esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Device Modeling Technique Based on MLP Neural Network for Model Parameter Extraction
## 5 Conclusions  
In this paper, a novel device modeling technique based on NNs for the optimal extraction of device model parameters is proposed and verified with the BSIM-SOI model. This technique does not require developers to manually implement physics-based analytical models, which vastly accelerates the developing of parameter extraction software. The development of the program related to the physics-based analytical models in the model  
Figure 14: IV fitting curves of the second set of measured data: (**a**) \(\rm I_{ds}\)–\(\rm V_{ds}\) curves with T = 25 \({}^{\circ}\)C, W = 800 nm, L = 500 nm; (**b**) \(\rm I_{ds}\)–\(\rm V_{gs}\) curves with T = 25 \({}^{\circ}\)C, W = 800 nm, L = 500 nm.  
Figure 13: IV fitting curves of the first set of measured data: (**a**) \(\rm I_{ds}\)–\(\rm V_{ds}\) curves with T = 0 \({}^{\circ}\)C, W = 654 nm, L = 200 nm; (**b**) \(\rm I_{ds}\)–\(\rm V_{gs}\) curves with T = 0 \({}^{\circ}\)C, W = 654 nm, L = 200 nm.  
parameter extraction software takes about six months, while the development of the NN model takes less than one week.  
The MLP neural network used in this paper obtained 4.14% in overall training error, with 3.83% in the non-subthreshold and 4.77% in the subthreshold regions. The NN model has good generalization ability. The overall test error is 5.38% of more than four thousand IV curves, with 6.89% in the subthreshold and 4.79% in the non-subthreshold regions. The trained NN model is used for parameter extraction, and the extraction error is less than 6%. Compared with the embedded SPICE, the extraction speed of the NN model is thousands of times faster.  
Conceptualization, H.K., Y.W. and L.C.; methodology, H.K.; software, H.K.; validation, H.K.; investigation, H.K. and Y.W.; data curation, H.K.; writing--original draft preparation, H.K.; writing--review and editing, H.K., Y.W. and X.Z.; supervision, Y.W.; funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Research on Modeling, Analysis and Optimization Technologies for ULP Circuit (E1 GW047002).  
Not applicable.  
The first author, H.K., hereby acknowledges the Institute of Microelectronics of Chinese Academy of Sciences (IMECAS) and the EDA Center.  
The authors declare no conflict of interest.
## 2 Materials and Methods
to keep the same settings of the reference data while changing the measured data meshes to achieve consistency in the comparisons; therefore, the area of interest remained the same for all the following comparisons and only the measured data were swapped. In order to superimpose the IOS mesh over the reference mesh, an \"initial alignment\" procedure was conducted. For a precise overlapping of the interest area, the \"best fit alignment\" procedure was executed (Figure 5). The \"3D Compare\" function of the metrology software presented the standard deviation results between the measured and reference data via projecting all paired points onto the reference data. The \"3D Compare\" function also rendered a color-coded map displaying the deviation patterns of the investigated surfaces between \(\pm\)0.05 mm (50 \(\upmu\)m). Outward displacement is presented on the color-coded map towards the red spectrum, inward displacement is presented towards the blue spectrum, and the green areas where there is less than \(\pm\)1 \(\upmu\)m difference indicate no deviation (Figure 6). The described protocol was conducted on all the measured data meshes of the in vivo and in vitro cases by comparison to their respective reference data to obtain the trueness values. To obtain the precision values, the same protocol was conducted by comparing each measured data mesh with all the other meshes within its group. The organized trueness and precision values were uploaded into the MedCalc statistical software to conduct the statistical analysis. The Kolmogorov-Smirnov test for normality was conducted on the whole set of data and indicated that the trueness values of both in vitro and in vivo cases were parametric while the precision values of both in vitro and in vivo cases were non-parametric. The nonparametric data set underwent Kruskal-Wallis test analysis and post-hoc analysis (Conover test) was also executed. The parametric data  
Figure 4: Isolated area of interest on the reference mesh of the in vivo case. Figure 5: (**a**) Reference mesh of the in vivo case. (**b**) Measured data mesh of the IOS scan. (**c**) Super-imposition of the two meshes. set underwent a one-way ANOVA test and the Student-Newman-Keuls test for all pairwise comparisons was also executed. The level of significance was set to \(\alpha=0.05\).
