esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
#### Limitations of the Study  
The methods used have generated good results and have met the objectives set out in this study; however, it is important to describe some limitations of the methodology so that future researchers can consider them. The main limitations are as follows:  
Number of variables: more spectral and environmental variables could be included to improve the effectiveness of the model; in this work it was complicated to do so due to the lack of information; for a long time, the Andean zones were little valued and studied.  
Satellite images: attention should be paid to the calibration, atmospheric, and topographic correction of the images.  
Learning methods: this method can relate variables as appropriate because it does not have a fixed distribution, so it can sometimes be a problem to choose an appropriate final answer. To confirm the effectiveness of the method, a confirmation of the data should be made from the control points collected in the field.  
Ecosystem valuation: the information obtained in the study related to the EV of the area could be subjective as there may be changes among individuals and societies that are part of the economic activities over time, which would change the condition of the variables and therefore the results [47].  
The methodology assumes that the monetary benefits from the exploitation of the natural resource will be increasing, and it is possible that this will change due to the viability of the country's economic development.  
Errors may occur in EV mapping with respect to land use due to the nature of the data used in the analyses. For example, the spatial distribution of production costs may vary depending on farmers' land use techniques, crop rotations, weather changes, and soil properties. Uncertainty can also be generated by the variability of product yields over time [49].  
The information obtained in relation to EV can be interpreted in different ways, as it will depend on the theoretical approach from which the analysis is conducted, which may influence the statements about the study area.
# Formulating Cybersecurity Requirements for Autonomous Ships Using the SQUARE Methodology
## 2 Background  
### Autonomous Ship  
The core technologies of autonomous ships comprise situational recognition and detection, judgment, action and control, and infrastructure technologies, as shown in Figure 1. In autonomous ships, detection, judgment, and action are performed using AI, and only the action outcome is monitored by the crew members or coastal control centers [8].  
The situational recognition and detection technology involves a vision system, such as radar, LiDAR, CCTV, and automatic identification system (AIS), that can accurately recognize weather conditions and different objects at sea.  
Judgment technology allows autonomous ships to automatically sail, berth, and moor based on the data collected and predicted using vision systems. It can automatically set a safe, economic navigation route according to sea conditions, predict failures, and take action in advance.  
Figure 1: Systems in autonomous ships.  
The action and control technology controls the ship's position, speed, and engine via AI with its judgment technology. It is a technology that allows remote control of the ship (in case of emergency) from the coastal control center.  
The infrastructure technology corresponds to a seaport automation technology that can operate autonomous ships and includes laws, institutions, and standards.  
Each technology is classified by its role, but in autonomous ships, these technologies and systems cannot be considered independent systems because they are recognized, detected, judged, and controlled based on each other's data.
## 4 System Model
### Fading Model
#### 4.3.1 Fading Due to Time Spread
The curve characterizes a degradation of reception. However, to obtain a successful reception with a BER of \(10^{-4}\), the signal-to-noise ratio must be increased to 54 dB. Thus, Expression (4) gives a sensitivity of \(-78.63\) dBm, and finally, according to (3), the range becomes \(3.5\) km. Therefore, the multiple paths taken by the locomotive's signal strongly affect the range of the railway telecommunication system. In general, when designing a wireless telecommunications system [23], a margin is defined for the sensitivity of the receiver, which is known as the fade margin. In the study of the railway telecommunication system, the sensitivity is \(-100\) dBm, with a fading margin of \(5\) dB. Therefore, the sensitivity of the receiver is fixed at \(-95\) dBm. For closed environments, the fading margin is between \(6\) and \(10\) dB. To confirm this result, let us discuss about the spreading delay value \((\sigma_{\tau})\) parameter representing the RMS of all multipath propagation time delay. The propagation channel has a bandwidth frequency parameter, which is coherence bandwidth and also known as Bc. Bc is often defined as \(\frac{1}{(2.\sigma_{\tau})}\) : if the signal bandwidth is less than Bc, the propagation channel is flat in frequency. This means that \(\sigma_{\tau}\) is much smaller than the symbol duration, and therefore, there will not be any intersymbol interferences (ISI). In the opposite direction, if the signal bandwidth is larger than the coherence bandwidth, it means that the symbol duration is lower than \(\sigma_{\tau}\), and therefore, there will be ISI, which might degrade the transmission. With the values used earlier for \(\tau\) in MATLAB simulations, \(\sigma_{\tau}\) is equal to \(5.916\) us. As the XTends module's transmission rate is \(125\) kbps, the symbol duration is \(8\) us, and the coherence bandwidth then equals \(84.5\) kHz: therefore, symbol duration and \(\sigma_{\tau}\) are very closed, meaning that there will be ISI, and this confirms the performed MATLAB simulations results. It is now interesting to consider the influence of the movement of the receiver, which will be the case with the Hi-Rail pickup leading to the Doppler effect.
