esa-sceva/satcom-chunk-collection huggingface dataset:
# Environmental Behavior and Remediation Methods of Roxarsone
## 6 Remediation Methods of ROX Contamination
### Combined Degradation and Adsorption
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Remediation Type** & **Remediation Method** & **ROX** & **References** \\ \multicolumn{4}{c}{**Initial Concentration and Removal Rate**} \\ \hline  & Cellulose-geochite composites & 0.04 mm / 24 h, 100\% & [94] \\  & HA-\(\kappa\)FeOOH & 20 mg/ L, 12 h, -60\% & [81] \\  & Modified sorghum straw biochar(MSSB) & 1.0 mg/ L, 4 h, 67.09\% & [84] \\ Adsorption method & magnetic profile (Fe\({}_{3}\)O) & 1-10 mg/ L, 4 h, 100\% & [82] \\  & Geochite modified biochar(GM) & 10 mg/ L, 24 h, 98\% & [95] \\  & Zerovalent Iron Nanocomposite & 30 mg/ L, 40 min & 86.74\% & [96] \\  & BIfunctional cationic cyclodextrin material (GD-DTAC) & 50 mg / L, 51\% & [97] \\  & Zr-based metal-organic frameworks & 1 mg/ L, \(\sim\) 99.1\% & [98] \\ \hline  & UV/Fe(II) & 10 mg/ L, 99\% & [87] \\  & UV/Fe(II) & 10 mg/ L, 99\% & [86] \\  & UV/chlorine + Fe(II) & 5 mm/ L, \(\sim\) 99\% & [53] \\  & UV/caulate herentic & 20 mg/ L, 8 h, 85\% & [88] \\ Photochemical oxidation + adsorption & UV/alpha-reduction/CA/persulfine & 20 mg/ L, 8 h, 85\% & [88] \\  & UV/hematine/sulfine & 19 mm/ L, 50 min, 92\% & [89] \\  & UV/D/ FeOOH hybrid & 10 mg/ L, 12 h, 96\% & [100] \\  & UV/a bifunctional membrane modified by BiOC\({}_{0.05}\)Bi\({}_{0.125}\) and polydopamine & 17.5 mg/ L, 5 h, 100\% & [101] \\  & UV/permanate & 14.29 mm/ L, 20 min, 78\% & [102] \\  & Fe\({}_{3}\) decorated research-formaldehydehyde series (Fe\({}_{3}\)-RFR) photocatalyst & 20 mg/ L, 29\% & [103] \\ \hline  & Co\({}_{0}\)Cu\({}_{2}\)-Y\({}_{2}\)O\({}_{3}\)+perosymosimosaic & 50 mmol/ L, 15 min, 100\% & [90] \\  & Ferrate & 5 um/ L, 10 min, 99\% & [61] \\  & KMmO\({}_{x}\)-Fe(II) & 103 mmol/ L, 24 min, 99\% & [92] \\ Chemical oxidation + adsorption & Heterogeneous Fenton & 10 mg/ L, 3 h \(\sim\) 80\% & [104] \\  & 0.1-ball milling multi-walled carbon nanotubes/electrolyte manganese & 40 mg/ L, 60 min, 90.96\% & [105] \\  & residue/peroxydisulfine & & \\  & Chlorination + Fe(II) & 5 um/ L, 120 min, 100\% & [106] \\ \hline Heat treatment + adsorption & Heat treatment + FeSO\({}_{4}\)-7H\({}_{2}\)O & 500 mg/kg (in Ag), 300\({}^{-}\)C, 60 min, 98\% & [93] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: ROX main pollution remediation methods.
# Resilience in the Internet of Medical Things: A Review and Case Study
## 3 Background
### Microservices  
IoMT networks run on a big infrastructure, as they host multiple patients connected with several wearable sensors and devices. Furthermore, they generate large amounts of data and transfer these data to the cloud storage of hospitals, where these data are utilized by different users. The response requirement should be fulfilled promptly, as the data might belong to the critical health status of a patient. Due to the heavy burdens on the existing resources of the cloud infrastructure of the IoMT network, sometimes issues like route congestion, cyber-attacks, and other faults or failures may occur [17].  
