esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The usage of semiconductor nanocrystals (NCs) as the light harvesting materials in thin film solar cells has attracted intense research over a period of nearly two decades [1; 2; 3; 4; 5; 6]. The solution-processed hybrid solar cells (HSCs) are recently developed and offer the potential advantages of low cost, low materials consumption, simple fabrication process, etc. Among all kinds of the NCs in solution-processed HSCs, CdSe [7], CdTe [8], ZnO [9], TiO\({}_{2}\)[10] and Pb5 [11] have been well used due to their appropriate bandgap, stability and high optical absorption coefficient. In general, solution-processed HSCs consist of a transparent conducting metallic oxide layer (such as ITO, FTO or AZO), an electron transfer (ETL), a photoactive layer, a hole transfer layer (HTL) and a back contact electrode layer. To prepare HSCs, a polymer and NCs are firstly dispersed into a solvent such as octane, toluene, alcohol, chloroform or water with typical concentrations around 10 mg/mL [12; 13]. The hybrid solution is then deposited on a substrate by spin-casting or printing. The photovoltaic performance of HSCs can be easily adjusted at the atomic or molecular level. Compared to organic solar cells (OSCs), the HSC emerged very late and the progress has been relatively slow, which is mainly due to the compatibility issue between organic polymers and NCs. For example, the solubility of the two materials in some solvents is quite distinctive due to the large difference of polarity, and the operation mechanism or physical essence for organic/NC junctions is complex and still controversial [14; 15]. There are many strategies to improve the HSC properties, in which the device architecture of the HSC shows a significant effect on the efficiency of the HSC. In this review, we report the advances in hybrid solar cells based on the solution-processed semiconductor NC/polymer and focus particularly on the optimized device design for improving HSC performance. Here, we begin with introducing the fabrication of HSCs by using a single bulk heterojunction (BHJ) active layer. Then, the fundamentals of hybrid bulk heterojunction active layers are depicted. The HSCs with multi-photoactive layers and complex structure are then presented. The hole/electron transfer equilibrium is also a key factor for improving NC solar cell performance. Here, we emphasize to address the numerous strategies using organic materials as hole/electron transfer layers for the efficient collection of holes and electrons. To extend the range of the spectrum response for HSCs, a tandem structure is preferred to further improve the device performance. In the end, we will emphasize the challenges and future prospects in the architecture design of HSCs towards ~15% power conversion efficiency (PCE) aiming for commercial application.
# A Dynamic Service Reconfiguration Method for Satellite-Terrestrial Integrated Networks
## 5 Simulation Experiment and Analysis
### Result Analysis
The MGA algorithm and TS-MAPSCH algorithm belong to heuristic algorithms, while the SEC-SM algorithm mainly considers the traffic change and has no obvious perception of the change of network topology state, so the effect is poor. Comparing (a) and (b) in Figure 5, the success rate of service reconfiguration of the four algorithms decreases, while the request arrival rate increases to \(\lambda=0.2\), and the performance of the MGA algorithm is slightly better than that of the TS-MAPSCH algorithm. The reason for the decline in reconfiguration success rate is that the migration reconfiguration algorithm may face the condition that nodes and links are occupied and computing and bandwidth resources have not been released when tackling service requests with high arrival density, so its reconfiguration success rate is low. The performance of the  
Figure 4: Comparison of end-to-end delay: (**a**) traffic load of 60%; (**b**) traffic load of 90%. Figure 3: Comparison of algorithm convergence. MGA algorithm is slightly better because the global solution ability and efficiency of the MGA algorithm based on the genetic algorithm are slightly better than the TS-MAPSCH algorithm based on the Tabu algorithm. Figure 6 shows the algorithm running time under different traffic loads. With the gradual increase in traffic load, the running time of each algorithm also increases. The simulation is set between 40-90% of the traffic load. Obviously, the average running time of the proposed FQGA-SR algorithm is much lower than the MGA algorithm, the TS-MAPSCH algorithm and the SEC-SM algorithm. Taking 90% load as an example, the average running time of the FQGA-SR algorithm is 2.27 s, the MGA algorithm is 4.38 s, the TS-MAPSCH algorithm is 5.15 s, and SEC-SM algorithm is 6.89 s. Compared with the latter three algorithms, the running time of the former is increased by 48%, 55%, and 67%, respectively. As the FQGA-SR algorithm uses the quantum superposition principle to accelerate the calculation and can quickly decide the migration and reconfiguration scheme, the calculation efficiency is much higher than the other three algorithms. The calculation efficiency of the MGA algorithm is higher than the other two algorithms when the solution space is large, but the solution obtained at this time may not be the global optimal solution.
