esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In the IoT scenario, encryption is important for keeping data safe and defending against attacks. If SCAs are not stopped, encryption keys can be stolen, which means sensitive information can be accessed and the whole system could be at risk. Traditional hardware-level defense mechanisms are not suitable for IoT devices because of the unaffordable cost like power overhead or circuit area overhead. Consequently, it is imperative to implement software-based defense strategies to guarantee system security without impairing computational performance.  
In this study, we proposed a dynamic key replacement mechanism for AES encryption, which effectively enhances the system's resilience against SCAs by modifying the encryption keys before an attack can successfully recover them. A lightweight key replacement mechanism was designed based on D-H key exchange protocol, which is straightforward to implement and particularly well-suited for resource-constrained microcontroller environments. This mechanism is not limited to AES encryption but can also be applied to other encryption systems requiring key negotiation.  
The experimental results demonstrate that as the frequency of key replacement increases, the system's ability to withstand SCAs is significantly enhanced. Specifically, when the key was replaced after every one or ten encryptions, the success rate of SCA dropped substantially. These findings confirm the effectiveness of the dynamic key replacement mechanism in enhancing the security of IoT devices against SCAs.  
Conceptualization, C.-W.K.; methodology, C.-W.K. and W.W.; software, W.W.; validation, C.-C.L., Y.-Y.H. and J.-R.L.; formal analysis, W.W. and K.-Y.T.; investigation, W.W. and C.-C.L.; resources, C.-W.K.; data curation, C.-W.K. and W.W.; writing--original draft preparation, C.-W.K. and W.W.; writing--review and editing, C.-W.K., W.W. and C.-C.L.; visualization, C.-C.L.; supervision, K.-Y.T.; project administration, C.-W.K.; funding acquisition, C.-W.K. All authors have read and agreed to the published version of the manuscript.  
This research work funded by National Science and Technology Council, Taiwan, under Grant NSTC 113-2221-E-035-075.  
Dataset available on request from the author.  
This work was supported in part by the Longmau Technology Co., Ltd., under Grant 12B1001T.  
We declare that we have no conflict of interest to report regarding the present study.
## 3 Results and Discussion
### Comparative Analysis  
#### 3.3.1 Analysis of the Results of the Feature Selection Algorithm Based on CARS  
In the study, the four different regression models were used to establish the prediction model of soil As content. The closer \(R^{2}\) is to 1, the higher the degree of fitting. The predictive power of the model is measured by RMSE and MAE. The smaller the value, the more accurate the prediction result of the model and the higher the accuracy. The prediction accuracies for the As concentration in the soil obtained with CARS using the four different regression models are given in Table 2. For the Honghu area, the four inversion methods obtain a lower accuracy. For the Daye area, the \(R_{p}^{2}\) values of the models constructed by the four regression methods are all below 0.5. The reason for this is that the full spectrum contains a lot of redundant and irrelevant information. CARS can remove the redundancy between the characteristic bands, but it cannot improve the correlation between spectral reflectivity and soil heavy metal As, resulting in a lower model accuracy.  
Figure 7: Variation of the correlation coefficient after the different pretreatments. (**a**) Honghu; (**b**) Daye.
