esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel STDM Watermarking Using Visual Saliency-Based JND Model
## 5 Experimental Results and Analysis
### Experiment of Robustness with SSIM = 0.982  
The test images were watermarked with a uniform fidelity, a fixed structural similarity index (SSIM) of 0.982. SSIM is a measure of the similarity of two indicators of the image. The overall SSIM image quality index for two images is computed by averaging the SSIM index \(SSIM(x,y)\) values computed for small patches of the two images [35].  
\[SSIM(x,y)=\frac{(2\overline{xy}+C_{1})(2\sigma_{xy}+C_{2})}{(\overline{x}^{2}+ \overline{y}^{2}+C_{1})(\sigma_{x}^{2}+\sigma_{y}^{2}+C_{2})} \tag{19}\]  
where \(\overline{x}\) and \(\overline{y}\) represent the mean of the luminance of the carrier image and the watermarked image, \(\sigma_{x}\) and \(\sigma_{y}\) represent the variances of the luminance of the carrier image and the watermarked image, and \(\sigma_{xy}\) represents the covariance of the above both.  
Figure 3 shows the response to additive white Gaussian noise. The STDM-AdpWM and STDM-RW perform significantly worse because of the mismatch problem. Our proposed scheme did not exceed 15% for the Gaussian noise with variance \(1.5\times 10^{-3}\)\(v\). Our proposed scheme has average BER values 2% lower than the LSTDM-WM. Obviously, our scheme outperforms others in the noise-adding-attacks in particular.  
The sensitivity to JPEG compression is demonstrated in Figure 4. From the robustness results, the STDM-AdpWM performs significantly worse. The STDM-RW performs worse than the LSTDM-WM. Our proposed scheme, on the other hand, has average BER values 3% lower than the STDM-RDWMm and 2% lower than the LSTDM-WM. Our model outperforms other models in robustness against JPEG compression.  
As shown in Figure 5, all the schemes, except STDM-RW, do have robustness to volumetric scaling due to the mismatch problem within the watermark embedding. Although the other three algorithms showed passable robustness to this attack, our proposed scheme has the best performance.  
Moreover, robustness of the above schemes is tested under some common attacks with the \(SSIM=0.982\) and the results are depicted in Table 1. From the average values, we can see our scheme outperforms other schemes and has good robustness for some common attacks.  
Figure 3: BER versus Gaussian noise for different watermarking algorithms with SSIM = 0.982.
## 4 Conclusions and Future Outlook  
In this work a transportable prototype QCL-based CM-CLaDS instrument designed for open-path sensing of atmospheric N2O concentration was developed. The CM-CLaDS system is based on molecular dispersion spectroscopy and provides sensitive and selective remote detection of N2O (1.2 ppmv\(\times\)m/Hz\({}^{1/2}\) at ambient conditions). The laboratory and field tests performed with this instrument confirmed unique properties of CLaDS technique _i.e._, baseline-free nature and no dependence of CLaDS signal on received optical power with only minor effect on SNR. The prototype sensor was deployed in the field setting to perform remote monitoring of atmospheric N2O. With only 250 \(\mu\)W of optical power being emitted towards retroreflector, the CM-CLaDS system enabled detection of N2O concentration changes at single ppbv levels. The operation of the sensor was not interrupted despite large (>20 dB) transmission changes caused by the atmospheric water condensation and several precipitation events. A cross-comparison measurement with another N2O sensing instrument confirmed the consistent performance of CM-CLaDS and its capability of reliable long-term open-path trace-gas detection. It is worth noting that the CM-CLaDS 2f signal amplitude is directly proportional to molecular number density and does not require any power normalization or baseline correction. This is a significant advantage over other optical techniques such as LAS or WMS, which rely on accurate normalization with respect to the received optical power and can suffer from baseline and/or signal drifts due to laser intensity variations or photodetector nonlinearities. This particular property of CM-CLaDS assures strong immunity to changes in environmental conditions and provides better long-term stability of the system.  
