esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Fluidity
#### 3.1.2 Effect of Composite Retarder on Fluidity of HBCSA Paste  
When the retarder \(\beta\)-CD and CA were mixed together (Figure 5a), the initial fluidity decreased to a certain extent. When the fixed content of \(\beta\)-CD was 0.5% and 0.7%, the mobility decreased with the increase in CA content. It can be seen that the fluidity loss over time between 0 and 90 min after the two retarders are remixed is very small. The combined dosage is 0.5% \(\beta\)-CD, 0.3% CA, and its overall fluidity retention ability is good. When the content of CA is 0.3%, and the content of \(\beta\)-CD is 0.5% and 0.7%, the initial fluidity is larger than other content, but the loss of fluidity over time from 0 to 60 min is larger, and there is no fluidity after 90 min. The combined dosage is 0.5% \(\beta\)- CD and 0.3% CA, no matter from the initial fluidity or the change law of the fluidity over time, are better than other dosage. Overall, the fluidity (Figure 5b) also showed a downward trend, and when the \(\beta\)-CD content was 0.3% and the B content was 0.5%, the initial fluidity was the highest; and the greater the B content, the more the fluidity decreased; but the decrease in fluidity over time is not obvious within 60 min. When the content of \(\beta\)-CD and B are both 0.5%, the fluidity over time is not much different from the initial fluidity, but with the increase in the content of B, the loss of fluidity over time increases, even disappearing after 30 min.  
Figure 4: Effects of PCE compounding \(\beta\)-CD, CA, and B on the fluidity of HB-CSA: (**a**) \(\beta\)-CD, (**b**) CA, and (**c**) B.  
Figure 5: Effects of PCE compound \(\beta\)-CD+CA and \(\beta\)-CD+B on the fluidity of HB-CSA: (**a**) \(\beta\)-CD+CA, (**b**) \(\beta\)-CD+B and (**c**) comparison of different combination dosage.  
According to the results of the above-mentioned compounding of \(\beta\)-CD with CA and B, respectively, the combined dosage with a relatively good fluidity retention ability was selected from the two cases, as shown in Figure 5c. It can be found in the figure that the fluidity of the cement slurry within 60 min is basically higher than the initial fluidity, and even some of the combined dosages have higher fluidity than the initial fluidity at 90 min. This shows that, although the initial fluidity of the two retarders after compounding is lower than that of the blank group (single-doped PCE), the fluidity retention ability of the two compound components is relatively good, and with the inhibition of cement hydration, PCE can be continuously adsorbed on the surface of cement particles and improve its dispersing ability.
## 4 Results and Discussion  
### Ka-Band RWG-to-Fish-Tail-Shaped MSL Design Model  
A 3D Model of the final designed and optimized Ka-band microstrip line-to-WR-28 transition, including the outer embodiment, is displayed in Figure 9a. Photographs of the  
Figure 8: Effect of fish-tail width on (**a**) insertion loss, S\({}_{12}\) (dB) and (**b**) return loss, S\({}_{11}\) (dB).  
WR-28 with a back-short [17], along with front and rear views of the fabricated nature-inspired fish-tail-shaped microstrip lines are shown in Figure 9b. A 50 \(\Omega\) microstrip line was fabricated on Roger's RT Duroid 5880 soft substrate using photolithography and chemical etching processes. A 50 \(\Omega\) connector was soldered at the strip end. The other end of the MSL was very close to the shape of a fishtail, as displayed by the arrow mark.
# User-Cell Association for Security and Energy Efficiency in Ultra-Dense Heterogeneous Networks
## 5 Numerical Results
### Without Macrocell  
We start considering the case of having users served only by the small cell layer (i.e., the macrocell is not considered). First of all we show the previous metrics as a function of the mean number of cells when \(\bar{\mathcal{N}}_{U}=30\) in Figure 3.  
