esa-sceva/satcom-chunk-collection huggingface dataset:
Finally, several solutions are only tested with simulators or emulators [22; 23; 24; 26], which prevents the evaluation of real operation conditions such as parasitic losses, bandwidth limitation, and inductor saturation, among others. This paper faces the previous drawbacks by designing a semi-active supercapacitor/battery hybrid energy storage system based on a bidirectional Sepic/Zeta DC/DC converter, which interfaces the SC and the battery/DC bus, as observed in Figure 1. This connection enables the indirect control of the battery current, thus avoiding high-frequency transients to the battery. The main contributions of the the proposed solution are:  
* The power stage (Sepic/Zeta converter) has the advantage of connecting devices to the DC bus with any voltage condition (lower, equal, or higher than the ESS voltage). * The Sepic side of the converter is connected to the battery/DC bus, which guarantees a continuous current (non-pulsating waveform) and prevents high-frequency transients in the battery. * A gain-scheduling approach is used to design the proposed LQG-FF controller, which adapts the controller parameters to the system's operating condition (step-down/step-up or unitary conversion ratio). This real-time adjustment makes the system highly adaptive and efficient, thus it provides a robust and flexible solution with the desired transient performance. The rest of the paper is organized as follows. Section 2 reports the power stage model, which is based on a bidirectional Sepic/Zeta converter. Then, a control-oriented model is obtained from the switched model, and it is validated against both the circuital and switched models through frequency response analysis. In Section 3, a feedforward/optimal Linear Quadratic Gaussian (LQG-FF) controller, with adaptive parameters, is proposed. This controller ensures that the high-frequency component of the load current is provided by the supercapacitor, and hence the reference current of the Sepic/Zeta converter is calculated using a high-pass filter. Such a control approach guarantees that the battery will only deal with low-frequency current components. Section 4 reports Matlab/Simulink simulations of the HESS in the three operation modes: step-up, step-down, and unitary gain; in the same way, Section 5 analyzes the results of the experimental tests for the same application cases. The conclusions are presented in Section 6.
## 4 Security Analysis
### Security Properties
PUF values cannot be extracted from the drones after they are closed. In our case, after the production, the PUF value is retrieved and stored on the blockchain  
Figure 7: ProVerif-Events. by the manufacturer, who closes the system. Hence, the adversaries cannot gain information about the PUF value, not even by physical tampering. * Denial of service: Since most of the drones are resource-constrained, it is essential for them to be able to withstand various denial-of-service attacks. We consider the DOS attack only on the protocol level. Regarding the registration protocol, it is an important aspect that all messages are authenticated. If the validation fails, the incoming request is dropped. On the other hand, all calculations, including verifications, are chosen to be lightweight, and a time limit is also set for incoming responses. * Forward and backward key secrecy: In the case of registration, the secret keys exchanged are randomized, and hence they are independent. If a secret key is leaked, neither the previous keys nor the latter ones can be calculated. Hence, forward and backward secrecy of the keys is assured. * Jamming attacks: Many anti-jamming techniques exist today, including frequency and channel hopping, frame masking or special antenna designs for spatial filtering, but most of them are not suitable in practice. However, LoRa can utilize redundancy to improve packet resilience against jamming attacks by configuring a code rate (CR) parameter, controlling the ratio of the actual data to the forward-error-correcting capability added to the payload. The disadvantage of this solution is that the data rate may be reduced.
## 4 Numerical Examples and Discussions
### Case of P Wave Incidence  
Figure 2 demonstrates the reflection coefficients of the body waves under different microstructural parameters when the P-wave is incident. For comparison with classical elastic waves, a reflection graph under the classical elastic condition is also presented. The results indicate that as the microstructural parameters increase, the reflection coefficient of the reflected P-wave gradually decreases, whereas that of the reflected SV wave gradually increases.  
Figure 3 indicates the reflection coefficients of surface waves at different wavelengths, such as the reflected SP and SS waves, as shown in Figure 1. Compared with the reflected body waves, the reflection coefficient of the reflected surface waves decreased significantly with increasing wavelength.  
Figure 3: The influence of wavelength on reflected body wave when P wave is incident (\(\varepsilon\!=0.05\)).  
Figure 2: The influence of microstructural parameters on reflected body wave when P wave is incident (\(\overline{\lambda}\!=8\)).  
