esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Analytical RP-HPLC  
To determine methylglyoxal content, a derivatization reaction with o-phenylenediamine (OPD) was carried out, giving rise to the quinoxaline adduct. Samples were incubated for 16h with 2% w/v OPD in phosphate buffer 0.5 M (pH 6.5). The reaction was performed at room temperature, in dark conditions. The samples were examined using an Agilent HPLC (1260 Infinity, Agilent Technologies, Santa Clara, CA, USA) equipped with a 20 \(\upmu\)L Rheodyne injection loop, a Multiple Wavelength detector and a reversed phase column (Synergi\({}^{\text{TM}}\) Hydro-RP 4 \(\upmu\)m 150 \(\times\) 4.6 mm, Phenomenex). An isocratic elution was performed at room temperature with a flow rate of 0.9 mL/min, using a mobile phase (Water/ACN 75/25). The column effluent was constantly monitored at 263 nm and the OPD derivative of methylglyoxal was eluted after 3.50 min. The ChemStation software for LC systems (Agilent Technologies) was used for data acquisition and processing. Methylglyoxal quantification was performed by comparing the peak areas with those acquired using the standard compound eluted in the same conditions. A linear response was observed for the standard curve (R2 = 0.995). Each analysis was performed in triplicate and results were reported as mean \(\pm\) standard deviation.  
The limit of detection (LOD) and limit of quantification (LOQ) of the analytical method applied were calculated and were, respectively, 0.04 mg/kg and 0.12 mg/kg.  
After evaluating the presence of MGO, only samples that showed the highest concentration of MGO were analyzed for minimum inhibitory concentration (MIC).
# Mitigating DDoS Attacks in LEO Satellite Networks Through Bottleneck Minimize Routing
## 1 Introduction
### Contribution  
This paper proposes a routing algorithm and statistically models the bottleneck path in LSN. We propose the K-Bottleneck Minimize routing algorithm, which aims to minimize the bottleneck weight during path generation. This algorithm increases the cost for the attacker to launch DDoS attacks and enhances the detectability of the attacker, providing greater security for LSN.
# LoRaWAN Networking in Mobile Scenarios Using a WiFi Mesh of UAV Gateways
###### Abstract  
Immediately after a disaster, such as a flood, wildfire or earthquake, networks might be congested or disrupted and not suitable for supporting the traffic generated by rescuers. In these situations, the use of a traditional fixed-gateway approach would not be effective due to the mobility of the rescuers. In the present work, a double-layer network system named LoRaUAV has been designed and evaluated with the purpose of finding a solution to the aforementioned issues. LoRaUAV is based on a WiFi ad hoc network of Unmanned Aerial Vehicle (UAV) gateways acting as relays for the traffic generated between mobile LoRaWAN nodes and a remote Base Station (BS). The core of the system is a completely distributed mobility algorithm based on virtual spring forces that periodically updates the UAV topology to adapt to the movement of ground nodes. LoRaUAV has been successfully implemented in ns-3 and its performance has been comparatively evaluated in wild area firefighting scenarios, using Packet Reception Ratio (PRR) and end-to-end delay as the main performance metrics. It is observed that the Connection Recovery and Maintenance (CRM) and Movement Prediction (MP) mechanisms implemented in LoRaUAV effectively help improve the PRR, with the only disadvantage of a higher delay affecting a small percentage of packets caused by buffer delays and disconnections.  
Keywords:LoRaWAN; Unmanned Aerial Vehicles; topology control; virtual spring forces; firefighting communications +
Footnote †: journal: Accepted: 5 April 2020; Published: 10 April 2020
## 2 Analytical Investigation of Electromagnetic Damper Model
### Induced Voltage and Machine Constant  
The permanent magnet inside the cylindrical damper is magnetized in the axial direction. However, the magnetic field radiates through the iron poles, affecting the conductor and magnetic material of the stator. Therefore, the circular cross-section of the cylindrical device can be flattened to ensure the induced voltage or the electric power generated in the coil by the movement of the driver can be derived. By applying [PERSON]'s law of electromagnetic induction to define the induced voltage generated in the coil due to the magnetic flux of the flattened magnet and constant air gap, Equation (14) can be derived.  
