esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Analysis
### Accuracy Verification
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Image Size** & **Multi-Looking Ratio** & **Number of PS Points** & **Peak Memory Usage** \\ \hline Traditional Method—TerraSAR-X & 14,500 \(\times\) 13,000 & 5:5 & 416,089 & \(\sim\)20.7 GB \\ Traditional Method—Sentinel-1 & 9000 \(\times\) 1900 & 5:1 & 349,674 & \(\sim\)22.0 GB \\ Proposed Method—TerraSAR-X & 14,500 \(\times\) 13,000 & 1:1 & 12,921,707 & \(\sim\)15.1 GB \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Data specifications for different methods. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **PS Point Ratio** & \multicolumn{2}{c}{**Elevation Residual (vs. Proposed Method, m)**} & \multicolumn{2}{c}{**Deformation Rate (vs. Proposed Method, mm/y)**} \\ \cline{3-8}  & **Method** & **Mean Bias** & **SD** & **Cor** & **Mean Bias** & **SD** & **Cor** \\ \hline Traditional Method—TerraSAR-X & 3.2\% & \(-\)0.85 & 3.38 & 0.99 & 0.08 & 0.48 & 0.98 \\ Traditional Method—Sentinel-1 & 2.7\% & 0.02 & 2.77 & 0.99 & \(-\)0.63 & 2.43 & 0.75 \\ Proposed Method—TerraSAR-X & 100\% & 0 & 0 & 1 & 0 & 0 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracy comparison of different methods. densely populated low-rise buildings (highlighted in yellow in Figure 11), significant deformation was observed in the proposed method, while the traditional method did not detect any PS deformation points in these areas. The SD of the deformation rates for the traditional and proposed methods were 0.48 mm/y and 2.43 mm/y, respectively, with correlation coefficients of 0.98 and 0.75. Overall, the deformation rates and elevation residuals from the proposed method demonstrated high reliabilities. Figure 11: Deformation rate results from different methods (the blue rectangles indicate areas with large deformation rates and consistent results between methods, and the yellow rectangle indicates areas with significant deformation where results differed between methods). (**a**) Results from the proposed method based on the TerraSAR-X data. (**b**) Results from the traditional global PSInSAR method based on the TerraSAR-X data. (**c**) Results from the traditional global PSInSAR method based on the Sentinel-1 data. (**d**) Scatter density plot for (**a,b**). (**e**) Scatter density plot for (**a,c**). Figure 10: Elevation residual results from different methods (the red rectangles indicate areas with PS point distribution differences between methods). (**a**) Results from the proposed method based on the TerraSAR-X data. (**b**) Results from the traditional global PSInSAR method based on the TerraSAR-X data. (**c**) Results from the traditional global PSInSAR method based on the Sentinel-1 data. (**d**) Scatter density plot for (**a,b**). (**e**) Scatter density plot for (**a,c**).
###### Abstract  
Multi-beam satellite communication systems are promising architectures in the future. A packet is transmitted by multi-satellite and multi-beam cooperatively, which can provide efficient spectrum utilization, improve system throughput, and guarantee Quality of Services (QoS). In multi-beam satellite communication systems, multi-layer and multi-dimensional radio resources change dynamically, which leads to the discontinuity of optimal resources and the lack of mapping balance between packets and radio resources. To deal with these problems, we propose a cross-layer and cross-dimension radio resources optimization model based on the weighted discrete firefly algorithm and an adaptive packet segmentation scheme based on the irregular gradient algorithm. The cross-layer and cross-dimension radio resources optimization model based on the weighted discrete firefly algorithm simulates cross-layer and cross-dimension optimization for the high-dynamic and multi-dimensional radio resources by considering the channel state information (CSI) and QoS in the multi-beam satellite communication system. The optimal resources are taken as the weight of irregular gradient algorithm to segment packets and map packets to radio resources, which can realize the mapping balance between packets and radio resources and ensure the efficiency and reliability of communication. The simulations show that the new transmission scheme improves the normalized system throughput and user satisfaction index by 18.7% and 6.2%, respectively.  
multi-beam satellite communication systems; cooperative transmission; resource allocation; resource optimization; packet segmentation +
Footnote †: journal: Electronics
# A Comparative Analysis of Anomaly Detection Methods in IoT Networks: An Experimental Study
## 4 Results
### Results Obtained from the IoT-23 Dataset Using a Supervised Machine Learning Approach
#### 4.1.1 Research Analysis According to [45]  
Research [45] has investigated anomaly detection in IoT networks, utilizing the IoT-23 dataset. The methodology encompasses several key steps.  
