esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Correlation of Indirect and Direct Indexes  
In the above research, we have used both the indirect method and the direct method to analyze the geometric characteristics of FA and evaluated the effectiveness and sensibility of both kinds of methods. The indirect methods focus on the overall behavior of the graded aggregate instead of the individual particles, and the test procedure is easy and time-saving. By contrast, the direct methods  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \(SP\) & \(A_{I}\) & \(C_{R}\) & \(SF\) & \(D\) \\ \hline \(F\) & 0.21 & 0.18 & 0.25 & 1.02 & 0.44 \\ \(F_{0.05}\) & & & 5.59 & & \\ \hline \end{tabular}
\end{table}
Table 10: One-way ANOVA results of texture indexes.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Index** & **1** & **3** & **4** & **5** & **6** & **7** & **8** & **10** & **11** \\ \hline \(SP\) & 0.559 & 0.738 & 0.739 & 0.826 & 0.827 & 0.827 & 0.876 & 0.947 & 0.846 \\ \(A_{I}\) & 1.060 & 1.077 & 1.099 & 1.093 & 1.091 & 1.097 & 1.063 & 1.097 & 1.091 \\ \(C_{R}\) & 1.015 & 1.023 & 1.024 & 1.024 & 1.029 & 1.030 & 1.020 & 1.029 & 1.023 \\ \(SF\) & 1.162 & 1.284 & 1.291 & 1.276 & 1.266 & 1.251 & 1.217 & 1.271 & 1.232 \\ \(D\) & 1.032 & 1.036 & 1.029 & 1.031 & 1.035 & 1.037 & 1.036 & 1.032 & 1.039 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Angularity indexes of different FAs.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \(SP\) & \(A_{I}\) & \(C_{R}\) & \(SF\) & \(D\) \\ \hline \(F\) & 5.98 & 0.002 & 0.57 & 3.01 & 2.71 \\ \(F_{0.05}\) & & & 5.59 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 8: One-way ANOVA results of angularity indexes.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Index** & **1** & **3** & **4** & **5** & **6** & **7** & **8** & **10** & **11** \\ \hline \(SP\) & 0.123 & 0.163 & 0.257 & 0.244 & 0.305 & 0.245 & 0.196 & 0.298 & 0.230 \\ \(A_{I}\) & 1.052 & 1.097 & 1.106 & 1.098 &are time-consuming, but they provide more precise descriptions of the particle geometry. In order that the potential relevance between the indirect and direct methods was to be exploited, a statistical analysis was conducted in this part and correlations were run between the indirect and direct indexes, as shown in Figure 12.  
Figure 12 indicates that the indirect indexes were negatively associated with the form index \(R\) but positively associated with other direct indexes. In terms of the three indirect indexes, a good correlation was found to exist between the particle index \(I\) and the direct indexes. This means that the particle index \(I\) can better characterize the individual particle morphology. As for the direct indexes, the texture indexes exhibit the highest correlation with the direct indexes, followed by the angularity indexes and form indexes. The texture index convex ratio \(C_{R}\) displays strong correlation with all the three indirect indexes. It is likely that the texture morphology is the major factor that affects the overall behavior of FA rather than form and angularity.
## 3 Materials and Methods
### Phase Extraction  
As shown in Figure 2a, we used a HeNe laser (Thorlabs, Newton, NJ, USA, HRS015B) working in a stable mode. After the laser beam emitted by a HeNe laser passed through the optical isolator, 1/2 wave plate, and polarizer, we adjusted the polarization to a horizontal direction to ensure the maximum modulation efficiency of the SLM ([PERSON], Berlin, Germany, PLUTO-VIS-016, resolution 1920 pixels \(\times\) 1080 pixels, pixel size 8 \(\upmu\)m \(\times\) 8 \(\upmu\)m). Then, beam splitter 1 was used to divide the incidence beam into two perpendicular laser beams: one beam passed through beam splitter 2, objective lens 1, diffuser (Thorlabs, DG100X100-120, Ground Glass) and objective lens 2, and at last was reflected by a mirror and injected into beam splitter 3; as a reference light, the other beam passed through lens 1 and filter and lens 2, the beam diameter became 18 mm, and at last the object light and reference light were combined at beam splitter 3 to enter the DOPC system together. In our experiment, the reference light was injected perpendicularly into the SLM, and the object light was injected at a specific angle with the reference light. We used a CMOS camera in the DOPC system to collect the interference patterns on the SLM, and then we performed spectrum analysis of the patterns collected before. Finally, we obtained the conjugate phase of the speckle light field by frequency domain filtering and inverse Fourier transform calculation.  
