esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Device Security Application for Unmanned Surface and Aerial Systems
### UxS Security  
Related research on UxS secure links focuses on ciphertext algorithm performance [8; 9] or on the improvement of network topography to allow for current cryptographic protocols to continue to function [10; 11]. Secure communication protocols such as TLS and DTLS have been tested for efficiency in IoT [12; 13], a somewhat related domain to unmanned systems. Those analyses conclude that TLS and DTLS, being not designed for IoT and the complexity of its infrastructure, were sub-optimal. Both protocols add latency to the network which should be considered as more devices join--a consideration that likewise applies to unmanned system uses. Prior work on using the MQTT protocol on unmanned systems [14] has shown an approximate overhead of 350 bytes of throughput with security--however, the security achieved by MQTT is considerably lower than what MLS provides.  
While these works are relevant under the ever-increasing number of UxS and sensors that require interoperability, it continues to assume existent communication protocols designed for internet connections are the main available option for unmanned systems. Such works, therefore, do not cover the use or application of emergent protocols or communication standards that might address UxS needs in a holistic manner--link security, efficiency, and interoperability.
Sensors_**2015**, _15_, 733-759; doi:10.3390/s150100733  
Sensors  
ISSN 1424-8220  
www.mdpi.com/journal/sensors  
Modeling and Development of INS-Aided PLLs in a GNSS/INS Deeply-Coupled Hardware Prototype for Dynamic Applications  
**[PERSON], [PERSON], [PERSON], [PERSON] *, [PERSON] and [PERSON]**  
GNSS Research Center, Wuhan University, No 129 Luoyu Road, Wuhan 430079, China;  
E-Mails: [EMAIL_ADDRESS] (T.Z.); [EMAIL_ADDRESS] (X.N.); [EMAIL_ADDRESS] (Y.B.); [EMAIL_ADDRESS] (C.S.); [EMAIL_ADDRESS] (J.L.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-133-8758-1970; Fax: +86-27-6877-8971.  
Academic Editor: [PERSON]  
Received: 1 September 2014 / Accepted: 4 December 2014 / Published: 5 January 2015  
**Abstract:** A GNSS/INS deeply-coupled system can improve the satellite signals tracking performance by INS aiding tracking loops under dynamics. However, there was no literature available on the complete modeling of the INS branch in the INS-aided tracking loop, which caused the lack of a theoretical tool to guide the selections of inertial sensors, parameter optimization and quantitative analysis of INS-aided PLLs. This paper makes an effort on the INS branch in modeling and parameter optimization of phase-locked loops (PLLs) based on the scalar-based GNSS/INS deeply-coupled system. It establishes the transfer function between all known error sources and the PLL tracking error, which can be used to quantitatively evaluate the candidate inertial measurement unit (IMU) affecting the carrier phase tracking error. Based on that, a steady-state error model is proposed to design INS-aided PLLs and to analyze their tracking performance. Based on the modeling and error analysis, an integrated deeply-coupled hardware prototype is developed, with the optimization of the aiding information. Finally, the performance of the INS-aided PLLs designed based on the proposed steady-state error model is evaluated through the simulation and road tests of the hardware prototype.  
**Keywords:** GNSS/INS deeply-coupled integration; INS-aided PLLs; PLL tracking error; steady state error model; hardware prototype
## 4 System Architecture  
The architecture of the proposed SCADA system was designed to ensure robust monitoring, control, and data management. The system is composed of four main components, as can be seen in Figure 2: (1) RTUs (remote sites), (2) Communication Network, (3) Data Server (central site), and (4) 3D Visualization Application (user site). Each component plays a critical role in the system's functionality and performance.
## 2 Simplified Vulnerability Assessment Methods for Masonry and RC Building Structures
### Application of Methods of Group A
#### 3.3.1 Results of the SAVE Method  
As explained in Section 2.1.1, the SAVE method [18] assigns an index, SPD, and a vulnerability class from A (high vulnerability) to D (low vulnerability) based on the information about the typology of vertical structures. In Table 11, the indexes and the classes provided by the SAVE method were calculated for 935 buildings (the 75 buildings with'mixed' typology were, indeed, not considered by the method): 228 masonry buildings and 707 RC buildings distinguishing the masonry buildings in regular (226) and irregular (2). It is worth noting that all the RC buildings fell in class \(D_{v}\), all the masonry buildings fell in class \(B_{v}\), with exception of the 2 irregular masonry buildings that fell in class \(A_{v}\).  
