esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Analysis in an Industrial Refrigeration System  
The electrical power and condensation pressure were monitored continuously in an industrial refrigeration system, accounting for 20 measurement samples.  
The flow rate of air through EC has a fundamental role in the heat exchanger capacity. Higher fan rotations cause high volumetric flow rates of air and electricity consumption in the condenser. However, the heat and mass transfer coefficients are also increased, resulting in lower condensation pressures for the same thermal load to be rejected. Despite this fact, for a certain magnitude of the flow rate of air, the increase in its magnitude does not lead to a significant decrease in the condensation head pressure. This behavior occurs due to the approximation of the condensation pressure of the refrigeration system with that magnitude obtained for the wet bulb temperature of the external air. Thus, the heat exchanger significantly reduces its capacity. If the differences between the condensation temperature and the wet bulb temperature of the ambient air are very small, the increase in the mass flow rate of air may not significantly increase the influence of the EC heat transfer rate. Therefore, the operation of the fans with rotation greater than necessary (usually nominal) can lead to a waste of electrical energy.  
Figure 4 demonstrates the behavior previously explained for 10 data samples acquired in the industrial refrigeration system under study. The results show that for the conditions of the wet bulb temperature and the thermal load tested, it was not possible to obtain condensation temperatures below 24 \({}^{\circ}\)C, even when the flow rate of the air of the condenser was at the maximum level.  
For these samples, the best performance from an economic viewpoint was obtained when the ratio between the current air mass flow rate (\(\dot{m}_{\rm air}\)) and the total air mass flow rate (air mass flow rate with a motor drive at a frequency of 60 Hz)--\(\dot{m}_{\rm air}/\dot{m}_{\rm total}\)--was in the range between 0.6 and 0.8. For magnitudes of the ratio \(\dot{m}_{\rm air}/\dot{m}_{\rm total}>0.8\), the results shown in Figure 4 indicated that an increase in the mass flow rate of air just caused waste of electricity, because it did not provide a significant reduction in the condensation pressure and, consequently, in the compression ratio. It is also possible to observe that each sample had a specific mass flow rate that corresponded to a minimum condensation pressure for the same system, demonstrating the importance of variable pressure condensation control.
###### Abstract
Here, the authors show the advantages of the TLS technique over SfM in the following aspects: TLS needs less GCP; with SfM, the interior and exterior of the building must be processed separately (with the subsequent consumption of time spent on processing); TLS can be employed in low or no visibility situations (even at night). It should also be noted that the studies on heritage conservation used TLS to compare different methods performed with SfM, i.e., TLS was used as a control method, where 99.99% of the points have an accuracy of 1.2 cm. Therefore, these authors considered TLS a more accurate technique than SfM [19]. This approach has also been claimed by authors comparing the two techniques for bridge inspection and monitoring [20]. Where all authors agree is that the SfM technique is cheaper than TLS. Up to now, no comparison has been made for archaeological mosaics. For this reason, this case study presents a comparison of the results of both methods, TLS and SfM, applied to a Roman mosaic for its 3D virtual re-creation.
## 5 Conclusions  
We use a convolutional neural network as the baseline to predict the grasping stability through the output of tactile sensing array. In order to propose a unified tactile representation and an end-to-end prediction system, we extend the framework of baseline with the global pooling layer, multiple fully-connected heads, and the mask prediction branch. The global pooling layer can deal with variable input sizes and multiple fully-connected heads can deal with different characteristics of sensors. The feature after the global pooling layer performs well when it is transferred to the grasping stability prediction of other sensors and we define it as the tactile representation. In addition, these two optimizations also have the influence of improving classification accuracy. The mask prediction branch determines which fully-connected to be selected during inference and makes the whole system work in an end-to-end manner. Finally, our proposed MM-CNN achieves the high accuracy of (99.49%, 94.25%) on GKD and GSD.  
Conceptualization, X.S., C.L., and T.L.; methodology, X.S.; validation, X.S.; writing--original draft preparation, X.S.; writing--review and editing, X.S., C.L., and T.L.; visualization, X.S.; supervision, C.L. and T.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 61802363 and 61774157, Key Research Program of Frontier Sciences, CAS, grant number QYZDY-SSW-JSC037, and the Natural Science Foundation of Beijing, grant number 4182075.  
