esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
A 5-30 Gb/s receiver analog front-end (AFE) cascading transimpedance amplifier (TIA) and continuous-time linear equalizer (CTLE) were implemented using a Taiwan Semiconductor 180 nm process. The system comprises a two-stage differential input pair CTLE, TIA, and a differential termination resistor \(R_{\mathrm{m}}\). A source-degenerated transconductance stage was adopted in the CTLE, and source follower and shunt feedback resistor stages were adopted in the TIA. The proposed CTLE could achieve high frequencies by altering the tail current with fixed degenerate capacitance \(C_{\mathrm{S}}\) and resistance \(R_{\mathrm{S}}\). The proposed AFE achieved high bandwidth, and the use of a feedback resistor \(R_{\mathrm{f}}\) and inductor \(L_{\mathrm{f}}\) improved its high-frequency performance. Simulation results revealed that the CTLE can compensate for 16 dB of channel loss at a 3 GHz Nyquist frequency and can open closed eyes in a 6 Gb/s non-return-to-zero signal with a bit error rate of 0.16 \(\times\) 10\({}^{-12}\) for a 2\({}^{31}-1\) pseudorandom binary sequence input. The AFE could compensate for 12 dB of channel loss at a 15 GHz Nyquist frequency and can open closed eyes in a 30 Gb/s PAM4 signal from a pseudorandom binary sequence input; it consumed 27 mW of power at 1.8 V.  
continuous-time linear equalizer; transimpedance amplifier; pulse amplitude modulation; no-return-to-zero; analog front-end; source follower +
Footnote †: journal: Electronics Letters
# Accelerated Correction of Reflection Artifacts by Deep Neural Networks in Photo-Acoustic Tomography
## 4 Results
### Experimental Setup
#### 4.1.4 Evaluation Metrics  
For the evaluation of image quality, we used the peak signal-to-noise ratio (PSNR) and SSIM in our experiments. The SSIM has been defined in (5) and PSNR is defined as follows:  
\[\text{PSNR}(\mathbf{I}_{\text{est}},\mathbf{I}_{\text{IS}})=10\log_{10}\Big{(}\frac{R^ {2}}{\text{MSE}(\mathbf{I}_{\text{est}},\mathbf{I}_{\text{IS}})}\Big{)}, \tag{7}\]  
where \(R\) is the maximum possible pixel value of the images, which is 1 in this study as the images are normalized into \([0,1]\).
## 3 Results and Discussion  
### Chemical Profile  
Table 1 reports the amounts of phenolics and flavonoids in the tested extracts, as well as the results of phosphomolybenum assay. Total phenolic content was highest for the flower extracts, followed by the leaf extracts, and finally extracts from the roots. A previous investigation reported that the methanol extract of _C. officinalis_ flower possessed highest phenolic and flavonoid contents as comparedto the corresponding leaf extract [22]. Regarding the flavonoid content, the leaf extracts showed highest content, followed by extracts from the flower and roots (Table 1). Table 2 shows detailed chemical composition of the different extracts along with the concentration of specific compounds present. It was noted that extraction method influenced the biochemical composition of extracts. For instance, fumaric acid concentration was highest in flower extract obtained by Soxhlet extraction; catechin was identified in the flower and root extracts obtained by Soxhlet extraction only. Hesperidin, astragalin, and rutin were identified in the flower and leaves only. Hesperidin and rutin concentration were highest in the flower extracts (3.076-2.618 and 4.810-3.656 mg/g extract, for hesperidin and rutin, respectively). Caffeic acid concentration was highest in Soxhlet extracts (0.635, 0.963, and 0.734, for the flower, leaf, and root, respectively), suggesting that Soxhlet extraction might be more suitable for caffeic acid extraction. Nicotiflorin was identified in the flower extracts only while amentoflavone occurred only in the roots (Table 2). Cosmosinin was extracted from _C. officinalis_ leaves using conventional maceration and Soxhlet extraction methods. The flower extracts obtained by homogenizer assisted, maceration, and ultrasound assisted extraction showed highest total antioxidant capacity (2.14-2.08 mmol TE/g extract). In the literature, the different level of total and individual compounds in the different parts of _C. officinalis_ were reported by several authors [6; 23; 24; 25; 26]. The different level could be explained by climatic, geographical and genetic factors. Taken together, _C. officinalis_ might be regarded as a potential raw material for bioactive constituents.
