esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Security Analysis, Performance Evaluation, and Discussion
### Performance Parameters
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Original** & **Ref. [21]** & **Ref. [24]** & **Ref. [25]** & \multicolumn{2}{c}{**Proposed**} \\  & **AES-128** & **Ref. [21]** & **Ref. [24]** & **Ref. [25]** & \multicolumn{2}{c}{**SARNC Technique**} \\ \hline PSNR & 36.1236 & - & 54.26 & - & 48.1648 \\ \hline MSE & 16 & - & 0.24 & - & 1 \\ \hline SSIM & 0.7495 & - & 0.99 & - & 0.99996 \\ \hline Correlation coefficient & \(-\)0.0121 & \(-\)0.0036 & - & 0.001178542895092 & \(-\)0.0074 \\ \hline NPCR (\%) & 99.4141 & 99.60 & - & 99.7570 & 99.5117 \\ \hline UACI (\%) & 33.2802 & 33.41 & - & 39.12 & 33.2837 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of performance parameters. where \(E_{1}(i,j)\) and \(E_{2}(i,j)\) define the grayscale area's value of a pixel in the \((i,j)\) coordinate of the encrypted images \(E_{1}\) and \(E_{2}\). NPCR and UACI values are calculated using Equations (26) and (27), as shown below:  
\[NPCR=\frac{1}{x*y}\sum_{i,j}[X(i,j)]*100 \tag{26}\]  
\[UACI=\frac{1}{x*y}\sum_{i,j}(E_{1}(i,j)-E_{2}(i,j))/\ \left(2^{l}-1\right)*100 \tag{27}\]  
The parameters x and y indicate the dimensions of the original image. The greater values of UACI and NPCR ensure better security of the encryption algorithm. Results Discussion:  
A comparison study was carried out between proposed SAARC technique and AES-128, and is tabulated in Table 4. The greater values of PSNR, MSE, and SSIM indicate the better quality of received decrypted image. The greater values of the correlation coefficient, NPCR, and UACI are indicative of better quality of encryption algorithm. By referring to Table 5, we can conclude that the proposed SARNC technique provides improved security and reliability for the transmitted image. Figures 7 and 8 show the variation of PSNR and BER with SNR. Histogram Analysis. An image histogram represents the number of pixels as a function of their intensity [24]. Results Discussion:  
A comparative study was carried out between the original and received decrypted image histograms, as shown in Figure 9. The histogram of the original and received decrypted image is almost the same, with a negligible histogram error of 0.005157. Figure 8: BER curve for proposed SARNC technique. Figure 9: Result of histogram analysis for the SARNC technique.
# Visible Spectrum and Infra-Red Image Matching: A New Method
## 3 Proposed Method
### Image Datasets  
We use two different image datasets: (i) RGB-NIR dataset [4], and (ii) Multimodal Stereo dataset (MSD) [5]. The RGB-NIR dataset consists of Visible Spectrum (VS: 400-700 nm) and Near-Infra-Red  
Figure 1: Proposed regression based method for image matching between Visible Spectrum–Infra-Red (VS-IR) images.  
(NIR: 750-1100 nm) images of 477 different indoor and outdoor scenes. The MSD dataset consists of VS and Long-Wave-Infra-Red (LWIR: 800-1500 nm) images of 100 different outdoor scenes. We randomly divide each dataset into two disjoint sets: Training set and Test set. The training set consists of 10 percent image pairs of the dataset and only used for regression model training. Whereas the test set consists of the remaining 90% image pairs and used only to evaluate the proposed descriptor (Reg-SIFT) and to compare its performance with other state of the art descriptors.
# Experimental Research on Braking Feedback and Taxing Feedback System of New Energy Vehicles
## 2 New Energy Vehicle Feedback-Related Technology Research
### Research on the Braking Feedback of New Energy Vehicles
#### 2.1.2 Coordination  
The principle of coordinated braking energy recovery strategy is to give priority to the use of feed-back braking force, while the hydraulic braking force is adjusted accordingly, so that the total braking demand is coordinated with the sum of the two braking forces. As shown in Figure 2, this strategy feed-back efficiency compared with the above-mentioned superposition strategy is more efficient, braking feeling is better, but the traditional hydraulic braking system needs to be redesigned or modified, the implementation is more complex. With the development of technology, coordinated feed-back braking, which has great advantages in many aspects such as feed-back efficiency, braking sensation and braking safety, has gradually become the mainstream of research and development.  