# Secure LoRa Firmware Update with Adaptive Data Rate Techniques +
### Contributions and Outline  
This work expands upon prior ADR research in [19] by applying it to a firmware update process. The research described in this paper makes the following contributions:  
1. A method is proposed for energy optimized firmware updates for mobile LoRa devices that is both secure and reliable.
2. The method is designed to work in LoRa ad-hoc networks without LoRaWAN.
3. The method leverages LoRa ADR techniques to minimize energy consumption by reducing the firmware image receive time.
4. A battery consumption approximation technique is provided to quantify the process energy expenditure.  
Figure 1: Roper device in housing and the sensor board printed circuit board (PCB).  
A security assessment is performed to evaluate privacy and authenticity.
## 3 Experiment
### Visualization Analysis  
To further validate the effectiveness of our algorithm, we tested the segmentation performance of the trained model on the Ship dataset and visualized some of the segmentation results, as shown in Figure 13. The image segmentation results of our method are superior to DeepLabv3+, Pyramid Scene Parsing Network (PSPNet), and FCN. Firstly, compared to DeepLabv3+, our method performs better in detail information such as small objects and edges, and also segments edges and small objects more clearly. For example, in the first row of images in the figure, the segmentation performance of the ship's edge and the segmentation line of the water surface has been improved. Compared to the visualization results of FCN segmentation, FCN's segmentation results are too rough. For instance, large objects like ships are coarsely segmented at close range, and the labels of pixels inside the object contours are also confused, resulting in a loss of detailed information for most objects and easily leading to internal discontinuities.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Background\%** & **Ship\%** & **mIoU\%** & **FreqW Acc\%** & **Overall Acc\%** & **mAcc\%** \\ \hline DeepLabv3+ & 96.31 & 48.41 & 72.43 & 95.61 & 96.32 & 90.33 \\ PSPNet & 97.56 & 54.41 & 75.96 & 96.26 & 96.39 & 90.41 \\ Danet & 98.18 & 61.74 & 80.01 & 96.52 & 96.85 & 91.62 \\ FCN-16s & 96.84 & 41.98 & 69.48 & 95.81 & 96.77 & 91.25 \\ FCN-32s & 96.77 & 43.92 & 70.36 & 95.53 & 96.84 & 91.39 \\ Denseaspp & 98.33 & 70.14 & 84.71 & 96.97 & 97.84 & **93.12** \\ DeepLabv3 (ResNet50) & 98.74 & 66.31 & 83.65 & 96.76 & 97.39 & 91.66 \\ Ours & **98.92** & **79.86** & **89.17** & **97.52** & **98.72** & 92.61 \\ \hline \hline \end{tabular} Bold and underline indicate the best and second best performance, respectively.  
\end{table}
Table 2: Result of different models on the Infrared-ship dataset.  
Figure 12: Lateral temperature distribution of abnormal ship hull samples.  
For the images in the second and third rows, compared to DeepLabv3+, the segmentation of each category is more refined, and our proposed method also performs better in details such as edges and small objects. For the images in the fourth and fifth rows, FCN and PSPNet still only roughly segment objects of each category, and FCN's segmentation results are still relatively rough. The segmentation results of the DeepLabv3+ algorithm show that some pixel categories are confused for objects such as ships near the dock. Our method not only enhances the localization of boundary pixels for these targets but also alleviates the discontinuity and label confusion problems of large targets.
## 3 Results  
### Data Sources and Operating Environment  
The effectiveness of the proposed method was demonstrated based on three standard databases, namely AR [22], Extended Yale B [23], and ORL [24]. These databases contain several deviations from ideal conditions, including pose, lighting, occlusion, and gesture changes. Appropriate experimental results have demonstrated that the developed method performs well for severe continuous occlusion with small changes in pose, scale, illumination, and rotation. All the above experiments were run on the Windows 10 operating system (Intel Core i7-4770 CPU M620 @ 3.40 GHz and 8 GB RAM), and the programming environment was Python 3.7.
