esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Thermal Network Correction
### Thermal Parameter Correction  
The correction of parameters in the thermal network model is an inversion problem, which involves inferring the parameter values based on the experimental temperature data of each node. The Monte Carlo algorithm is utilized to derive optimal estimates of the sensitive parameters.  
The process of correcting thermal parameters using the Monte Carlo algorithm is shown in Figure 9. The specific correction procedure is delineated as follows:  
(1) The range of values for the key thermal parameters is as follows:  
\[V_{\rm min}\leq V\leq V_{\rm max} \tag{10}\]  
where \(V_{\rm min}\) and \(V_{\rm max}\) are the lower and upper limits of the key parameters, respectively, corresponding to the range of values in Table 2.  
Figure 8: Calculated and measured temperatures subsequent to the thermal network structure correction: (**a**) window; (**b**) window barrel.  
Figure 9: Process for correcting thermal parameters using the Monte Carlo algorithm.  
where \(n_{u}\) is the sampling number for calculated and measured temperature curves under the \(u\)-th heating condition, \(T_{i}^{(m,n)}(\tau_{r})\) is the calculated node temperature resulting from the \(m\)-th combination of parameters, and \(L\) is the total number of parameter sets that fulfill the requirements of Equation (12).  
By minimizing the objective function shown in Equation (12), the optimal values of the key parameters can be determined.  
By setting the sampling number at 3000, 6000, and 12,000, respectively, it is possible to obtain the different optimal parameter combinations. It is important to note that the required computation time increases with the sampling number, as illustrated in Figure 11. However, it is important to note that as computer processor speed, memory size, and parallel computing capability improve, the computation time will reduce.  
Furthermore, the predictive efficacy of the modified thermal network model can be quantified by the correlation index, \(R\)-squared:  
\[R^{2}=1-\frac{\sum\limits_{v=1}^{11}\sum\limits_{i=1}^{23}\sum\limits_{r=1}^{n _{u}}\left[T_{is}^{(u)}(\tau_{r})-T_{i}^{(u)}(\tau_{r})\right]^{2}}{\sum \limits_{v=1}^{11}\sum\limits_{i=1}^{23}\sum\limits_{r=1}^{n_{u}}\left[T_{is}^ {(u)}(\tau_{r})-\overline{T_{is}}\right]^{2}} \tag{13}\]  
where \(T_{i}^{(u)}(\tau_{r})\) is the calculated node temperature resulting from the optimal parameter combination and \(\overline{T_{is}}\) is the average value of the measured temperature points.
## 3 Methods
### Data Preprocessing  
A bitmask band with cloud mask information (QA60) for the Sentinel-2A image is provided on the GEE platform. Cloud and cloud shadow affecting observation quality were identified by the code based on the Function of Mask (CFMask) [51], and they were marked in the QA60 band. The clear-sky pixels were selected according to QA60, and the Sentinel-2A images in 2020 with less than 20% cloud and cloud shadow cover were used in this study.  
Figure 2: Approach proposed in this study.
