esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Results of Measurements and Correlation Analysis  
The basic descriptive statistics of CPT data, soil water content, soil bulk density, and shear strength parameters of the 83 soil measurement points are shown in Table 2. It can be seen from the table that the median and mean are closely distributed for all parameters, which indicates that the statistical distribution of these parameters for experimented samples is nearly normal. As shown in Table 2, the measured value of cone tip resistance ranges from a lower value of 138 N to a high value of 570 N. The average and median values of cone tip resistance are 317.36 N, and 317.42 N, respectively. The cone side pressure values range from 2.57 N to 261.51 N, with an average value of 106.58 N and a median value of 96.43 N. The cohesion value ranges from 1.74 to 38.95 kPa, with a mean of 20.77 kPa and a median of 20.91 kPa. The measured values of the internal friction angle ranged from 8.66\({}^{\circ}\) to 32.16\({}^{\circ}\), with mean and median values of 18.73\({}^{\circ}\) and 17.70\({}^{\circ}\), respectively. The measured water content values range from 11.88% to 25.84%, with a mean value of 17.68% and a median value of 17.25%, respectively. Among the measured parameters, the spatial variability of bulk density was the lowest ranging from 1.17 g/cm\({}^{3}\) to 1.96 g/cm\({}^{3}\), with mean and median values of 1.60 g/cm\({}^{3}\) and 1.64 g/cm\({}^{3}\), respectively.  
To determine the strength of the linear relationships between variables included in our study, the bivariate correlation technique was adopted to analyze the variables in the dataset, which is represented in a correlation matrix (Table 3). In the correlation analysis,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Statistics** & **\(\omega\) (\%)** & **\(d\) (g/cm\({}^{3}\))** & **\(R\) (N)** & **\(P\) (N)** & **\(c\) (kPa)** & **\(\varphi\) (degree)** \\ \hline Minimum & 11.88 & 1.17 & 138.00 & 2.57 & 1.74 & 8.66 \\ Maximum & 25.84 & 1.96 & 570.92 & 261.51 & 38.95 & 32.16 \\ Average & 17.68 & 1.60 & 317.36 & 106.58 & 20.77 & 18.73 \\ Medium & 17.25 & 1.64 & 317.42 & 96.43 & 20.91 & 17.70 \\ Standard Deviation & 3.63 & 0.19 & 98.768 & 71.55 & 10.29 & 5.46 \\ \(n\) & 83 & 83 & 83 & 83 & 83 & 83 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Basic descriptive statistics for the original data set.  
The predicted results for cohesion and the internal friction angle by BPNN models are illustrated in Figure 11. The results predicted with the calibration set are \(R_{C}{}^{2}\) = 0.89, \(RMSE_{C}\) = 3.39 (cohesion); \(R_{C}{}^{2}\) = 0.79, \(RMSE_{C}\) = 2.33 (internal friction angle). The prediction results of the validation set are \(R_{V}{}^{2}\) = 0.87, \(RMSE_{V}\) = 4.08 (cohesion); \(R_{V}{}^{2}\) = 0.75, \(RMSE_{V}\) = 3.28 (internal friction angle). Generally, if a proposed model gives \(R^{2}\) > 0.8, there is a strong correlation between the measured and predicted values for all the data available in the database; therefore, the results suggest that the BPNN model is well suited to predicting soil cohesion, but it is not suitable for predicting the internal friction angle.
## 3 Future Work
### Prosumer Relationships
#### Consumer Engagement  
This section outlines the need for further research in the area of consumer engagement. Suggested research areas include the identification of socio-economic motivations for consumer engagement, regulatory measures, and sustainable funding policies to support the crowd energy concept [74]. Further study is also needed to design and implement mechanisms to handle the automation and control in support of the decentralization [74]. These mechanisms would involve the use of information and communications technologies to integrate the decentralized production, storage and load, business relationships and prosumers.  
A comparative study to determine the factors influencing smart grid acceptance among current and potential consumers is another area for investigation [76]. The results would lead to more reasonable suggestions for policies and measures for smart grid participation. Future studies could also consider exogenous and endogenous variables [76].  
