esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Case Study
### Denial of Service (DoS) Attack
#### 5.3.2 Missing Data (MD) Attack  
The real application of missing data attack and mitigation is completely similar to DoM attacks, which use detection from DC current sensors as a measurement. In the event of a missing data attack, as shown in Figure 16, the actual current showed a significantly different response from the prediction value, i.e., there will be no current output in actual measurement, especially when the contingency occurs. Instinctively, the power system operator should mitigate this problem to avoid large mismatches of balancing arrangement control. A similar explanation of the proposed algorithm in the previous section would increase balancing arrangement stability.  
Furthermore, a noticeable difference could be seen in VSC current response. As mentioned in the previous study, mitigation control was enhanced through the VSC line reflected in the ramp-rate deviation value, which is calculated from the actual current between two lines as described in Equation (9). Moreover, when missing data attack mitigation control is activated, there will be complexity due to the characteristic of different maximum power rating values for each line. Adjustment of additional power through VSC is also important, such as increment, especially when additional power through the LCC line is limited at a certain value, i.e., zero power response. The effect of the proposed methodology is shown through frequency nadir enhancement value in the Jeju Island system.  
As illustrated in Figures 22 and 23, the active power from LCC and VSC showed remarkable differences in terms of balancing arrangement operations. When missing data occur and create zero response after a contingency operation, this situation showed the worst condition because there were huge differences in the power balancing of the HVDC line after the first HVDC interconnection trip. However, balancing arrangement is not always the mismatches of the amount of active power. The fast response is also important to maintain the frequency of nadir operation. This is clearly explained through active power description; when the active power response is attacked, the frequency nadir will lower than 59 Hz and the load shedding must be activated. However, when mitigation operation is activated, the adaptive response from VSC#3 control cannot help much to avoid load shedding. This situation could describe the maximum power rating in the VSC#3 line.  
As illustrated in Figures 24 and 25, it is important to explore mitigation control more advanced with other ancillary services when a missing data attack has occurred. As mentioned earlier, even though the mitigation operation is activated, the frequency is still lower than the standard value, i.e., 59.185 Hz due to the limitation of the maximum power rating in the VSC#3 line.  
Figure 21: Mainland frequency in the delay of measurement attack operation.  
Figure 23: VSC active power response in the missing data attack operation.  
Figure 24: Jeju Island frequency in the missing data attack operation.  
Figure 22: LCC active power response in the missing data attack operation.
## 4 Platform Design
### Communication Protocol Design
Furthermore, in Figure 1(b), we see that for a network traffic made of one heartbeat per hour per sensor node and a gateway window of slightly less than six hours, we achieve very low false positives for fields up to about 1000 nodes. This is realistically large, since we expect at most a few hundred nodes per gateway in practical deployments. Furthermore, a six-hour delay in reporting missing nodes is considered suitable for scheduling network maintenance operations. Figure 2: Simulation results of the field communication protocol for design space exploration. The rate of the missing node erroneous reportsincreases with the number of nodes and with the heartbeat period and lowers with the increase of the heartbeat listening window of the gateway (**a**); missing nodes can be detected in less than 6 h with a negligible rate of erroneous reports, for a 1 h heartbeat period (**b**). Thus, the main characteristics of the communication protocol can be summarized as:  
* under normal conditions, the sensor nodes send one heartbeat per hour;
* fire alert messages are sent with much higher frequency (every few seconds) to ensure their timely propagation, even at the cost of losing concurrent heartbeat messages. The communication channel capacity can support the alert traffic well, since, assuming no collisions, 1000 nodes sending one 0.3 s heartbeat message per hour each occupy at most 8.3% of the channel capacity;
* the gateways can detect a missing node in less than six hours. This simple protocol satisfies the requirements of the application well. It does not need a receiver on the sensor nodes, which primarily lowers node cost and energy consumption. Moreover, without a receiver, the operation of the sensor nodes is defined only by their own programming. This means that the energy consumption for each node in normal conditions is predictable, and we calculate it in Section 4.2. In the alert state, the node will spend much more energy in order to propagate in a timely manner the alert messages to the gateways, which can significantly reduce its energy reserves. However, this is not an issue, since the heat of a fire will very likely destroy the node shortly after detection.