## 5 Empirical Analysis and Results
### Assessment of Measurement Model
#### 5.2.1 Individual Item Reliability  
The measurement model was assessed by examining the outer loadings of each construct item [57; 64]. According to the latter, the rule of thumb retains items with loadings below 0.40; and 0.70, out of 52 items, 19 were deleted because their loading was below this threshold of 0.40. The remaining 34 items had loadings between 0.705 and 0.989 (see Table 3).
###### Abstract  
Interference detection is one of the key technologies used in satellite communication systems to cope with complex electromagnetic environments. To accommodate the power consumption and resource-constrained application scenarios of communication satellite payloads, this paper proposes an efficient differential amplitude interference detection algorithm based on non-uniform adaptive segmentation, building upon the classical forward consecutive mean excision (FCME) algorithm in the field. The proposed algorithm replaces the traditional point-based iterative approach with a segment-based iterative strategy and employs a non-uniform adaptive segmentation scheme to achieve rapid and efficient iterative updates of detection thresholds. This significantly reduces the complexity of the whole interference detection process. Simulation experiments were conducted on different types of interference signals, and the results verified the effectiveness of the proposed algorithm.  
s 2025 14 1792
## 2 Numerical Methods and Experimental Procedure
### Propagation and Attenuation  
We use 3D FEM to solve the dynamic equation, and derive the displacement field due to the wave propagation. We used an explicit integration scheme because it requires less memory space that an implicit scheme. It is faster and better suited to problems such as the present study, where the frequency band of interest necessitates a huge number of elements to discretise the geometries. Moreover, temporal increments need to be small for the explicit scheme to be stable. Consequently, it is well suited to modelling transient waves. To calculate signals as close as possible to the experimental signals, we have to consider damping. In the present study, we use Rayleigh damping, for which the damping matrix is a linear combination of mass and stiffness matrices:  
\[[C]=\ \alpha[M]+\beta[K] \tag{1}\]  
The Rayleigh coefficients \(\alpha\) and \(\beta\) are linked to the loss factor \(\kappa\) and the damping coefficient \(\xi\) as follows:  
\[\kappa=2\xi=\ \frac{\alpha}{\omega}+\beta\omega \tag{2}\]  
Table 1 shows a numerical value of the loss factor for aluminium for a specific frequency (500 kHz). We do not have access to values of \(\kappa\) at several frequencies, so it not possible to deduce a value of \(\alpha\) and \(\beta\). We thus choose to use only \(\alpha\), which is set to 700, as it gives a less frequency-dependent damping.  
The FEM simulations are implemented with the commercial software ABAQUS(Dassault Systemes Simulia Corp., Providence, RI, USA) using first order hexahedral elements with reduced integration (C3D8R). The validity of the results with the model demands a careful choice of the size of the elements and the time step of the simulation. The typical size of an element (\(l_{e}\)) is:  
\[l_{e}=\frac{\lambda_{min}}{R} \tag{3}\]  
where \(\lambda_{min}\) is the minimum wavelength of interest and \(R\) a resolution parameter that must be between 5 and 20. The minimum wavelength is:  
\[\lambda_{min}=\frac{c_{R}}{f_{max}} \tag{4}\]  
with \(f_{max}\) the maximum frequency of interest and \(c_{R}\) the Rayleigh wave velocity.  
If damping is considered, the critical time step is:  
\[\Delta t_{lim}=\frac{2}{\pi f_{max}}\left(\sqrt{1+\xi_{max}^{2}}-\xi_{max}\right) \tag{5}\]  
where \(f_{max}\) is the highest eigen frequency of the system and \(\xi_{max}\) the damping coefficient at the highest eigen frequency. Therefore, one should know the highest eigen frequency in order to calculate the critical time step, which, in practice, is not trivial. However, considering the time needed by the fastest wave (compressive wave) to cross a single element, an approximate value of the critical time step is:  
\[\Delta t_{lim}\simeq\frac{l_{e}}{c_{p}} \tag{6}\]  
where \(c_{p}\) is the compressive wave speed.  
