esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Slangther Surveys and Physico-Chemical Analyses  
At 83 days of age, a total of 40 randomly chosen birds (10 birds from each experimental group, 2 birds for each replication) were individually weighed, labelled and processed under commercial conditions. The carcass weight was obtained by removing the head, neck, shanks, and abdominal fat from bled, plucked, and eviscerated chickens; the carcass yield was calculated. Then, breast (including pectoralis major and pectoralis minor muscles), leg (thigh + drumstick), and wing weights were recorded; cut yields were calculated based on hot carcass weight. On the right pectoral muscle (PM), pH and color were recorded at 24 h  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feed Administered a** & **SOY** & **PEA** \\ \hline Ingredients, g/kg DM & & \\ Wheat bran & 491.5 & 474.6 \\ Durum wheat & 254.2 & 262.7 \\ Corn meal & 152.6 & 127.1 \\ Fava bean & 67.8 & 67.8 \\ Pea bean & - & 67.8 \\ Soybean flaked, 37\% CP & 33.9 & - \\ \hline Total & 1000 & 1000 \\ \hline Analyzed results & & \\ DM, g/100 g & 88.9 & 89.2 \\ CP, g/100 g DM & 18.6 & 18.6 \\ EE, g/100 g DM & 4.82 & 4.45 \\ Ash, g/100 g DM & 4.97 & 4.83 \\ \hline Calculated analysis & & \\ CF, g/100 g DM & 8.16 & 8.14 \\ Lys, g/100 g DM & 0.67 & 0.69 \\ Met, g/100 g DM & 0.25 & 0.24 \\ aME, MJ/100 g DM & 1.33 & 1.32 \\ \hline Fatty acids, \% of total fatty acids & & \\ C14:0 & 0.10 & 0.09 \\ C16:0 & 15.87 & 14.60 \\ C18:0 & 1.45 & 2.33 \\ \(\Sigma\)SFA & 17.41 & 17.03 \\ C16:1 & 0.06 & 0.08 \\ C18:1n9 & 20.91 & 21.34 \\ \(\Sigma\)MUFA & 20.96 & 21.42 \\ C18:2 n-6 & 57.50 & 56.18 \\ C18:3 n-3 & 4.12 & 5.37 \\ EPUFA & 61.63 & 61.55 \\ n6/n3 & 13.95 & 10.46 \\ \hline \hline \end{tabular} \({}^{\pi}\) SOY = control diet including flaked soybean (3.39 g/100 g DM); PEA = experimental diet including pea beans (6.78 g/100 g DM); DM = dry matter; CP = crude protein; EE = ether extract; CF = crude fibre; Lys = lysine; Met = methionine; aME = apparent metabolizable energy; SFA = saturated fatty acids; MUFA = monounsaturated fatty acids; PUFA = polyunsaturated fatty acids; n6/n3 = n6 PUFA/n3 PUFA.  
\end{table}
Table 1: Ingredients, chemical composition, energy content and fatty acid composition of the diets.  
post-mortem. The pH was measured on the upper part of the left-side breast filter using a portable pH meter (FiveGo, Mettler-Toledo, Switzerland) equipped with a penetrating glass electrode. Tri-stimulus color coordinates (lightness, L\({}^{*}\); redness, a\({}^{*}\); yellowness, b\({}^{*}\)) were measured on the bone-side surface of the left-side breast filter using a Chroma Meter CR-300 (Konica Minolta B. S. Italia Spa, Milan, Italy). The left PM was vacuum packaged and frozen (\(-20\)\({}^{\circ}\)C) until chemical analysis. Water-holding capacity, expressed as expressible juice, was measured on PM 24 h after chilling using the press method [26].