# A Set of Active Disturbance Rejection Controllers Based on Integrator Plus Dead-Time Models +
## 3 Extended State Observer
### ADRC1 Controller Design  
ESO is a dynamic system into which the control signal and the difference between the outputs of the controlled system and its model enter. Most simple case with process model \(S_{1}(s)\), the extended state observer above results in the following expressions (Figure 2) yields the state equations (the model output \(y_{m}\) which is given by the state \(x_{1}\) will be denoted as \(\overline{y}\); disturbance \(d_{m}=\overline{d}\) corresponds to \(x_{2}\))  
\[\begin{array}{c}\dot{x}_{1}=(x_{2}+u(t-T_{d}))K_{s}+p_{1}(y-\overline{y});\ \ \overline{y}=x_{1}\\ \dot{x}_{2}=\ \ \ \ \ \ \ \ \ \ \ \ \ \ +p_{2}(y-\overline{y})\end{array} \tag{29}\]  
The expression (29) can be written as:  
\[\begin{array}{c}\dot{\mathbf{x}}=\left[\begin{array}{cc}-p_{1}&K_{s}\\ -p_{2}&0\end{array}\right]\mathbf{x}+\left[\begin{array}{c}K_{s}\\ 0\end{array}\right]u(t-T_{d})+\left[\begin{array}{c}p_{1}\\ p_{2}\end{array}\right]y;\\ \overline{y}=[1\ 0]\mathbf{x};\ \ \overline{d}=[0\ 1]\mathbf{x}\end{array} \tag{30}\]  
For equally delayed plant and model outputs \(y\) and \(\overline{y}\) (\(T_{m}=\overline{T}_{d}\)), the extended state observer dynamics described as:  
\[det\big{(}s\mathbf{I}-\overline{\mathbf{A}}\big{)}=\left[\begin{array}{cc}s +p_{1}&-K_{s}\\ p_{2}&s\end{array}\right]=s(s+p_{1})+K_{s}p_{2} \tag{31}\]  
does not explicitly depend on \(T_{d}\). By selecting the pair of poles at \(s=-1/T_{c}\), the calculated ESO parameters are chosen according to  
\[p_{1}=\frac{2}{T_{c}};\ \ p_{2}=\frac{1}{T_{c}^{2}K_{s}} \tag{32}\]  
With respect to the time constant of the stabilizing control (9), we may expect as a reasonable choice \(T_{c}\approx T_{d}\). The disturbance rejection, especially in the steady-state, can be achieved by subtracting \(\overline{d}\) from the inner proportional controller. When taking into account the limitations of the controller output, the solution results in ADRC\({}_{1}\) controller (see Figure 2). Of course, with respect to possible uncertainty in approximating real loop dead-time \(T_{d}\) by the chosen model dead-time \(\overline{T}_{d}\), \(T_{c}\) may not be chosen too short.  
Figure 2: Control block scheme with ADRC\({}_{1}(T_{d})\) controller (modified ESO blocks with blue background) derived for the model \(S_{1}(s)\), \(\overline{T}_{d}=T_{dm}\), \(y_{m}\) displays \(\overline{y}\) and \(d_{m}\) displays \(\overline{d}\).
## 4 Hardware Implementation
### Client Device and Additional Hardware
#### 4.2.2 Additional Hardware  
The currents that enter the MRAM device are used to determine the resistance of the specific cells. To read the MRAM cells and compare them, it is necessary to convert the currents into voltages. Therefore, additional circuitry was required.  
A fixed-value resistor was placed in series with the pins connected to MRAM cells. The resistor was placed in series for two reasons. The first reason was limiting the current flowing into the device to prevent internal damage. The second reason was to measure the voltage drop of known resistance in series with the electrical resistance of the MRAM cell, which allowed us to extract the MRAM cell's relative resistance. Since the voltage drop of the fixed resistor was low (\(<\)20 millivolts), a precision amplifier was used to amplify the voltage drop to compare the resistances with greater accuracy.  
After the voltage drops of the responses were amplified, they were fed to a comparator which output a 0 or 1 depending on which voltage was higher. The comparator was not as crucial to this implementation as the differential amplifier; however, a precise comparator with a fast output swing is preferred. The comparator that was used for this hardware implementation was the ADA4625 op-amp. This op-amp was chosen because it has high precision and a fast output swing. A picture of the hardware circuitry and client device is shown in Figure 3.
## 4 Results and Discussion
### Fracture Trajectory Results
#### 4.2.1 Mode I Fracture
This figure obviously shows the difference of the fracture trajectory in these two materials. The fracture surface of the tested asphalt samples with different geometries are also presented in Figure 8a. According to this figure although the overall fracture surface is flat but there is a rough surface due to propagation of fracture mainly from the boundary of the aggregates. However, the fracture surface shows some evidences for breakage of the tight aggregates, demonstrating that the fracture can either grow through the coarse aggregates. Using image processing technique, the observed fracture surface was divided into three parts: (i) passed through the aggregates, (ii) passed through the mastic and (iii) passed through the interface of aggregate and mastic. For example, these three regions are shown in Figure 8b with white, green and black colors, respectively for mode I fracture surface of SCB, ENDB and SENB samples tested at low temperature conditions. Using the utilized image processing method, the contribution of different regions or cracking mechanisms can be studied better for the asphalt mixtures. For example, for the SENB specimen tested under low temperature, it was observed that approximately 15% of the fracture surface belongs to the broken aggregates. In addition, it was seen from the observations of the fracture surface that typically about 20% of the fracture surface grows along the interface of aggregate/mastic or boundary of coarse aggregates. However, a significant part of the fracture surface in the tested mode I samples (i.e., approximately 65%) passes via the inside of soft mastic part. Figure 8: \(Cont.\)  
Figure 7: Comparison of mode I fracture path obtained from isotropic and heterogeneous mixture; (**a**) asphalt, (**b**) Polymethylmethacrylate (PMMA).
