esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Test Results  
### Compressive Strength  
The compressive strengths of each HFLMC material are shown in Figure 3. Mixtures containing fibers exhibited slightly lower compressive strengths after 4 h. This is due to the surface hydrophilicity of the fibers, which tend to absorb water and suppress evaporation during the early stages of curing.  
All of the mixtures created in this study satisfied the requirement of 21 MPa compressive strength after 4 h. After 7 days, the fiber-reinforced and plain mixtures had similar compressive strengths. After 28 days, all mixtures met the target compressive strength of 35 MPa. After 28 days, the fiber-reinforced materials showed greater splitting tensile strengths than the plain mixtures, with enhancements of 2.62%, 3.31%, 3.39%, 3.83%, 3.91%, and 5.01%, respectively, for mixtures containing PVA, jute, nylon, jute+ nylon, PVA+jute, and PVA+nylon fibers. The compressive strengths of fiber-reinforced mixtures were 2.62-5.01% higher than those of plain mixtures. Furthermore, hybrid fiber mixtures were more effective than single fiber mixtures in terms of strength enhancement. This can be attributed to the greater diversity in fiber length and diameter of the hybrid mixtures, which was more effective for filling voids within the concrete, thereby suppressing crack  
Figure 2: Test specimen manufacturing process. (**a**) Filling of coarse aggregate in the mold. (**b**) Fiber-reinforced rapid-set latex-modified materials pouring. (**c**) Pressurized vibration compaction, (**d**) Completion of manufacture of test specimen.  
formation and growth [27; 28]. Notably, the PVA+nylon hybrid fiber mixture developed the highest compressive strength. The variety in fiber dimensions suppressed both macro and micro-crack formation. However, batch-to-batch variation in concrete quality is a concern. From a quality control perspective, synthetic nylon and PVA fibers are preferred because their properties can be readily controlled by the manufacturer. In contrast, the properties of jute fibers, as a natural material, are difficult to control.
# Deep Learning-Based Detection Technology for SQL Injection Research and Implementation
## 4 Deep Learning Model Based on Improved TextCNN and Bi-LSTM  
### Description of the Model of Improved TextCNN and Bi-LSTM (ITCBL)  
Traditional machine learning methods usually require manual feature engineering, and many features are extracted by researchers, such as \"percentage of uppercase characters,\" \"the existence of typical SQL injection keywords,\" etc. However, these features themselves make it difficult to effectively detect different types and variants of SQL injection statements. To this end, we propose a SQLIA method based on deep learning. In the network structure, the improved TextCNN algorithm is combined with the Bi-LSTM and ATTENTION mechanisms, supplemented by the word vector representation extracted by BERT, so that the model can learn finer features and perform better in detecting various variants of SQL injection attacks.  
The deep model network structure proposed in this paper for detecting SQLIA is shown in Figure 2. First, the SQL injection samples are input into the TF-IDF and Word2 Vec fusion algorithms to obtain their vectorized representations. Then, the pre-trained word vectors are input to the improved TextCNN network to learn local feature representation, extract local complex advanced features, and form n features for each sample after the pooling operation. These features serve as input for the Bi-LSTM network to learn sequence timing information and extract sequential features. The ATTENTION mechanism is then employed to strengthen the weight of important features, weaken the influence of noise, and obtain the feature vector s. Additionally, the BERT algorithm is utilized for migration  
Figure 1: The overall architecture of the SQL injection attack detection system.  
learning to obtain the Transformer's bidirectional encoder representation of each word in the sample. These representations are flattened to obtain the feature vector bert_s, which is subsequently combined with s. Finally, a fully connected layer is introduced, followed by the application of SoftMax for probability normalization, culminating in the attainment of the ultimate category classification.  
Figure 2: Deep learning model based on improved TextCNN and LSTM algorithms.
## 5 Discussion
### Future Directions  
A promising avenue for future research is to explore more sophisticated models and to utilize advanced transfer learning techniques. These efforts aim to improve the accuracy and applicability of readability assessment in the context of medical research abstracts.  
Another compelling area of future study involves investigating the direct influence of readability assessment on the decision making processes and patient care outcomes of medical professionals. Understanding the tangible benefits of improved readability in medical literature can further underscore the importance of our research.  
One interesting possibility for future research is the incorporation of text complexity factors into the IR ranking algorithm. Although our study has demonstrated their potential advantages, additional research is required to examine the feasibility of this integration. A future study could focus on implementing text difficulty aspects into IR ranking algorithms to improve the retrieval of relevant medical information for practitioners. The study should address algorithmic refinement, adaptability, and user experience assessment for practical implementation.