## 2 Model Description
### Vehicle Model
The dependency of the drag reduction on the inter-vehicle distance, which was experimentally validated for HDVs in [21], is introduced in the model with the following equations:  
\[c_{x,i}=c_{x,0}\cdot k_{cx} \tag{4}\]  
\[k_{c_{x}}=1-\Delta C_{x}\Delta C_{x}(\%)=\left(1-\frac{a_{3,i}d^{3}+a_{2,i}d ^{2},a_{1,i}d+a_{0,i}}{b_{3,i}d^{3}+b_{2,i}d^{2}+b_{1,i}d+b_{0,i}}\right)\times 100 \tag{5}\]  
where \(d\) represents the relative distance, \(c_{x,0}\) is the undisturbed drag coefficient (i.e., the drag coefficient of the isolated vehicle), and \(a_{n,i}\) and \(b_{n,i}\) are empirical coefficients obtained by experimental data fitting [21]. Figure 2 shows the trend of the drag coefficient reduction versus the inter-vehicle distance for a platoon composed of three HDVs. It is noticeable that the middle (red line) and last (black line) vehicles are more affected by the drag reduction than the leader. The map, derived from the literature, was included in the platoon model to evaluate the potential for energy savings. Figure 3 shows the energy consumption comparison related to a set of three trucks following a WLTP Class 3 driving cycle: the group of bars on the left represents the vehicles moving far enough apart not to be affected by the slipstream of the preceding vehicle, while the bars on the right refer to the same vehicles moving close together thanks to a platoon control system. The results demonstrate that each vehicle in the platoon shows energy savings; in particular, the advantage increases from the first to the last vehicle. Obviously, the quantitative results depend on vehicle data and platooning control parameters; Figure 3 refers to the numerical values used in Section 5. The inter-vehicle distance and its derivative are defined as:  
\[\left\{ \begin{aligned} & d_{i-1,i}=x_{i-1}-x_{i}-l_{i-1}\\ &\dot{d}_{i-1,i}=v_{i-1}-v_{i}\end{aligned} \right. \tag{6}\]  
The vehicle platooning control system is conceived to exploit the advantages of a collaborative ACC. More specifically, the test scenario is based on a lead vehicle following the reference speed of a driving cycle, thanks to a PID controller, whereas the string of followers is controlled by a centralized control unit, which exploits the information from the sensors, i.e., vehicle velocity \(v_{i}\) and inter-vehicle distance \(d_{i-1,i}\), to track a reference value of inter-vehicle distance (according to the spacing policy) and a target speed of the platoon. The reference values are computed by the centralised controller, which in turn evaluates the amount of torque to be applied by each actuator in the platoon in order to compensate for the velocity and distance errors. All the data exchange (feedback signals, references, and inputs) is instantaneous. Thus, neither delay nor disturbance are accounted for. The idea of a centralised control unit is investigated considering two different control solutions: the linear quadratic controller (LQC) and a reinforcement learning-based controller (RL).
# Trends in Quantification of HbA1c Using Electrochemical and Point-of-Care Analyzers
## 8 Challenges in Diagnosis Based on HbA1c Measurements  
The total Hb values vary between individuals, and hence measurement of HbA1c alone results in errors in the diagnosis of diabetes, especially in anemic individuals [107]. Therefore, it becomes necessary to measure both total Hb and HbA1c levels in individuals for error-free diagnosis. Another key challenge in diabetes diagnosis using HbA1c arises due to glycated hemoglobin variants that exist due to various congenital disorders of globin chain synthesis, called \"hemoglobinopathies\". Some of these variants directly interfere with the HbA1c detection, leading to erroneous values, while some other variants cause interference by premature turnover of red blood cells (RBC). In cases where an individual is heterozygous, the individual will be asymptomatic and have normal red cell survival [108]. However, the presence of these variants can lead to the under- or over-estimation of glycated hemoglobin. Therefore, initial testing needs to be performed to screen the person for hemoglobinities before HbA1c measurements to avoid errors. Similarly, conditions leading to blood loss or hemolytic anemia have also been reported to result in erroneous HbA1c values [109]. Another facet in diagnosis using HbA1c levels is the variations in the predictions based on HbA1c with those based on blood glucose and glycated albumin levels, which are referred to as the hemoglobin glycation index (HGI) and glycation gap (GG), respectively [110]. Both glucose and glycated albumin are extracellular entities, while HbA1c formation is intracellular. Therefore, HGI and GG have been attributed to the difference in permeation of glucose through the red blood cell (RBC) membrane to glycate Hb, the rate and position of glycation and deglycation in the globin chains, as well as the lifespan of the RBCs where the non-enzymatic glycation takes place [111].
