esa-sceva/satcom-chunk-collection huggingface dataset:
# Progress in Improving Photovoltaics Longevity
## 4 Results
### PV Longevity Improvement
#### 4.4.1 Knowledge Improvement
Figure 5: Progress in photovoltaic system’s lifetime over the years, according to literature sources. Warranty lifetime data for PV modules over time are also shown [155]. \begin{table}
\begin{tabular}{c c c} \hline
**Degradation** & **Incomplete Knowledge** & **Material Advancements** & **References** \\ \hline PID & \begin{tabular}{c} A fully microscopic interpretation of the \\ phenomenon, materials components’ behavior \\ under high voltage stresses, appropriate \\ material selection, and cost-effectiveness \\ \end{tabular} & \begin{tabular}{c} Polyolefin elastomer (POE) encapsulants \\ (higher volumetric resistance), fluoropolymer \\ backsheets (limited moisture ingress) \\ \end{tabular} & [58; 156; 157] \\ \hline Temperature-induced degradation (TID) & \begin{tabular}{c} Effects of thermal fatigue on PV module’s \\ components, precise models for the \\ development and enlargement of the cracks \\ \end{tabular} & \begin{tabular}{c} Polyolefin encapsulants (increased hydrolytic \\ degradation resistance and enhanced thermal \\ stability), polyamide and PET (Poly-Ethylene \\ Terpethplate-based backsheets (increased \\ thermal stability), fluoropolymer backsheets \\ (increased thermal stability), advanced \\ anti-reflecting coatings \\ \end{tabular} & \
\begin{tabular}{c} Polyolefin encapsulants (increased hydrolytic \\ degradation resistance and enhanced thermal \\ stability), polyamide and PET (Poly-Ethylene \\ Terpethplate-based backsheets (increased \\ thermal stability), fluoropolymer backsheets \\ (increased thermal stability), advanced \\ anti-reflecting coatings \\ \end{tabular} &
\begin{tabular}{c} [158; 159; 160; 161] \\ (1621) \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 9: Incomplete knowledge and corresponding material advancements that have been introduced as a result of continuous research regarding PID, TID, UV exposure, and LID.
## 3 Materials and Methods
### Materials
#### 3.1.2 Traversal Algorithm
Through our custom traversal algorithm, we were able to obtain the following data for each project:  
* Maximum date: Latest date of the project found in our dataset, padded by one month;
* Minimum date: Earliest date of the project found in our dataset, padded by one month;
* Project time frame: Calculated by using the maximum and minimum date;
* Total commits: The total number of commits made during the project time frame;
* Active committers: The number of active committers during the project time frame;
* Active authors: The number of active authors during the project time frame;
* Commit rate per minute: Commits made per minute calculated as outlined in Equation (1). In addition, we obtained the following for each bug:
* The bug type;
* The code author's email who fixed the commit;
* The time and date the code author fixed the bug;
* The code author's email who introduced the bug;
* The time and date the code author introduced the bug;
* Range of line numbers where the code that introduced the bug was located;
* The total number of commits made in the project;
* The number of active committers in the project;
* The number of authors;
* Latest date of project;
* Earliest date of project;
* Number of minutes it took to fix a bug;
* Project time frame;
* Commit rate per minute;
* Number of Commit Cycles it took to fix each bug. Out of the 643 projects, 592 projects were available for analysis due to the shortcomings outlined in the following Section 3.1.3. From the 592 projects available, we obtained 36,407 unique commits. Although some bugs showed up multiple times with different bug types, we only counted these once. From the subset of projects available and our algorithm, we discovered that the projects' activity ranged from 2001 to 2019. The information obtained would be used to answer our research questions, thus allowing us to evaluate factors affecting bug fix times.
## 2 Spectral UAV Sensors
### Spectral 2D Imagers
#### Multi-point spectrometer  
Multi-point spectrometers use a beam splitter to divide the 2D image into sections, of which the signal is spread in the spectral domain [78]. An example is the Cubert Firefleye [79]. The camera and a controlling computer weighs about 1 kg and records an image cube with 50 \(\times\) 50 pixel of spectral data from 450-900 nm with an FWHM of 5 nm (460 nm) to 25 nm (860 nm). Simultaneously, a one megapixel grey image with the same extent is taken and can be used to collate the images into a full scene [43]. Multi-point snapshot cameras have been used to derive chlorophyll [42], plant height [43] and leaf area index [80] in crops.  
