esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Multi-Criteria Decision-Making Method for Emergency Communication Protocol (MCDM-ECP)
### Weighed Multi-Criteria Matrix  
After the calculation of weights, we continue to process the TOPSIS algorithm [41] for selecting the next-hop node. With respect to the selection, the AHP-TOPSIS algorithm dynamically evaluates the mobility characteristics of the nodes involved in the DAWN.  
Firstly, we build the multi-criteria matrix by the four decision criteria aforementioned:  
\[A=\left[\begin{array}{cccc}e_{11}&c_{12}&h_{13}&d_{14}\\ \vdots&\vdots&\vdots&\vdots\\ e_{n1}&c_{n2}&h_{n3}&d_{n4}\end{array}\right] \tag{5}\]  
where \(e_{i1}(i=1,\ldots,n)\) indicates energy consumed, \(c_{i2}(i=1,\ldots,n)\) indicates the numbers of connected nodes, \(h_{i3}(i=1,\ldots,n)\) indicates hop counts to ECVs, and \(d_{i4}(i=1,\ldots,n)\) indicates distance to the nearest ECVs. Subscript \(n\) denotes the number of neighbors.  
Secondly, we normalize matrix \(A\) by:  
\[e_{i1}^{\prime}=e_{i1}/\sqrt{\sum_{i=1}^{n}e_{i1}^{2}}(i=1,\ldots,n) \tag{6}\]  
\[c_{i2}^{\prime}=c_{i2}/\sqrt{\sum_{i=1}^{n}c_{i2}^{2}}(i=1,\ldots,n) \tag{7}\]  
\[h_{i3}^{\prime}=h_{i3}/\sqrt{\sum_{i=1}^{n}h_{i3}^{2}}(i=1,\ldots,n) \tag{8}\]  
\[d_{i4}^{\prime}=d_{i4}/\sqrt{\sum_{i=1}^{n}d_{i4}^{2}}(i=1,\ldots,n) \tag{9}\]  
where \(e_{i1}^{\prime},c_{i2}^{\prime},h_{i3}^{\prime},d_{i4}^{\prime}(i=1,\ldots,n)\) stands for each element of normalized matrix \(A^{\prime}\), which is illustrated by:  
\[A^{\prime}=\left[\begin{array}{cccc}e_{11}^{\prime}&c_{12}^{\prime}&h_{13}^ {\prime}&d_{14}^{\prime}\\ \vdots&\vdots&\vdots&\vdots\\ e_{n1}^{\prime}&c_{n2}^{\prime}&h_{n3}^{\prime}&d_{n4}^{\prime}\end{array}\right]\]  
Thirdly, we multiply each column in \(A^{\prime}\) by the weights \(W\) that we get from Equation (4), and get the weighted multi-criteria matrix \(M\):  
\[M=\left[\begin{array}{cccc}e_{11}^{\prime}\overline{w_{1}}&c_{12}^{\prime} \overline{w_{2}}&h_{13}^{\prime}\overline{w_{3}}&d_{14}^{\prime}\overline{w_{ 4}}\\ \vdots&\vdots&\vdots&\vdots\\ e_{n1}^{\prime}\overline{w_{1}}&c_{n2}^{\prime}\overline{w_{2}}&h_{n3}^{ \prime}\overline{w_{3}}&d_{n4}^{\prime}\overline{w_{4}}\end{array}\right]\]
## 3 Application Sectors and Valuable Products from Microalgae Biomass  
Technologies related to the production and utilisation of microalgae biomass are increasingly seen as promising solutions that can be applied in various economic sectors as well as in the field of environmental protection [21]. Previous studies have shown that microalgae-based systems can be effectively used in wastewater and leachate treatment, waste and sewage sludge neutralisation, carbon dioxide biostorage, biogas upgrading, and flue gas cleaning [22; 23; 24; 25; 26; 27]. Microalgae biomass is also a promising raw material for the production of renewable fuels, the use of which is associated with a significant reduction in pollutant emissions compared to conventional fossil fuels [28]. Figure 2 shows the generalised use of microalgae biomass. The main energy carriers obtained from microalgae include biodiesel produced from the lipids accumulated in their cells, hydrogen produced by photobiological processes, biomethane produced by fermentation processes, and biomass that can be used by pyrolysis, gasification, or direct combustion technologies [29; 30; 31]. The application of technologies based on microalgae biomass in the broader context of environmental protection is shown in Table 1.  