The authors declare no conflict of interest.  
Figure 5: T-distributed stochastic neighbor embedding analysis of embedding after global pooling in mask branch.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Input Size**} & \multirow{2}{*}{**Train Set**} & \begin{tabular}{c} **(Avg \(\pm\) Std)** \\ **Accuracy on GKD** \\ \end{tabular} & \
\begin{tabular}{c} **(Avg \(\pm\) Std)** \\ **Accuracy on GSD** \\ \end{tabular} &
\begin{tabular}{c} **Highest Accuracy** \\ **on GKD and GSD** \\ \end{tabular} \\ \hline \multirow{2}{*}{Mask branch} & \(\begin{array}{c}16\times 10\\ \text{and}\ 12\times 12\end{array}\) & GMD & \(100.00\%\pm 0.00\%\) & \(99.82\%\pm 0.06\%\) & (100.00\%, 99.89\%) \\ \hline \multirow{2}{*}{MM-CNN} & \(\begin{array}{c}16\times 10\\ \text{and}\ 12\times 12\end{array}\) & Pretrain on & \multirow{2}{*}{\(99.49\%\)} & \multirow{2}{*}{\(94.25\%\)} & \multirow{2}{*}{-} \\ \hline \multirow{2}{*}{MM-CNN} & \(\begin{array}{c}16\times 10\\ \text{and}\ 12\times 12\end{array}\) & Pretrain on & \multirow{2}{*}{\(99.49\%\)} & \multirow{2}{*}{\(94.25\%\)} & \multirow{2}{*}{-} \\ \cline{1-1} \cline{5-5}  & \(\begin{array}{c}12\times 10\\ \text{and}\ 12\times 12\end{array}\) & GSD & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Overall results of MM-CNN trained on grasping merged dataset (GMD).
## 3 Methods
### AnimalEnvNet Network
#### 3.2.2 Environmental Feature Extraction  
We use remote sensing data obtained from both Sentinel and Google Earth images, which exhibit distinct spatial resolutions. Performing direct upsampling of low-resolution images or downsampling of high-resolution images would result in either significant processing expenses or reduced accuracy. In order to achieve a balance between computing economy and accuracy, we use several convolutional structures to resize the features of the remote sensing images to a uniform size. This helps to decrease the processing requirements. Ultimately, we resize the Sentinel-2A picture to a dimension of (201, 201), with a pixel size of 10 m, and the Google Earth image to a dimension of (334, 334), with a pixel size of 6 m. Both include an area of roughly 1 square kilometre.  
Within the module for extracting features from remote sensing images, we first use three distinct convolutional blocks to individually process the Sentinel-2A and Google Earth images, ensuring that the feature maps are standardised to the same dimensions. Abstract characteristics are retrieved gradually using a series of convolution and pooling procedures at many levels [39]. The selection and use of convolution and subsampling techniques in this work are based on their notable benefits in processing remote sensing image data. The convolution procedure efficiently extracts spatial features by systematically moving the filter across the input image and finds crucial environmental information associated with animal movement. Subsampling minimizes computing complexity and memory needs by decreasing the spatial dimensions of the feature map, while maintaining important characteristics and limiting overfitting. The computing formula for the convolution kernel at a certain location in the image is provided as follows:  
\[W_{ij}=f(\sum_{x=1}^{s}\sum_{y=1}^{s}p_{i+x-1,j+y-1}\cdot k_{xy}+b) \tag{7}\]  
\(W_{ij}\) is the output value at position \((i,j)\) in the output feature map. \(f\) is the activation function. \(s\) is the size of the filter. \(P_{i+x-1,j+y-1}\) is the input value at position \((i+x-1,j+y-1)\) in the input feature map. \(K_{xy}\) is the value of the filter at position \((x,y)\). \(b\) is the bias term.
