esa-sceva/satcom-chunk-collection huggingface dataset:
# Stiffness Estimation and Equivalence of Boundary Conditions in FEM Models
## 3 Equivalence Method Based on Finite Element Model Updating
### Results and Discussion  
The natural frequencies of this model are listed in Table 6. They are compared to measured frequencies that are considered as reference. Percentage deviations are calculated with respect to them. For interest, Table 6 also lists the frequencies of the cantilever beam model with ideal boundary conditions. The mode shapes of the final equivalent FE model can be seen in Figure 10.  
The natural frequency values of the equivalent FE model differ from the reference values in the range from 0.56% to 7.35%. The average percentage deviation is 3.98%. However, it is evident that the equivalent FE model provides greater accuracy compared to the clamped beam model with a perfectly rigid constraint. Given that the torsional stiffness of the bushing was not taken into account when deriving the equivalent model, the results obtained can be considered acceptable. In addition, it should be recalled that the stiffness coefficients were not determined using multi-objective optimization.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Mode**} & \multirow{2}{*}{**EMA**} & \multicolumn{2}{c}{**FEM**} & \multicolumn{1}{c}{**FEM**} \\  & & **The Equivalent Model** & **Clamped-Free Beam Model** \\ \hline
1 & 46.95 & 45.09 (\(-\)3.97\%) & 52.743 (12.34\%) \\
2 & 289.44 & 291.07 (0.56\%) & 330.11 (14.05\%) \\
3 & - & 411.26 & 516.60 \\
4 & 660.99 & 682.50 (3.25\%) & 724.09 (9.55\%) \\
5 & 810.21 & 829.92 (2.43\%) & 924.37 (14.09\%) \\
6 & 1567.15 & 1682.42 (7.35\%) & 1812.71 (15.67\%) \\
7 & 1998.37 & 2064.50 (3.31\%) & 2192.14 (9.69\%) \\
8 & - & 2463.03 & 2992.52 \\
9 & 2562.31 & 2741.52 (6.99\%) & 2998.71 (17.03\%) \\ \hline \hline \end{tabular} The values in parentheses indicate the percentage deviation from the reference values. The frequencies that exceed the tolerance are expressed in red. The frequencies that are within tolerance are marked in green.  
\end{table}
Table 6: Natural frequencies of the beam obtained by EMA and FEM (Hz).  
Figure 9: Design variables plot.
## 3 Methodology
### Reconstruction of 3D Coseismic Deformation Fields  
This paper reconstructed 3D coseismic deformation fields by integrating LOS and MAI azimuth deformation data sets. The retrieval of the east-west (E-W) (\(D_{e}\)), north-south (N-S) (\(D_{n}\)), up-down (U-D) (\(D_{u}\)) components of the surface deformation followed methods described in previous studies [13; 14; 15; 16; 17; 18; 19; 20; 21; 22]. Figure 4 shows the 3D coseismic displacements maps.  
The deformation field covers an area of about \(60\times 70\) km\({}^{2}\) with two clear deformation lobes which divided by the trending bound AB mentioned in Section 3.1.  
The heading and length of the arrows indicate the direction and magnitude of the horizontal motion, respectively. The U-D component map (Figure 4a) shows somewhat similar displacement patterns with the LOS deformation maps (Figure 2) because vertical motion has dominant during the earthquake. Uplift appears in the lobe G, while subsidence appears in the lobe H. The maximum uplift and subsidence values are 100 cm and -50 cm, respectively, thus reaching up to a relative offset of 150 cm in the vertical direction. It can be confirmed that the U-D components are the most violent, and the westward motion plays a major role in the horizontal motion. The vectors reflect the characteristics of a pure thrust fault. The E-W component map (Figure 4b) clearly shows a pushing effect of plates. Both the maximum displacement in lobe G and H is approximately 50 cm toward the  
Figure 3: The along track deformation maps derived from MAI (multiple aperture InSAR) processing. (**a**,**b**) derived from ascending and descending ALOS-2 MAI processing, respectively. The red star represents the epicenter of the mainshock; The red solid squares represent the city Halabja and Sarpol-e-Zahab. The black line AB is the deformation bound.  
west. The earthquake demonstrated dominantly vertical and westward motion along a thrust fault, but it also encompassed movement in the lobe G of the N-S component map (Figure 4c), the maximum displacement in N-S direction is approximately 70 cm. The city of Sarpol-e-Zahab is located at the region that had mixed motion in the horizontal and vertical directions, so it suffered the highest intensity (the intensity is VIII) and destructions (about 12,000 buildings collapse). At the same time, the surface ruptures confirmed by [PERSON] [1] distributed in the uplift region mostly.
