esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study
### Results and Discussion
#### 4.3.2 Values of Route Resilience  
According to the changes in the daily operational resilience values for routes in the central and eastern regions shown in Figure 11, the resilience values show two distinct fluctuations corresponding to periods of high flight activity. These fluctuations occur during peak operational hours, which coincide with higher flight volumes. This pattern suggests that flight demand in the central and eastern regions is significant, thus putting pressure on Xi'an Xianyang International Airport's transportation capacity during peak hours. As the airport becomes busier, its ability to maintain steady operational resilience appears to be constrained, thus leading to reduced resilience values. Conversely, during off-peak periods, when the demand for flights is lower, the resilience of these routes improves.  
Figure 12 shows the daily operational resilience value changes for routes in the western and remote regions. Compared to the central and eastern areas, the resilience values for routes in the west and remote regions fluctuate more significantly. This indicates that airports in the western and remote areas have lower operational resilience and higher volatility when faced with changes in average daily flight volumes. This phenomenon could be related to relatively underdeveloped airport facilities, fewer flight volumes, and less sophisticated scheduling and management systems in these regions.  
By comparing the operational resilience values of routes in the two regions, the following conclusions can be drawn: Airports in the central and eastern areas have higher operational resilience, with more minor fluctuations in resilience values, and can better maintain operational stability under high flight volumes. In contrast, airports in the western and remote regions have lower operational resilience, with more significant fluctuations in resilience values, which indicates poorer stability when facing changes in flight volumes.  
Figure 11: Changes in daily operational resilience values of routes in central and eastern regions.
## 2 Experimental Systems and Methods  
### Experimental Systems  
In the physical simulation of water invasion in gas reservoirs, previous studies only considered the elastic expansion of formation water when simulating the edge-and-bottom aquifer [20; 21]. However, the compressibility of formation water is between 3.4 \(\times\) 10\({}^{-4}\) and 5.0 \(\times\) 10\({}^{-4}\) MPa\({}^{-1}\) in gas reservoirs with the finite edge-and-bottom aquifer [22]. Thus, the elastic expansion capacity of an aquifer is very weak and the aquifer expansion caused by the formation pressure drop is small. For example, when the pressure drop of formation water is 50 MPa, the aquifer expansion only accounts for about 2% of aquifer reserves. That is why water coning will occur at 20 times, 100 times and even infinite aquifer considering the aquifer expansion [21]. Therefore, there will be a false understanding that water coning will occur in strong aquifers if water invasion only considers the elastic expansion capacity of an aquifer, but this contradicts that water invasion in some gas reservoirs takes place without a strong aquifer.  
In order to simulate water invasion of gas reservoirs with the edge-and-bottom aquifer accurately, the energy source of water invasion is not just the elastic expansion of aquifer, and the effects of rock pore compression and water soluble gas expansion should be considered. The solubility of natural gas in water is mainly affected by formation pressure, temperature, and the salinity of formation water [23; 24]. With the increasing of formation pressure and temperature, the gas solubility in water rises gradually. The solubility declines gradually with the increasing of water salinity. According to the previous studies [23], there is a gas reservoir with origin formation temperature of 150 \({}^{\circ}\)C, formation pressure of 60 MPa, and water salinity of 19,029 mg/L. When the aquifer is 5 times and pressure drawdown is 40 MPa, the limit adsorption of natural gas in formation water is about 3.7% of reservoir reserves. However, when reservoir pressure declines to 40 MPa in the development of gas reservoirs, the overall pressure drop of formation water is much less than 40 MPa. Therefore, the desorption of water soluble gas can be negligible in contrast to reservoir reserves. As it is very difficult to dissolve gas in water completely, the experiment only considers the elastic expansion energy of water soluble gas, which ignores the desorption effect due to pressure drop. The experimental system of water invasion in gas reservoirs with edge and bottom aquifer was shown in Figure 1, which was composed of nitrogen gas source, ISCO pump, confining pressure pump, core holder, pressure sensor, gas flowmeter and data acquisition system, etc.