Figure 2: Use of microalgae biomass. An important direction in the utilisation of microalgae is their use as a substrate for the production of numerous economically valuable chemical compounds, which increases both the economic and environmental benefits of these technologies [73; 74]. Microalgae are a rich source of carbohydrates, proteins, fatty acids, enzymes, and dietary fibre [75]. Its biomass provides almost all essential vitamins, including A, B1, B2, B6, B12, C, E, biotin, niacin, folic acid, and pantothenic acid, and it is also characterised by a balanced composition of micronutrients such as sodium, potassium, calcium, magnesium, iron, and zinc [76; 77]. One of the most important products from microalgae is pigments, which are widely used in the cosmetics and food industries. The most commonly used pigments include
## 5 Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Techniques** & **Advantages** & **Shortcomings** & **Application Scenarios** \\ \hline \multirow{6}{*}{Vibration Analysis} & Sensitive to common faults, & & Sensor installation requires \\  & with high detection & & high-speed rotating \\  & accuracy, supporting & Sensitive to & equipment, long-term \\  & online monitoring, and & environmental noise & operation equipment status \\  & usually low cost & & detection, and predictive \\  & & & maintenance \\ \hline \multirow{6}{*}{Acoustic Emission} & Early fault sensitivity, & & Destructive detection, \\  & suitable for high-frequency & affected by environmental & \\  & & noise, signal analysis is & \\  & signal analysis & complex, and acoustic & \\  & & detection equipment is & \\  & & usually expensive & \\ \hline \multirow{6}{*}{Ultrasonic} & Early fault sensitivity, & & \\  & non-contact detection, & & \\  & suitable for high-frequency & & \\  & signal analysis & & \\  & & & \\  & & & \\  & & & \\  & & & \\ \hline \multirow{6}{*}{Current Analysis} & Non-contact, low cost, & & \\  & suitable for remote & & \\  & monitoring, sensitive to & & \\  & load changes & & \\  & & & \\ \hline \multirow{6}{*}{Electromagnetic Detection} & Non-contact, accurate & & \\  & detection of shallow & & \\  & defects, suitable for online & & \\  & monitoring, insensitive to & & \\  & temperature changes, with & & \\  & low requirements for & & \\  & bearing surfaces & & \\ \hline \multirow{6}{*}{Infrared Thermography} & Non-contact, capable of & & \\  & quickly and extensively & & \\  & scanning the entire device, & & \\  & suitable for high & & \\  & temperature environments & & \\  & & & \\ \hline \multirow{6}{*}{Radiographic Detection} & Highly sensitive, capable & & \\  & of detecting defects at the & & \\  & micrometer level & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison and application scenarios of different techniques.
###### Abstract  
With the increasing convenience of location-based services (LBSs), there have been growing concerns about the risk of privacy leakage. We show that existing techniques fail to defend against a statistical attack meant to infer the user's location privacy and query privacy, which is due to continuous queries that the same user sends in the same location in a short time, causing the user's real location to appear consecutively more than once and the query content to be the same or similar in the neighboring query. They also fail to consider the hierarchical structure of the address, so locations in an anonymous group may be located in the same organization, resulting in leaking of the user's organization information and reducing the privacy protection effect. This paper presents a dummy generation scheme, considering the hierarchical structure of the address (DGS-HSA). In our scheme, we introduce a novel meshing method, which divides the historical location dataset according to the administrative region division. We also choose dummies from the historical location dataset with the two-level grid structure to realize the protection of the user's location, organization information, and query privacy. Moreover, we prove the feasibility of the presented scheme by solving the multi-objective optimization problem and give the user's privacy protection parameters recommendation settings, which balance the privacy protection level and system overhead. Finally, we evaluate the effectiveness and the correctness of the DGS-HSA through theoretical analysis and extensive simulations.  
location-based service; location privacy; query privacy +
Footnote †: journal: Accepted: 8 January 2020; Published: 11 January 2020
# Singulation of Objects in Cluttered Environment Using Dynamic Estimation of Physical Properties
###### Abstract  
This paper presents a scattering-based technique for object singulation in a cluttered environment. An analytical model-based control scattering approach is necessary for controlled object singulation. Controlled scattering implies achieving the desired distances between objects after collision. However, current analytical approaches are limited due to insufficient information of the physical environment properties, such as the coefficient of restitution, coefficient of friction, and masses of objects. In this paper, this limitation is overcome by introducing a technique to learn these parameters from unlabeled videos. For the analytical model, an impulse-based approach is used. A virtual world simulator is designed based on a dynamic model and the estimated physical properties of all objects in the environment. Experiments are performed in a virtual world until the targeted scattering pattern is achieved. The targeted scattering pattern implies that all objects are singulated. Finally, the desired input from the virtual world is fed to the robot manipulator to perform real-world scattering.  