Step 1: Data Preparation and Processing  
This study utilized the IoT-23 dataset, containing data from various IoT devices, and IoT Network Intrusion, simulating IoT network traffic [34]. To ensure data quality and consistency, several preprocessing steps were implemented. Subsampling was employed to address class imbalance, standardizing features to prevent dominance, and encoding categorical variables for compatibility with ML algorithms.  
Step 2: Model utilization  
The Random Forest algorithm was selected due to its ability to handle large datasets, reduce overfitting, and provide feature importance. Hyperparameters were carefully tuned to optimize model performance. The model was then trained on the preprocessed data, learning patterns, and relationships between features and class labels.  
Step 3: Model evaluation  
To assess the model's effectiveness, various metrics were employed. Accuracy, Precision, Recall, and F1-score provided comprehensive measures of overall performance. Confusion matrices visualized the model's ability to correctly classify instances. Receiver operating characteristic (ROC) curves evaluated the trade-offs between true positive and false positive rates. Feature importance analysis identified the most influential features in the classification process.  
Step 4: Result verification  
The Random Forest model demonstrated exceptional performance in detecting intrusions in IoT networks. High scores for Accuracy, Precision, Recall, and F1-score indicated its effectiveness. The confusion matrix and ROC curve confirmed the model's ability to accurately classify both benign and malicious connections. Feature importance analysis revealed key features that contributed significantly to the model's predictions, providing valuable insights into the underlying patterns of intrusions.
# Coefficient of Thermal Expansion of RCA Concrete Made by Equivalent Mortar Volume
## 4 Test Results
### Concrete COTE Test Results
#### 4.2.2 Predicted COTE Values  
Since concrete is a composite including various materials, the COTE of concrete may be determined from the COTEs of the individual components. Two models have traditionally been presented for this: one is the parallel model; the other is the series model [10]. [PERSON] [46] combined the two models and presented the following composite equation:  
\[\alpha_{c}\ =X(\alpha_{m}V_{m}+\alpha_{a}V_{a})+(1-X)\frac{\alpha_{m}V_{m}E_{ m}+\alpha_{a}V_{a}E_{a}}{V_{m}E_{m}+V_{a}E_{a}} \tag{4}\]  
where  
\(\alpha_{m}\), \(\alpha_{a}\) = COTE of mortar and aggregate, respectively  
V\({}_{m}\), V\({}_{a}\) = volume fraction of mortar and aggregate, respectively  
Figure 9: COTE values in terms of total coarse aggregate volume ratio.  
E\({}_{m}\), E\({}_{u}\) = elastic modulus of mortar and aggregate, respectively  
\(X\) = relative proportions of materials conforming with the upper and lower bound solution.  
It should be noted that [PERSON]'s model becomes the series model when XX = 1, while it becomes the parallel model when XX = 0.  
Tables 3 and 4 summarizes the input variables of the [PERSON] model. The elastic moduli of the CNR, CRR68, CRR33, ER68 and ER68-2 specimens at the age of 28 days in mix series 1 were 27.1, 24.8, 25.8, 26.1 and 26.9 GPa, respectively [42], and those of the CNP, CRP25, CRP50, ERP25 and ERP50 specimens in mix series 2 were 27.7, 27.7, 26.3, 26.4 and 27.2 GPa, respectively [43]. Since the elastic modulus of concrete and that of the mortar used in the present study, as shown in Table 4, were very similar to each other, the parallel part in the [PERSON] model was neglected. Therefore, the condition of XX = 1 was applied.  
Under the assumption that XX = 1, the input variables in Table 3 were substituted into the [PERSON] model to predict the COTEs of the specimens prepared under different conditions; the results of the curve fitting of the COTEs are shown in Figure 10. Figure 10a,c shows that the predicted COTE values fit well to the measured COTE values with determination coefficient of 0.85 and 0.86, respectively by linear relationships in Mix 1 and Mix 3. However, the determination coefficient was lower in mix series 2 than in mix series 1 or 3, because RCA replacement ratios were 33% and 68% in mix series 1 and 25% and 50% in mix series 2. As a result, the distribution of the total coarse aggregate ratio, represented by the \(x\)-axis of the plot shown in Figure 9, is closer among the specimens in mix series 2 than in mix series 1. The difference between the predicted values and the measured values may have resulted because the \(\alpha_{a}\) and \(\alpha_{m}\) values were obtained using a dilatometer type TMA, but the COTE value of concrete was obtained under saturated conditions in a bath in which RH = 100% according to the AASHTO-TP60.  
