esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Meteorological Factor
#### 3.1.1 Descriptive Statistics and Topological Data Analysis of the Meteorological Factor  
The distributions of the measurements for all subregions are shown in Figure 3. A kernel-based method was used for the computation of the distributions of major diagonal [22]. A good agreement between the distributions of all regions except Lakkomata with respect to the pyranometer (and thus reference evapotranspiration) was observed. This difference provoked a further investigation, and thus a topological data analysis was performed and the computed manifold was projected in the 2D embedding of Figure 4. The main idea of this method [23] is to preserve the topological information of the high dimensional space (i.e., 5D in this case: pyranometer, rainfall, wind speed, relative humidity, air temperature, reference evapotranspiration) and via a non-linear transformation to project the points into the _xy_-plane. In particular, it can be defined as a non-linear principal component analysis where the points that are similar/neighbors in the 5D space will be close to each other in 2D space, while the dissimilar ones are projected very far away. In Figure 4, a clear separation of Lakkomata can be seen, as well as a seasonality pattern for all subregions, which splits the data into two clusters, i.e., April-May (left cluster) and June-August (right cluster), while September is split between those two clusters. This shows a very strong indication of special microclimatic conditions of the Lakkomata subregion, which is investigated in the next section.  
Figure 3: Pairwise plots of wind speed (km/h), relative humidity (%), pyranometer (W/m\({}^{2}\)), air temperature (\({}^{\circ}\)C), ETo (mm) and rainfall (mm) from Kastoria.
## 4 Numerical Results  
This section provides a numerical validation of the proposed detection algorithms, considering different system scenarios for an uplink massive MIMO wireless communication system. We evaluate the symbol error rate (SER) performance of various detection techniques and compare the efficacy of our proposed linear methods against conventional AOR and SOR detectors. Furthermore, we benchmark these iterative algorithms against the linear MMSE exact matrix inversion scheme. The channel matrices are generated utilizing an i.i.d. Rayleigh flat-fading channel model. To ensure a fair comparison, different system configurations are examined, utilizing higher-order modulation schemes such as 64-QAM and 16-QAM. In addition, we compare the complexity of the proposed approach with that of recently introduced iterative approaches.
## 1 Introduction  
Nondestructive testing/evaluation (NDT/NDE) is a collection of scientific techniques which are able to evaluate the properties and condition of materials without inflicting permanent damage on them. They were originally designated for inspecting parts, products, and structures of high value. The number of published research reports on NDT rapidly increased in the beginning of the 20 th century because of industrialization and the two World Wars. There have been wide applications for NDT technologies in numerous fields such as industrial manufacturing [1, 2], aerospace and aeronautics [3], civil engineering [4, 5], and material science and technology [6, 7]. Practical NDT techniques used for materials characterization can reduce maintenance costs and enforce lifetime management [8]. From the viewpoint of NDT on components made from solid elastic materials such as alloys, polymers and composites, quality is assessed by inspecting the possible existence of defects and changes in mechanical strength [9, 10]. Conventionally, the mechanical strength of a material is measured with a tensile machine and the stress-strain relation is determined by dynamic tensile testing from load-free to failure [11, 12, 13]. The stress-strain data are recorded on samples of cylindrical, center-necked shape and contact stress/strain measurement modules such as adhesive transducers are usually part of the machine. Such tensile tests are destructive because samples undergo irreversible deformation during the tensile test. To resolve the problem associated with mechanical strength testing,several nondestructive methodologies have been developed for the purpose of stress-strain evaluation. Some of them focus on noncontact approaches which characterize the change of stress-strain state by other physical fields during tensile testing. Other methodologies do not involve tensile testing but are devoted to measuring surface or internal residual stress distribution in an elastic sample both qualitatively and quantitatively. Nevertheless, the ultimate goal always is pre-diagnosing the mechanical strength of a component before actual cracks and failure occur.  