# An Efficient Method to Learn Overcomplete Multi-Scale Dictionaries of ECG Signals
## 4 Numerical Results
### Sparse ECG Representation of Other Channels  
In this section, we investigate the feasibility of using the dictionary learnt on lead V4 to represent ECGs recorded in other leads. To do so, we used the constructed dictionary to model all 15 channels (leads) available for Patient 104. Let us emphasize that no signals at all from any of the other 14 leads available were used to derive the dictionary, since all samples used from all the patients during the dictionary construction stage correspond to lead V4. Table 1 displays the results for \(\lambda=1\) and \(K=2\), showing that good results are obtained in general for most of the leads. Indeed, the performance for several leads (II, aVR, V5, V6 and Vx) in terms R-SNR is better than for lead v4, which was used to construct the dictionary, and poor R-SNR results were only attained for leads aVL and Vy. Similar conclusions were obtained when considering other values of \(\lambda\) and \(K\). Overall, this shows the feasibility of constructing a single multi-scale and overcomplete dictionary (possibly using QRS complexes extracted from several leads) for multiple channels.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Channel** & **Lead** & **C-Sp (\%)** & **S-Sp (\%)** & **NMSE (\%)** & **R-SNR (dB)** \\ \hline
1 & I & 86.5245 & 11.0095 & 4.1476 & 13.8220 \\
2 & II & 83.6901 & 5.1302 & 1.8310 & 17.3732 \\
3 & III & 92.0191 & 38.1580 & 5.1706 & 12.8646 \\
4 & aVR & 85.4093 & 6.0408 & 2.5404 & 15.9510 \\
5 & aVL & 92.9162 & 61.9314 & 10.2057 & 9.9116 \\
6 & aVF & 88.1383 & 12.0894 & 3.0323 & 15.1823 \\
7 & V1 & 87.4629 & 5.5182 & 3.1652 & 14.9960 \\
8 & V2 & 90.7240 & 51.0356 & 3.0689 & 15.1302 \\
9 & V3 & 86.5196 & 40.1319 & 2.3935 & 16.2097 \\
10 & V4 & 83.1699 & 26.8689 & 2.9814 & 15.2557 \\
11 & V5 & 80.9004 & 5.1181 & 2.1296 & 16.7170 \\
12 & V6 & 81.0859 & 3.4931 & 1.6270 & 17.8862 \\
13 & Vx & 80.1663 & 4.3333 & 1.7768 & 17.5037 \\
14 & Vy & 93.0671 & 62.7804 & 11.4420 & 9.4150 \\
15 & Vz & 94.0583 & 69.9991 & 5.8457 & 12.3316 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance of the constructed dictionary (using waveforms extracted from lead V4) on other leads from Patient 104 not used to construct the dictionary.  
Figure 10: Different performance metrics: (**Left**) results on signals from training set; and (**Right**) results on signals from test set (i.e., signals not used to build the dictionary).
## 3 Use Case: Watson Analytics as AaaS to Identify HF Patients Analyzing Only the ECG Signal
### Results
#### 3.4.1 Long-Term HRV Results  
For the Long-Term HRV analysis many parameters have been spotted, having a Predictive Strength (PS) of 90% on the Target HF State, in \"single predictor\" mode. The most influent predictors are:  
- In the Time Domain: SDNN (PS = 90%), SDANN (PS = 90%), SDNNIDX (PS = 88%)  
- In the Frequency Domain: TOT_PWR (PS = 90%), ULF_PWR (PS = 90%)  
Figure 11 shows, as an example, the screenshot for the parameter TOT_PWR. It can be noted that the results are displayed as numbers, text and graphics.  
Figure 10: Screenshot from WA. On the left, the choice of the number of parameters to be used for the prediction, to balance intelligibility and prediction power.  
Figure 9: WA has automatically selected logistic regression as the best model to deal with our data.  
Increasing the number of predictors to be used for the analysis, we can find many combinations with a maximum overall PS of 92%. Figure 12 shows, as an example, the combination of TOT_PWR and LF/HF.
# Dynamical Manipulation of Surface Plasmon Polaritons
## 2 Directional Excitation of Surface Plasmon Polaritons (SPP)
### Polarization Controlled Excitation of SPP
Figure 1c schematically shows a curved plasmonic waveguide which can realize directional SPP propagation at a deep subwavelength scale [50]. The circular slit provides an extrinsic orbital angular momentum (OAM) [89; 90] to match the spin angular momentum (SAM) of incident phonon, which enables the spin-orbit interaction (SOI). Two counter propagating modes can be sustained in such a  
Figure 1: (**a**) Polarization controlled directional excitation of surface plasmon polaritons (SPPs) [48]; (**b**) Directional SPP excitation with interfacial phase discontinuity [49]; (**c**) Directing SPP flows based on spin and orbit angular momentum interaction [50]; (**d**) Wavelength dependent directional SPP excitation [79]; (**e**) Broadband directional SPP excitation with a single compact antenna [80]. Reproduced with permissions from: [48], AAAS, 2013, [49], Springer Nature, 2013, [50], ACS, 2018, [79], Springer Nature, 2011, and [80], ACS, 2015.  
