esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Concept Description
The needed heat of evaporation can be provided at Low Temperature (LT) by an industrial process that needs to be cooled (I.C.E., air compressors, mould cooling, etc.). The sorption storage system is preferably used in this way because, by actively cooling processes during discharge, the heat of evaporation is a \"useful heat\" too. This leads to a heat storage efficiency > 1, which means that the storage system is actively saving additional energy. If there is no process available to be cooled, the heat of evaporation may be absorbed from the air. In this case, the storage efficiency is <1 and the advantage compared to common sensible pressurised water storage is a much higher storage density and lower thermal losses. It is worth spending some further explanation to better clarify this point. In TES systems, the energy storage efficiency is defined as the ratio of useful energy output to  
Figure 3: The discharging phase of an adsorption TES system combined with a concentrated solar field. Figure 2: The charging phase of an adsorption TES system combined with a concentrated solar field. the energy input (see next section). For sorption-based TES systems, the efficiency can exceed 1 in specific configurations and operating conditions due to the active cooling effect and energy recovery mechanisms. Indeed, during the discharging phase, the evaporator absorbs heat from a low-temperature source (e.g., ambient air or a cooling process). This heat, along with the stored thermal energy, is delivered to the loads (at different temperatures). The additional heat absorbed from the surroundings increases the total useful energy output, allowing the efficiency to surpass 1. This phenomenon is similar to the performance of heat pumps, where low-grade heat is effectively upgraded for useful applications. Moreover, in a well-designed TES system, heat released during condensation in the condenser can be recovered and reused, either for preheating or other low-temperature applications. Similarly, the adsorption process releases significant exothermic heat, which can also be utilized. These mechanisms allow for enhanced utilization of the input energy. By leveraging these effects, sorption-based TES systems can achieve efficiencies greater than 1, effectively providing more useful energy than direct input energy, thus maximizing energy savings and reducing waste.
# LogBD: A Log Anomaly Detection Method Based on Pretrained Models and Domain Adaptation
## 3 Methodology  
### Overview  
Logs record information about the state of software systems when they are running. They have become an important resource to ensure the reliability and continuity of software systems. However, it is quite difficult to discover system anomalies from massive log data. Based on deep learning and domain adaptation, we propose a domain adaptation-based log anomaly detection method, LogBD, which is outlined in Figure 2.  
The first step is log parsing, which extracts log contents from unstructured raw logs, and then removes numbers and punctuation marks from logs by regular matching. After comparing some log parsing methods, we use Drain for log parsing, which efficiently converts preprocessed log constants into log templates.  
After log parsing, the second step is feature extraction. The log templates are fed into the pretrained model BERT, a bidirectional encoder based on transformer [25] proposed by Google, through which semantic vectors containing semantic information of the log templates are obtained. After representing the log templates as numeric vectors that the neural network can use, the BERT model uses a sliding window method segmentation of the original log sequence into datasets.  
The third step is anomaly detection, where the log template sequence after the digital vector representation is fed to a deep learning model for training to learn the patterns of normal log sequences and generate an anomaly detection model based on the distance of the log sequence mapping to the center of the hypersphere space.
## 7 Conclusions  
This study proposes an ontology-based modeling process for architecting SoS in the early design phase of the SoS to translate SoS missions into strategic and operational architectures through specific modeling steps based on the UAF views implemented by SysML. The novelty of this work lies in providing a novel perspective to model strategic and operational domains of AFs for an SoS architecture based on relationships and flows. The contributions of this study are summarized as follows:  
1. The metamodel for the SoS is customized using the UAF DMM, which provides a comprehensive view of the mission from both structural and behavioral perspectives. Mapping rules are then proposed to enable the construction of an ontology from the metamodels, thereby enhancing the semantics for developing SoS architecture.
2. The proposed SoSCO and SoSOO rely on the relationships and flows between architectural elements, thus enabling a shared comprehension of the strategic and operational architectures of the SoS.
3. The detailed modeling steps of SoS strategic and operational architectures provide a structured approach to defining capabilities, bridging strategic and operational domains, and identifying the behavioral process of operational activities. These steps enable the selection and construction order of architecture views for AFs during early design.  
Continued refinement and enhancement of the metamodel, ontologies, and modeling process can lead to more sophisticated approaches for designing SoSs.  
