esa-sceva/satcom-chunk-collection huggingface dataset:
# Survey on Unmanned Aerial Vehicle for Mars Exploration: Deployment Use Case
## 3 Preliminary Studies
### Different Types of UAVs in Space
#### 3.2.2 Vtol  
VTOL is created by incorporating the advantages of both multi-copter and fixed-wing aircraft. Multi-copters can perform vertical take-offs and landings, but their rotary-wing rotor could not pass the sound velocity. On the other side, aircraft can go higher, but heavy lift requires an airstrip. Similar is the case for space exploration. A separate control technique is necessary to transform flight modes between horizontal and vertical configurations in other solar bodies. However, it is possible to imagine hybrids of flying wing and rotary wing UAVs as the future of space UAV setups [77]. The latest studies have focused on the possibility of designing aerial VTOL vehicles that could assist in our solar system's discovery of different celestial bodies. Specifically, the effectiveness of VTOL vehicles is being researched to support missions to Mars' surface, Titan, and Venus. The NASA Ames Research Centre studied different rotary-wing aero-mechanics and proof of concept problems underlying the production of vertical lift aerial vehicles for planetary science missions [78]. In addition, the performance of rover and rotor measurements by creating a required condition of atmosphere on Mars' surface, co-axial helicopter through radio control to promote studying Mars' surface, VTOL aircraft for studying titan surface [79]. NASA's Mars helicopter mission 2021 is a groundbreaking vehicle in space. At 2400 rota tions per minute, one of these rotors would be about four feet long, tip to tip, and turn. That is about ten times the velocity of an Earth helicopter, representing the extra work taken for the craft to travel in the thin atmosphere of Mars. Indeed, provided the air pressure at the planned landing scale of Mars 2021 is equal to around 100,000 feet above the surface of the Earth, it would mean that the Mars Helicopter would be operating at altitudes never achieved by any terrestrial chopper.
# Criteria for Cutting Head Clogging Occurrence during Slurry Shield Tunneling
## 2 Cutter Head Clogging at the Beijing South-to-North WATER Diversion Auxiliary Project
Due to the cutting head clogging, the driving parameters experienced remarkable changes after ring 145. Sharp increases of the total thrust and the cutting head torque were found with a total thrust of 7200-12,000 kN, a machine advance rate of almost 25 mm/min, and the cutting head rotation speed of 0.  
thrust of more than 20,000 kN and the cutting head torque over 3500 kN\(\cdot\)m at the ring 320. At rings 311 and 322, an anti-clogging agent was added to the excavation chamber to soften the mud cake in and on the cutting head. Between rings 320 and 340, the total thrust and the cutting head torque decreased gradually to 13,000 kN and 1300 kN\(\cdot\)m, respectively. After ring 340, an increase was not seen with the total thrust and the cutting head torque fluctuating around 17,000 kN and 1500 kN\(\cdot\)m, respectively. Between ring 145 and ring 320, the advance rate of the machine decreased from 25 mm/min to 5 min/min due to cutting head clogging, and the cutting head rotation speed increased gradually to 2 r/min. In the following rings, the advance rate rose to over 30 mm/min, firstly because of the adding of the anti-clogging agent into the excavation chamber, and then declined to 25 mm/min little by little or even less at the end. The cutting head rotation speed was increased to more than 2 r/min, then gradually decreased to 1.5 r/min or even less. For a tunneling shield machine, the cutting head torque is closely related to the total thrust, the advance rate, and the cutting head rotation speed, and is a combined result of many influencing factors [15]. Cutting head torque increases due to the cutting head clogging, but the torque increase does not mean the occurrence of clogging. For example, the increase of the penetration depth per revolution of the cutting head, which is defined as the ratio of advance rate to cutting head rotation speed, can also lead to the increase of the cutting head torque. This is true of the total thrust of the machine. So two composite parameters are introduced: total thrust/penetration depth and cutting head torque/penetration depth, which are plotted in Figure 7. From the development of the two new parameters, the clogging at ring 145 and the effects due to the countermeasures taken at rings 311 and 322 can be more clearly and directly presented. The newly-defined parameters magnify the side effects of the cutting head clogging on shield machine driving, which can be used to evaluate the occurrence of the cutting head clogging.
