esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Discussion
### Test Problems  
Three realistic scenarios with different dimensions are tested in the proposed approach. The geographic information is provided by the National Institute of Statistics and Geography of Mexico (INEGI). INEGI organizes the information of Mexico according to map scales. The digital elevation model (DEM) and topography are used to generate the cumulative cost matrix. Figure 7 shows the organization in topographic charts of the Mexican regions at two scales (1:250,000 and 1:50,000). The 1:50,000 map scale is considered to calculate the distance of the nodes sequence \(p\), in kilometers (km), as follows:  
\[dist(s,t)=\frac{p.50}{1000} \tag{7}\]  
For formulating the three test problems, the topographic charts shown in Table 1 are used. From these topographic charts, the maximum elevation in the area comprising the first two scenarios is 80 m above sea level, so in the present work, the half of this value (40 m) is taken as the \(e_{max}\) value. For the third scenario, 1/20 of the maximum elevation (5610 m above sea level) is used, so \(e_{max}\) is set as 280.5.  
Figure 5: Distance between nodes and \(s\) connection and the _pseudo_-random path to create \(s^{\prime}\).  
Figure 6: Corridor’s layout.  
Figure 8 shows the names of the topographic charts of the Mexican territory used in this experimental study, which corresponds to the geographic information for the year 2015. The first test problem is a small network of \(586\times 732\) nodes covering the Mexican port of Veracruz. The second one consists of a medium-sized network of \(585\times 1467\) nodes, including the Joachin and Naranjos cities, both in Veracruz state, Mexico. Finally, the third problem is an extensive network of \(5341\times 6727\) nodes comprising part of the Mexican states of Morelos, Guerrero, Puebla, and Veracruz.  
The topographic restrictions used for the test problems are based on a classification defined by the feasibility penalties of the different topographic layers.  
Table 2 shows the penalties used in the first two test problems and Table 3 shows those used in the third one.  
\begin{table}
\begin{tabular}{c c} \hline
**Problem Dimension** & **Topographic Charts** \\ \hline \(586\times 732\) & E14B49 \\ \hline \(585\times 1467\) & E14B59 Y E14B79 \\ \hline \(5341\times 6727\) & E14B41-49, E14B51-59, E14B61-69, E14B71-79 Y E14B81-89 \\ \hline \end{tabular}
\end{table}
Table 1: Topographic charts that correspond to each problem.  
Figure 8: Topographic charts of the study area.  
Figure 7: Topographic charts at scales (**a**) 1:250,000, and (**b**) 1:50,000.
# Deriving High Spatial-Resolution Coastal Topography From Sub-meter Satellite Stereo Imagery
## 6 Conclusions  
The beach topography of a 40-km-long sandy coastal stretch, located in the southwest of France, was surveyed by satellite Pleiades stereo-imagery. The computed DEM was compared with those obtained from traditional survey methodologies (RTK-GPS and LiDAR). Present findings indicate that Pleiades stereo-imagery allows the acquisition of high resolution DEM with a RMS error that ranges from 0.35 to 0.48 m. The largest errors were observed at the dune face, in regions with large shadow patches. Near-perfect agreement between Pleiades and concurrent methods (all computed CC were above 0.9) provides strong indications that this method can be used as a surveying tool to monitor detailed coastal morphological changes over large spatial domains.  
Conceptualization, L.P.A., R.A. and E.B.; data curation, L.P.A. and E.B.; methodology, E.B. and L.P.A.; validation, L.P.A.; writing--original draft preparation, L.P.A., B.A., E.W.J.B. and R.A.; writing--review and editing, all authors; field data acquisition, L.P.A., R.A., E.W.J.B., P.B., E.G. and O.A.D.; administration and funding acquisition, L.P.A. and R.A.  
[PERSON] was funded by a post-doctoral fellowship from the French Space Agency (CNES) to develop this work. [PERSON] acknowledges fundings from CNES through the TOSCA program. The work of [PERSON] was supported by FCT research contract IF/00661/2014/CP1234. [PERSON] acknowledges the financial support to CESAM (UID/AMB/50017/2019), to RCT/MCTES through national funds, and the co-funding by the FEDER, within the PT2020 Partnership Agreement and Compete 2020.  