## 4 Results and Discussion
### Leakage Analysis of Underground Powerhouse and Karst Pipes  
There are five karst pipes in the dam site, of which Nos. 2 and 3 are finally merged into a karst pipe. Karst pipes 2, 3, and 4 are distributed on the right bank, and Nos. 1 and 5 on the left bank (Figure 5). The water flow in the pipes was discharged into the reservoir area under natural conditions. However, the water level of the reservoir was higher than the elevation of pipes after reservoir impoundment. The reservoir water will be discharged into the pipes and flowed out of the reservoir owing to the difference of water levels, which will affect the reservoir impoundment. The leakage of karst pipes and the underground powerhouse was calculated under the normal and failure impervious curtain (Table 8). The reservoir level of cases 1 and 2 is 1030 m, and that of Case 3 and 4 is 985 m. It can be seen from the Table 8 that the leakage in the case of impervious failure (Case 2) was larger than that of normal seepage control (Case 1), which shows that plugging karst pipes was very necessary. Because the vertical curtain did not reach the elevation of No. 5 karst pipe, there was little difference in leakage. It is suggested that the impervious curtain should be deepened at No. 5 karst pipe. Whether the seepage control of karst pipes is normal or not  
Figure 9: Contour maps of groundwater level for karst pipes with and without the impervious curtain ((**a**) planar maps with an elevation of 950 m, (**b**) cross sections of underground powerhouse, and (**c**) cross sections of No. 4 karst pipe).  
has little effect on the leakage of the underground powerhouse. During the operation of the reservoir, the leakage of the powerhouse was estimated to be about 1000-3000 m\({}^{3}\)/d.
# A Survey on Unmanned Surface Vehicles for Disaster Robotics: Main Challenges and Directions
###### Abstract  
Disaster robotics has become a research area in its own right, with several reported cases of successful robot deployment in actual disaster scenarios. Most of these disaster deployments use aerial, ground, or underwater robotic platforms. However, the research involving autonomous boats or Unmanned Surface Vehicles (USVs) for Disaster Management (DM) is currently spread across several publications, with varying degrees of depth, and focusing on more than one unmanned vehicle--usually under the umbrella of Unmanned Marine Vessels (UMV). Therefore, the current importance of USVs for the DM process in its different phases is not clear. This paper presents the first comprehensive survey about the applications and roles of USVs for DM, as far as we know. This work demonstrates that there are few current deployments in disaster scenarios, with most of the research in the area focusing on the technological aspects of USV hardware and software, such as Guidance Navigation and Control, and not focusing on their actual importance for DM. Finally, to guide future research, this paper also summarizes our own contributions, the lessons learned, guidelines, and research gaps.  
survey; disaster management; unmanned surface vehicle; USV; unmanned surface craft; USC; autonomous surface craft; ASC; autonomous boat; disaster robotics; floods; landslides; hurricanes; tsunamis; hazard; search and rescue +
Footnote †: journal: Sensors
# Extraction of Joint Entity and Relationships with Soft Pruning and GlobalPointer
## 3 Methodology
### Graph Model
#### 3.2.2 KL Divergence
To gain more information about the structure of large graphs, we refer to the dense connection [43]. With the help of dense connections, we can train deeper models, allowing rich local and non-local information to be captured for learning a better graph representation. The \(p\) is represented as the connection between the initial node representation and the layer 1, 2,..., \(l-1\) node representation. \[p_{j}^{(l)}=[v_{j};h_{j}^{(1)};h_{j}^{(2)};\ldots;h_{j}^{(l-1)}] \tag{6}\]  
Each densely connected layer has L sub-layers. Each sub-layer has multi-layer GCNs. Our graph model concatenates the outputs of each sub-layer to form new representations. Therefore, unlike GCN models, where the hidden dimension is more than or equal to the input dimension, our graph module's hidden dimension diminishes as the number of layers grows, improving parameter efficiency. Thus, for \(N\) dense connections and the resulting \(N\) attention-guided adjacency matrices, we obtain the output representation of the \(t\)-th graph according to the following equation:  
\[h_{t_{i}}^{(l)}=\rho\left(\sum_{j=1}^{n}\mathrm{A}_{ij}^{(t)}\mathrm{W}_{t}^{ (l)}p_{j}^{(l)}+\mathrm{b}_{t}^{(l)}\right) \tag{7}\]  
where \(t=1,2,\ldots,N\) and \(\mathrm{A}^{(l)}\) is \(t\)-th the attention guided adjacency matrix. \(\mathrm{W}_{t}^{(l)}\in\mathbb{R}^{d_{hidden}\times d^{(l)}}\) represents the weight matrix, where \(d^{(l)}=d+d_{hidden}\times(l-1)\). Finally, the linear combination layer is applied to integrate the outputs of the \(N\) different densely connected layers. Formally, the output representation of the graph module after the linear combination layer can be written as  
\[ \begin{array}{l}h_{comb}=[h^{(1)},h^{(2)},\ldots,h^{(N)}]\\ h_{out}=\mathrm{W}_{comb}h_{comb}+\mathrm{b}_{comb}\end{array}  \tag{8}\]  
where \(h_{comb}\in\mathbb{R}^{d\times\mathrm{N}}\) is obtained by connecting \(N\) densely connected layers, \(b_{comb}\) is a bias vector for the linear transformation, and \(\mathrm{W}_{comb}\in\mathbb{R}^{(d\times\mathrm{N})\times d}\) is the weight matrix.