Figure 3 shows the difference between superimposed and coordinated braking energy recovery system strategies. This strategy has a higher feedback efficiency and a better braking feeling, but it needs to be modified or redesigned for the traditional hydraulic braking system, which is more complicated to implement. With the development of technology, coordinated feedback braking, which has significant advantages in feedback efficiency, braking feel, and braking safety, has gradually become the mainstream in research and development.  
From the above discussion, it can be seen that compared with the superimposed energy feedback control strategy, research on the coordinated energy feedback strategy shows great advantages in terms of braking efficiency and comfort. In the integration and application of a brake energy recovery system on a pure electric bus and a hybrid bus, it is still necessary to test and compare the difference in the economic contribution rates of braking energy recovery for the whole vehicle under different braking energy recovery control strategies.
# A Review of the High-Mix, Low-Volume Manufacturing Industry
## 1 Introduction
### Industry 4.0 for Production Flexibility  
Industry 4.0 is a concept of an interconnected system of real and virtual factories represented by cyber-physical systems (CPSs). A cyber-physical system is a system of collaborating computational elements that control physical entities. The CPSs are physical and engineering systems whose operations are monitored, coordinated, controlled, and integrated, based on the computing and communication core. Embedded system relieson sensors to record data and manipulate physical processes with the help of actuators operated in digital networks. With the core elements of connectedness, smart machines and products, decentralisation, big data, and cybersecurity [9]. These elements deliver intelligence, connectedness, and responsiveness to changes [10].  
This flexibility of the Industry 4.0 concept also provides vital capabilities for HMLV manufacturers to handle the operation on small batches, down to batch-size-one. Industry 4.0 envisioned real-time, zero-setup-time production flexibility to meet the demand for personalisation and mass customisation [11]. Two of the pillars of Industry 4.0 best reflect the production flexibility in HMLV--additive manufacturing and product identification, and traceability.  
One of the vital Industry 4.0 technologies for flexible production is additive manufacturing and 3D printing technology [12]. Three dimensional design data enables components to be built in layers by depositing material in fine powder form. Despite the initial high investment, additive manufacturing has a cost advantage for small volumes, with the capability for complex design, shorter time-to-market, and flexibility down to batch-size-one.  
Traceability and product identification are the fundamentals of Industry 4.0 production flexibility. The ability to assign a unique ID to each component enables real-time control of the value chain over the product life cycle. Unique IDs make individual components identifiable throughout the production process; this allows dynamic, efficient production path planning down to individual components. Information on each component, such as origin, storage, state, and location, is retrievable instantly.  
With the increase in costs in a high-wage economy, automation and robotic production has become more attractive for HMLV's flexible manufacturing system [13]. Flexible end effectors, advanced integrated peripheries, and easy programming are key enablers that make collaborative robots flexibly attractive for the HMLV industry.
# Joint Delay-Doppler Estimation Performance in a Dual Source Context
### Contributions  
Keeping in mind the problem of interest, that is, the dual source delay-Doppler estimation with a generic band-limited signal, the main contributions of this article are as follows:  
* A compact Fisher Information Matrix (FIM) for the general dual source narrowband CSM (i.e., where the Doppler effect on the band-limited baseband signal is not considered and amounts to a frequency shift) is provided in Section 3. This extends the work of [17; 18] dealing with the single source narrowband CSM and completes the work of [28] in which the FIM is given for the specific case of a static, ground-based receiver, leading yo an identical Doppler effect for the two paths;
* The FIM is expressed in an easy to use form, based on the baseband signal samples;
* The theoretical results are supported and validated by the implementation of a dual source CMLE [1] and sub-optimal estimators such as a CLEAN RELAX Estimator (CRE) [40];
* The formal connection with existing literature [28] and possible uses of the new CRB are discussed;
## 2 Impact of MAC Delay on Localization
### Length of MAC Delay  
The medium access delay is the waiting time until the channel status becomes empty. The longer the time to transmit a packet is, the longer the MAC delay to avoid packet collision is needed. Therefore, the MAC delay is proportional to the channel occupation time per transmission. In underwater communication, the channel occupation time required for a transmission is considerably long even in the case of sending a small amount of data due to the following reasons:  
1. Low transmission rate: Acoustic signal is significantly attenuated as the frequency and the communication distance increase. This means that the available bandwidth is very limited. In [12], it is stated that the bandwidth is less than 10 kHz in the case of the communication range is 1 to 10 km or more. Furthermore, communication between moving platforms such as the AUV is strongly affected by the Doppler effect, accordingly the use of modulations providing massive transmission like orthogonal frequency-division multiplexing (OFDM) is difficult. Therefore, the low transmission rate induces the long transmission delay.