# Two-Stage Single-Channel Speech Enhancement with Multi-Frame Filtering
## 3 Method
### Multi-Frame MVDR  
The MFMVDR was first proposed in [12]. It considers the correlation between consecutive time-frames to obtain better enhancement performance.  
The signal model for MFMVDR is as follows:  
\[y(t)=x(t)+n(t), \tag{5}\]  
where \(y(t)\), \(x(t)\), and \(n(t)\) are the noisy speech, clean speech, and the additive noise, respectively. Using STFT, (5) can be rewritten as:  
\[Y(t,f)=X(t,f)+N(t,f). \tag{6}\]  
In order to model the interframe correlation, a \(L\)-dimensional noisy speech vector \(\mathbf{y}(t,f)\) is defined as:  
\[\mathbf{y}(t,f)=\big{[}\ Y(t,f)\ \ \cdots\ \ \ Y(t-L+1,f)\ \big{]}^{T}, \tag{7}\]  
where \(L\) is the number of frames used to calculate the MFMVDR filter. \(\mathbf{x}(t,f)\) and \(\mathbf{n}(t,f)\) can be defined similarly.  
The formula of MFMVDR is as follows:  
\[\mathbf{h}_{\text{MFMVDR}}(t,f)=\frac{\mathbf{\Phi_{n}^{-1}}(t,f)\mathbf{\Phi _{y}}(t,f)-\mathbf{I}_{L\times L}}{\text{tr}\big{[}\mathbf{\Phi_{n}^{-1}}(t,f )\mathbf{\Phi_{y}}(t,f)\big{]}-L}\mathbf{i}_{1}, \tag{8}\]  
where \(\mathbf{I}_{L\times L}\) is an \(L\times L\) identity matrix, \(\mathbf{i}_{1}\) is the first column of \(\mathbf{I}_{L\times L}\), \(\text{tr}[\cdot]\) is the trace operation, and  
\[\mathbf{\Phi_{y}}=E[\mathbf{y}(t,f)\mathbf{y}^{H}(t,f)],\] (9  
It should be noted that calculating \(\mathbf{\Phi_{n}}\) using Equation (10) yields a matrix of rank 1, i.e., a singular matrix, but in Equation (8) the inverse matrix of \(\mathbf{\Phi_{n}}\) is required. To address this issue, we use the following methodology in this study. First, we calculate \(\mathbf{\Phi_{y}}\) and \(\mathbf{\Phi_{n}}\) using the following equations, instead of Equations (9) and (10):  
\[\mathbf{\Phi_{y}}(t,f) =\lambda_{y}\mathbf{\Phi_{y}}(t,f-1)+\big{(}1-\lambda_{y}\big{)} \mathbf{y}(t,f)\mathbf{y}^{H}(t,f), \tag{11}\] \[\mathbf{\Phi_{v}}(t,f) =\lambda_{n}\mathbf{\Phi_{n}}(t,f-1)+(1-\lambda_{n})\mathbf{n}(t, f)\mathbf{n}^{H}(t,f), \tag{12}\]  
where \(0<\lambda_{y}<1\) and \(0<\lambda_{n}<1\) are the forgetting factors. Second, we apply diagonal loading [23] to \(\mathbf{\Phi_{y}}\) and \(\mathbf{\Phi_{n}}\) to improve the robustness of training stage.
# Small Infrared Target Detection via a Mexican-Hat Distribution
## 2 Mexican-Hat Distribution of the Adjacent Region around an IR Target  
Let \(I(x,y)\) be an image. Then its DoG-filtered image, \(I_{DoG}\), is formed by (1) [23]:  
\[I_{DoG}(x,y)=G(x,y,\sigma_{1})*I(x,y)-G(x,y,\sigma_{2})*I(x,y) \tag{1}\]  
where \(\sigma_{1}\) and \(\sigma_{2}\) are the standard deviations of the two Gaussian kernels, \"*\" is the convolution operation given by (2).  
\[G(x,y,\sigma)=\frac{1}{2\pi\sigma^{2}}e^{-\frac{x^{2}+y^{2}}{2\sigma^{2}}} \tag{2}\]  
After careful observation, we find the pixel intensity of the adjacent region around an IR target shows a regular bright-dark-bright pattern along the radial direction. Specifically, Figure 1 illustrates such bright-dark-bright pattern. The center pixel of a target patch is the brightest, and then the intensity decreases gradually along the radial direction until reaching the lowest value. After that, the intensity increases again. As shown in Figure 1c, the profile of such bright-dark-bright pattern is like a Mexican hat, and thus we call it Mexican-hat distribution in this paper.  