## 1 Introduction
This system integrates high-precision temperature collection devices, ARM processing modules for data transcoding and transmission, and fiber optic transceivers and cables to form an underground network using 5G routers for network access, in conjunction with cloud servers and data collection software (Wireless Measurement System v1.0.0.0). The system provides a scientific basis for mining extraction and construction, reduces the risk of geological disasters, and enhances the safety and sustainability of mine construction.
# RSIMS: Large-Scale Heterogeneous Remote Sensing Images Management System
## 2 Materials and Methods
### RSIData: Large-Scale Image Files Storage and GDAL-Compatible Interfaces Design
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Product** & **Start Time** & **Latest Time** & **Resolutions** & **Dimensions** & **HLevel** & **Size** \\ \hline Landsat 4 MSS & 6 August 1982 & 15 October 1992 & 80 m & 185 km \(\times\) 185 km & 6 & 341,759 \\ Landsat 4 TM & 22 August 1982 & 18 November 1993 & 30 m & 185 km \(\times\) 185 km & 6 & 65,870 \\ Landsat 5 MSS & 4 March 1984 & 7 January 2013 & 80 m & 185 km \(\times\) 185 km & 6 & 1,206,592 \\ Landsat 5 TM & 5 March 1984 & 5 May 2012 & 30 m & 185 km \(\times\) 185 km & 6 & 4,553,146 \\ Landsat 7 ETM & 28 May 1999 & 30 July 2019 & 30 m & 185 km \(\times\) 185 km & 6 & 4,281,514 \\ Landsat 8 OLJ/TIRS & 18 March 2013 & 30 July 2019 & 30 m & 185 km \(\times\) 185 km & 6 & 2,554,265 \\ MODIS MCD12Q1 V6 & 1 January 2001 & 31 December 2019 & 500 m & 1200 km \(\times\) 1200 km & 4 & 5983 \\ MODIS MOD9A1 V6 & 24 February 2000 & 15 February 2021 & 500 m & 1200 km \(\times\) 1200 km & 4 & 283,456 \\ MODIS MYD09A1 V6 & 4 July 2002 & 2 February 2021 & 500 m & 1200 km \(\times\) 1200 km & 4 & 251,623 \\ MODIS MYD09 GA V6 & 4 July 2002 & 24 February 2021 & 500 m & 1200 km \(\times\) 1200 km & 4 & 2,173,235 \\ MODIS MYD11A1 V6 & 4 July 2002 & 16 February 2021 & 1000 m & 1200 km \(\times\) 1200 km & 4 & 2,142,872 \\ Sentinel-1 SAR GRD & 3 October 2014 & 24 February 2021 & 10 m & 400 km \(\times\) 400 km & 5 & 41,915 \\  & & & 10 m & & & \\ Sentinel-2A MSI & 28 March 2017 & 24 February 2021 & 20 m & 290 km \(\times\) 290 km & 6 & 9,064,645 \\  & & & 30 m & & & \\ \hline \end{tabular}
\end{table} 
Table 1: Popular products in RSIMS. VSIFileManager, VSIFilesystemHandler and VSIVirtualHandle are the key abstract classes provided by GDAL library. Storage system inheriting and implementing these classes can be accessed directly by GDAL library. VSIFileManager is used for new virtual filesystems registration and management. VSIFilesystemHandler and VSIVirtualHandle are the abstract classes for a new virtual filesystem implementation; VSIFilesystemHandler is responsible for file attributes management and directory operations; VSIVirtualHandle is responsible for file data read and write. RadosFilesystemHandler and RadosVirtualHandle are the concrete classes implementing the corresponding abstract classes. Converting the operations on virtual image file to the real image object in RSIData is implemented in RadosVirtualHandle. For better performance, we added a module, called CachedFileBlock, used for cache frequently visited data blocks in memory. These blocks are parts of remote sensing image files with fixed size. Every block is assigned a unique id generated from the image file and the offset. Considering the limit of memory space, these cached data blocks are regularly updated with the LRU algorithm.