object singulation; clutter environment; dynamic estimation; impact mechanics; physics engine; dynamic manipulation Article
# Real-Time Terrain-Following of an Autonomous Quadrotor by Multi-Sensor Fusion and Control
## 2 Quadrotor System Models
### Quadrotor Dynamic Models
 \right]^{T}\)\(\in\)\(R^{4}\). (18)  
The state equation is further written as:  
\[\left\{ \begin{array}{c}\dot{\phi}=p\\ \dot{\theta}=q\\ \dot{\psi}=r\\ \dot{p}=\frac{\tau_{x}+\tau_{xx}}{\tau_{xx}}\\ \dot{q}=\frac{\tau_{y}+\tau_{yy}}{\tau_{yy}}\\ \dot{r}=\frac{\tau_{z}+\tau_{xx}}{\tau_{zz}}\\ \dot{v}=g\phi+\frac{f_{yy}}{m}\\ \dot{w}=\frac{f_{zz}-\dot{r}}{m}\\ \dot{x}=u\\ \dot{y}=v\\ \dot{z}=w\end{array} \right.. \tag{19}\]  
Ignoring the influence of wind, it can be written as:  
\[\left\{ \begin{array}{c}\dot{\phi}=p\\ \dot{\theta}=q\\ \dot{\psi}=r\\ \dot{p}=\frac{\tau_{x}}{\tau_{xx}}\\ \dot{q}=\frac{\tau_{y}}{\tau_{yy}}\\ \dot{r}=\frac{\tau_{z}}{\tau_{zz}}\\ \dot{v}=g\phi\\ \dot{w}=\frac{-f_{t}}{m}\\ \dot{x}=u\\ \dot{y}=v\\ \dot{z}=w\end{array} \right.. \tag{20}\]  
That is, the linearized model of the hovering state equation can be written as:  
\[\dot{\mathbf{x}}=A\cdot\mathbf{x}+B\cdot\mathbf{u}, \tag{21}\]where \(A\) and \(B\) can be expressed as follows:  
\[A= \begin{array}{c}\frac{\partial\mathbf{f}(\mathbf{x},\mathbf{u})}{ \partial\mathbf{x}}\Big{|}_{\begin{array}{c}\mathbf{x}=\mathbf{x}\\ \mathbf{u}=\mathbf{u}\end{array}}-\\ \\ \end{array}  \tag{22}\] \[= \begin{bmatrix}0&0&0&1&0&0&0&0&0&0&0&0\\ 0&0&0&0&1&0&0&0&0&0&0&0\\ 0&0&0&0&0&1&0&0&0&0&0&0\\ 0&0&0&0&0&0&0&0&0&0&0\\ 0&0&0&0&0&0&0&0&0&0&0\\ 0&0&0&0&0&0&0&0&0&0&0\\ 0&-g&0&0&0&0&0&0&0&0&0&0\\ g&0&0&0&0&0&0&0&0&0&0\\ 0&0&0&0&0&0&0&0&0&0&0\\ 0&0&0&0&0&1&0&0&0&0\\ 0&0&0&0&0&0&1&0&0&0\end{array}\end{bmatrix} ,\]  
\[B=\left.\frac{\partial\mathbf{f}(\mathbf{x},\mathbf{u})}{\partial\mathbf{x}} \right|_{ \begin{array}{c}\mathbf{x}=\mathbf{x}\\ \mathbf{u}=\mathbf{u}\end{array} }=\left[ \begin{array}{cccc}0&0&0&0\\ 0&0&0&0\\ 0&\frac{1}{1_{\text{err}}}&0&0\\ 0&0&\frac{1}{1_{\text{err}}}&0\\ 0&0&0&\frac{1}{1_{\text{err}}}\\ 0&0&0&0\\ 0&0&0&0\\ 0&0&0&0\\ 0&0&0&0\\ 0&0&0&0\end{array} \right]. \tag{23}\]
## 1 Introduction
Some of the attractive reasons behind choosing 802.15.1 are as follows: it operates in the unlicensed spectrum; it has reasonable data rates and transmission coverage; and it can be easily integrated with small UAVs. On the other hand, the transmission speed of 802.11 is higher than that of 802.15.1. For this reason, we propose a hybrid wireless communication scheme, which can utilize the strong features of each of the two options: i.e., the high-speed data transmission rate of 802.11 and the low-power consumption of 802.15.1. The remainder of the paper is organized as follows: Section 2 presents various communication architectures for networking of UAVs with a discussion on the pros and cons; Section 3 elaborates existing communication technologies to address the communication needs of FANET; Section 4 details resource management and energy efficiency; Section 5 describes the proposed hybrid communication scheme; Section 6 presents our Optimized Network Engineering Tool (OPNET) simulation setup and performance metrics; Section 7 contains results and analysis; Section 8 concludes the work.