Future efforts can focus on two areas: (1) Validation and evaluation through analysis of UAF model types (i.e., constraints and parameters) and domains (i.e., criteria/standard and actual resources); (2) Establishment of a comprehensive knowledge base to support SoS architecture design, such as knowledge-based reasoning for automating the matching process from operational architecture to resource architecture.  
Conceptualization, Y.F., Q.Z. and C.Z.; methodology, Y.F., Y.L. and Q.P.; writing original draft preparation, Y.F. and Q.Z.; writing--review and editing, C.Z., Q.P. and Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (grant number 2018 YFB1700901); the National Science Foundation of China (grant numbers 61873236 and 62102355); the fund from the Fifth Electronic Research Institute of Ministry of Industry and Information Technology (grant number HK07202200877); and the Civil Aerospace Technology Pre-Research Program (grant number D020101).  
Not applicable.  
The authors declare no conflict of interest.
## 2 Theory and Methodology
### Odor Feature Extraction Based on HOG  
Inspired by the application of image features in computer and engineering science [33; 34], this paper proposed an odor feature extraction method based on the HOG algorithm. Firstly, the response images are drawn according to the E-nose response signals, and then the image features are extracted by using the HOG algorithm, as illustrated in Figure 2.  
Firstly, the horizontal gradient value \(G_{a}(x,y)\) and the vertical gradient value \(G_{b}(x,y)\) are calculated by sliding the discrete derivative mask [\(-1\), \(0\), \(1\)] and [\(-1\), \(0\), \(1\)]\({}^{\mathrm{T}}\) with the following formulas.  
\[G_{a}(x,y)=\frac{\partial I}{\partial x}=I(x+1,y)-I(x-1,y) \tag{1}\]  
\[G_{b}(x,y)=\frac{\partial I}{\partial y}=I(x,y+1)-I(x,y-1) \tag{2}\]  
where \(I(x,y)\) is the calculated pixel value of the image. The horizontal and vertical gradient values obtained above are then used to calculate the gradient magnitude \(G(x,y)\) and gradient direction \(\theta(x,y)\) of the image.  
\[G(x,y)=\sqrt{G_{a}(x,y)^{2}+G_{b}(x,y)^{2}} \tag{3}\]  
\[\theta(x,y)=tan^{-1}(\frac{G_{a}(x,y)}{G_{b}(x,y)}) \tag{4}\]  
After the abovementioned calculation, the image could be divided into small cells with the size of \(m\)\(\times\)\(m\) (\(m\) = 4, 6, 8, 10, 12). A local histogram is then created for each cell, which contains 9 bins and its values are distributed between (0, \(\pi\)). Weighted voting is subsequently performed on each bin based on the gradient magnitude.  
Figure 2: Flowchart for extracting HOG features from images.  
\[\left\{\begin{array}{c}R_{i}+G(x,y),\;\;if\;\theta(x,y)=\frac{\pi}{9}i\\ R_{i}+\left[\frac{\pi}{9}(i+1)-\theta(x,y)\right]G(x,y)\\ R_{i+1}+\left[\theta(x,y)-\frac{\pi}{9}i\right]G(x,y)\end{array}\right\},\;\;\; \;if\;\frac{\pi}{9}i<\theta(x,y)<\frac{\pi}{9}(i+1) \tag{5}\]  
where \(R_{i}\) (\(i=0,\,1,\,2,\,\ldots,8\)) represents the current value of the \(i\)th bin, which is accumulated by iterative weighted voting. Particularly, when the angle is greater than \(160^{\circ}\), the corresponding gradient magnitudes will be accumulated in the 0 th and 8 th bins, respectively.  
Finally, we combine \(n\,\times\,n\) (\(n=1,\,2,\,3,\,4\)) cells to a larger block by sliding windows to overlap 50% of the cells. The L\({}_{2}\)-norm is then used to normalize the spliced local histogram and all the normalized results are combined to the final feature vector.  
\[V_{N}=\frac{V_{k}}{\sqrt{\left\|V\right\|^{2}+\varepsilon}} \tag{6}\]  
where \(V\) is the block vector to be normalized, \(V_{k}\) represents the \(k\)th element in the block vector \(V\), \(V_{N}\) is the normalized block vector, and \(\varepsilon\) is a very small amount, which is used to solve the problem with a denominator of zero.