# Support Vector Machine-Based Transmit Antenna Allocation for Multiuser Communication Systems
## 3 SVM-Based Antenna Allocation
### Task 3. Parameter Adjustment  
For the multiclass SVM algorithms, several parameters and strategies can be selected and tuned in order to achieve superior sum-rate performance. Here, we note that machine learning algorithms with low prediction complexity are favorable to antenna selection in communication systems. Based on numerical results regarding the sum-rate performance and prediction runtime performance, we discuss the selection of kernel function from linear, polynomial, and Gaussian kernel functions, and the selection of multiclass strategy from between OVA and OVO. Through rigorous simulation and comparison, we find that the SVM algorithm with the OVA strategy and the Gaussian kernel provides nearly the best sum-rate performance with a relatively low prediction runtime, and is thus adopted in our antenna selection system. For example, Figures 3 and 4 verify our observation when \(N_{t}=5\) and \(U=3\), where solid red curves represent the use of SVM algorithms with the OVA strategy while dashed blue curves represent the use of SVM algorithms with the OVO strategy. In contrast, SVM algorithms with the OVO algorithm show a relatively high prediction runtime that grows fast with \(L^{\prime}\), although the sum-rate performance is slightly superior to that of the OVA counterparts. The SVM algorithm with the OVA strategy and linear kernel provides the lowest prediction runtime performance but the sum-rate performance degradation is severe.  
Figure 3: Support vector machine (SVM) performance evaluation results when \(N_{t}=5\) and \(U=3\): the empirical cumulative density function (CDF) of the sum rate for various SVM kernel functions.  
For each combination of multiclass strategies and kernel functions, proper values of crucial parameters such as the variance \(\sigma^{2}\) and the penalty parameter \(C\) are found by extensive experimentation via a heuristic search targeting the minimum cross-validation loss. For instance, when the OVA strategy and the Gaussian kernel function are adopted, \(\sigma^{2}\) and \(C\) are determined by 21.56 and 7.67, respectively.
# A Dual-Stage Vocabulary of Features (VoF)-Based Technique for COVID-19 Variants' Classification
## 4 Experimental Results  
The proposed framework was evaluated based on different performance parameters. The performance parameters include accuracy, \(F1\)-score, sensitivity, specificity, Cohen's kappa, and classification error. The accuracy of the model can be evaluated by Equation (1).  
\[Accuracy=\frac{TP+TN}{TP+FP+TN+FN} \tag{1}\]  
where \(TP\) denotes true positive, \(TN\) denotes true negative, \(FP\) represents false positive, and \(FN\) means false negative. Accuracy can be stated as the ratio of the correct prediction of COVID-19 to the total number of images in the database. Accuracy is very important performance parameter. Similarly, specificity can be calculated by Equation (2). Specificity is the ratio of the prediction of normal images present in the database.  
\[Specificity\left(Sp\right)=\frac{TN}{TN+FP} \tag{2}\]  
Another performance parameter is the sensitivity of the model, and it is defined as the ratio of the prediction of COVID-19. It can be determined by Equation (3).  
\[Sensitivity\left(Se\right)=\frac{TP}{TP+FN} \tag{3}\]  
Like accuracy, the \(F1\)-score is also an important parameter for the evaluation of the model performance. It is the average of the specificity and sensitivity and the formula to evaluate the \(F1\)-score is given in Equation (4).  
\[F1-score=\left[\frac{\left(Se*Sp\right)}{\left(Se+Sp\right)}\right]*2 \tag{4}\]  
where \(Se\) and \(Sp\) denote sensitivity and specificity, respectively. Another performance parameter considered in this article is Cohen's kappa. It can be calculated by using Equation (5).  
\[Cohen\prime\kappa Kappa\left(\kappa\right)=\left[\frac{\left(TP.TN-FP.FN\right) }{\left(TP+FP\right).\left(FP+TN\right)+\left(TP+FN\right).\left(FN+TN\right) }\right]*2 \tag{5}\]  
These parameters were used to evaluate the performance of our proposed method.
## 3 Results and Discussion
### Changes in the Mycotoxin Concentration in Wheat Wholemal during Fermentation  
The mycotoxin concentration in the WW samples is shown in Table 6. In 'Ada' samples (nontreated and fermented) and nonfermented and LUHS122 strain fermented 'Sarta' samples, all the tested mycotoxin concentrations were below the detection limits. However, in 'Sarta' samples fermented with the LUHS29 and LUHS245 strain, the deoxynivalenol  
Figure 5: Volatile compounds that are \(<\)1% in wheat wholemal samples (LUHS29—fermented with _Paliococcus acidilacidic_ LUHS29 strain; LUHS245—fermented with _Liquidobacillus uarum_ LUHS245 strain; LUHS122—fermented with _Liquidintillus plantarum_ LUHS122 strain. Data are represented as means (\(n=3\))).  