## 2 Materials and Methods
### Imaging System and Image Acquisition  
A schematic diagram of the image acquisition system is shown in Figure 1. Typically, a vision system consists of the illumination component to illuminate the sample under test; the camera to acquire an image; personal computer or microprocessor system to provide disk storage of images and computational capability.  
In an image acquisition system, choosing the right lighting strategy remains a difficult problem because there is no specific guideline for integrating lighting and machine vision application. Despite this, some rules of thumb exist [29] which suggest that fluorescent bulbs are inherently more efficient and produce more intense illumination at specific wavelengths. Moreover, the fluorescent light provides a more even, uniform dispersion of light from the emitting surface [30]. A 25-40 kHz ring-shaped compact fluorescent light is used for illumination in this setup. Apart from the illuminant, the surface geometry is also important in the illumination design. In this present work, a diffuse illuminator is used to produce uniform lighting as shown in Figure 1. Such a setup is extremely useful for visual inspection of grains and oilseed with a success rate almost reaching 100% [31].  
The system was enclosed in a dark chamber to prevent exposure to stray light. The digital camera (Canon EOS 1300D) was set in the manual mode for image acquisition with an ISO of 400 and a shutter of 1/30 s. The images were taken with a black background for basmati rice sample with different orientation and quality in a scattered arrangement for training and testing. Camera aperture and focus were adjusted to make individual grain boundaries distinguishable in the picture. A total of 40 images were acquired and saved in raw format, in which no adjustment (e.g., white balance) was applied.
# Passive Intermodulation at Contacts of Rough Conductors
## 3 Thermal Nonlinearities at Contacts of Good Conductors
### Electro-Thermal Effect in Contact Joints and PIM Generation
The ET-PIM generated by joints of rough surfaces is related to their effective contact resistance \(R_{u}\) defined in (3) and depends on the effective size \(a_{\mathit{off}}\) of a contact area and an averaged contact resistivity \(\rho_{\mathit{env}}\) that increases with temperature as defined by (5). This results in the higher ET-PIM level at rougher surfaces that was observed in the printed circuits and conductor joints [49, 32, 64].
## 3 Effect of Strain Rate and BRB on Seismic Response of a CFST Frame Structure
### Influence of BRBs on the Seismic Response of the CFST Structure  
BRBs are a type of metal damper. By providing a restraining casing that surrounds an ordinary brace, the buckling of the brace under compression can be suppressed without limiting the free expansion and contraction of the brace under stress, so that the brace can yield over the entire cross section either in compression or in tension. Through reasonable design, BRBs can yield first under a strong earthquake. The input seismic energy is dissipated through the hysteresis of the BRBs to protect the main frame structure.  
The CFST structure without BRB (CFST-A) and the CFST structure with BRBs (CFST-B) are used in the nonlinear seismic response analysis. The maximum roof displacement and ISDR are taken as the structural response indexes to extract the time history analysis results. Table 2 lists the maximum roof displacements and ISDRs of CFST-A and CFST-B and the corresponding reduction percentages (RPs) under various conditions. The results show that the responses of CFST-B are generally smaller than those of CFST-A. Specifically, the maximum roof displacement decreases by 14.1% to 42.7%, and the maximum ISDR decreases by 21.5% to 40.2%.  
Figure 11 compares the roof displacement time history curve and ISDRs of CFST-A and CFST-B under Taft ground motion (PGA = 1.0 g). The maximum values of the roof displacement and ISDR of CFST-B are smaller than those of CFST-A. The ISDRs of CFST-B significantly decrease compared with those of CFST-A. Under the Taft ground motion, the maximum ISDR of CFST-B is \(7.62\times 10^{-3}\), with a decrease of 37.5% compared with that of CFST-B.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Input Motions**} & \multirow{2}{*}{**PGA (g)**} & \multicolumn{4}{c}{**Roof Displacement (mm)**} & \multicolumn{4}{c}{**ISDR (10\({}^{-3}\))**} \\ \cline{3-8}  & & **CFST-A** & **CFST-B** & **Difference (\%)** & **CFST-A** & **CFST-B** & **Difference (\%)** \\ \hline \multirow{8}{*}{San Fernando} & 0.1 & 25.46 & 19.34 & \(-\)24.0 & 0.85 & 0.64 & \(-\)24.7 \\  & 0.2 & 50.39 & 38.65 & \(-\)23.3 & 1.66 & 1.