As we can see, the proposed association policy allows to significantly reduce the power consumption while maintaining a good level of security (both in terms of secure area and secure throughput). In particular, when only 1 or 2 cells are active, the microcell is active (i.e., one microcell is always present in the scenario), for all methods, thus the consumed power is comparable. When the number of cells increases the Max-SecureEE optimization procedure tends to turn off the high power cell and to activate cells with  
Figure 3: Association performance vs. number of small cells \(\bar{\mathcal{N}}_{C}\) when \(\bar{\mathcal{N}}_{U}=30\).  
lower power. The power slightly increases with the number of cells (for \(\mathcal{N}_{C}\geq 3\)) to not reduce the throughput, thus achieving the highest EE. The Max-SINR association policy requires higher transmission powers thus achieving the highest secure throughput. The Max-Secure Area method has a power consumption that is in the middle, but it has the lowest secure throughput and EE. For all methods, the power consumption obviously increases with the number of cells in the area (i.e., also the cells in idle mode contribute to the power consumption). In case all the cells are active, obviously the power consumption is the highest and we can observe that the high interference generated by cells increases the security in terms of secure area, but the throughput is reduced due to the SINR reduction. Finally, we can observe that the proposed heuristic is a very good approximation of the optimal Max-secure EE association.  
In Figure 4 the performance metrics described before are provided when \(\mathcal{N}_{U}\) varies and \(\bar{\mathcal{N}}_{C}=10\). As it can be observed from Figure 3(b)-d, when the number of users increases the power consumption as well as the averaged secure throughput per user and averaged secure area per user do not change significantly. The secure area slightly increases for the proposed utility (Figure 3(b)), while the power consumption slightly increases for the max-SINR method (Figure 3(d)). The secure EE method improves the performance when the number of users increases, outperforming all the other methods (Figure 3(a)). The heuristic algorithm is always able to produce solutions whose quality is very close to that of the solutions returned by the exhaustive search, leading us to propose it as a lower complex method with similar performance. The secure EE is summed over all the users, hence it increases with \(\bar{\mathcal{N}}_{U}\).
# A Multilevel Switched Capacitor Inverter with Reduced Components and Self-Balance
## 3 Power Losses Analysis
### Conduction Losses  
The conduction losses can be produced by the impedance of semiconductor devices and capacitors, which include the conduction resistance \(R_{\rm s}\) of switches, the internal resistance \(R_{\rm d}\) of diodes, and the equivalent series resistance \(ESR_{\rm C}\) of capacitors. Figure 6 provides the predigested circuits for all operating states.  
Table 3 shows the equivalent parameters of each operating state. Herein, \(V_{\rm o,j}\) and \(R_{\rm eq,j}\) are the output voltage and equivalent parameters, respectively. For the interval of \([0,t_{1}]\), the energy loss can be expressed as  
\[E_{0\sim V_{\rm dc}}=\int_{0}^{t_{1}}\left[I_{\rm o}\sin(2\pi f_{\rm o}t) \right]^{2}\times[R_{\rm eq,1}\frac{A_{\rm ref}\sin(2\pi f_{\rm o}t)}{A_{\rm c }}+R_{\rm eq,0}(1-\frac{A_{\rm ref}\sin(2\pi f_{\rm o}t)}{A_{\rm c}})]dt. \tag{13}\]  
For other intervals, the energy losses can be calculated in the same way.  
\[E_{V_{\rm dc}\sim 2V_{\rm dc}}=\int_{t_{1}}^{t_{2}}\left[I_{\rm o}\sin(2\pi f_{ \rm o}t)\right]^{2}\times[R_{\rm eq,2}\frac{A_{\rm ref}\sin(2\pi f_{\rm o}t)- A_{\rm c}}{A_{\rm c}}+R_{\rm eq,1}(1-\frac{A_{\rm ref}\sin(2\pi f_{\rm o}t)- A_{\rm c}}{A_{\rm c}})]dt, \tag{14}\]  
