esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Instrument Design
### Detailed Designs
#### 4.2.5 Electronics Design  
The electronics design of the HEPD mainly includes the front-end analog electronic circuit of each probe, the back-end digital acquisition and processing circuit, and the satellite bus interface. The front-end signal processing electronics mainly realizes the functions of pre-amplification, pulse shaping, main amplification, and peak holding for the charge pulse signal output by the semiconductor detector in each probe. Subsequently, this signal is sent to the back-end digital circuit part, and the ADCs complete the analog-to-digital conversion of the peak holding signal. The FPGA circuit mainly completes the functions of data processing, compression, storage, and packaging of the signals collected by the ADC, and communicates with the satellite through the interface bus to download the detection data packages. In addition, the instrument electronics also includes power supply modules, noise detection circuits, telemetry interfaces, temperature detection circuits, and sensor bias circuits. The electronics principle of the high-energy particle detector is shown in Figure 12. The sensor and front-end electronics in Figure 12 only show the probe of the \(-\)X direction, and the probes of the other two directions have similar electronics.  
\begin{table}
\begin{tabular}{c c c} \hline
**Energy Range** & **Flux** & **FY-3E Satellite Maximum** \\
**(MeV)** & **(cm\({}^{2}\)ssr)** & **(Counts/s)** \\ \hline
3–5 & \(1\times 10^{1}\)–\(3\times 10^{3}\) & \(1\times 10^{3}\) \\
5–10 & \(1\times 10^{1}\)–\(2\times 10^{3}\) & \(6\times 10^{2}\) \\
10–26 & \(1\times 10^{1}\)–\(1\times 10^{3}\) & \(3\times 10^{2}\) \\
26–40 & \(1\times 10^{1}\)–\(6\times 10^{2}\) & \(2\times 10^{1}\) \\
40–100 & \(1\times 10^{1}\)–\(1\times 10^{3}\) & \(3\times 10^{2}\) \\
100–300 & \(1\times 10^{1}\)–\(1\times 10^{3}\) & \(3\times 10^{2}\) \\ \hline \end{tabular}
\end{table}
Table 4: Estimation of maximum count rate for high-energy proton probes.  
\begin{table}
\begin{tabular}{c c c} \hline
**Energy Range** & **Flux** & **FY-3E Satellite Maximum** \\
**(MeV)** & **(cm\({}^{2}\)ssr)** & **(Counts/s)** \\ \hline
0.15–0.35 & \(5\times 10^{5}\)–\(10^{6}\) & \(4\times 10^{4}\) \\
0.35–0.65 & \(1\times 10^{3}\)–\(4\times 10^{5}\) & \(2\times 10^{4}\) \\
0.65–1.20 & \(1\times 10^{3}\)–\(5\times 10^{4}\) & \(2\times 10^{2}\) \\
1.20–2.0 & \(1\times 10^{3}\)–\(1\times 10^{4}\) & \(5\times 10^{1}\) \\
2.0–5.7 & \(1\times 10^{3}\)–\(1\times 10^{4}\) & \(5\times 10^{1}\) \\ \hline \end{tabular}
\end{table}
Table 5: Estimation of maximum count rate for high-energy electron probes.