Figure 4 illustrates the variation in the reflection angle with the incident angle for different microstructural parameters. In classical elastic solids, the reflection angle reaches 90 degrees, and the incident angle is 30 degrees. These findings strongly suggest that, as the microstructure size increases, the reflection angle increases proportionally.  
Figure 5 presents the reflection coefficients of the different waves under various external magnetic field intensities. The reflection coefficients under these conditions are also provided to facilitate a comparison with the cases of classical elasticity and no external magnetic field. The results indicate that the reflection amplitude ratio of the reflected SV wave decreases with an increase in H0, whereas the reflection amplitude ratios of the reflected P wave, reflected SP wave, and reflected SS wave all exhibit an increasing trend with an increase in H0. Furthermore, compared with the reflected body waves, the reflected surface waves (i.e., the reflected SP wave and reflected SS wave) show a significant increase with an increase in the external magnetic field. These findings suggest that reflection surface waves are more sensitive to variations in the external magnetic field than body waves.
## 3 Results and Discussion  
### Effect of Zeolite Content on Swelling Characteristics of Bentonite  
Figure 5 shows zeolite-bentonite mixture specimens with different zeolite-to-bentonite mass ratios (12.50, 25, and 50%) were prepared using 2 g of bentonite as the basic dilator. The specimens were subjected to free-swelling tests in DIW and ZnCl\({}_{2}\) solutions of different concentrations. It was investigated that the zeolite content and the swelling capacity of the bentonite were related. Zeolite-bentonite mixture specimens with different zeolite-to-bentonite mass ratios (12.50, 25, and 50%) were prepared using 2 g of bentonite as the basic dilator. The specimens were subjected to free-swelling tests in DIW and ZnCl\({}_{2}\) solutions of different concentrations. It could be indicated that the zeolite content and the swelling capacity of the bentonite were related.  
When in contact with DIW, pure bentonite had a maximum swelling capacity of 8.50 mL/g. The specimens with zeolite contents of 12.50, 25, and 50% had swelling capacities of 9.75, 8.38, and 8.13 mL/g, respectively, indicating that overall, the addition of zeolite had an insignificant effect on the swelling characteristics of the bentonite. When in contact with the ZnCl\({}_{2}\) solution, the swelling capacity of pure bentonite decreased with increasing contaminant concentration. The swelling capacities at ZnCl\({}_{2}\) concentrations of 0.01, 0.03, 0.05, 0.1, and 1.0 mol/L were 6.75, 4.50, 3.13, and 2.50 mL/g, respectively; the swelling capacity of the pure bentonite at the ZnCl\({}_{2}\) concentration of 1.00 mol/L was 3.40 times lower than that of the pure bentonite in contact with DIW. The specimen with 12.50% zeolite content had swelling capacities of 9.13, 5.50, 5.38, 4.75, and 4.25 mL/g for contaminant concentrations of 6.75, 4.50, 3.13, 2.50, and 2.50 mL/g, respectively. These values were higher than those for pure bentonite by 2.38, 1.00, 2.25, 2.25, and 1.75 mL/g, respectively, indicating that adding the zeolite improved the swelling. At low contaminant concentrations, increasing the zeolite content from 12.50 to 25 and 50% did not change the swelling capacity significantly.  
Figure 5: Variation of the swelling volume of mixed soil with different zeolite admixtures.
## 3 Results and Discussion
### Delimitation of Coastal Lakes
#### 3.4.3 Eutrophic Lakes--Xuno  
The total surface area of the Xuno lake is 2.3 ha and hydrophilic vegetation is currently colonizing almost all of the lake. The surface area occupied by open waters was small (0.005 ha) and decreased by more than half in the study period [15]. The lake basin is occupied by reed bed (77.5%) in the distal part and by _Scirpus maritimus_ at the front (14.8%), with the presence of small areas occupied by _Thypha latifolia_ (Figure 7).  
The limits of the Xuno lake proposed in this work are almost consistent with those established by the PDRN. Nevertheless, in the PDRN, 99.5% of the surface area was registered as Coastal lagoon, which appears to be an error. The Xuno lake is located at an elevation of 5 m.a.s.l. and as the channel only acts as a drain, there is no marine influence [15]. This also appear clearly in the analysis of this area, as summarized in Table 2 and Figure 2.  