## 3 Results
### Antenna Measurements  
The radiation pattern measurements were carried out in an anechoic chamber. Figure 3 shows photographs of the custom-designed antennas LaYUA and TuSA, which were implemented, respectively, on FR-4 substrates and mounted in the anechoic chamber. Figure 4 illustrates the simulated and measured reflection coefficient's magnitude and normalized pattern at 2.4 GHz, when both the antennas are radiating into free space. There is a good agreement between the simulations and measurements. In this way, the simulated gain of the LaYUA and TuSA are about 9 dBi and 3 dBi, respectively.  
Figure 3: Photographs of the antenna prototypes mounted in an anechoic chamber, along with the definition of their spherical coordinates. (**Left**) LaYUA. (**Right**) TuSA.
# Quadratic Clustering-Based Simplex Volume Maximization for Hyperspectral Endmember Extraction
## 3 Results and Discussion
### Parameter Analysis
#### 3.2.2 Spectral Clustering Analysis  
There are two parameters involved in spectral clustering: the weighting factor \(\lambda_{2}\) and the number of clusters k. The experimental basis of spectral clustering analysis is the completion of spatial clustering with s of 6 and \(\lambda_{1}\) of 0.1 and representative endmember generation with Pt of 0.4. To choose the best value for \(\lambda_{2}\) and k, the following experiments are carried out. First, set \(\lambda_{2}\) to 0.4, calculate Scatt, AvgPI and SvdPI on the spectral clustering results with k from p to \(10^{4}\)p at an interval of p. The results are shown in Figure 9. Then, set k to \(5^{\circ}\)p, and indexes are calculated on the k-means clustering results as \(\lambda_{2}\) increases from 0 to 1 with an interval of 0.05. The results are demonstrated in Figure 10.  
It can be seen from the results that the response range of each index to k is greater than that to \(\lambda_{2}\), which indicates that k has a greater impact on spectral clustering than \(\lambda_{2}\). Similar to spatial clustering, indexes on Cuprite are the most stable compared to that on Samson and Jasper Ridge. This has relations with the different spectral contrast of endmembers in the datasets.  
Figure 8: The results of (**a**) Scatt; (**b**) AvgPI; (**c**) SvdPI with the change of Pt.  
Figure 10: The results of (**a**) Scatt; (**b**) AvgPI; (**c**) SvdPI with the change of \(\lambda_{2}\).  
Figure 9: The results of (**a**) Scatt; (**b**) AvgPI; (**c**) SvdPI with the change of k.  
When k is below 5*p, AvgPI and SvdPI rise rapidly, and then tend to stabilize. Scatt shows a fall trend, and roughly takes k of 5*p as the cut-off point. That is, when the number of clusters k is more than five times the number of endmembers, the spectral clustering keeps good performance with low dispersion degree and high spectral correlation within classes. At the same time, a large k may lead to over-clustering, as the same feature with spectral variability will be divided into different classes. Taking all this into consideration, set k to 5*p. This number improves the computational efficiency of searching for the maximum simplex volume compared with other simplex-based algorithms.  
With the increase in \(\lambda_{2}\), all indicators show a falling trend. That is, the reinforcement of spectral distance constraint enhances the central tendency but weakens the spectral shape similarity. To keep low dispersion degree and good spectral correlation, the best value of \(\lambda_{2}\) is set to 0.4.
## 4 Preliminary Performance Considerations for IPDALLS
### First Order Estimation of the Link Budget and Measurement Accuracy
The offline wavelength and both the online wavelengths of highest relevance within the UTLS and with highest absorption coefficient have been chosen to illustrate the range of signal shot noise. **(b)** Signal-to-noise ratio (carrier-to-noise ratio) for the water vapour sounding wavelengths. **(c)** Relative random error of the water vapour two-way path-averaged optical depth for the various wavelength pairs. The coloured regions with the solid vertical lines correspond to the optimal measurement ranges (using geometric means) for each wavelength as defined in Figure 6.  
altitudes of 45 to 50 km (not shown), we hypothesize that significantly lower water vapour concentrations can be detected at lower heights where the effective absorption path increases. However, more advanced modelling and system definitions would be required to translate the modelled random error into errors on mass mixing ratios and to account for systematic errors and additional factors influencing the random error.
