esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methodology  
#### 2.2.1 Artificial Neural Network  
As a data-driven method, the Artificial Neural Network (ANN) is widely used in machine learning. ANN is composed of nodes within an input layer, hidden layers and an output layer. These nodes constitute the basic unit for computing, and each layer is connected by several links with assigned connection weights. The traditional ANN model is based on multi-layer perceptron (MLP), which has at least one hidden layer between the input and output layers, which can account for complex nonlinear relationships between factors flexibly [12].  
In this study, the back-propagation algorithm with feed-forward nets is applied. The following Formulas (1) and (2) are the bases of the ANN learning process. The set of output values (\(y\)) is computed using the activation function (\(f\)) and connection weights through the hidden layer when the set of input values of the \(n\)-dimensional vector (\(x\)) is rendered to the input layer. Then the Formula (3) is used which can minimize the error (\(E\)) to adjust the connection weights.  
\[Z_{j}=f\left(\sum_{i}w_{ij}^{1}x_{i}+b_{j}\right) \tag{1}\]  
\[y=\sum_{j}w_{jk}^{2}z_{j}+b_{k} \tag{2}\]  
\[E=\tfrac{1}{2}(y-t)^{2} \tag{3}\]  
where, \(i,j\) and \(k\) are the number of nodes in the input, hidden, and output layers, respectively. And the \(w_{ij}^{1}\) is the connection weight between the first two layers; \(b_{i}\) is the bias unit; \(w_{jk}^{2}\) is the hidden layer and the output layer's connection weight. \(t\) is the target value and \(y\) is the predicted value.  
The BP-ANN model was trained using the overlapped NDVI3g and FPAR\({}_{\text{MCD15A2}}\) pixel by pixel for the period from 2003 to 2015. Then, the trained BP-ANN net was applied to predict the FPAR data against the NDVI3g from 1980 to 2015. When the ANN network was built, a total of 598 samples (46 data points per year for 13 years from 2003-2015) were used of which 70% were for training and 30% for testing for the randomly selected pixels. To balance accuracy and computation speed, the network was set as the three layers of 1 \(\times\) 34 \(\times\) 1 net number for its higher test accuracy. After the network was determined, all data in 2003-2015 were used to train the network and the FPAR was estimated for the period of 2003-2015 and evaluated on accuracy and consistency.
# Simulation of Forest Carbon Fluxes Using Model Incorporation and Data Assimilation
## 4 Results  
### Optimization of MOD_17 Model  
The original MODIS GPP products were validated against the EC measurements at the forest flux site. The results showed that the original GPPs (GPP_Default in Figure 2a,b) contained significant underestimation compared to the EC measurements, especially in winter and summer (Figure 2b, \(\mathrm{R^{2}}\) = 0.65, RMSE = 26.510 \(\mathrm{gC\cdot m^{-2}\cdot(8d)^{-1}}\)). Similar underestimation of a forest flux site was alsoreported by [PERSON] et al. (2016). To analyze the impact surrounding the input of parameters collected from three sources (meteorological, biome-specific, and /PAR parameters) on model behavior, three optimized simulations were designed to improve the performance of the GPP.  
The first experiment (GPP_MOD1) was conducted using DAYMET data, including Tmin, VPD, and PAR, as well as the default \(\mathrm{LUE_{max}}\) and MODIS /PAR. As the blue line shows in Figure 2a, the simulated GPPs improved significantly, especially during growing seasons, even though some underestimation still persisted. Better results were obtained in the GPP_MOD1 experiment regarding R\({}^{2}\) and RMSE (Figure 2b, R\({}^{2}\) = 0.87, RMSE = 16.835 gC\(\cdot\) m\({}^{-2}\cdot(8d)^{-1}\)). A further optimization (GPP_MOD2) was performed using the DAYMET data, as well as calibrated \(\mathrm{LUE_{max}}\) (calculated from the measured GPP and APAR during growing seasons), and MODIS /APR. The calibrated value of \(\mathrm{LUE_{max}}\), 1.658 gC\(\cdot\) MJ\({}^{-1}\) APAR, was much greater than the default value obtained from the BPLUT (1.116 gC\(\cdot\) MJ\({}^{-1}\) APAR). As shown in Figure 2b, the good accordance obtained in the GPP_MOD2 experiment was supported by the R\({}^{2}\) and RMSE (R\({}^{2}\) = 0.89, RMSE = 12.941 gC\(\cdot\) m\({}^{-2}\cdot(8d)^{-1}\)).  