These challenges can be mitigated by transforming this monolithic architecture into a microservices architecture (MSA), which provides a loosely coupled architecture. The distributed and independent module structure of the microservices architecture can overcome the problem of a single point of failure in the IoMT networks [18; 19; 20]. Due to the deployment of small independent function modules in terms of fog/edge servers, MSA provides partial availability to IoMT networks for urgent medical data transmission during faults and failures [21]. MSA can also be utilized if the system is required to significantly scale up from tens of patients to thousands of patients, without impacting the quality of service of IoMT. It also brings higher flexibility into the network by enabling the independence of one module operation from another.
# Performance Evaluation of Asphalt and Asphalt Mixtures Modified by Fuel-Resistant Admixture
## 3 Results and Discussion
### Performance of SMA-13 Mixture
#### 3.2.2. Cantabro Test  
The results of scattering loss are illustrated in Figure 11. As the FRM concentration increased, the pre-immersion and post-immersion scattering loss of asphalt mixture specimens initially decreased and then increased. Among the specimens, those containing 1% FRM exhibited the least scattering loss. In comparison to the absence of FRM, all FRM-MAM specimens showed a reduction in scattering loss, mainly attributed to the enhanced adhesion between aggregates and asphalt resulting from the co-solubilization of FRM and asphalt, forming an elastic network structure [7,24]. Additionally, post-immersion treatment led to a significant increase in scattering loss for all specimens. The scattering loss of the non-FRM-modified specimens reached 28.9%, an increase of 18.7% compared to pre-immersion. This is attributed to the dissolution of asphalt in the fuel after immersion treatment, leading to reduced asphalt film thickness and a substantial decrease in adhesion between aggregates. Consequently, the scattering loss significantly increased. Specimens containing 1% FRM exhibited the smallest difference in scattering loss, only 5.0%. When the FRM concentration exceeded 1%, the difference in scattering loss increased, which may be related to the change in rheological properties of asphalt. All FRMA samples showed larger scattering loss differences than the control group, consistent with the results of the massing test.  
Figure 11. Results of the Cantabro test.  
Figure 10. Variation curve of quality losses with different FRM concentrations.
## 2 Data and Methods
### Earth Observation Data
WorldView-2 (eight spectral bands with a 1.84 m resolution and one panchromatic band with 0.46m resolution), WorldView-3 (eight multi-spectral bands in the visible near-infrared region with a 1.24 m resolution, one panchromatic with 0.31 m resolution, and eight bands in the short-wave infrared region with 3.7 m resolution) and WorldView-4 (four multi-spectral bands in the visible near-infrared region with a 1.23 m resolution, and one panchromatic with 0.31 m resolution) satellites also constitute options for very-high resolution imagery [47]. Combining very-high spatial resolution (below one meter) with multi-frame image acquisitions can allow for the monitoring of a diverse range of coastal features [48]. In the context of SAR data alternatives to Sentinel-1, the ALOS PALSAR 2 and TerraSAR-X missions operate in six image acquisition modes with resolutions that range from 3 m to 100 m, and from 0.25 m to 40 m, respectively, and while supporting different scene sizes and polarization modes. The combined use of ascending and descending image pairs via L-band and C-band data can support coastal applications for land-water discrimination [49].
# Pipeline Damage Identification Based on Additional Virtual Masses
## 3 Numerical Simulation
### Damage Identification  
Using theoretical FEM, we multiply the stiffness \(\mathbf{Ki}\) of several substructures using the theoretical damage vector \(\mathbf{\mu}\) to simulate the actual damage. In this section, damage identification is carried out  
Figure 8: Changes of the first frequency with different masses (FEM).  
Figure 9: Changes of the first frequency with different masses (VDM).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline
**Virtual Structure** & **G1** & **G2** & **G3** & **G4** & **G5** & **G6** & **G7** & **G8** & **G9** & **G10** \\ \hline
**Mass (kg)** & 7 & 7 & 5 & 6 & 8 & 8 & 6 & 5 & 7 & 7 \\
**FEM (Hz)** & 177.38 & 175.86 & 161.12 & 137.43 & 117.87 & 118.07 & 137.63 & 161.47 & 175.70 & 177.26 \\
**VDM (Hz)** & 177.43 & 175.51 & 160.79 & 137.62 & 117.54 & 118.15 & 137.91 & 161.09 & 175.48 & 177.54 \\ \hline \end{tabular}
\end{table}
Table 5: Frequency comparison of ten virtual structures.  
in two kinds of conditions: single and multiple damages. Case 1 is that of damage occurring in substructure 5 with \(\mu=0.7\), whereas case 2 reflects the multiple damage that occurs on substructures 3, 4, and 7. Table 6 presents the specific damage vectors \(\mu\).  
