esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Experiments on port water quality identification based on random image sets have been conducted. The reliability and development requirements of the method have been evaluated showing that computer vision tools are suitable for these monitoring tasks. Several computer vision techniques were considered for use in real-time marine pollution monitoring, with the decision that Image Classification was the most adequate for such tasks due to its high accuracy rates and low training requirements. These requirements and the possibility to improve accuracy through retraining with increased image sets were considered due to the difficulty in obtaining port spill images, finding that progressive implementation can not only offer functional monitoring systems in a shorter time frame, but also reduce the total development cost for a system with the same accuracy level. A novel performance metric for the case of computer vision systems in the port environmental management application was put forward and tested, providing meaningful conclusions.  
Future lines of research include the development of additional methods that improve the time taken to obtain spill and waste images, ultimately increasing the applicability and speed in which it provides meaningful information to port decision makers. In addition, future works include the consideration of a new class for combined spill and waste for those ports that receive mixed (i.e., waste and spills) discharges from waterways or water collection infrastructure. In addition, image preparation and pre-filtering could also yield algorithms with higher performance metrics and help overcome limitations for monitoring systems where camera location is not optimal or where reflected sunlight makes images hard to classify.  
Conceptualization, M.E., M.G. and C.G.; Data curation, M.M. and A.P.; Formal analysis, M.M. and P.P.; Funding acquisition, C.G.; Investigation, M.M. and A.P.; Methodology, P.P., M.E. and M.G.; Project administration, M.M.; Software, A.P.; Supervision, M.E., M.G. and C.G.; Writing--original draft, M.M. All authors have read and agreed to the published version of the manuscript.  
This work is supported by ECOBAYS project (MCIN/AEI/10.13039/501100011033) funded from the Agencia Espanola de Investigacion (Spanish Research Agency).  
Not applicable.  
Data Availability Statement: Project datasets are not publicly available as images used are property of Garau Ingenieros, SLU.  
The authors are grateful for the help provided by Ports de Balears for providind the site; to Vicepresidencia i Conselleria d'Innovacio, Reecerca i Turisme del Govern de les Illes Balears through Direccio General d'Innovacio i Recerca for support to SPILLSURVEY project; and to Secretaria d'Universitats i Recerca del Dpt. d'Economia i Coneixement de la Generalitat de Catalunya (ref. 2014 SGR1253) for supporting the research group.  
The authors declare no conflict of interest.
## 3 Case Study: Measurement Campaign Goal and Setup
### RADARSAT-2 Products  
The RADARSAT-2 SAR system operates at a center frequency of 5.405 GHz. The mode-dependent bandwidth goes up to 100 MHz. Center frequency and maximal bandwidth are therefore identical to those of the Sentinel-1 mission for which the Kalibri transponder was designed.  
All eight RADARSAT-2 products were acquired in the _Wide Ultra-Fine_ mode in VV polarization and delivered as single-look complex (SLC) images. The products offer a high nominal single-look  
Figure 1: Artist’s rendering of DLR’s new C-band Kalibri transponder, mounted on a two-axis positioner.  
geometric resolution of \(1.6\,\mathrm{m}\times 2.8\,\mathrm{m}\) (range \(\times\) azimuth resolution) [19]. A high-resolution mode was chosen as to increase the target-to-clutter ratio for the used point targets. Special care had to be taken in choosing the dynamic range of the processed products so that the peak of the transponder pulse response is still within the processed image's dynamic range.  
An overview of all overpass times and beams is shown in Table 1.
# Single Trace Analysis against HyMES by Exploitation of Joint Distributions of Leakages
## 2 Related Works
### Code-Based Cryptography  
Error correction codes were first developed in 1947 by [PERSON]. The technique was developed to enable the reliable delivery of digital data over unreliable communication channels. In this technique, the sender encodes the data using an error-correcting code prior to transmission. The additional information added by the code is used by the receiver to recover the original data. However, decoding is only efficient for linear codes with efficient decoding algorithms. The code-based cryptosystems make use of the fact that decoding the syndrome of a general linear code is known to be NP-hard, while efficient algorithms exist for the decoding of specific linear codes. The Goppa codes are an example of efficient correcting codes that can be turned into a secure coding scheme when the decoding functions are kept secret. Only an attacker in possession of the secret decoding function can remove the secret mapping and recover the plaintext. Before describing the Goppa codes, the definition of binary linear code is presented in Definition 1. The definitions of the generator matrix \(G\) and the parity check matrix \(H\) are as denoted in Definition 2.  