\[E_{2V_{\rm dc}\sim 3V_{\rm dc}}=\int_{t_{2}}^{t_{3}}\left[I_{\rm o}\sin(2\pi f_{\rm o }t)\right]^{2}\times[R_{\rm eq,3}\frac{A_{\rm ref}\sin(2\pi f_{\rm o}t)-2A_{ \rm c}}{A_{\rm c}}+R_{\rm eq,2}(1-\frac{A_{\rm ref}\sin(2\pi f_{\rm o}t)-2A_{ \rm c}}{A_{\rm c}})]dt, \tag{15}\]\[E_{3V_{\rm dc}\sim 4V_{\rm dc}} =\int_{t_{3}}^{t_{4}}\left[I_{\rm o}\sin(2\pi f_{\rm o}t)\right]^{2} \times[R_{\rm eq4}\frac{A_{\rm ref}\sin(2\pi f_{\rm o}t)-3A_{\rm c}}{A_{\rm c}}+R _{\rm eq3}(1-\frac{A_{\rm ref}\sin(2\pi f_{\rm o}t)-3A_{\rm c}}{A_{\rm c}})]dt, \tag{17}\] \[E_{4V_{\rm dc}\sim 5V_{\rm dc}} =\int_{t_{4}}^{t_{5}}\left[I_{\rm o}\sin(2\pi f_{\rm o}t)\right]^{2} \times[R_{\rm eq5}\frac{A_{\rm ref}\sin(2\pi f_{\rm o}t)-4A_{\rm c}}{A_{\rm c}} +R_{\rm eq4}(1-\frac{A_{\rm ref}\sin(2\pi f_{\rm o}t)-4A_{\rm c}}{A_{\rm c}})]dt,\] (18) \[E_{5V_{\rm dc}\sim 5V_{\rm dc}} =\int_{t_{5}}^{t_{6}}\left[I_{\rm o}\sin(2\pi f_{\rm o}t)\right]^{2} \times[R_{\rm eq6}\frac{A_{\rm ref}\sin(2\pi f_{\rm o}t)-5A_{\rm c}}{A_{\rm c}} +R_{\rm eq5}(1-\frac{A_{\rm ref}\sin(2\pi f_{\rm o}t)-5A_{\rm c}}{A_{\rm c}} )]dt. \tag{19}\]  
Based of the above, the \(P_{\rm con}\) of the proposed 13-level topology can be obtained by  
\[P_{\rm con}=(4E_{0\sim V_{\rm dc}}+4E_{V_{\rm dc}\sim 2V_{\rm dc}}+4E_{2V_{ \rm dc}\sim 3V_{\rm dc}}+4E_{3V_{\rm dc}\sim 4V_{\rm dc}}+4E_{4V_{\rm dc}\sim 5V_{ \rm dc}}+2E_{5V_{\rm dc}\sim 6V_{\rm dc}})\times f_{\rm ref}. \tag{20}\]
# Simultaneous Analysis of Heavy Metal Concentration in Soil Samples
## 2 Materials and Methods
### Fourier Transform Infrared Spectroscopy  
FTIR analyses of the 100 soil samples were carried out in the laboratory of Central Mining Institute (Katowice, Poland) using a Thermo Scientific Nicolei iS50 spectrometer. Spectral analyses were performed using samples prepared according to the procedure described earlier: dried, grounded and sieved. Two spectral techniques based on attenuated total reflectance and diffuse reflectance were applied to samples, as these techniques enable direct measurements of the surface of solid samples without a need for pellet preparation; thus, potential application of these techniques in rapid estimation is considered. The first technique, the attenuated total reflectance (ATR) was used in the far infrared (FIR) range. The Nicolei iS50 FT-IR spectrometer has built-in diamond iS50 ATR sampling station. A sample was placed on a crystal diamond and then pressed onto the top surface of the crystal. In this technique, the attenuated reflectance is measured. During the measurement, the infrared radiation propagates through the crystal with such an angle that enables one to obtain internal reflectance. Infrared radiation is reflected but penetrates into the surface of a sample pressed onto the crystal. The second technique, the diffuse reflectance infrared Fourier Transform (DRIFT) technique, was used in the mid infrared (MIR) range. The absorption and scattering of light by the sample in all directions (diffuse reflectance) was measured in the DRIFT technique. Samples were placed in a stainless steel cup, located in a diffuse reflectance accessory and scanned. The spectrometer was equipped with a DLaTGS (deuterated L-alanine doped triglycene sulphate) detector and a KBr beam splitter for mid IR and solid substrate for far IR. In the near IR region, a Thermo Scientific NIR Integrating Sphere equipped with an InGaAs detector and CaF\({}_{2}\) beamsplitter was used. The sample was placed on the sapphire window. In the module, near-infrared energy passes through the window and is diffusely reflected by the sample. Then the reflected light is efficiently collected by the sphere and directed onto the dedicated InGaAs detector.  
All spectra were collected after a nitrogen purge. The spectrum for air was used as a background in the ATR technique, and as a pure potassium bromide (KBr) in the DRIFT and NIR techniques. The number of scans was set at 64 with a resolution of 4 cm\({}^{-1}\). Spectra were measured over a far  
Figure 1: The location of the copper smelter “Glogów” (Source: Google Map).  
infrared range of 1800-100 cm\({}^{-1}\), a mid-infrared range of 4000-400 cm\({}^{-1}\), and a near-infrared range of 12,500-4000 cm\({}^{-1}\). Data obtained from the laboratory analyses together with the collected spectra were used to develop and validate the partial least squares regression models. The spectra in the form of csv files were the input to the model created in MATLAB. Spectra were not modified before the development of PLS models.