###### Abstract  
GPS has become an attractive feature for geolocalization enabling asset tracking IoT devices. GPS satellite antennas radiate RHCP (right-hand circularly polarized) electromagnetic waves; thus, the typical antenna at the receiver is also RHCP. However, when the orientation of the receiving device is random, linear polarization antennas operate better in terms of TTFF (time to first fix). Through field measurements (urban and field) and considering different positions of the device in a vehicle, an RHCP microstrip patch antenna and a linear non-resonant antenna element called an antenna booster were compared. TTFF averaged for several positions was 7 s better for the linearly polarized antenna booster than for the microstrip RHCP patch antenna. The results demonstrate that the behavior of the linear polarization antenna booster technology is more robust in terms of TTFF to the arbitrary position of the IoT device while keeping a small size and simplicity.sdf  
antenna; GPS; antenna boosters; polarization; IoT; TTFF (time to first fix) Article
## 3 System Model
### Traffic Load Model  
In the traffic load model, every node relay decides its load. We quantize the load using Equation (2) according to the assumption that each node produces \(\lambda_{i}^{s}\) data packets in each round.  
\[l_{i}=\lambda_{i}^{s}+\sum_{j\in\text{BF}(i)}l_{j}, \tag{2}\]  
where \(l_{i}\) denotes the traffic load of node \(v_{i}\), whereas \(l_{j}\) is the traffic load of node \(v_{j}\). \(BF(i)\) represents the children set of node \(v_{i}\), i.e., the nodes which upload data via the relay of node \(v_{i}\). \(l_{i}\) reflects the node's traffic load, referring specifically to the number of data packets set to be uploaded every round. The nodes which fall in the traffic concentration region bear a high load.  
Figure 1: Example of system model with a mobile vehicle with both wireless charging and data gathering functions (MCGV).
## 3 The Optimization and Reconfiguration of UDNs Incorporating VPPs
### Objective Functions
#### 3.1.1 Minimizing Voltage Deviation  
Voltage deviation is an indicator of the stability of the UDN. A more minor voltage deviation indicates a more stable system. The expression for voltage deviation is as follows:  
\[\min\theta_{1}=\sum_{i=1}^{v}\lvert U_{i}-U_{n}\rvert \tag{23}\]  
where \(\theta_{1}\) is the system voltage deviation; \(U_{i}\) is the voltage magnitude at bus \(i\); \(U_{n}\) is the rated voltage.
# Random Access Using Deep Reinforcement Learning in Dense Mobile Networks
## 2 AI and Recent RA Enhancements in Literature
### Information Redundancy for RA  
3 GPP first introduced the concept of redundant preambles for narrowband (NB)-IoT, and the authors in [25] tested the feasibility of the proposal in improving initial access probability for 5G mmWave networks for a massive V2X use case, where a massive number of sensors were deployed on vehicles, and subsequently, massive machine-type communications (mMTC) are no longer commonly spatially static. In practice, mMTC traffic tends to flow in bursts, and beam alignment problems along with the harsh propagation conditions of mmWaves are huge challenges to the reliability of the RA process.  
Redundant preamble transmission aims to quickly acquire data transmission opportunities in addition to improving the reliability (successful access probability). The mmWave base stations (eNodeBs, gNodeBs, and access points) send variable j in addition to ACB and uniform back-off window (UBW) variables. Here j denotes the number of times a user is allowed to send a selected preamble after selecting o from the set of preambles available. However, the major concern is to dynamically allocate j based on the traffic load. For this, the authors in [26] adopted a previously developed algorithm calculating an optimal j by solving an optimization problem.  