Figure 10: Predicted COTE values versus measured COTE values in various mixes.
# A Microservices-Based Control Plane for Time-Sensitive Networking
## 4 Tests and Discussion of Results
### Laboratory Setup  
The elements included in the setup are shown in Figure 12. We will start by distinguishing between the data plane and the control plane. The data plane consists of all the components within the red block. These elements have hardware interfaces that are capable of communicating according to TSN standards:  
* Two TSN switches (SW0, SW1). We used Soc-e MTSN kits during the development and testing phase [31]. Each switch has four TSN interfaces; it communicates with the other switch through the Eth3 interface and with its respective end station through the Eth0 interface. PTP messages are exchanged in the TSN links for synchronization purposes.
* Two end stations. In our testbed, we used two computers running Linux (Ubuntu 22.01 LTS). Intel i210 network interface cards were used to support the precise timestamping required by PTP. The computers ran PTP daemons to manage synchronization with the switches. On the other hand, the elements inside the blue block correspond to the control plane:
* The Port Z interfaces of the TSN switches. These NICs were used only for management tasks by means of NETCONF and SSH.
* A regular Ethernet switch that works as an out-of-band control network.
* The \(\mu\)TSN-CP Controller, deployed on a single computer with Docker-compose. This highlights another advantage of using microservices, as our controller can run on any machine with Docker. However, to take full advantage of the microservices architecture in a more realistic deployment, the \(\mu\)TSN-CP Controller should ideally be in a private or public cloud, running on some container orchestration tool like Kubernetes, OpenShift, or Docker Swarm.  
Figure 12: Laboratory setup.
# Safety Evaluation of a Water-Immersed Bridge Against Multiple Hazards via Machine Learning
## 3 Effect of Immersing in Water  
As the natural frequency of the river-crossing bridge will vary according to the water level, the influence of immersing the structure in water should be discussed. The immersing effect is considered using the Morison equation [16].  
The Morison equation is a semiempirical equation for calculating the force acted on a body under oscillatory flow. There are two parts in the Morison equation, an inertia force that depends on the acceleration of the surrounding flow and a drag force that depends on the velocity of the surrounding flow. Two empirical hydrodynamic coefficients are needed when the Morison equation is adopted: an inertia coefficient (\(C_{m}\)) and a drag coefficient (\(C_{d}\)). These two coefficients depend on the Keulegan-Carpenter number, Reynolds number, and surface roughness [17]. [PERSON] and [PERSON] [18] proposed the added mass ratio method (AMRM) according to the Morison equation, by which a rational method was inducted for calculating the added mass and was verified with the finite element method. In this article, AMRM was adopted, and the added mass (\(\Delta m_{cir}\)) calculation equation is described as below:  
\[\Delta m_{cir}=\rho_{cou}\cdot\frac{\pi D^{2}}{4}\cdot p_{cir}(H,D), \tag{2}\]  
Figure 8: Sectional mesh of caisson in this study.  
Figure 7: Sectional mesh of pier in this study.  
where \(\rho_{con}\) represents the density of concrete, and the added mass ratio \(p_{cir}(H,D)\) of each cylinder section is calculated as below:  
\[p_{cir}(H,D)=[0.0133\ln(H)-0.112]\times\ln(D)+0.0002H+0.4, \tag{3}\]  
in which \(H\) means the cylinder height, and \(D\) means the diameter of the cylinder section. Because the bridge piers of this study were tapered, their added mass was calculated from the pier bottom for every 1 m interval, and then the calculated added mass will be applied to each point, as shown in Figure 9.
