=P_{L(urban)}-4.78[log\ (f)]^{2}+18.33 log\ (f)-40.49} \tag{6}\]  
where \({\rm P_{L}}\) is Path Loss (dB), \(f\) is Carrier Frequency (GHz), \({\rm h_{t}}\) is Transmitter Antenna Height (km), \({\rm h_{r}}\) is Receiver Antenna Height (km), \({\rm d}\) is Distance of Transmission (km), and \({\rm a(h_{r})}\) is Antenna Correction Factor.
## 5 The Query Management Scheme
### The Query Processing Phase (QPP)
#### 5.3.1 Query Set Start Time  
When the node receives a query set \(\Sigma_{i}\), it automatically assigns a query set start time \(I_{i}\), based on the time indicated by each node clock. The query set start time is the zero time point for all the temporal information within the set. The set start time for
# A Review of Fault Diagnosing Methods in Power Transmission Systems
###### Abstract  
Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field.  
AC networks; artificial intelligence (AI), deep learning (DL), fault detection (FD), fault-type classification (FC), fault location (FL), machine learning (ML) +
Footnote †: journal: Applied Sciences
## 1 Introduction
While UK CEH LC and Crops are products that have a dynamic role for identifying main habitats, land cover/use and change and connectivity, and to support land parcel boundary mapping and principal crop types, VHR imagery can capture field boundary features (e.g., hedgerows and field margins), seasonal aspects related to agricultural management practices, vegetation patterns, evidence of ditches, forest canopy, etc. [72; 83; 84]. Insufficient collaboration with biodiversity experts is frequently cited as a limitation in using remote sensing for monitoring biodiversity and developing relevant indicators [85]. A deep understanding of habitat-species relationships must be available to use remotely sensed information effectively. Gaps still exist in our knowledge regarding the potential of EO imagery at the higher resolutions for projects such as the SH-AONB 'Making Space for Nature' Defra Test and Trial:  
1. VHR satellite data use in identifying the availability of suitable and connected habitat, and
2. how effectively do EO approaches at the highest resolutions add new insights to overall habitat assessments for various species of importance for biodiversity conservation and recovery. Our aim in the present research was, therefore, to work with ecologists to establish habitat criteria for five example species/species groups (henceforth referred to as the'species') and then to use this information to examine the potential role of EO in habitat evaluation for these species at particular sites and more widely in the SH-AONB. The research was conducted in three linked steps (see Figure 1): Step 1, knowledge acquisition of species-habitat requirements; Step 2, use of readily-available EO and similar resources to evaluate broad habitat suitability and connectivity for the species; and Step 3, exploration of the additional contribution of the increasingly available Very High Resolution (VHR) EO data (0.7-4 m in this study) for habitat assessment.
## 4 Results
### Time Series Validation  
The time series of the water levels were calculated using a strict water mask polygon. The water levels of the altimeter footprints within the mask were considered, selected and averaged for each date. Figures 6a and 7 show the water levels for different water bodies derived using the three retrackers: the two-step physical-based retracker, OCOG retracker and threshold retracker, combined with the waveform portion selection method. The retracked results were compared with the in situ measurements and the Level-2 results using the ocean retracker.  
Among the eight water bodies monitored in this study, the water levels over the Mequinenza Reservoir, Cavallers Reservoir and San Salvador Reservoir, all of which were tracked in an open loop tracking mode (on-board DEM dependent), could not be retracked. The difference between the on-board tracking heights derived from the received waveforms and the heights from the SRTM DEM with reference to the geodetic ellipsoid was almost 50 m over the Mequinenza Reservoir, more than 1000 m over the Cavallers Reservoir, and approximately 80 m over the San Salvador Reservoir. These findings indicate that the SRAL sensor lost its track and was unable to acquire the waveforms reflected from these water body surfaces.  
Figure 6: Time series water levels over the Ebro Reservoir: using the waveform portion selection method (**a**); and using the whole waveform (**b**).  
Figure 7: _Cont_.  
The RMSE and unbiased RMSE (ubRMSE) (Equation (8)) were calculated, as listed in Table 5. The unbiased RMSE is more reliable as it removes the bias.  
\[ubRMSE=\sqrt{E\Big{\{}[(WL-E[WL])-(insitu-E[insitu])]^{2}\Big{\}}} \tag{8}\]  
where \(E[\cdot]\) is the expectation operator and _WL_ is the water level derived from satellite data. The _RMSEs_ for different water bodies varied from 17 cm to more than 1 m. A comparison of the performances for the different retrackers over different water bodies is shown in Table 5. The best result was obtained for the Ribarroja Reservoir, which had an ubRMSE of approximately 16 cm. The results over the Ebro Reservoir, Sotonera and Ribarroja Reservoirs were also satisfactory. The RMSEs for the Itoiz Reservoir and Irabia Reservoir were relatively large with values greater than 1 m. The results using the three different retrackers do not show large differences except for the Sotonera Reservoir and Ribarroja Reservoir. The OCOG retracker gave the smallest ubRMSE for the Sotonera Reservoir with a value of approximately 38 cm. The two-step physical-based retracker and threshold retracker performed better over the Ribarroja Reservoir than the OCOG retracker, which had an ubRMSE of approximately 16 cm. All retrackers gave better results than the Level-2 ocean retracker without the waveform portion selection method.
## 6 Performance Evaluation
### Parameter Analysis
#### 6.3.1 Storage Cycle Analysis  
Let the storage cycle be 200 s, 400 s, 600 s, 1000 s and 1200 s, respectively, and the numbers of storage cycles in the total simulation time 12,000 s are 60, 30, 20, 12 and 10, accordingly. The results are shown in Figure 6.  