## 3 Proposed Methods
### Recurrent Networks Classifiers
#### 3.2.3 Gated-Recurrent Unit  
Gated Recurrent Unit (GRU) was introduced by [54] to enable each recurrent unit to adaptively capture dependencies at different time scales. It resembles LSTM but has a simpler cell design. The GRU comprises gating units that influence the flow of information inside the unit but without memory cells. GRU computes two gates known as update and reset gates that regulate the flow of information through each hidden unit. Therefore, the update and reset gates, which are depicted by colored boxes in the GRU cell in Figure 7, can be calculated using the following equations [39,57]:  
\[\mathbf{z}_{t} = \sigma(\mathbf{W}_{xx}\mathbf{x}_{t}\ +\ \mathbf{W}_{lx}\mathbf{h}_{t-1}\ +\ \mathbf{b}_{z}) \tag{9}\] \[\mathbf{r}_{t} = \sigma(\mathbf{W}_{xx}\mathbf{x}_{t}\ +\ \mathbf{W}_{lx}\mathbf{h}_{t-1}\ +\ \mathbf{b}_{r})\] (10) \[\widetilde{\mathbf{h}}_{t} = tanh(\mathbf{W}\mathrm{xhx}_{t}\ +\ \mathbf{W}_{hh}(\mathbf{r}_{t}\ \odot\ \mathbf{h}_{t-1})\ +\ \mathbf{b}_{h})\] (11) \[\widetilde{\mathbf{h}}_{t} = \mathbf{z}_{t}\ \odot\ \mathbf{h}_{t-1}+(1\ -\ \mathbf{z}_{t})\ \odot\ \widetilde{\mathbf{h}}_{t} \tag{12}\]  
where \(\mathbf{z}_{t}\) is the update gate, \(\mathbf{r}_{t}\) is the reset gate, \(\mathbf{W}\) represents the weight vector, \(\odot\) represents the element-wise multiplication, and \(\sigma\) is the sigmoid function. At each time \(t\), it takes input \(\mathbf{x}_{t}\) and hidden state \(\mathbf{h}_{t-1}\) from the previous time \(t-1\). Next, it outputs a new hidden state \(\mathbf{h}_{t}\) for the following time \(t\). In order to find \(\mathbf{h}_{t}\), two steps are needed in GRU. The first is generating the candidate hidden state. It multiplies the input and hidden state from the previous time \(t-1\) by the reset gate output \(\mathbf{r}_{t}\). This information is then passed to the tanh function, and the result is the candidate's hidden state. In this equation, the reset gate value controls how much the previous hidden state influences the candidate state. If \(\mathbf{r}_{t}=1\), all information from \(\mathbf{h}_{t-1}\) is considered. Otherwise, if \(\mathbf{r}_{t}=0\), the previous hidden state's information is ignored. Once we obtain the candidate state, it is used to generate \(\mathbf{h}_{t}\), and it is where the update gate is involved. In GRU, instead of using a separate gate like in LSTM, it utilizes a single update gate to regulate both the historical information (\(\mathbf{h}_{t-1}\)) and the new information from the candidate state. Furthermore, \(\mathbf{z}_{t}\) is critical in this equation. It can determine how much past knowledge needs to be passed into the future. The \(\mathbf{z}_{t}\) value ranges from 0 to 1.  
Figure 6: LSTM unit architecture.
# Stability Analysis of Jinchuan Hydropower Station Hydraulic Tunnels during Excavation and Unloading
## 4 Numerical Modeling of the Excavation of Adjacent Tunnels  
### FLAC3D Finite-Difference Software  
The Fast Lagrangian Analysis of Continua in 3 Dimensions (FLAC3D) is numerical modeling software developed by Itasca Consulting Group, Inc. in the U.S. It is also a numerical analysis method that uses 3D finite differences to simulate the mechanical properties of the rock-soil mass or other materials under different stresses. The method involves dividing the computing object into several elements, establishing a linear or nonlinear constitutive model, and imposing boundary conditions. The element deforms along with the material if the material yields or flows plastically under stress. It is capable of solving differential equations and accurately simulating the yielding, softening, plastic flow, and large deformations of the material using a mixed/discrete finite element model and an explicit finite difference method.