For masonry buildings, the corrected vulnerability index was calculated considering four independent parameters: the presence of isolated columns, position of the building (isolated or aggregated), the presence of ties, and regularity in plan or along the height. This assumption leads to having 16 combinations of parameters able to modify the original values of SPD. In Table 11, since specific information were not available for the whole dataset, the worse (case 1) and the best (case 2) modified values of SPD within the 16 combinations are listed. The worse combination, leading to an increase of the index, was obtained by considering the presence of isolated pillars, aggregated position, absence of ties, and irregularity in plan or along the height. Due to these assumptions, all masonry buildings fell in class A, with an increase of the SPD of about 20%, which determined an increase of the vulnerability class only for the regular masonry buildings. Conversely, under the best combination of parameters, the index reduced by about 18% leading to all masonry buildings being in class B and, thus, to an improvement of vulnerability only for the irregular buildings. The analyses allowed evidence also that the position of the building was the most influencing parameter in terms of SPD variation.  
For RC buildings, the corrected vulnerability index was calculated considering three independent parameters: the position of the building in the aggregate (isolated, internal, and angle), regularity in plan or along the height, and regularity of infill walls. Thus, seven combinations of parameters were considered and in Table 11 the values of SPD and the classes corresponding to the worse and best combinations are listed. The analyses evidenced that the most significant parameter was the regularity of infill walls. Under the best combination (case 2), the index reduced by 18% and all buildings remained in class D, while, under the worse one (case 1), the index increased by 18% and all the buildings fell in class B.
## 4 Data Acquisition and Analysis
### One-Dimension Inversion Results  
The 1D inversion results are presented as stitched geoelectrical sections in Figures 6 and 7, corresponding to the NW-SE and NE-SW lines, respectively. Both figures reveal a noticeable resistivity contrast that reflects the sedimentary structure beneath the observation stations. Along the NW-SE line, both the TE and TM modes show low-to-medium-resistivity layers, typically around 10 \(\Omega\)m to less than 150 \(\Omega\)m, extending from the surface to depths of less than 500 m. Beneath this low-resistivity layer, a high-resistivity zone of less than 500 \(\Omega\)m, with variable thicknesses between approximately 2 and 3 km, is observed. The northeastern side is more resistive compared to the southern side, where the high-resistivity layer at stations F2 and N2 appears to extend to the greatest depth. Below this high-resistivity zone exists a moderate-resistivity layer, generally below 200 \(\Omega\)m.  
Figure 6: Geoelectrical stitched sections from 1D inversion for the NW-SE profile, shown in TE mode (**top**) and TM mode (**bottom**). Higher resistivity is represented in red. The white dashed lines indicate the estimated sediment interfaces. The red triangle marks the station at the intersection of the NE-SW and NW-SE profiles.  
A similar pattern is observed along the NE-SW line (Figure 7), where the near surface is marked by the low-medium-resistivity layer of approximately 10-130 \(\Omega\)m. The underlying high-resistivity layer is relatively uniform compared to the NW-SE profile, with a depth ranging from approximately 2 to 2.5 km. The top low-resistivity layer is likely associated with Quaternary sediments deposited in the Plain, while the higher-resistivity layer may correspond to older sediments from the late Neogene. This interpretation is consistent with the sedimentary bedrock described in Section 3.
## Materials and Methods
### Agricultural Drought Indices
#### 2.5.2 Standardized Soil Moisture Index (SSMI)  
In order to analyze the agricultural drought condition, a standard normalization was performed on the volumetric SM introduced in Section 2.4, and the result was processed as an index. This index is named standardized soil moisture index (SSMI). The SSMI is calculated as follows.  
\[\text{SSMI}=\text{Z}(\frac{\text{SM}_{\text{i}}-\text{SM}_{\text{min}}}{\text{ SM}_{\text{max}}-\text{SM}_{\text{min}}}), \tag{7}\]  
where Z is the standardized normalization, SM\({}_{\text{max}}\) the maximum SM value, SM\({}_{\text{min}}\) the minimum SM value, and SM\({}_{\text{i}}\) the SM in the i-th time point.