# Differential Signalling in Free-Space Optical Communication Systems
###### Abstract  
In this paper, we review the differential signalling techniques and investigate its implementation of in free-space optical (FSO) communication systems. The paper is an extended version of our previous works, where the effects of background noise, weak turbulence and pointing errors (PE) were investigated separately. Here, for the first time, we present a thorough description of the differential signalling scheme including for combined effects. At first, we present an extension of the analysis of differential signalling to the case of moderate to strong atmospheric turbulence. Next, we investigate a more general case where both channel turbulence and PE are taken into consideration. We provide closed-form expressions for the optimal detection threshold and the average bit-error-rate, and present a set of numerical results to illustrate the performance improvement offered by the proposed differential signalling under various turbulence and PE conditions.  
free-space optical communication; differential signalling; atmospheric turbulence; pointing errors; optimal signal detection; NRZ-OOK +
Footnote †: journal: Applied Sciences
# A Self-Calibration of Capacitor Mismatch Error for Pipeline ADCs
## 2 Proposed Self-Calibration Technique
A block diagram of the 1.5-bit pipeline stage and its transfer curve for the stage's analog input and output voltages are shown in Figure 3, according to which the digital output is \"01\" and the MDAC output is twice the input when the input amplitude is within \(\pm\mathrm{V_{REF}}/4\). Generally, the capacitor mismatch is small, and \(\mathrm{V_{E}}\) remains within \(\pm\mathrm{V_{REF}}/4\) even after several amplifications. Therefore, suppose the digital output is \"01\" even with no sub-ADC. In that case, the 1.5-bit stage operation is performed whenever \(\mathrm{V_{E}}\) is amplified by a fact of two. For self-calibration, redundant stages are required to accurately measure the mismatch errors generated by the front pipeline stages. For example, suppose the capacitor mismatch error that occurs in the first stage is converted to the remaining stages in a 12-bit pipeline ADC that performs a 1-bit or 1.5-bit conversion per stage. In that case, the converted error voltage has only 11-bit accuracy, which is insufficient for correction. At least two redundant stages must be added to increase the accuracy by more than two bits. As previously explained, amplification of the small-error voltage by a factor of two corresponds to the addition of one redundant stage. In other words, the accuracy of the error voltage is increased to 13 bits by amplifying the error voltage twice before it is converted by the remaining stages, which allows self-calibration with no additional circuitry. Calibration must only be performed for a few MSB stages according to the capacitor-matching characteristics provided in the CMOS process and proceeds in the reverse direction from the rear stage. A comparison of the dynamic performance of a 12-bit pipeline ADC before and after calibration is shown in Figure 4. In the simulations, only capacitor mismatch was considered, and calibration was performed for the first two stages. The mismatch between  
Figure 3: Block diagram of a 1.5-bit pipeline stage (a) and its transfer curve (b). \(\text{C}_{\text{S}}\) and \(\text{C}_{\text{F}}\) was assumed to be 0.5% (\(\varepsilon\) is 0.005) based on the technology information. As shown in the simulation results, the mismatch calibration effectively improved the ADC performance. SNDR and SFDR increased from 59.9 dB to 73.9 dB and 62.3 dB to 93.6 dB, respectively. The dynamic performances were also compared according to the number of redundant bits or stages. As shown in Figure 5, the performance improved with the number of redundant bits and saturated at 2 or more bits. Therefore, in this study, the number of redundant bits was determined to be two. Figure 4: Simulated dynamic performances of a 12-bit pipeline ADC: (**a**) Before calibration. (**b**) After calibration. Figure 5: Comparison of the dynamic performance according to the number of redundant bits: (**a**) No calibration. (**b**) No redundancy. (**c**) 1-bit redundancy. (**d**) 2-bit redundancy.
## 4 Experiments, Results, and Discussion
### Performance Evaluation
#### 4.4.4 Discussion  
Our proposed FL-based intrusion detection model is more effective for the following reasons.  
* By using FL instead of conventional ML methods, IIoT devices are able to provide data that is both more secure and require less bandwidth for transmission. Less bandwidth is required as clients do not share whole data but only the learnings of their respective local models are shared.
* The massive amounts of private and secret information are no longer accessible through a single server. This ensures the privacy of the users and the security of the data.
* Due to the local representation of the models, devices are able to independently predict and recognize network anomalies even when they are disconnected. So, in case of any disconnection, local clients are still able to train their models and detect any kind of intrusions.
* As the number of FL rounds progressed, we achieved similar intrusion detection accuracy to centralized ML models. This is due to the fact that after each round, the models' performance gets enhanced by the learnings from all the client-end learnings, and they perform as accurately as a centralized model.
## 4 Experiments and Analysis  
### Experimental Environment Configuration  
Table 1 outlines the specific parameters configured for the experimental setup. The SGD optimization strategy was utilized, with the training process consisting of a fixed 300 epochs. The input image scale was uniformly altered to 640 \(\times\) 640 pixels, and the learning rate was tuned to 0.001. A batch size of 16 was selected for efficient training. To mitigate overfitting and strengthen the model's generation, mosaic data augmentation was employed during in training stage, whereas the mix-up method was deliberately omitted from the augmentation strategy.