## 3 Results and Discussion
### Sorption Properties
#### 3.2.3 Sorption Isotherms
The \(\mathrm{q_{m,L}}\) values increase from 0.626 \(\pm\) 0.028 mmol Ce g\({}^{-1}\) to 1.305 \(\pm\) 0.022 and 2.275 \(\pm\) 0.017 mmol Ce g\({}^{-1}\) for MC-UR and MC-UR/S, respectively. The affinity coefficient (i.e., \(\mathrm{b_{L}}\)) for MC (\(\approx\)1.506 \(\pm\) 0.156 L mmol\({}^{-1}\)) is about 3 times lower than the values obtained for the functionalized sorbents (\(\approx\)5.817 \(\pm\) 0.720 and \(\approx\)5.379 \(\pm\) 0.092 L mmol\({}^{-1}\) for MC-UR and MC-UR/S, respectively). The grafting of the urea onto MC doubles the maximum sorption capacity and almost quadruples the affinity of the sorbent for Ce(III). After sulfonation, the affinity slightly decreases (by 8%), while the maximum sorption capacity increases by 74% (proportionally to the change in the total number of reactive groups). MC-UR/S represents a good compromise in terms of the enhancement of the Ce(III) sorption properties. These properties can be compared to those of alternative sorbents. Table S8 shows that MC-U/S is comparable to the best sorbents reported in the literature. The sorbents with the higher sorption capacities (i.e., higher than 2 mmol Ce g\({}^{-1}\)) are based on magnetic cellulose [75], a carboxymethylcellulose highly opened monolith [76], a polystyrene/poly(hydroxamic acid) copolymer sorbent [77], or a HKUST-1 metal-organic foam [78]. It is noteworthy that MC-UR/S is characterized by one of the highest affinity coefficients (with the remarkable exception of HKUST-1-MOF). Combining these remarkable characteristics, the fast kinetics, and the easy synthesis procedure, MC-UR/S is one of the most promising sorbents described in the literature for Ce(III) recovery. Based on the effect of the pH and the FTIR analysis, it is possible to suggest a series of mechanisms involved in the binding of Ce(III) onto MC-UR/S (Scheme 1). In addition to the modes of interaction of lanthanides with sulfonate groups (as identified by [PERSON] et al. [42] for the Lewatit Monoplus SP112 macroporous cation exchanger), the amine/amide groups present on urea moieties may also contribute to metal binding. These sorption mechanisms may be affected by the reported oxidation of Ce(III) to Ce(IV). However, XPS analyses were not performed to verify the natural occurrence of oxidation mechanisms under the selected experimental conditions.
# Retina-like Imaging and Its Applications: A Brief Review
## 3 Applications
### Three-Dimensional Acquisition and Reconstruction
After extracting multi-scale keypoints from 3D point cloud data, the keypoint descriptors with constant scaling, rotation, and translation are generated on the basis of the scaling and rotation invariance of log-polar coordinate strategy and derivative mapping calculated from local height blocks around the keypoints. Then, the keypoints of the height map are matched, and the registration results are obtained. Figure 5: Information extraction of the image depth. (**a**) The original input image. (**b**) Linear detect process with LPT. (**c**) The output image. [PERSON] [44] used log-polar height map (LPHM) to establish the corresponding relationship for achieving the coarse registration of multiple range images. This map represents the shape of a local surface as a height mapping of the log-polar coordinate system relative to the tangent plane. Figure 7 illustrates an overview of the LPHM-based registration method. For the input range images, the corresponding log-polar depth maps are established via LPHM. Subsequently, the paired range images are registered in a robust manner by the RANSAC algorithm, and the incorrect correspondences are eliminated as outliers. Finally, the fine registration is completed on the basis of the coarse registration results. In the application of 3D object reconstruction based on a skeleton, a retina-like descriptive operator algorithm is constructed on the basis of the attention mechanism of human eyes and the ability to deal with complex tasks of visual scenes. The retina-like descriptive operator algorithm improves the performance of artificial bee colony algorithm in 3D object reconstruction. The description operator of retina-like mechanism also cooperates with digital elevation model (DEM). The purpose is using the variable resolution characteristics of the human retina to achieve high resolution while increasing the efficiency of 3D image  
Figure 6: Overview of the height map image point matching approach for co-registering 3D multi-sensor point clouds. Figure 7: Overview of the registration method based on LPHM. reconstruction as much as possible. [PERSON] et al. [45] presented a continuative variable resolution DEM (cvrDEM) for the representation of 3D terrain, which has the characteristics of retina-like high and variable resolutions. The final cvrDEM product with resolution varying from 0.004 m to 0.067 m is displayed in Figure 8a. Figure 8c is the grid DEM used for performance comparison, which has a resolution of 0.03 m per pixel. Figure 8b,d are enlarged views of the area outlined by the rectangle in Figure 8a,c, respectively.
# Inter-System Differencing between GPS and BDS for Medium-Baseline RTK Positioning
## 2 Combined GPS and BDS Observation Model
### Ambiguity Fixing Strategy for NL AR  
As we can see from Equations (2), (3) and (15), the estimable DD NL ambiguities only have the wavelengths of 10.70 cm and 10.83 cm for GPS and BDS, respectively. Such short wavelengths are easily affected by the residual atmospheric errors, observation noises, and other errors. This is more difficult for the new-rising satellites, the ambiguities of which usually need longer convergence time after they first become visible. In the AR search strategy under ILS condition, if some estimated float ambiguities have large residuals, the validation process (e.g., Ratio test [27]) will reject the ambiguity fixing due to biases. Actually, for combined GPS and BDS, not all the ambiguities must be fixed in simultaneously since there are sufficient satellites available. Thus, a partial fixing strategy will be used in this paper. A simple criterion involving the satellite elevation angle and the continuous tracking number is employed. If a satellite is under a threshold of the elevation or continuous tracking number, the NL ambiguity of the satellite will not be fixed and is pending as a float value. In this paper, the threshold of the elevation to fix ambiguities is set as 20\({}^{\circ}\). The continuous tracking number depends on the sampling interval (e.g., 300 for the 1-s sampling interval, 50 for 15-s sampling interval, and 20 for 30-s sampling interval).  