Other studies in [27; 28] considered an information redundancy approach in which RA response (RAR) messages are redundantly sent to users to reduce the collision rate and hence support an envisioned a large number of users. In the legacy system, if more than one user gets the same preamble, they collide or one of them obtains a RAR response. Owing to the redundant RAR responses, a user randomly selects a single RAR message as they are different from each other and has a chance to proceed further in the procedure. They experimentally demonstrated that the performance of RA increases as the number of redundant RAR messages increases. This tends to occur when user density is very high. 5G NR adopted the legacy RA procedure from LTE; however, a beam selection enhancement was added. Users are expected to synchronize with a selected beam to perform the procedure.  
The authors in [29; 30; 31; 32; 33] suggested an interesting analysis that can be applied to various RA processes. Users participating in the RA procedure are considered analogous to stochastic experiments involving the computation of a probability distribution and expectations of some of the random variables involved in the analysis. The random variablesare the control parameters discussed in this paper. Assuming the RA channel (RACH) as a queueing system, probabilistic upper bounds for performance metrics have also been suggested in [34]. However, in this analysis process, some questionable assumptions were made. For instance, a Poisson-type arrival process for bursty machine-type communication traffic was assumed. These initial assumptions are critical as they influence the final results.
# Effect of Hydrogen Ion Presence in Adsorbent and Solution to Enhance Phosphate Adsorption
## 3 Results and Discussion
### Zeta Potential (\(\xi\))  
When the solid acted as an adsorbent in the solution, a region of the solid surface was dispersed with a positive or a negative charge due to the electrical inhomogeneity at the solid-solution interface, i.e., the electrical double layer. This can be defined as the zeta potential (\(\xi\)) that characterizes the electrical properties of the solid particles [4; 20]. The zeta potential is one of the surface properties of the GAC that is used to understand the adsorption mechanism between the adsorbent and the ions, this being related to the surface charge. Figure 2 shows the zeta potential of the GAC and GACA at different solution pH values. Regardless of the type of adsorbent, the surface charge decreases with the increase in the solution pH, and the pH of the point of zero charge (pH\({}_{\text{pzc}}\)) estimates to be 4.5 and 6.0 for the GAC and GACA, respectively. The pH\({}_{\text{pzc}}\) of 4.5 for the GAC was similar to that of commercial GAC, which is measured at approximately 4-4.5 [21], and the increased pH\({}_{\text{pzc}}\) was observed when the GAC was in the presence of the NaCl and FeCl\({}_{3}\) solutions [22; 23]. Therefore, the increased pH\({}_{\text{pzc}}\) could be explained by the presence of the pretreated hydrogen ions on the surface of the GAC, resulting in enhanced protonation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Element**} & \multicolumn{2}{c}{**GAC**} & \multicolumn{2}{c}{**GAC-A**} \\ \cline{2-5}  & **Apparent Concentration** & **wt.\%** & **Apparent Concentration** & **wt.\%** \\ \hline C & 11.87 & 78.35 & 12.99 & 83.58 \\ \hline N & 0.00 & 0.00 & 0.00 & 0.00 \\ \hline O & 4.77 & 11.88 & 4.00 & 10.18 \\ \hline Al & 0.95 & 2.83 & - & - \\ \hline Si & 1.00 & 3.13 & - & - \\ \hline S & 1.03 & 3.81 & 1.68 & 6.24 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Energy-dispersive X-ray (EDX) spectroscopy analysis of GAC and GACA.  
Figure 1: Scanning electron microscope (SEM) image of granular activated carbon conditioned with deionized water (GAC) (**a**) and with hydrochloric acid (GACA) (**b**).
# Vehicle Acceleration Prediction Based on Machine Learning Models and Driving Behavior Analysis
## 4 Discussion  
### Experimental Content  
We used LSTM and GRU to predict vehicle acceleration in the car following condition. The experimental subjects are divided into three groups: 10 drivers with similar driving behaviors in group 1, and the other 20 drivers in group 2. In order to validate the performance of the proposed prediction approach, all 30 drivers were considered as the control group (group 3). In this study, the Min-Max function was used to normalize the data to improve the convergence speed and prediction performance of the model.  