## 2 Theoretical Discussion
### Dynamic Factor Calculation Methods Outlined
#### 2.3.3 Trackside Measurement Based Calculation Method (DAF\({}_{\text{Field}}\))  
Dynamic behavior analysis of track system, especially wheel load, should not only consider the static load but should consider the sum of static load and dynamic effect; the coefficient for the dynamic effect of the wheel load values obtained from field measurement data is used to calculate the wheel load fluctuation coefficient.  
The reduction of the wheel weight occurs due to the vibration effect (e.g., hunting oscillation motion of the vehicle). At this time, the ratio of the static wheel load (\(V_{static}\)) to the wheel weight reduction value (dynamic load subtracted by static load) is called the wheel load fluctuation ratio. Wheel load fluctuation is calculated with the following equation:  
\[\text{{Wheel load fluctuation coefficient}}=\frac{\text{{Dynamic Load} \left({V_{Dynamic}}\right)-Static\ Load \left({V_{static}}\right)}}{V_{Static}} \tag{10}\]  
Dynamic factor is influenced by the total spring constant of track, rail surface irregularities, and so on. The probability distribution of wheel load for dynamic factor analysis can be assumed as a normal distribution and shown in Figure 3 below:  
Above normal distribution has maximum probability value at the mean (m) and descends as it moves away from the mean (m) and inflect x = m \(\pm\)\(\sigma\). In addition, as it moves away from the average,  
Figure 3: Wheel load fluctuation calculation method and parameter based on standard deviation.  
the probability approaches 0, and the distribution curve and the area enclosed by the x-axis represent the probability. In this paper, the dynamic factor is calculated by considering the values of the range inclusive of t = 3 parameter (\(m\pm 3\sigma\)). Trackside measurement dynamic factor is calculated based on the standard deviation of dynamic wheel load (wheel load fluctuation), in which case can evaluate the level of the wheel-rail interaction force and the amplification level of the dynamic load acting on the track structure. The calculation method is as follows:  
\[DAF_{Field}=1+at \tag{11}\]  
where,  
_a_: the slope of the regression line of the relation between the standard deviation of wheel load fluctuation and speed of the train vehicle  
_t_: reliable area (within Figure 3) determining the inclusion range of the wheel load fluctuation coefficient standard deviation
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 5 Critical Analysis and Open Research Challenges
### Trade-Off Between Performance and Computational Complexity  
AI-based methods, including DL and RL [233, 221], have shown great potential in achieving adaptive and intelligent ICO. However, these solutions frequently suffer from high computational overhead, slow convergence times, and the need for extensive training datasets. Their deployment on resource-constrained edge devices remains a challenge, especially in time-sensitive applications such as URLLC [213, 214].
## 4 Results and Discussions
### Validation of the Mechanical System
#### 4.1.3 Case 3--DC-DC Buck Circuit  
In Figure 11, the power curves as a function of frequency generated by the DC-DC buck converter circuit, LTC-3588-1, are presented. The circuit's output power was evaluated by varying the excitation frequency in 5 Hz increments.  
In Figure 11, it is observed that the system generated a power of 4.53 mW and 1.38 mW for the PZT-5H and P5-13B transducers, respectively. The Simulink graph shows higher density in the power peak regions, including the system's resonance region. With this data, it is evident that the P5-13B device, due to the low power generated in this experiment, is limited in its applicability in monitoring systems.  
Table 8 presents the results of the Simulink simulation and experimental values, demonstrating the approximation between the values and highlighting the DC-DC buck converter circuit, LTC-3588-1, as an ally for the extraction and storage of piezoelectric energy. It is important to note that the IC allows voltage selection, enabling the choice of the best option for storing energy in the rechargeable battery and application. The DC-DC buck converter circuit had an output setpoint of 3.3 V.  