## 2 Materials and Methods
### Chemicals and Standards  
NaOH, phenolphthalein, tartaric acid, Na\({}_{2}\)CO\({}_{3}\), and gallic acid were from Tianjin Damao Chemical Reagent Factory (Tianjin, China); Folin-Ciocalteu phenol assay reagent was from Shanghai Ruiyong Biological Technology Co., Ltd. (Shanghai, China); HCl was from Tianjin Miou Reagent Co., Ltd. (Tianjin, China); ethanol, NaHCO\({}_{3}\), NaCl were from Sinopharm Chemical Reagent Co., Ltd. (Shanghai, China); methanol and formic acid were from Thermo Fisher Scientific Co., Ltd. (Shanghai, China); LC-MS grade water was from Honeywell International Co., Ltd. (Morris Plains, NJ, USA).  
Aroma volatile standards, isoamyl alcohol, isoamyl acetate \(\geq\)99.7%, acetoin \(\geq\)98.0%, and nonanal \(\geq\)98.0% were from Sigma Aldrich (St. Louis, MO, USA); Sinalool \(\geq\)96.0%, 1-octanol \(\geq\)99.7%, methionol \(\geq\)99.0%, and 4-methyl-2-pentanol \(\geq\)98.0% were from Tokyo Chemical Industry (Tokyo, Japan); phenolic standards, (+)-catechin 99.2%, (\(-\))-epicatechin 99.7%, (\(-\))-epicatechin gallate 98.1%, 3,4-dihydroxybenzoic acid 99.9%, and gallic acid 90.9% were from the National Institutes for Food and Drug Control (Beijing, China); gallocatechin 98.54% and epigallocatechin 99.77% were from Chengdu Biopurify Phytochemicals Ltd. (Chengdu, China); quercetin dihydrate \(\geq\)98.0%, myricetin \(\geq\)98.0%, isorhamnetin \(\geq\)98.0%, and syringe acid \(\geq\)98.0% were from Solarbio Science and Technology Co., Ltd. (Beijing, China); caffe acid 98.0% was from Yuanye Bio-Technology Co., Ltd. (Shanghai, China).  
Figure 1: Distribution map of wine samples of six vintages (2013–2018), from the six sub-regions from the Ningxia region of China. (The colored numbers represent the number of samples from each vintage that were collected from each sub-region.).
## 8 Conclusions  
This paper presented a comprehensive technical analysis of Dual Two-Level Inverter (DTLI) topologies for motor drive applications. The investigation covered the fundamental operating principles of DTI systems and examined three key structural configurations: the conventional Common DC Bus (C-DCB), the Two-Isolated DC Bus (TI-DCB), and the more recent Floating Capacitor-based DC Bus (DCB-FC). A detailed mathematical model of the DTI system was developed to support this analysis, highlighting voltage distribution mechanisms and control strategies specific to each architecture.  
To evaluate system performance, several critical criteria were considered, including total harmonic distortion (THD), common-mode voltage (CMV), zero-sequence voltage (ZSV), energy efficiency, floating capacitor voltage behavior, and the risk of DC bus overcharging. In addition, this study included a comparative summary (Table 6) of several PWM techniques, emphasizing their influence on waveform quality and overall system efficiency across the three configurations.  
Among the architectures studied, the dual C-DCB configuration stood out for its simplicity and robustness, attributed to its symmetrical design and straightforward control implementation without the need for additional hardware. However, its limitations in suppressing ZSV, alongside the DC bus overcharging risks associated with the TI-DCB configuration, motivated further interest in the DCB-FC structure. The DCB-FC demonstrated superior performance thanks to its self-regulated floating capacitor voltage and the elimination of isolation requirements.  