The advantage of multi-point 2D imagers is that they record all of the spectral information for each point in the image at the same time, and typical integration times are very short due to the high light throughput. However, the disadvantage is the relatively low spatial resolution of the spectral information.
# Modeling Network-Controlled Device-to-Device Communications in SimuLTE
## 6 Performance Evaluation
### Profiling
The influence on the execution time of the number of UE pairs and system packet rate in the D2D scenario can be formally assessed through \(2^{k}r\) factorial analysis, using the parameter ranges in Table 2. The results in Table 3 show that about 98% of the variation in the execution time is due to the number of UE pairs, and that the remaining 2% is due to the packet rate. The reason behind this large imbalance (confirmed by Figure 17) is that CQI computation and storage, which is done per UE, is performed quite frequently (every 5 ms), and it involves a relatively large amount of computations, whereas MAC-layer transmissions occur comparatively less often. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Min** & **Max** \\ \hline
**\#UE pairs** & 10 & 50 \\
**System packet rate** & 100 Pkt/s & 500 Pkt/s \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameter range for factorial analysis. Figure 16: Execution time for an increasing number of UEs, obtained with a per-UE inter-packet-time of 0.1 s.  
Figure 17: Breakdown of execution times with 10 UE pairs, 0.1 s inter-packet time (leftmost bars in Figure 16). Observing the above results, we can summarize that introducing D2D communications does not increase the processing overhead of packets flowing within the LTE protocol stack. On the other hand, the execution time is affected by the need of computing and storing additional channel information for D2D links.
# An Overview of Position Sensorless Techniques for Switched Reluctance Machine Systems
## 2 The Composition of SRM Drive System  
### Structure of an SRM System  
A typical SRM drive system, shown in Figure 3, is composed of a controller, inverter, motor body, power supply, and various sensors. The motor converts the electric energy provided by the DC power supply into mechanical energy to drive the load. The controller generates the corresponding driving signal through the feedback signal of the sensor to control the motion state of the motor. It is extremely important to detect accurate and effective rotor position signals for SRM control. The position sensorless method can significantly increase the stability of the system. These methods estimate the rotor position by adding hardware, a magnetic model, or control algorithm.  
The converter is important to the SRM system because of the sampling of phase current, bus current, and phase voltage. As shown in Figure 4, the A phase of the three-phase half-bridge converter is used as an example to illustrate the process of converter operation. Figure 4a is the circuit topology of a single-phase half-bridge, which consists of two controlled switches, \(S_{A1}\) and \(S_{A2}\), and two diodes, \(D_{A1}\) and \(D_{A2}\). The converter has three modes: magnetization, zero freewheeling, and demagnetization. As shown in Figure 4b, when winding A needs to establish a magnetic field, \(S_{A1}\) and \(S_{A2}\) are turned on. In the freewheeling mode, only \(S_{A1}\) or \(S_{A2}\) is turned on, as shown in Figure 4c. When winding A no longer needs to establish a magnetic field, \(S_{A1}\) and \(S_{A2}\) are turned off at the same time, which will force the current commutation of winding A to achieve the purpose of eliminating the magnetic field. Meanwhile, the voltage across the winding is the negative phase voltage -\(U_{dc}\).  
Figure 2: Classification of control methods of position sensorless.
# Accelerated Charge Dissipation by Gas-Phase Fluorination on Nomex Paper
## 3 Results and Discussion
### Space Charge Accumulation and Dissipation  
To investigate the influence of gas-phase fluorination on the space charge behavior of oil-impregnated Nomex paper, PEA was used to measure the space charge accumulation and dissipation of sample F0 and F30. Figure 7 shows the space charge distribution of sample F0 and F30 under different polarization times. Firstly, a large amount of space charge accumulated at the sample-electrode interface, i.e., positive charges accumulated near the anode (peak A) and negative charges accumulated near the cathode (peak B). With the increasing of polarization time, the positive charges accumulated near the anode gradually migrated to the matrix and positive charge peaks C appeared in the middle of the sample. The peak charge volume increased with polarization time, which indicated that the induced positive charge migrated from the anode to the internal part. The positive charge peak in F30 was lower than that in F0 before 60 s, indicating that fluorination could suppress the charge injection to some extent. A similar phenomenon could also be found in fluorinated LDEP [31]. From Figure 7a, it can be seen that for the non-fluorinated samples, the charge volume of peak B gradually increasedand finally reached a stable value with the extension of polarization time, but the peak position still maintained in the sample-electrode interface. However, negative peak charge volume of sample F30 increased first and then decreased after the 60 s of polarization and the peak position tended to move inward in samples.  