## 2 Maximum Dynamic Shear Modulus Tests
### Specimen Preparation, Saturation, and Consolidation  
The Yangtze River floodplain's soft soil is mainly distributed between 5 and 25 m below the surface. The typical original floodplain soft soils, 51 in total, were extracted using an open-end thin-wall sampler. Because the underground water level is about 1-2 m, all the undisturbed samples are saturated soils. Table 1 lists the fundamental physical and mechanical properties of the soft soils in each drill hole. The density \(\rho\), moisture content \(w\), void ratio \(e\), and plasticity index \(I_{\text{P}}\) are measured using ASTM D4318 [25]. It indicates that the \(w\) values of the floodplain soft soils are between 37 and 42%, and \(I_{\text{P}}\) is distributed between 14.8 and 17.8. The samples are classified into four groups based on the depth H of the samples: Group A: H = 5-10 m; Group B: H = 10-15 m; Group C: H = 15-20 m; Group D: H = 20-25 m. \(\sigma_{\text{3c}}\)\({}^{\prime}\) was set to 50, 85, 120, and 150 kPa for samples in groups A, B, C, and D, respectively. The consolidation of each group was performed with OCR = 1, 2, and 3 in combination with the \(e\) distribution in each group. Table 1 details the test conditions.  
Figure 2: Microstructure of undisturbed Yangtze River floodplain facies soft soil.  
The standard dimensions of samples (50 \(\times\) 100 mm) were prepared and saturated using the vacuum saturation method according to the ASTM D5311-13 [26]. The samples were saturated in the saturation vessel for 10 h. Then, the specimen was placed in the apparatus, and the back pressure saturation was conducted. Back pressure was applied step by step until the value of Skempton's B was greater than 0.97, which can guarantee the saturation to prevent differences in sample preparation from affecting the test results. Then they were placed in the pressure chamber and consolidated according to the corresponding consolidation conditions. When the average strain rate of the soil sample was less than  
\(1\times 10^{-3}\%/\min\), consolidation was achieved, followed by the bender element test. The time of consolidation was about 4-5 days.
## 2 Background Knowledge
### Conv-TasNet  
An effective SE method, the fully convolutional time-domain audio separation network (Conv-TasNet), adopts and extends the ideas of two other methods: the time-domain audio separation network (TasNet) [36] and temporal convolutional network (TCN) [37]. Briefly speaking, TasNet models the input waveform with a convolutional encoder-decoder architecture. The encoder possesses a nonnegativity constraint on its output, and a linear decoder inverts the (enhanced) encoder output back to the sound waveform. In particular, a separation (enhancement) layer consisting of a deep LSTM network is located between the encoder and decoder to implement the denoising. In contrast, TCN consists of a series of 1-D convolution blocks with different dilation factors, and it is used in Conv-TasNet as the layer structure of separation (enhancement), which is shown in Figure 1.  
The architecture of Conv-TasNet is depicted in Figure 2, which contains three parts: the encoder, mask-estimation network, and decoder. In the following, we describe the process of each of the three parts.  
Figure 1: 1-D convolutional block.
# Topology Inference and Link Parameter Estimation Based on End-to-End Measurements +
## 4 Performance Evaluation
### Results and Discussion
& 16 & 10.7957 & 5.1186 & 3.3833 & 7.5524 & 4.7407 & 4.0048 & 7.7135 & 4.7855 & 3.6110 & 7.1554 & 4.7276 & 3.3867 \\ Bin. & 24 & 9.0789 & 5.2434 & 4.0306 & 8.9049 & 4.7218 & 3.4756 & 7.7404 & 4.7545 & 3.6221 & 7.6445 & 4.6965 & 3.4926 \\ Bin. & 32 & 13.4703 & 7.4144 & 4.4306 & 14.6285 & 10.1443 & 6.5357 & 10.9308 & 7.0454 & 4.34 of \(\alpha\) (Figure 3) under the loss-based metric. As can be seen, the error varies significantly as the employed update formula changes. In this particular case, it appears that the best result is obtained for \(\alpha=0.79\). Finally, Table 2 lists the execution time (in seconds) for the various experimental scenarios, as recorded in a reference machine with the following specifications: Intel Core i5-6500 @4x 3.6 GHz CPU and 16 GB DDR4 2133 MHz RAM. These measurements were taken using 5105 multicast probes, which were transmitted in 60 s (a time period that is not considered in this table). As it is evident, although no special optimizations have been made to that end, the required times are indeed rather small and depend on the size of the examined topology (the differences between alternative configurations applied to each examined tree are negligible). Nevertheless, an implementation in a compiled language is expected to further reduce the execution time, if so desired. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Physical Tree**} & **Single** & **Complete** & **Average** & **Weighted** \\ \hline
**Type** & **\# Nds** & \multicolumn{3}{c}{**Loss Rate Estimation Execution Time (s)**} \\ \hline Bin. & 8 & 0.2665 & 0.2614 & 0.2592 & 0.2645 \\ Bin. & 16 & 0.3005 & 0.3042 & 0.2988 & 0.2989 \\ Bin. & 24 & 0.3950 & 0.3940 & 0.4008 & 0.3976 \\ Bin. & 32 & 0.4626 & 0.4586 & 0.4578 & 0.4555 \\ Gen. & 10 & 0.2879 & 0.2847 & 0.2876 & 0.2878 \\ Gen. & 20 & 0.3669 & 0.3655 & 0.3699 & 0.5344 \\ Gen. & 30 & 0.4797 & 0.4857 & 0.4798 & 0.4793 \\ Gen. & 40 & 0.5346 & 0.5285 & 0.5357 & 0.5281 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Execution time of experimental scenarios. Figure 3: RMSE for different values of \(\alpha\).