\[-\frac{\partial}{\partial t}\int_{S}B_{c}\cdot ds=-\frac{d}{dt}(B_{c}2\pi rx )=-2\pi B_{c}rv\, \tag{14}\]  
where \(x\) represents the axial coordinate of the magnet, and \(v\) represents the relative velocity between the translator and the coil. By substituting the left equation of Equation (7), which defines the magnetic field in the coil, into Equation (14) based on [PERSON]'s law, the voltage induced in a single coil can be expressed as follows:  
\[\oint_{C}\overset{\rightarrow}{E}\cdot d\overset{\rightarrow}{l}=-V_{s}=\frac {\pi r_{m}^{2}B_{m}}{l_{p}}v. \tag{15}\]  
Considering the active coils connected in series through the magnetic flux of the air gap, the total open-circuit voltage induced by the damper can be expressed as follows:  
\[V_{emf}=pN_{a}\frac{\pi r_{m}^{2}B_{m}}{l_{p}}v=k_{t}v\,\ k_{t}=pN_{a}\frac{ \pi r_{m}^{2}B_{m}}{l_{p}}\, \tag{16}\]  
where \(p\) represents the number of active coils, and \(N_{a}\) represents the number of active-coil turns of the portion affected by the pole piece flux. \(k_{t}\) represents the electromotive force constant of the device,which is the machine constant of the damper. The machine constant of the damper can be expressed as follows according to the geometry and magnetic properties of the machine.  
\[k_{t}=pN_{a}\pi r_{m}^{2}B_{r}\ \left/\ \left[\frac{1}{l_{p}}+\frac{\mu_{m}r_{m}^{2}} {\mu_{0}l_{m}}\left\{\frac{1}{2\mu_{Fe}}+\ln\left(\frac{r_{c}}{r_{m}}\right)+ \frac{\left(l_{m}+l_{p}\right)l_{p}}{\mu_{F}l_{y}(r_{c}+r_{s})}\right\}\right]\.\right. \tag{17}\]  
The derived machine constant is comparable to the motor constant that determines the characteristics of the motor and determines the damping force and speed relationship.
## 2 Mathematical Modeling
### Adsorption Isotherms and Kinetics  
The extended temperature-dependent dual-site Langmuir (TD DSL) model described by Equations (6)-(10) is used to express the adsorption isotherms of AC and zeolite 13X [25]. Meanwhile, the adsorption kinetics is presented by the LDF model in Equation (11).  
\[{q_{i}}^{*}=\frac{q_{m1,i}b_{1,i}p_{i}}{1+\sum_{j=1}^{N}b_{1,j}p_{j}}+\frac{q_ {m2,i}b_{2,i}p_{i}}{1+\sum_{j=1}^{N}b_{2,j}p_{j}},i=1,2,\ldots,N \tag{6}\]  
\[q_{m1}=k_{1} \tag{7}\]  
\[b_{1}=k_{2}\text{exp}\bigg{(}\frac{k_{3}}{T}\bigg{)} \tag{8}\]  
\[q_{m2}=k_{4} \tag{9}\]  
\[b_{2}=k_{5}\text{exp}\bigg{(}\frac{k_{6}}{T}\bigg{)} \tag{10}\]  
\[\frac{\partial q_{i}}{\partial t}=w_{i}(q_{i}^{*}-q_{i}),i=1,2,\ldots,N \tag{11}\]  
where \({q_{i}}^{*}\) is the equilibrium adsorption amount; \(q_{m1,i}\), \(b_{1,i}\), \(q_{m2,i}\), and \(b_{2,i}\) are the DSL isotherm parameters for component '\(i\)'; \(k_{1}\) to \(k_{6}\) are the TD DSL isotherm parameters; and \(w_{i}\) is the LDF coefficient.
## 3 Mathematical Modeling and Design Methodology
### System Design Methodology
#### 3.4.3 System Operation and Optimization  
The objective of the system optimization in this paper was to determine the optimal reliable system configuration. The objective function is the index of reliability (IR). Further, the decision variables are the number of PV modules (\(N_{PV}\)) and the volume of the lower reservoir (\(V_{LR}\)), with respect to technical constraints. Moreover, PSO and WOA are hourly testing of all possible configurations in order to meet the load consumption and determines the one that has the optimal value of IR. The energy balance Equation of the entire system is hourly tested and is expressed by Equation (20):  
\[P_{PV}(t)\;N_{PV}\;f_{PV}\;\eta_{inv}+P_{T}(t)+P_{grid_{(Brown)}}(t)=P_{PVP}(t)+ P_{L}(t)+P_{grid_{(sales)}}(t) \tag{20}\]  
The system operation flow chart is shown in Figure 9. It describes the energy management strategy of the two modes of operation. In other words, it traces hydro operation with respect to PV generation as modelled by the energy balance Equation of Equation (20). Furthermore, Figure 9 helps researchers to simply follow the design methodology and the operation strategy modeling of the system described in Figure 1.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Value** \\ \hline T\&D losses (\%) & 14.57\% \\ Electricity emission factor (tCO\({}_{2}\)/MWh) & 0.6439 \\ \hline \end{tabular}
\end{table}
Table 2: Emission constants data in Jordan.