# High Linearity DC-38 GHz CMOS SPDT Switch
## 3 Results
Moreover, for V\({}_{\mathrm{DD}}\)/\(\mathrm{V}_{\mathrm{SS}}\) of 2.5 V\(/\)\(-\)2.5 V, the SPDT switch achieves TX-to-RX isolation better than 25.4 dB for 0-28 GHz and 23.6 dB for 0-38 GHz. The result is slightly better than the simulated one (TX-to-RX isolation better than 24.4 dB for 0-28 GHz and 22.6 dB for 0-38 GHz) for V\({}_{\mathrm{DD}}\)/\(\mathrm{V}_{\mathrm{SS}}\) of 1.8 V\(/\)\(-\)1.8 V because transistors M\({}_{4}\) and M\({}_{5}\)/M\({}_{6}\) are closer to being open and short, respectively. P\({}_{\mathrm{out}}\) versus P\({}_{\mathrm{in}}\) measurement of the SPDT switch was conducted by a Keysight N9030A PXA signal analyzer in conjunction with a Keysight E8257D PSG analog signal generator. Figure 4d shows the measured power gain versus P\({}_{\mathrm{in}}\) characteristics of the SPDT switch at 28 GHz and V\({}_{\mathrm{GI}}\) (equal to V\({}_{\mathrm{DD}}\)) of 1.8 V in the condition of P1-to-P2 being on and P1-to-P3 being off. As can be seen, the SPDT switch with V\({}_{\mathrm{G2}}\) and V\({}_{\mathrm{B}}\) (equal to V\({}_{\mathrm{SS}}\)) of \(-\)1.8 V achieves IP\({}_{\mathrm{1 dB}}\) of 25 dBm and power gain of \(-\)4.3 dB (at P\({}_{\mathrm{in}}\) of 0 dBm), better than those (IP\({}_{\mathrm{1 dB}}\) of 20 dBm and power gain of \(-\)5.6 dB at P\({}_{\mathrm{in}}\) of 0 dBm) of the SPDT switch with V\({}_{\mathrm{G2}}\) and V\({}_{\mathrm{B}}\) of 0 V. Moreover, in the bias condition of V\({}_{\mathrm{DD}}\)/V\({}_{\mathrm{SS}}\) equal to 2.5 V\(/\)\(-\)2.5 V, the SPDT switch achieves measured IP\({}_{\mathrm{1 dB}}\) of 25.6 dBm, close to the simulated value of 28 dBm. To the authors' knowledge, this is one of the best IP\({}_{\mathrm{1 dB}}\) results ever reported for mm-wave SPDT switches. Clearly, positive/negative gate-bias and negative body-bias are beneficial for linearity enhancement because larger P\({}_{\mathrm{in}}\) (i.e., AC signal with larger negative V\({}_{\mathrm{in}}\)) is required to conduct the off-state series switch transistor M\({}_{4}\). Table 1 is a summary of the SPDT switch and recent reported state-of-the-art SPDT switches with similar operation frequency. Compared with the SPDT switch in 0.1 \(\mathrm{\SIUnitSymbolMicro}\) GaAs PHEMT in [1], our SPDT switch achieves better IP\({}_{\mathrm{1 dB}}\). Compared with the SPDT switch in 0.18 \(\mathrm{\SIUnitSymbolMicro}\) SOI CMOS in [2], our SPDT switch achieves comparable return loss, insertion loss, and better isolation and IP\({}_{\mathrm{1 dB}}\). Compared with the SPDT switch in 90 nm CMOS in [3], our SPDT switch achieves better insertion loss and isolation. Compared with the SPDT switches in 28 nm and 0.18 \(\mathrm{\SIUnitSymbolMicro}\) CMOS in [4; 5], our SPDT switch achieves comparable return loss, insertion loss, and isolation, and better IP\({}_{\mathrm{0.3 dB}}\)/IP\({}_{\mathrm{1 dB}}\). Overall, the results indicate that the SPDT switch is suitable for multi-band 5G communications.
# Traffic Light and Arrow Signal Recognition Based on a Unified Network
## 4 Results
### Dataset  
Seenarios where our approach was to be applied pose two technical challenges. First, currently available public datasets contained vertically arranged traffic lights, which differed from the horizontally arranged traffic lights. Second, although arrow signals appeared frequently on Taiwan's roads, most studies only used classifiers to recognize circle lights. The establishment of a self-collecting dataset can solve these problems. Furthermore, the lack of arrow light images perplexed the training process of the network.  