Figure 10 compares the selected natural frequencies. The figure shows that the change of the first frequency after damage is small with a 5 Hz reduction. The damage can be initially judged. In comparison, by attaching to the corresponding virtual masses, the frequency is significantly reduced at 112 Hz.  
In the theoretical damage model, the excitation shown in Figure 4 is applied to the intermediate position of 10 substructures and their acceleration responses at the corresponding excitation position are calculated. Placing excitation and response while considering 5% noise in Equation (4), the frequency corresponding to the respective optimal virtual masses was constructed. Then, the frequency changes before and after the damage were combined with the sensitivity matrix of Formula (9), which was dominant and convergent. Hence, the damage factor of the simulation was iterated in Formula (10).  
Figure 11 shows the single damage identification of substructure 5, and Figure 12 shows the multiple damage identification of the substructure 3/5/7. From the result of the numerical simulation, we conclude that damage identification of the substructure can be effectively realized by adding the virtual masses to the substructure and using the sensitivity matrix iteration.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Substructure** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline
**Damage vector:**\(\mu\) & 1.0 & 1.0 & 0.8 & 1.0 & 0.4 & 1.0 & 0.6 & 1.0 & 1.0 & 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Theoretical damage vectors of case 2.  
Figure 10: Substructure 5: Frequency changes before and after damage.
## 5 Numerical Results with Adaptive Time-Step Sizes
Now, we substitute \(LE_{\mathrm{C}1L2}\) and \(u^{n+1}\) into Equation (21) to obtain the norm of the local error estimation. Considering the previous calculations and Equations (22) and (23), an adaptive time-step controller is constructed, and it is denoted by ALNe3-C1L2. The local error can be estimated based on the first and third stages as well. Repeating the same step as in the previous lines, another adaptive time-step controller is obtained, and it is denoted by ALNe3-C1L3. The third possibility is when the local error is estimated based on the first and the third stages and the applied controller will be denoted by ALNe3-L2L3. **The CLL method**  
The CLL method consists of three stages. The first stage uses the CNe scheme with time-step length \(ph\), while the second step uses the LNe scheme with time-step length \(\frac{2}{3}h\). If we take \(p=\frac{2}{3}\) in the first stage, then an error estimation can be made as in Equation (24), where \(\hat{u}^{n+1}\) is calculated by Equation (16), considering that \(p=\frac{2}{3}\), while \(u^{n+1}\) is calculated by Equation (17). Substituting Equation (24) into Equation (21), and then considering Equations (22) and (23), an adaptive controller can be implemented, and it will be denoted by ACLL-C1L2. If \(p=1\), another local error estimation can be considered as follows:  
\[LE_{\mathrm{C}1L3}=\left|\hat{u}^{n+1}-u^{n+1}\right| \tag{25}\]  
where \(\hat{u}^{n+1}\) is calculated by Equation (16), taking \(p=1\), while \(u^{n+1}\) is calculated by Equation (18). Substituting Equation (25) into Equation (21), and then considering Equations (22) and (23), an adaptive controller will be implemented, and it will be denoted by ACLL-C1L3. **Runge-Kutta Cash-Karp Method RKCK**  
Because it is a well-known method, and explained in detail in [55] (p. 717), we think that it is not necessary to describe the tedious processes of implementing the method. The local error estimation in Equation (16.2.6) and the fourth-order solution in Equation (16.2.5) in [55] can be plugged into our equation (21) to obtain the norm of the local error estimation. Again, using Equations (22) and (23), the Runge-Kutta Cash-Karp is obtained, and it is denoted by RKCK. **Runge-Kutta-Fehlberg Method**  
Plenty of references discussed and implemented this method. Here, we will refer to [56], where the authors show how to estimate the local error using Equation (5.55) in that reference. The numerical solution generated by Equation (5.53), along with the local error estimated by Equation (5.55) in that reference, can be substituted into our Equation (21) to obtain the norm of the error estimation. That norm can be used to adapt the time-step size using Equation (22), resulting in the so-called Runge-Kutta-Fehlberg 4(5), or RKF45 method, and it will be referred to as RKF in our paper. Two numerical experiments are conducted to check the performance of these adaptive controllers and to compare their performances. The numerical computations are carried out using the MATLAB R2020b software on a desktop computer Intel Core (TM) i11-11700F.