# An Overview of the Public Health Challenges in Diagnosing and Controlling Human Foodborne Pathogens
## 6 Control of Foodborne Microbial Pathogens
### Phytobiotics  
A phytobiotic is an element that is non-nutritive and has antibacterial, anti-inflammatory and transcriptional modulating properties [52]. As phytobiotics contain a large number of pharmacologically active chemicals, they have been suggested as a promising therapeutic option [286]. Phytocompounds have the ability to bind free radicals, inhibit xenobiotic activation enzymes and activate detoxication enzymes. These compounds have antiviral, antimicrobial and immunomodeling properties, in addition to being able to stimulate appetite and act as flavors [287]. A study by [PERSON] et al. [288] evaluated both pre- and post-harvest effects of plant-derived essential oils against _Campylobacter_ in poultry production chains, including carvacrol, thymol and cinnaldehyde. _Campylobacter jejuni_ counts were reduced by 2.4-4 logs by washing broiler chickens' skin with a 2% carvacrol suspension, as this essential oil has been proposed as an antibacterial wash treatment for postharvest poultry. Likewise, lactic acid, citric acid and citrus extract have been shown to be effective against _Escherichia coli_ O157 in vitro and in a model rumen system, and this cocktail may be useful in controlling this pathogen in animals [289].
# Experimental Study on the Torsional Behaviour of Prestressed HSC Hollow Beams
## 3 Global Analysis of the Experimental Results
### Torsional Moments vs. Twists
Hence, the calculation of the balanced ratio of the longitudinal to transverse reinforcements should account for the area of the prestressed steel (\(m_{b,tot}=(A_{sl}+nA_{sp})s/A_{st}u\) with \(n=E_{p}/E_{s}\)), which leads to an excess of longitudinal reinforcement of about 20%. Therefore, the transverse reinforcement should yield before the longitudinal reinforcement, as observed in Figure 8.  
Table 3 presents, for each tested beam, the main properties of \(T\)-\(\theta_{m}\) curves, namely--the cracking torque and correspondent twist (\(T_{cr}\) and \(\theta_{cr}\)), the torsional stiffness in State I (\((GC)^{\text{I}}\)), the torsional stiffness in State II (\((GC)^{\text{II}}\)), the torque corresponding to the yielding of the transverse reinforcement and correspondent twist (\(T_{ty}\) and \(\theta_{ty}\)), the resistant torque (peak torque) and correspondent twist (\(T_{r}\) and \(\theta_{T_{r}}\)). Since the yielding of the longitudinal reinforcement occurs after the peak torque, the corresponding values are not presented. The torsional stiffness in State I was calculated dividing \(T_{cr}\) by \(\theta_{cr}\) (with \(\theta_{cr}\) in radians unit). Prior to the calculation of the torsional stiffness in State II, the equation of the line of \(T\)-\(\theta_{m}\) curve in the linear elastic stage was previously calculated from linear interpolation. For this calculation, the points of the \(T\)-\(\theta_{m}\) curves located in the zone that can be identified as belonging to State II were selected. Only the zone of the curves that is approximately a straight line was considered. After the calculation of the equation, \(T=a\theta+b\) (see Table 3), the stiffness \((GC)^{\text{II}}\) is equal to the slope \(a\) of the line (with twists converted to radian units). The analysis of the values displayed in Table 3 confirms the trends observed in the \(T\)-\(\theta_{m}\) curves from Figure 8 and previously discussed.
## 3 Results
### Analysis of the Driving Factors of Farmland Abandonment
#### 3.3.3 Abandoned Farmland Accessibility Analysis  
The accessibility of transportation affects farmland in several ways, such as facilitating the migration of agricultural labor, influencing the circulation of agricultural products, and shaping the spatial distribution of abandoned farmland. As the agricultural workforce continues to age, farmland located further from field roads is more likely to be abandoned. Therefore, the distribution of field roads plays a critical role in agricultural production. To assess the relationship between farmland abandonment and transportation accessibility, we calculated the Euclidean distance to roads in the study area and divided it into five concentric buffers, each with a 50 m interval: 0-50 m, 50-100 m, 100-150 m, 150-200 m, and greater than 200 m. Figure 8a shows the distribution of perennial abandoned farmland at different distances from the road, While Figure 8b illustrates the distribution of seasonal abandoned farmland. By combining the vector map of abandoned farmland with the road buffer zones, we calculated the proportion of farmland in each buffer zone relative to the total farmland area, as well as the abandonment rate within each zone Figure 8c. The distribution of abandoned farmland in relation to road proximity reveals that approximately 39% of both perennial and seasonal abandoned farmland is located within 50 m of roads.  
Figure 7: Irrigation water system and abandoned farmland overlay distribution, as well as the ratio of cultivated land area and abandonment rate within the buffer zone. (**a**) Perennial abandonment of farmland. (**b**) Seasonal abandonment of farmland. (**c**) Cultivated land ratio and abandonment rate.  