# Implementation and Evaluation of Dynamic Task Allocation for Human-Robot Collaboration in Assembly
## 2 Materials and Methods
### Software Framework for Block-Based No-Code Programming  
Based on the desire to ensure a broad usability of the envisaged approach for task distribution and an easy adaptation to different use cases or robotic systems, a visual no-code programming framework was chosen as a basis for the implementation of HRC task allocation. The framework _ComFlow_[47, 48] satisfies these requirements by enabling an intuitive generation of process flows for various technical systems via a web-based user interface by means of system-specific functional blocks. Figure 3 shows the system architecture of the _ComFlow_ software framework including the integrated module for task allocation proposed in this article. The framework consists of a server to which different technical systems can be connected, a process flow manager for creation and control of process flows, a digital twin for virtual testing of processes during the planning phase and real-time monitoring of systems during the execution phase, and the module for HRC task allocation, which is detailed in the following section. The process flow manager and the digital twin are both integrated into the web-based user interface, which can be accessed by different end-devices. The user interfaces comprise two views--a planning view (planning HMI) and an execution view (execution HMI)--as schematically illustrated in Figure 4.  
Figure 2: Research approach for the study following the Design Science Research procedure model from [46].  
For each technical system to be controlled, functional blocks are defined which map to control commands of the respective system, such as movement to waypoint or control of gripper. In addition, system-independent functional blocks can be created, which enable human interaction in terms of information provision and requests for confirmation on the one hand, or control the process flow, for example by means of _and/or_ connections, pauses, or multi-output decision nodes. Using these functional nodes, the user can create process flows by drag-and-drop blocks and connect blocks to create the logical process sequence (see Section 2.2.3). The use of _and_ nodes also allows easy synchronization between different systems, as these ensure that all connected preceding functional blocks have been completed.  
Further details on the _ComFlow_ software framework, including a research study validating the time-effectiveness and intuitive usability of the planning software, are provided in [47; 48].  
Figure 3: System architecture of the software framework _ComFlow_ for block-based no-code programming with integration of the task allocation module and the specific hardware setup within this article for enabling human–robot collaborative assembly (extended and adapted based on [47]).
# A Review of Image Inpainting Methods Based on Deep Learning
## 5 Applications
### Object Removal
#### 5.1.2 Remote Sensing Image Object Removal  
With the rapid advancement of remote sensing technology, remote sensing images are being widely used across various fields. The demand for detailed information on the Earth's surface in remote sensing image analysis is expanding. [PERSON] et al. [2; 3] attempted to fill areas in remote sensing images that were obscured by clouds. [PERSON] et al. [4; 5] restored and filled areas in remote sensing images where mountains and buildings were obscured.
# Performance Modeling Framework for IoT-over-Satellite Using Shared Radio Spectrum
## 5 Optimal Operating Parameters  
In order to maximize the availability of the IoT-over-Satellite channel, we utilize the framework derived in Section 4 and find the optimal operating parameters of LEO satellite serving IoT devices. In particular, we focus on the antenna beamwidth and the optimal spreading factor (SF) of LoRa modulation when used for then uplink channel to increase link-availability.
# Near-Field-Based 5G Sub-6 GHz Array Antenna Diagnosis Using Transfer Learning
## 3 Experiments with Simulation-Based Data
### Classification of Normal/Abnormal Operations of Array Antenna  
First, uniform excitation of the array antenna (0.9 \(<\) amplitude \(\leq\) 1, \(-10^{\circ}\)\(<\) phase \(<\) 10\({}^{\circ}\) for all port excitation signals) was assumed to be its normal operation, and all other cases were assumed to be abnormal (See Table 2). An example of the near field according to the conditions is shown in Figure 3. For the near-field image, only the co-polarization component of the antenna was used. The amplitude of the near field was normalized to the maximum value when the uniform signals were excited to all ports, and the amplitude and the phase distribution image were arranged in parallel (left: amplitude, right: phase) as input image data. A total of 10,000 data were obtained by extracting 5000 normal data and 5000 abnormal data. The stochastic gradient descent method was used as the optimization algorithm, and the learning rate was set as 0.001. A maximum of 100 epochs were trained, and the batch size was set as 128.  