Several commonly used datasets can be obtained for detecting and recognizing traffic lights. However, they each used different formats and were therefore unsuitable for network training involving traffic lights in Taiwan. Therefore, we worked with the Industrial Technology Research Institute (ITRI). We collected a dataset for evaluation and training, and the dataset comprises data on two routes. The first was the route from Hsinchu High-Speed Railway Station to the ITRI campus, and the second was the route from Chiayi High-Speed Railway Station to National Chung Cheng University. The first and second routes, respectively, spanned 16 and 39 km and took 40 and 50 min to record. Two cameras having different focal lengths (3.5 and 12 mm) were placed below the rearview mirror of a vehicle for acquiring images. The image sequences were captured at 36 fps. The resolution of the captured image was a size of 2048 \(\times\) 1536. Additionally, LiDAR data were recorded using a Velodyne Ultra Puck VLP-32C and used to segment approximate regions of traffic lights. The first and second routes were recorded thrice and once, respectively. We sampled five frames per second for processing and labeling the positions of traffic lights and the classes of light states. The labeled images contained 26,868 images and 29,963 traffic lights. Only the traffic lights with obvious light states were labeled and 14 classes of light state combinations were established.  
In the LISA dataset, traffic lights were arranged vertically (see Figure 8 as an illustration). The traffic lights in Taiwan were arranged horizontally, as depicted in Figure 9. Additionally, the light states were distinct. In the LISA dataset, only a single light can be shown at a time in the traffic lights, whereas traffic scenarios in Taiwan can involve multiple combinations of traffic lights with multiple arrow light types. Our dataset contained mostly data on circular lights and several arrow lights. With respect to ROI size of traffic lights, the LISA dataset mainly contained traffic light regions in the range of 15 to 30 pixels. In our dataset, the images captured with a 3.5 mm lens camera consisted of traffic light regions in the ranges of 10 to 20 pixels, and the images obtained with a 12 mm lens camera consisted of traffic light regions in the ranges of 15 to 50 pixels.  
Figure 7: Augmented data. (**a**) Training image, (**b**) augmented image 1, and (**c**) augmented image 2.
## 5 Experiments and Results
### Fabric Hyperledger
#### 5.1.3 Results  
Table 1 details a comparison of query times with and without aggregation. All four unaggregated queries were executed at a similar time due to the limited nature of the GetHistoryForKey API, which always returns all entries in the same order without any filtering; as a result, aggregation functions like avg, count, min, max, cannot be performed until all the records are fetched.  
First and second queries could not be optimized, because all transactions are accessible only via GetHistoryForKey API, which does not provide a filtering mechanism; therefore all transactions have to be fetched. The aggregation state does not hold information that could be optimized similar to the Stellar approach. However, this is not possible due to the lack of offset filtering of GetHistoryForKey API.
* The execution time of 3 rd query was decreased 2244 times. This huge improvement is possible by storing pre-computed averages in the state.
* The fourth query, similarly to the third query, was significantly optimized by storing the total number of records for each time frame in the state.
# An Efficient MAC Protocol for Blockchain-Enabled Patient Monitoring in a Vehicular Network
## 3 Proposed Superframe Structures  
In this work, a TDMA-based MAC protocol is proposed to prioritize adaptive patient data to compute their Nonce value for a valid hash value.