Based on the above observations, we propose to detect small targets in IR images via the Mexican-hat distribution. As shown in Figure 2, we first divide the adjacent region of a small target into three sub-regions along the radial direction that are respectively termed as \(R_{0}\), \(R_{1}\), and \(R_{2}\). For a small target positioned at \((r,c)\), \(R_{0}\) is a \((2L+1)\)\(\times\)\((2L+1)\) square image region centered at \((r,c)\), and \(R_{1}\) is one-pixel square border just outside \(R_{0}\), and \(R_{2}\) is one-pixel square border without four corner pixels outside \(R_{1}\). According to the Mexican-hat distribution, we know that the intensity relationship between the three sub-regions of a target roughly meets the bright-dark-bright pattern, and thus have the following rule:  
_Rule 1_: If the mean intensity of \(R_{0}\) is both larger than \(R_{1}\) and \(R_{2}\), and the mean intensity of \(R_{1}\) is smaller than \(R_{2}\), then the center of \(R_{0}\) can be a candidate target center.
## 4 Application of the CrSA for Parameter Extraction of the Solar Module
### Procedure for Estimating PV Module Parameters  
The steps below outline how to use the proposed algorithm to compute the parameters of a solar PV module:  
1. Set the initial values for the variables of the solar PV module and the algorithm according to the information provided in Tables 1-3.
2. Validate the upper limit of the iteration count before proceeding to the subsequent phases. If the condition is not met, proceed to Step 7.
3. Consider the SDiM and DDiM for the solar PV module under discussion using Equations (7)-(21).
4. Utilize Equations (3)-(6) to execute the proposed CrSA for the study work being examined.
5. Minimize the overall error, as defined by Equations (25) and (29), for Steps 3 and 4 in each iteration.
6. Increment the iteration count and go to Step 2.
7. Conduct a comprehensive analysis of different solar PV modules and identify the optimal values for the corresponding circuit parameters.  
primary goal of the study is to identify the five and seven parameters of the SDiM and DDiM of the PV panel provided. After using the suggested procedure across 500 iterations for 20 cycles of testing, the findings are detailed below.
# Advancing Multi-UAV Inspection Dispatch Based on Bilevel Optimization and GA-NSGA-II
## 4 Experimental Verification
### Parameter Settings
#### 4.3.1 The Result of Unit Task Segmentation  
Following the algorithm's procedure, we performed unit task segmentation in the power grid scenario, resulting in 25 task fragments, as illustrated in Figure 15. In the figure, each unit task (ftask) is denoted by a unique color and symbol and is numbered from 0 to 24.
# Radiometric Method for Determining Canopy Stomatal Conductance in Controlled Environments
## 2 Materials and Methods
### Temperature Measurements
For example, if \(T_{\mathrm{glass}}\) = 30 \({}^{\circ}\)C, \(T_{\mathrm{wall}}\) = \(T_{\mathrm{air}}\) = 23 \({}^{\circ}\)C, \(T_{\mathrm{canopy,IR}}\) = 24 \({}^{\circ}\)C (e.g., 20% wall and 80% glass temperature), and \(\varepsilon_{\mathrm{c}}\) = 0.97, then the difference between \(T_{\mathrm{R}}\) and \(T_{\mathrm{canopy,IR}}\) is only 0.14 \({}^{\circ}\)C. If the water filter under the lamps is removed, \(T_{\mathrm{R}}\) increases by \(\sim\)0.5 \({}^{\circ}\)C, \(T_{\mathrm{glass}}\) = 45 \({}^{\circ}\)C, and the difference between \(T_{\mathrm{R}}\) and \(T_{\mathrm{canopy,IR}}\) rises to \(\sim\)0.5 \({}^{\circ}\)C (Equation (1)). These conditions are unique to controlled-environment conditions because such high \(T_{\mathrm{sky}}\) temperatures are never observed in the field.