## 3 Overall Algorithm Design
### Ground Control Station System  
The Ground Control Station (GCS) serves as the core control hub of a UAV system, enabling both within visual line-of-sight (VLOS) and beyond visual line-of-sight (BVLOS) operations. Throughout all mission phases--from the initial preparation and critical task execution to final data processing and dissemination--the GCS plays a pivotal role. Its mission is to monitor the aircraft's flight status and payload operational conditions, empowering ground operators to effectively command both the airframe and mission equipment. Core functions encompass mission planning, flight monitoring and control,  
Figure 8: Control flowchart for UAV algorithms.  
imagery display and payload management, system monitoring, intelligence dissemination, and data recording.  
The GCS system deployed in this research enables the real-time display of situational imagery and continuous monitoring of UAV kinematic status. This system further supports the control of flight modes and trajectory planning. Wireless communication between the GCS and UAV is established via the MAVLink protocol. Additionally, operators can remotely pilot the UAV using the Yun Zhuo H16 All-in-One Remote Controller, while concurrently monitoring real-time environmental imagery data. The workflow diagram is illustrated in Figure 9.
# Requirements Engineering for a Drone-Enabled Integrated Humanitarian Logistics Platform
## 3 AIRDROP Overview
### Warehouse Inventory Management  
This service pertains to the utilization of drones for inventory management of emergency relief warehouses. It encompasses essential tasks, such as stock taking, cycle counting, locating missing items, and retrieving hard-to-reach stocks, while also conducting warehouse capacity calculations. The system incorporates a blockchain and distributed ledger to store specific inventory data acquired by UAVs, ensuring their validation, trustworthiness, and accessibility to relevant stakeholders.
## 3 Experimental Results
### Evaluation Metrics  
The proposed algorithm uses the Dice, Jou, Recall \(R\), and Precision \(P\) as the evaluation metrics, which are respectively shown in Equations (3)-(6). The higher the values, the better the model.  
\[Dice=\frac{2*area(S_{1}\cap S_{2})}{area(S_{1})+area(S_{2})} \tag{3}\]  
\[Iou=\frac{area(S_{1}\cap S_{2})}{area(S_{1}\cup S_{2})} \tag{4}\]  
\[R=\frac{TP}{TP+FN} \tag{5}\]  
\[P=\frac{TP}{TP+FP} \tag{6}\]  
where \(S_{1}\) is the edema region predicted by the network, \(S_{2}\) is the annotated edema region by the expert 1, \(TP\) is the true number of pixels for the predicted edemas, and \(FN\) is the number of pixels with true edemas predicted as non-edemas. \(FP\) is the number of pixels where non-edemas are predicted to be edemas.
# A Deep-Learning Approach for Identifying a Drunk Person Using Gait Recognition
## 2 Materials and Methods
### Gait Analysis
#### 2.2.1 Background Subtraction  
During image-based object detection, the recognition algorithm sometimes confuses the target object with the background because the image processing contains an analysis of the color difference and the contour lines from the raw data. However, ambiguity due to similar colors, shadows, and lighting causes the accuracy of the detection algorithm to be reduced. In order to leave only information regarding walking in the input data, it is necessary to remove the background. This is also called the background subtraction technique. The background subtraction technique consists of four steps. The procedure of background subtraction is as follows. The first step is to convert the raw data to an input format. The second step is to calculate the statistical property whether each frame belongs to the background or foreground. And the result of the detection algorithm is compared with the movement in the next frame. This was required in order to verify the results, namely, what was classified as foreground or background.  
Typical background subtraction techniques include GMG (Kalman filters and Gale-Shapley matching) [31], MOG (mixture of Gaussian), and MOG2. First of all, GMG is an algorithm that combines the statistical background image removal method and pixel-by-pixel Bayesian segmentation. In general, GMG has been evaluated to be superior in background removal performance, but there is no significant difference in the background detection degree between MOG2 and GMG. However, there is also the advantage that MOG2 runs faster and is not affected by scene changes due to changes in lighting conditions. Therefore, in this study, MOG2 was selected as the image background removal method.  