The key to digital optical phase conjugation based on off-axis holography is to separate the spectral information which contains the phase of the object light field. According to Equation (4), the larger the angular deviation of the two beams (the smaller \(\upalpha\)), the smaller the spacing of adjacent bright (dark) stripes and the denser the interference fringes. In the case where the resolution of the camera is not exceeded, that is, Equation (7) is satisfied, the angular difference between the reference light and the object light increases as much as possible to ensure that the optical spectrum of each stage is separated as much as possible during the Fourier transform. Furthermore, according to Equation (9), as long as the relative incident angle of the reference light and the object light is kept constant, the distance between the object light field and the central light field in the frequency domain remains unchanged. Therefore, by fixing the relative angle of the reference light and the object light and setting a suitable  
Figure 2: Experimental setup: (**a**) phase extraction; (**b**) time reversal. WP: waveplate; BS: beam splitter; M1: mirror; SLM: spatial light modulator; DOPC: digital optical phase conjugation; OL: objective lens; CMOS: complementary metal oxide semiconductor camera.  
spatial filter around the object light field in the frequency domain, the phase of the object light field is better restored.
# Review of Photogrammetric and Lidar Applications of UAV
## 3 Classification of UAV
### Based on Wings and Rotors
#### 3.4.3 Fixed-Wing UAVs  
Fixed-wing UAVs have a single fixed wing that resembles that of a piloted airplane in design and function. Fixed-wing UAVs cannot hover in the air--they can only move forward with the achievement of lift and energy efficiency. However, this makes it difficult to launch these UAVs. Depending on the size of the UAV, it is necessary to launch the UAV using a runway or catapult. The advantages of fixed-wing UAVs include covering greater distances, mapping much larger areas, reaching a higher speed, and having a longer flight time. This type of UAV can fly at higher altitudes than other UAVs (often limited by flight restrictions). However, fixed-wing UAVs can be expensive (EUR 25,000-120,000). Another disadvantage is that training is usually required to fly a fixed-wing UAV. Fixed-wing UAVs are ideal for long-range precision aerial mapping and photogrammetry. With a flight time of 90 min, they can cover a large part of the Earth, have a range of 7.5 km, and can map the topography of the Earth's surface with an accuracy of \(\pm\)2-5 cm. They are used in aerial mapping, forestry and environmental surveys, monitoring pipelines, and power lines in agriculture or construction [23; 24].
# Design and Implementation of a 2D MIMO OCC System Based on Deep Learning
## 4 Experimental Setup
### Evaluation Metrics  
The performance of the LED detection network is evaluated using the following metrics:  
\[\text{IoU}=\frac{B\cap B_{gt}}{B\cup B_{gt}} \tag{6}\]  
\[\text{Average IoU}=\frac{\sum_{l=1}^{N}IoU_{i}}{N} \tag{7}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{FP}+\text{TP}} \tag{8}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{FN}+\text{TP}} \tag{9}\]  
where \(B\) is the predicted box, \(B_{gt}\) represents the ground truth box, and \(N\) represents the total number of objects in the images.  
The IoU value will thereafter be employed to determine the true positive (TP), false positive (FP), true negative (TN), and false negative (FN) for each prediction. The detection box is assigned TP if the IoU between it and the LED bounding box is greater than 0.5. In all instances other than the one being discussed, the detection box is designated as an FP. In cases where the LED bounding box does not have a corresponding detecting box, it is indicated as an FN. The terms TP and FP refer to the accurate and inaccurate identification of LEDs, respectively, while FN indicates the count of LEDs that were not identified. The inclusion of TN is unnecessary for the purposes of this binary classification task, as the foreground is consistently maintained for LED detection. The Average IoU in Equation (7) is computed by summing the IoU values for each object or segment and then taking their  
Figure 8: The overall process in the receiver-side with the sample images.  
average. A higher Average IoU value indicates better model performance in accurately recognizing and delineating objects.  