## 3 Results and Discussion
### Deposition Characteristics of Refrigeration in a Confined Space After the Leak Ends  
Figure 8 illustrates the concentration distribution of different refrigerants on the yz (x = 2 m) plane 30 min after free diffusion following a leakage event. It can be seen from Figure 8 that after the leak stops, free diffusion of the refrigerants results in significant variations in their spatial distribution, with distinct deposition characteristics observed at the bottom of the space. Refrigerants with high densities, such as R1234 yf, exhibit higher concentrations near the floor (1.69%) and lower concentrations at the top of the room (0.7%). For refrigerants with lower densities, this trend reverses: R22 (1.63% bottom, 0.77% top), R32 (1.44% bottom, 0.85% top), and R290 (1.32% bottom, 0.94% top). This behavior is attributed to the initial downward diffusion of denser-than-air refrigerants, leading to accumulation near the floor. Subsequently, the gravity, viscosity, and concentration gradients drive a downward flow towards the upper region. Due to their slower diffusion rates, the denser refrigerants exhibit a more pronounced deposition near the floor. Conversely, R717, being less dense than air, accumulates at the top of the room (5.24%) with significantly lower concentrations near the floor (0.16%). Furthermore, a comparison of R290 and R744, which have similar densities, highlights the influence of viscosity. R744, with its higher viscosity, exhibits a greater concentration at the bottom of the room (1.39%) compared to R290. This suggests that a higher viscosity can impede refrigerant diffusion, leading  
Figure 7: The refrigerant concentration at (**a**) 0.5 m and (**b**) 0.7 m above the leakage space varies with time.  
to increased deposition. These results underscore the significant impact of both density and viscosity on the distribution and deposition characteristics of different refrigerants following a leakage.
# Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives
## 1 Introduction
[PERSON] and [PERSON] [29] focused on the principles of thermal remote sensing, the relationship between surface and air UHIs, and the potential use of thermal remote sensing data in urban climate models. [PERSON] et al. [7] presented a systematic review of the generation as wellas determination and mitigation of UHI based mainly on air UHI studies. [PERSON] [28] reviewed the methods and applications of thermal remote sensing, but concentrated on land surface temperature (LST) retrieval, LST-vegetation relationships, and some typical applications of thermal infrared remote sensing data in SUHI studies. [PERSON] and [PERSON] [37] examined the literature trends of UHI research from 1991 to 2015 using bibliometric methods, regardless of UHI types. The most recent work by [PERSON] et al. [30] focused solely on SUHI, but reviewed only the spatial and temporal factors affecting the SUHI based on the publications after 2007. We extend this review literature by conducting a systematic review of the features, current status, and prospects of SUHI research. This study aims to provide a comprehensive and broad review on the SUHI studies from their origin in 1972 to the present. The specific objectives of this review are to: (1) explore the characteristics of research output on SUHI, (2) review the space borne sensors and methods employed to measure the SUHI, and (3) outline the key research findings, challenges, and potential future directions of the SUHI research. The current review concentrates on the satellite-based SUHI studies, even though airborne sensors were also used to study SUHI in several cities. Our review will provide important insights not only for scientists on understanding the SUHI phenomenon, but also for practitioners to formulate the associated mitigation and adaptation strategies.
# Simheuristics Approaches for Efficient Decision-Making Support in Materials Trading Networks
## 6 Addressing the Performance Challenge: Exploiting Domain-Specific Information
### Defining Relations between Actions  
The last DSI that we propose is the correlation between two actions and their impact on the performance of the network. In this context, the joint impact of a sequence of two actions on the performance of a network is compared to their expected impact if they are applied individually. In Figure 7, the expected impact of applying actions \(a_{1}\) and \(a_{2}\) is an increase of 0.19% in the service level, given by the summation of the impact of the single actions \(a_{1}\) (0.12%) and \(a_{2}\) (0.07%). Actually, the sequence \(a_{1}\) followed by \(a_{2}\) increases the service level by only 0.15%. Thus, applying these two actions as \([a_{1},a_{2}]\) has a negative influence (\"\(-\)\"). The sequence \([a_{2},a_{1}]\) causes an increase in the service level by 0.28%. Thus, it has a positive impact (\"\(+\)\") compared to the expected impact of the single actions. Another example of the positive impact is the sequence \([a_{1},a_{3}]\). If the expected impact of a sequence does not differ from the actual impact, the relation is a weak relation (\"\(\sim\)\"), for example, sequence \([a_{2},a_{3}]\) in Figure 7.  
Figure 6: Impact of actions on the service level.  
The correlation between actions could be tabulated in a correlation matrix. Rows represent the first applied action in the sequence, and columns represent the second applied action. The cells show the relation between the impact of a sequence of actions and their expected impact on the network as \"+\", \"\(\sim\)\", and \"\(-\)\" for positive, weak, and negative relations, respectively. The relations extracted from Figure 7 are tabulated in Table 2. The rest of the correlation matrix shows relations between other actions in the search space.  
The cell between \(a_{1}\) and \(a_{1}\) is a duplication of an action. An action can be duplicated by applying it twice. If an action increases the stock level of an SKU at a site by 10 units, its duplication increases the stock level by 20 units. Accordingly, the performance of the network is affected and might vary from the expected impact.  