## 4 Discussion
Further work is needed on debugging the software and adding features for digitizing error checking. The point cloud processing method may also be proposed for removing gross and systematic errors of digitization, merging point clouds from multiple digitizations, and designing suitable parameters of the surface polygonization process from the obtained point cloud. Adding a graphical user interface, a control panel with buttons, a representation of errors and calibration accuracy, a real-time display of the digitized surface and the current point cloud, and real-time control of adjustable internal camera parameters may be important additions. Future research may focus on the design of a desktop or portable prototype digitizing device for industrial use in digitizing physical objects. Implementing the use of external reference points for merging point clouds from multiple digitizations could enable sequential scanning of objects as the device is moved within a scene, providing accurate calculation of 3D coordinate points and synchronizing live streaming of two real-time images using ESP32-CAM devices, thus performing digitization from continuous surface scanning. Eventually, the use of structured light, multiple laser line projectors and a more precise laser projector with higher light intensity might be explored. Research could also focus on the accuracy and precision of the point cloud obtained from the prototype digitizing device under different lighting conditions, using different laser or other structured light projectors, with different external and internal device parameter settings, at different distances from the surface to be digitized, and using different optical sensors and lenses. Finally, future research may explore the use of the prototype digitization device in the automation and robotization of manufacturing processes, e.g., computer vision, and its use in the sensing of packages on a conveyor belt or robotic vision.
## 3 Results
### Meteorology
The results demonstrated significant correlations between the two types of data in terms of Tavg (R2 = 0.92) and Tmin (R2 = 0.90) (Figure 9a,b). This meant that the meteorological reanalysis datasets could be used to effectively estimate the on-site temperature variations. However, in the case of VPDavg, the correlation between the NCEP-DOE Reanalysis II meteorology data and the site meteorology data was poor (R2 = 0.31) (Figure 9c) because the meteorological reanalysis datasets failed to estimate the on-site moisture conditions. Regarding PAR, the NCEP-DOE Reanalysis II meteorology data commonly overestimated the site meteorology data (Figure 9d). The correlation (R2 = 0.62) was stronger than that of VPDavg but weaker than those of Tavg and Tmin.
## 4 The DABC Algorithm for an EAHFSP
### Initialization  
For an EAHFSP with \(n\) products and \(w=\sum_{k=1}^{n}\lvert N_{k}\rvert\) components, which are manufactured in \(m\) processing stages and an assembly stage, the part-based encoding and decoding method [21] is used. A solution can be represented as a component scheduling string \([\theta_{1},\theta_{2},\cdots,\theta_{w}]\), a machine assignment string \([\pi_{11},\cdots,\pi_{1m},\cdots,\pi_{n\pi_{1}},\cdots,\pi_{n\pi}]\), and a speed selection string \([z_{11},\cdots,z_{1m},\cdots,z_{w1},\cdots,z_{wm}]\), where \(\theta_{r}\in\{1,2,\cdots,w\}\), \(\pi_{ri}\in S_{i}\) is the unrelated parallel machines assigned for the processing of component \(\theta_{r}\) at stage \(i\), and \(z_{ri}\in V\) indicates the processing speed selected by processing machine \(\pi_{ri}\).  
Decoding is described as follows: Beginning with the first component \(\theta_{1}\), select the corresponding processing machine and processing speed. The first \(m\) machines are assigned to the \(m\) stages of component \(\theta_{1}\), then the second \(m\) machines are arranged for the processing of the \(m\) stages of component \(\theta_{2}\), and so on. Once the processing of all required components of product \(P_{k}\) is completed and the assembly machine is ready, product \(P_{k}\) can be assembled until all products are finished.  
To guarantee the diversity of solutions, the initial population \(P\) with \(N\) solutions is randomly generated. For each solution \(x_{i}\), the fitness value \(F_{i}\) is computed as  
\[F_{i}=max\{rank_{i}\}-rank_{i}+crowd_{i}\Big{/}\sum_{k\in\Theta_{\pi_{rank_{i} }}}crowd_{k} \tag{16}\]where \(rank_{i}\) is the rank value after executing non-dominated sorting [46] on \(x_{i}\), \(crowd_{i}\) is the crowding distance defined in [47], and \(\Theta_{rank_{i}}\) is a set of solutions with the rank value of \(rank_{i}\).  