There is also habitat division in this location, which includes coastal lagoon habitat, dune (2130) (0.01 ha) and alluvial forest (91E0) (almost non-existent). Currently these zones are occupied by reed bed, _Scirpus maritimus_ and floating aquatic vegetation (_Potamogeton natas, Nymphaea alba_).  
Figure 6: Evolution of Vixan lagoon SE sector. From left to right the images correspond with Interministerial flight (1973–1986); Coastal flight in October 1989 and PNOA image in June 2017. Data: (IGN, [20]). Red polygons in 1989 represents burnt reedbed. The burning in this lagoon part was frequent at the end of summer.
# Discoloration Mechanisms of Natural Rubber and Its Control
## 5 Discoloration Mechanism of Natural Rubber
### Enzymatic Browning  
NR causes discoloration during processing, similar to that in food processing. It is mainly due to the phenols and aminophenols in the latex combining with oxygen to form \(o\)-quinones under the catalysis of polyphenol oxidase (PPO) [21]. The substrates (mainly polyphenols) that participate in enzymatic browning are present in plastids, whereas the enzymes are located in the cytoplasm. PPOs interact with the substrates flowing out of the plastid during processing. Phenolic substances in plants are oxidized to quinone under the catalysis of phenolase and peroxidase, and the quinone undergoes a non-enzymatic reaction (to be discussed later) to produce brown pigments, such as melanins [22]. The reaction mechanism of enzymatic browning is shown in Figure 3. The extensive difference in the hue and intensity of color pigments relies on the source of polyphenols and environmental causes of the oxidation process during the browning process [23].  
Afterward, non-enzymatic reaction with molecular oxygen gives rise to auxiliary reactions of formation of complex products, such as indole-5,6-quinone from tyrosine. Then, \(o\)-benzoquinones covalently react with another polyphenol to provide intensive-colored compounds ranging from red, yellow, green, and blue, to black. \(O\)-benzoquinones, upon their reaction with thiol compounds and aromatic amines and those in proteins, provide a great range of products, consisting of high-molecular-weight protein polymers.  
The \(o\)-quinone, which leads to phenolic discoloration, is blocked by glutathione (\(\gamma\)-glutamyl cysteineylglycine, GSH), and it is reduced by \(o\)-quinone back to catechol. If GSH is not present, \(o\)-quinone can react with other phenolic compounds or amino acids (in protein) or self-polymerize into melanin-like brown pigment [24]. With an increase in L-cysteine concentration, there is a corresponding decrease in the rate of browning reaction and PPO activity. The proposed mechanism is confirmed by the FTIR spectrum of acetone extract of the rubber sample.  
Researchers have gradually deepened their understanding of the discoloration mechanism of NR. In the beginning, they did not know what role PPO or polyphenol substances played but found in their research that the discoloration of NR was related to the content of polyphenol substances or polyphenol oxides.
# Ultrashort Vortex Pulses with Controlled Spectral Gouy Rotation
###### Abstract  
Recently, the spatio-spectral propagation dynamic of ultrashort-pulsed vortex beams was demonstrated by 2D mapping of spectral moments. The rotation of characteristic anomalies, so-called \"spectral eyes\", was explained by wavelength-dependent Gouy phase shift. Controlling of this spectral rotation is essential for specific applications, e.g., communication and processing. Here, we report on advanced concepts for spectral rotational control and related first-proof-of-principle experiments. The speed of rotation of spectral eyes during propagation is shown to be essentially determined by angular and spectral parameters. The performance of fixed diffractive optical elements (DOE) and programmable liquid-crystal-on silicon spatial light modulators (LCoS-SLMs) that act as spiral phase gratings (SPG) or spiral phase plates (SPP) is compared. The approach is extended to radially chirped SPGs inducing axially variable angular velocity. The generation of time-dependent orbital angular momentum (self-torque) by superimposing multiple vortex pulses is proposed.  
orbital angular momentum; vortex beams; femtosecond pulses; structured beams; spiral gratings; Gouy phase shift; spectral Gouy rotation; spectral anomalies Article
# A Fairness Index Based on Rate Variance for Downlink Non-Orthogonal Multiple Access System
###### Abstract
The comparison of our proposed fairness index with Jain index and GUI index is given in Table 1.  