Another strategy employed to the NL ambiguity fixing in this paper is the 'fix and hold' mode [28]. This strategy takes the concept of feeding information derived from the current epoch forward to subsequent epochs one step further. At first, float ambiguities are resolved in the usual way but once the integer solutions are verified by the validation process at a certain epoch with enough high reliability, the tight constraint to the integer solutions is introduced into the next update of the filter. A fixed ambiguity is held to an integer value until a cycle-slip occurs or the filter diverged with large residuals. The biggest advantage of this strategy is that other parameters (e.g., unfixed ambiguities, DISBs) can be improved with fixed solution, and thus will be beneficial for the subsequent epochs. Besides, it can also protect the fixed ambiguities from being disturbed by the new-rising satellites of other errors in the next epochs.  
During the data processing in this paper, the least-squares ambiguity decorrelation adjustment (LAMBDA) method [29] is used for integer ambiguity resolution. In order to ensure the AR reliability, the solved integer ambiguities at each epoch will be validated with Ratio and ambiguity dilution of precision (ADOP) [30] with strict thresholds of 3.0 and 0.12 (corresponds to an ambiguity success rate larger than 99.9%), respectively.  
Besides the observation equations, AR model, and fixing strategies above, additional processing options used in this paper are listed in Table 1.
# Unmanned Aerial Vehicles Rescue System Design and Traffic Model Planning
## 2 Materials and Methods  
### Selection of UAV  
Among various types and brands of UAVs, eight UAVs meeting the requirements were selected according to their overall dimensions, payload capacity and configuration functions. The potential candidate UAVs considered by UAV formation are shown in Table 1.  
In order to select a UAV with excellent comprehensive performance (called strong UAV), the performance characteristics of different UAVs were comprehensively evaluated by the normalization method [24], and these characteristics were defined as service capability.  
The ideal flight distance is equal to the average speed times the time of no cargo flight. Using this formula, the ideal flight distance of various types of UAVs can be calculated.  
\[s_{0}=\overline{v}\times t_{0}, \tag{1}\]  
where \(s_{0}\) is the flight distance of UAV, \(v\) is the average flight speed of UAV, and \(t_{0}\) is the flight time of UAV.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Shipping Container Dimensions**} & \multicolumn{3}{c}{**Performance Characteristics/Capabilities**} & \multicolumn{3}{c}{**Configurations Capabilities**} \\ \hline \multirow{2}{*}{**Drone**} & **Length** & **Width** & **Height** & **Max Payload** & **Speed** & **Right Time** & **Video** & **Medical** & **Drone Cargo** \\  & **(in.)** & **(in.)** & **(in.)** & **Capability** & **(km/h)** & **No Cargo** & **Capable** & **Capable** & **Bay Type\({}^{*}\)** \\ \hline A & 45 & 45 & 25 & 3.5 & 40 & 35 & Y & Y & 1 \\ B & 30 & 30 & 22 & 8 & 79 & 40 & Y & Y & 1 \\ C & 60 & 50 & 30 & 14 & 64 & 35 & Y & Y & 2 \\ D & 25 & 20 & 25 & 11 & 60 & 18 & Y & Y & 1 \\ E & 25 & 20 & 27 & 15 & 60 & 15 & Y & Y & 2 \\ F & 40 & 40 & 25 & 22 & 79 & 24 & N & Y & 2 \\ G & 32 & 32 & 17 & 20 & 64 & 16 & Y & Y & 2 \\ H & 65 & 75 & 41 & N/A & N/A & Indefinite & N & N & N/A \\ Tethered & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: UAV configuration of each size.  
