esa-sceva/satcom-chunk-collection huggingface dataset:
# Multiple-Diffraction Subtractive Double Monochromator with High Resolution and Low Stray Light
## 5 Discussion  
Compared to previous studies of multi-diffraction dispersion [9; 10], the MSDM demonstrates a higher OSNR combined with superior spectral resolution capabilities. A comprehensive comparison is presented in Table 1.  
Both multiplexed grating spectrometers and triple dispersion spectrometers are single-monochromator schemes, which cannot suppress stray light by optical layouts and have lower OSNRs. Based on the conclusion from Equation (9), increased number of diffraction times lead to higher angular dispersion. Higher focal lengths and narrower intermediate slit widths yield superior spectral resolution for fixed angular dispersion. Through optimized system parameter design, the MSDM achieves enhanced resolution performance. The double-diffraction monochromator used in this study serves as just one example of the MSDM. By increasing the number of diffraction events in a similar configuration, spectral resolution can be further enhanced.  
The proposed MSDM exhibits superior performance compared to two leading commercial dual monochromators. To maintain objectivity and avoid potential commercial sensitivities, these instruments are anonymized as Commercial Instrument 1 and Commercial Instrument 2 in the comparative analysis presented in Table 2. Both are widely adopted in applications like dense wavelength division multiplexing (DWDM) due to their exceptional specifications.  
Notably, the proposed system reaches an internationally advanced spectral resolution (18.8 pm, surpassing Commercial Instrument 2 by 68.7%) and maintains an OSNR within <6% of top-tier commercial devices. However, its current wavelength coverage (400 nm) remains a primary technical limitation compared to the 1100 nm operational range typical of advanced commercial systems.
# Some Applications of ANN to Solar Radiation Estimation and Forecasting for Energy Applications
## 3 ANN Method and Implementation  
### General Description of ANN Structure  
An ANN [27] is a modelling tool able to find complex relationships between inputs and outputs. It is considered as \"intelligent\" because it works as a human brain:  
* A neural network acquires knowledge through learning; and
* a neural network's knowledge is stored within inter-neuron connection strengths known as synaptic weights.  
ANNs can represent both linear and non-linear relationships and have their ability to learn these relationships directly from the measured data. Among the various ANN methods, the multilayer perceptron (MLP) using feed-forward back-propagation is often used for empirical estimation in general [28], and in particular [19; 29; 30], for the estimation of solar radiation. An ANN has a parallel structure with an input layer receiving data, an output layer sending the computed data, and one or several hidden layers lying between the input and output layers as illustrated in Figure 1.  
A neuron receives input from other neurons and/or an input data, which represents an external source. In the feed-forward propagation MLP configuration, this connection is unidirectional. Each input, \(x_{k,j}\), has an associated weight, \(w_{k,j}\) (related to the j-th neuron among p of the k-th layers), which can be modified during the learning phase. The weighted sum, \(\sum_{j=1}^{p}w_{k,j}x_{k,j}\), is called the net input to unit j, often written net. The unit computes a function, \(f\), of this weight sum and is called the activation or transfer function; this function, \(f\), produces an output, \(O\), of a neuron if this sum exceeds a given threshold denoted biases. A bibliographical study conducted to use as activation transfer functions a sigmoid one for the hidden layers and a linear function for the output layer. For the \(j\)th neuron of the layer (k+1), \(O\) is given by [31]:  
\[O=f(net)=f\left(\sum_{j=1}^{p}w_{k,j}x_{k,j}\right) \tag{6}\]  
This output is then distributed to other neurons as inputs.
# Reflective Fiber Surface Plasmon Resonance Sensor for High-Sensitive Mercury Ion Detection
## 3 Performance Test
### Measurement Experiment of Mercury Ion Concentration  
In order to test the sensing characteristics of the reflective SPR Hg\({}^{2+}\) sensor, a series of Hg\({}^{2+}\) solutions with different concentrations were prepared by diluting 1 mmol/L standard solution of Hg  
Figure 8: Comparison of wavelength shifts for SPR sensors with different numbers of layers of sensitive film.  