The precision rate refers to the ratio of accurately predicted positive instances by the YOLOv8 model. On the contrary, the recall rate pertains to the ratio of correctly identified true positives by the model within the provided dataset. The explicit evaluation of detection accuracy is hindered by the relationship between precision and recall rates. Hence, in order to quantify the reliability of LED detection, the AP was incorporated into Equation (10). The quantity AP represents the integral of the precision and recall values over the whole range of the precision-recall curve. Furthermore, the AP denotes the average accuracy rate of detection, which is measured on a scale ranging from 0 to 1. The expression is articulated in the following manner:  
\[\text{AP}=\int_{0}^{1}Pr(Re)dRe \tag{10}\]  
\[\text{mAP}=\frac{1}{n}\sum_{k=1}^{k+n}AP_{k} \tag{11}\]  
In Equation (11), mAP is used to characterise a model's overall quality. The mAP is computed by averaging the AP for each class. mAP takes into consideration the trade-off between precision and recall and the number of FP and FN. Therefore, this property is suitable for the majority of detection applications.
## 3 Numerical Analyses
### Response Characteristics for Vertical Excitation
The maximum acceleration of RIS decreased 0.101 g (30%) to 0.233 g, while the isolator (I) decreased 0.15 g (45%) to 0.184 g. Even when only horizontal excitation works, the isolator (I) with vertical damper performs better than the RIS. \begin{table}
\begin{tabular}{c c c c c} \hline  & & **El Centro** & **Isolator (I)** & **Isolator (II)** \\  & & **Earthquake** & **(Figure 7a)** & **(Figure 7b)** \\ \hline \multirow{2}{*}{Maximum absolute acceleration} & Horizontal direction & 0.335 g & 0.185 g (\(\blacktriangledown\)0.15 g) & 0.273 g (\(\blacktriangledown\)0.131 g) \\ \cline{2-5}  & Vertical direction & 0.226 g & 0.135 g (\(\blacktriangledown\)0.091 g) & 0.187 g (\(\blacktriangledown\)0.039 g) \\ \hline \multirow{2}{*}{Maximum relative displacement} & Horizontal direction & - & 7.98 cm & 9.24 cm \\ \cline{2-5}  & Vertical direction & - & 9.09 cm & 9.2 cm \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison of maximum vibration response values. Figure 17: System response to two directional earthquake excitations in case of \(f_{y}=3\) Hz, \(\alpha=0.1\), \(\mu=0.02\), \(R=0.6\) m, and \(\zeta=0.3\): (**a**) Horizontal response. (**b**) Vertical response.
## 5 Application Examples  
This section aims to explain some applications for the algorithm described in this research. Three application examples are used to show the performance and results of the proposed approach. These examples are defined under different working conditions and fitness functions.
## 4 Evaluation
### Evaluation Results  
The simulation results for the proposed electrode placements for stimulating the lower side of the eyeball surface in order to present phosphenes in the lower portion of the human visual field are shown in Figure 7. Each cell in Figure 7 represents the simulation results for a different electrode, and consists of two transformed 2D planes corresponding to the surfaces of the right and left eyeballs. The vertical axis is the height coordinate of the eyeball, and the horizontal axis is the angular coordinate of the eyeball. The stimulated  
Figure 6: Method for transforming eyeballs into two planes for evaluation.  
area is colored yellow to orange, and the blue area is not stimulated. The area below the red line on the vertical axis corresponds to the lower part of the eyeball surface. The area from around zero to three on the horizontal axis is further divided into three areas in Figure 7. The left side of the divided area corresponds to the temporal right eyeball or the nasal left eyeball. The central part of the divided area corresponds to the central area of the right or left eyeball. The right side of the divided area corresponds to the nasal right eyeball or the temporal left eyeball.  
Our hypotheses, and the expected area stimulated by each electrode, are summarized in Table 3. On the basis of Figure 7, all of the hypotheses regarding the areas stimulated by particular electrodes were validated. All of the areas that were stimulated by each electrode placement were located below the red line on the horizontal axis. Therefore, it can be seen that all of the proposed electrodes can potentially allow the the stimulation of the lower side of the eyeball. Regarding the target stimulated area, all of the proposed electrodes stimulated the expected stimulated area on the eyeball surface, as stated in Table 3. However, local stimulation remains an issue, in the sense that the electrode placements stimulate areas outside the target area, in addition to the target area. For example, electrode 3, designed to stimulate the lower central area of the right eyeball, as  
Figure 7: Simulation results with proposed electrodes stimulation the lower side of the eyeball surface: (**a**) simulations results for electrode 1; (**b**) Simulation results for electrode 2; (**c**) simulation results for electrode 3; (**d**) simulation results for electrode 4; (**e**) simulation results for electrode 5.  
shown in Table 3, also stimulated the lower temporal area of the right eyeball, which is outside of the target stimulation area.