Figure 2: Seasonal performances (**a**) and comparisons (**b**) of eight-day GPPs obtained from the original products (GPP_Default), three simulations (GPP_MOD1, GPP_MOD2, and GPP_MOD3), and EC measurements (GPP_EC) at Changbai Mountains forest flux site between 2003 and 2007.  
To mitigate the influence of noise (e.g., clouds, aerosols, water vapor) in the original MODIS/PAR products on GPP values, the GLASS _f_PAR products were introduced into the GPP_MOD3 simulation. The original MODIS/PAR products contained some serious noise and exhibited dramatic fluctuations for the forest flux site. In contrast, the temporal profiles for the GLASS _f_PAR performed relatively smoothly during 2003-2007. Generally, GPP simulations from the GPP_MOD3 yielded the highest agreement with EC measurements (Figure 2b, R\({}^{2}\) = 0.93, RMSE = 8.736 gC- m\({}^{-2}\) - (8d)\({}^{-1}\)), therefore, the simulations of the selected 10 plots were conducted by means of GPP_MOD3 as next calibration of the Biome-BGC model.
## 4 Results and Discussion
As explained previously, however, the Reynolds numbers are higher for the circular tube, the heat transfer rate decreases due to the greater hydraulic diameter value for circular cross-section (13 mm) compared to the elliptical one (12 mm) at the same area. Figure 10 shows the contributing factors in determining \(\mu_{Th}\) in one diagram. \(Q_{u}\) and \(\mu_{Th}\) are plotted by bar charts and \(h_{fi}\) is shown by line graphs, respectively. The bar chart colour and thickness are related to \(F_{R}\) and \(\dot{m}\) values, respectively. In addition, the darkest and the lightest blue in the bar charts are related to the maximum and minimum \(F_{R}\) values, respectively. The thickness of the line graphs represents \(Nu\) amounts. As it can be seen, the maximum \(Q_{u}\) and \(\mu_{Th}\) (493.8 W and 56%, respectively) are accessible using the elliptical cross-section tube with (\(Nu\) = 16.5, \(F_{R}\) = 0.539, and \(h_{fi}\) = 827 \(\left(\mathrm{Wm^{-2}K^{-1}}\right)\)). It means that the larger contact area for the elliptical tube compared to the circular one will improve \(F_{R}\) and \(Q_{u}\) by an average of 2%. Figure 9: Effects of the \(Re\) on \(h_{fi}\), \(F_{R}\), \(Q_{u}\), and \(\mu_{Th}\).
## 1 Introduction
The study done here is connected with many outputs, such as evaluating the potential of predicting EEG with Artificial Intelligence-based systems, and also the success rate of an ANN-ALO (ALO trained ANN) system with this problem. Furthermore, the practical value of the ANN-ALO system is analyzed thanks to applications made by physicians recently. The motivation of this study and the main contributions considered in it are as follows:  
The study deals with the remarkable, widely-followed research interest of time series prediction and focuses on predicting especially 'chaotic' ones. Additionally, the study also considers the healthcare field and tries to predict electroencephalogram (EEG) time series, which are important medical data for analyzing brain activity and obtaining information about possible diseases (like depression, autism, epilepsy, and even Alzheimer's disease) or environmental factors that interact with the brain. While performing predictions over EEG time series, the study contributes to the associated literature by introducing the ANN-ALO hybrid system which has not been employed for this purpose yet. As EEG is the target data to be predicted, the study aims to predict an advanced time series with chaotic characteristics. Furthermore, the study generally proves that hybrid Artificial Intelligence techniques are effective enough in predicting time series, even when they are chaotic. In this way, the more practical and effective sides of artificial intelligence techniques according to traditional approaches can be understood by readers. Finally, the study also proves, generally, that healthcare needs more use of artificial intelligence to solve some issues. The issue considered here, the 'prediction' over data flow, is one of the key elements for healthcare, especially in terms of early diagnosis. In this context, the study proves the advantages of the introduced approach thanks to performed evaluations including both comparative and experiences from experts (physicians).