# Secure Authentication and Credential Establishment in Narrowband IoT and 5G
## 2 Related Work
In previous work, LO-CoAP-EAP was tested with a real LoRa network (a LoRaFabian network) [10]; in this work, we intend to continue the process of verifying that LO-CoAP-EAP can serve the purpose of providing a bootstrapping solution for an LPWAN. In this case, we employ the NB-IoT technology. In this sense, we implement LO-CoAP-EAP to provide the device with the necessary key material to authenticate with an external server, once the device is a trustworthy part of the network, and generate the necessary key material to establish a security association with the external server.
# A Survey on Satellite Communication System Security
## 3 Background  
This section covers the background knowledge related to SCSs. First, we will address the overall architecture of SCSs, including the basic hardware payload that satellites carry.  
Figure 2: Paper organization.  
We then deal with the unique environmental aspects of SCSs, as well as the characteristics of frequency and bandwidth specific to SCSs. Furthermore, we discuss the main protocols used in satellites and the features of space-to-ground integrated networks. The above content includes key concepts that will be used later when discussing threats and solutions.
## 2 Materials and Methods
### DBD Plasma Generation  
The DBD plasma box comprises two electrodes and a high voltage power supply. Electrode 1 is made of stainless steel (Figure 1). Electrode 2 is made of stainless steel covered with ceramic and positioned at an interval of 3-7 mm from electrode 1. For the DBD plasma step, a high voltage was applied using an alternating power supply (V\({}_{\text{PP}}\) (peak-to-peak voltage) of 34 kV; frequency of 15 kHz; NTO-1501, NT Electronics).  
Figure 1: Schematic illustration of the hydrogen peroxide gas plasma instrument (RENO-S130) used to investigate prion inactivation. Electrode 1 (stainless steel) and electrode 2 (stainless steel covered with ceramic) are located in the dielectric barrier discharge (DBD) plasma region and air input flow (flow rate of 5-10 L/min) was applied. The sterilization chamber box is located next to the DBD plasma region. The pressure of the sterilization chamber box was monitored using a pressure gauge. The discharge voltage V\({}_{\text{PP}}\) (peak-to-peak voltage) was set to 22 kV and a frequency 15 kHz was applied. For the corona discharge plasma, the discharge voltage V\({}_{\text{PP}}\) was 14–34 kV and the frequency was 15 kHz. An aliquot (10 uL) of 10% scrapie prion (Chandler strain)-infected mouse brain homogenate was air-died on a cover glass and sealed in a Tyvek pouch. Samples were then placed in the sterilization chamber. H\({}_{2}\)O\({}_{2}\): hydrogen peroxide.
## 4 Evolution of the Smart Grid Infrastructure: SDN-Enabled Smart Grid
### Network Design of the SDN-Enabled Smart Grid System  
The SDN-enabled smart grid system can be simply installed over a legacy IP network infrastructure without the need for any additional wired or network installation. This is because the SDN functions are implemented by application software with a general operating system, such as Linux, instead of traditional hardware-dependable network systems. Figure 3 shows an example of an SDN-enabled smart grid. In this example case study, the message exchange between HES and the edge AMI meter is realized using a switch and gateway intelligent electronic device (IED). This communication architecture is the same as the traditional smart grid communication infrastructure, and it can be simply enhanced to become an SDN-enabled smart grid infrastructure by using centralized SDN controllers [4]. In addition, the resource and service capability of the SDN controller is determined depending on its service domain (tier) as a s. The same architecture is used for the smart grid WAN and HAN. The service function as a communication policy and smart grid (power system) application can be realized within the SDN controller. Meanwhile, the service functions between communication infrastructure and the power grid system application can share the information to optimize the smart grid system.  
* WAN SDN case study: The WAN SDN controller needs to be designed to enforce the communication policy and smart grid application of the smart grid system. The WAN SDN controller is generally called a master controller and handles the back-end HES service applications, such as SCADA and intrusion detection system (IDS) control. In addition, its service capability can be extended to micro-grid service applications, such as AMI monitoring systems, depending on the network scale and system resources. [PERSON], [PERSON] et al. proposed a WAN SDN controller architecture that is designed for the back-end SCADA management infrastructure of the smart grid [18,44]. In this system, the master SCADA controller is implemented for one of the SDN applications and controls slave SCADA systems in the power grid network. This WAN SDN controller system handles the global optimization of the smart grid system, such as end-to-end routing, network capacity planning and back-end network management, but it also needs to consider the system resources, such as storage, link capacity and computational power.