## 3 Methodology
### Feedback Factor Calculation Based on Probabilistic Soft Logic  
Probabilistic soft logic is a framework based on first-order logic rules for probabilistic reasoning. The difference is that it uses the [0, 1] interval of soft truth values of random variables, not only the extreme values 0 and 1 [39]. The following will introduce the relevant concepts of PSL:  
**Definition 1**.: _Given a set of atomic statements \(\hat{I}\), \(I(l)\) denotes the soft truth value that satisfies the atomic statement \(l\)._  
**Definition 2**.: _A rule \(\bar{r}\) usually consists of a disjunctive clause for an atomic statement or a negative atomic statement, for example \(\eta_{r}:B_{1}\wedge B_{2}\wedge\cdots\wedge B_{m}\to H_{1}\wedge H_{2}\wedge \cdots\wedge H_{n}\), where \(B_{1}\wedge B_{2}\wedge\cdots\wedge B_{m}\) is the body \(r_{body}\) of the rule, \(H_{1}\wedge H_{2}\wedge\cdots\wedge H_{n}\) is the head \(r_{head}\) of the rule, and \(\eta_{r}\in[0,1]\) is the weight of the rule \(r\), indicating the prior confidence of the rule._  
**Definition 3**.: _A specific atomic statement \(l\) and a specific rule \(r\) are instantiations of \(\hat{l}\) and \(\bar{r}\), such as \(l_{10}\), \(r_{3}:l_{6}\wedge l_{2}\wedge(\
eg l_{7})\to l_{10}\)._  
**Definition 4**.: _Define the computation of the base logic operator in PSL according to the Lukasiewicz equation:_  
\[I(l_{1}\lor l_{2})=\min\{I(l_{1})+I(l_{2}),1\} \tag{30}\]  
\[I(l_{1}\wedge l_{2})=\max\{I(l_{1})+I(l_{2})-1,0\} \tag{31}\]  
\[I(\
eg l_{1})=1-I(l_{1}) \tag{32}\]  
\[I\Big{(}r_{body}\rightarrow\tau_{head}\Big{)}=I\Big{(}\
eg r_{body}\lor\tau_{ head}\Big{)} \tag{33}\]  
**Definition 5**.: _According to the interpretation 1, \(d_{r}(I)\) denotes the satisfaction distance of rule \(r\). The calculation is as follows._  
\[d_{r}(I)=\max\Big{\{}I\Big{(}r_{body}\Big{)}-I(r_{head}),0\Big{\}} \tag{34}\]  
Definitions 1-5 describe how PSL calculates the soft truth values and satisfiability distances of compound rules in detail. PSL provides theoretical support for calculating the satisfiability probabilities of compound logic rules.  
Combine the probability that each atomic statement in \(l_{1}\
eg l_{18}\) is satisfied and its corresponding rule \(r\). After the logical operation defined in PSL, the distance of satisfaction of this time atomic statement, the formula is shown in (35). If \(d_{r}>0\), the model predictions are contradictory, and the model needs to be adjusted by negative feedback, using \(d_{r}\) as a feedback factor to amplify its loss.  
\[d_{r}=\max\Big{\{}I\Big{(}r_{body}\Big{)}-I\big{(}r_{head}\big{)},0\Big{\}}= \max\Big{\{}I\Big{(}r_{body}\Big{)}-I(r_{correct}),0\Big{\}} \tag{35}\]
## 2 System Model
### Object Detection and Tracking  
The captured image from the surveillance camera is fed to the control room. The control room performs detection and tracking of the precious object inside the object showcase room. If the object is not present in the captured image frames, then a control signal is given to the museum alarm systems and the object protection system. The process of detecting and tracking the precious object is given below, and the flowchart representation of object detection and tracking is presented in Figure 9.  
Step 1: Read the initial or beginning image frame received from the surveillance camera.  
Step 2: Read the next or current image frame received from the surveillance camera.  
Step 3: Perform image segmentation for the obtained black & white image and select only the region that does not contain a display screen or region that contains the precious object.  
Step 4: Find the absolute difference between an initial frame and the current frame. This gives the distortions or disturbances between the two image frames. i.e., the movement of the precious object. This step results in a black and white image with the disturbances represented by white pixels and stationary locations represented by black pixels.  