Figure 10: N2O concentration data measured using the CM-CLaDS instrument (concentration data in gray, 30 s average of data points recorded in 2 s intervals) and the WMS sensor (red, 30 s average of data points recorded in 0.1 s intervals) [32].  
The performance of the present system can be improved by using optical frequency shifters with larger (\(\sim\)1 GHz) frequency shift for optimum signal enhancement and better AR coatings for fringe noise suppression. Application of a more powerful QCL laser and a more sensitive photodetector can additionally improve the CLaDS SNR by increasing the carrier-to-noise ratio at the input of the FM demodulator, thus reducing demodulation noise. A long-term deployment of the CM-CLaDS system that will focus on environmental studies of N\({}_{2}\)O concentration, effects of precipitation on N\({}_{2}\)O emission, and on seasonal trends is currently being planned.
## 5 LPWAN Application Requirements
### Security and Privacy  
The security requirements for LPWAN devices are particularly stringent because of the massive number, vulnerabilities, and simplicity of the devices. The essential attributes of authorization, authentication, trust, confidentiality, data security, and non-repudiation need to be supported. The security support should be able to handle malicious code attacks (such as worms); handle hacking into LPWAN devices and system; and manage eavesdropping, sniffing attacks, and denial of service (DoS) attacks [8]. It is also important to protect the device identity and its location privacy from the public. Additionally, it should also support security for the forward and backward transmission as required in various applications.
# A Portable Optical Sensor for Microplastic Detection: Development and Calibration
## 3 Results and Discussion  
We used commercially purchased microplastics for this study to ensure consistency and reliability. The microplastics were analyzed directly on a glass slide, thereby demonstrating the versatility of the method and the optical sensor's capacity to detect microplastics under various conditions without the need for a special collection process.  
Three stages of evaluation were used to ensure accuracy. Initially, the system was tested in an uncontrolled environment to evaluate its adaptability to external factors. Second, laboratory testing was conducted under controlled conditions to assess baseline performance and identify issues. Finally, we test the sensor in a controlled environment and assess its robustness and stability in real-world conditions.  
The prototype systematically scans each sample, acquiring 2000 data points per LED, with a total testing time of 20 s. This balanced approach ensures optimal resolution and efficiency. While augmenting the number of data points enhances resolution, it also extends operating time. Once collected, the data is stored in CSV format for later analysis with various software tools.  
The system supports partial automation, enabling users to configure and run multiple samples with different settings. The GUI facilitates user interaction by allowing users to select specific parameters and LEDs. However, the process is not fully automated, as manual intervention is required to select particular parameters and LEDs. Future developments aim to enhance the system's automation capabilities, making it more user-friendly and efficient for large-scale applications.  
Overall, the meticulous testing process and careful design of the sensor ensure its reliability and accuracy in detecting microplastics, making it a valuable tool for environmental monitoring and research.  
The following protocol must be followed to operate the sensor:  
* Personal protective equipment (PPE): wear clean lab coats, gloves, and safety goggles;
* Disinfect work surfaces: wipe down all work surfaces, including benches, tables, and equipment, with a disinfect solution (e.g., 70% isopropyl alcohol or a bleach solution);
* Microplastic-handling tools: clean tools (tweezers, scissors, etc.) by rinsing them with distilled water and wiping them with a lit-free cloth soaked in a disinfect solution;  
Figure 12: The figure shows the microscope mode interface, which showcases control options for precise X- and Y-axis motor positioning via dropdown menus. Features include buttons for capturing and saving images (“Snap”! and “Save Snap”!), alongside initialization and configuration options (“Initialize” and “Set Microscope”).  
* Control samples: Use control samples or blanks to verify the absence of external contamination. Handle these samples with care to avoid introducing contaminants.