**Definition 3**: _Binary linear code \([n,k]-C\) is a one-to-one function that takes a \(k\)-bit string message and outputs a \(n\)-bit string codeword._  
**Definition 4**: _If the row vector space of \(k\times n\) matrix \(G\) spans \(C\) over finite field \(F\) and the dimension of row vector space is \(k\), then matrix \(G\) is the generator matrix of \([n,k]-C\) over \(F\). For the \((n-k)\times n\) matrix \(H\), if the column vectors of \(H^{T}\) are the basis of the null space of \(G\), \(H\) is the parity check matrix of \(C\)._
## 4 Simulation and Experimental Analysis
### Wearable Experimental Results and Discussion  
To verify the effectiveness of the self-adaptive-coefficient double power sliding mode control method, a wearable experiment was conducted on the lower limb rehabilitation exoskeleton robot prototype using selected healthy testers. The gait trajectory tracked by the lower limb rehabilitation exoskeleton robot still adopted the trajectory curve shown in Equation (21), and during the experiment, the lower limb of the tester was completely driven by the lower limb rehabilitation exoskeleton robot. Figure 9 shows the seven intermediate motion states taken from the video of a complete gait cycle recorded in the experiment. The tracking trajectory curve of each robot joint is shown in Figures 10-13, and the tracking error curve is shown in Figure 14.  
Figure 11: Trajectory tracking curve of the right knee joint.  
Figure 12: Trajectory tracking curve of the left hip joint.  
Figure 13: Trajectory tracking curve of the left knee joint.  
Figures 9-13 show that based on the self-adaptive-coefficient double power sliding mode control method, the trajectory tracking error of the left and right hip joints ranged from \(-0.55^{\circ}\) to \(0.2^{\circ}\), and the trajectory tracking error of the left and right knee joints ranged from \(-1.5^{\circ}\) to \(1.65^{\circ}\) when the lower limb rehabilitation exoskeleton robot prototype drives the tester to track the gait trajectory. Compared with the previous simulation results, the tracking error change of the hip joint was unobvious. The tracking error of the knee joint was increased, but it still had good tracking accuracy. Human gait movement was uncertain. Therefore, the movement of the tester wearing the lower limb rehabilitation exoskeleton robot can be regarded as a random disturbance. When the human wearing the exoskeleton carries out the gait experiment, the tracking error was very small. This shows that the self-adaptive-coefficient double power sliding mode control method proposed in this paper not only achieved good control accuracy, but also had good robustness. In this analysis, the tester was wearing the lower limb rehabilitation exoskeleton robot while walking on the ground, thus, when the legs were grounded, they were greatly disturbed by the ground, which increased the tracking error of the knee joint. In the follow-up research, we will deeply analyze the impact of the ground on the lower limb rehabilitation exoskeleton robot, establish the corresponding mathematical model, and design a controller with better performance.
# Real-Time Simulation and Sensor Performance Evaluation of Space-Based Infrared Point Target Group
## 5 Results and Discussion  
The improvements of proposed method compared to others are shown in Table 8. In terms of simulation performance, while the original techniques excelled in establishing simplistic space-based infrared simulation scenarios and presenting results in an intuitive manner [7,29], they encountered challenges when confronted with complex scenarios. Furthermore, they lacked support for fast infrared simulation output. Conversely, the proposed method achieved real-time simulation of space-based sensors on space point targets, thereby enhancing its practicality. In terms of background generation, the original STK-based methods could generate star backgrounds without requiring complex setups [7]. However, the absence of customization options for star catalogs and the inability to export star simulation results limited their practicality. Other star background simulation methods accurately calculated star positions [17,18], yet they did not provide further discussion of spatial targets within the FOV. In this regard, the proposed method generated high-quality star background for the targets, effectively compensating for STK's deficiency in star simulation. In terms of target features, the original techniques primarily focused on the influence of target attitude while setting the temperature to a fixed value or a linear function of time [7,37,41,43]. In contrast, the proposed method embraced a dynamic temperature model and accounted for the impact of micromotion during the simulation process. In terms of development mode, the original methods fulfilled their specific application requirements through secondary development of STK, leveraging its data analysis capabilities [20, 21, 22, 23], but they did not mention the program maintainability and expandability. The proposed method employed contemporary tools and connection modes to enhance the system's scalability. In short, we established the foundation of a modern space-based simulation platform.