Step 5: The white pixels in the black & white image represent movement of the precious object. Compute the centroid of all the white pixels in that image, i.e., the centroid of the precious object.  
Step 6: If there are no white pixels in the black & white image. Then, it is not possible to determine the centroid of the object.  
Step 7: Alert the museum alarm systems and send command information to the object protection system through the current stego image.  
Step 8: Initialize the Kalman filter parameters [27] such as the covariance matrix, error in measurement matrix, error in estimation matrix, initial object location (centroid), and so on.  
Step 9: Give the measured centroid value of the object to the Kalman filter; the output obtained is the estimated centroid value of the object.  
Step 10: Assign the current image frame as an initial image frame for the next iteration.  
Step 11: Repeat from Step 2 to Step 10 for continuous tracking of the precious object.  
Figure 9: Flowchart representation for object detection and tracking.
## 3 Methodology
### Filling Data Gaps of Monthly Land-Water Mask
Therefore, a surface area error is computed which includes the index error from Section 3.2.4 but also error information from the gap-filling step. The gap-filling error uses the water probability threshold \(p\) as additional information in the error assessment step. Initially, the complete filled area is assumed to be an error. But this is very pessimistic as the errors of gap-filling occur mainly near the lake shores where the probability threshold \(p\) is computed. Within the lakes or reservoirs, where the long-term water probability is increased the gap-filling error decreases. Therefore, we compute a confidence fill derived from the computed probability threshold \(p\) plus 5% which is the limit of 54%. This corresponds to a quasi-error band along the lake shores. The areas of filled water pixels with a water probability larger than 54% are summarized and used as confidence fill which is assumed to be correctly filled. The value of 60.22 km\({}^{2}\) is then subtracted from the initial area error 72.25 km\({}^{2}\) which results in a more realistic gap-filling error of 12.03 km\({}^{2}\). Finally, we add an index error of 2.11 km\({}^{2}\) computed in the classification step where not all water indexes show the same results. The outcome of filling the data gap is a surface area with error and a land-water mask without data gaps. Figure 11: (**a**) Monthly mask with data gaps for Lake Ray Roberts in October 2012 (**b**) Long-term water probability mask \(\cap\) AOI mask (**c**) Long-term water probability mask \(\cap\) AOI mask \(\cap\) water pixels of monthly mask (**d**) Filled monthly land-water mask with \(A_{initial}\) (red) and \(A_{fill}\) (green).
## 4 Result and Discussion
### Packet Loss  
Packet loss can be triggered by a mixture of circumstances, including signal deterioration owing to multi-path fading on the network media. In WSNs, packet loss is conceivable. In order for attackers to simply acquire the data. Packet loss occurs when one or more sent packets fail to reach their intended destination. The Packet Delivery Ratio is reduced when packets are lost. The packet loss of the different method is compared and its mentioned in Table 5  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No of Nodes** & **DWMA** & **CBR-RPL** & **WEMER** & **GG-Conv\_Clus-FSO** \\ \hline
0 & 0 & 0 & 0 & 0 \\
20 & 17.32 & 24.55 & 28.91 & 32.63 \\
40 & 32.67 & 35.46 & 38.64 & 42.03 \\
60 & 41.04 & 48.75 & 52.06 & 50.17 \\
80 & 63.93 & 67.11 & 69.47 & 71.90 \\
100 & 71.46 & 79.02 & 82.03 & 95.14 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the packet delivery ratio.  
Figure 4: Comparison of the packet delivery ratio.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No of Nodes** & **DWMA** & **CBR-RPL** & **WEMER** & **GG-Conv\_Clus-FSO** \\ \hline
0 & 0 & 0 & 0 & 0 \\
20 & 89 & 76 & 59 & 31 \\
40 & 143 & 137 & 147 & 98 \\
60 & 221 & 226 & 204 & 149 \\
80 & 349 & 358 & 269 & 223 \\
100 & 520 & 440 & 320 & 282 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of packet loss.  
In Figure 5, packet loss during data transmission for different number of node is illustrated. The packet loss of proposed approach is minimal than existing approaches, namely DWMA, CBR-RPL, WEMER and PEGASIS.