This review article discusses typical NDT methodologies that measure stress or strain by means of both contact and noncontact configurations. Noncontact methods can be used under extreme testing conditions or for in-situ inspection. Features of these noncontact NDT modalities are discussed with regard to their physical basis and state-of-the-art applications. No ranking of the various techniques is made or conclusions drawn with respect to their relative advantages because they all have distinctive features and limitations. It is expected that this review article will help interested readers to learn about the underlying physical principles, development histories and modern applications of a number of major NDT methods used for industrial quality control to-date.
# Beyond Static Estimates: Dynamic Simulation of Fire-Evacuation Interaction in Historical Districts
## 2 Methods
### Multi-Case Fire Development
#### 2.4.1 Fire Development Conditions  
Ignition Point Location: Based on previous research, ignition points are typically located in areas with high combustible loads, such as restaurants [48; 49]. A total of 265 Points of Interest (POIs) for restaurants and hotels in the historic district are identified using Baidu Map data. Four locations (A, B, C, D), situated in areas densely populated with restaurants and hotels, are selected as potential ignition points (Figure 1b).  
Wind Direction and Speed: Meteorological data indicate a predominant southwest wind direction, with an additional easterly wind condition observed during the historical fire. The wind speed is set to 5 m/s for the simulation [50].  
Ambient Temperature: Temperatures corresponding to summer (15 \({}^{\circ}\)C) and winter (0 \({}^{\circ}\)C), when fires are more likely to happen, are used, based on meteorological data [51].  
Combining the four ignition points (A-D) with the two wind directions and two seasonal temperatures yields a full-factorial set of 4 \(\times\) 2 \(\times\) 2 = 16 cases across these three key factors. This design brackets the most common and the most critical physical conditions for Dukezong. In particular, ignition Point D lies on the sole northbound corridor to Diqing Square, thereby capturing the worst-case situation in which the designated evacuation route is ignited at its origin.
## 3 BM-RAGAM Retrieval Scheme in the Vertical Domain of LLMs
### BM25 Search  
The BM25 (a sparse lexical search algorithm), as a classical search algorithm based on a probabilistic model, has excellent performance in classical document retrieval tasks due to its flexible regulation of the term frequency (_TF_), document length, and inverse document frequency (_IDF_). Its core idea is to calculate the relevance score of a document to a query by considering factors such as the _TF_, _IDF_, and document length. The BM25 assigns higher weights to rare words through the _IDF_ so that important words that are distinguishable to queries can play a greater role [43]. The retrieval process of the BM25 is shown in Figure 4, and the formula is as follows:  
\[\text{BM25}(q,d)=\sum_{i=1}^{n}IDF(q_{i})\cdot\frac{f(q_{i},d)\cdot(k_{i}+1)}{ f(q_{i},d)+k_{i}\cdot(1-b+b\cdot\frac{|d|}{a\overline{\Sigma}_{d}})} \tag{1}\]  
\[IDF(q_{i})=\log(\frac{N-n(q_{i})+0.5}{n(q_{i})+0.5}+1) \tag{2}\]  
\begin{table}
\begin{tabular}{l} \hline \hline
**Algorithm for an LLM Application Using RAG** \\ \hline Index Process: \\
1. embeddings \(\leftarrow\) load(”embedding\_model”) \\
2. doc \(\leftarrow\) load(“file\_name”) \\
3. chunks \(\leftarrow\) chunker.chunk(doc,txt,pdf) \\
4. ce \(\leftarrow\) embeddings.embed(c) \\
5. db \(\leftarrow\) index(ce) \\ Query Process: \\
1. INITIALIZE sys\_prompt \(\leftarrow\) “I want to...” \\
2. INITIALIZE model \(\leftarrow\) load(“Qwen2-72B”) \\
3. while TRUE do \\
4. q \(\leftarrow\) get\_user\_query() \\
5. q \(\leftarrow\) embeddings.embed(q) \\
6. chunks \(\leftarrow\) db.search(qe) \\
7. context \(\leftarrow\) merge(chunks) \\
8. prompt \(\leftarrow\) create\_prompt(sys\_prompt, q, context) \\
9. answer \(\leftarrow\) model\_generate(prompt) \\
10. end while \\ \hline \hline \end{tabular}
\end{table}
Table 4: RAG algorithm used by large language models.  