Figure 7: Resonant spectra (**a**) and linear fitting result (**b**) of a reflective fiber SPR sensor in refractive index measurement.  
with ultra-pure water. Figure 9a shows the change of the wavelength shift with time when detecting different concentrations of Hg\({}^{2+}\) solution. When the detection time reached about 20 min, the spectrum stopped changing and reached stability. A linear fitting relationship between the spectral wavelength shift and Hg\({}^{2+}\) concentration is shown in Figure 9b. With the increase of the Hg\({}^{2+}\) concentration, the wavelength shift increases when the spectrum finally reaches stability. The sensitivity of the sensor is 0.5586 nm/uM in the concentration range of 0 to 30 uM, 0.1239 nm/uM in the concentration range of 30 to 100 uM, and 0.02 nm/uM in the concentration range of 100 to 200 uM. From the experimental results, it can be seen that the concentration sensitivity of the sensor decreases with the increase of the Hg\({}^{2+}\) concentration in the solution. This is because the chelating sites of Hg\({}^{2+}\) in the sensing film gradually reach saturation, and the refractive index of the sensing layer gradually reaches the maximum value.
# Performance Assessment and Scooter Verification of Nano-Alumina Engine Oil
## 1 Introduction
The main role of nanoparticles added to oils is to improve mechanical performance due to effects such as: the rolling-sliding effect [26; 27; 28], protective film [29; 30; 31], third-body effect [26; 32], mending and smoothing effect of surface roughness [14; 26; 33; 34], cleaning effect on surfaces [35; 36], and shear thinning of nano-oils [9; 19; 22] that could be adopted to mitigate mechanical friction, to enhance anti-wear performance, and to facilitate the energy-efficient operation of machinery. The main advantages of incorporating nanoparticles into lubricants include their sizes and shapes, which are suitably adapted for a perfect feeding of the sliding interface, and for potentially reducing the friction coefficient and increasing the load-carrying capacity of coupled surfaces in mechanical systems [13; 15; 22; 25; 36; 37; 38]. Furthermore, using nanoparticles with high thermal conductivity to enhance the heat-transfer performance of lubricants can enhance the heat-dissipation performance of a mechanical system [21; 39; 40; 41]. By literature survey, it can be found that studies of nano-oils focused primarily on the indexes of nano-oils characteristics itself, wear, friction coefficient, power consumption in the past. However, studies of IC engines with nano-oils for fuel efficiency (consumption) were rarely seen. In this study, ultrafine alumina (Al\({}_{2}\)O\({}_{3}\)) nanoparticles were added to engine oil (EO), and a stable suspension was maintained to form nano-alumina (Al\({}_{2}\)O\({}_{3}\)) engine oil (NAEO). The density, viscosity, specific heat, and tribological performance of the NAEO with various weight fractions of Al\({}_{2}\)O\({}_{3}\) (0 wt. %, 0.5 wt. %, 1.5 wt. %, and 2.5 wt. %) were evaluated to assess the relevant characteristics. Finally, we conducted various real engine operation tests with the EO and NAEO to evaluate the practicability of NAEO in a 125 c.c. spark ignition engine of a scooter in a ECE40 driving cycle by analyzing the fuel consumption of a scooter using those oils.
# An Improved V-Net Model for Thyroid Nodule Segmentation
## 1 Introduction
This process is carried out by ML methods, applying it to features obtained from classical techniques, whereas DL methods implement it internally and automatically derive their features. Most of the methods in this category provide important advantages, suchas insensitivity to nodule echogenicity, automatic segmentation, and high segmentation accuracy and efficiency. However, they also encounter challenges, such as requiring extensive labeled datasets and prolonged training times [10]. Recent studies have focused on developing and using DL methods for segmenting nodule regions from thyroid images, leading to segmentation models that outperform the existing methods. By leveraging their advantages in image classification, numerous studies have proposed innovative models utilizing the U-Net framework for segmentation tasks [14; 16; 17]. The U-Net architecture is commonly utilized in this area because of its straightforward structural design, facilitation of generalization through symmetric training, and capability to learn features across various levels, enabled by skip connections [14]. Nonetheless, this method struggles to deal with the unclear and blurry edges of nodule regions in the segmentation process and tends to be insensitive to small nodules [17]. This study addressed the complexity of tissue boundaries surrounding the thyroid by proposing an improved V-Net model for two-dimensional image segmentation tasks, considering the limitations of ultrasound imaging techniques and the U-Net method. The proposed model represents an architecture based on V-Net and SE mechanisms, aiming to be applied to various data. The distinguishing features of the improved V-Net model compared to similar studies in the literature can be summarized in the following three points:  
* Strengthening the encoder layers of the V-net through triple fusion. * Enhancing the capability to select more significant features by employing SE mechanisms in the encoder layers. * Enabling rapid and accurate detection of important features with SE mechanisms applied to the final layer before the output in the decoder layers. The remainder of this paper is organized as follows: Section 2 covers similar studies in the literature. Section 3 discusses the materials and methods used in this study. Section 4 presents experimental studies, their results, and comparisons with the literature. The Section 5 provides an overall evaluation of this study and discusses future research directions.