The ideal flight distance and the maximum payload are normalized, respectively, to calculate the service capability of different UAVs. The normalized results are shown in Table 2.  
\[s_{0i}^{*}=\frac{s_{0i}-s_{0 min}}{s_{0 max}-s_{0 min}} \tag{2}\]  
\[w_{i}^{*}=\frac{w_{i}-w_{min}}{w_{max}-w_{min}} \tag{3}\]  
\[z=v_{i}^{*}\times s_{0i}^{*}, \tag{4}\]  
where \(s\) is the flight distance of the UAV, \(w\) is the maximum payload capability of the UAV, and \(z\) is the service capability.
## 4 Discussion  
### Applicability for Bulk Bioremediation  
While petroleum hydrocarbons can be generally biodegraded reasonably well, many factors can reduce the effectiveness of the biodegradation process [9; 35; 36; 37; 38; 39]. Prior to bulk bioremediation measures, therefore, it is common to undertake laboratory tests similar to those undertaken in this study in order to assess the effectiveness of the proposed bioremediation procedures [4; 28; 35; 39; 40; 41]. As such, our two-tier experimental process reflects a process of trial and error in the search for a successful combination of bacterial medium and process.  
The experiments described were initiated by the need of bioremediation of soil highly polluted by petroleum hydrocarbons in the order of thousands of tons. Before such bulk bioremediation, trial small-scale experiments are necessary. In our case we started with massive repetitive bioaugmentationusing _Odnroductrum_ sp. and _Bacillus_ sp., microbial consortium routinely used in previous bioremediation applications (commercial activities, not published), and compared them to biostimulation using BSM and combination (Experiment A). This experiment, despite its long time period, proved this approach inapplicable for a bulk remediation method as no decrease of hydrocarbons was detected in any of the variants. The reasons for the unsuccessful application of an otherwise successful consortium can be only speculated upon, and are likely to be too high a concentration and age of the petroleum contamination along with the inhibiting presence of heavy metals. Nevertheless, it confirms that growth of microbes on the petroleum hydrocarbons as a sole source of carbon and energy in liquid media does not implicate biodegradation in real soils. At least microbial activities throughout the test suggested a positive effect on soil function regeneration. Absolute values of microbial activities were however generally low and corresponded more with developing, damaged or poor soils than healthy soils [29; 42].  
Consequently we had to try different approaches, especially testing new strains of highly resistant actinobacteria (Experiment B). In this case, a significant decrease of aliphatic hydrocarbons was achieved in several variants despite an order of magnitude shorter experimental period. The positive result cannot be explained by different amount of bioaugmented bacteria, as the experiment A was bioaugmented periodically with high amounts of degrading bacteria, while experiment B was inoculated only once in the beginning roughly by minimally two orders of magnitude lower amount of bacteria. The results of Experiment B suggest that both the _G. rubripertincta_ CWB2 and _R. erythropolis_ S43 strains show clear promise for applications in bioremediation of petroleum contaminated soil; although the actual mechanism by which they achieve biodegradation remains somewhat unclear.
# Content-Based Video Big Data Retrieval with Extensive Features and Deep Learning
## 3 Background
### Object Detection and Feature Extraction Using Deep Learning
#### 3.3.1 Deep Neural Networks for Object Detection  
Residual Neural Network (ResNet)  
The Microsoft Research Team proposed ResNet [34] in 2015. They demonstrated that ResNet is easier to optimize and has higher accuracy than previous models. ResNet is an efficient architecture that won the ImageNet competition by using skip connections. The main challenge in training deep learning models is that accuracy decreases with the depth level of the networks. ResNet converges very quickly and can be trained with hundreds or  
Figure 1: Tesseract recognition algorithm—OCR.  
Figure 2: Flowchart of speech recognition process.  
thousands of layers. At the same time, ResNet is easy to optimize and can achieve accuracy gains from greatly increased depth, producing better results [35]. Skip connections help to keep information from being lost by connecting the earlier layer to the layer behind and skipping some intermediate layers. Therefore, we propose to apply ResNet for the feature extraction of objects on video frames.  
To diversify experiments for object features extraction, we propose to use Inception ResNet architecture [36]. It is a model built on the advantages of Inception block and Residual block. Inception ResNet achieves astonishing accuracy with this combination. The complete Inception network consists of many small Inception modules. The idea of the Inception modules is very simple, instead of using a Conv layer with a fixed kernel_size parameter, Inception uses multiple Conv layers at the same time with different kernel_size parameters (1, 3, 5, 7, etc.) and connects the outputs. The input to the model is 299 \(\times\) 299 images and the output is a list of class prediction results.  