Table 9 presents the power densities of the PZT5H and P5-13B transducers, which have volumes of 1.368 cm\({}^{3}\) and 0.820 cm\({}^{3}\), respectively, for comparative purposes. It is observed that, despite the circuit with a resistor generating more power, the reached voltage was 21.9 V, which makes it impractical for use in TTL circuits. On the other hand, the circuit with a bridge rectifier presented lower generated power due to diode polarization, Joule effect losses, and capacitor loading.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Power** & **PZT-5H** & **P5-13B** \\ \hline Analytical & 4.50 mW & 1.35 mW \\ Experimental & 4.53 mW & 1.38 mW \\ Error & 0.66\% & 2.22\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Powers of the DC-DC buck converter circuit.  
Figure 11: Frequency vs. power curves—DC-DC buck circuit: **(a)** PZT-5H and **(b)** P5-13B.  
Considering the volume parameters of each transducer in cm\({}^{3}\) and the power density, which allows evaluating the generation per unit volume, it is concluded that the resistive circuit presented the highest current availability, but the voltage is not continuous enough to be applied to an IC. On the other hand, the LTC-3588 consumes a portion of the generated power, and although the circuit has lower power generation, it has a stable average voltage of 3 V, which is interesting for use in TTL-level integrated circuits of 5 V.
###### Abstract  
No consensus exists on how to model human articulations within MSK models for the analysis of gait dynamics. We propose a method to evaluate joint models and we apply it to three models with different levels of personalization. The method evaluates the joint model's adherence to the MSK hypothesis of negligible joint work by quantifying ligament and cartilage deformations resulting from joint motion; to be anatomically consistent, these deformations should be minimum. The contrary would require considerable external work to move the joint, violating a strong working hypothesis and raising concerns about the credibility of the MSK outputs. Gait analysis and medical resonance imaging (MRI) from ten participants were combined to build lower limb subject-specific MSK models. MRI-reconstructed anatomy enabled three levels of personalization using different ankle joint models, in which motion corresponded to different ligament elongation and cartilage co-penetration. To estimate the impact of anatomical inconsistency in MSK outputs, joint internal forces resulting from tissue deformations were computed for each joint model and MSK simulations were performed ignoring or considering their contribution. The three models differed considerably for maximum ligament elongation and cartilage co-penetration (between 5.94 and 50.69% and between \(-0.53\) and \(-5.36\) mm, respectively). However, the model dynamic output from the gait simulations were similar. When accounting for the internal forces associated with tissue deformation, outputs changed considerably, the higher the personalization level the smaller the changes. Anatomical consistency provides a solid method to compare different joint models. Results suggest that consistency grows with personalization, which should be tailored according to the research question. A high level of anatomical consistency is recommended when individual specificity and the behavior of articular structures is under investigation.  
personalization; musculoskeletal model; anatomical consistency; gait analysis +
Footnote †: journal: Applied Sciences
# An EV SRM Drive and Its Interconnected Operations Integrated into Grid, Microgrid, and Vehicle
## 2 System Configuration of the Developed EV SRM Drive  
The developed EV SRM drive with G2V/V2G/V2V/M2V/V2M functions is depicted in Figure 1. The battery-powered EV drive consists of an interleaved bilateral battery interface converter, an asymmetric converter fed SRM, a mechanically coupled DC generator (DCG) with load resistor, a flywheel (FW) mounted on the motor shaft, and a dynamic brake leg. The DCG serves as the dynamic load for the studied SRM. The FW is added to increase the stored kinetic energy for facilitating the regenerative braking operation when the motor is decelerated. The incremental encoder (EC) with (_A_,\(B\),_Z_) signals is used to detect the SRM rotor position. The resolution of the employed EC is (2500 pulse/rev).  
In driving mode, the SRM drive varied DC-link voltage is established by the battery via an interleaved bidirectional boost/buck converter. The improved motor driving performance over a wide speed range with reduced battery energy consumption can be achieved. In idle mode, the grid-connected G2V/V2G operations are conducted through the bidirectional CLLC resonant converter and the bidirectional 3P3 SW inverter. The bidirectional inverter is operated in inverter mode under V2G operation, and conversely as an SMR when conducting the G2V charging operation. With the same schematic, the  
Figure 1: System configuration of developed EV SRM drive.  
vehicle-to-home and vehicle-to-building operations are directly applicable. The critical loads normally powered by the grid can be transferred to the EV, providing uninterruptible power as the grid power failure occurs. In addition, the V2V and/M2V/V2M operations can also be conductible. The functional blocks are shown in Figure 2a,b.