Based on the proposed fairness index, this paper studies the power allocation problem of a downlink NOMA system and proposes a joint power allocation algorithm intra and inter clusters to maximize throughput based on fairness constraints. The paper is organized as follows. First, the system model is discussed. Second, the new fairness index based on sample variance of users' transmission rates is introduced. Third, the power allocation problem of maximizing system throughput is studied under the constraints of minimum intra and inter cluster fairness. Finally, simulation results followed by the conclusion are provided.
# Generation of Stereo Images Based on a View Synthesis Network
## 4 Results
### Quantitative Evaluation  
The Middlebury 2014 data sets consist of 33 data sets of various scenes. Each data set contains imperfect and perfect pairs of stereo images under the default setting, and under different illuminations and exposures. The imperfect and perfect pairs are the stereo images with and without calibration errors, respectively. In addition, 23 of these datasets have calibration information, such as for baseline. We selected five imperfect and five perfect pairs under the default setting from these 23 data sets as the input images for the quantitative evaluation. The baseline \(b\) is from the calibration information, and we selected the focal length \(f\) that produces the result that is most similar to the ground truth.  
The structural similarity (SSIM) index [34,35] is sensitive to nonstructural geometric distortions such as translation, rotation and scaling [36]. In our model, we emphasized the rotation of objects to enhance the stereoscopic sense. We used the complex wavelet SSIM (CW-SSIM) index [36] in this experiment because it is not affected by enhanced rotation and focuses on the SSIM between two images. The CW-SSIM is an extension of the SSIM to the CW domain. A higher CW-SSIM index indicates higher similarity in two images. Let \(c_{x}=\left\{c_{x,i}\middle|i=1,\dots,N\right\}\) and \(c_{y}=\left\{c_{y,i}\middle|i=1,\dots,N\right\}\) be two  
Figure 6: An improvement of the inpainting method proposed by [PERSON] et al. (**a**) The result using the method of [PERSON] et al., (**b**) the result using our improved method.  
sets of coefficients of the two images in the complex wavelet transform domain, respectively. The CW-SSIM index is given by  
\[\overline{S}(c_{x},c_{y})=\frac{2\left|\sum\limits_{i=1}^{N}c_{x,i}c_{y,i}^{*} \right|+K}{\sum\limits_{i=1}^{N}\left|c_{x,i}\right|^{2}+\sum\limits_{i=1}^{N} \left|c_{y,i}\right|^{2}+K}\]  
where \(c^{*}\) is the complex conjugate of \(c\) and \(K\) is a constant.  
The CW-SSIM indices are presented in Table 1. Our model obtains the highest average CW-SSIM index, which implies that it better retains the structure when enhancing the effects of rotation. Moreover, the average value of perfect-pair images is lower because the images are relatively complicated; the average value of imperfect-pair images is higher because the images are relatively simple. The images of this experiment are presented in Figures 7 and 8.
# Hybrid Deep Learning Approach for Automated Sleep Cycle Analysis
## 3 Methodology
### Class Balancing  
Class imbalance can affect model quality and generalization, particularly in supervised classification tasks. When certain classes have significantly more samples than others,  
Figure 4: Proposed methodology for EEG signal processing and sleep stage classification.  