## 5 Performance in 6G-Specific Scenarios
* The ARMOR framework is designed to conduct combinatorial auctions for heterogeneous spectrums while safeguarding bidders' sensitive information. This is achieved through advanced cryptographic techniques that protect location and valuation data. ARMOR's objectives are to ensure high levels of customer satisfaction and social welfare while providing verifiable protection against fraudulent payment schemes [29]. Similarly, the PROST framework offers a double-auction mechanism that preserves privacy and improves the efficiency of spectrum allocation while protecting user privacy in online auction settings [30]. * The integration of artificial intelligence (AI) and machine learning (ML) in auction design for the 6G spectrum is essential to managing the expected surge in wireless communication demands. The incorporation of artificial intelligence (AI) in auction design aims primarily to optimize the spectrum allocation process, making it more dynamic and responsive to real-time network demands. Using AI algorithms, spectrum auctions can be designed to predict network usage patterns with more accuracy, ensuring a more effective distribution of this finite resource. AI and ML contribute significantly to creating more efficient 6G networks by enabling intelligent resource management. This integration allows for self-configuration and self-healing capabilities, vital for managing the complexities of 6G wireless networks [31]. In addition to resource optimization, the integration of AI and ML in auction design addresses security concerns associated with the usage of the 6G spectrum. The move towards intelligent network solutions includes robust security measures to protect against adversarial attacks that could compromise network integrity [32]. Spectrum auctions not only facilitate the allocation of spectrum bands necessary for 6G implementation but also ensure that this is done efficiently and securely. They support dynamic resource management by encouraging technological innovation and fair competition, aligning with the broad operational spectrum and the high demands expected for 6G networks.
# Two-Line Element Outlier and Space Event Detection Method Based on Multi-Strategy Genetic Algorithm
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**NORAD**} & \multirow{2}{*}{**Maneuvers**} & \multicolumn{4}{c}{**MGMMMGA**} & \multicolumn{4}{c}{**GMM**} \\ \cline{3-8}  & & **Precision** & **Recall** & **F1** & **Precision** & **Recall** & **F1** \\ \hline
27386 & 177 & 93.1\% & 91.0\% & 92.0\% & 97.7\% & 73.4\% & 83.9\% \\
36508 & 179 & 99.3\% & 84.3\% & 91.2\% & 99.2\% & 67.6\% & 80.4\% \\
41240 & 47 & 92.2\% & 100.0\% & 95.9\% & 85.5\% & 100.0\% & 92.2\% \\
41335 & 73 & 83.9\% & 100.0\% & 91.3\% & 100.0\% & 68.5\% & 81.3\% \\
43476 & 39 & 84.2\% & 82.1\% & 83.1\% & 56.5\% & 33.3\% & 41.9\% \\
73477 & 39 & 80.6\% & 74.4\% & 77.3\% & 56.7\% & 43.6\% & 49.3\% \\
37781 & 58 & 89.2\% & 100.0\% & 94.3\% & 46.0\% & 100.0\% & 63.0\% \\
39086 & 63 & 94.2\% & 77.8\% & 85.2\% & 96.2\% & 39.7\% & 56.2\% \\ Total & 675 & 91.7\% & 88.9\% & 90.3\% & 81.6\% & 68.3\% & 74.4\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Maneuver detection results. Figure 6: Anomaly detection results. Figure 7: ROC Plot. of the targets, and we recognize that these characteristics may vary depending on the target type, orbital height, orbital inclination, and orbital shape. Thus, further studies are necessary to clarify the effectiveness of our method.
# An Architecture for a Tri-Programming Model-Based Parallel Hybrid Testing Tool
## 2 Background
### An Overview of Common Run-Time Errors
#### 2.3.5 Mismatches  
Mismatching is another common problem that can occur under many circumstances, such as when the wrong type or number of arguments is made, when more than one call is made simultaneously, when collective calls are made, or when tasks are assigned to the wrong processors or are not properly synchronised, thereby causing delays, decreasing performance, and yielding incorrect outputs.  