curved single-mode waveguide and, considering the longitudinal angular momentum conservation, the corresponding probabilities of the two modes can be expressed as  
\[|a_{r}|^{2} =\frac{1+\sigma_{\pm}/\beta R}{2} \tag{4}\] \[|a_{l}|^{2} =\frac{1-\sigma_{\pm}^{2}/\beta R}{2}\ \.\]  
\(\beta\) is the wave vector of the waveguide modes and \(R\) is radius of the circular waveguide. When \(\beta\) satisfies the geometric dynamical condition (\(\beta=\sigma/R\)), the propagation direction of the surface plasmon is determined by the handedness of incident light. In the experiment, the operation wavelength is 1.55 \(\upmu\)m and the corresponding radius \(R\) is 500 nm. The results show that the directionality (the ratio of emitted intensity) of the subwavelength plasmonic network for both LCP and RCP light can reach 95%.
## 4 Analysis of Influencing Factors
### Effect of Sampling Density  
Keeping all other conditions the same, Figure 11 shows the inversion results of the elastic wave information when the sampling density from left to right was 3 m, 2 m, and 1 m, respectively. As can be seen from the figure, when the sampling density was 3 m, the elastic wave information collected was too sparse, resulting in rough inversion results. The stratigraphic delineation in the middle section was not clear enough in the range of 15-33 m, and even a large anomaly zone appeared in the depth range of 45-50 m. When the sampling density was 2 m, comparing the inversion results of the sampling density of 1 m, it was found that the contents of the two maps were basically the same. There was only a  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Profile** & **Depth/m** & **Pre-Pressing Speed/m/s** & **Post-Pressing Speed/m/s** \\ \hline \multirow{3}{*}{C1-C3} & 0–14 & 1800 & 1500 \\  & 14–43 & 1650 & 1650 \\  & 43–56 & 1650 & 2250 \\  & 0–14 & 1000 & <900 \\ \multirow{3}{*}{C3-C5} & 14–33 & 1700 & 1550 \\  & 33–43 & 2800 & 2800 \\  & 43–56 & 2400 & 2700 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Variation of cross-pile profile velocity before and after grouting.  
slight difference in the stratigraphic delineation in the depth range of 33-43 m. Therefore, it is suggested that the sampling density should be flexibly adjusted according to the size and accuracy requirements of the detection area and generally set to 1-2 m. When the detection area is small, the data collection points can be appropriately encrypted.
## 4 Discussion
### Uncertainty  
In the current investigation, there are few areas where there is room for uncertainty. As the study area has a considerable spatial extent and is associated with a larger dataset, there is a greater likelihood of overgeneralization. The incorrect classification of pixel values is an important aspect of this problem. If the NDVI value does not reflect a higher value during the peak greenish period of the crop cycle because of spectral or radiometric errors, then there is a greater chance that those pixels will be omitted. The mixed-pixel classification represents another significant component of the geospatial characterization of cropping patterns and intensity. The current study was associated with a very small amount of the natural vegetation, and this vegetation was excluded from the entire process to obtain the final result. However, regions that are associated with a greater proportion of natural vegetation may be subject to challenges regarding the identification and removal of natural vegetation. The resolution of the image that is being used is an important factor to take into consideration. Numerous studies concluded that a resolution of fewer than 30 m on satellite images is insufficient for crop area mapping [15; 42; 43; 44]; consequently, high-resolution images of 10 meters were incorporated into the present investigation. During the field verification, it was found that the field area is also an important component of the overall process, and that it should be selected with great care to achieve the best results. Because the spatial resolution of the satellite images was 10 m, agricultural fields with an area greater than 100 m\({}^{2}\) were considered in the present work only.
## 3 The Proposed MOA-CV Method
### Effect of Variance of the Random Normal Distribution  
In the demonstration of the MOA-CV method, the variance \(\sigma\) of the random normal distribution is fixed at \(0.02d_{\min}\). In this section, ten different values of variance \(\sigma\) are set to explore the effect on the accurate rates of ranking surrogate models. The 1D function as shown in Equation (3) is still used, and the training samples are the same to those in Section 3.2. Since the key point of the MOA-CV method is to move the validation point, the virtual validation point can not be too far away form the original one. That is, \(\sigma\) cannot be too large. Set \(\sigma\) is equal to \(k*d_{\min}\), in which \(k\) is set to be 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09 and 0.1. The ranking results of surrogate models by MOA-CV with different \(\sigma\) are listed in Table 4; the corrected orders are in italic bold. From Table 4, it is observed that the MOA-CV method with \(k=0.02\) has the highest accurate rate and as the \(k\) increases from 0.03 to 0.09, the corrected rate of ranking is likely to decreases. Overall, in this work, the variance \(\sigma\) is set to be \(0.02d_{\min}\).