In the formula, \(q\) is the query; \(d\) is the knowledge base; \(|d|\) represents the length of the retrieved documents; \(N\) is the total number of documents in the knowledge base set; \(f(q_{i},d)\) is the term frequency of term \(q_{i}\) in the documents; \(n(q_{i})\) is the total number of documents containing term \(q_{i}\); \(k_{i}\) and \(b\) are free parameters--usually \(k_{i}=1.2\) and b = 0.75 are chosen; \(|d|\) is the length of the document in words; and \(avg_{d}\) is the average document length in the text collection.
# An Ultra-Thin Composite Metasurface with Hybrid-Damping Modes for Broadband Sound Absorption
## 6 Conclusions  
In this study, we introduced an ultra-thin acoustic composite metasurface, which can achieve above 85% continuous absorption of over 750-10,000 Hz with a thickness of 31 mm. A 20 mm-thick compound HR structure was first designed with the hybrid-damping modes, by which the band was broadened by about 70% and the high-order peaks were optimized to obtain 100% absorption. This advancement serves as a solid foundation for the achievement of compact metasurfaces. By gradually incorporating the parallel porous cells and series layer, the absorption spectrum can be expanded to 10,000 Hz. Compared to the porous foams of the same thickness, the performance of this metasurface is obviously improved below 1600 Hz. The utilization ratio of porous materials in this structure has been significantly reduced to about 35%, allowing the low-frequency advantages of the metamaterial to be fully exploited. The metasurface would offer a new approach to obtain ultra-broadband absorption with a thin structure and show great potential in modern equipment.  
Conceptualization, C.L.; Methodology, C.L.; Software, Z.X.; Validation, Z.X.; Formal analysis, X.L.; Writing--original draft, C.L.; Writing--review & editing, X.L.; Supervision, C.L.; Funding acquisition, C.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Aeronautical Science Foundation of China under Grant No. 20220015070001, the National Natural Science Foundation of China (NSFC) under Grant No. 52105120, and the China Postdoctoral Science Foundation under Grant No. 2021M692552.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
## Theory and Methods
### The Bistatic GNSS-R Soil Moisture Retrieval Method
Generally, due to the attenuation of signal power caused by surface reflection and the presence of fading noise introduced by surface scattering, 1 ms of integration is not enough to get the correlation peak, consecutive 1 ms coherent correlations must be averaged. This process is known as non-coherent integration. There is not a defined rule governing the choice but it should only be determined by the specific application and by its own situation. In this study, 500 ms of non-coherent integration time is used since it is examined to be long enough to eliminate the effects of speckle noise and short enough to have a good resolution of the surface by multiple experiments. After that, the reflectivity is used to obtain the permittivity through the bistatic radar equations [53]. We must note that the permittivity is strongly related to the soil moisture content. The relationship between soil permittivity and soil moisture is given by the soil permittivity models. Due to its complex structure, simplified empirical or semi-empirical models are used as a function of permittivity in practical applications [54; 55; 56]. The performance of GNSS-R soil moisture retrieval is an important issue, which is determined by many factors: (1) The geometric and physical characteristics of the reflected surface, such as the statistical distribution of the ground height, the composition of the soil, etc., which impact the received SNR. (2) Parameters of the GNSS-R system, such as the behavior of the transmitting and receiving antennas (\(G_{r}\), \(P_{n}\)), and the elevation angle (\(\theta\)), etc. Except for the above-concerned parameters (input variables), some random factors, the appearance of the surface roughness, vegetation, e.g., leaf orientation, height of the vegetation, etc., may influence the signal collection in the real case. Due to the complex interaction of these parameters, the retrieval of soil moisture content using GNSS-R is commonly based on semi-empirical models. The random factors can be regarded as system noise and suppressed by machine learning methods. Here we considered the retrieval procedure as a nonlinear regression problem with the input variables (\(SNR\), \(P_{n}\)... ) and output variables (permittivity, soil moisture) based on the early work [46] as shown in Figure 2.