# Insights into Native Fermentation Process of Apples (_Malus domestica_) in Low Sodium Conditions
## 3 Results
### Color Analysis
The strong positive correlations between the level of lactic acid and that of total polyphenols and flavonoids (r > 0.9) indicate that fermentation could have the potential to increase the antioxidant and antiradical activity of apples. Also, the enzymatic hydrolysis processes of complex carbohydrates (pectic substances, hemicellulose and, to a lesser extent, cellulose) influence the physical parameters of apple tissue such as firmness and cohesiveness, which are closely correlated (r = \(-0.778\div-0.964\)) with the accumulation of bioaccessible biologically active compounds such as polyphenols and flavonoids, thus increasing their availability [48]. Thus, the entire dynamics of the fermentation process can be explained: as the firmness and cohesion of apple tissue decrease (r = 0.857 \(\div\) 0.899), the fermentative biochemical processes intensify, leading to the accumulation of acidity due to the reduction in fermentable carbohydrates (sugars and uronic acids) and to the permanent exchanges between organic compounds and the microbiota in apple pulp and brine solution. The values of the Pearson coefficients emphasize that the type of salt used for spontaneous fermentation of apples does not significantly influence the physicochemical profile of the products [49]. To identify the degree of similarity of apple samples fermented with the three types of salts, the unweighted average method on pairs and the Pearson coefficient as a measure of similarity were used, without imposing a fixed number of clusters. The grouping was performed taking into account the level of chemical compounds present throughout the fermentation period (sugars, uronic acids, lactic acid, biologically active compounds), but also the physical parameters firmness and cohesiveness. The analysis classifies the samples throughout the fermentation period into three groups (Figure 7). The first group contains all apple samples from the first 2 fermentation stages: 7 and 14 days, regardless of the different brine solution used. Apples pickled for 3 weeks form cluster 2, and those from the final weeks (28 and 35 days of fermentation, respectively) are all grouped in the third cluster. This distribution demonstrates that the type of brine used for apple fermentation--NaCl, KCl or MgCl\({}_{2}\)--does not greatly influence the dynamics of the fermentation process. After each 7-day pickling stage, the chemical composition and texture of the apples do not differ greatly. Therefore, it can be stated that replacing NaCl-based brine with MgCl\({}_{2}\) and KCl represents, in each case, a good alternative in obtaining pickled apples by spontaneous lactic fermentation [50; 51; 52].
## 2 Theoretical Background
### Fault Classification Model
#### 2.2.3 Fully Connected Layer  
The fully connected layer first integrates multi-dimensional data into one-dimensional data. There may be more than one fully connected layer, and the layers are usually connected by weights, as shown in Figure 1.  
Assume that the neurons of the one-dimensional input layer after high-dimensional data integration are p\({}_{1}\), p\({}_{2}\), and p\({}_{3}\). The nodes in the middle layer are \(a_{i}^{(l)}\), which represents the activation values of the \(i\)-th neuron in layer \(l\). \(w_{i,j}^{(l)}\) represents the weight values of the connection parameter between the \(j\)-th neuron of layer \(L\) and the \(i\)-th neuron of the next layer. \(b_{i}^{(l)}\) is bias item of the \(i\) neuron of layer \(l\). It can be seen from Figure 1 that the output of layer \(L\) neurons is the input of layer \(L+1\) neurons. According to Figure 1, the following expression can be obtained:  
\[a_{i}^{l}=\sum\limits_{j=1}^{n}w_{i,j}^{(l-1)}a_{j}^{l,-1}+b_{j}^{l,-1} \tag{6}\]  
The above process is also called the coding process, which reflects the internal characteristic information of the original signal. The last hidden layer outputs the most advanced features of the input signal. The classification result of the whole network is related to the selection of parameters in the model. In order to further improve the robustness of the model, the feature quantity with large inter-layer correlation is extracted as the subsequent processing object to eliminate the influence of interference factors on the results, as shown in Equation (7).  
\[a_{i}^{l}=\left\{\begin{matrix}0&a_{i}^{l}\leq 0\\ a_{i}^{l}&a_{i}^{l}>0\end{matrix}\right. \tag{7}\]
## 4 Results and Discussions
### Optimization of sCO\({}_{2}\)-BC Parameter  
The above-discussed results reveal that the cycle's performance was susceptible to the operating parameters associated with sCO\({}_{2}\)-BC, i.e., compressor inlet temperature \((T_{1})\), compressor inlet pressure  
algorithm was carried out to optimize the cycle's operating conditions and maximize the cycle efficiency. The following deductions were made from the study:  
* The performance of the cycle modules significantly affected the overall performance of the cycle. A 2% variation in the efficiency of turbines or compressors changed the cycle's performance by 1%. Simultaneously, a 2% variation in the effectiveness of both recuperators affected the cycle's performance by 3%. Thus, the cycle's efficiency was most sensitive to the performance of the recuperator.