# Rapid-DEM: Rapid Topographic Updates through Satellite Change Detection and UAS Data Fusion
## 4 Discussion
### DEM Updates  
Rapid-DEM presents opportunities for government or non-profit organizations to implement an API to crowd-source patch DEM updates improving national and statewide DEM databases. The dataset could be filtered by date and return the most updated DEM mosaics for a requested area. Validation methods should be implemented similar to other systems such as OpenTopography ([[https://opentopography.org/data/contribute](https://opentopography.org/data/contribute)]([https://opentopography.org/data/contribute](https://opentopography.org/data/contribute)), accessed on 6 December 2021) [26]. Additionally, the fused DEM could be added in either a stable state (i.e., post-construction) or during a period of disturbance to create a robust spatial-temporal dataset of topographic change.
## 2 Results  
The first aim of this research was to develop a straightforward methodology, to provide effective characterization of the VOC aromatic profile of dietary fiber recovered from pre-waste melon fruits. Furthermore, from this study, we aimed to obtain a suitable screening method for sampling VOCs from melon pomace and DF at different aging steps. By extending the study to other crop varieties, it could be possible to produce a robust correlation between the characteristics of the cultivar and the shelf life of the DF itself. Figure 1 shows the vacuum-freeze-dried melon pomace \"As-it-Is\" at t\({}_{0}\) time (M_AI_V_t\({}_{0}\)), and the bleached fiber immediately after the H\({}_{2}\)O\({}_{2}\) treatment (M_B_O_t\({}_{0}\)) and after a storage period of 24 months in the closed vial, in the dark, and at room temperature (M_B_O_t\({}_{24}\)). Figure 2 shows the HS-SPME chromatogram obtained from the vacuum-freeze-dried melon pomace \"As it Is\", immediately processed with the GC-MS instrumentation (t\({}_{0}\) time), and Table 1 summarizes the identified compounds. Figure 1: Images of the M_AI_V_t\({}_{0}\) (**left**), M_B_O_t\({}_{0}\) (**middle**) and M_B_O_t\({}_{24}\) (**right**) samples from winter melon pomace fiber. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Compound** & **LRI** & **ID** \# & **Aroma** & **Area** & **Ref** \\ \hline _Alcohols_ & & & & & \\ \hline
1-Propanol & 538 & A, B & & & \\  & & & & & \\ \hline
2-methyl-1-Propanol & 639 & A, B, C & & & \\ \hline
1-Penten-3-ol & 720 & A, B & & & \\  & & & & & \\ \hline Propylene glycol & 789 & A, B & - & 1.2 \(\pm\) 0.1 & [22; 23] \\ \hline
1-Pentanol & 829 & A, B, C & & & \\ \hline (R,\(R\))-2,3-Butanediol & 845 & A, B, D & & & \\  & & & & \\ \hline (R,\(S\))-2,3-Butanediol & 858 & A, B, D & & & \\  & & & & \\ \hline
3-Pentanol & 875 & A, B & & & \\  & & & & \\ \hline
3-Hexen-1-ol & 937 & A, B & & & \\ \hline
1-Hexanol & 948 & A, B & Green, flowery & 0.53  & [10; 20; 24; 26; 28; 30] \\ \hline
1-acetoxy-2-Propanol & 955 & A, B & - & 0.67  & - \\ \hline
2-acetoxy-1-Propanol & 967 & A, B & - & 0.49  & - \\ \hline
1-Octen-3-ol & 1065 & A, B & & & \\  & & & & \\ \hline
2-ethyl-1-Hexanol & 1112 & A, B, C & & \\  & & & & \\ \end{tabular}
\end{table} 
Table 1: VOC composition of vacuum-freeze-dried melon pomace, at t\({}_{0}\) time (M_AI_V_t\({}_{0}\)) identified through HS-SPME-GC-MS analysis, grouped by chemical classes. Data are expressed as mean (\(n=3\)). TIC area \(\times\) 10\({}^{7}\)\(\pm\) SD\({}_{(3)}\). Figure 2: Total ion current chromatogram of VOCs from the M_AI_V_t\({}_{0}\) fibrous sample, obtained using HS-SPME-GC-MS (a.u. = arbitrary units).