# A Comprehensive Evaluation of Possible RNSS Signals in the S-Band for the KPS
## 5 Discussion  
The S-band has emerged as the desirable frequency band for the next-generation RNSS signal. Therefore, South Korea is considering using the S-band for the KPS. For this reason, we investigated possible RNSS signals in the S-band and analyzed their RF compatibility, spectral efficiency, ranging performance, and receiver complexity using the FoMs.  
The signal performance of the candidates is evaluated differently by the reception environment. Therefore, this section summarizes and discusses the simulation results under the specific receiver parameters in Table 6 and the multipath delay of 51 m.  
Figure 8 shows the SSCs of the existing S-band signals with each candidate. A low SSC means that the RF compatibility between the two signals was good. The SSCs between candidates and the NavIC SPS signal had a different tendency than SSCs between the NavIC RS signal. In other words, candidates having good RF compatibility with the NavIC SPS signal had poor RF compatibility with the NavIC RS signal. BOC\({}_{\text{c}}\)(4,4) and BOC\({}_{\text{s}}\)(3,1) had the best RF compatibilities with the NavIC SPS signal and RS signal, respectively. Meanwhile, BOC\({}_{\text{c}}\)(4,4) had the best RF compatibilities with the Globalstar and itself.  
The spectral efficiencies for each candidate are shown in Figure 9. A high spectral efficiency indicated that the signal power distribution was concentrated within 16.5 MHz. BPSK signals had high spectral efficiency and BOC\({}_{\text{c}}\)(4,4) had the worst spectral efficiency.  
The ranging performance was analyzed using the Gabor bandwidth and the RA of the MPEE. Figure 10 shows the Gabor bandwidth and the RA of the MPEE for each candidate. A high Gabor bandwidth and a small RA of the MPEE imply that the signal has good ranging performance. BOC\({}_{\text{s}}\)(5,2) was the best in terms of the Gabor bandwidth (also DLL error) and BOC\({}_{\text{c}}\)(3,3) was the best in the multipath error performance at a typical multipath delay corresponding to urban areas.  
Figure 8: The SSC values between the existing S-band signals and each candidate.  
Figure 9: The spectral efficiency for each candidate.  
As seen in Figure 11, the AMSR of the BPSK signals was close to zero. BOC\({}_{\text{s}}\)(3,1) and BOC\({}_{\text{c}}\)(3,1), which had the highest modulation order, had high AMSRs of 0.67 and 0.70, respectively. Since a signal with a high AMSR requires a relatively complex receiver implementation, BPSK modulation schemes are preferred for civilian uses.
# A Privacy-Preserving Record Linkage Method Based on Secret Sharing and Blockchain
## 3 Preliminaries and Background
### Smart Contracts  
Smart contracts are self-executing code protocols deployed on the blockchain that automatically enforce predefined rules [20]. They are the core mechanism for automating and enforcing the protocol processes of this method, handling specific operational logic, including implementing fine-grained role definitions and data access control policies, coordinating the storage and distribution of secret shares and masked data on IPFS to computational nodes, and managing the audit and verification of final results, as well as dynamically updating the reputation scores of computational nodes.