* The sensitivity of the cycle's performance to the recuperator's effectiveness increased with a rise in \(x\). Simultaneously, it decreased with an escalation in the values of the isentropic efficiencies of the turbomachinery components.
* The value of \(\eta_{cyc}\) was susceptible to the cycle's operating parameters if the compressors' inlet conditions were close to carbon dioxide's critical point. Consequently, the optimization results suggested placing the compressor away from the critical point to ensure better off-design performance.  
Conceptualization, K.A.; Data curation, A.A.; Formal analysis, B.B.; Investigation, K.A.; Methodology, Y.A., A.M.; Resources, K.A., Y.A.; Software, A.M., B.B.; Writing--original draft, K.A.; Writing--review & editing, K.A., A.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the privacy policy of the organization.  
The authors declare no conflict of interest.
## 1 Introduction
### Related Works  
For performance evaluation, with the increase in the complexity of communication systems/algorithms, statistical analysis based on ordered RVs in a given distribution becomes increasingly important. However, this task is difficult compared with that in conventional communication systems. Note that, even if the original unordered RVs are independent, the ordered ones are dependent, owing to the inequality relations among them, making it challenging to analyze the joint statistics. It is much more difficult to obtain the target statistical result, that is, the probability density function (PDF) or moment generating function (MGF) of the partial sums of ordered RVs, compared with the sum of all ordered RVs or the best-ordered RVs. Therefore, an approximated or a simulation approach is often used [25, 26, 27, 28]. To derive the exact closed-form expressions of the target statistics, several attempts have been made to convert dependent ordered RVs to independent unordered ones by applying the spacing method [29], the conditional PDF [30], and a successive conditioning approach [31]. However, these methods are applicable to only some simple special cases. In [32, 33, 34], although the statistical results for some types of partial sums considering all RVs were mentioned, the statistical results for partial sums of the best RVs have not been considered.  
Fortunately, owing to the approach proposed in [35, 36], where unified analytical frameworks were introduced to determine the required target joint statistics of the partial sums of ordered i.i.d. and i.n.i.d. RVs by extending the interesting results in [32, 33, 34], we can systematically derive the joint statistics of any case of the partial sums of ordered statistics in terms of the MGF and PDF, which are extremely important equations for performance analysis in communication theory.  
All the previous results dealt with closed-form statistical results only for the Rayleigh distribution case. However, in this study, the Gamma distribution is considered, which is mathematically challenging. Note that the Gamma distribution is related to the Nakagami-\(m\) distribution in that both are viewed as a generalization of the Chi-squared distribution. Specifically, the Nakagami-\(m\) distribution exhibits a central Chi-squared distribution with \(2m\) degrees of freedom [37]. This distribution is classically known as a versatile fading model because it is appropriate for modeling multipath fading in urban [38] and indoor [39] environments with a wide range of fading severity via the fading parameter, \(m\), including the Rayleigh and Rice distributions. Owing to the significance of the performance analysis of the Nakagami-\(m\) (or Gamma) distribution, considerable research in this regard is still actively underway [27; 40; 41; 42; 43; 44].
# Implementation and Evaluation of Activity-Based Congestion Management Using P4 (P4-ABC)
## 5 P4-Based Implementation of ABC
### Egress Control Flow  
The egress control flow sends any received packet. In addition, it copies the egress port specific queue length to the corresponding register.
# Evaluation of Tip Loss Corrections to AD/NS Simulations of Wind Turbine Aerodynamic Performance
## 6 Simulation Results
### Results of Glauert-Type Corrections
#### 6.1.2 Comparison with BEMT Results  
The Glauert tip loss correction was originally proposed for BEMT. As applied to AD/NS simulations, the correction factor \(F\) works in a different way. It is worth to mention that the specific difference in the final results would be caused by the transplantation from BEMT to AD/NS. In order to perform a comparison, BEMT computations under the same conditions of the present study are conducted.  