###### Abstract  
Objective: To analyze the efficacy of transcutaneous electrical nerve stimulation (TENS) on parotid glands in patients with xerostomia. Material and Methods: A simple, blinded, randomized, longitudinal, and prospective study was performed with 50 patients with xerostomia, divided into two groups; 25 patients received TENS and 25 received a sham treatment. The treatment group took part in three sessions with TENS that lasted 15 min each for three weeks. The production of saliva was measured through sialometry at the start and end of the treatment. The perception of xerostomia was assessed with the Visual Analog Scale (VAS-X) and the Xerostomia Inventory (XI), also at the beginning and the end. The impact on oral health (OHIP-14) was assessed in the first and third sessions. Results: A progressive improvement was observed after three treatment sessions. With respect to the initial VAS scores, these significantly decreased from the start (\(7.52\pm 1.92\)) to the end (\(6.84\pm 1.84\); \(p<0.001\)). The sialometry values significantly increased, showing an increase in the salivary fluid from the start to the end of the treatment (\(p<0.001\)). With respect to the Xerostomia Inventory (XI) (Thomson), the scores decreased from the initial 36.88 \(\pm\) 7.78 to 35.60 \(\pm\) 7.42 after the sessions (\(p=0.01\)). No significant changes were observed in the OHIP-14 in patients treated with TENS. Conclusions: The TENS therapy is presented as a promising and non-invasive alternative for the management of xerostomia, as it is able to objectively increase the salivary flow and improve the subjective perception of mouth dryness.  
Keywords:saliva; transcutaneous electrical nerve stimulation; dry mouth; treatment Article
# An Energy-Efficient and Secure Routing Protocol for Intrusion Avoidance in IoT-Based WSN
## 5 Network Model and Discussion
### Average Communication Cost  
Figure 10 illustrates the comparison of ESR protocol with other protocols in terms of average communication cost under varying nodes density topologies and in the presence of malicious nodes. Remarkably, with a varied number of nodes and malicious nodes, a network led to traffic jamming and network disconnections, which resulted in increases in communication cost and transmission interruption. However, based on Figure 9, it is seen that ESR protocol reduced the communication cost in the comparison of other schemes especially when network load increased. ESR protocol had achieved an average of 29% reduction in communication cost under varying nodes topologies. This was due to making use of neighbor's information, and in the selection process of cluster heads only a limited number of nodes contributed. Moreover, the re-election for cluster heads was re-called based on the demand and network analysis. Furthermore, support of data security and reliability, ESR protocol decreased the number of route re-transmissions, which resulted in shortening the communication cost.  
Figure 9: End-to-end delay in varying nodes density topologies.
## 1 Introduction
Nevertheless, there is a serious concern that a negative voltage is required to control the on/off operation of the transistor, which is incompatible with conventional CMOS electrical level. Currently, to create positive voltage-controlled DSAs for better digital compatibility, novel realization schemes have been proposed in [30; 31]. However, it affects the signals in the path and introduces resonance points at extremely low-frequency, which is not expected. Therefore, a more efficient form of positive voltage supply is highly demanded. Figure 1: Typical schematics of attenuators (**a**) T-type; (**b**) \(\pi\)-type; (**c**) switched-path; (**d**) distributed. To address the above-mentioned issues, a novel positive voltage-controlled 6-bit DSA with low loss and high accuracy is proposed and designed in this paper, which mainly consists of several different structures, including improved simplified T-type, improved \(\pi\)-type, and switched-path. In contrast to conventional passive attenuators, the major contributions of the proposed DSA are as follows: (1) an improved and simplified T-type structure is utilized in place of the 0.5/1/2 dB attenuation units to provide low insertion loss and accurate phase compensation while maintaining a compact layout. (2) Aiming at the simplified T-type structure, the phase transfer characteristics as well as mathematical expressions for various compensation types at different frequencies are investigated and characterized for the first time, including the forms of series tail capacitor and shunt bypass capacitor. Moreover, the applicable attenuation range of the modified structure and the corresponding limiting factors are also analyzed and indicated. (3) the transfer function of the \(\pi\)-type unit is analyzed and the corresponding mathematical expressions are given to reveal the mechanism of bandwidth expansion. (4) In addition, the level shifting structure based on direct-coupled FET logic (DCFL) circuit is proposed to realize the positive voltage control of PHEMT transistor, which is favored to avoid a complex control signal loading scheme and exhibits a simple structure and fast speed with extremely low power consumption. Ultimately, this paper demonstrates a 10-20 GHz 6-bit DSA using 0.15-\(\upmu\)m GaAs pHEMT technology.