Figure 8 presents the space charge distribution of sample F0 and F30 after voltage-off. It is observed that the density of negative charge near the cathode and the positive charge in middle of the sample decreased over time, meanwhile a negative peak appeared near the anode and then decreased to zero finally. By comparing the space charge decay rate of samples F0 and F30, it is evident that the fluorinated sample showed a better space charge suppression effect. From the trap energy and density distribution of the samples shown in Figures 5 and 6, it can be inferred that lower surface hole and electron deep trap density and trap energy level in the fluorinated sample were also beneficial for the space charge dissipation after the voltage off.
# On the Use of Structured Prior Models for Bayesian Compressive Sensing of Modulated Signals
## 2 Proposed Method
### Problem Formulation
#### 2.1.3 Bayesian Framework  
The CS problem is considered through a Bayesian framework. This will offer not only an automatic recovery algorithm to compute point estimates, but also a full posterior distribution to compute additional information, such as credible bars. Furthermore, Bayesian approaches with sparsity-inducing priors offer a large flexibility via hierarchical models and require less restrictions when compared to standard approaches involving \(\ell^{0}\) and \(\ell^{1}\) penalty functions [47].  
In the sequel, the coefficients of \(\mathbf{\omega}\) are assumed to be i.i.d Gaussian variables of unknown variance \(\tau>0\). The observation distribution density is then given by  
\[\mathrm{p}(\mathbf{y}|\mathbf{x},\tau)=(2\pi\tau)^{-\frac{M}{2}}\exp(-\frac{ \|\mathbf{y}-\mathbf{H}\mathbf{\Phi}^{-1}\mathbf{x}\|^{2}}{2\tau}) \tag{2}\]  
We aim at estimating \(\tau\) jointly with \(\mathbf{x}\). Hence, we assign a non-informative Jeffrey prior for \(\tau\): \(p(\tau)\propto\tau^{-1}\)[48].  
The use of sparsity-favoring prior distributions is the core of Bayesian CS modeling. In this paper, we particularly consider hierarchical models, as they exhibit large flexibility to take the structures of sparse coefficients through latent variables into account. In this paper, we will consider two families of models, namely continuous hierarchical models and spike-and-slab models (see Appendix A, for a review of these families of models). In the following, we will discuss their extensions to take the structure of the target spectrum into account.
# Micro-Gear Point Cloud Segmentation Based on Multi-Scale Point Transformer
## 4 Results and Discussion
### Ablation Experiment  
To further verify the effectiveness of the network, an ablation study was designed, as shown in Table 4. Since the MFF-PT, compared to the original PT network, primarily adds multiscale fusion for global feature extraction and an Up-Down-Up point cloud expansion module, the ablation study was organized into five groups.  
Model A is the original PT model, serving as the control group.  
Model B includes an added pathway of Farthest Point Sampling (FPS), MLP, and a stacked upsampling module (FMU). After FPS, the number of points is halved, but because the Down-Up-Down (DUD) module's comparison is for subsequent models (like Model D), another form of upsampling was necessary. Therefore, the upsampling method from the DUD module was used, which involves computing k-nearest neighbors and performing bilinear interpolation, as depicted in Figure 12b (upsampling unit). Due to having only one pathway of FMU, the max pooling operation is omitted.  
Model C is similar to Model B but includes two FMU pathways, hence requiring the first max pooling operation.  
Model D is essentially the same as Model C but includes three FMU modules.  
Model E employs the DUD model, which corresponds to the MFF-PT model.  
Table 4 shows that adding FMU modules indeed improves segmentation precision, but the degree of improvement is significantly related to the number of modules. A single FMU module increases mIoU by only 0.1%, possibly due to the absence of subsequent Max-Pooling, leading to poor global feature extraction. However, adding multiple FMU  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **1 FMU** & **2 FMU** & **3 FMU** & **UDU** & **mIoU** \\ \hline A & & & & & 85.2 \\ B & \(\surd\) & & & & 85.3 \\ C & & \(\surd\) & & & 85.5 \\ D & & & \(\surd\) & & 85.7 \\ E & & & \(\surd\) & \(\surd\) & 86.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation experiment.  
Figure 16: Training process performance indicators. (**a**) Multilayer Feature Fusion PointTransformer; (**b**) PointTransformer.  
modules can substantially enhance mIoU, with three modules achieving a 0.5% improvement. Replacing the upsampling module with the UDU module also contributes a 0.6% increase in the model's mIoU. This improvement is attributed to the UDU module's ability to effectively increase the number and features of newly generated point clouds through its multiple upsampling and downsampling processes.