The authors would like to thank the CNRS/LEGOS for funding the project the COMBI (2017) that allowed the development of the field campaign for the validation of the Pleiades products. We would like to thank the Mairie and Port de Capbreton for offering accommodation during the field campaign, and all the amazing support given during the experiment.  
The authors declare no conflict of interest.
## 7 Risk Analysis
### WiFi
#### 7.5.3 Spoofing  
1. **Component**  
_Security risk_: If the information exchange is not properly protected, the attacker can gain access to WiFi and execute a spoofing attack by exploiting the Bluetooth interface and then gain access to the TCU and then to the WiFi module. Spoofing might allow the manipulation of certain features by an unauthorized user, leading to major safety complications. On the privacy side, the attacker could trick users into sharing personal information, which could be used for identity theft and fraudulent activities.  
_Security measures_: In order to mitigate this risk, the latest security protocol should be implemented, wireless protected access (WPA3), which provides stronger encryption and protection against brute force attacks.
## 3 Results and Discussion
### Effect of Ageing  
A principal component analysis (PCA) was carried out to study the main variation sources in the data and to determine the grouping of samples. The first two principal components of the PCA accounted for the 61% of the total sample variability (Figure 1), the main variation source being explained by PC1 (39%), which was related with the ageing of the brandies (Figure 1A). It was observed that _Solera_ and _Solera Reserva_ brandies with a more similar period of ageing, averaging 6-9 and 12-18 months, respectively (Figure 1A), displayed a similar ester profile, characterized by higher levels of isoamyl acetate, hexyl acetate, isobutyl acetate, ethyl butyrate, methyl hexanoate, propyl acetate, ethyl octanoate, phenylethyl acetate, ethyl valerate, isobutyl hexanoate, methyl octanoate, isoamyl octanoate, ethyl dihydrocinnamate, and ethyl hexanoate (Figure 2A). Meanwhile, the ester profile of the _Solera Gran Reserva_ brandies (averaging 6-12 years of ageing) displayed a more differentiated ester profile, with an overall increase of the rest of the esters analyzed, including ethyl 2-methylbutyrate, ethyl nonanoate, ethyl phenylacetate, ethyl isovalerate, ethyl isobutyrate, ethyl decanoate, ethyl dodecanoate, ethyl propanoate, ethyl heptanoate, isoamyl butanoate, ethyl myristate, and ethyl cinnamate. Among these, ethyl isobutyrate, ethyl 2-methylbutyrate and ethyl decanoate have been described as olfactive markers of a similar product (Calvados), as well as isoamyl acetate [30], which, conversely, displayed lower concentrations in the _Solera Gran Reserva_ brandies. These results demonstrated the distinctive and exclusive character of the brandies with a longer ageing period. Another study [31] also found that \"Brandies de Jerez\" _Solera Gran Reserva_ displayed a clear difference in phenolic and furanic derivative profile from other brandies produced in different regions, indicating their highly specific character. The higher concentration of esters observed for the _Solera Gran Reserva_ brandies may be related to the continuous esterification of fatty acids and ethanol, and losses of water and ethanol through evaporation effects and perspiration through the pores of the wood during ageing [26; 32].