## 4 Location Estimation-Based Congestion-Aware Routing Protocol (LECAR)--Detailed Proposal
### Defining Message Format and Interval
#### 4.2.3 Global Pheromone Map and Location Information Message Format  
Figure 8 represents the format for the global pheromone map and location information message. The UAVs periodically exchange this message to update the pheromone map and location information of each other. Global_Pheromone_Map and Location_Information represents the global pheromone map and location information in Figures 2 and 5, respectively.
## 3 Materials and Methods  
### Materials  
We implemented this work using MATLAB version R2020a 64-bit, The MathWorks, Inc., Natick, Massachusetts, United States, 2020. For segmentation, we utilized datasets consisting of 50 Sentinel-2 satellite water bodies and 50 medical images of dermoscopic skin lesions. The Sentinel-2 dataset was obtained from the ESA Sentinel-2 Pre-Operations Hub [5], and it was downloaded from [[https://scihub.copernicus.eu](https://scihub.copernicus.eu)]([https://scihub.copernicus.eu](https://scihub.copernicus.eu)) (accessed on 25 July 2022). The dermoscopic skin lesion dataset was sourced from the International Symposium on Biomedical Imaging database (ISBI 2016) [30]. To assess the conceptual validity of our proposed method, we specifically chose sub-datasets characterized by skewed histograms. In these sub-datasets, the Sentinel-2 images exhibited right skewness, while the dermoscopic images displayed left skewness. This selection allowed us to investigate the impact of mean estimation resulting from the Maximum Likelihood Estimation (MLE) of the Gumbel distribution definition.
## 4 Methodology
### The 9-Parameter Transformation
#### 4.5.2 Inverse Transformation  
To perform the inverse transformation for the 9-parameter transformation, Equation (64) needs to be modified by expressing the element \(\mathbf{X}_{i}\) on the left side of the equation:  
\[\mathbf{X}_{i}=\mathbf{S}^{-1}\mathbf{R}^{-1}(\mathbf{X}_{o}-\mathbf{t}) \tag{70}\]  
where matrix \(\mathbf{R}^{-1}\) is defined by Equation (54) or (55), and the inverse of the scale matrix can be easily calculated:  
\[\mathbf{S}^{-1}=\begin{bmatrix}\mu_{x}^{-1}&0&0\\ 0&\mu_{y}^{-1}&0\\ 0&0&\mu_{z}^{-1}\end{bmatrix} \tag{71}\]
# Comparing Monitoring Networks to Assess Urban Heat Islands in Smart Cities
## 3 Choice of System Components and Programming
### Considerations of the WMO
#### 3.1.1 Automatic Weather Stations
Peripheral equipment  
This equipment complements such functions of the CPU as: continuous energy supply with 12 V accumulators or batteries (which can be rechargeable or not); temporal accuracy using RTC, which keeps the clock ticking during short power cuts, thus guaranteeing accurate readings, sampling intervals and exact timings; and the system's quality control through incorporated testing equipment, which supervises the performance of the components of the AWS, providing visual information available locally or sent to the CPU for analysis, thus facilitating both predictive and corrective maintenance. 4. Auxiliary elements  
In order to ensure that the measuring devices reflect the true temperature of the environment, it is best to use radiation screens or shields as they allow the free circulation of the air, thus maintaining a uniform internal temperature equivalent to that of the surrounding air. These screens exclude radiant heat, precipitation, and other phenomena, minimizing errors and accidental damage. A screen with a high thermal conductivity, such as a metal, dark colors, and sizes smaller than 200 mm in diameter and 250 mm in height, will result in a poorer performance. There should be forced or natural ventilation to favor the convective process within the radiation screen.