## 2 Data and Methods
### Methods  
#### 2.3.1 Calibration of Water Turbidity Algorithms  
Based on the existing field measurements and knowledge in the Gironde Estuary, test sites 1 and 2 present two different water types: highly turbid in the MTZ with SPM concentrations ranging from  
Figure 2: Pleiades-derived water reflectance at 842 nm (\(\rho\)842, dimensionless) recorded on 24 May 2017. The enlarged image locates the Pauillac autonomous station (0.742644 W, 45.198511 N).  
\(\sim\)50 to \(\sim\)3000 g\(\cdot\)m\({}^{-3}\) within surface waters [16] and moderately turbid with SPM concentrations ranging from \(\sim\)0 to \(\sim\)60 g\(\cdot\)m\({}^{-3}\)[21], respectively, corresponding to different optical signatures (spectral water reflectance values). Moreover, according to [PERSON] et al. (2017), different spectral bands should be considered to establish robust relationships between SPM and the water reflectance: NIR band in test site 1 and red band in test site 2 [19].  
In test site 1, in situ hyperspectral water reflectance from SeaSWIR 2013 campaign [42; 43] was weighted by the relative spectral response (RSR) of each sensor to obtain equivalent water reflectance values in the green, red and NIR bands of Pleiades, OLI and MODIS satellites sensors. The central wavelengths of the selected bands are 558, 655 and 842 nm for Pleiades, 561, 655 and 865 nm for OLI, 555, 645 and 859 nm for MODIS. A second-order polynomial was established between turbidity measured in Nephelometric Turbidity Unit (NTU) and Pleiades reflectance at 842 nm (Equation (1)) for test site 1:  
\[\text{Turbidity (NTU)}=37661\times\rho 842^{2}+1845\times\rho 842\ (\text{R}^{2}=0.78) \tag{1}\]  
In test site 2, the water reflectance in the NIR band is less sensitive to variations of SPM concentrations below \(\sim\)60 g\(\cdot\)m\({}^{-3}\) while the red band reflectance to turbidity relationship is linear [19; 21]. A linear relationship between water reflectance in the red band and turbidity was hence used for test site 2, where SPM concentration is generally lower than \(\sim\)60 g\(\cdot\)m\({}^{-3}\)[19; 21]:  
\[\text{Turbidity (NTU)}=531.5\times\rho 655/0.88\ (\text{R}^{2}=0.89) \tag{2}\]
# A Heat Load Prediction Method for District Heating Systems Based on the AE-GWO-GRU Model
## 3 Case Study
### Evaluation Indicators  
To evaluate the prediction performance of the proposed AE-GWO-GRU model, this paper employs three four performance indicators: Mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), and standard deviation of absolute percentage error (SDAPE). These indicators are calculated using the following formulas. Smaller values of MAE, MAPE, and RMSE signify more accurate prediction results from the model. The smaller the SDAPE value is, the higher is the prediction stability of the model.  
\[MAE=\frac{100\%}{n}\sum_{i=1}^{n}\lvert y_{i}-\hat{y}_{i}\rvert \tag{9}\]  
\[MAPE=\frac{100\%}{n}\sum_{i=1}^{n}\left\lvert\frac{y_{i}-\hat{y}_{i}}{y_{i}}\right\rvert \tag{10}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2}} \tag{11}\]  
\[SDAPE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(\left\lvert\frac{y_{i}-\hat{y}_{i}}{ y_{i}}\right\rvert-MAPE\right)^{2}} \tag{12}\]  
where \(y_{i}\) represents the actual value of the heat load of the \(i\) th data sample, \(\hat{y}_{i}\) represents the heat load prediction value of the \(i\) th data sample, \(n\) represents the total number of predicted samples.