## 2 Materials and Methods
### Strains and Plasmids  
_E. coli_ strains and plasmids used in the present study are listed in Table 1. Bacterial cells were cultivated in LB medium supplemented with antibiotics as required: ampicillin (0.15 mg/mL) and chloramphenicol (0.015 mg/mL). The ER2738 strain was employed for molecular cloning and plasmid propagation, whereas Lemo21(DE3) was used as the expression strain for surface display. The pBAD-AppA plasmid (Figure 1a) served as a template to amplify the sequence encoding mature AppA and the pESE plasmid (Figure 1b) was used as the backbone vector to display AppA on the bacterial surface.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Strain** & **Genotype** & **Source** \\ \hline ER2728 & F\({}^{\prime}\) proA+B+ lacIq \(\Delta\)(lacZ)M15 zzzf:Tn10(Tet\({}^{\text{R}}\))/fluA2 glnV & New England \\  & \(\Delta\)(lac-proAB) & Biolabs \\  & thi-1 \(\Delta\)(hsdS-mcrB)5 & \\ \hline BL21(DE3) & \(\lambda\) DE3 = \(\lambda\) sBamHt\(\Delta\)EscoR-I-B int:(lacI::PlacUV5::T7 gene1)i21 & Novagen \\  & \(\Delta\)nin5 & \\ \hline Lemo21(DE3) & \
\begin{tabular}{c} BL21(DE3)/pLemo \\ pLemo = pACYC184-PhaBAD-lysY (Cam\({}^{\text{R}}\)) \\ \end{tabular} &
\begin{tabular}{c} New England \\ Biolabs \\ \end{tabular} \\ \hline MWC-22b & Lemo21(DE3)/pET22b(+) & This study \\ MWC-ESE & Lemo21(DE3)/pESE & This study \\ MWCB-phytase & Lemo21(DE3)/pESE-AppA & This study \\ \hline Plasmid & Features & Source \\ \hline pBAD33 & araBAD promoter (arabinose regulation), p15A origin, Cam\({}^{\text{R}}\) & ATCC \({}^{\text{1}}\)[44] \\ pBAD-AppA & pBAD33-based, periplasmic expression of AppA & Lab Stock [45] \\ pET22b(+) & T7 lacO promoter (lactose regulation), ColE1 origin, Amp\({}^{\text{R}}\) & Novagen \\ pESE & pET22b(+)-based, OM-linked expression of eLpp-OmpA & Lab Stock [41] \\ pESE-AppA & pESE-derived, OM-linked expression of eLpp-OmpA-AppA & This study \\ \hline \hline \end{tabular}  
\({}^{\text{1}}\) American type culture collection.  
\end{table}
Table 1: _E. coli_ strains and bacterial plasmids used throughout this study.
## 1 Introduction
### Fracture Healing Process  
Fracture healing is a complex process, (described in detail in [2]), which is triggered by the inflammation phase. Inflammatory response reaches a peak approximately 24 h following the injury releasing inflammatory mediators, which recruit inflammatory cells and promote angiogenesis. Proper fracture healing requires two key conditions, as follows:  
1. Proper blood supply through the nutrient artery (80%) and periosteal vessels (20%), which triggers the release of growth factors, stimulates local angiogenesis (which is another key process in bone healing [3]), vasodilation and ultimately, formation of new bone tissue. It has been shown [4] that blood supply not only provides nutrients but is also a source of mesenchymal stem cells that ultimately differentiate into osteoblasts. It is known that metaphyseal bone section heals significantly faster than diaphyseal bone because of a more intense vascularization of the first.
2. Mechanical stability of the fracture. Relative displacement of bone fragments interrupts the development of new bone tissue and may cause malunion.  
Bone healing is a physiological process of proliferative nature. Depending on the relative position of the bone fragments created by the fracture line, the following types of healing process occur:  
1. Primary healing occurs when the bone fragments are rigidly fixed in correct anatomic position with no gap in between [5]. Primary healing consists of remodeling of lamellar bone, the Haversian canals and the blood vessels without callus formation. The process can last from months to years.
2. If a gap of less than 0.01 mm the healing mechanism--contact healing--is slightly different. In this case, cutting cones, consisting of osteoclasts from across the sides of the fracture line, generate cavities at a rate of 50-100 \(\upmu\)m/day, which subsequently are filled up with the Haversian system  
Figure 1: Schatzker classification system of tibial plateau fracture types—from [1].  
and osteoblasts lay down new bone tissue. Cutting cone mechanism occurs in diaphysis region of the bone (cortical tissue) and not in metaphysis region (such as tibial plateau).
3. In fractures with larger gaps (0.8-1 mm) the fracture region is filled by osteoclasts and then by lamellar bone orthogonal to the axis of the bone. The process is known as gap healing.
4. Secondary healing consists of endochondral ossification (which is one of the two fundamental mechanisms of bone tissue formation during fetal development of the mammalian skeletal system) and is the healing process most frequently encountered. Secondary healing occurs in cases where fracture is treated using orthopedic cast, or external or internal fixation.