## 2 Datasets and Method  
### Description of Datasets  
Two datasets, (1) Dayton Annotated Laser Earth Scan (DALES) datasets provided by University of Dayton, Ohio, and (2) Vaihingen dataset of the International Society for Photogrammetry and Remote Sensing (ISPRS) Benchmark Datasets, were used. The DALES datasets and ISPRS Vaihingen datasets are for both training and testing the DL model, and evaluating the trained DL mode as new data, respectively.
## 2 Characterization of FBG  
Light that meets the Bragg condition is reflected and its peak wavelength is called the center wavelength \(\lambda_{B}\)[17; 18]. The expression relation is as follows:  
\[\lambda_{B}=2n_{eff}\Lambda \tag{1}\]  
Fiber-optic gratings can cause \(\Lambda\) and \(n_{eff}\) to change when subjected to temperature or stress, and the reflected light wavelength \(\Delta\lambda_{B}\) can be shown as follows:  
\[\Delta\lambda_{B}=2\Delta n_{eff}\Lambda+2n_{eff}\Delta\Lambda \tag{2}\]  
From the equation, it is shown that \(\lambda_{B}\) varies as a result of changes in \(n_{eff}\) and \(\Lambda\).  
Since the change in \(\lambda_{B}\) depends on \(n_{eff}\) and \(\Lambda\), stress and temperature changes will directly change \(\lambda_{B}\). Based on this principle, sensors with different functions can be developed.
# Research on O-ring Dimension Measurement Algorithm Based on Cubic Spline Interpolation
## 4 Experiments and Discussion  
### Calibration Experiments  
To get the actual size of the O-ring from the sub-pixel image, we have to know the actual size of each pixel point of the camera, so the pixel calibration must be done in advance [27].  
We have selected a standard block with a diameter of 17 mm using a sub-pixel edge measurement algorithm based on third spline interpolation for 20 measurements to obtain the pixel diameter size of the fitted circle [28]. The image of standard gauge block is shown in Figure 10. Then its actual value is calculated as a ratio to the pixel length, here we call it K-value. k-value is calculated as follows:  
\[K_{subpixel}=r/d_{pixel} \tag{27}\]  
where r is the diameter of the block, \(d_{pixel}\) is the pixel diameter size of the block, and the \(k_{subpixel}\) value is in \(mm/pixel\).  
Figure 10: Images of the 17-mm standard gauge block.  
Figure 9: O-ring inner and outer circle fitting diagram. (**a**) Fitting of the O-ring outer diameter circle; (**b**) fitting of inner diameter circle of the O-ring.  
The measurement results are shown in the data in Table 1, and we averaged the 20 values obtained so as to obtain a standard \(k_{subpixel}\) value of 0.037623 \(mm/pixel\).  
In order to compare with the results of the algorithm in this paper, the classical algorithms, the Sobel and Canny algorithms, were chosen here for a standard block with a diameter of 17 mm and also 20 measurements were carried out, consistent with the above principle, and obtained \(k_{sobel}=0.037202\) mm/pixel, \(k_{canny}=0.037305\) mm/pixel respectively.