## 4 Conclusions
The correlation between the deep levels and intrinsic defects offers the opportunity to adjust the Sb\({}_{2}\)Se\({}_{3}\) growth parameters and post-deposition treatments. This adjustment aims to minimize or eliminate intrinsic defects responsible for the formation of deep levels. Preventing deep levels acting as traps for charge carriers would enhance free charge accumulation in the junction region, thereby improving the built-in potential, photovoltage, and photocurrent. It is crucial to carefully consider the lattice mismatch between the ETL layer and Sb\({}_{2}\)Se\({}_{3}\) to mitigate the formation of the interfacial states responsible for recombination centers. These recombination centers can significantly reduce the internal quantum efficiency, leading to adverse effects on photovoltaic conversion efficiency. The insights gained in this study can help to improve the efficiency of Sb\({}_{2}\)Se\({}_{3}\)-based solar cells, stimulate a broader research effort, and attract commercial attention. Indeed, the transition from laboratory-scale to industrial applications for Sb\({}_{2}\)Se\({}_{3}\)-based solar cells using DC/RF sputtering and close-spaced sublimation (CSS) involves key considerations for scalability, cost-effectiveness, device stability, and compatibility with existing production infrastructure. Low-temperature sputtering and CSS are scalable techniques with low energy consumption and high deposition rates, making them suitable and cost-effective for large-area solar cell production. Reducing deep-level defects and lattice mismatches is crucial for ensuring long-term device performance and stability. The techniques are compatible with existing solar cell production lines, hence reducing initial investment and speeding up commercialization. Efficiency improvements and optimization will help Sb\({}_{2}\)Se\({}_{3}\)-based solar cells compete with CdTe and CIGS. In summary, Sb\({}_{2}\)Se\({}_{3}\)-based solar cells have strong potential to become commercial players. [PERSON], [PERSON], [PERSON] and [PERSON] wrote the original draft of this manuscript; [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] revised and confirmed the manuscript, [PERSON] offered financial support. I.C. did the TEM measurements and evaluation of data. [PERSON] contributed to the EDS evaluation. All authors have read and agreed to the published version of the manuscript. Not applicable. Data Availability StatementData will be made available on request. We would like to thank [PERSON] of the University of Parma for his support in the optical measurements of the layers used for this study. The authors declare no conflicts of interest.
# CWSXLNet: A Sentiment Analysis Model Based on Chinese Word Segmentation Information Enhancement
## 5 Results and Discussion  
The experimental results of the ChnSentiCorp dataset are shown in the following Table 5 and Figure 13, and the LTP process was  
From the experimental results, both the CWSXLNet model and the CWSXLNet-BiGRU-Attention model achieve better results in dealing with Chinese sentiment analysis tasks.  
On ChnSentiCorp dataset, CWSXLNet achieved 89.91% precision, 91.53% recall rate and 90.71% F1-score, and CWSXLNet-BiGRU-Attention has achieved 92.61% precision, 93.19% recall rate and 92.90% F1-score. For comparison, Chinese pre-trained XLNet model proposed by [31] achieved 88.83% precision, 87.92% recall rate and 88.37% F1-score on the same dataset.  
On Weibo_senti_100k dataset, CWSXLNet achieved 95.02% precision, 94.83% recall rate and 95.01% F1-score, and CWSXLNet-BiGRU-Attention has achieved 95.67% precision, 95.48% recall rate and 95.57% F1-score. For comparison, Chinese pre-trained XLNet model proposed by [31] achieved 94.28% precision, 94.15% recall rate and 94.21% F1-score. The experimental results indicated that the Chinese word separation information can help the XLNet model to understand Chinese semantics, and the performance of CWSXLNet-BiGRU-Attention is better than that of CWSXLNet alone, indicating that the BiGRU network and the self-attention mechanism are more accurate and effective in controlling the sentiment keywords.