models tend to bias their predictions towards the majority class, leading to reduced performance in minority classes [36]. Although there are various class-balancing techniques to address this issue, special care must be taken when dealing with temporal models such as LSTM. Common techniques like random oversampling or undersampling at the sample level can break temporal coherence, thus affecting the model's ability to learn long-term patterns [37]. Other techniques, such as the synthetic minority oversampling technique (SMOTE), are not suitable because they generate synthetic samples that alter sequences, compromising temporal relations [38]. To overcome this, we introduce an epoch-level random undersampling (E-LRUS) technique that preserves the sequential structure of the data. E-LRUS operates by randomly removing entire epochs from the majority classes while maintaining the continuity of the original sequence. Since the EEG signals in PSG are annotated every 30 s, the segments are treated as single epochs and assigned unique indices to retain their original temporal order. The epochs are grouped by class, and the class with the fewest epochs is identified. For each of the remaining classes, a subset of epochs equal in number to that of the minority class is randomly selected, and the rest are discarded. After the undersampling process, the selected epochs of all classes are recombined and reordered according to their original temporal indices. Mathematically, the E-LRUS algorithm is presented below. Considering a sequence set \(X\),  
\[X=(x_{i},y_{i})|x_{i}\in\mathbb{R}^{d},\,y_{i}\in 1,2,\ldots,C, \tag{2}\]  
where \(x_{i}\) denotes the \(i\)-th sample within the data collection, which, in this case, is a scalar value (\(d=1\)), and \(y_
# Positioning Methods and the Use of Location and Activity Data in Forests
## 4 Location-Based Services, the Internet of Things, Wearable Technology, and Big Data
### The Internet of Things (IoT)  
The Internet of Things (IoT) encompasses a global network of interconnected physical objects that can interact with one another and with their environments to accomplish common goals [213; 214; 215]. Each object possesses a unique identifying Internet Protocol (IP) address that is recognizable via an internal computer board, RFID tag, barcode, or any other networked means of identification [213; 214; 215; 216] Sensors associated with the object collect information about its physical environment, such as temperature, motion, or location [213; 214]. Sensor data, along with objects' identifying addresses, are then transferred through wireless networks to a database for storage and processing. This big data is typically too immense for physical hardware to accommodate and is thus usually transmitted to a virtual, shared cloud network, where it can be stored and processed by middleware. After processing, the middleware prepares visualization of the data to be easily interpreted and understood by end-users [213; 215] for a variety of analytics.  
Similar to location-based services, the IoT entails sharing potentially personal information through a wireless network. Maintaining user security and privacy, such as through data confidentiality, in which only authorized entities can access information, is a critical challenge for the IoT [213; 214; 217]. Another challenge is the need for enough addressing space to accommodate the billions of interconnected objects in the network, each with its own distinctive identifying address [213; 214]. Additionally, the IoT requires increased processing capabilities to accommodate the immense number of service requests. Recent studies have highlighted the use of fog computing to complement cloud centers [213; 218; 219]. Fog devices are smaller, cheaper, and generally have fewer performance delays than cloud centers, making them the ideal choice for handling real-time services requiring immediate processing [213; 219]. Fog devices can route other requests requiring extensive analysis or more permanent storage to a cloud computing center [219].  
IoT technology has been applied across multiple industries. In transportation and logistics, it has been used for collision avoidance systems, augmented maps, self-driving vehicles, traffic incident information, route optimization, and delivery time status [213; 214]. IoT enables industrial automation, in which computerized robots complete manufacturing tasks, from parts production to quality control. Also, with IoT, health care providers can receive real-time physiological data on patients. IoT is integral to smart home and building technology, allowing users to control appliances, electronics, lighting, security, and temperature remotely or to program activity based on conditions like time of day for lighting or current use for electrical equipment [213; 214].
# Energy Harvesting for TDS-OFDM in NOMA-Based Underwater Communication Systems
## 7 Conclusions  
In this paper, a novel energy-harvested TDS-OFDM-NOMA scheme is introduced for UWA communication. The proposed energy harvested scheme uses the long UWA channel tap delay in harvesting energy using TS-SWIPT and increases the battery lifetime. The UWA TDS-OFDM-NOMA communication can efficiently harvest the energy via the long guard interval. By the proposed scheme, the drawback of the long guard interval used in UWA communication is converted to be useful in energy harvesting. On the other hand, TDS-OFDM-NOMA fulfills high throughput communication over the limited UWA channel bandwidth. It is observed that the proposed TDS-OFDM-NOMA performs better as compared with the conventional CP-OFDM-NOMA in the UWA channel. Results were driven in terms of BER, total system throughput, energy efficiency, and harvested energy.  
Conceptualization, H.E.; formal analysis, H.S.; investigation, H.E. and H.S.; methodology, H.E. and H.S.; project administration, H.E.; resources, H.S.; software, H.E.; supervision, H.E.; writing--original draft, H.E. and H.S.; writing--review & editing, H.E. and H.S. All authors have read and agreed to the published version of the manuscript. This research was funded by Science, Technology & Innovation Funding Authority (STDF) under grant number 46143. The authors declare that the data used to support the findings of this study will be available from the corresponding author upon request. This paper is based upon work supported by the Science, Technology & Innovation Funding Authority (STDF) under grant number 46143. The authors declare no conflict of interest.