For the manufacturing example in Section 2, a solution consists of a scheduling string \([9,10,2,7,6,4,1,5,3,8]\), a machine assignment string \(\{M_{11},M_{21},M_{12},M_{22},\)\(M_{12},M_{22},M_{11},M_{21},M_{22},M_{12},M_{12},M_{22},M_{12},M_{22},M_{12},M_{22}\}\), and a speed selection string \([v_{1},v_{1},v_{2},v_{1},v_{5},\)\(v_{1},v_{4},v_{1},v_{5},v_{3},v_{1},v_{3},v_{1},v_{1},v_{4},v_{1},v_{2},v_{4},v_{5}]\), \(V=\{1.0,1.3,1.55,1.8,2.0\}\), \(E_{ijl}=4\times v_{i}^{2}\), \(SE_{ij}=1\), \(AE_{k}=4\), and \(ASE=1\). Figure 3 displays the schedule plan of this example. \(f_{1}=121.59\). At the manufacturing stage, the total energy consumption of machines in the processing mode is \(EC_{1}=1631.21\); the energy consumption of the processing machines in the stand-by mode is \(EC_{2}=32.16\). At the assembly stage, the energy consumption in the assembly mode is \(EC_{3}=268.02\); the energy consumption in the stand-by mode is \(EC_{4}=54.59\), and \(f_{2}=1985.97\).
# Responses of Seasonal Indicators to Extreme Droughs in Southwest China
## 2 Materials and Methods
### Data Collection and Processing
#### 2.2.2 Vegetation Data  
A total of six parameters were used to analyze the vegetation phenology derived from the Leaf Area index (LAI) (Figure 2). The parameters of the growing season were extracted from LAI image series in TIMESAT 3.3 software by the Dynamic Threshold method, which is suitable for studying phenology by eliminating the influence of background and vegetation types [52; 53]. The Dynamic Threshold method defines the growth nodes by the ratio of the seasonal variations in LAI. In Figure 2, the curve represents the LAI series from the product of Global LAnd Surface Satellite (GLASS). A period between a minimum value (marked by \"c\" in Figure 2) and the next minimum value (marked by \"d\") is defined as a growth season. Peak (marked by \"p\") means the maximum value in a growth season. The Start of a Season (SOS) (marked by \"a\") is defined from the curve at the time when the LAI has increased by 35% of the difference between (c) and (p). The parameter of 35% was selected referring to previous research [53; 54; 55]. The End of the Season (EOS) (marked by \"b\") is defined in a similar way. Length of Season (LOS) (marked by \"h\") means time between SOS and EOS [52].  
Figure 2: Seasonal parameters: (**a**) start of season, (**b**) end of season, (**c**) minimum Leaf Area index (LAI) value in the left, (**d**) minimum LAI value in the right, (**h**) length of season, (**p**) peak. (DOY means day of year).  
LAI used in this study is the product of GLASS, a long-term and high-precision (0.05\({}^{\circ}\)) global surface remote sensing product based on multi-source remote sensing data and ground measured data [56; 57]. It was obtained from National Earth System Science Data Sharing Infrastructure ([[http://www.geodata.cn/](http://www.geodata.cn/)]([http://www.geodata.cn/](http://www.geodata.cn/))). This study excluded multiple growth seasons, such as multiple cropping, because crops only contributed 15.8% to the all vegetation in the study area. All vegetation types were regarded as one growth season every year. SOS, EOS and LOS were extracted for each pixel annually using TIMESAT software.  
The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI) and GPP were used to analyze the changes in vegetation due to droughts. NDVI and EVI were used to examine the vegetation greenness and GPP for productivity. Both NDVI and EVI data are from MOD13Q1 V6 product of Moderate-resolution Imaging Spectroradiometer (MODIS) with a spatial resolution of 250 meters and a temporal resolution of 16 days [58]. The MOD17A2H V6 GPP product used in this study is a cumulative 8-day composite with 500 meters resolution [59].