## 3 Results  
Table 1 shows the average temperature and number of pest type individuals detected during the experiment for each week. As shown in the table, a density of more than 1300 insects per week of a single type of pest can be detected, as well as a lower density of _Anthonomus eugenii_, and variable amounts of other types of pests. A relationship between temperature and the number of insects detected can also be observed. Through the SIFT algorithm, tests were carried out to detect the different genera of insects (Table 2). In the images, five genera were found; in the data obtained in Week 1 of the experimentation, the percentages obtained in the detection reached an average precision of 90%. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Genus**} & \multicolumn{3}{c}{**Number of Insects**} & \multicolumn{2}{c}{**Number of Insects**} & \multirow{2}{*}{**Accuracy Rating**} \\  & **Manual Method** & & & & & \\ \hline _Bactericen cockrell_ & 12 & 12 & 100\% \\ _Thrips tabacci_ & 56 & 28 & 50\% \\ _Bentisia tabaci_ & 36 & 36 & 100\% \\ _Epitrix cucumeris_ & 1 & 1 & 100\% \\ _Myzus persicae_ & 7 & 7 & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Detection percentages of specimens according to the five genera. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Time in Weeks**} & \multicolumn{5}{c}{**Number of Specimens**} & \multirow{2}{*}{**Time in Weeks**} \\  & **Temperature** & _Bactericera_ & _Thrips tabacci_ & _Bemisia tabaci_ & _Epitrix cucumeris_ & _Anthonomus eugenii_ & _Myzus persicae_ \\  & \({}^{\circ}\)**C** & _cockrell_ & _Thrips tabacci_ & _Bemisia tabaci_ & _Epitrix cucumeris_ & _Anthonomus eugenii_ & _Myzus persicae_ \\ \hline  
\hline \hline \end{tabular}
\end{table} 
Table 1: Weekly numbers of specimens by type of pest and average temperature. In the insect pest detection images, 17 insects of different species were found; the results (Table 3) for each genus were obtained, showing up to 100% accuracy for the detection of _Anthonomus eugenii_, _Myzus persicae_, and _Epitrix cucumeris_. Figure 2 shows the results after applying image processing, starting with the original image of each genus, CIE*L*A*B filter, and ROI, and applying the same processing to each type of pest detected. Table 4 shows the invariant characteristics of each genus, obtaining the mathematical values of each insect, and the size in square millimeters representing the approximate real size of each insect.
# DFT Characteristics of Charge Transport in DBTP-Based Hole Transport Materials
## 1 Introduction  
Solar cells based on the perovskite structure from methylamine and lead-halide have attracted considerable attention in recent years. Perovskite solar cells (PSCs) are a promising photoelectric conversion device because of their fast charge transport and high power conversion efficiency (PCE). To produce high performance, suitable HITMs play a significant role in quickly obtaining and collecting holes generated by light in the perovskite layer [1]. An excellent hole transport material (HTM) should reduce the rate of charge recombination, generate continuous charge injection, and have high hole mobilities. The state-of-the-art HTM is 2,2',7,7'-Tetrakis-(N,N-di-4-methoxyphenylylamino)-9,9'-spirobiltoorene (spiro-OMeTAD), which has still many deficiencies in some aspects: difficult synthesis processes, high cost, and low hole mobility. The hole mobilities of HITMs in PSCs can be improved using p-dopants and some doping reagents contains cobalt(III) complexes and tert-butylpyridine (TBP) [2]. Concurrently, from the viewpoint of HTM, molecular design strategies are used to control their structures and high hole mobilities [3, 4], and HTM materials have been developed, such as triphenylamine [5], cyclopentadithiophene [6], small amide molecules [7], and Cyclopenta[hi]aceanthrylene [8]. The di(1-benzoth)[3,2-b-2',3'-d]pyrrole (DBTP) moiety contains the 4H-dithieno[3,2-b-2',3'-d]pyrrole (DTP), electron withdrawing acyl substituents, and an extended \(\pi\)-conjugated system, which strengthens \(\pi\)-electron delocalization and improves charge carrier transport [9]. Based on the recently reported HITMs containing DBTP as a core unit [10], the strong electron-donating moieties and two arylamine wings are used to synthesize mDPA-DBTP and pTPA-DBTP (can be shown in Figure 1) [11]. In this study, DFT calculations were used to investigate the structure and charge mobility of HITMs (mDPA-DBTP and pTPA-DBTP) to reveal the relationship between the structure and performance of HTM.
## 4 Discussions of Engineering Applicability of Theoretical Solutions
### The Influence of the Distance between Two Adjacent Wall Columns  
Based on the parameters of the example in Section 3.1, the distance between two adjacent wall columns is adjusted to 2.5 m, 3 m, 3.5 m, 4 m, and 4.5 m, respectively. The relationships between the bending moment at the bottom of the column and the distance of two adjacent wall columns are calculated by three-dimensional Midas gen software, and analytical solutions are obtained via Equation (38), as shown in Figure 17.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Height Difference between Two** & **Analytical** & **Simulation** & **The Error between** \\
**Adjacent Layers** & **Solution** & & **The Dem** \\ \hline
2 & 3532.68 & 3784 & \(-\)6.64\% \\ \hline
1 & 3532.68 & 3760 & \(-\)6.05\% \\ \hline
0 & 3532.68 & 3717 & \(-\)4.96\% \\ \hline \(-\)1 & 3532.68  
We can see in Figure 17 and Table 3 that analytical solutions are well coincident with the results obtained by software under the different distances between the wall columns, and the maximum error is less than 13%. Hence, it shows that the analytical solution of Equation (38) applies to various column spacing.