2. Low propagation speed: Sound velocity in water varies with pressure, temperature, and salinity. In common, it can be approximated to 1500 m/s. If the communication distance is short, the transmitted signal can reach the receiver within short time. However, the network size in underwater is generally up to several km, leading to long propagation delay. For example, 1 km communication distance results in about 0.67 s propagation delay, which is non-negligible time.
3. Long preamble: In most underwater studies, long preamble is ignored, but it should be considered in the practical system design. Acoustic modem appends preamble at the beginning of transmission for the signal detection and frame synchronization. In a commercial acoustic modem, the preamble lasts for a considerable time. For instance, ref. [13] proves that the preamble lasts for about 1.5 s on the ATM series Teledyne Benthos Modem, which is widely used in underwater communication.  
Due to the aforementioned factors, the channel occupation time per ANs significantly increases, even though the packet size is small. Unfortunately, the main goal of the conventional underwater MAC protocol in underwater is not to reduce or eliminate the delays, but is to improve the energy efficiency or data reliability [14; 15; 16]. For instance, Ref. [17] proposes the MAC protocol considering the AUV's mobility, but the mobility estimation is just used to seamlessly find a better link. Actually, the development of contention-free MAC protocols supporting simultaneous transmission is not a concern in the current underwater MAC literature. It is very important in the AUV localization though.
# Opal-Like Photonic Structuring of Perovskite Solar Cells Using a Genetic Algorithm Approach
## 4 Results
### Angular Dependency  
We are now interested in the angular dependency of the optimal combination found by the GA. Unpolarized incident EM radiation is sent in the PSCs. Figure 6 shows the iQE as a function of the incident angle \(\theta\), with steps of 5\({}^{\circ}\). The iQE is found to be reasonably stable regarding the angle of incidence up to 75\({}^{\circ}\). The main factor limiting the absorption of light is the Fresnel reflection at the FTO interface. It should be noted that no other specific light-trapping strategies were developed here, except for the photo-active layer. Anti-reflection coatings or surface structuration of the FTO layer may increase the light coupling.
# Improved Video Action Recognition Based on Pyramid Pooling and Dual-Stream C3D Networks
## 2 Methodology
### Feature Fusion Based on Dynamic and Static Features
#### 2.2.3 PCA-Based Feature Fusion  
Principal component analysis (PCA) is a commonly used data dimensionality reduction method aimed at mapping high-dimensional data into a low-dimensional space while retaining as much information as possible. PCA determines the principal components by calculating the eigenvectors of the covariance matrix and reconstructs the data using these principal components. In feature fusion, static and dynamic features can be considered as 2D data containing 1000 dimensions each, and after dimensionality reduction by PCA, the top m principal components (m < k) are selected as the new feature representations. PCA improves the efficiency of the feature representations by retaining the maximum variance and reducing redundant features [17]. In this section, PCA-based feature fusion processes dynamic and static features into 2D data containing 1000 dimensions, extracts principal components through PCA, and finally uses the first principal component as the new feature. Specifically, the original dimension of our feature vector is 1000 dimensions (static and dynamic features). Through PCA dimensionality reduction, we select the top m principal components (where m < 1000), retaining as much variance as possible. The explained variance of each principal component indicates the contribution of that principal component to the variability of the data, and we choose which dimensions to retain based on the cumulative percentage of variance. We usually choose dimensions that retain more than 95% of the variance to ensure that the downscaled features still contain most of the information.