## 3 Methodology
### Stream Data Preprocessing  
The signals from the patient's brain are read using EEG sensors, which are placed in particular places in the cortex, as explained in Section 2.1. These sensors produce a large amount of data because there are multiple channels, so the preprocessing stage aims to prepare the EEG data in an appropriate form for building the classification model. In the first step, the important features are extracted from the EEG signal using Fast Fourier Transform (FFT). FFT is an implementation of Discrete Fourier Transform, which is explained in Section 2.1. The importance of this step is based on the fact that building a classification model depends on choosing the best feature for the data splitting process during building decision trees. For this reason, the quality of extracted features and their correlation to the target of the classification task has significant importance in preprocessing operations.  
While the previous step is trying to reduce the complexity of the data steam horizontally by extracting the most valuable features, the aggregation aims to make a vertical reduction in the data stream by minimizing the number of rows. This step includes the aggregation of a set of patient rows that are generated during the same time moment and with the same target class value. The preprocessing ends with sending the preprocessed EEG data stream to the cloud where the adaptive classifier is built. Algorithm 1 summarizes the steps of preprocessing stage.  
```
1:procedureEEG Preprocessing
2: S \(\leftarrow\) Raw EEG signals received at time t
3: F \(\leftarrow\) Set of extracted features from S using FFT \(\triangleright\) based on Equation (2)
4:for each subset A in F, where \(|A|\) = sample rate of S do
5: ifIsIdentical(Class(A)) = True then R = R + Aggregated(A)
6:endif
7:endfor
8: Upload Preprocessed stream R to the cloud side
9:endprocedure
```  
**Algorithm 1** EEG Stream data preprocessing--Edge Node.  
The classification in this part of the proposed model can be considered static, whose primary goal is a fast response due to the rapid generation and large quantity of EEG signals. If the signal is classified as an epileptic seizure, the patient, the contact person, and  
Figure 1: The diagram of ARFC for classifying EEG data stream in the Cloud.  
the medical authority responsible for his care are notified. The patient's actual status is acquired after the notification, and then it combines with the feature data of that instance before sending them to the master node across the cloud. The patient or the medical staff may dismiss the notification, thinking it is a false warning or due to the lack of information to confirm this at present. The feedback for the actual value of the patient's status will wait for a specific period. If there is no response after the time expires, the class label is considered a missing value, and only the features values are sent to the master node.
## 4 Influence of the Cathode's Geometry
### Study of the Cylindrical Height  
At 20 kV, with xenon as the working gas and starting from the initial cathode height value of 21 mm, we begin by reducing the cathode's length down to 12.6 mm. These  
Figure 3: Influence of the cylindrical inner radius in the thruster’s performance at 20 kV: (**a**) variation of the output thrust \(T\) in (\(\mu\)N) for several radius (mm); (**b**) variation of the thrust-to-power ratio \(T/P\) in (mN/kW) for several radius (mm).  
modifications can cause significant changes in the electric potential distribution that could improve our thruster. As we were reducing its length, we observed that values under 12.6 mm would not improve, but instead would unfold instabilities in the plasma. We conclude that at 20 kV, in the case of Ar, the cylinder height should be approximately 10.5 mm, and in the case of Xe, the cylinder height should be around 14.7 mm (see Figure 4a,b) to optimize the output. As we can see, the cathode's height is not a determinant parameter in the efficiency of the EHD thrusters under study in the range considered.  