# Scanning Electron Microscopy Techniques in the Analysis of Gunshot Residues: A Literature Review
## 3 Results
### SEM Applications Beyond Forensic Science  
While the primary focus of this study is the application of scanning electron microscopy (SEM) in forensic science, particularly in gunshot residue (GSR) analysis, SEM is widely used across various scientific disciplines [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13].  
In materials science, SEM is an essential tool for characterizing the microstructure, composition, and surface properties of a wide range of materials, including metals, ceramics, polymers, and composites. High-resolution SEM imaging allows researchers to analyze the fracture surfaces, phase distribution, and grain boundaries in metallic alloys, providing crucial insights into material failure mechanisms. Additionally, SEM-EDS is extensively used to study corrosion processes, enabling the identification of elemental composition changes in degraded materials [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13]. The ability to perform in situ environmental SEM (ESEM) also allows researchers to observe real-time structural changes in materials under different environmental conditions, such as high humidity or elevated temperatures.  
In nanotechnology, SEM plays a critical role in imaging and characterizing nanomaterials, nanoparticles, and nanostructures [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15]. Due to its high-magnification capabilities, SEM is used to examine carbon nanotubes, graphene, quantum dots, and nanocomposites,providing detailed morphological and compositional information. The use of field-emission scanning electron microscopy (FE-SEM) enhances resolution even further, enabling the visualization of structures at the sub-nanometer scale. Additionally, SEM is widely used in semiconductor research, where it facilitates failure analysis, contamination detection, and quality control of microelectronic components.  
Another significant application of SEM is in biological and medical research, where it is used to study cellular structures, bacteria, viruses, and biofilms. SEM provides detailed three-dimensional images of biological specimens, revealing intricate surface morphologies that are not visible using traditional optical microscopy. In medical device development, SEM is instrumental in evaluating the surface properties of biomaterials, implants, and tissue-engineered scaffolds, ensuring their compatibility with biological systems.
# Assessing the Long-Term Evolution of Abandoned Salinized Farmland via Temporal Remote Sensing Data
## 3 Methods
### Analyzing Temporal Remotely Sensed Imageries  
Detecting and excluding vegetative areas was the first step in detecting abandoned salinized farmland. The traditional mono-temporal VI threshold-based method is sensitive to the selected threshold, and it may be influenced by the timing of the detection (e.g., flooded irrigation and the variability of the planting schedule may degrade its performance). Therefore, time-series VI was used to analyze the dynamics of the land cover from different years to pursue higher accuracy. Potential land types were classified by the _K_-means clustering method and the land types of interest were detected and merged via the shape-model-fitting method. The irrigation strategy is determined by cropping structure, which may affect the evolution of abandoned salinized farmland. As a result, the regional cropping structure was additionally detected from clustering results via the shape-model-fitting method [33].
# A Look at the Importance of Chirality in Drug Activity: Some Significative Examples
## 5 Local Anesthetics  
The main local anesthetics in use are bupivacaine (**28**), lidocaine, benzocaine, ropiivacaine (**29**), and chloroprocaine and are used primarily as injectable anesthetics. Differences in (\(R\))- and (\(S\))-enantiomers of local anesthetics were found in terms of pharmacodynamic and pharmacokinetic activity, as well as toxicity [91]. For the local anesthetic bupivacaine, the (\(S\))-enantiomer is significantly less cardiotoxic than the (\(R\))-enantiomer and the racemate. Thus, the chiral switching to the levorotatory enantiomer resulted in the development of a local anesthetic drug with a clinical profile similar to that of the previously marketed racemate, but with a decrease in cardiovascular toxicity: levobupivacaine was approved in the United States in 2000 [92]. Ropivacaine was the first local anesthetic to be used in clinical practice as a pure (\(S\))-enantiomer [93]. Moreover, some clinical data suggest that ropivacaine is clinically safer than bupivacaine [94].