In addition, the frames required in this study were those in which the background was black and the foreground was white. To this end, the noise was removed by adding a morphological calculation to the existing MOG2. The opening technique was used during the morphology calculation, which is a technique to remove noise on the image by applying the dilation calculation immediately after the erosion is performed.
## 4 The Monte Carlo Simulation and Experimental Results  
### An Illustrative Example of the Dynamic Adjusting Model  
This research intends to utilize the data simulated in Formosa Freeway of Taiwan as our case study as shown in Figure 1 (Google Map). There are three road sections marked in red and defined as Road Section 1, 2, and 3 based on its direction toward Taipei. The detail information of these three road sections can be stated below. Each road section is with a traffic light and each road length can be 400, 300 and 1000 m with respect to its order. In addition, there are four lanes in each road section. The Road Section 3 is adjacent the System Interchange of Formosa Freeway of Taiwan. In this study, we assume that vehicles entry ramp in the inside lane and drive straight to Taipei in the outside lane. Suppose that one drives a vehicle toward Taipei through Road Section 1, 2 and 3. Once the timing traffic signal control is not flexible during the peak hours, there may be the traffic block either in Road Sections or Formosa Freeway of Taiwan. This situation would be occurred during traffic rush hour or weekend.  
In Taiwan, the RFID hardware, the tag and reader, is not yet implemented in the vehicles or traffic signal controllers because of the cost. This follows that we cannot collect the real data of the traffic flow. To solve this problem, our study applies Monte Carlo simulation to simulate the traffic flow during the heavy traffic of each road section. Our proposed model would derive optimal results based on available data. As a result, the dynamic traffic control mechanism can be developed.
# Hybrid Cooperative Spectrum Sharing Protocols Based on OMA and NOMA in Cognitive Radio Networks
## 2 System Model and Protocol Descriptions  
As illustrated in Figure 1, we consider a hybrid cooperative spectrum sharing system based on OMA and NOMA consisting of a pair of PUs and a pair of SUs. The PUs consist of a primary transmitter (PT), and a primary receiver (PR), while the SUs consist of a secondary transmitter (ST), and a secondary receiver (SR). All nodes are equipped with one antenna and operate in the half-duplex mode.  
We supposed that all channels experience independent but not necessarily distributed quasi-static Rayleigh fading. The channel fading coefficients \(h_{i}\) is modeled as independent complex Gaussian random variables denoted by \(h_{i}\sim CN(0,\lambda_{i})\), where \(i=PP,PS,PR,SP,SS\) for the channels between node pairs \(\text{PT}\rightarrow\text{PR},\text{ PT}\rightarrow\text{ST},\text{ PT}\rightarrow\text{SR},\text{ ST}\rightarrow\text{PR},\text{ and ST}\rightarrow\text{SR},\text{ respectively}\). We assume that all channels have an additive white Gaussian noise (AWGN) with mean zero and variance \(\sigma^{2}\). The transmit power at PT and ST are denoted by \(P_{P}\) and \(P_{S}\), respectively. The achievable data rate for PUs and SUs are denoted by \(R_{P}\) and \(R_{S}\), respectively.  
The proposed spectrum sharing protocol switches between non-CR, underlay mode and overlay NOMA/OMA corporative transmission modes according to feedback information from PR. In the non-CR transmission mode, PUs alone access to the spectrum. The SU remains silent and eavesdrops on the PU's packets at the same time. In the underlay OMA mode, the PU and SU access the spectrum simultaneously, but the transmit power of the SU must be within the spectrum threshold tolerated by PUs. In overlay NOMA cooperative transmission mode, the PU remains silent and ST superimposes its own data packets with the data packets that the PUs failed to transmit for the first time to PR and SR. PUs' packets are allowed to be retransmitted once, while SUs' packets are transmitted only once or not. For another model proposed, OMA is used in overlay cooperation mode. Other modes are similar to those mentioned above. In overlay OMA cooperative transmission mode, the PU stays silent and ST acts as a relay to transmit the PU's previously failed packets to PR. Therefore, the flow chart of cooperative cognitive radio network based on OMA/NOMA is shown in Figure 2.