# An Embedded Sensor Node Microcontroller with Crypto-Processors
## 4 Implementation and Verification
### Production Tests  
Testing of the fabricated TNODE5 chips was performed on the Agilent Verigy 9300 industrial tester. The Verigy 9300 is a cycle-based industrial tester for digital circuits, which supports both wafer and package testing. The tester operates on the data extracted from an EVCD (extended value change dump) file. The EVCD file is generated by the simulation framework and includes the value change information for all input and output signals. In a standard test procedure, the input stimuli extracted from the EVCD file are applied to a chip. The output signals of the chip are sampled by the tester and compared with the expected output values. Since the TNODE5 design cannot execute an external program, the stimuli data must be written into its Flash memory first. Therefore, a set of initial \(\mathrm{I^{2}C}\) program sequences was generated for storing the test program in the Flash memory. Such a programming sequence includes all \(\mathrm{I^{2}C}\) instructions to erase the Flash memory, to write the test program and to verify the written data. After loading the Flash memory, a functional test is started by booting the processor. The booted program performs the functional test and writes the results to the processor outputs, which are sampled and verified by the tester. The TNODE5 chips were tested on the complete set of production tests.  
The analog components, the ADC and the DCO, cannot be comprehensively tested on the digital tester. Nevertheless, the AD conversion of predefined voltage levels feed at the ADC analog inputs can be tested by comparing the ADC output with the expected values obtained from analog simulations. More detailed tests of the ADC are performed on a special test board by using appropriate testing equipment. The same board was used for testing the DCO as well.
# Recent Advances in Optical Detection of Aminoglycosodies
## 4 Other Optical Assays  
### Chemiluminescence Assays  
Chemiluminescence (CL) is a phenomenon of light radiation accompanied by chemical reactions. The CL analysis method is a widely applied trace analytical method with advantages such as its high sensitivity, wide linear range, simple equipment, and easy operation [67]. The CL of luminol was reported by [PERSON] in 1928. Streptomycin was reported having an enhancement effect on the luminol-potassium periodate-Mn\({}^{2+}\) CL reaction [68]. Luminol-H\({}_{2}\)O\({}_{2}\) is one of the most investigated CL reactions. Without a catalyst, the CL emission of the luminol-H\({}_{2}\)O\({}_{2}\) reaction is weak. Au NCs have been reported to have catalytic activity in the luminol-H\({}_{2}\)O\({}_{2}\) CL reaction to produce enhanced CL signals [69]. [PERSON]'s group employed polyA-containing probe DNA as the template to synthesize Au NCs, which could catalyze the luminol-H\({}_{2}\)O\({}_{2}\) CL reaction to emit light. The kanamycin-aptamer was immobilized on the surface of magnetic beads and then hybridized with the probe DNA-templated Au NCs. The hybridized composites were used for the CL detection of kanamycin. The binding to kanamycin to the aptamer immobilized on the magnetic beads surface resulted in the release of Au NCs in the solution phase. After an external magnetic separation, the released Au NPs contained in the supernatant triggered the luminol-H\({}_{2}\)O\({}_{2}\) to emit light. The CL intensity was employed to quantify the concentration of kanamycin. The label-free CL assay showed a LOD of 0.035 nM for the detection of kanamycin [70]. The G-quadruplex DNAzyme with peroxidase-like properties can also catalyze the luminol-H\({}_{2}\)O\({}_{2}\) CL reaction. In [PERSON] and [PERSON]'s work, beta-cyclodextrintrins and ionic liquid-functionalized graphene oxide aerogel (beta-CD/IL@GOGA) were employed as three-dimensional supports to assemble aptamers and DNAzyme. The graphene oxide aerogel provided a large surface area. Beta-CD and ionic liquids increased the biocompatibility and stability of the composite. The binding of streptomycin to the aptamer triggered the release of DNAzyme, which catalyzes the luminol-H\({}_{2}\)O\({}_{2}\) CL reaction to emit light. The CL sensor showed a linear range of \(1.4\times 10^{-12}\) to \(2.8\times 10^{-9}\) M and a detection limit of \(9.2\times 10^{-14}\) M. The CL assay was successfully used for streptomycin detection in cucumber and milk samples [71].