## 3 Training and Testing Results
### Crack Detection Results  
Four crack images with different crack degrees and different environments were input into our model to verify the crack detection performance. The surface crack detection results of our model are shown in Figure 14. Our model accurately identified the locations and shapes of the cracks in all four images and detected surface cracks of varying degrees with good results.
## 2 Materials and Methods
### Acoustic Pressure Field Calculation  
The US waveform transmitted from the elements is generated by the product of a multiple-cycle sine wave and a Hanning window of equal length:  
\[w(t)=sin(2\pi f_{c}t)\cdot 0.5\bigg{(}1-cos\bigg{(}\frac{2\pi t}{T_{w}}\bigg{)} \bigg{)}\;\;for\;\;0\leq t\leq T_{w} \tag{1}\]  
\[T_{w}=\frac{n_{cycles}}{f_{c}} \tag{2}\]  
where \(n_{cycles}\) is the number of cycles in the waveform, \(f_{c}\) is the center frequency of the waveform. The US elements are arranged in the \(xy\) plane, and the direction of the beam axis is along the \(z\)-axis. US wave transmitted from the \(k\)-th element at time \(t_{k}\) is observed at the point \(P\) as:  
\[p_{k}(t)=P_{0}\cdot B(a_{k},\;b_{k})\cdot D(\theta_{k},\varphi_{k},\;a_{k},\;b _{k})\cdot G(r_{k})\cdot A(r_{k})\cdot w(t)*\delta\bigg{(}t-t_{k}-\frac{r_{k} }{c}\bigg{)} \tag{3}\]  
where \(a_{k}\) and \(b_{k}\) are the side lengths of the \(k\)-th element in the \(x\)- and \(y\)-directions, respectively. \(r_{k}\) is the distance between the element center \(O_{k}\) and the observation point \(P\). \(\theta_{k}\) and \(\phi_{k}\) are the angles between the \(z\)-axis and the projections of the vector \(\mathbf{OP}\) onto the \(xz\) and \(yz\) planes, respectively. \(t\) is the observation time, and \(c\) is the speed of sound. \(P_{0}\) represents the reference sound pressure. \(B\) represents the transmitting sensitivity of the element. \(D\) represents the directivity of the element. \(G\) represents the diffusion attenuation. \(A\) represents the absorption attenuation by the medium. \(\delta\) is the Dirac delta function. The specifics of these functions are detailed below. The transmission sensitivity \(E\) is proportional to the area of the element and can be expressed as follows:  
\[B(a_{k},\;b_{k})=a_{k}\times b_{k} \tag{4}\]  
The directivity \(D\) is calculated by considering that the US wave transmitted from the element is the summation of spherical waves emitted from the infinitesimal areas \(dS\) that constitute the element over the entire area \(S\). To improve computational efficiency, a far-field approximation is employed, based on the assumption that \(r_{k}\) is sufficiently large compared to \(a_{k}\) and \(b_{k}\). Furthermore, while the US wave comprises multiple frequency components, only the center frequency \(f_{c}\) is taken into account in the directivity calculation. Under the far-field approximation, the amplitudes of the spherical waves are regarded as constant independent of the locations of the source infinitesimal areas. Consequently, only the phase differences need to be considered. The phase difference \(\Delta\Phi_{E}\) at observation point \(P\) between the spherical waves generated from the element's center point \(O\) and the point \(E\) on the element is given by:  
\[d=\frac{\mathbf{OP}}{\|\mathbf{OP}\|}\cdot\mathbf{OE} \tag{5}\]  
\[\Delta\Phi_{E}=2\pi\cdot\frac{f_{c}}{c}\cdot d \tag{6}\]where \(\dot{d}\) is the length of the projection of the vector \(OE\) onto the vector \(OP\).
Sensors_**2010**, _10_, 8963-8980; doi:10.3390/s101008963  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**A Monitoring System for Vegetable Greenhouses based on a Wireless Sensor Network**  
**[PERSON] 1,2,*, [PERSON] 1,2,*, [PERSON] 2 and [PERSON] 2**  
1 College of Global Change and Earth System Science, Beijing Normal University, Xinjiekouwai Street No.19, Beijing, 100875, China  
2 State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, P.O.BOX 9718, Beijing, 100101, China; E-Mails: [EMAIL_ADDRESS] (K.Y.); [EMAIL_ADDRESS] (P.G.)