The ABAQUS software automatically calculates the time-step, which is inferior to the critical time step:  
\[\Delta t_{lim}\simeq\frac{l_{e}}{c_{p}}=\frac{400.10^{-6}}{6407}=1.62.10^{-6}s \tag{7}\]  
Table 2 shows the values used for spatial and temporal discretisation in the present study. The total simulation window is 100 \(\upmu\)s.
# Automatic Feature Construction-Based Genetic Programming for Degraded Image Classification
## 3 The Proposed Approach
### Accompanying Evolution
#### 3.3.2 Replacement Strategy  
In the replacement strategy, to determine the appropriate timing for substituting some redundant individuals with accompanying individuals, the parameter \(q\) is introduced, as defined in Equation (3),  
\[q=\begin{cases}q+1&\text{if }f_{\text{best},g}\leq f_{\text{best},g-1}\\ 0&\text{otherwise}\end{cases} \tag{3}\]  
where \(f_{\text{best},g}\) denotes the fitness value of the best solution in the population at the \(g\)-th generation, and \(q\) represents the number of times the best individual in the population fails to update.  
During the evolutionary process, if the population's \(f_{\text{best}}\) fails to update, the algorithm determines that the population's evolution has stagnated, leading to an increment in the value of \(q\). When \(f_{\text{best}}\) updates successfully or when \(q\) reaches the predetermined threshold \(T\), the algorithm resets the value of \(q\) to zero.  
When \(q\) reaches the designated threshold \(T\), set at 5 in this study, for the first time, the algorithm selectively removes a certain proportion of redundant individuals from the population and selects individuals from \(A_{1}\) for replacement. The number of individuals selected follows Equation (4),  
\[\Gamma_{1}=\beta\times P_{\text{redundant}} \tag{4}\]  
where \(P_{\text{redundant}}\) represents the number of redundant individuals identified in the current population, while \(\Gamma_{1}\) denotes the number of individuals selected from A1. \(\beta\) is a fixed proportionality coefficient, set at 0.25 here.  
When the value of \(q\) reaches the threshold \(T\) again, the algorithm evaluates whether the previous replacement strategy was effective. If replacing individuals with those from \(A1\) has led to an improvement in the fitness of the best individual, the algorithm will continue with this strategy. Otherwise, individuals from \(A2\) will be selected. At this point, the number of individuals chosen follows Equation (5),  
\[\Gamma_{2}=\min((\beta\times P_{\text{redundant}}),P_{\text{updated}}) \tag{5}\]  
where \(P_{\text{updated}}\) represents the number of individuals in the population whose performance has successfully improved, while \(\Gamma_{2}\) denotes the number of individuals selected from \(A2\). Equation (5) ensures that the number of individuals chosen from \(A2\) does not exceed the upper limit of accompanying individuals in \(A2\). By using Equations (4) and (5), the appropriate quantity of replacements is determined to substitute the redundant individuals in the population. When the fitness growth of the best individual in the population slows down or even stagnates, this will assist the algorithm in exploring better individuals.
## 5 Conclusions and Suggestion for Future Work  
This paper proposes an optimized framework for AV through cooperation communication in order to enhance the connectivity for vehicular communication to achieve efficient and reliable road traffic communications. The proposed model is based on an optimization model in terms of finding the best position of the inter-vehicles to efficiently communicate with AV or to relay messages between autonomous vehicles and everything. Furthermore, based on this optimization model, the system performance is evaluated in terms of the reliability and effectiveness of the proposed model. It has been shown that the autonomous vehicle best position and the optimum transmission distance between V2V and V2I in a vehicular network can vary and change under certain environmental conditions such as outage probability, transmission power and path loss exponent, vehicle mobility, vehicle density and vehicle speed. The results presented in this paper can be used to form an adaptive intelligent transmission system, where each autonomous vehicle, based on the  
Figure 15: Packet loss rate (PLR) vs. number of vehicles at variable vehicles’ velocity.  