## 1 Introduction
Reliability in AE source localization can be enhanced by combining iterative and closed-form methods i.e., of non-iterative nature. In order to avoid local convergence and improve the level of noise tolerance, the latter method can be used to estimate the initial value of the iterative calculations [33]. In addition to time domain methods based on the use of the least-square technique [34; 35], those based on Bayesian filters revealed to be an efficient alternative thanks to the simplicity of their implementation [36]. The aim of this paper is to present results related to the use of the extended Kalman filter (EKF) technique applied on AE signals in order to monitor the creation and propagation of a crack propagating in thin aluminum plates (as shown in Figure 1). Conventionally, AE based SHM techniques use the first arrival time of AE signals detected at multiple receiving points to locate the damage. This approach works relatively well for simple metallic structures, when the group velocity \(\left(V_{g}\right)\) of the involved guided wave(s) and the arrival time \(\left(t_{i}\right)\) at sensors' locations are known. In general, the arrival time of the detected elastic waves and their related velocities are two uncertain parameters, where uncertainties can be caused by random and systematic errors [13; 37; 38; 39; 40; 41; 42]. These uncertainties can reduce the reliability of source localization approaches in particular in the case of an automatic damage detection. To overcome the limitations related to the abovementioned uncertainties, several methods have been proposed for damage or impact location [13; 37; 43; 44; 45; 46; 47; 48]. EKF was used in passive sensing in order to estimate AE source location in guided wave conditions, where the AE coordinates and guided waves velocity were treated as unknown gaussian variables [13]. On the other hand, the Kalman filter was also used with the triangulation method to track multiple AE sources created successively. Despite the good accuracy of this method (i.e., in the case where AE sources occur in a straight line), results become less accurate as soon as the AE events move away from the straight line [49]. This paper presents the application of EKF in the case of AE sensors to locate impacts created using the pencil lead breaks procedure. After this calibration procedure, EKF is applied in situ when samples are submitted to a mechanical tensile test. Initial values were taken as uniformly distributed random variables. Analyses are performed on the basis of the generated Lamb waves during the creation and propagation of the cracks, which do not evolve as a straight line. In such a case, results show that by taking into account uncertainties, the localized AE sources are in accordance with the experimental results.
# Robust Hybrid Beamforming Scheme for Millimeter-Wave Massive-MIMO 5G Wireless Networks
## 5 Complexity Analysis  
In this section, the complexity of the algorithms is compared and analyzed under conditions given in Section 4 [28; 29]. The OMP hybrid beamforming algorithm in [20] utilizes the error matrix of pure digital matrix and hybrid beamforming matrix to iteratively update the hybrid beamforming matrix from the candidate set. Its algorithm complexity is mainly calculated by the candidate set and the error matrix. Correlation determines and selects the appropriate analog beamforming vector with a complexity of \(\boldsymbol{O}(\boldsymbol{N}_{t}^{2}\big{(}\boldsymbol{N}_{t}^{\text{RF}} \big{)}^{2}\boldsymbol{N}_{s})\). The MD-HP algorithm in [24] uses phase information of analog matrix \(\boldsymbol{F}_{\text{RF}}\) to update the process of digital matrix \(\boldsymbol{F}_{\text{BB}}\), which involves the pseudoinverse of the analog matrix. Its algorithm complexity is \(\boldsymbol{O}(\boldsymbol{N}_{t}^{2}\big{(}\boldsymbol{N}_{t}^{\text{RF}} \big{)}^{2})\). The proposed Alt-MMSE algorithm is different from the above two algorithms. The phase information of analog matrix \(\boldsymbol{F}_{\text{RF}}\) is obtained by matrix \(\boldsymbol{F}_{\text{BB}}\), and the analog beamforming matrix is constructed. The algorithm complexity is mainly from Algorithm 1, Step 5, for calculation of the equivalent matrix, so the complexity of the proposed Alt-MMSE algorithm is \(\boldsymbol{O}(\boldsymbol{N}_{t}\boldsymbol{N}_{t}^{\text{RF}}\boldsymbol{N }_{s})\). In the actual system, since \(\boldsymbol{N}_{t}>\boldsymbol{N}_{r}\gg\boldsymbol{N}_{t}^{\text{RF}}\geq \boldsymbol{N}_{s}\), the proposed Alt-MMSE algorithm has lower complexity than that of the OMP [20] and MD-HP [24] algorithms. Figure 9 compares the number of flops required for algorithms under an increasing number of BS antennas. As can be seen from Figure 9, the proposed Alt-MMSE algorithm requires a smaller number of flops as compared with existing algorithms and has similar complexity performance with that of the optimal digital  
Figure 8: Bit error rate (BER) comparison of different algorithms under various SNR levels.  
precoding algorithm. This clearly indicates that the proposed algorithm was simpler to implement than the traditional algorithms, and it is robust in performance. Table 2 summarizes the complexities of the algorithms.