In conclusion, both the C-DCB and DCB-FC configurations exhibit strong potential for modern high-performance power conversion systems, offering practical trade-offs between simplicity, control complexity, and electromagnetic performance. These findings serve as a foundation for the development of more efficient and reliable inverter systems in future industrial and renewable energy applications.  
This research received no external funding.  
The authors declare no conflict of interest.
# Effect of Plasma Treatment of Titanium Surface on Biocompatibility
## 3 Results
### Protein Adsorption  
We measured the quantity of bovine serum albumin (BSA) adsorbed to the plasma-treated and untreated titanium disk surfaces after incubation for 1, 3, 6, and 24 h. More BSA was adsorbed to the plasma-treated than the control disks at every time point. (Figure 5).  
Figure 4: Contact angle of titanium and plasma-modified titanium. There was a clear difference between the contact angles (titanium; 32\({}^{\circ}\), plasma-modified titanium; 0\({}^{\circ}\)) of the plasma-treated titanium disks and those of the titanium disks (Figure 4).  
Figure 3: X-ray photoelectron spectroscopy analysis of titanium and plasma-modified titanium. It showed the intensity of the O1s peaks increased while that of the C1s peaks decreased in response to plasma irradiation.  
Figure 2: Scanning probe microscopy of titanium and plasma-modified titanium. No changes in surface roughness (titanium; Ra = 7.5, plasma-modified titanium; Ra = 8.2) were detected on the test disks observed by scanning probe microscopy (SPM) (Figure 2).
# Time-Series Prediction of Failures in an Industrial Assembly Line Using Artificial Learning
## 3 Materials and Methods
### Modelling Using the NAR Network
\(y_{i}\) is outputs coming from the previous layer. The \(j^{th}\) neuron's output is \(out\). It is calculated via an activation function as seen in Equation (6). Its threshold can be [0, 1]. \[out=f_{act}\big{(}net_{j}\big{)} \tag{6}\]  
If \(out\) is less than the threshold, then \(out\) can be called weak information. It is forgotten, in the current iteration, by creating no output. Thus, the network can become stronger. Tangent hyperbolic, \(\tanh\bigl{(}net_{j}\bigr{)}=\frac{en_{j}^{*}-e^{-net_{j}}}{e^{*T_{j}}+e^{-net_{j}}}\), and sigmoid, \(\sigma(net_{j})=\frac{1}{1+e^{-net_{j}}}\) are two popular activation functions. The sigmoid function compresses values to the [0, 1] interval. The output neuron has a linear activation function. Training employs backpropagation with weight updates using \(w=w+\Delta w\) where \(\Delta w=-\eta\underbrace{\frac{\partial\mathcal{L}}{\partial w}}_{gradient}+ \alpha(\Delta w)^{previous}\) where \(\alpha\) is the momentum and determines the amount of influence from the previous iteration on the present one. \(\eta\) is the step size and is called the learning rate, and \(\mathcal{L}=MSE\) loss. \(\alpha\) and \(\eta\) are scalar hyper-parameters within the interval [0, 1]. \(\eta\) governs the magnitude of weight updates during gradient descent, directly influencing optimisation dynamics. A greater \(\eta\) expedites training convergence but risks overshooting minima, potentially yielding suboptimal model performance. Conversely, lower \(\eta\) values reduce convergence speed and may induce oscillatory behaviour near local optima. To mitigate this instability, the optimisation process incorporates \(\alpha\), which proportionally adds gradients from prior iterations to stabilise weight updates. \(\alpha\) thereby attenuates oscillations and enhances the likelihood of converging to a global optimum. Figure 4 shows the NAR network's topology used in this study. In light of the above explanations and findings, the remaining hyper-parameters are determined through trial and error. The optima are given in Table 1. \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Stopping criteria of training & MSE = 0.0 or 6 consecutive validation fails or max 1000 epochs or min \\  & \(1\times 10^{-7}\) gradient or min \(1\times 10^{10}\) mu \\ Number of hidden layers & 2 \\ Number of neurons in each hidden layer & 15 and 10, respectively \\ Activation functions & Hyperbolic tangent sigmoid in hidden layers, linear in the output layer. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Hyper-parameters of the NAR technique. Figure 4: NAR network’s topology used.