# High-Performance Flux Tracking Controller for Reluctance Actuator
## 2 Hammerstein Model
### Bouc-Wen Model and Its Inverse Model  
The Bouc-Wen model is widely used in hysteresis modeling due to its simple structure, few parameters to be identified, and ease of implementation. Here, the Bouc-Wen model is described in the form of a differential equation, as shown in Equation (1):  
\[\left\{\begin{array}{l}\dot{x(t)}=a_{0}x(t)+a_{1}I(t)+a_{2}h(t)\\ h(t)=\alpha\dot{I(t)}-\beta|\dot{I(t)}|h(t)-\gamma\,I(t)|h(t)\\ B(t)=x(t)\end{array}\right. \tag{1}\]  
where \(x(t)\) represents the state variable of the system, \(I(t)\) and \(B(t)\) are input current and output flux of the reluctance actuator, respectively. \(h(t)\) represents hysteresis, \(\alpha,\beta,\gamma\)\(a_{0},a_{1},a_{2}\) stands for the identification parameters of the Bouc-Wen model. It should be noted that \(I(t)\) and \(B(t)\) can be directly measured, while \(h(t)\) is an intermediate variable that cannot be measured.  
The inverse model of the Bouc-Wen model, which can be obtained by a note from (1) that \(\dot{x(t)}=a_{0}x(t)+a_{1}I(t)+a_{2}h(t)\), implies:  
\[I(t)=\frac{1}{a_{1}}[B_{\mathrm{d}}(t)-a_{0}B_{\mathrm{d}}(t)-a_{2}\dot{h}(t)] \tag{2}\]  
where \(B_{d}(t)\) stands for input trajectory, \(I(t)\) represents the input current, \(a_{0},a_{1},a_{2}\) is the model parameter, \(\dot{h}(t)\) denotes the estimated hysteresis. In addition, it should be indicated that there is no analytic solution to the inverse model, which effectively reduces the mathematical calculation of the controller. What is more, in order to avoid the algebraic ring problem in the algorithm process, the output of the previous time is used instead of the input of the current time \(I(t)\).
## 4 Review of Literature
### Ridesharing Problem
#### 4.1.2 Heuristic and Metaheuristic Algorithms
Hence, the uncertain travel time for the driver can be higher when compared to the case with no rideshare. A ridesharing problem with consideration for travel time uncertainty is then proposed by [29]. Their study focussed on the static single driver and multiple riders'tabaring model. To achieve computational tractability, a robust optimisation approach (a hybrid heuristic algorithm with the cluster-first route-second approach) is proposed to handle the ridesharing problem. Before employing a hybrid heuristic algorithm, the cluster-first route-second approach is utilised to decompose the large scale problem into different sub-groups. The two clustering approaches used in the approach are greedy heuristic and k-means clustering. After clustering, the hybrid heuristic algorithm, an extended insertion algorithm in conjunction with a TS, is employed to solve the solution iteratively. Through numerical experiments, the proposed robust optimisation approach can potentially produce a viable solution for solving the large-scale problem. The events of a natural disaster or man-made disaster can be considered as extreme events that necessitate the short-notice evacuation from a populated area. The authors of [66] presented a two-phase route optimisation method for ridesharing matching with transfers in short-notice evacuations. In the first phase, a vehicle-space-time hyper-dimensional network is constructed based on the obtained network topology information and personal requests. For the second phase, a new time-discretised multiple-ride multiple-driver network flow model is created based on the created vehicle-space-time network to ease the formulation of the ridesharing with transfers. The Lagrangian relaxation solution approach is designed to solve the model. Multiple Origins-Multiple Destinations Ridesharing Problem  
A large-scale ridesharing problem with flexible participants, time window and transfer points is proposed by [67] in addressing both the assignment and sequencing decision in ridesharing. The flexible participants represent flexible drivers who own a car but are willing to be a passenger in a trip. The proposed model provides participants flexibility in deciding which participants act as drivers and relocating alternative locations for the participants to act as passengers. The chosen benchmarks in the study are urban area instances and commuter instances to evaluate the performance of the late acceptance hill climbing and integer programming. The late acceptance hill climbing has advantage in solving large instances. Through the simulation results, it indicated that the cost can be greatly minimised at the condition where 30% of the drivers are flexible. The increase of flexible drivers can further improved the overall solution but suffered from diminishing returns.
# Integration of Sentinel 1 and Sentinel 2 Satellite Images for Crop Mapping
## 4 Discussion
The reason for this is that in some studies, the classification performed was related to the level [36], or filtration was used in the classification [31], but in this study, the unfiltered method of classification in pixels was used in addition to a small amount of OA. It is difficult to group similar crops together, but if we look into several specific classes of different crops, the accuracy of crop classification will improve. This obvious difference in the structure of plants will facilitate classification if the plants are classified in terms of structure and family [37; 38], such as the classification of horticultural and agricultural crops [39], paddy and grassland. The findings of our study are in line with those of previous studies. Data and their properties can be prioritized over pixels in such studies, allowing the data from the two sensors Sentinel 1 and Sentinel 2 to be used as input. Closed space can also be used with the classification method, which has the advantage of lowering the signal-to-noise ratio. We can look into the differences between radar and optical data, spectroscopic interference, optical image SWIR bands, and plant phenological differences as one of the input factors for classification in the future.