The comparisons were shown by percentages, which represent the relative change of blade loads before and after using a tip loss correction,  
\[\delta_{n}=\frac{F_{n,No}-F_{n,Gl}}{F_{n,No}}\times 100\%, \tag{58}\]  
\[\delta_{t}=\frac{F_{t,No}-F_{t,Gl}}{F_{t,No}}\times 100\%, \tag{59}\]  
where \(F_{n,No}\) and \(F_{n,Gl}\) are the normal forces obtained from computations with no tip loss correction and with the Glauert correction (the standard Glauert correction in BEMT and the Glauert-type corrections in AD/NS), respectively, \(F_{t,No}\) and \(F_{t,Gl}\) are the corresponding tangential forces.  
The above parameters are calculated independently using BEMT and AD/NS. The results are shown in Figure 7 where the Glauert-BEMT denotes the results for the standard Glauert correction used in BEMT, and the Glauert-A/B/C denotes the results for the Glauert-type corrections used in AD/NS. A certain degree of difference between the Glauert-BEMT and the Glauert-A/B/C results is observed. The Glauert-C again shows the best performance, since it is generally most consistent with the Glauert-BEMT, which means the transplantation of the tip loss correction from BEMT to AD/NS  
Figure 6: Normal force \(F_{n}\) and tangential force \(F_{t}\) along the _NREL 5 MW_ blade.
## 4 Results
### Analysis of Factors' Relative Importance  
Figure 5 reports the importance of the two investigated factors in generating the results reported in Figure 4. The analysis thus provides insight into which of the two building blocks composing the proposed approach (the data management block and the model learning block) would most affect the final results if changed. More specifically, the results reported in Figure 4 were provided as input to a random forest regression model, and the importance of each factor on the results was estimated according to the methodology described in [45].  
The results show that the data management block was more important in generating the final results when considering the _RMSE_ and _MAE_ metrics, with the data management stage accounting, respectively, for 85% and 73% of the final results. In contrast, the model learning block was more important when considering the results related to the _SMAPE_ error, in which the importance of the data management block was only 39%.  
Figure 4: Percentage of times that a predictive approach results in the one reporting the lower error over the considered forecast.
## 5 Major Test Results and Findings
### Flutter in Uniform Wind  
Due to the presence of self-excited vibration, the response of the deck in uniform wind is not zero, as shown in Figure 19 a,b, respectively. The vertical and torsional RMS of deck responses at the mid-span and closure in the service state were not higher than 0.464 m and 0.040\({}^{\circ}\), respectively, for all sections and wind directions concerned, up to a wind speed of 58.24 m/s (Flutter text wind speed). Furthermore, the aerodynamic instability phenomena did not occur up to a wind speed of 75 m/s, showing that the flutter critical wind speed of service state is much higher than flutter text wind speed, indicating that the bridge has sufficient flutter stability margin in uniform wind. Furthermore, the vertical response of each measure points is the largest when the wind attack angle is \(-3^{\circ}\), indicating that the bridge is more susceptible to negative wind attack angles. In addition, the response is largest in 3-3 cross-section, which corresponds to the vertical mode's maximum value above. In terms of torsion, the bridge is more susceptible to positive attack angles, and the 2-2 cross-section's response is largest when the attack angle is \(+3^{\circ}\), which corresponds to the maximum value of the torsion mode above. Therefore, it can be inferred that, because the maximum position of vertical and torsional response of asymmetric bridge is not in the same section, the vertical and torsion are not easy to couple, so as to improve the flutter critical wind speed.  
Figure 19: The root mean square (RMS) value of the displacement of each measure point of the service state in uniform wind. (**a**) Vertical; (**b**) torsional.  
Figure 18: The root mean square (RMS) value of the displacement of mid-span and closure of the service state in uniform wind. (**a**) Vertical; (**b**) torsional.