Similar to the previous section, applying actions affects the performance of the network, and their impact might be used to guide the selection of actions to construct promising action plans. The selection probability of actions is increased if the actions are in a positive relation with already selected actions. For example, if action \(a_{2}\) is selected, then the recommended action is \(a_{1}\) to increase the service level (Table 2). Selecting \(a_{2}\) does not increase the service level as expected, and selecting \(a_{3}\) does not influence the expected increase in the value. Thus, the selection probability of \(a_{1}\) becomes higher than \(a_{3}\).
# Performance and Control Strategy of Real-Time Simulation of a Three-Phase Solid-State Transformer
## 1 Introduction
Despite the fact these components are not necessary for control design, this simplification causes the loss of realism in the use of RTS. On the other hand techniques like hardware emulation like shown in [16; 17] can generate a highly accurate machine-state based model for a VSC converter, however the complexity of the implementation increases. Real-time implementation for a full-circuit model of the SST is a good method to test in advance control schemes, soft startup strategies, transient on-state switching currents, resonance points, among others; which is of particular interest in the design of these type of higher frequency converters, due to the use of reduced coupling inductances which commonly leads to the appearance of inrush currents. DAM models neglect the switching dynamics and preserve the fundamental signal component, considering the converter as the union of continuous current and voltage controlled sources. Nevertheless this is based on the assumption that the commutation effect has low impact on the fundamental component dynamics. In the case of higher power ratings, where low commutation switching frequency is used, these type of models reduces their accuracy. Besides, there are certain types of transient effects, resonance and other phenomena that can not be recreated through fundamental DAM models. The aim of the paper is to show the implementation of a three stage solid-state transformer in an Opal-RT platform, using an electronic hardware solver (eHS), in order to achieve very small simulation time-steps (\(\approx\)200 ns) for the electric circuit, adapting the RTE-Library of the Opal software in conjunction with SimPowerSystems library in Matlab for the topology. The paper is organized as follows: in Sections 2 and 3 the SST topology, control strategy and the simulation parameters used for the simulation are shown; Section 4 introduces the RTS platform, the simulation scheme and the implementation of the circuit; showing the modulation schemes and its overall operation. In Section 5 the RTS results for the topology are depicted, finally in Section 6 the concluding remarks are presented.
# Recent Advances in Tunable External Cavity Diode Lasers
## 3 Research Progress of ECDL
### External Cavity Quantum Well Laser
#### 3.1.2 F-P Cavity Configuration  
F-P cavity is another common external cavity structure. The FP external cavity structure has better mode stability, but it is limited by the tuning method, and the tunable range is not very large [40]. [PERSON] et al. proposed an innovative external cavity configuration by inserting an all-dielectric thin film Fabry-Perot filter (AFPF) into the external cavity [41]. The AFPF had good model selection, like a solid Fabry-Perot etalon. The planar mirror acted as an external cavity mirror. When the AFPF was rotated around a pivot point, a linear interrelationship was attained between its peak transmittance wavelength and the cosine value of the incident angle of the laser beam. The ECDL achieved a mode-hop-free tuning range over 40 nm range around 1550 nm. The ECDL can be applied in various fields, including environmental monitoring, atomics, and so on.  
A well-designed external cavity structure is highly effective in narrowing the line width of a laser. Another effective way is using the DFB laser as a gain chip to obtain the narrow linewidth output. DFB diode laser and F-P cavity were combined to achieve an ultra-narrow-linewidth [42]. This design offers two significant advantages: first, FP cavity and DFB laser can achieve double mode selection. Second, the F-P cavity simplified the coupling of light into the cavity and made it insensitive to the misalignment due to mechanical vibrations or temperature changes. Consequently, the laser achieved an ultra-narrow line width and excellent mode stability. The measured white frequency noise levels reached 5 Hz\({}^{2}\)/Hz at a Fourier frequency as low as 20 kHz, which was over 5 orders of magnitude lower than that of the same solitary diode laser operating without resonant optical feedback.  
The tuning method also has great influence on the mode stability of output laser. [PERSON] et al. demonstrated a simple and stable laser frequency stabilization method by utilizing piezoelectric ceramic actuators to control a Fabry-Perot cavity [43]. To mitigate piezoelectric drift, a duplex symmetrical structure was adopted for the piezoelectric actuators, utilizing their inherent tendency to drift in opposite direction. Therefore, the external cavity diode laser can achieve stable output. A tuning range of 14.7 GHz was obtained. This laser frequency stabilization system was significantly simplified.