* NAN case study: The NAN SDN controller (sub-controller) provides local optimization of the resource-constrained smart grid network. The service capability of the sub-controller is generally limited to a single subnet of the smart grid network. In Figure 4, the gateway IED is directly connected to the sub-controller, and its service capability is migrated from the master SDN controller. The sub-controller framework can be installed in the gateway IED of the smart grid system to achieve resource-constrained AMI network management.  
Figure 3: Illustration of the multi-controller-based SDN-enabled smart grid infrastructure.
# Position Control of a Mobile Robot through Deep Reinforcement Learning
## 2 Reinforcement Learning
### Deep Deterministic Policy Gradient (DDPG)  
The deep deterministic policy gradient (DDPG) agent is an off-policy algorithm and can be considered as a DQN for continuous action spaces. This agent uses two neural networks, one of which is called Actor and is in charge of learning the action policy (\(\mu\)), and the other is called Critic and works as an approximation of the Q-function. The policy is deterministic given that the action is to be performed directly, without the need to make an argmax of the \(Q\)-values, as is carried out in DQN. A pseudoexplanatory code of the DDPG algorithm is presented in Algorithm 2.  
```
Randomly initialize critic network \(Q(s,a|\theta^{Q})\) and actor \(\mu(s|\theta^{\mu})\) with weights \(\theta^{Q}\) and \(\theta^{\mu}\), respectively  Initialize target network \(Q^{\prime}\) and \(\mu^{\prime}\) with weight \(\theta^{Q^{\prime}}\leftarrow\theta^{Q},\theta^{\mu^{\prime}}\leftarrow\theta ^{\mu}\)  Initialize replay buffer \(R\) for\(\text{episode}=1,\ M\)do  Initialize a random process \(N\) for action exploration  Receive initial observation state \(s_{1}\) for\(t=1,\ T\)do  Select action \(a_{t}=\mu\big{(}s_{t}|\theta^{\mu}\big{)}+N_{t}\) according to the current policy and exploration noise  Execute action \(a_{t}\) and observe reward and observe new state  Store transition \((s_{t},a_{t},r_{t},s_{t+1})\) in \(R\)  Sample a random minibatch of \(N\) transitions \((s_{i},a_{i},r_{i},s_{i+1})\) from \(R\)  Set \(y_{i}=r_{i}+\gamma Q\Big{(}s_{i+1},\mu^{\prime}\Big{(}s_{i+1}|\theta^{\mu} \Big{)}\big{)}\theta^{Q^{\prime}}\Big{)}\)  Update critic by minimizing the loss :  Update the actor policy using the sampled policy gradient: \(\
abla_{\theta\mu}J\leq\frac{1}{N}\sum\limits_{i}\
abla_{a}Q(s,a|\theta^{Q}) \Big{|}_{s=s_{i},\mu=\mu(s_{i})}\
abla_{\theta\mu}H(s|\theta^{Q})\Big{|}_{s_{ i}}\)  Update the target networks: \(\theta^{Q^{\prime}}\leftarrow\tau\theta^{Q}+(1-\tau)\theta^{Q^{\prime}}\) \(\theta^{u^{\prime}}\leftarrow\tau\theta^{u}+(1-\tau)\theta^{u^{\prime}}\) endfor endfor
```  
**Algorithm 2** Deep Deterministic Policy Gradient algorithm [33]  
To balance the exploitation and exploration problem, we can introduce a random process that adds noise to the action determined by the actor and allows for exploration. For this effect, the Ornstein-Uhlenbeck process [45] is used, which is adapted for the problems of physical control with inertia.
## 1 Introduction
### Routing in WMSNs
Our proposed routing protocol aims to cluster the nodes, so that cluster heads can do some aggregation and reduction of data in order to save energy consumption and bandwidth usage, and to find the maximum number of paths suitable for the different requirements of handling different traffic classes. More specifically, the proposed routing protocol aims to satisfy the following design goals: (1) supporting different traffic classes of different delay and bandwidth requirements by choosing the suitable path for each data type, (2) maintaining minimum end-to-end delay suitable for real-time and non-real-time data packets to meet their playout deadlines, (3) achieving high throughput and packet delivery ratio by selecting the paths with better link quality and avoiding collisions and interferences, (4) saving energy at sensor nodes by moving the multimedia processing complexity as well as the aggregation process to the cluster heads' side, along with preventing path loops and path cycles in establishing the routes, (5) providing load balancing and reliable data delivery by using multi-path routing protocol and two-level QoS-aware scheduling, and (6) supporting a secure communication among the nodes by implementing a light-weight distributed security scheme for key management as explained in the following subsection.