## 2 Modeling of Strain Actuation on Curved Beams
### FE Modeling
#### 2.2.1 Effect of the Curvature  
At first, the impact of the curvature on the transmission was investigated. Twenty different values of the curvature were considered, assuming the piezoelectric device to be located on the middle of the arc, as shown in Figure 2. The thickness of the piezo device, the thickness of the beam, and the lengths of the piezo device and the beam were fixed at the following values:  
* Piezo device thickness: 3 mm;
* Beam thickness: 2.7 mm;
* Piezo device's tangential extension: 10 mm (corresponding to a value of 14.75 deg for the internal reference radii of 0.075 m)
* Beam length (main line): 119.9 mm;  
With the tangential extensions of both the beam and the piezo device being fixed, their angular sweep and piezo angular location were calculated for each curvature case investigated. In Table 2, the main features of the numerical test cases are reported. The figures of the reference configuration for this parameterization and for the ones reported in the next sections are highlighted in bold characters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Parameter** & **Value** \\ \hline  & material & PVC \\  & elastic modulus (GPa) & 2.89 \\ Arc & Poisson ratio & 0.41 \\  & yield strength (MPa) & 52 \\  & mean line radius (mm) & 75 \\  & thickness (mm) & 2.7 \\ \hline  & material & PK-11 \\  & elastic modulus (GPa) & 60 \\ piezoelectric & Poisson ratio & 0.35 \\  & thickness (mm) & 3.0 \\  & d\({}_{31}\) (m/V) & 179 \(\times\) 10\({}^{-12}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main characteristics of the model.  
Figure 5: Tasks executed by the parameterization code.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Internal Radii (m)**} & \multirow{2}{*}{\begin{tabular}{c} **Curvature** \\ **(1/m)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Angular Position of** \\ **PZT (deg)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Angular Extension** \\ **of PZT (deg)** \\ \end{tabular} } \\ \hline
0.025 & 40.00 & 135.00 & 41.37 \\
0.030 & 33.33 & 112.50 & 35.04 \\
0.035 & 28.57 & 96.43 & 30.40 \\
0.040 & 25.00 & 84.38 & 26.84 \\
0.045 & 22.22 & 75.00 & 24.02 \\
0.050 & 20.00 & 67.50 & 21.74 \\
0.055 & 18.18 & 61.36 & 19.86 \\
0.060 & 16.67 & 56.25 & 18.28 \\
0.065 & 15.38 & 51.92 & 16.93 \\
0.070 & 14.29 & 48.21 & 15.76 \\
**0.075** & **13.33** & **45.00** & **14.75** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Curvature test cases.
## 4 Experimental Results
### The Experimental Results of the Proposed VCS with Color Images  
Similarly, the experimental results from Algorithm 6 for the \((5,\,5)\) case are shown in Figure 10. All of the color images are 512 \(\times\) 512 pixels in size. Figure 10a shows the secret  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Images** & **PSNR** \\ \hline \(R1\) & 19.7081 \\ \(R2\) & 19.4408 \\ \(R3\) & 19.6550 \\ \(R4\) & 19.5888 \\ \(R5\) & 19.7651 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The PSNR of the reveal image and shares generated by Algorithm 5 for the \((5,\,5)\) case.  
Figure 9: The secret and 15 different covers: (**a**) man; (**b**) boat; (**c**) couple; (**d**) architect; (**e**) cars; (**f**) baboon; (**g**) castle; (**h**) scientists; (**i**) agriculture; (**j**) butterfly; (**k**) pepper; (**l**) houses; (**m**) Barbara; (**n**) hill; (**o**) jet; (**p**) secret.  
image. Figure 10b-f show the cover images. Figure 10h-l show the share images generated by Algorithm 6. Figure 10g shows the revealed image via XORing all shares. Although there are some traces of encryption in the shares, we were still clearly able to analyze the meaning on the shares. Table 4 illustrates the PSNR of the images in Figure 10, all with a PSNR of between 19 to 20. We also calculated \(C(15,\,n)\) times to obtain the average PSNR in different cases. The 15 images are shown in Figure 11 (from [[https://pixabay.com](https://pixabay.com)]([https://pixabay.com](https://pixabay.com)), accessed date: 5 July 2022).  
The experimental results are shown in Table 5. From Table 5, we can obviously see that as \(n\) increases, the average visual quality of shares becomes better. It is easy to see that the average PSNR in Table 5 is similar to the value in Table 3 for each case.  
\begin{table}
\begin{tabular}{c c} \hline
**Images** & **PSNR** \\ \hline \(R1\) & 19.5757 \\ \(R2\) & 19.1196 \\ \(R3\) & 19.2015 \\ \(R4\) & 19.4861 \\ \(R5\) & 19.1398 \\ \hline \end{tabular}
\end{table}
Table 4: The PSNR of the reveal image and shares generated by Algorithm 6 for the (5, 5) case.