# Dissimilarity Space Based Multi-Source Cross-Project Defect Prediction
## 2 Related Work
### Multi-Source CPDP  
In recent years, multi-source CPDP methods are beginning to be considered to improve the performance of defect prediction [16; 17]. Compared with a random selection of a single data set from the source domain as a training set, the multi-source CPDP method can avoid negative transferring, over-fitting problems, and make better performance of transfer learning [11]. Many researchers have proved that the multi-source cross-project software defect prediction can improve the performance of the prediction model and enhance generalization.  
[PERSON] and [PERSON] [13] proposed two multi-source CPDP methods: the first one is to establish multiple candidate weak classifiers according to the source domain data set in each iteration, and select the one with the lowest error rate as the weak classification of the current iteration; The second method is divided into two stages: firstly, all candidate weak classifications are obtained through multiple iterations, and then the classifier with the smallest error rate among the candidate weak classifiers is used as the current weak classifier in each iteration.  
[PERSON] et al. [10] proposed a multi-source TrAdaBoost (MsTrA) approach to construct prediction model. This method calculates the similarity between each candidate source data set sample and the target sample and gives weight to each sample at first. Then, on the basis of the TrAdaBoost algorithm, the classifier with the smallest error rate is selected as the weak classifier in each iteration, and the prediction model is obtained through multiple iterations.  
[PERSON] and [PERSON] et al. [11] proposed HYDRA method, which constructs a multi-source CPDP model through two stages. In the first stage, they merge each candidate source and target dataset as the training set to construct multiple base-classifiers, then use the genetic algorithm (GA) to find the optimal combination of base-classifiers. The second phase is similar to the AdaBoost algorithm, and the GA model is generated in each iteration. Finally, a linear combination of GA models is obtained as a prediction model.  
[PERSON] et al. [18] based on the HYDRA method proposed the S\({}^{3}\)EL method that constructs basis classifier with feature mean values, and after that, GA is also used to assign weights to the basis classifier. Experiments show that those methods are superior to the conventional single-source CPDP methods.  
The above methods provide important research approaches and ideas for multi-source CPDP research, but there are still some shortcomings. The MsTrA algorithm selects the optimal weak classifier in each iteration, which is prone to over-fitting problems. The HYDRA and S\({}^{3}\)EL method use the GA to search for the optimal weight combination of the classifiers in each iteration, thus the time complexity is higher. Therefore, this paper focus on solving the problems of data noise, generalization, and time complexity.
# Big Data Collection in Large-Scale Wireless Sensor Networks
## 2 Overview of WSNs
### Topologies of WSNs
#### 2.3.3 Hybrid Topology  
Hybrid topology combines both the star and mesh topologies [45]. It provides a robust network with several communication functions while keeping sensor energy consumption to a minimum.  
Figure 3: Types of WSNs.  
Sensor nodes that have higher energy capacities are enabled to provide multihop communications, while low power nodes are disabled.
## 4 Conclusions  
As security breaches become more prevalent, new authentication techniques must incorporate agriculture professionals' biometrics to improve the system's security. To cater for this need, a robust multi-gateway authentication scheme for agriculture WSN is proposed in this paper. The proposed scheme exploits the advantages of the fuzzy extractor to design a secure authentication system. The study proposed a multi-gateway model to overcome single point failure that exists in a single gateway communication model. This paper pointed out that multi-gateway WSN can allow users to access data from multiple sensor areas (a typical IoT deployment). Furthermore, the study added a new joined phase to enable new sensors to join the agriculture field. The proposed scheme is resistant to various known attacks, including the sensor node capture attack, as proved by formal and informal security research. The proposed scheme is secure against replay and man-in-the-middle attacks, as demonstrated by the extensive formal security verification performed using the AVISPA tool. Furthermore, we demonstrated that our scheme is efficient and provides additional functionality as compared to previous schemes through performance results. In terms of performance, the proposed scheme is better suitable for IoT deployment, where devices deployed in agriculture are generally resource constrained. The future works of this research can be summarized as follows: First, this paper provides secure communication for a multi-gateway environment with efficient results. This solution can be extended to enable the environment with different communication methods used in the same area (e.g., Bluetooth, ZigBee, and WiFi) to guard against interference. Second, due to the efficiency of the proposed scheme, it can further be extended to provide secure user authentication to monitor the field progress. Third, we plan to extend the proposed scheme to protect against distributed denial of service attacks (DDoS) that mainly target sensor nodes.  