# N-Trans: Parallel Detection Algorithm for DGA Domain Names
## 4 Experiment and Analysis
### Experimental Results and Analysis
#### 4.3.2 Parallel Detection Model Construction Experiments  
In order to select a malicious domain detection model with better results, possible combinations of N-gram algorithms in the proposed parallel detection model are analyzed. In the single N-gram experiment when \(N\geq 3\), although more features can be captured and the ability to predict characters is significantly improved, a large number of sparse matrices are generated in the model. This can lead to distortion of the calculated probabilities, and a large parameter space can cause a dimensional disaster; thus, the model will have a considerable time and memory loss during training, resulting in the experiment not being carried out smoothly. Based on the original N-gram and Transformer detection models, the features are optimized using a feature selection algorithm based on L1 regularization. After feature gain processing, in addition to achieving the filtering feature purpose, the high-dimensional matrices are also dimensionally reduced, which reduces the experimental time and memory loss, making the experiments feasible.  
Different combinations of \(N\) were tested to explore the most suitable N-gram combination and Transformer composition for the parallel detection model. The experimental procedure is shown in Table 4.  
The experimental results show that, for a single N-gram algorithm, the number of phrase elements in the domain names must be reduced because the basic units of the phrase elements are expanding, and the number of phrase elements matching the same category in the domain names must be reduced as the N-value changes. Therefore, the effective features obtained from training are reduced. Taking _\"linkedin\"_ as an example, there are 749 _\"li\"_ phrase elements in the dataset. After trigram processing, there are 192 _\"lin\"_ phrase elements in the dataset, which is less than the result of binary phrase element processing. At the same time, the accuracy of the trained model decreases, and the recall increases, indicating overfitting.  
The features extracted by a single algorithm are expanded for the parallel detection model by stitching the features from different N-gram algorithms and Transformer processing. As can be seen from Table 4, the parallel detection model, including bigram, has better detection results and achieves the best results at \(N=2\), 3, and 4. However, it is not the case that the more parallel approaches are included, the better the detection results are. It is found that the model detection accuracy and recall decrease when 5-g is included, but then the detection results become better again if \(N=1\) is included in the model. The domain location and text features are extracted  
After comparing the experimental results, the model using the parallel combination of bigram, trigram, 4-g, and Transformer at \(N=2\), 3, 4 was more effective in identifying malicious domain names.
## 2 Materials and Methods
### Biomechanical Testing  
The maximal insertion torque was measured with an electronic torque wrench (OLY 921/6 NB, New Taipei City, Taiwan) at the last thread of screw insertion. The same insertional depth was confirmed using X-ray imaging prior to the pullout test using a material testing machine. Following screw insertion, the instrumented vertebral specimens were embedded in acrylate resin (#20-3568; Buehler, Lake Bluff, IL, USA) to allow clamping during the screw pullout test. The method for the screw pullout test was identical to that used in our previous study [15,24]. Each prepared specimen was secured to a custom-made grip mounted on the platform of the testing machine (E10000/E10 BMTB19359., Instron Com., Norwood MA, USA) to conduct axial pullout tests with the screws. The polyaxial screw head was fixed to a 10-mm diameter cylindrical rod with an outer thread that matched the inner thread of the screw head cup. The cylindrical rod was then clamped to the upper wedge grip of the Instron testing machine. The potted specimen was clamped on a lower custom-made grip capable of x-y plane translation and rotation to achieve the coaxial alignment of the pedicle screw with the pullout ram (Figure 3). After the specimens were mounted, pullout force was applied at a constant crosshead rate of 5 mm/min [15,24]. During the pullout test, the relation between the applied force and displacement was simultaneously recorded in 0.1 mm increments until failure. The peak force recorded was defined as the maximum pullout strength for comparison.  
Figure 1: Schematic drawings (**upper**) and photographs (**lower**) of six types of pedicle screws with different thread profiles (single- or dual-thread) and projection shapes (cylindrical, conical, or proximal-conical shape). From left to right: single-thread and cylindrical shape (S-Cy), single-thread and conical shape (S-Co), single-thread and proximal conical shape (S-PCo), dual-thread and cylindrical shape (D-Cy), dual-thread and conical shape (D-Co), and dual-thread and proximal-conical shape (D-PCo).