MobileNet-V2  
Although the DNN models introduced above are highly accurate, there is a common limitation that they are not suitable for mobile applications or em- bedded systems with low computing capacity. To develop these models for real-time applications, we need an extremely powerful machine configuration (GPU/TPU). A \"lighter\" model is necessary for embedded systems (Raspberry Pi, Nano pc, etc.) or applications running on smartphones. On the same ImageNet dataset, MobileNet V2 has the same accuracy as other models, such as VGG16 and VGG19, while the number of parameters is only about 3.5 M (about 1/40 of the parameters of VGG16) [37]. Thus, MobileNet-V2 has the advantages of being fast, lightweight, and highly accurate, which is suitable for training with limited datasets. The key point that helps MobileNet models reduce the amount of computation is to apply depthwise separable convolutions.
## 4 Monitoring Scheme
### Earth Pressure Monitoring of Diaphragm Wall  
Earth pressure monitoring for the diaphragm wall is performed using earth pressure cells. The earth pressure cell employed was the VWE model vibrating wire earth pressure cell (Geonan/GEOKON, Nanjing, China). This sensor features a measurement accuracy of 0.5% FS (Full Scale) and has passed the long-term stability certification from the Geotechnical Instrument Quality Inspection and Test Center, Ministry of Water Resources (annual drift rate < 0.1% FS), making it particularly suitable for long-term automated monitoring of soil-structure interface pressures in excavation support systems. Eight monitoring points (numbered WTL-1 to WTL-8) are uniformly distributed along the central axis of the diaphragm wall and positioned on its exterior wall surface. At each monitoring point, four measurement devices are installed vertically at intervals of 4 m, 10.5 m, 16 m, and 21 m below the capping beam top, as illustrated in Figure 10.
# Q-Learning Based Fair and Efficient Coexistence of LTE in Unlicensed Band
## 3 LTE-U Coexistence Mechanism
### LTE-U DC and CA Model  
To devise a duty-cycle based LTE-U transmission, we consider a TDD configuration, as shown in Figure 4. In this structure, an LTE-U frame of 10 ms is divided into multiple subframes while maintaining the same frame length as that of LTE. In Figure 4, the dark blue slots represent blank subframes, which can be used by Wi-Fi systems, with the remaining subframes used by LTE-U systems. Four different duty cycles are considered (20%, 40%, 60%, and 80%), which can be configured by the operators according to the network requirements. As shown, LTE will transmit for (1-\(\Theta\)) percent of the time from the allocated duty cycle period and will be mute for \(\Theta\) percent of the time. A channel selection is used to choose the operating carrier by LTE-U BS. Therefore, by choosing the cleanest  
Figure 3: LTE-U deployment scenario.  
channel based on the received power measurements, it can be used as a frequency domain coexistence mechanism to guarantee that LTE-U is a \"good neighbor\" in the unlicensed band [30]. The design of a proper channel allocation functionality can significantly increase the overall efficiency of the LTE-U operation. Specifically, we can see from Equation (1) that the selection of channels will have an impact on the achieved throughput performance mainly through the \(\Theta_{k}\) and \(SINR_{m_{k}}\)terms. Thus, if the selected \(k\)th channel is not used by other cells, a higher throughput will follow. In addition, if the selected \(k\)th channel is affected by low interference levels, a high \(SINR_{m_{k}}\) will be observed along with higher throughputs. Therefore, the channel selection for an LTE-U BS should be able to dynamically identify and capture the key information regarding the present utilization of the channels allowing the most suitable ones to be selected. Therefore, solutions recognizing the best channel are of high interest in exploiting the full potential of LTE-U. However, it is always not the best case. This is because (as environment remains the same) LTE-U will try to remain within the same parameters (i.e., channel and duty cycle) as long as it does not find other parameter that can maximize its cost function. Residing with the same parameters for a long period of time will continuously decrease the access opportunity for the Wi-Fi user of that channel hence decreasing the fairness. Our algorithm intended to address this problem by avoiding an LTE-U to occupy the same channel for a long duration and thus providing more access opportunities for Wi-Fi users under low traffic load condition. Therefore, the proposed Q-learning algorithm helps LTE-U to switch its current transmission from the optimal channel to a second optimal channel easily in runtime without any increase in complexity in the algorithm. In addition to this, Q-learning with its simple modeling approach provides LTE-U BS to learn from the environment and automatically adapt to an appropriate parameter providing robustness to its dynamic and uncertain operating environment.