changing environmental conditions, will be able to decide which the appropriate inter-vehicle to communicate with is. Additionally, this enables determination of the optimum required transmission distance between AV2V and V2X to achieve the preferable QoS in a dynamic way, in terms of achieving the highest throughput, packet delivery ratio and fairness index with the lowest possible packet loss rate and average delivery latency, which makes this proposed model a very promising solution for future development of vehicular communication systems. The proposed model is built based on assumption and network parameters that may change due to channel conditions. The model is suitable for choosing one relay node, but multiple relay nodes should be considered in future work to show what system enhancement can be achieved. Furthermore, the proposed model can be used for traffic control and course plotting for AV in an urban area demanding continuous course correction, leading to a decrease of AV accidents by obtaining continuous reliable and efficient information. However, further investigation is needed for more practical related applicability assessment, as several aspects can deteriorate the system performance, like distance detection proximity sensor dependency, crossings/bridge road changes in maps, and interference with power line towers, etc.  
Conceptualization, R.A.O. and A.K.A.; methodology, R.A.O.; software, R.A.O.; validation, R.A.O.; formal analysis, R.A.O. and A.K.A.; investigation, R.A.O. and A.K.A.; resources, R.A.O. and A.K.A.; data curation, R.A.O.; writing--original draft preparation, R.A.O.; writing--review and editing, R.A.O. and A.K.A.; visualization, R.A.O.; supervision, A.K.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare
## 3 Results and Discussions
### Baseplate Effect  
In this section, the final cantilever deformations after cutting all the supporter legs were compared with the cases of the cantilevers attached on the artificial supporter and the baseplate during the heat treatments. Figure 7 shows the final deformations of the cantilevers with various thicknesses after the aging heat treatments. The deformations shown in Figure 7a are from the cantilevers that were aging heat treated while they were attached on the artificial supporters made by the maraging steel, whereas in Figure 7b, the deformations of the cantilevers attached to the medium-carbon steel baseplate directly by the legs during the heat treatments are shown. The results shown in Figure 7 evidently indicate that the aging heat treatment with the medium-carbon steel baseplate does not alter the residual stress state compared with the case of the aging heat treatment of all the maraging steel components. As can be seen in Figure 7, the deformations observed in the cantilevers that were heat treated on the maraging steel artificial supporters depend only on the thickness of the cantilever, but not on the baseplate or the artificial supporter on which they were attached during the heat treatment. This behavior is clearly indicated by Figure 7, where the cantilevers heat treated with the steel baseplate show almost identical deformations compared with the cantilevers heat treated with the artificial supporters.  
The results shown in Figure 7 indicate that the stress relieving heat treatment of the maraging 18 Ni-300 steel components produced by the LPBF can be performed while they are attached to carbon steel baseplates, without any special consideration of the thermal strain misfit effect due to the dissimilar material joint between the maraging components and the steel baseplate. This is probably because the stress relievi  
Figure 7: Comparisons of deformations caused by cutting supporter legs, between cantilevers heat treated with maraging supporter and directly on steel baseplate. (**a**) Cantilevers heat treated with maraging supporter and (**b**) heat treated directly on steel baseplate.  
can occur at a relatively low aging temperature of 500 \({}^{\circ}\)C. At such a low temperature, the carbon steel is in a ferritic state since the temperature is below the A1 temperature and stays in the same state while cooling to room temperature after the aging heat treatment. The marging steel is expected to be in the same ferritic state in the temperature range between 22 and 500 \({}^{\circ}\)C since the austenite transformation start temperature of the LPBF-processed marging steel is about 620 \({}^{\circ}\)C [30]. Therefore, it is expected that both the maraging steel component and the carbon steel baseplate will be in the ferritic states during the aging heat treatment and cooling without any significant phase transformation-induced volumetric change.
# From Bottom of Sea to Space: Quo Vadis IoT? So What About Security?
## 3 Security Challenges and Mechanisms
### IoT in Space
#### 3.5.3 Internet of Satellites  
Some research work has studied the Internet of Satellites in the area of anti-jamming technology as smart jamming has become more of a threat in recent years. The authors of [210] use the Stackelberg game and reinforcement learning to minimize the cost of creating an efficient anti-jamming protocol. To do this, the authors create the problem as a Stackelberg game for anti-jamming. The scheme for anti-jamming is broken into two parts. To find the best routing that is available, the authors use a deep reinforcement learning routing algorithm. To make fast decisions, a fast response anti-jamming algorithm is implemented. The scheme uses these methods together to make the best decision for anti-jamming. The scheme shows low routing cost and solid anti-jamming protection when tested.  