## 3 Results and Discussion
### Microstructural Analysis  
As illustrated in Figure 3, transmission electron microscopy images depict SiO\({}_{2}\)@Fe\({}_{3}\)O\({}_{4}\) nanoparticles synthesized via hydrolysis of varying quantities of TEOS. Clearly discernible from the images is the encapsulation of Fe\({}_{3}\)O\({}_{4}\) within SiO\({}_{2}\). Notably, the sample prepared with 2 mL of TEOS exhibited suboptimal encapsulation and SiO\({}_{2}\) coating, displaying a degree of disorder. Conversely, the utilization of 8 mL of TEOS resulted in an excessively thick SiO\({}_{2}\) coating attributed to an overabundance of TEOS, leading to the gradual formation of Fe-O-Si bonds through interactions with solution-derived silicon species (Si\({}^{4+}\)). This phenomenon compromised the quality of Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\) nanoparticles for photothermal applications, rendering them unsuitable for biological contexts. In contrast, the introduction of 5 mL of TEOS yielded a homogeneously distributed sample devoid of irregularities, ensuring effective encapsulation of Fe\({}_{3}\)O\({}_{4}\) within SiO\({}_{2}\) to form approximately 18 nm coatings. Elemental mapping analysis, as depicted in Figure 4 for SiO\({}_{2}\)@Fe\({}_{3}\)O\({}_{4}\), confirmed the encapsulation success by revealing a confined spatial distribution of Fe elements enveloped by Si and O species. Further scrutiny via scanning electron microscopy images in Figure 5 showcased the successful integration of Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\) with scaffold materials for bone scaffold fabrication. Both the PVA/HA/\(\beta\)-TCP/PCL composite bone scaffold and the 3%, 5%, and 7% photothermal composite scaffolds exhibited uniform micro-pore structures without localized aggregation, underscoring the proficient preparation of SiO\({}_{2}\)@Fe\({}_{3}\)O\({}_{4}\) and its efficacious implementation in bone tissue engineering scaffolds, thereby mitigating the detrimental impact of Fe\({}_{3}\)O\({}_{4}\) aggregation on cellular behavior.  
Figure 3: Transmission electron microscopy image of SiO\({}_{2}\)@Fe\({}_{3}\)O\({}_{4}\). (a) SiO\({}_{2}\)@Fe\({}_{3}\)O\({}_{4}\) prepared with 2 mL TEOS; (b) SiO\({}_{2}\)@Fe\({}_{3}\)O\({}_{4}\) prepared with 5 mL TEOS; (c) SiO\({}_{2}\)@Fe\({}_{3}\)O\({}_{4}\) prepared with 8 mL TEOS.  
Figure 2: XRD patterns of Fe\({}_{3}\)O\({}_{4}\) and SiO\({}_{2}\)@Fe\({}_{3}\)O\({}_{4}\).
# Integrated Deep Renovation of Existing Buildings with Prefabricated Shell Exoskeleton
## 3 Application to a Real Case Study  
The AdESA system was first applied as a prototype to a case study, a gymnasium hall built in 1981, owned by the Municipality of Brescia, a city in northern Italy classified as being in a moderate-high seismic-prone zone (ag = 0.168, [14]) and Climatic Zone E (HDD = 2410, DPR 412/93). The gymnasium hall was first surveyed with laser-scanner techniques and validated with traditional geometric surveys, in order to obtain a georeferenced and measurable 3D model of the building envelope with high accuracy. From this first geometric model, additional structural and energy surveys were carried out, and a 3D BIM model was developed and later used as a reference for the architectural drawings and as the basis to develop the structural and energy models of the building in its as-is condition [16].  
Structural, energy, and architectural/functional audits were then carried out to analyze the major vulnerabilities of the building and to define the performance objectives of the intervention. The design of the AdESA solution was carried out following a multidisciplinary approach, on the basis of joint energy, structural and architectural considerations. The project was then implemented in the BIM model, later used to extract construction documents, bills of quantities, drawings and specifications for the carpentry pieces, and assembly/construction instructions. The structural system was entirely prefabricated in the factory, shipped to the construction site, and assembled in situ. In this prototype case study, the three layers were assembled in sequence, upon completion of the innermost layer (structural layer, then energy and finishing layer). The ongoing re-engineering of the technique, particularly of the connections, will enable the preinstallation of the energy and finishing layers in the factory, to substantially reduce construction time; in situ operations will encompass the assemblage and covering of the interfaces between adjacent sandwich panels.