## 4 Conclusions  
In this work, a Rh2O3-rGO modified GCE has been successfully fabricated for detecting BPA, which presents good catalytic performance. The enhanced electrocatalytic property can be attributed to the synergism of rGO and Rh2O3-NPs. Wide linear range, low detection limit, and good stability for detecting BPA have been obtained for this prepared Rh2O3-rGO/GCE sensor. More excitingly, BPA in some drinks could be directly and successfully detected after simple filtration. As a whole, a Rh2O3-rGO/GCE sensor, combining the catalytic properties of Rh2O3 and the conductivity of rGO, should be a useful supplemental strategy to common methods based on GC-MS and HPLC-MS, especially for rapid field detection.  
Conceptualization, Z.Z. and R.S.; formal analysis, R.S.; funding acquisition, A.L. and Z.Z.; investigation, X.Y.; methodology, A.L.; supervision, Z.Z.; validation, X.Y.; writing--original draft, A.L.; writing--review and editing, M.X.  
This work was supported by the China Postdoctoral Science Foundation (2016M602210 and 2018T110715) and the Open Fund supported by Key Laboratory for Environmental Factors Control of Agro-product Quality Safety, Ministry of Agriculture.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **Added (\(\mu\)M)** & **Found (\(\mu\)M)** & **Recovery (\%)** \\ \hline \multirow{4}{*}{Beverage 1} & 0 & 3.22 \(\pm\) 0.09 [1] & - \\  & 5 & 8.10 \(\pm\) 0.18 & 98 \\  & 10 & 12.66 \(\pm\) 0.43 & 94 \\  & 15 & 17.22 \(\pm\) 0.62 & 93 \\ \hline \multirow{4}{*}{Beverage 2} & 0 & 4.69 \(\pm\) 0.09 & - \\  & 5 & 9.46 \(\pm\) 0.18 & 95 \\  & 10 & 14.34 \(\pm\) 0.37 & 97 \\  & 15 & 19.49 \(\pm\) 0.43 & 99 \\ \hline \multirow{4}{*}{Beverage 3} & 0 & 3.96 \(\pm\) 0.04 & - \\  & 5 & 8.63 \(\pm\) 0.09 & 93 \\  & 10 & 13.85 \(\pm\) 0.21 & 98 \\  & 15 & 17.80 \(\pm\) 0.49 & 93 \\ \hline \multirow{4}{*}{Beverage 4} & 0 & 0 & - \\  & 5 & 4.86 \(\pm\) 0.10 & 97 \\  & 10 & 9.57 \(\pm\) 0.30 & 96 \\  & 15 & 14.73 \(\pm\) 0.24 & 98 \\ \hline \multirow{4}{*}{Thermal paper} & 0 & 6.46 \(\pm\) 0.23 & - \\  & 5 & 11.33 \(\pm\) 0.18 & 97 \\  & 10 & 13.85 \(\pm\) 0.21 & 98 \\  & 15 & 20.95 \(\pm\) 0.54 & 97 \\ \hline \hline \end{tabular} \({}^{1}\) Average \(\pm\) Standard deviation.  
\end{table}
Table 2: The results of determined BPA in practical samples.