(DON) concentration was, on average, 19.5 ug/kg. Also, DON was established in all the tested DS8472-5 blue and DS8526-2 purple WW, with the highest DON concentration in samples fermented with LUHS29 (163.0 and 392.3 ug/kg, respectively). In DS8472-5 blue WW samples, after fermentation with all the tested LAB strains, the DON concentration was increased in comparison with the nontreated samples (in samples fermented with LUHS29 by 69.1%, in samples fermented with LUHS245 by 39.0%, and in samples fermented with LUHS122 by 20.3%). Different tendencies of the DON concentration in DS8526-2 purple WW samples were established, and after fermentation with LUHS29 and LUHS245, an increase in DON was found (by 43.0% and 18.0%, respectively); however, after fermentation with LUHS122, a 17.6% lower concentration of DON was established in the WW samples. It was reported that the anti-fungal activity of sourdough LAB is mainly attributed to the inhibitory metabolites produced during fermentation [89] and the synergistic interactions of the LAB metabolites with dough or microbial derived compounds, and this inhibitory effect may even change depending on the type of flour used [90; 91]. However, the reduction of mycotoxins by LAB mainly depends on the binding of the toxin to the bacterial cell wall or, more rarely, due to the metabolism of the toxin [92]. Despite the fact that the main role in the removal of mycotoxins is attributed to the bacterial cell-wall components, [93] it was reported that the organic acids and antifungal ingredients such as phenyllactic acid, hydroxyphenyllactic acid, and indole lactic acid produced by the LAB also have anti-aflatoxigenic effects. To summarize, differences in the structure of the bacterial cell wall, the initial concentration of the toxin, the bacterial population, the incubation conditions, the type of the LAB, and probably the types and the amounts of the LAB metabolites are important in this effect [93; 94]. Finally, it could be suggested that by changing the duration of the fermentation, moisture content of the fermentable substrate, etc., the results of the mycotoxin concentration in fermented WW could vary. Further research is needed to evaluate the influence of technological parameters on mycotoxin degradation in WW samples.
## 6 Unifying Semantic Legal Policies
### Query at the _TLD\({}_{\alpha\cap\beta}\)_ for Data Exchange  
In Figure 5, an _agent\({}_{\alpha}\)_ in _TLD\({}_{\alpha}\)_ enforces privacy protection law, and an _agent\({}_{\beta}\)_ in _TLD\({}_{\beta}\)_ enforces national security law. When a data usage context satisfies the conditions of national security law enforcement, such as a data user's _role_ as a national security officer, a data owner's _consent_ is absent, and the _purpose_ of data disclosure falls on national security; then, we enter the _TLD\({}_{\alpha\cap\beta}\)_ legal domain for data exchange. We model the enforcement of national security law as the privacy policy's exceptions. Whenever a national security officer queries data at the _sp\({}_{\alpha\cap\beta}\)_, the nationality principle shown in Section 1.2 allows another jurisdiction's privacy protection law to bend. However, only the anonymous PII are disclosed because we still have to somehow ensure that the privacy protection law \(\alpha\) is not violated. This approach balances personal privacy rights and national security needs in the cloud.  
We manually unify two types of legal policies, which are translated from privacy protection law and national security law to demonstrate how data are collected from peers who have been registered at the _sp\({}_{\alpha\cap\beta}\)_. Two types of queries are available subject- and pattern-based queries, where a subject-based query allows us to access a specific data owner's complete profile. Conversely, a pattern-based query does not have specific access targets, so only data that satisfy pre-defined filtering conditions are disclosed.  
At the _sp\({}_{\alpha\cap\beta}\)_, we only provide pattern-based queries. This is in contrast with the queries provided at the _sp\({}_{\alpha}\)_ and _sp\({}_{\beta}\)_, where we provide both. Similar to the privacy appliance [8], a trusted _agent\({}_{\alpha\cap\beta}\)_ at the _sp\({}_{\alpha\cap\beta}\)_ is a guardian, who follows the laws and provides privacy protection and national security legal services while disclosing data from its registered peers within _TLD\({}_{\alpha\cap\beta}\)_.  
In summary, we manually unify privacy protection legal policies with national security legal policies at the intersection of TLDs while enforcing data exchange. This is not only to ensure privacy but also to encourage sharing data for national security purposes without the fear of privacy rights being violated.