**Author Contributions:** Conceptualization, H.K. and S.J.H.; methodology, H.K., S.J.H.; software, H.K.; validation, H.K.; results interception, H.K. and S.J.H.; formal analysis, H.K.; writing--original draft preparation, H.K.; writing--review and editing, H.K. and S.J.H.; supervision, S.J.H., S.M.S.A., F.H. and M.A.C.; and project administration, S.J.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data sharing not applicable.  
**Acknowledgments:** We would like to thank the reviewers for their careful, constructive and insightful comments in relation to this work.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Mask R-CNN-Based Stone Detection and Segmentation for Underground Pipeline Exploration Robots
## 2 Materials and Methods
### Distance Measurment of the Segmented Stones  
In this research study, an Intel RealSense LiDAR L515 camera was used, which uses laser scanning technology and is compact in size (61 mm \(\times\) 26 mm) and specially designed for an indoor environment. The camera depth output resolution is 1024 \(\times\) 768: the field of view is 70\({}^{\circ}\)\(\times\) 55\({}^{\circ}\); and the measurement range is 0.25 m to 9.0 m. And the RGB frame resolution is 1920 \(\times\) 1080, and the field of view is 70\({}^{\circ}\)\(\times\) 43\({}^{\circ}\). Moreover, it has the ability to generate 23 million depth points per second, and this advantage was utilized for real-time distance calculation.  
The basic idea was to align the RGB and depth frames obtained from the lidar camera. By using the stone detection model based on Mask R-CNN, stones are segmented into the bounding box. The bounding box of the segmented stone is defined by the coordinates of its top-left (\(x_{1}\), \(y_{1}\)) and bottom-right (\(x_{2}\), \(y_{2}\)) corners. And we calculated the middle point coordinates of the segmented stone using the following equations:  
\[x=\frac{x_{1}+x_{2}}{2} \tag{2}\]  
\[y=\frac{y_{1}+y_{2}}{2} \tag{3}\]  
We utilized the Intel RealSense camera APIs to align the depth frame. Then, the center points of each stone were estimated from the bounding box coordinates using Equations (1) and (2). The distance value of the center point coordinate was obtained from the aligned depth map. Finally, the distance between the explorer robot and each segmented stone was shown with a label. The algorithmic process of distance measurement is illustrated in Figure 4. A measuring tape with a precision of \(\pm\)0.5 mm is used to verify the estimated distance from our model.
# HISEA-1: The First C-Band SAR Miniaturized Satellite for Ocean and Coastal Observation
## 2 Typical Data and Preliminary Results
### Coastal Observation in Delaware Bay  
An image of Delaware Bay is shown in Figure 5, which also contains the information of ocean and coastal observations. Figure 5a is the whole image, in which the stripe information of wind and wave, rivers, and ships could be recognized clearly. Wind and wave are important phenomena which could provide reference for human activities, and  
Figure 4: Three-meter resolution striping-mode SAR image of the Kerch Strait; the gridding size is 2000 \(\times\) 2000 (horizontal \(\times\) vertical).  
retrieve underwater terrain. The images in the red rectangles show more specific details. In Figure 5b, the freshwater rivers are shown as a dark color because the salinity of freshwater is lower than seawater. HISEA-1 could also be used to retrieve the areas with terrestrial freshwater bodies, and the shape of freshwater plumes could be found in the coastal estuaries which are marked with red circles in Figure 5b. In Figure 5c, the bright spots are the sailing ships whose wakes are clearly visible. The ship identification and the ship wakes play important roles in maritime and navigation management. In Figure 5d, several strip-shaped buildings in the sea could be recognized clearly, which are embankments or piers. The red circles and ovals mark the area of University of Delaware Lewes campus, fishing pier Cape Henloppen, Cape Henlopen State Park and University of Delaware Air Sea Interaction Lab, and Cape Henlopen, shown in the order from left to right. In addition, some dark spots appear in the coastal region, which represent the discharge of sewage.