The factors influencing industrial consumers' smart grid participation were derived based on a qualitative approach using data from four Danish companies [77]. Denmark is an emerging nation in the field of the future intelligent grid, and the research findings apply to Danish smart grid context. Thus, it is recommended to extend the study of this topic through cross-national comparisons. Also, the study used a qualitative approach. Hence, further qualitative or quantitative approaches could be used to validate the research findings [77].  
Trust management is essential in customer engagement, but there is limited work that relates trust in the smart grids. Therefore, future work could include investigating the process of trust and distrust attribution in the smart grid [82]. This could be further extended by analyzing or simulating dataset extracted from a real social network, to understand trust measures that can do profile matching to strengthen community cohesion [80].
# Micromechanical Characterization of Diatom Frustules of Multiple Origin
## 2 State-of-the-Art
### Scanning Electron Microscopy as a Technique for the Characterization of Frustules  
Scanning Electron Microscopy (SEM) is a technique that allows visualization at small scales, using instruments that apply radiation other than visible light [14]. This microscope does not form an exact image of the object but constructs a virtual image from some of the signals emitted by the sample. If a non-conductive material is observed, it is coated with a thin metallic layer, e.g., gold [14]. Alternative procedures have considered coating samples with resins and subsequently covering them with graphite [17].  
The study of materials through SEM consists of amplifying the surface views of the observed structures and examining their properties. The samples are approached using an electron beam generated in a vacuum environment, which is directed with electromagnetic lenses, thus creating high-resolution images. These interactions provide information on topography, morphology, shape, size, composition, and crystallographic structure. The size of the samples must be defined depending on the material, and their surface must be clean. The dimensions of the sample must be adjusted to the chambers of the equipment [15].  
An advantage of this technique is the possibility of obtaining information about the mineralogy of the particles since an energy-dispersive X-ray spectrometer (EDS) can be operated simultaneously. By comparing the standard spectrum of each mineral, it is possible to determine its presence within the sample [15,16] if its concentration is higher than 1% [14].  
The behavior of some soils, especially fine soils (silts and clays), cannot always be understood from their physical and mechanical properties. In such cases, SEM is necessary to assess the microstructure. SEM is used to understand some soil types' differential or problematic performance, verify upgrading or stabilization processes, and identify cementation and contact structures that may occur around the particles. SEM offers high image resolution, detailed surface topography, ease of use, fast image capture and generation of data in digital format. The main disadvantage is the high cost of microscopy services [15].  
This SEM practice in DS is mainly associated with the identification of the species according to morphology, size, pore structure, chemical composition, interaction with other types of particles, state of preservation and arrangement within the soil matrices that contain them [18,19,48,49,50,48].
# Transfer Convolutional Neural Network for Cross-Project Defect Prediction
## 4 Experimental Setup
### Evaluated Datasets  
To verify the validity of the TCNN method, we selected 10 open-source projects as our evaluation datasets. The source code and corresponding PROMISE data for all 10 projects are public and have been widely used in SDP research [21; 25; 26; 27]. In our experiments, we extracted DL-generated features from the Java source code and adopted the static code metrics and data labels from the PROMISE repository. Table 2 shows the essential information of selected projects, including project name, project version, number of instances, and defect rate (the percentage of defective instances). To guarantee the generality of the evaluation results, the experimental datasets consisted of projects with different sizes and defect rates (the maximum number of files was 1077, and the minimum number of files was 32; the minimum defect rate was 6.3%, and the maximum defect rate was 98.8%). Table 3 shows the static code metrics contained in the PROMISE repository, and for the descriptions, the readers are referred to [21]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Project Name** & **Project Version** & **\# of Instances** & **Defect Rate** \\ \hline Camel & 1.6 & 965 & 19.5\% \\ Forrest & 0.8 & 32 & 6.3\% \\ Ivy & 2.0 & 352 & 11.4\% \\ Log4j & 1.2 & 205 & 92.2 \% \\ Lucene & 2.4 & 340 & 59.7\% \\ Poi & 3.0 & 1077 & 63.6\% \\ Synapse & 1.2 & 256 & 33.6\% \\ Velocity & 1.6.1 & 229 & 34.1\% \\ Xalan & 2.7 & 909 & 98.8\% \\ Xerces & 1.4.4 & 588 & 74.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The 10 projects used in this paper. Regarding the composition of the CPDP tasks, we first selected a project as the target project and then respectively used the remaining 9 projects as the source project (e.g., Ivy-2.0 data as a training set, camel-1.6 data as a test set). Thus, 90 test pairs were collected to perform the CPDP. In fact, the class imbalance problem of software defect data is a typical characteristic in CPDP tasks [28; 29]. As shown in Table 2, no matter which project was used as the source projects, the training data had a class imbalance problem. If the classifier was trained on a highly imbalanced dataset, the predictive model tended to support the majority class, and the ability to detect the minority class was weak. For this reason, it was necessary to handle the class imbalance problem. Because class imbalance learning is only used as an application in this paper, we adopted a standard random oversampling to avoid imbalanced data rather than complex approaches in some theoretical approaches [25; 29].