There are multiple mismatch issues in MPI that warrant understanding to fully comprehend. For instance, a type mismatch occurs when the type of data sent by the sender does not match the type of data that the receiver anticipates. Meanwhile, a tag mismatch occurs when the tag that the sender tags the data with does not match the tag that the receiver anticipates.
# IoT: A Long-Reach Fully Passive Low-Rate Upstream PHY for IoT over Fiber
## 3 Results
### Analog Communication Channel Characterization (\(M_{2}\))
#### 3.2.1 Interaction of Codification-Modulation Device with Communication Channel  
Figure 6 shows the pulse response of the system and the modulation electric signal in solid and dashed lines, respectively. As it can be observed, for pulse periods lower than 2.5 ms, the underdamped response was increased. The resonance frequency of the system (570 Hz) was evidenced in the underdamped temporal response. Therefore, if either OOK or ASK modulation were used, the intersymbol interference (ISI) would be drastically increased when compared with FSK modulation, which is a good candidate for the proposed modulation scheme. Although, FSK does not guarantee total free ISI. This event was evidenced in the large transition from high to low frequencies of the sub-carriers (such as variations from 700 Hz to 100 Hz).  
Figure 5: (**a**) Peak to peak relation between wavelength shift and electrical amplitude as a function of the modulation frequency and (**b**) transfer function of the proposed AOM.  
By using the system transfer function, it is possible to pre-emphasise the signal amplitude, phase, or both, in order to improve the demodulation process. Since the square-law detector was applied to demodulate the signal, the issue related with the phase component can be neglected [36]. For amplitude pre-emphasis, the modulation carrier was multiplied by the inverse function of the amplitude response, presented in Figure 5b.  
Figure 7 displays the fast Fourier transform of the received signals for 8-FSK modulation in both cases: uncompensated (a) and applying pre-emphasis equalization (b), for an arbitrary symbols sequence with 3000 bits. The uncompensated spectrum response shows an unleveled amplitude of the carriers. On the other hand, the pre-emphasis spectrum response shows a subcarriers' amplitude distribution which is more equalized, which simplifies the demodulation process and improves the overall system performance. The right corner inset of both figures depict the received signal temporal evolution for an arbitrary period, being evidenced the effect of the time-domain pre-emphasis.
## 2 Materials and Methods
### Methodology
#### 2.2.3 Post-Processing  
The editing of the HRC consisted of replacing erroneous or unavoidable misclassified pixels due to the presence of shadows, clouds, or temporal features (e.g., vehicles or pedestrians). This procedure was run in three stages: (1) the reclassification of shadows and clouds; (2) the manual editing of specific pixels; and (3) superimposing specific MC layers (see post-processing steps in Figure 2). These operations were carried out using the Post-processing and Band Calc tools provided by SCP plugin, along with the Raster Calculator Tool included in QGIS.  
In the first stage, single raster files containing only clouds or shadows were generated from the HRC. Shadows were reassigned based on the premise that they originated from trees or buildings and projected on any of the LULC classes except clouds. Thus, the initial classification was transformed considering that: shadows amid forests represent trees, amid consolidated urban blocks represent buildings, and at the boundary of two different classes represent the lower one. In the case of clouds, if the image transparency allowed us to visually recognize a specific feature, it was manually edited. Otherwise, both the older and more recent available images were checked to define whether the cloud occurred in an area with a specific LULC. These changes were updated in each single raster file, and finally, updated in the HRC.  
The second stage consisted of a manual edition of specific zones in the HRC whose initial classification was inaccurate or invalid. To avoid time-consuming and unnecessary efforts improving the outcome, this step was performed only where particular features required a proper reclassification and the area was sufficiently large in the urban landscape. An example of edited areas are open-air concrete stairs in a stadium classified in the HRC as paved streets, but expected as building or mobile elements, such as cars temporally parked on sealed surfaces.  
Finally, the third stage consisted of overlapping layers. This superimposing method makes use of single raster files obtained from the HRC or derived from OSM. The data obtained from this database were used to define the street and river network. To do so, an OSM file limited by the study area boundaries was exported and transformed into shapefile. Afterwards, the lines contained in the file were converted to polygons and edited to match the width and location of the streets in each image by using the tools Buffer and Toggle Edition. Subsequently, the street polygons were superimposed as a raster in the HRC. Since that procedure may alter additional desired outputs, such as the location of trees above streets, a new single layer containing that information was also derived from the HRC and superimposed again, after overlapping the OSM layers. A similar approach was used to define the river. In our case, the river raster layer was overlapped initially over 'Shadows' to represent only the area visible in the image.