With argon as the propellant gas, we verified that the width was already optimized in 12 mm, as any other variation, upper or lower, would decrease the output thrust and the thruster efficiency. On the other hand, when using xenon, we concluded that the optimal hollow radius was 20 mm. With the change of the cathode's length, we found that the xenon propellant would attain its optimal state with a length of 14.7 mm. Overall, the xenon thruster delivered an output thrust of 3.80 \(\mu\)N and the thrust-to-power ratio of 434 mN/kW, values that when comparing with the current state-of-the-art of the modern electric propulsion systems reach the same order of magnitude.
# An Overview of Autonomous Vehicles Sensors and Their Vulnerability to Weather Conditions
## 3 Weather Effects
### Dust Storm  
Dust storms are the formations of sand or dust walls typically caused by strong wind currents, which are typically offshoot from a large thunderstorm and/or, on some rarer occasions, the movement of pressure fronts [47]. These dust particles, for which its size can range from 2.5 to 10 microns, are pushed by the current causing visibility concerns and may also facilitate cloud growth [48]. Since visibility concerns are present, camera performance is severely debilitated relative to whether there may be poor visible conditions in the air or the camera lens becoming dirty. For the former, over the span of a year, 549 incidents involving casualties were reported to be caused by dust-related storms [47]. According to Sky Harbor International Airport, dust storms debilitate visibility to less than half a mile (0.8 km). Smoke, which shares some physical characteristics with dust storms, also cause performance issues. Energy absorption and soot particles, similarly to the dust particles, have higher effects relative to sensors with smaller wavelengths and can be around 400-750 nm in the camera's case [49].  
LiDAR is also affected negatively by smoke but not as severely as the camera [48]. Similarly to how fog or rain affects LiDAR, dust storms particles will cause Mie scattering to the 905 and 1550 nm wavelengths, since their sizes are around 2.5-10 microns. This scattering will not be as powerful, however, because water as a material has a high absorption coefficient [13]. RADAR will only be affected minorly by Raleigh scattering since particle size and wavelength differ three magnitudes.  
Since ultrasonic sensors are extremely dirt-tolerant, their efficiency shines, especially in harsh working environments where process reliability is not jeopardized by dust, smoke, mist, or other impurities. However, this is only with respect to the particles contained within dust storms. Wind speeds of 6 m/s begin to disturb wave propagation [39]. That being said, more than half of the dust storms observed in Phoenix, Arizona, had maximum wind speeds between 36 and 57 mph (16-25 m/s) [47].
## 2 Materials and Methods
### Data Analysis  
The database consists of the four gait parameters: stride interval, swing interval, stance interval, and double-support interval, shown in Figure 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Anthropometric Parameter** & **CON (n = 16)** & **PD1 (n = 6)** & **PD2 (n = 9)** \\ \hline Age (years) & 45.66 \(\pm\) 9.14 & 66 \(\pm\) 14.3 & 67.33 \(\pm\) 8.7 \\ \hline Height (m) & 1.83 \(\pm\) 0.085 & 1.88 \(\pm\) 0.12 & 1.85 \(\pm\) 0.17 \\ \hline Weight (kg) & 68.935 \(\pm\) 10.75 & 81.33 \(\pm\) 14.3 & 70.88 \(\pm\) 17.66 \\ \hline Gender (male: female) & 2:14 & 1:5 & 4:5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Anthropometric details of all subjects.  
The first step was the visualization of the data, which was done by plotting the histogram of the four parameters for each side and for each subject. The next step was computing the mean and variance of the four parameters for each subject and for both the right and left sides, i.e., mean stride interval (\(\mu_{st}\)), mean swing interval (\(\mu_{sw}\)), mean stance interval (\(\mu_{sta}\)), mean double-support interval (\(\mu_{ds}\)), variance stride interval (\(\sigma_{st}\)), variance swing interval (\(\sigma_{sw}\)), variance stance interval (\(\sigma_{sta}\)), and variance double-support interval (\(\sigma_{ds}\)), with \(\sigma_{ds}\) being common for the left and right sides.  
The stride interval was analyzed to determine the ratio of the stance and swing intervals. This was performed to test whether there was a relative increase in the stance phase among PD patients, and this increased with the severity of the disease.