## 3 Results  
Figures 4-7 show the most interesting radargrams derived from a measuring frequency of 15 and 25 MHz, obtained by means of the data processing chain described in the previous section. The processed data allow for identifying several areas (highlighted by dark blue rectangles) with strong reflections due to variations in the dielectric permittivity of the subsoil, likely related to the presence of buried and/or outcropping faults. These areas are well correlated with the positions of the 2017 earthquake's co-seismic ruptures from [PERSON] et al. (2018) [3] and of the active fault system from [PERSON] (1988) [10] and [PERSON] and Vezzoli (1998) [11] (see Figure 9). We also note that the layers detected by our GPR data (shown by white solid lines in Figures 4-7) are compatible with the geological sections in [PERSON] et al. (2022) [42] and [PERSON] et al. (2023) [29]. Furthermore, the layers from GPR data match with the outcome of our TDEM loops (TDEM 1-4, light blue lines) and the electro-layers from [PERSON] et al. (2023) [29] (black lines), especially for ISH2 and ISH4 (Figures 5 and 7).  
One of the main parameters of the EM wave is its attenuation, which manifests itself in the absorption of the wave energy and in the dispersion of the speed, depending on the crossed medium. Knowledge of the attenuation behavior of GPR waves in the medium allows rock types to be distinguished. The Q-factor is a frequency attribute of the wave field expressed by the ratio between the central frequency of the signal spectrum and its width. The Q-factor is inversely proportional to the absorption coefficient and represents its attenuation behavior ([PERSON], 1955). It is given by Q = \(\varpi\)/((28)\), where \(\varpi\) = 2\(\pi\)f (with f antenna frequency), and \(\delta\) = v\(\pi\) is the damping rate (with v the phase speed and \(\alpha\) the attenuation coefficient). Previous studies demonstrated how some GPR attributes were successful for highlighting normal faults within volcanic environments, e.g., [31].  
We computed the Q-factor on a portion of the ISH2 section acquired using a 100 MHz antenna to infer the fault system dip (Figure 8). The reasoning for this analysis is that the ISH2 profile crosses over the 2017 earthquake's co-seismic ruptures ([PERSON] et al., 2018) [3]. This antenna choice allowed for the identification of an anomaly between the progresses 0.84 and 0.93 km in the depth range of 5-20 m. Our Q-factor analysis focused on this portion of ISH2 and highlighted a large step that may be the fault displacement of the rupture. Thus, we infer a subvertical contact dipping towards the north that may represent the synthetic fault system of the Casamicicola Holocene graben, activated in 2017.  
Figure 7: Processed data for the ISH4 LDGPR profile (25 MHz). Refer to the caption of Figure 4 for explanation [29].
# Estimating Underwater Light Regime under Spatially Heterogeneous Sea Ice in the Arctic
## 2 Material and Methods
### 3D Monte Carlo Numerical Simulations of Radiative Transfer
#### Estimation of Reference and Local Light Profiles  
To explore how the melt pond influences the averaged underwater irradiance and radiance profiles (Figure 1), data from the Monte Carlo simulation were averaged according to six different radii, corresponding to varying melt pond spatial proportions. The simulated light profiles were averaged within the following surface areas: (1) 10 m radius (25% melt pond cover), (2) 11.18 m radius (20% melt pond cover), (3) 12.91 m radius (15% melt pond cover), (4) 15.81 m radius (10% melt pond cover), (5) 22.36 m radius (5% melt pond cover) and (6) 50 m radius (1% melt pond cover). For each of these six configurations, the corresponding averaged light profile, \(\overline{E_{d}}(z)\), was subsequently viewed as an adequate description of the average underwater light field. For the remainder of the text, these averaged profiles are referred to as reference light profiles. Furthermore, 50 light profiles, evenly spaced by 1 m from the melt pond centre, were extracted to mimic local measurements of light and to calculate associated diffuse attenuation coefficients.