## 4 Results and Discussion
### Discussion
#### 4.2.3 Type-Wise Contrastive Learning  
In general, the accuracy scores in the last column in Table 4 indicate that classification using type-wise contrastive learning produced higher scores than those using instance-wise learning, with improvements in OA and F1 scores of 2.5% and 6.8%, respectively. This is evident since type-wise learning requires that samples with identical types share the same feature representation. This presumption necessitates contrastive learning to investigate the inherent characteristics of distinguishing crop types.  
Let us examine the improvements made to various crops in further detail. We discovered a more substantial improvement in minor categories, specifically for _\"winter triticale\"_ (0.22), _\"sunflower\"_ (0.21), and _'fallow'_ (0.18), as measured using F1 scores. Possible causes include the fact that a loss function applied to positive sample pairs from two instances with the same crop types might reduce the inherent feature space of crops. Furthermore, the compression of space is more severe for major crop types than for minor types. These compressions would promote crop classification by balancing the distribution of the sample.
# Research on Rock Strength Test Based on Electro-Hydraulic Servo Point Load Instrument
## 4 Results and Discussion
### Analysis of Optimal Loading Rate and Stable Size Range for the Different Rock Samples  
#### 4.4.1 Optimal Loading-Rate Analysis  
The effect of loading rate on I\({}_{\text{s(50)}2}\) was considered to be non-negligible. The I\({}_{\text{s(50)}2}\) values of different sizes of rock samples at the same loading rate were firstly obtained, and then the I\({}_{\text{s(50)}2}\) values of different sizes of rock samples were averaged, and finally the average I\({}_{\text{s(50)}2}\) values of three rock samples at different rates were obtained, as shown in Tables 5-7. The optimal loading rate was explored by analyzing the degree of variation in I\({}_{\text{s(50)}2}\) for different sizes (the coefficient of variation index was used to evaluate).  
Figure 10: The relationship between I\({}_{\text{s(50)}}\) and size of the three rock samples under different loading rates.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method Loading Rate (kN/s)**} & \multirow{2}{*}{**Size (mm\({}^{3}\))**} & \multicolumn{2}{c}{**I\({}_{\text{s(50)2}}\) (MPa)**} & \multirow{2}{*}{**Standard Deviation**} & \multirow{2}{*}{**Coefficient of Variation**} \\ \cline{3-3}  & & & **Strength Value** & & & \\ \hline \multirow{4}{*}{I\({}_{\text{s(50)2}}\)} & 50\({}^{3}\) & 3.974 & & & \\  & 40\({}^{3}\) & 3.985 & 3.962 & 0.039 & 0.010 \\  & 20\({}^{3}\) & 3.905 & & & \\  & 50\({}^{3}\) & 4.050 & & & \\ \cline{1-1} \cline{2-3} \cline{5-6}  
rate is obtained, and it can be seen that the coefficient of variation is the smallest for fine-crystalline granite, coarse-crystalline granite, and siltstone at the point load loading rates of 0.5, 0.5, and 1.0 kN/s. This indicates that the Is(50) solved using method II for different sizes rock samples at this loading rate is more reasonable and credible.