## 2 Materials and Methods  
### Site Description  
The field experiment was carried out with a two-factor randomized trial of double cropping rice in Zengjia village, Shanggao County (28.28\({}^{\prime}\) N, 115.12\({}^{\prime}\) E) and Gaohu village, Xingguo County (26.48\({}^{\prime}\) N, 115.32\({}^{\prime}\) E), Jiangxi Province, China, during 2018 and 2019. The mean annual solar radiation, annual maximum temperature, annual minimum temperature and precipitation were 4270.54 and 4401.69 MJ m\({}^{-2}\), 23.36 and 24.33 \({}^{\circ}\)C, 15.81 and 16.28 \({}^{\circ}\)C and 1486.90 and 1532.34 mm at Shanggao and Xingguo, respectively, and the two ecological regions are characterized by a subtropical monsoon climate. The Shanggao and Xingguo soil types are classified as red acid soils. The soil organic matter, total N, total phosphorus, total potassium, pH, available N, available phosphorus and available potassium for the 15 cm layer of Shanggao and Xingguo are 32.9 and 21.3 g kg\({}^{-1}\), 1.99 and 1.35 g kg\({}^{-1}\), 0.83 and 0.54 g kg\({}^{-1}\), 22.2 and 14.9 g kg\({}^{-1}\), 5.41 and 5.23, 154.8 and 103.5 mg kg\({}^{-1}\), 14.4 and 8.8 mg kg\({}^{-1}\) and 124.6 and 65.8 mg kg\({}^{-1}\), respectively. The main nutrient demands of rice consist of four elements: carbon, N, phosphorus and potassium. Eight rice field soil nutrient parameters, including soil organic matter, total N, total phosphorus, total potassium, available N, available phosphorus, available potassium and pH, were selected as the indicators for the comprehensive evaluation of soil fertility [32; 33]. The soil fertilities of Shanggao and Xingguo are high and medium, respectively, among the rice fields of South China. The solar radiation, maximum and minimum tempera tures and precipitation in the double cropping rice seasons at Shanggao were 3277.35 and 3087.36 MJ m\({}^{-2}\), 29.23 and 29.08 \({}^{\circ}\)C, 20.91 and 20.68 \({}^{\circ}\)C and 876.55 and 1238.57 mm in 2018 and 2019, respectively. The solar radiation, maximum and minimum temperatures and precipitation in the double cropping rice seasons at Xingguo were 3243.23 and 3113.48 MJ m\({}^{-2}\), 30.71 and 30.64 \({}^{\circ}\)C, 20.96 and 21.17 \({}^{\circ}\)C and 887.42 and 1239.21 mm in 2018 and 2019, respectively (Figure 1).
## 2 Materials and Methods
### Inclusion and Exclusion Criteria  
Once the results were reviewed, the final selection of the apps to be analyzed was performed. In the search and selection of the apps, the following inclusion criteria were established: the apps were either updated or created from 2015 to 2020, the minimum user rating was four stars in both portals, and the user profile was children or families.  
In the 60 apps initially found, 28 were excluded because they were not directly related to the object of study, nor did they present any type of pedagogical or educational interest.  
Apps that were variations of others selected apps or came from the same developer were not included; therefore, a total of 32 apps were considered for the content analysis.  
To elaborate on the inclusion criteria, we did not close criteria around the gender of the target audience or the age range of the participants, and we excluded applications that required previous knowledge or were for professional use.  
The apps finally selected for this analysis were Kids Doodle, 2020; Doodle Master-Glow Art, 2020; Kawaii Easy Drawing, 2020; PaperColor, 2020; PaintX, 2020; Coloring Book, 2020; Painting for Kids, 2020; How to Draw Cute Food, 2020; Paintastic, 2020; Penup, 2020; ColorPlanet: oil painting, paint by numbers, 2020; Paint Dropper, 2020; Drawely-How to Draw Girls, 2020; Drawing-Paint Free, 2016; Pocket Painting: Drawing and Editing, 2020; Coloring Book for Me and Mandala, 2020; Sandbox Pixel Colorin, 2020; Kawaii Glitter Coloring Games, 2020; Draw Kawaii, 2019; Masha and the Bear: Animal Coloring Drawings, 2020; Sketcher PRO, 2020; FlipaClip, 2020; PicsArt Color Paint, 2020; Glitter Dress Up Coloring and Drawing, 2020; How to Draw Anime Step by Step, 2020; Simple Drawing Kids Line, 2019; Paint and Learn Animals, 2020; Tiny colors-Coloring Book Kids Paint and Draw,2020; Coloring and Painting for Kids, 2020; Coloring on line, 2020; Sand Drawings, 2019; SketchBook, 2020.