# Discrimination of Severity of Alzheimer's Disease with Multiscale Entropy Analysis of EEG Dynamics
## 2 Materials and Methods  
### Participants  
The study group comprised 84 patients with AD (49 women, 35 men) and with a mean age of 77.0 years (SD: 8.0) who were recruited from the Dementia Clinic at the Neurological Institute, Taipei Veterans General Hospital in Taiwan. The diagnosis for AD was based on the criteria of the National Institute of Neurological and Communicative Disorders and the Stroke/Alzheimer's Disease and Related Disorders Association ([PERSON] et al., 1984). All patients had received neurological examinations, laboratory tests, EEG monitoring, and neuroimaging evaluation during the diagnostic process. Our study was approved by the Institutional Review Board of Taipei Veterans General Hospital to conduct retrospective analysis of the patients' clinical and EEG data. We excluded patients who had other conditions that caused secondary dementia, such as vascular dementia, Parkinson's disease, hypothyroidism, vitamin B12 deficiency, syphilis, and prior history of major psychiatric illness (e.g., major depression, bipolar disorder, or schizophrenia).  
The severity of dementia was assessed by the Clinical Dementia Rating (CDR) scale ([PERSON], 1993). Patients were categorized as having AD that was mild (CDR = 1; N = 69) or moderate to severe (CDR \(\geq\) 2; N = 15). The control group included 15 healthy participants (9 women, 6 men) with a mean age of 69.9 years (SD: 9.5). All healthy subjects were cognitively normal and symptom-free, and they were free of neurological disease or psychiatric illness. For convenience, the healthy control, mild AD, and moderate to severe AD groups are called for short as HC, AD1, and AD2, respectively, in the following context.
# Energy Consumption Model for Sensor Nodes Based on LoRa and LoRaWAN
## 6 Numerical Results and Discussions
### Full Communicating Sensor-Energy Consumption Results
#### 6.3.3 Consumed Energy: Scenario 3  
For this scenario, the communicating sensor transmits data to the gateway and receives the RX1 acknowledgment which contains transmission error for example. The sensor node must demodulate the RX2 acknowledgment to verify the transmission success (which means that it will consume more energy than Scenario 2). The dissipated energy by the communicating sensor is given in Figure 19. We remark that the consumed energy is tdouble that consumed by the LoRa receiver (\(E_{R}\) = 0.54 mJ).  
Figure 17: Energy consumption of sensor node: Scenario 2.  
Figure 18: Sensor node autonomy: Scenario 2.  
The sensor node lifetime is depicted in Figure 20. The autonomy of the node is estimated at 4 years and 21 days (48.7 months) for this case (i.e, there is a loss equal to 22.2% of the battery life compared to Scenario 1).
# Detection of Large-Scale Floods Using Google Earth Engine and Google Colab
## 2 Materials and Methods
### Data
#### 2.2.4 Data Used for Application  
To simulate a calculation covering the entire territory of Madagascar, flood detection is extended over a 12-day period. This approach aims to ensure complete coverage of the territory using Sentinel images, as the revisit frequency of Sentinel-1 satellites is every 12 days. To ensure a significant presence of flooding, the period following the passage of Cyclone Batsirai on 5 and 6 February 2022, is chosen. A total of 30 Sentinel-1 scenes were required. They were acquired between 5 and 17 February 2022. Radar images acquired at the same period as these post-flood images for each previous year were used as reference images.