## 3 Materials and Methods
### Method Validation Parameters  
Method validation was designed according to Eurachem Guidelines [46] and performance quality evaluation was based on reference parameters of Commission Implementing Regulation (EU) 2021/808 of 22 March 2021 for quantitative methods in food applications [47]. Infusions prepared from Ceylon tea (named quality controls, QCs) were analyzed by IS-SPME-GC-MS and LC-UV/DAD and quantitative results collected over a period of six weeks were used for repeatability (intra-week) and intermediate precision (inter-week) assessments. Results on precision based on chromatographic responses (normalized over the internal standard for GC-MS and absolute areas for LC-UV/DAD) are reported in Supplementary Table S1 (ST1), expressed as percent coefficient of variation (CV%) together with chromatographic information on retention times (\(t_{R}\) min), Target/Qualifier Ions (\(m/z\)) (GC-MS), detection wavelengths (nm) (LC-UV/DAD), calibration functions and determination coefficients (\(R^{2}\)), and accuracy results from spiked QC infusions at two levels expressed as percent recovery (% Rec). Accuracy was tested at +10 and +25 \(\mu\)g/L for aroma compounds and +10 and +25 mg/L for tastants and quality markers.
## 5 Conclusions  
This research assessed four OBIA-deep learning integration frameworks and compared them with a patch-based convolutional neural network (CNN) and a pixel-based artificial neural network (ANN) for the classification of high-resolution aerial photographs. The evaluated frameworks were OBIA ANN, OBIA-CNN feature fusion, decision fusion, and patch filtering. The best results were obtained by the patch filtering method for both the training and test areas.  
Land cover mapping applications play a crucial role in many urban and environmental planning and management tasks. As those maps get more accurate, the plans made by the decision-makers for urban and environmental management can get better and become more efficient. The importance of integrating OBIA into deep learning for a high-resolution satellite image is highlighted in this research. However, there are still several challenges in this research area. For future works, the integration of OBIA-CNN should be more flexible by providing effective ways of combining image objects and image patches. Additional research should be carried out to improve the Patch filtering framework by finding better methods to merge image objects and image patches. Moreover, ensemble frameworks may also be applied. For example, OBIA features can be used in decision fusion methods by refining with segmentation a classification map produced with an integrated model (e.g., feature fusion).  
[PERSON] conceptualized, supervised, and obtained the grant for the research. [PERSON] and H.Z.M.S. collected and analyzed the data, performed the analyses and validation, wrote the manuscript, and contributed to the re-structuring and editing of the manuscript. [PERSON], [PERSON], [PERSON] and N.A.B.H. professionally optimized the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the Ministry of Higher Education Malaysia (MOHE) under the Fundamental Research Grant Scheme (FRGS) with project code: : FRGS/2/2014/TK02/UPM/02/2 (03-02-14-1529 FR (Vote no:5524613)).  
Not applicable.  
The authors acknowledge the resources and financial support provided by the Ministry of Higher Education Malaysia (MOHE). Universiti Putra Malaysia (UPM) is also acknowledged for the facilities provided, and the comments given by anonymous reviewers are highly appreciated.  
The authors declare no conflict of interest.
# Efficient Multi-UAV Path Planning for Collaborative Area Search Operations
## 3 Path Planning Design
### Search Path for UAV
#### 3.1.3 Peano Curve Path  
In 1890, [PERSON], an Italian mathematician, invented a curve that can fill a square, which is called the Peano curve [44]. The construction method of this curve is to first take a square, then to divide it into nine equal small squares, and to connect the centers of these small squares with line segments. Next, each small square is divided into nine equal small squares again and their centers are connected. This process is repeated infinitely many times--the resulting curve is the Peano curve. We apply this curve to the path-planning algorithm of UAVs.  
When the target area is divided into \(9^{n}(n\geq 1)\) square cells with side length \(D\), the UAV enters the cell on the left side of the space area near the starting point S from the starting point \(S\). It moves in the shape of the letter \"S\" and connects the center points of all cells in the target area with a non-intersecting curve by rotation or translation according to the Peano transformation rule, and returns to the starting position \(S\) from the cell opposite to the starting point on the right side of the space area, as shown in Figure 7. The pseudo-code to generate the Peano curve path is in Algorithm 2.  
```
0: Matrix \(\text{A}=[C_{0,0},\dots,C_{m-1,0},\dots;C_{0,n-1},\dots,C_{m-1,n-1}]\)
0:P
1:P: The path of UAVs
2:A: Explore points within a region
3:X: \(m\) Columns
4:Y: \(n\) Rows
5:ifX and Y are 3 to the Nth power then
6:\(\text{N}\leftarrow\log_{3}(X)\)
7://Stage 1: Initialize starting point and direction
8:\(\text{Peano}=\text{PeanoCurve(N)}\)
9://Stage 2: Generate Path P based on the Peano curve rule
10:\(P=\text{Peano.PeanoRule}(C)\)
```  
**Algorithm 2** Make Peano Curve Path