# Accuracy and Precision of Three-Dimensionally Printed Orthognathic Surgical Splints
### Variables in 3D Printing
#### 1.3.4 Printing Materials  
The choice of printing material is primarily determined by the type of 3D printing technology used. An SLA printer is a type of vat photopolymerisation that uses liquid photopolymers such as acrylates and epoxides, similar to material jetting printers. Material extrusion printers, on the other hand, use polylactic acid or acrylonitrile butadiene styrene, and powder bed fusion printers use polyamides or thermoplastic polyurethanes in powder form.  
Over the years, manufacturers have developed many different formulas with varying compositions to enhance certain physical and mechanical properties. The use of different resin materials is one of many factors that may influence the accuracy and precision of 3D-printed OSSs, but currently, there is limited published evidence in the literature evaluating the relationship between them.
## 2 Preliminary Background
### Signcryption  
Signcryption is a public key authenticated encryption introduced by [PERSON] [21]. The key idea behind signcryption is to integrate encryption and signature operations into a single computation, thereby reducing the computational overhead and improving the efficiency compared with performing the operations separately. The cryptography primitive aims to simultaneously achieve confidentiality, integrity, and authentication at a lower cost. [PERSON] [22; 23; 24] extended the principles of signcryption to the key encapsulation mechanism and provided security definitions for the integration. The resulting signcryption KEM (SKEM) became a variant of the signcryption primitive capable of generating authenticated symmetric keys. The characteristics of SKEM motivated the exploration of the connection between SKEM and one-pass key establishment (OPKE) by [PERSON] et al. [25]. Based on the one-pass HQMV scheme [26], [PERSON] et al. proposed SKEM-based OPKE, which provides a highly efficient method to generate ephemeral symmetric keys with confidentiality and authenticity. This study aimed to realize the practicality of OPKE based on SKEM to meet the demands of wireless sensor networks.  
The proposed protocol applies elliptic curve cryptography as the mathematical foundation of both the ECQV and SKEM schemes. Therefore, the parameters of a selected curve can be shared for the two schemes. For a specific curve, let \(\mathcal{G}\) be an elliptic curve group, and let \(G\in\mathcal{G}\) be a generator of order (prime) \(q\) of the group \(\mathcal{G}\). The ECQV requires a hash modulo function \(\mathcal{H}_{1}\), which computes the hash and then performs modulo to the order \(q\). The function hash without modulo is denoted as \(\mathcal{H}_{2}\). In addition, the ECQV algorithm is added with a function of a PUF with a challenge-response pair (CRP) denoted as \((\mathcal{C}^{t},\mathcal{R}^{t})\). Table 1 shows the notions used in the two schemes.
# A Review of Detection Technologies for Underwater Cracks on Concrete Dam Surfaces
## 4 Application of Image Processing Techniques to Underwater Crack Detection in Dams
### Image Processing of Underwater Cracks  
#### 4.2.1 Underwater Crack Image Preprocessing  
Under the influence of inhomogeneous illumination, absorption and scattering of light by water, nonlinear mapping of photodetectors, and other factors [68], underwater images suffer from problems such as color distortion, low contrast, low signal-to-noise ratio, and few details, which can affect the identification of cracks [74]. Therefore, it is necessary to pre-process underwater images with noise reduction and enhancement to improve image quality and enhance crack recognition accuracy. Among them, removing inhomogeneous illumination and enhancing image contrast are the main methods for underwater image preprocessing.  
The cancellation of inhomogeneous illumination is mainly based on the statistical properties of the illumination in the normal regions, and the anomalous regions are pre-processed to obtain underwater cracks under uniform illumination. Currently, methods to eliminate inhomogeneous illumination in underwater crack images include affine shadow transform-based methods [25; 32; 46], coarse set-based methods [71], dark channel-based enhancement algorithms [75], and methods based on biological principles [74; 76]. Although these methods eliminate the influence of inhomogeneous illumination in a certain way, they are slow in processing speed and do not have real-time features, requiring multiple human interventions. Increasing the contrast between cracks and background regions is another direction for underwater image preprocessing. [PERSON] et al. [46] used adaptive histogram equalization to improve the contrast of underwater crack images; [PERSON] et al. [74] proposed an adaptive enhancement method based on the principles of biological vision, introducing a two-dimensional lateral inhibition network to highlight boundaries and designing a boundary highlight rule to enhance the gray contrast of faint object edges at different luminosities. Underwater image contrast is effectively enhanced by the above-mentioned methods, but there are issues such as slow speed and manual intervention. The methods and results used in the corresponding documents are shown in Table 1.