###### Abstract  
In this paper, the packed bed dielectric barrier discharge (DBD) with needle-plate electrode configuration is presented to study the effects of electrical parameters, such as pulse duration and pulse rising and falling time, on discharge characteristics under air and nitrogen. The waveforms of the voltage and the discharge current, discharge evolution images, and the emission spectral of N\({}_{2}\) (C\({}^{3}\Pi_{\text{u}}\rightarrow\) B\({}^{3}\Pi_{\text{g}}\)) and N\({}_{2}\)\({}^{+}\) (B\({}^{2}\Sigma_{\text{u}}\rightarrow\) X\({}^{2}\Sigma_{\text{g}}\)\({}^{+}\)) are collected to investigate the discharge current, as well as the spatial distribution of the discharge modes and the reactive species in the packed bed reactor specifically. It is found that the pulse duration and pulse rising and falling time can regulate the discharge current. Firstly, increasing the pulse duration and the pulse rising and falling time can both increase the discharge duration. Secondly, the peak value of the discharge current has an obvious increasing trend with the pulse duration. Finally, the discharge start time can be delayed by increasing the pulse rising and falling time. A bright discharge channel is distributed at the top of the reactor, while the discharge is diffused at the bottom of the reactor. The generation of N\({}_{2}\)\({}^{+}\) (B\({}^{2}\Sigma_{\text{u}}\)\({}^{+}\)) tends to depend on the existence of the streamer channel, and N\({}_{2}\) (C\({}^{3}\Pi_{\text{u}}\)) can be generated in the entire discharge area. In addition, the discharge operated in pure nitrogen can reach higher current values, a stronger discharge intensity, and longer existence time for the reactive species than in the air.  
packed bed dielectric barrier discharge; voltage pulse shape; gas composition; optical emission spectrum; discharge images +
Footnote †: journal: Applied Sciences
## 1 Introduction
This sequential process not only balances the class distribution but also improves data quality by reducing noise, leading to better classification performance compared to using SMOTE or ENN alone [11]. Building on the work of [PERSON] et al., who applied regressive machine learning algorithms for the real-time monitoring of bedridden patients at risk of falls [13], this study aims to enhance predictive performance using the SMOTEENN technique. [PERSON] et al. analyzed six unique movements commonly performed by patients on hospital beds and developed an automated risk assessment system to reduce the need for constant monitoring by hospital staff [14]. They addressed class imbalance in their dataset using SMOTE to improve model performance. This study evaluates the effectiveness of SMOTE and SMOTEENN in addressing class imbalance and improving predictive accuracy. By applying SMOTEENN to the same dataset used by [PERSON] et al., the goal is to determine whether this hybrid approach provides improvements over SMOTE alone. Assessing the performance of SMOTEENN in this context has the potential to contribute to the development of more accurate and reliable real-time monitoring systems for fall risk assessment in bedridden patients, ultimately reducing the incidence of falls without requiring constant supervision by hospital staff. This study focuses on demonstrating the practical efficacy of existing resampling algorithms, namely SMOTE and SMOTEENN, in addressing class imbalance within the context of regression-based ML models. While these techniques are not novel in themselves, their application and performance in the specific domain of fall risk assessment for bedridden patients using real time data have not been extensively explored. By conducting a comprehensive comparative analysis across various sample sizes and regression models, this research aims to provide insights into the effectiveness of SMOTE and SMOTEENN in improving predictive accuracy and model generalization in this critical healthcare application. The findings of this study can contribute to the development of more reliable and accurate real-time monitoring systems, ultimately reducing the need for constant patient supervision and enhancing patient safety.
# Production of Microfibrilated Cellulose from Fast-Growing
## 2 Materials and Methods
### Pulps and MFCs Characterization
#### 2.5.5 X-ray Diffraction Analysis (XRD)  
The crystallinity of the fibers in pulps and MFC samples was analyzed by powder X-ray diffraction (XRD) using a Bruker D8 Advance instrument (Bruker, Billerica, MA, USA) equipped with a Cu anode (CuK\(\alpha\) radiation) and a Ni filter. For this analysis, the samples were pressed as pellets. The crystallinity index (CI) was calculated by the Segal method as seen in Equation (4)  
\[\text{CI}=(\text{I}_{200}-\text{I}_{\text{AM}})/\text{I}_{002}\cdot 100 \tag{4}\]  
where I\({}_{002}\) is the intensity of the (002) reflection and I\({}_{\text{AM}}\) is the intensity attributed to the amorphous phase at approximately 2\(\Theta\) = 18.5\({}^{\circ}\).  
The crystallite size t was calculated according to the Scherrer equation using the (002) reflection:  
\[\text{t}=(\text{k}\cdot\lambda)/(\beta\cdot\cos\Theta) \tag{5}\]  
where k is the correction factor (0.9), \(\lambda\) is the radiation wavelength (0.15418 nm), \(\beta\) is the width of the peak at the half of maximum height (in radians), and \(\theta\) is the diffraction angle of the peak.