# The Usefulness of Gradient-Weighted CAM in Assisting Medical Diagnoses
###### Abstract  
In modern medicine, medical imaging technologies such as computed tomography (CT), X-ray, ultrasound, magnetic resonance imaging (MRI), nuclear medicine, etc., have been proven to provide useful diagnostic information by displaying areas of a lesion or tumor not visible to the human eye, and may also help provide additional recessive information by using modern data analysis methods. These methods, including Artificial Intelligence (AI) technologies, are based on deep learning architectures, and have shown remarkable results in recent studies. However, the lack of explanatory ability of connection-based, instead of algorithm-based, deep learning technologies is one of the main reasons for the delay in the acceptance of these technologies in the mainstream medical field. One of the recent methods that may offer the explanatory ability for the CNN classes of deep learning neural networks is the gradient-weighted class activation mapping (Grad-CAM) method, which produces heat-maps that may offer explanations of the classification results. There are already many studies in the literature that compare the objective metrics of Grad-CAM-generated heat-maps against other methods. However, the subjective evaluation of AI-based classification/prediction results using medical images by qualified personnel could potentially contribute more to the acceptance of AI than objective metrics. The purpose of this paper is to investigate whether and how the Grad-CAM heat-maps can help physicians and radiologists in making diagnoses by presenting the results from AI-based classifications as well as their associated Grad-CAM-generated heat-maps to a qualified radiologist. The results of this study show that the radiologist considers Grad-CAM-generated heat-maps to be generally helpful toward diagnosis.  
CNN; Grad-CAM; assisted diagnosis +
Footnote †: journal: Medical Diagnoses  
12748
# Short Text Clustering Algorithms, Application and Challenges: A Survey
## 3 Components of Short Text Clustering
### Dimensionality Reduction
#### 3.3.2 Linear Discriminant Analysis (LDA')  
It is a standard data-mining algorithm used for supervised or unsupervised learning. LDA is commonly used for dimensionality reduction [132]. It determines the projection hyperplane with the lowest interclass variance and the most significant distance between the projected means of the classes [133]. LDA is beneficial when the within-class frequencies are unequal and their performances have been assessed using randomly generated test data.  
Let us say \(X_{j}\in\mathbb{R}^{d\times n_{j}}\), which are d-dimensional samples, and \(y_{i}\in\{1,2,\cdots,k\}\) denotes the class label of the \(i-\)th sample, where \(n\) is the number of documents, \(d\) is the data dimensionality and \(k\) is the number of classes. Equations (8)-(10) calculate the number of samples in each class:  
\[n=\sum_{j=1}^{k}n_{j} \tag{8}\]  
\[n_{j}=\sum_{x\in w_{i}}(x-\mu_{i})(x-\mu_{i})^{T} \tag{9}\]  
\[\mu_{i}=\frac{1}{N_{i}}\sum_{x\in w_{i}}x \tag{10}\]  
In discriminant analysis [134], three scatter matrices are defined as within-class, between-class and total scatter matrices, as shown in Equations (11)-(13):  
\[S_{w}=\frac{1}{n}\sum_{j=1}^{k}\sum_{x\in X_{j}}\left(x-c^{(j)}\right)\left( x-c^{(j)}\right)^{T} \tag{11}\]  
\[S_{b}=\frac{1}{n}\sum_{j=1}^{k}n_{j}\left(c^{(j)}-c\right)\left(c^{(j)}-c \right)^{T} \tag{12}\]  
\[S_{t}=\frac{1}{n}\sum_{i=1}^{n}\left(x_{i}-c\right)\left(x_{i}-c\right)^{T} \tag{13}\]where \(c^{(j)}\) It is the centroid of the \(j-\) th class and \(c\) is the global centroid. It follows from the definition that \(S_{t}=S_{b}+S_{w}\). Furthermore, trace (\(S_{w}\)) measures the within-class cohesion, and tracing (\(S_{b}\)) measures the between-class separation.