## 4 Experimental Results and Analysis
### Implementation of Previous Work  
We evaluate the previously proposed algorithms for homogeneous multi-core processors, HP, RRP, and DVP on the Cell BE architecture. Firstly, these algorithms are implemented with using only SPEs and SIMD instruction set for the SPE. Figure 10 presents execution time on the decoding operation that was discussed in Section 2.4. Experimenting with the entire coefficient matrix size, HP and RRP exhibit  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**Sony PlayStation3**} & **Intel Quad Core** & **AMD Quad Core** \\ \hline \multirow{8}{*}{**SPEC**} & **CPU** & Cell BE & Intel Core 2 quad Q9400 & AMD phenom-X4 9550 \\  & **Clock** & 3.2 GHz & 2.66 GHz & 2.2 GHz \\  & **RAM** & 512 MB & 2 GB & 4 GB \\ \cline{1-1}  & **Cache** & L1 : 32 KB & L1 : 4 \(\times\) 64 KB & L1 : 4 \(\times\) 128 KB \\ \cline{1-1}  & **Size** & L2 : 512 KB & L2 : 2 \(\times\) 3 MB & L2 : 4 \(\times\) 512 KB \\ \cline{1-1}  & **OS** & Linux & Linux & L3 : 2 MB shared \\ \cline{1-1}  & **Number** & & Yellow Dog Linux 6.1 & Fedora Core7 & Fedora Core8 \\ \cline{1-1}  & **of Cores** & & (1 + 6) & 4 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental Environments.  
similar performance. In contrast, DVP exhibits even better performance. As the SPEs decode the data without a data cache, the dissimilarity between the HP and RRP algorithms does not affect the required decoding time.  
The maximum performance difference between HP and RRP is only 1.69% and on average, there is only 0.04% difference. In addition, DVP shows a maximum 31% enhancement over HP and RRP. Therefore, in the next section, we perform the remaining experiments using DVP. As in the homogenous multi-core processor, the advantage of DVP in terms of load balancing brings better results. Detailed explanation on DVP can be found in [31].  
The difference between Horizontal Partitioning (HP) and Row by Row Partitioning (RRP) comes from the different manner by which row is distributed to the different cores in _Stage E_. The results can be explained by the presence (or lack thereof) of a data cache. However, the Cell BE does not have a data cache on its SPEs. Therefore, there would be no distinctive difference between the two algorithms when implemented upon this architecture. In other words, the heterogeneous processor which has a simplified memory hierarchy to access local memory fast cannot provide efficiency of horizontal partitioning, even though it is a different and well balanced approach for cache embedded systems.
# Picosecond Q-Switched 1064/532 nm Laser in Tattoo Removal: Our Single Center Experience
## 4 Discussion  
The QS Nd: YAG laser is reported as the optimal tattoo removal device because it can obtain excellent cosmetic results with minimal skin damage, low scarring risk, and low incidence of hypopigmentation [17,18]. A two-month interval between sessions permits the removal of tattoo pigmented particles via the phagocytic cells, and it is necessary before carrying out any other session or procedure [19,20]. Red ink is more frequently associated with long-term reactions, including granulomatous and pseudolymphnotous phenomena or morphea-like lesions and vasculitis. The exacerbation of preexisting psoriasis, atopic dermatitis, and pyoderma gangrenosum may occur after tattooing [21]. Preoperative assessment must include the evaluation of scars under the tattoo, as removing the pigment may expose them [22]. Scars exposed after tattoo removal may then be treated as all other marks are: with a pulsed dye laser device or with some of the new lasers that target connective tissue. The results, however, may not be always cosmetically acceptable [23,24] (de las alas, cannarozzo). Q-Switched lasers have been on the market for numerous years; the technology and pulse duration have been modified during this time, and the picosecond pulsed laser is considered the gold standard in tattoo removal [25,26]. Even if they can be considered the latest technological finding in tattoo removal, Q-switched picosecond lasers cannot remove professional tattoos altogether; complete clearance was only reported in about 30% of participants. Patients must be informed of the possibility of residual pigmentation after treatment, especially in the case of professional tattoos [27]. The risk of adverse events, such as hyperpigmentation, hypopigmentation, and skin darkening, must be communicated to patients [28,29]. Colored tattoos respond poorly to removal--especially when the pigment is yellow or green--even when using other types of Q-switched lasers, such as alexandrite (755 nm). Moreover, poor responses to treatment may be due to larger tattoo dimensions (larger than 30 cm), pigment density, anatomical sites (lower extremities), and the age of the tattoo (three or more years old) [30]. Our experience suggests that picosecond laser treatment may help reduce the mean number of sessions and associated side effects, with cosmetic results similar to nanosecond pulse devices. However, these results may be biased because picosecond sources are often used for harder-to-treat tattoos, such as professional tattoos with mixed colors. Photoacoustic imaging may be useful as a clinical adjunct for the objective preoperative evaluation of tattoos and potentially as a guide/monitor for laser-based tattoo removal procedures [31].