## 5 Use Case of 6G-OPENSEC-Security
### Application Deployment Scenarios
#### 5.5.1 Integrating SCLA in V2X Slicing  
Vehicle-to-Everything (V2X) communication is one of the currently emerging application scenarios for network slicing. V2X enables seamless interaction between vehicles, infrastructure, pedestrians, and networks. Network slicing is pivotal in meeting the diverse and stringent Quality-of-Service (QoS) requirements of such V2X scenarios, ensuring that safety-critical applications, traffic management, and infotainment services can coexist efficiently without compromising performance.  
The framework detailed in [113] proposes a three-tier architecture for implementing network slicing in V2X communications. Leveraging SDN and NFV, the system dynamically allocates resources, manages traffic, and maintains service quality across small base stations (SBS), macro base stations (MBS), and the core network. The integration of Recurrent Neural Networks (RNN) and Deep Q-Networks (DQN), forming the RDQ3N model, facilitates intelligent slice management. This model predicts resource availability and QoS, enabling proactive resource allocation and efficient slice instantiation.  
Incorporating the SCLA into this framework would provide significant enhancements. The SCLA continuously monitors network slices, analyses traffic patterns, and dynamically adjusts security policies based on real-time threat assessments. Each slice monitored by the SCLA receives tailored security measures appropriate to its specific requirements, ensuring optimal protection and security service granularity without compromising performance. For instance, safety-critical slices would benefit from stringent security protocols to prevent data breaches and ensure the integrity of critical communications, while slices that are not categorized as mission-critical would allow for more flexible security policies. This proactive security approach helps maintain high QoS levels by preventing performance degradation due to security incidents and ensures efficient resource allocation, as intended by the proposed framework as well.
## 1 Introduction
The combination of all possibilities for configuration search is \(2^{4096}\), or effectively infinite. So, the challenge is that the IRS configuration is in discrete space; therefore, defining optimization objectives, loss function, and the gradient is not straightforward. Furthermore, if we somehow map the IRS to continuous space, the complexity of the mapping function results in the non-convex optimization problem. So, our algorithms must effectively address all these constraints in real-world wireless communication applications. However, in practical terms, obtaining passive elements that can be tuned to a continuous range of values is not feasible [20]; hence, to cater to the needs of simple hardware, we restrict the value that an IRS element can take to either \(-1\) or \(+1\). The IRS is a \(64\times 64\) surface with \(4096\) tunable elements. Hence, the total number of possible configurations is \(2^{4096}\), making it impractical to check each configuration. The implementation methodology for the IRS involves two steps: channel estimation and maximizing the rate function. We use the dataset's pilot configurations to estimate the controllable and uncontrollable channels. Secondly, the linear model is used to characterize the system. Then, we estimate the channel. Searching for the best configuration for a high-data-rate genetic algorithm with a gradient descent algorithm could also be used sequentially to optimize each user's IRS configuration. The proposed work and its two algorithms for the best IRS configuration for higher data rates are explained in Sections 3 and 4 in detail. Genetic algorithms (GAs) are employed in RIS-assisted OFDM systems to optimize the configuration of reflection coefficients. Each potential solution, or chromosome, represents a set of RIS reflection coefficients. The process starts with initializing a diverse population of these chromosomes. Each chromosome's effectiveness is then evaluated using a fitness function that measures key performance metrics, such as signal-to-noise ratio (SNR) or throughput. The best-performing chromosomes are selected through a roulette wheel or tournament selection, ensuring that the most promising solutions are carried forward. Following selection, crossover and mutation operations are applied to introduce variation and create new offspring chromosomes. Crossover combines parts of two chromosomes, while mutation introduces random changes, maintaining genetic diversity and preventing premature convergence on suboptimal solutions. This iterative process continues until the fitness function stabilizes, indicating an optimal or near-optimal set of RIS reflection coefficients.
## 3 Results
### Optical Readouts  
Figure 3 shows the total readouts of both sampling years, colored according to the qualitative parameters. Data from the two different sources, i.e., the pre-prototype [22] and the new version of the device and the two different years, were merged to observe the sample reflectance in the whole optical range covered by the sensors. Even if the device is not able to provide continuous spectra, the increasing and decreasing trends of the measured parameters are visible by observing the readouts in their entirety.  