## 3 Results and Discussion
### Proposed Degradation Pathway of KP  
The photodegradation and intermediates of KP were examined by LC-MS coupled with Q-TOF using the negative ion ESI mode. These experiments were conducted to further investigate if indeed KP was photodegraded in these experiments. The LC-MS data showed that the fresh KP test solution only had one KP peak while after 5 h of filtering in the presence of light, four additional peaks were observed within the measured mass range and elution time. The full description is given in S14. The chromatograms and mass spectrum are shown in Figure S6 while the proposed mechanism is in Figure S7.
## 4 Results
### LULC Changes at the Catchment Scale
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**1994–2004** & **Area (km2)** & **2004–2014** & **Area (km2)** \\ \hline Conversion to cropland & 1968.94 & Conversion to cropland & 5744.11 \\ Grassland to cropland & 1093.15 & Grassland to cropland & 4981.32 \\ Open woodland to cropland & 838.67 & Open woodland to cropland & 762.79 \\ Swamp to cropland & 37.12 & & \\ \hline Conversion to teak & 97.14 & Conversion to teak & 176.4 \\ Bare soil to teak & 0.31 & Bare soil to teak & 0.88 \\ Grassland to teak & 10.57 & Grassland to teak & 24.56 \\ Open woodland to teak & 86.26 & Open woodland to teak & 113.64 \\ Closed woodland to teak & 37.32 & & 37.32 \\ \hline Changes within natural classes & 12,496.49 & Changes within natural classes & 12,808.00 \\ Open woodland to grassland & 5378.69 & Open woodland to grassland & 1316.24 \\  & & Closed woodland to open woodland & 2401.3 \\ Grassland to bare soil & 1835.23 & & \\ Swamp to grassland & 39.05 & & \\ Bare to open woodland & 136.51 & Bare to open woodland & 554.43 \\ Bare to grassland & 3268.5 & Bare to grassland & 93.94 \\ Grassland to open woodland & 1838.51 & Grassland to open woodland & 4128.38 \\  & & Closed woodland to montane forest & 2888.71 \\  & & Open woodland to closed woodland & 1425.00 \\ \hline Other changes & 1156.97 & Other changes & 0.00 \\ Cropland to grassland & 945.24 & - & \\ Cropland to open woodland & 211.73 & - & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Change detection statistics based on post-classification comparison results of the Kilombero catchment. The flow duration curves of the land use change scenarios from 1994 to 2014 do not show significant changes in exceedance probabilities (Figure 5) whereas a more detailed look at single components of the water balance (Table 4) illustrates changes in all runoff components and in all four land use scenarios (Figure 6). It can be seen that baseflow, overall water yield (baseflow, lateral runoff and surface runoff), and the deep aquifer recharge increase compared to the 1970s scenario, though not in a chronological order with regard to land use changes. Surface runoff decreases in the 1994 land use scenario and increases for the following scenarios of 2004 and 2014. Lateral runoff and evapotranspiration decrease in all scenarios compared to the reference scenario.
# Error Resilient Coding Techniques for Video Delivery over Vehicular Networks
## 5 Real Scenarios Evaluation  
In the previous tests we have been using a simple probabilistic loss model to simulate the packet losses in video delivery. As we have shown, real world details may change the behavior of the proposed error resilient techniques, so, in this section, we will use a realistic vehicular scenario to evaluate all the protection proposals presented before, to check their performance in this type of scenarios, where local network traffic interferences, source video signal strength, node mobility pattern, handover procedure, etc., determine the real packet loss patterns.  
Also, note that different layouts (encoding modes, til/rm layouts, FEC protection levels, etc.) produce very different packet streams (number of packets, size of each packet, etc.), and this has influence on the final network conditions. For example, encoding a video sequence with a tile layout of 10 til/rm with LP encoding mode will probably generate many small network packets and, on the contrary, encoding a video sequence with a tile layout of 1 til/rm and with AI mode will produce packets with the maximum size allowed. Also, protecting a video sequence with a protection level of  
Figure 8: TLR for every PLR, for the AI and LPI4 encoding modes. FEC-protected bit streams with a protection level of 10% and 20% using a protection period of 333 ms and a repair symbol size of 192 bytes.  