## 6 Results and Discussion
In particular, it is possible to see that both the performances present very low changes when the logistic spaces are located at the same side or consecutive sides, but they get worse when the logistic spaces are located at opposite sides. This results suggests to adopt indifferently mainly logistic spaces at the same side or consecutive sides. On another hand, to locate the logistic spaces at opposite sides could lead to a worsening of the performance. The matter is different for the robot speed. The trend of total time and collaborative time are reported according to the robot speed. It is interesting to see that the collaborative time has a linear decreasing trend with the robot speed whereas the performance of the total time has a increasing trend (Figure 11b). The analysis of the results leads to the following considerations. The minimum distance among the resource is not a important control factor on the KPIs, therefore it can be set following the criteria of minimum cost or another criterion. The relative position of the logistic spaces has a high impact on the use of space and the SMART utility value. In particular, when the logistic spaces are at opposite sides, there is a general worsening of these performances. Still, the robot speed presents a conflicting result. It is impossible to maximize the performance of the total time and collaborative time at the same time, even though the middle robot speed is a good compromise to achieve the best possible condition for both the KPIs. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**ANOVA Analysis**} \\ \hline
**SMART** & \multicolumn{3}{c}{**Impact on space**} \\ \hline Minimum distance & 8.14\% & Minimum distance & 8.93\% \\ Robot speed & 2.98\% & Robot speed & 0.40\% \\ Logistic spaces & 88.88\% & Logistic spaces & 90.68\% \\ \hline
**HRC relevance** & \multicolumn{3}{c}{**Time**} \\ \hline Minimum distance & 0.00\% & Minimum distance & 0.03\% \\ Robot speed & 99.96\% & Robot speed & 98.60\% \\ Logistic spaces & 0.04\% & Logistic spaces & 1.37\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Results of the ANOVA analysis.
# Quantitative Analysis of Forest Water COD Value Based on UV-vis and FLU Spectral Information Fusion
## 1 Introduction
Combined with modern wireless sensor network technology, this COD detection method helps to track the impact of human activities on key water ecosystems and provides rapid early warning of sudden water pollution disasters, which is an alternative approach to improving the national water resources security system. Considering the complex interference factors of water bodies and the limitations of the single-spectrum model, we combined UV-vis and FLU spectra to detect forest water COD through information fusion technology. To further improve the predictive accuracy, we compared the results of the single-spectrum model, feature-level fusion, and decision-level fusion. Through the introduction of decision-level data fusion, the accuracy of predictive models was significantly improved. For the problem of large amounts of spectral information, high data dimensionality, and noise interference, we extracted features from the data using SPA and CARS algorithms [23; 24; 25; 26; 27]. Due to the difficulty of collecting actual water samples, we used the least squares support vector machine (LSSVM) algorithm suitable for small sample sizes to model and analyze the two spectra. By exploring data fusion methods to achieve information complementarity, our study lays the foundations for more comprehensive access to forest water quality information.
## 4 Experimentation and Results
### Comparison with Traditional Methods  
Table 17 compares the performance of the YOLOv5 + DeepSORT combination with traditional object detection and tracking methods. The comparison covers detection accuracy, tracking accuracy, real-time processing capability, and insight detail level. The conventional method described in this comparison contains:  
Figure 19: Inventory waste levels before and after system integration.  
* Background Subtraction: This method commonly has difficulty with dynamic conditions and detects moving objects by comparing the current frames to a static backdrop model.
* Haar Cascades: A machine learning technique that utilizes predefined profiling to detect things. Although beneficial in some conditions, it is less intense than the current deep learning method.
* Kalman Filters: In situations with non-linear proposals or closures, this statistical method for estimating the track of moving objects may need to be more helpful.
* Optical Flow: An analytically challenging and noise-sensitive method of tracking object displacement based on variance in pixel intensity.  
The YOLOv5 + DeepSORT approach significantly outperforms traditional methods with a detection accuracy of 91.67% compared to 70.2% for conventional methods. Tracking accuracy is also higher at 89.47% versus 60.5%. The real-time processing capability of 25 frames per second (fps) showcases the efficiency of the YOLOv5 + DeepSORT approach, which is superior to the 15 fps of traditional methods. Additionally, the level of insights provided by the YOLOv5 + DeepSORT approach is described as high in contrast to the lower detail level offered by traditional methods.  
Figure 20 shows the advantages of the novel methodology in terms of accuracy, handling speed, and level of detail by comparing the performance criterion of YOLOv5 + DeepSORT with conventional methods.  
Figure 21 shows the confusion matrices for six different models used to classify information. The confusion matrices are clustered to raise the comparative, and respectively, the storyline shows the performance of a unique model.  
\begin{table}
\begin{tabular}{c c c} \hline
**Metric** & **YOLOv5 + DeepSORT** & **Traditional Methods** \\ \hline Detection Accuracy & 91.67\% & 70.2\% \\ Tracking Accuracy & 89.47\% & 60.5\% \\ Real-Time Processing Capability & 25 fps & 15 fps \\ Insights Detail Level & High & Low \\ \hline \end{tabular}
\end{table}
Table 17: Comparison of YOLOv5 + DeepSORT vs. traditional methods.  
Figure 20: Comparison of YOLOv5 + DeepSORT vs. traditional methods.