# Crescent Microstrip Antenna for LTE-U and 5G Systems
## 5 Dual-Band Microstrip Antenna Designed for LTE-U and 5G Systems
 \tag{4}\]  
Since the parameter \(a\) is less than 1.52, the width and length of the feed line are determined from the following equation [19; 38; 39; 40]:  
\[ \begin{array}{c}W_{f}=A=\frac{2}{\pi}\Big{\{}b-1-\ln(2b-1)+\frac{\varepsilon_ {r}-1}{2\varepsilon_{r}}\Big{[}\ln(b-1)+0.39-\frac{0.61}{\varepsilon_{r}} \Big{]}\Big{\}}*h=6.99\,\mathrm{mm}\\ L_{f}=3\cdot h=3\cdot 1.57=4.71\,\mathrm{mm}\end{array}  \tag{5}\]  
The determination of the length of the feed line completes the stage of determining the input data for the initial simulation model. Based on the calculations made, assuming the basic data for the 3.6 GHz frequency, a model was obtained, the preliminary dimensions of which are shown in Table 1. Unfortunately, in this model, for the frequency equal to 5.5 GHz, no resonance was obtained, and therefore it was decided to correct the calculations using the optimization process available in the CST Microwave Studio software. Thanks to this process, the final version of the antenna was obtained after modifications were made to the following: the shape of the radiating patch, by making a circular indentation in the circular patch with a diameter resulting from the radiator size for f = 5.5 GHz; the dimensions of the feed line; and the size of the reference plane (antenna shield). The final dimensions of this antenna are presented in Table 1. The appearance of the final version of the antenna model with a crescent radiating patch is shown in Figure 4, while Figure 5 shows the appearance of the physical antenna model [41; 42]. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Layer**} & \multicolumn{2}{c}{**Preliminary**} & \multicolumn{2}{c}{**Final Dimensions**} \\  & \multicolumn{2}{c}{**Dimensions [mm]**} & \multicolumn{2}{c}{**[mm]**} \\ \hline Ground plane width & \(W_{\mathrm{e}}\) & 42.36 & 40.00 \\ \hline Substrate width & \(W_{\mathrm{s}}\) & 42.36 & 40.00 \\ \hline Substrate length & \(L_{\mathrm{s}}\) & 36.67 & 60.00 \\ \hline Ground plane length & \(L_{\mathrm{e}}\) & 36.67 & 39.60 \\ \hline Patch diameter & \(2R_{1}\) & 17.19 & 18.70 \\ \hline Patch radius & \(R_{1}\) & 8.59 & 9.35 \\ \hline Patch cut diameter & \(2R_{2}\) & 11.56 & 13.02 \\ \hline Patch cut radius & \(R_{2}\) & 5.78 & 6.51 \\ \hline Copper thickness & Cu & 0.035 & 0.035 \\ \hline Substrate thickness & h & 1.57 & 1.57 \\ \hline Permittivity & \(\varepsilon_{r}\) & 2.2 & 2.2 \\ \hline Feed line width & \(W_{\mathrm{f}}\) & 6.99 & 2.80 \\ \hline Feed line length & \(L_{\mathrm{f}}\) & 4.71 & 33.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Preliminary and final dimensions of the proposed antenna.
## 3 Related Work
In [20], mathematical modelling is used to simulate and analyse the performance improvement using frequency hopping spread spectrum with popular modulation techniques using Matlab-Simulink. The baseband signal is combined with a randomly generated carrier frequency. The carrier frequency is controlled by a Pseudorandom Noise (PN) sequence generator and the signal is transmitted over AWGN. The received data is demodulated using the same PN code, and compared to the original input data to calculate the BER. Theses simulators deal only with frequency hopping systems with pseudorandom frequency sequences, and do not consider adaptive frequency hopping techniques, where the selected frequency channels are adapted to the conditions and characteristics of the environment interferences. The method presented here takes into consideration different hopping techniques and channel quality metrics found in the literature. These hopping techniques and metrics are interlaced to analyse different possible solutions and obtain the best subset of channels as well as a predetermined hopping sequence for a given scenario where the network under study has to coexist with unwanted interfering networks.