## 5 Methods and Models
### Outlier Detection Algorithm  
Each SN of the proposed system runs a distributed event detection algorithm for local events, while the BS runs a global event detection algorithm. Generally, the backbone of any event detection approach is outlier detection, which consists of observations of significant differences from a normal dataset of values [39]. Outliers could be noise or sensor faults. Therefore, the event identification algorithm of each SN is designed in such a way that they identify an event if four consecutive readings of outliers are received by the SN. Although many outlier detection approaches exist, clustering was considered as the most appropriate because of its simplicity, high outlier detection, low false positivity, and high reliability. Among partitioning clustering approaches [40], K-means has gained popularity as it is able to incorporate multiple attributes and can be implemented in an unsupervised manner. The time series equation of a K-means algorithm was used for data partitioning. It follows the rule of \(k\leq n\), where k is the number of clusters and n is the number of observations. Track duplication of _dist_ (\(a\), \(b\)) has been removed by using the Euclidean distance from the Malinowski space using the following relationship  
\[(d_{ab})^{2}=(\sum_{a=1}^{n}{(x_{ia}-c_{ka})})^{2} \tag{7}\]  
Here, \(d_{ab}\) is the distance of the \(i\)-th data point to the center of cluster \(k\), \(x_{i}\) is the \(i\)-th data, and \(C_{ka}\) is the centroid of the cluster. A steadfast verification of cluster analysis is the _Silhouette_ value (\(\zeta i\)) [41], which is in the range of \(-1\leq\zeta i\leq 1\) and has an easy graphical representation. For n clusters, the silhouette coefficient values are  
\[\zeta_{i}=(1/n)\sum_{i=1}^{n}{((\beta_{i}-\alpha_{i})/\max(\alpha_{i},\beta_{ i}))} \tag{8}\]  
Here, \(\alpha_{i}\) is the average distance of the \(i\)-th object in an individual group of the same cluster. If \(\alpha_{i}\) is unknown, then the minimum distance (\(\beta_{i}\)) is chosen from another nearby cluster. This enables the easy identification of an outlier from the collected data in a mine environment. The cumulative Euclidean distance derived from K-means is stored and used to determine the contribution of each weight by following these steps: (i) the contribution of each attribute is determined from their mean and covariance, (ii) the Euclidean distance is calculated again for each attribute using the mean and variance of the remaining attributes, (iii) the newly calculated Euclidean distance is subtracted from the cumulative Euclidean distance \(D_{m}\), and (iv) the results of step two are divided by the \(D_{m}\) to determine the contribution of attributes.
## 9 Conclusions  
This paper presents a technique to detect the presence of distortions at the tracking stage level of a GNSS receiver. In particular the method has been tailored to MP detection, but can be applied also to other impairments. The specific goal is to assist the modules of integrity monitoring of RAIM. In the near future, the presence of a multi-constellation of satellites like GPS, Galileo, GLONASS, Beidou and of receivers able to demodulate all these signals, will provide an increasing number of satellites in view. This will foster the development of signal processing techniques of FDE, since they perform better with a high number of satellites. In general, this kind of exclusion approach is sensitive to the DOP variation, and the effects of the satellite geometry could be greater than a simple MP phenomenon in terms of accuracy. For this reason a method to combine GDOP control and FDE is also proposed in this paper.  
Another way to integrate SQI into RAIM suggested in this work is the exclusion of satellites with lowest SQI in case when RAIM declares a navigation solution as unreliable.  
In this work, we have not taken into consideration a possible use of a carrier-smoothing techniques, that might be useful to protect against sporadic MP distortions, because usually they are used in the case of good visibility of the satellites in view.  
The proposed technique could be also employed for other kinds of distortion, like spoofing attacks that can bypass RAIM tests. Work is in progress on some modifications of the algorithm, for application of spoofing detection.  
[PERSON] analyzed the data, performed the experiments in post-processing and implemented the algorithms. [PERSON] have contributed to design of the algorithms and reviewed the paper.  
The authors declare no conflict of interest.