The total training time was 1 h 07 min 46 s, and the accuracy of the network for 2000 test data was 99.6% (1992/2000). The minimum number of sample data for normal/abnormal diagnosis was determined by reducing the data for each class at the same rate. Table 3  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Normal** & When 0.9 \(<\) amplitude \(\leq\) 1, \(-10^{\circ}\)\(<\) phase \(<\) 10\({}^{\circ}\) for all port excitation signals \\ \hline
**Abormal** & When 0.9 \(<\) amplitude \(\leq\) 1, \(-10^{\circ}\)\(<\) phase \(<\) 10\({}^{\circ}\) for any port excitation signal \\  & is not satisfied \\ \hline \hline \end{tabular}
\end{table}
Table 2: Normal/abnormal classification criteria for array antennas.  
Figure 3: Examples of near-field data in case of (**a**) normal and (**b**) abnormal operations.  
Figure 2: Near-field data extraction simulation environment in CST MWS.  
shows a comparison of the accuracy and execution time of the trained network by the number of sample data while maintaining the training option. The accuracy reaches approximately 99% or more, and the minimum number of sample data that achieves 99% accuracy is 1000. Therefore, for normal/abnormal diagnosis of the \(4\times 4\) array antenna, the accuracy of the network can be ensured with only 1000 sample data.
# Determination of Axial Force in Tie Rods of Historical Buildings Using the Model-Updating Technique
## 4 Case Study Using the Proposed Methodology
### Numerical Simulation  
The numerical model of the tie rod was developed as the Euler-Bernoulli beam element in SAP2000 software (Computers and Structures, Walnut Creek, CA, USA). The geometrical characteristics of tie rods--width and height--were assumed to be constant over their entire length and the rods were assumed to be homogenous throughout their volume. The material properties were based on literature review and taken as invariable; the elastic modulus E was considered as 185 GPa, the Poisson coefficient \(\
u\) as 0.3 and material density \(\rho\) as 7850 kg/m\({}^{3}\). Once the modal parameters were experimentally obtained, a numerical model was updated while changing the boundary conditions from fixed to hinged. To construct a real-life model, boundary conditions were additionally tuned with spring coefficients (stage 2 of the proposed methodology).
# Arabic Hate Speech Detection Using Deep Recurrent Neural Networks
## 4 Experimental Results and Discussion
### Positive and Negative Arabic Comment Classification  
The first experiment was based on the binary classification of comments as negative or positive. Table 4 shows the results of using the DT, MLP, NB, and LR classifiers. It can be seen from Table 4 that the highest accuracy (63.94%) was achieved using LR; however, this was still a very low percentage. The precision, recall, and F1 score were the highest for LR as well, with values of 0.63, 0.61, and 0.65, respectively.  
Figure 4: Architecture of the convolutional operations for the hate speech detection from the extracted features.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Evaluation Metrics** & **Equation** & **Description** \\ \hline Confusion Matrix & NA & A table that summarizes true positives, true negatives, false positives, and false negatives. \\ Precision & \(\frac{\text{T}_{\text{P}}}{\text{T}_{\text{P}}+\text{T}_{\text{P}}}\) & Quantifies the portion of the positive test cases that were correctly classified. \\ Recall & \(\frac{\text{T}_{\text{P}}}{\text{T}_{\text{P}}+\text{T}_{\text{P}}}\) & Also known as sensitivity, quantifies the portion of \\ F1 Score & \(\frac{2\text{T}_{\text{P}}}{\text{T}_{\text{P}}+\text{T}_{\text{P}}+\text{T}_ {\text{P}}}\) & actual positive test cases correctly classified. \\ Accuracy & \(\frac{\text{T}_{\text{P}}+\text{T}_{\text{P}}}{\text{T}_{\text{P}}+\text{T}_ {\text{P}}+\text{T}_{\text{N}}}\) & The harmonic mean of precision and recall. \\ \hline \multirow{2}{*}{
\begin{tabular}{l} Accuracy \\ \end{tabular} } & \(\frac{\text{T}_{\text{P}}+\text{T}_{\text{P}}}{\text{T}_{\text{P}}+\text{T}_ {\text{N}}+\text{T}_{\text{P}}+\text{T}_{\text{N}}}\) & Quantifies the ratio of the sum of all correct \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evaluation metrics used for the experimental results. T\({}_{\text{P}}\)—true positive; T\({}_{\text{N}}\)—true negative; F\({}_{\text{P}}\)—false positive; F\({}_{\text{N}}\)—false negative.  