# A Survey on Intelligent-Reflecting-Surface-Assisted UAV Communications
## 5 Research Challenges of IRS-Assisted UAV Communications
### UAV Energy Consumption  
Energy consumption plays an important role in the design of the entire system. However, in most studies on IRS-assisted UAV communications systems, the battery capacity of UAVs was not considered. Even when it was considered, practical scenarios and constraints were not reflected. The authors of [110] pointed out that UAV implementation requires realistic assumptions, such as payload weight, velocity, weather conditions, and UAV temperature after a certain period of working [111]. It is required to establish practical energy models for UAVs. In addition, there is a need for efficient charging techniques, as well as efficient energy usage. In particular, wireless power transfer for UAV charging is a promising area under development [112]. Wireless charging can work in a contact or non-contact manner by attaching a separate charging unit to the UAV. A charging technology based on laser forming is also a promising research topic to secure flight time [113; 114]. Hence, it is necessary to study various models of UAV energy consumption and charging technologies to ensure complete automation and flight time [115].
# Estimating Recycling Return of Integrated Circuits Using Computer Vision on Printed Circuit Boards
## 4 Experiments and Results  
### Experimental Setup  
For the development of the IC object detector, we used the PCB DSLR Dataset [10]. Initially, this dataset has 748 RBG images of WPCBs, with a resolution of 4928 \(\times\) 3280 pixels, from 165 different boards (three to five images per board). Furthermore, the integrated circuits present on the boards are all labeled, totaling 9313 components.  
The WPCBs were extracted from a recycling facility, and some WPCBs had broken parts and dust all over them. The WPCBs had different sizes and shapes, as can be seen in Figure 5. This makes the dataset very close to what is encountered in a real-world scenario. For each PCB, the dataset has images in different positions. This is important because usually, WPCBs are placed on conveyor belts with arbitrary orientation, and a computer-vision system has to perform visual inspection despite the PCB orientation.  
Figure 4: Calculated IC surface density for each package and average.  
In order to obtain the IC surface area, we needed to evaluate the dataset image scale. The silkscreen printing easily identified some IC part numbers on the package. With the measurement of the IC dimensions present in the datasheet, it is possible to calculate the real object size scale. It is important to note that the dataset authors do not provide information about the camera calibration. Table 2 shows the pixel area estimate for a particular IC (part number RTL810L).  
For all experiments, we used the pre-trained YOLOv3 provided by Darknet [16]. The fine-tune strategy was to restore the weights from all layers except the last three convolutional layers. Additionally, the YOLOv3 head was fine-tuned with a momentum optimizer and piecewise learning rate starting at \(1\times 10^{-6}\), decaying at every five epochs (decay factor of 0.96) until it reached \(1\times 10^{-6}\).  
With the augmented images dataset, 30 original WPCB images were reserved for the test dataset, and the other images were split into training (80%) and validation (20%). The metric used to evaluate the IC detection was the all-point mean average precision (mAP) [24]. Since there was a single class, the mAP was the same as the _AP_ (average precision) for the IC class, defined as  
\[AP=\sum_{n}(R_{n+1}-R_{n})P_{interp}(R_{n+1})\, \tag{4}\]  
where \(R\) is a recall level and  
\[P_{interp}(R_{n+1})=\underset{\vec{R}:\vec{R}\succeq R}{\text{max}}P\Big{(} \vec{R}\Big{)}. \tag{5}\]
## 1 Introduction
It is particularly effective in channels experiencing shadowing, fading, and hidden terminal problems. Like cooperative spectrum sensing [11, 12, 13], the sensor nodes perform spectrum sensing to determine the status of the PUs locally and send their results to the SU, which combines them using a certain rule. This feature of the sensor network is important for resource scheduling in CR-based SA system. So, we assume that the perfect detection of the presence of the PUs can be obtained through the sensor network in this paper. We first introduce a sensor network-assisted resource scheduling structure for SA in the CR condition. Then the proposed scheme is analyzed in detail in terms of performance including the factors that are important to its performance. Finally, simulations are carried out to verify the analysis on the proposed scheme. The remainder of this paper is organized as follows. Section 2 introduces the related works and Section 3 gives the system model of the proposed scheme. Section 4 designs the proposed scheme in detail. The simulation results are shown and analyzed in Section 5. Finally, some conclusions are drawn in Section 6.