The readouts represented in Figure 3a are colored based on total soluble solids, the readouts in Figure 3b are colored based on pH, while the readouts in Figure 3c are colored based on titratable acidity. The progressive ripening trend determines the color variations in the samples, which are strictly related to the ripening evolution. Variations in green color occur; initially, it is very bright then it tends to turn gradually towards light green, almost yellow. The decreasing trend of the optical outputs is due to high reflectance values corresponding to the less ripe (and therefore greener) grapes; as the ripening progresses, the reflectance values of the samples tend to decrease [25]. Evidently, these effects are more noticeable in the visible region readouts (460-650 nm).
# Modelling the Microphone-Related Timbral Brightness of Record Signals
## 3 Brightness Modelling
### Model Refinement  
Since the chosen metric is the logarithm of the product of the \(SC\) and \(\text{Ratio}_{2}\), the metric can be rewritten as  
\[\log_{10}(\text{Ratio}_{2}\cdot C)=\log_{10}(\text{Ratio}_{2})+\log_{10}(C). \tag{5}\]  
Using Equation (5), a multilinear regression modelling approach could be taken to find appropriate weights for both \(\text{Ratio}_{2}\) and \(SC\) metrics independently. However, applying this approach did not lead to an improvement in the performance of the model, achieving the same Pearson's r, RMSE, and [PERSON]'s rho to three significant figures. Upon performing a multilinear regression on the standardised values of the \(SC\) and \(\text{Ratio}_{2}\) components, the coefficients for each were 46.1 and 42.9, respectively (to three significant figures). Since these coefficients are very similar, the relative contribution of each metric is almost equal. This explains why weighting them independently does not improve the performance of the model. Therefore, the model is a linear regression of the product of \(SC\) and \(\text{Ratio}_{2}\).  
The finalised brightness can be expressed as in Equation (6). The full model is summarised in Figure 3. Its performance is shown in Figure 4.  
\[\text{Brightness}=-95.9388+44.5552\log_{10}(\text{Ratio}_{2}\cdot C). \tag{6}\]  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Best Pearson’s r \& RMSE** & **Best Spearman’s rho** \\ \hline Metric & \(SC\) \& \(\text{Ratio}_{2}\) & \(SC_{fc}\) \& \(\text{Ratio}_{1}\) & \(SC_{fc}\) \& \(\text{Ratio}_{1}\) \\ Audio weighting & None & None & None \\ Frequency scale & Hertz & Hertz & Hertz \\ Crossover freq. (\(f_{c}\)) & 500 Hz & 1250 Hz & 1250 Hz \\ Spectral smoothing & \(\frac{1}{3}\)-octave & \(\frac{1}{3}\)-octave & \(\frac{1}{3}\)-octave \\ Log transform & Yes & Yes & No \\ Pearson’s r & 0.922 & 0.919 & 0.646 \\ Spearman’s rho & 0.902 & 0.906 & 0.906 \\ RMSE & 8.52 & 9.928 & 16.779 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Best-performing (lowest root mean square error (RMSE), highest Pearson’s r, highest Spearman’s rho) metrics from the original 8484 candidates.  
Figure 4: Listeners’ brightness ratings against the best-performing model, across all sources.  
Figure 3: Flow chart of the developed brightness model (FFT = Fast Fourier Transform).
## 3 Proposed Architecture
### Data Gathering and Communication Module  
The vehicles are equipped with various sensors (lidar, radar, ultrasonic, camera, etc.) which are placed internally and externally to acquire information about the vehicle itself (its speed, direction, steering wheel movements, tire slip, distance between the lane and other nearby vehicles, and so forth) and the condition of roads (congested roads, inadequate traffic signs, potholes, ice patches, or other hazards); a wireless transceiver device which supports different wireless technologies that enable communications with other entities; a GPS device that provides precise information about location; and an OBU which controls the communication of vehicle with other entities and offers computing, storage, and networking facilities [44]. We have included all these components in the data gathering and communication module, as shown in Figure 3 (also referred to separately as data gathering module and communication module in Figure 1).  
Figure 2: Layered architecture of cloud-fog-edge SDN-VANETs.  
Once the data is sensed and processed, the vehicles then share with one another important information through beacon messages. They broadcast these beacons periodically to other vehicles via V2V communication links to increase cooperative awareness between them. The beacon messages are critical for our approach as well. Once the vehicles receive them, they extract the information they need, which for IFS-CMR is about their neighbors' geographic position, speed, direction, transmission power, available storage, available computing power, etc. Then, IFS-CMR makes the necessary calculations to update the current condition of all the input parameters.