A repeat of the same experiment using the proposed deep RNN model showed a significant improvement in the binary classification of Arabic comments, as shown in Table 5. There were two proposed architectures: one consisting of five deep layers with 231,841 parameters, referred to as DRNN-1, and the other with 10 deep layers and 1,682,787 parameters, referred to as DRNN-2. Using the DRNN-1 architecture, the training accuracy increased to 99.73%, with a training loss of 0.0044 and a validation loss and validation accuracy of 0.94 and 83.22%, respectively.  
With DRNN-2, the highest training accuracy achieved was 98.18% with a training loss of 0.0578, while the validation loss and validation accuracies were 0.3574 and 90.30%, respectively. DRNN-1 took 328 s to train the model, while DRNN-2, as a more complex network, took 492 s to train the model. It was observed that DRNN-1 performed better than the DRNN-2, while both achieved better results than the classifiers listed in Table 4.
## 3 Design of Hybrid Control Scheme
### Hybrid Control Scheme  
From the design above, the hybrid control scheme is obtained and its schematic diagram is shown in Figure 9.  
The hybrid control scheme consists of a constant velocity feed-forward compensator and double-loop cascade composition control strategy. The constant velocity compensator generates compensation input \(u_{\mathrm{vc}}\) to eliminate most of the extraneous force. The disturbance observer-based inner loop compensator generates compensation input \(u_{\mathrm{do}}\) to restrain disturbances besides the remaining extraneous force, and makes the actual plant tracking approximately a nominal model in a certain frequency range. In addition, the robust outer loop controller generates \(u_{\mathrm{c}}\) to achieve the desired force-tracking accuracy, and guarantees system robustness in the high-frequency region.  
Thus, the control law of the hybrid control scheme is obtained. The control input \(u_{\mathrm{i}}\) consists of three parts with different functions as follows  
\[u_{\mathrm{i}}=u_{\mathrm{c}}+u_{\mathrm{vc}}-u_{\mathrm{do}} \tag{20}\]  
Figure 8: Equivalent structure of the outer loop.  
Figure 9: Schematic diagram of the hybrid control scheme for the loading system.  
Under the same model perturbation, the tracking performance of the robust controller that can be achieved is limited. The direct robust method might achieve the same disturbance suppression ability (in dB) with the hybrid control scheme. However, the tracking error of the direct robust method is far greater than that of the hybrid control scheme, and therefore the tracking performance of the direct robust method is lower than that of the hybrid control scheme.
## 4 Results and Discussion
### The Added Mass and Its Coefficient Characteristics
For the pipe with buoyancy coverage of 25%, helical strakes coverage has little effect on the total mass in the CF direction. The average total mass in the CF direction with and without helical strakes is 5.15 kg/m and 5.27 kg/m, respectively. For the pipe with uneven buoyancy coverage of 50%, the average total mass changes do not change much, 5.84 kg/m and 5.42 kg/m with and without helical strakes, respectively. In general, the helical strakes coverage has little effect on the added mass coefficient of the riser with buoyancy modules at the same flow rate. Moreover, it can be seen that although riser displacement response decreases with the helical strakes coverage, the total mass and added mass coefficient of the riser change little. This phenomenon indicates that the riser displacement response of VIV has little effect on its added mass coefficient, which is consistent with the results of the rigid cylindrical forced oscillation test [1; 2].
## 2 Background
### Data Sharing in IoMT  
The Internet of Medical Things (IoMT) facilitates the interconnection of communication-enabled medical-grade devices and their integration into wider-scale health networks in order to improve patients' health. As shown in Figure 1, patient health data are continually transferred and exchanged between medical institutions, medical workers, medical devices, and medical applications in the IoMT context.  
However, IoMT entities often lack sufficient trust, and there is some skepticism regarding IoMT data-sharing platforms. This mistrust creates huge obstacles for IoMT-based data sharing. Therefore, there is an urgent need for IoMT data sharing approaches to fully utilize the potential value of IoMT data.