## 3 Evaluation of Surface Quality, Material Utilization, and Efficiency
### Effects of Process Parameters on the Performances  
Based on Equations (6), (8) and (9), the effects of single process parameter on surface roughness, material utilization, and construction time are analyzed, as shown in Figure 6. This is obtained by varying one process parameter while keeping the other process parameters at zero level (according to Table 1). The other unknown parameters are set as follows as an example:  
\[T_{\text{W}}\ =\ 7\ \text{mm},L\ =\ 1200\ \text{mm},\ \frac{m_{\text{plate}}}{m_{ \text{stiffener}}}\ =\ 4,\ t_{\text{cooling}}\ =\ 15\ \text{min},t_{\text{tool\_ changing}}\ =\ 2\ \text{min} \tag{10}\]From Figure 6a, it is known that the surface roughness appears to be a nonlinear decreasing function of travel speed and spindle speed. Higher travel speed leads to smaller bead width according to Equation (2) and, therefore, smaller cutting depth according to Equation (5). Both smaller cutting depth and higher spindle speed contribute to lower surface roughness according to Equation (4). On the other hand, the surface roughness increases almost linearly with increased wire-feed rate. This is because higher wire-feed rate leads to larger cutting depth according to Equations (2) and (5) and, therefore, higher surface roughness according to Equation (4). Additionally, the surface roughness decreases slightly and then increases greatly with the increased tool-feed rate. These results basically agree with the observations in previous research. A reasonable increase of spindle speed would decrease the cutting force due to the thermal softening and, therefore, lead to lower surface roughness [27]. An increase of cutting depth and tool-feed rate means removing more volume per unit time, which would result in larger cutting force and, therefore, higher surface roughness [28]. From Figure 6, we can also know that the steeper the slope of a curve, the larger the corresponding parameter's contribution to the surface roughness. To make a fair comparison, the slopes of these curves are compared at zero level. Then it can be obtained that the order of these parameters' contribution to the surface roughness is \(S_{\text{S}}>W_{\text{FR}}>T_{\text{FR}}>T_{\text{S}}\).  
Figure 6b shows the dependence of the material utilization on travel speed and wire-feed rate. Higher wire-feed rate and lower travel speed are likely to lead to larger bead width according to Equation (2) and, therefore, lower material utilization. The two process parameters must match each other in order to ensure a high material utilization. By comparing the slopes of the two curves, it is known that the wire-feed rate plays a dominant role in determining the material utilization. From Figure 6c, it is observed that the construction time is a decreasing function of both the travel speed and the tool-feed rate, which is easy to explain by combining Equation (8). Additionally, the tool-feed rate plays a dominant role in determining the construction time.
# An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision
## 1 Introduction
### Methodology and Articles Selection
#### 1.3.1 Data Collection and Preprocessing  
To find pertinent research papers, a set of thorough search questions was created in the first phase of this SLR. Boolean operators were used to create the following search terms:  
* \"Domain adaptation\" AND \"computer vision\";
* \"Domain adaptation\" AND \"robotic vision\";
* \"Domain adaptation\" AND \"visual domain transfer\";
* \"Domain shift\" AND \"computer vision\";
* \"Domain shift\" AND \"robotic vision\".  
Several respected academic resources were searched using these queries, including IEEE, ACM Digital Library, SpringerLink, ScienceDirect, and Google Scholar. To include current developments on the subject, the search was limited to publications published between January 2019 and September 2023.  
The following is a definition of the inclusion criteria for article selection:  
* Articles that explicitly discuss domain adaptation strategies in relation to computer and robotic vision;
* English-language articles are selected to ensure accessibility and comprehension;
* Publications that are accessible in full-text format as preprints, journal articles, or conference papers.  
Exclusion criteria were established to omit research that was repetitive or irrelevant:  
* Articles that have nothing to do with visual domain transfer or domain adaptation in computer or robotic vision;
* Articles written in languages other than English;
* Articles with little information or without access to the full text.  
A total of 264 pertinent articles were found after the search queries were run and the inclusion and exclusion criteria were used. Figure 1 provides a summary of the entire technique.