Figure 5: Simulation results. (**a**) access ratio; (**b**) average access delay; (**c**) number of content replicas; (**d**) transmission overhead.  
From Figure 6, we observe that when the storage cycle increases, the access ratios reach plateaus after sharp rises and the access delays always rise. On the one hand, the longer the storage cycle is, the more roadside unit candidates for the content carriers there are. Thus, the contents can be better allocated to roadside units with a result of higher access ratios. On the other hand, after the storage cycle reaches a threshold, an even longer cycle does little to help raise the access ratio since the bottleneck of the performance improvement changes to be the storage spaces of roadside units and the contact probability between vehicles and roadside units. Moreover, more elements in \(g(i)\) result in a longer delivery latency. As a conclusion, an appropriate storage cycle is important for DAS. In general, the storage cycle could be selected according to the network requirement of the transmission latency and by sampling analysis in pre-processing experiments.
## 3 Macro-Control and Micro-Autonomy Pathfinding Strategy
### Overall Design of Macro-Control and Micro-Autonomy Pathfinding System
#### 3.1.1 Execution Process  
The execution process of the macro-control and micro-autonomy pathfinding system is as follows:  
1. Task Decomposition: The central system first breaks down transport tasks that are too difficult for a single AGV to complete, such as cross-floor or cross-workshop tasks, into smaller subtasks that a single AGV can handle. For each subtask, the system identifies key waypoints like a pickup node and a drop-off node. If the loading or unloading involves other machinery, a pre-transfer waiting point is also defined.
2. Task Scheduling: Then, the central system assigns subtasks. Subtasks without prerequisites, or whose prerequisite tasks have been assigned, are then allocated to AGVs based on the priority of the task and the estimated distance from the A* pre-planned path. Under full-load operation with no idle AGVs available, each AGV, upon completion of its current subtask, immediately requests and receives the next subtask from the central system.
3. Pathfinding: For each AGV that actively executes a subtask, the central system uses A* to calculate a pre-planned path from its current node to the next critical waypoint. The map, pre-planned path, and positions of the neighbor AGVs are transmitted to the vehicle. The AGV then combines this information with real-time LiDAR data and runs its distributed planning strategy to dynamically avoid obstacles and refine its local path.  
Feedback and Calibration: Upon entering each new node, the AGV scans the ground code (a QR code on the floor) to recalibrate its position and reports its updated coordinates to the central system. If the AGV has deviated from its original pre-planned route, the central system recalculates and issues an updated path.
## 6 Discussion
### Simple Illustrative Example  
We use a simple chatbot for illustration purposes only. Specifically, we use administrative chatbot of an eye-care-center which gives information: (1) about the center services, (2) appointment availability, (3) various forms, (4) procedures to be followed.  
**Stage 1:** A quick analysis of the use-case and requirements reveals heavy dependency on text communication; however, it is clear that many of the clients have real eye-sight problem, so that the option of speech communication is crucial. The communications scope is limited in a way that can keeps it structured; it is assumed that dealing with emotions is less important.  
**Stage 2:** AI Requirements:  
* \"Heavy dependency on text communication\" \(\rightarrow\) Human machine interaction through text leading to natural language understanding.
* \"Speech communication is crucial\" \(\rightarrow\) Speech interaction ability.
* \"Structured communication\" \(\rightarrow\) Easy text mining.  
**Stage 3:** Required AI Technologies:  
From the stage 2 it is clear that NLP is needed for the implementation. So, from Figure 3 we conclude that: \"text mining\" is needed as well as \"text recognition\".  
From stage 2 it is clear that Speech interaction ability is required. Therefore, from Figure 3: \"text to speech\", \"speech recognition\", and \"text recognition\" are needed.  
**Stage 4:** Final inclusion/exclusion of AI technologies  
From previous stages it is clear that most of the AI technologies are fully required. Therefore, the included technologies are as follows:  
\"Text recognition\"  
\"Text mining\"  
\"Speech recognition\"  
\"Text to speech\"  
**Stage 5:** Steps for AI technology integration  
The outcome of stage 4 leads to the following AI implementation plan:  
\"Text recognition\" \(\rightarrow\) \"Text mining\"  
\"Speech recognition\" \(\rightarrow\) \"Text to speech\"  
Note that the implementation is done with two parallel progress paths.
## 1 Introduction
[28], taking a two-story house as an example, used the Ladybug, Honeybee, and Octopus plugins of the Grasshopper platform to conduct optimization calculations with thermal comfort, building energy consumption, and life-cycle cost as objectives, obtaining the optimal design parameters applicable to different targets in five climate zones. [PERSON] et al. [29], based on the Grasshopper platform, optimized three objectives of life-cycle carbon emissions, annual thermal discomfort hours, and global cost for single-story rural houses in cold regions, and they obtained the Pareto-optimal solution set and optimal reconstruction scheme. In summary, existing studies have mainly focused on two or three objectives, lacking in the comprehensive consideration of carbon emissions and photovoltaic utilization. There have been few studies with four or more objectives, and increasing the number of objectives will greatly increase the complexity of design decisions. Moreover, there have been few studies conducted in high-rise residential buildings, especially the comparative study of their design in different cities considering solar energy utilization. Therefore, this paper aims to simultaneously minimize four objectives for high-rise residential buildings in two cities (Yulin and Xi'an) with differences in solar radiation. There are three research objectives: (1) to obtain the Pareto non-dominated solution set for the four conflicting objectives of building energy consumption, thermal comfort, life-cycle cost, and carbon emissions; (2) to unveil the influence of climate, solar radiation, and objective-oriented differences on the values of the design parameters; and (3) to determine optimal design solutions using the TOPSIS method.