In our survey, we have not identified any published research focusing on the security of this extension.
# Comparative Accuracy Analysis of Truck Weight Measurement Techniques
###### Abstract  
Roads and bridges are designed to meet the transportation demands for traffic volume and loading. Knowledge of the actual traffic is needed for a rational management of highway infrastructure. There are various procedures and equipment for measuring truck weight, including static and in weigh-in-motion techniques. This paper aims to compare four systems: portable scale, stationary truck weigh station, pavement weigh-in-motion system (WIM), and bridge weigh-in-motion system (B-WIM). The first two are reliable, but they have limitations as they can measure only a small fraction of the highway traffic. Weigh-in-motion (WIM) measurements allow for a continuous recording of vehicles. The presented study database was obtained at a location that allowed for recording the same traffic using all four measurement systems. For individual vehicles captured on a portable scale, the results were directly compared with the three other systems' measurements. The conclusion is that all four systems produce the results that are within the required and expected accuracy. The recommendation for an application depends on other constraints such as continuous measurement, installation and operation costs, and traffic obstruction.  
truck; weight; measurements; WIM; B-WIM; portable scales; truck weigh station Article
# Demystifying MLOps and Presenting a Recipe for the Selection of Open-Source Tools
## 4 Workflow Stages in MLOps
### Data Management Phase
#### 4.2.3 Data Quality  
Assessing the quality of a data set is a complex and multidimensional problem [23]. According to defined production workflows, data are produced by the product, e.g., a sensor. An evaluation of the actual production workflow, the produced data quality, and a comparison of the assessed quality with similar products is carried out by a domain expert in order to pinpoint data error sources, quality assurance, compliance procedures, and data processing flowcharts [23].  
There are multiple factors to the data quality dimensions as mentioned in [16], such as, for example, completeness of data in which problem-related missing values are considered and must be addressed. According to the authors of [17], completeness, data format and structure (e.g., consistency), accuracy (e.g., realistic values), and timeliness are the cases of the intrinsic quality dimension. To compute a data quality dimension score by applying the feasible metrics, other representational (e.g., interpretability or ease of understanding) or contextual quality dimensions like reputation or relevancy can also be measured. Additionally, ML-related quality dimensions like data dimensionality reduction, data heterogeneity, or data duplication are considered according to authors in [42].  
Regardless of whether the data is applied in an ML pipeline, most scenarios depend upon automated data quality checks and perform them by either incremental or batch processing of the new data.
## 4 Case Study
### Virtual Sensor Fault-Detection Performance Metrics
#### 4.3.3 Recall  
Recall indicates the proportion of actual faulty data that the virtual sensor correctly identifies as a fault. Recall ranges from 0 to 1. The proportion of FN decreased as the recall increased. The recall is calculated using the following equation:  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{12}\]
## 6 Discussion and Implications
### Implications  
#### 6.2.1 Theoretical Implications  
This study provides an important theoretical contribution in that it analysed the impact of the use of VR technology on the effectiveness of safety training using the TAM as a theoretical basis. By developing a theoretical research model, we were able to overcome the lack of a theory-based approach to the topic of training transfer, which was pointed out by [PERSON] [11]. Our TAM-based research model could be extended to other training/education contexts wherein a new technology is emerging as a training tool.  
By analysing the survey data using a SEM-based approach, we were able to understand how the use of VR helps improve the effectiveness of VR safety training. Prior studies that simply compared the results of different training methods failed to explain or provide implications on how to improve effectiveness of a newly introduced method. However, the results of our study could be used for developing and further improving VR safety training content to increase the training effectiveness.  
Finally, we utilize the concept of telepresence, which is the key characteristic that distinguishes VR technology from other media. By analysing the effect of the two main dimensions of telepresence, vividness, and interactivity, we were able to understand how each dimension affects the trainee's perception toward the training. The results of our study showed that although both vividness and interactivity are important, the vividness of the VR directly affects both the perceived usefulness and ease of use for the trainee, and these are critical factors that affect the effectiveness of the training.