Additionally, 25% of the abandoned farmland is situated between 50 m and 100 m from roads. In total, more than 60% of abandoned farmland in the study area is located within 100 m of field roads. This suggests that transportation accessibility in the region is moderate, and while it does play a role, it is not the primary factor driving farmland abandonment.
# PC5-Based Cellular-V2X Evolution and Deployment
## 5 PC5-Based C-V2X Deployment in Taiwan
### V2V and V2N Field Testing  
Cellular infrastructure provider Nokia and its Partner research institutes in Hsinchu have been in cooperation to test C-V2X since 2018. Our contribution is to integrate all the systems with the most advanced technologies to prove C-V2X technology workable. The test involves autonomous driving vehicles RSU/OBU, which were both provided by Nokia. RSU and OBU were developed based on Qualcomm 9150 Chipset, which gives optimum performance. PC5-based C-V2X network topology is showed in Figure 9.  
The test steps are described below:  
1. One RSU is installed to extend the test vehicle's field of vision. The RSU transmits predefined road information such as pedestrian detection to OBU by PC5-based C-V2X.
2. OBUs are installed in the test vehicles to receive RSU information as well as between OBUs via PC5-based V2V application.
3. RSU/OBU information is transferred back to \"Traffic Control Centre\" by 4G LTE cellular network as a V2N application.  
V2V PC5 interface communication performance is evaluated below in Figures 10 and 11.  
From the V2V test result in Table 2, the average latency is 30 ms, which indicates that the device involved with PC5-based C-V2X has better performance than the Rel-14 standard defined maximum latency of 100 ms for V2X applications.  
Figure 10: Vehicle1 Performance.  
Figure 9: C-V2X Network Topology.
## 5 Comparative Evaluations
### Performance Limitations  
The proposed method has two limitations: (i) the accuracy of frame synchronization due to the number of reference frames depending on the signal length and (ii) limited embedding capacity due to the frame length of the LP analysis.  
For (i), the evaluation in Section 4.2 shows that the accuracy of frame synchronization depends on the number of reference frames. At least 128 reference frames are required for frame synchronization accuracy to exceed 80%, which means the frame window length of 20 ms would require at least 5.12 s for the host signal.  
For (ii), we used a frame window length of 20 ms for the proposed method as the commonly used analysis length for LP analysis. In that case, the number of frames per second is 50. Since the proposed method embeds one bit in two frames, the embedding capacity is limited to 25 bps. Generally, the bit rate of 4 bps is sufficient for speech watermarking. When speech watermarking is applied to tampering detection and spoofing detection, the precision of the tampering detection and spoofing detection depends on the bit rate. If the precision of 0.125 is required, a bit rate of at least 16 bps is needed.  
These limitations are due to the fact that the frame length is 20 ms. Reducing the frame length to shorter than 20 ms should lead to an increase in the number of frames, which in turn will lead to an increase in the embedding capacity and the number of reference frames.
## 4 Snake Robot Ethical Issues in SAR Response Phase
### Ethical Dilemmas Identified in the SAR Response Phase with Snake Robots  
1. Should snake robots be deployed in the SAR response phase given their potential to save or endanger lives depending on the situation?
2. What level of the snake's autonomy is appropriate to use, and how does that impact SAR operations?
3. Will the type and degree of human-robot interaction be important, and if yes, what kind and degree of interaction should be used in certain circumstances?
4. Will an autonomous snake robot receive the same treatment as a human rescuer?
5. Who will be held accountable if the snake robot behaves irresponsibly or malfunctions when interacting with the victim?
6. When given rights, what kind of rights should the snake robot have?
7. Will the snake robot be accepted by society for use in SAR response phase operations?
8. Is it possible to put adequate trust in snake robot operations?
9. Will society have higher expectations than what a snake robot is capable of?
10. How to handle the victim's sentiments and emotions as they are being managed by the snake robot?
11. Will snake robot behaviour be reliable and able to gain victims' trust?
12. Will snake robots carry out various tasks safely and with care?
13. Will the information gathered by the snake robot during the operation be stored securely?
14. Who has access to the data and how may it be utilised later?
15. Will data collection adhere to privacy laws?  
Some of the ethical problems and dilemmas mentioned early may be seen as design problems that could be resolved by integrating ethical standards into the design during the innovation process [44]. Along those lines, [PERSON] et al. and [PERSON] et al. [13; 42] used the \"Value Sensitive Design\" (VSD) approach for this. VSD method incorporates the ethical values of the robot or stakeholders (humans, rescue team, authorities, etc.) into the design throughout the development process. In this article, we will use a similar approach to figure out different design issues based on the mentioned ethical challenges above.