## 5 Simulation and Analysis
### Algorithm Simulation and Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & & **IMM-CKF** & **IMM-STCKF** & **AIMM-STCKF** \\ \multirow{4}{*}{Position/m} & \(x\) & 449.2 & 447.9 & 381.9 \\  & \(y\) & 213.7 & 207 & 171.3 \\  & \(z\) & 164 & 158.2 & 97.1 \\  & \(r\) & 537.3 & 530.2 & 448.6 \\ \hline \multirow{4}{*}{Velocity/(m/s)} & \(v_{x}\) & 52.7 & 37.9 & 19.4 \\  & \(v_{y}\) & 56.2 & 56.3 & 27.7 \\ \cline{1-1}  & \(v_{z}\) & 51.9 & 49.5 & 41.4 \\ \cline{1-1}  & \(v\) & 104.1 & 92.7 & 66.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Table of the mean RMSEs of the three algorithms. Figure 12: Model probability: (**a**) IMM-CKF, (**b**) AIMM-STCKF. It can be found through comparison that the algorithm in this paper enhanced the proportion of matching models in the IMM algorithm, reduced the probability variance in the model, and made the model transformation more robust.
## 2 Materials and Methods
### Measurement of Cell Cytotoxicity and Antioxidant Activities  
The cell cytotoxicity of the extract against human skin fibroblasts (CCD-986 sk) was evaluated by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolzolium bromide (MTT) method [16]: First, 6.0 \(\times\) 10\({}^{5}\) cells/mL of human skin fibroblasts was inoculated into a 96-well plate and cultured for one day at 37 \({}^{\circ}\)C in a 5% CO\({}_{2}\) incubator. Thereafter, the cultured cells were treated with various concentrations of the extracts and cultured again for one day under the same conditions described above. The supernatant of the treated sample was removed, and 1.0 mg/mL of MTT solution was added and cultured for 4 h at 37 \({}^{\circ}\)C while minimizing exposure to light. Then, the MTT solution was removed and 200 uL of Dimethyl Sulfoxide (DMSO) were added; the solution was left unattended for 30 min while light was blocked. The optical density was measured at a wavelength of 570 nm.  
To observe the antioxidant activities of _E. prostrata_ L. extracts, the \(\alpha\),\(\alpha\)-diphenyl-\(\beta\)-picrylhydrazyl (DPPH) free radical scavenging activity was measured by the Dietz method [17]: after mixing 150 uL of 0.1 mM DPPH solution prepared with methanol as the solvent and 150 uL of the extract, the mixture was wrapped in foil and left unattended for 30 min at room temperature while light was blocked. Thereafter, the optical density was checked at a wavelength of 517 nm by a microplate reader (Thermo Fisher Scientific, Waltham, MA, USA). The relevant values were estimated as DPPH radical scavenging activity (%).
## 3 Theoretical Foundations in Language Learning  
### Key Concepts in Language Acquisition  
Understanding language acquisition involves unraveling the complex interplay of cognitive, social, and environmental factors that influence language learning. This section highlights key concepts fundamental to the study of language learning, which are crucial for developing effective big-data-driven personalized learning systems.  
Innateness is a core concept in linguistics, positing that the ability to acquire language is hard-wired into the human brain [22]. The theory of Universal Grammar, proposed by [PERSON], suggests that the underlying structural principles of all languages are innate and that specific languages are learned through exposure. This concept has profound implications for designing learning systems that accommodate the natural language processing abilities inherent in all learners.  
Social Interaction plays a pivotal role in language acquisition. [PERSON]'s Sociocultural Theory emphasizes that language learning is deeply embedded in social contexts and interactions [23]. This theory advocates integrating collaborative tools in learning platforms that mimic real-life interactions, enhancing language skills through social engagement.  
Comprehensible Input is another significant concept introduced by [PERSON] [24]. It stresses the importance of delivering language input that is slightly beyond the current level of the learner's comprehension, known as \"i + 1.\" Effective personalized learning systems use big data analytics to adjust the complexity of language input to dynamically match each learner's proficiency level.  
Feedback and Error Correction are crucial for language development. Interactionist theories highlight the role of feedback in learning, suggesting that corrections during language use help learners refine their understanding and use of language structures. Personalized learning systems, therefore, must incorporate mechanisms for immediate and constructive feedback, leveraging data on individual learner responses to tailor feedback effectively.  
Motivation and Affective Factors also significantly influence language learning [25]. [PERSON]'s L2 Motivational Self System underlines the importance of motivation driven by a learner's vision of themselves as successful language users in the future. Personalized systems can enhance motivation by providing personalized goal-setting and rewards that align with individual aspirations and interests.  
These concepts collectively guide the development of sophisticated language learning systems that can adapt to each learner's unique needs. By leveraging big data to implement these theories in practical, scalable ways, educational technology can offer more nuanced and effective language learning experiences.