The LSTM model was implemented on a Windows platform based on a Tensorflow environment and optimized using Adaptive Momentum Estimation (Adam). All of the car-following data of each group were modeled separately, and 68% of the car-following segments were randomly selected as a training set, while the remaining 32% of the data were used as the test set. Each group was trained and predicted respectively, and therefore  
Figure 6: KS test of the driving behaviour semantics between the drivers.  
the model weights are different. Then, the number of neurons in the hidden layer and batch size were determined by the grid search method. Based on the experimental results, the super parameter values of the model are shown in Table 3.  
The GRU model was implemented on a Windows platform based on a Keras environment and optimized using Adam. The Adam optimization algorithm integrates the advantages of the AdaGrad and RMSProp algorithms for various gradient optimizations, and has the advantages of a simple operation and low memory requirements [31]. Therefore, both vehicle acceleration prediction models used the Adam algorithm as the first choice for the optimization of the error term. The data preprocessing of GRU was the same as LSTM, and then the grid search method was used to determine the super parameter values of the GRU, such as the number of hidden layers and the batch size. From the experiment, the super parameter values of the model are shown in Table 4.  
In this study, we use mean the square error (_MSE_), root mean square error (_RMSE_) and mean absolute error (_MAE_) to evaluate the prediction accuracy of each model.  
\[MSE=\frac{1}{m}\sum_{i=1}^{m}{{(y_{i}-\hat{y_{i}})}^{2}} \tag{18}\]  
\[RMSE=\sqrt{\frac{1}{m}\sum_{i=1}^{m}{{(y_{i}-\hat{y_{i}})}^{2}}} \tag{19}\]  
\[MAE=\frac{1}{m}\sum_{i=1}^{m}{{|y_{i}-\hat{y_{i}}|}} \tag{20}\]  
where \(y_{i}\) is the true value and \(\hat{y_{i}}\) is the predicted value. _MSE_, _RMSE_ and _MAE_ all reflect the difference between the true and predicted values, and all three values fall into interval \([0,+\infty)\).
## 1 Introduction  
The movement of vehicles and ships on land and sea is a crucial issue in transportation networks. In road transportation, roundabouts are used to effectively improve traffic flow and provide alternative solutions at intersections [1]. In maritime transportation, the International Maritime Organization (IMO) introduced ship routing systems that includes traffic separation schemes (TSSs), recommended routes, traffic lanes, and precautionary areas to enhance navigational safety. Precautionary areas are designated zones within certain bounds where ships must exercise extra caution to lower the risk of maritime accidents and ensuing ocean pollution [2].  
These precautionary areas serve as essential zones for guiding ships through high-density areas to enhance navigational safety. They are typically established in complex traffic areas with high collision risks [3]. Such risks arise from close proximity between passing and crossing ships, making proactive safety measures vital. In high-density, high-collision-risk areas, the IMO suggests that vessel traffic services (VTSs) be established to further mitigate these risks [4].  
Implementing these measures is crucial for maintaining maritime safety and reducing accidents in areas with a high likelihood of collisions. However, traditional precautionary areas, often designed as square or rectangular zones, are susceptible to increased collision risks due to complex crossing patterns and close-quarter interactions between ships, which can lead to navigational challenges. As the global maritime traffic continues to increase, these limitations have become a significant safety concern, with recent studies indicating persistently high collision risks, especially within the TSS and precautionary areas where ship convergence is common [5; 6]. Therefore, additional strategies are required to address these challenges.  
Studies in road transport have shown that by redesigning and replacing conventional intersections with roundabouts, they reduce overall crashes by 39% and injury crashes by 76%, largely because of the reduction in collision points at the intersections [7]. Therefore, we propose that similar benefits could be achieved in maritime settings, noting that among the various shapes of precautionary areas listed in routing measures, only eight out of 30 circular precautionary areas are designed as roundabouts [8].  
Drawing from road traffic engineering principles, this study aims to investigate whether roundabout designs can offer similar advantages in maritime traffic management. This approach is novel in its application of road transport roundabout principles to the maritime environment. It evaluates whether redesigning existing precautionary areas into roundabouts can enhance navigational safety, minimize crossing conflicts, and reduce collision risks in high-density marine traffic.