30% will generate far more packets and, as a consequence, higher bandwidth requirements. All these characteristics, combined with the real network conditions (video signal strength, background traffic level, etc.), have influence on the PLR and in the distribution of packet losses. So, every different layout does not \"work\" under the same network conditions, so it will behave differently and will have to confront a different situation. In the previous sections, when we analyzed, for example, how a PLR of 3% affected a certain layout versus another one, we did not take into consideration that one of these layouts may probably have a lower impact in the network conditions than the other.  
For the experimental tests presented in this section, we use several simulators working together in order to reproduce the conditions of vehicular networks (including vehicles mobility, wireless transmission of data, specific characteristics of the protocols defined for these networks, etc.). In Section 5.1, we describe the framework used to run our experiments and the selected network scenario for the tests. In Section 5.2, we will show and analyze the results obtained for the experimental tests evaluated.
# Graphical User Interface for the Development of Probabilistic Convolutional Neural Networks
## 2 Materials and Methods
### Convolutional Neural Networks  
CNNs are usually developed to be deterministic and are associated with pattern recognition in images or signals, presenting a high efficiency in classification. The process of data classification by the networks is carried out by means of the several layers that compose them [14]. These layers are typically of three types: convolution, pooling, and dense. A data alteration by ANNs takes place in all these layers, usually having non-linear AFs, such as the rectified linear unit (ReLU), to increase the learning capacity. The input and output layers are the ones that interact with the outside (they are not hidden).  
CNNs use filters that consist of randomly initialized weights, subsequently updated at each new input, through the back-propagation algorithm to learn the patterns. The region of the input, where the filter is applied, is called the receptive field. Pooling is used to reduce the information from the previous layer, decreasing the number of weights to be learned and avoiding overfitting [15]. As in convolution, an area unit is chosen to traverse the entire output of the previous layer. Furthermore, it is necessary to choose how the filtering will be done. The most used is the choice of the maximum value (maxpooling), where only the largest number of the unit is passed to the output.
# Segmenting Urban Scene Imagery in Real Time Using an Efficient UNet-like Transformer
## 3 Experiments
### Ablation Experiments  
#### 3.3.1 Each Component of the Model  
In order to evaluate the effect of each module of the proposed model on the final segmentation accuracy, a series of ablation experiments were conducted on the Vaihingen and UAVid datasets. The Baseline is a UNet network using ResNet18 as the backbone, which only considers the local context. In addition, we removed the SPT module and summed the global information in the global branch and the local information in the local branch (Baseline + GLIM-PT) to further evaluate the role of the position transformation operation. As shown in Table 4, the mIoU on the Vaihingen and UAVid datasets increased by 4.2% and 2.7%, respectively, after using the GLIM, and the position transformation operation can further improve the scores. In addition, the use of the FGH improved the mIoU by 0.6% and 1.3%, respectively. The results of the ablation experiments showed that each module of the proposed model is necessary and has positive effects on improving the final segmentation accuracy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Strategy** & **mIoU** \\ \hline Vaihingen & Baseline & 75.4 \\  & Baseline + GLIM-PT & 79.6 \\  & Baseline + GLIM & 81.7 \\  & Baseline + GLIM+ FGH & 82.3 \\ \hline UAVid & Baseline & 63.1 \\  & Baseline + GLIM-PT & 65.8 \\  & Baseline + GLIM & 66.9 \\  & Baseline + GLIM+ FGH & 68.2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation experiments for each module in the model.  
Figure 9: The segmentation results on the UAVid test set.
# Experimental Test of Continuous Wave Frequency Diverse Array Doppler Radar +
## 2 Data Collection I: Far-Field Beampattern
### Hardware and System Overview  
The antennas and components with respect to collection are given in Table 2. The connectors for each collection are SMA type, and common path-length cabling is used on the transmit paths. Each greenUSRP X310 houses a motherboard that supports two additional daughterboards, which can perform both analog-to-digital and digital-to-analog conversions. Each UBX-160 daughterboard is capable of producing one transmit signal; thus, the block diagram in Figure 1 shows a physical setup where three signals are produced from two X310 SDRs. The TwinRX daughterboard is used as the receive hardware in Data Collections I, II, III, and IV. The UBX-160 is transmit hardware for Data Collections I, II, and III. Data streaming limitations on the host PC require two signal generators as the transmit system for collection IV. The data is offloaded from the X310 SDR(s) to the host PC for display via a datastream. The Python (version 3.9.9) API (enabled during the CMake stage when building the source code) is used to configure both the devices and the metadata for the datastream.