* Authors to whom correspondence should be addressed; E-Mails: [EMAIL_ADDRESS] (X.L.); [EMAIL_ADDRESS] (X.C.); Tel.: +86-10-58802190.  
_Received: 28 July 2010; in revised form: 25 September 2010 / Accepted: 27 September 2010 / Published: 8 October 2010_  
**Abstract:** A wireless sensor network-based automatic monitoring system is designed for monitoring the life conditions of greenhouse vegetatables. The complete system architecture includes a group of sensor nodes, a base station, and an internet data center. For the design of wireless sensor node, the JN5139 micro-processor is adopted as the core component and the Zigbee protocol is used for wireless communication between nodes. With an ARM7 microprocessor and embedded ZKOS operating system, a proprietary gateway node is developed to achieve data influx, screen display, system configuration and GPRS based remote data forwarding. Through a Client/Server mode the management software for remote data center achieves real-time data distribution and time-series analysis. Besides, a GSM-short-message-based interface is developed for sending real-time environmental measurements, and for alarming when a measurement is beyond some pre-defined threshold. The whole system has been tested for over one year and satisfactory results have been observed, which indicate that this system is very useful for greenhouse environment monitoring.  
**Keywords:** wireless sensor network; embedded operating system; environment monitoring; data center; base station
# Recognition of Manual Welding Positions from Depth Hole Image Remotely Sensed by RGB-D Camera
## 4 Welding Position Recognition
### Recognition of Welding Positions  
The acquired 18,000 depth image dataset was divided into training set, validation set, and test set at a ratio of 6:2:2 and trained on the Inception v3 model. Learning was carried out using a model pre-trained with a visual database, ImageNet, and then fine-tuned on a depth image dataset. The loss function is defined as (3):  
\[f(s)_{i}=\frac{e^{s_{i}}}{\sum_{j}^{C}e^{s_{j}}},CE=-\sum_{i}^{C}t_{i}\log(f(s) _{i}) \tag{3}\]  
where \(s_{i}\) is the probability of belonging to the i-th class, \(t_{i}\) is the ground truth, and \(f(s)\), is the softmax function that expresses the probability of belonging to the ground truth. The smaller the value of the loss function, the better the model performance.  
The training was repeated 200 times to measure the accuracy and loss function values, and the results are graphically shown in Figures 19 and 20. The final accuracy and loss values are 94.84% and 0.1012 for the validation data with depth holes and 98.96% and 0.0562 for those with depth hole fillings.  
The computational environment used when running the CNN model as well as the algorithm proposed in this study is as follows:  
\(\circ\): Intel i7-10700K @ 3.80 GHz;  
\(\circ\): 16 GB RAM;  
\(\circ\): NVIDIA GeForce RTX 3070.  
The Inception v3 used as a CNN model consists of 42 layers. The last output layer was configured to be classified into 5 classes using the softmax as an activation function. It took about 15 min to train 200 times each for the two cases of (1) images with deep holes, and (2) images with reconstructed deep holes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Position** & **Before Correction** & **After Correction** & **Note** \\ \hline Flat & & & \\ Horizontal & & & \\ \hline Vertical & & & \\ \hline Overhead & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Examples of collected dataset: images with depth holes versus restored images with depth hole filling.  
The accuracy of the test data is compared in Table 3. This result reveals that the recognition accuracy of the welding positions improves when the depth hole caused by the arc is removed and restored.  
Figure 19: Accuracy of training data and validation data (red line for training data and blue line for validation data): (**a**) restored images with depth hole filling, and (**b**) images with depth holes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Image Dataset** & **Accuracy by SVM** & **Accuracy by CNN** \\ \hline Restored images & 74.37\% & 98.72\% \\ With depth holes & 60.45\% & 85.83\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy comparison with two different learning models.  
Figure 20: Loss of training data and validation data (red line for training data and blue line for validation data): (**a**) restored images with depth hole filling, and (**b**) images with depth holes.  
Figure 21: Confusion matrices for two different image sets: (**a**) restored image, and (**b**) image with depth holes.
## 2 Materials and Methods
### Methodology
#### 2.2.1 Data Acquisition  
Using six accelerometers (A1-A6) installed at different positions and inclinations on the gearbox, the vibration signal (Figure 4) was obtained. Each accelerometer sampled data at a frequency of 50 kHz, and a 10 s acquisition period produced 500,000 acceleration measurements per sensor.