## 1 Introduction  
After assessing benefits and risks to the patients, the choice of planning the fixed orthodontic treatment (FOT) should be made. Among problems during FOT, almost all of the patients suffer from pain and/or discomfort. The level of PP of the FOT patients varies in different stages [1]. During FOT, 56.8% and 34.4% of gingivitis reported as worldwide morbidity in adolescent and adult group [2]. [PERSON] et al. documented that sensitization to blunt-pressure stimuli occur in both the attached gingiva and the periodontal ligament following the insertion of an archive in orthodontic therapy [3]. Therefore, investigators are always interested in non-invasive and consistent techniques to overcome the chances of pain occurrence due to FOTIG. Current literature suggests that, as an adjunct with routine oral hygiene, scaling and root planing non-invasive practices are applicable to treat gingivitis [4; 5]. With meticulous maintenance of oral hygiene, these problems can be minimized. However, a number of the patients have failed to follow the oral hygiene instructions suitably, which sometimes results in irreparable impairment to the periodontal structures [6].  
Antioxidant therapy is in its beginning stage and is the most modern approach for the treatment of periodontal disease. Products based on nanotechnology, which contain antioxidants, may be excellent supplementary approach in managing periodontal diseases. Presently, the only commercially available such product based on nanotechnology globally is NBFG, which contains propolisis, vitamin C, and vitamin E in nanoemulsion. The beneficial outcome of propolis along with Vitamin C and E and nanotechnology amplified this effect in preventing disease progression. Recent reports indicate that locally delivered NBFG gel application exhibited a significant improvement compared with scaling and root planing (SRP) alone in managing chronic periodontitis [6]. Once applied, NBFG creates a nano-bioactive protective film, resulting in increased absorption and improved clinical effectiveness and visible results after application. [PERSON] and [PERSON] et al. (2021) found favorable clinical outcome in FOT using NBFG [7]. Several modalities are available for treating gingivitis during orthodontic therapy such as ozone and probiotics [8,9]. To date, the effect of NBFG has not been tested in PP during orthodontic therapy-induced gingivitis. Thus, this study aimed to assess the clinical effectiveness of locally delivered NBF gel as adjunctive therapy to SRP in pain perception in FOTIG.
# Magnetically Deployable Robots Using Layered Lamina Emergent Mechanism
## 2 Materials and Methods
### Magnetic Actuation  
The magnetic field has advantages over other actuating fields such as electrical, optical, or acoustics in tetherless manipulating forces and torques [37] to control the translational and rotational motions. Magnetic actuation includes actuation by using permanent magnets or electromagnets. While electromagnetic actuation provides highly precise control of a magnetic object, they are expensive and require a large workspace. Hence, we chose to use permanent magnets in this paper due to their simplicity at this initial research stage. Manual manipulation of the actuating permanent magnet (APM) is used to control and modulate the direction and strength of the induced magnetic field during the actuation of the robot prototypes.  
The principle of this method relies on manipulating the magnetic field acting on magnetized objects. When a magnetized object enters a magnetic field exerted by an APM, magnetic forces and torques are exerted on the magnetized object. Magnetic torque causes the object to rotate to align to the magnetic dipole of the actuating magnetic field. According to [38], the magnetic torque and force can be expressed as:  
\[\mathbf{\tau}_{\text{m}}=\mathbf{M}\times\mathbf{B} \tag{1}\]  
\[\mathbf{f}_{\text{m}}=(\mathbf{M}\cdot\bigtriangledown)\mathbf{B} \tag{2}\]  
where \(\mathbf{\tau}_{\text{m}}\) is the magnetic torque; \(\mathbf{f}_{\text{m}}\) is the magnetic force; \(\mathbf{M}\) is the total dipole moment of the magnetized object (A/m); and B is the flux density of the magnetic field (T) applied by the APM.  
This paper uses ferromagnetic materials (permanent neodymium magnets) as both the APM and the actuated components (internal magnets). We will design LEM prototypes with legs/flaps-like components to attach small neodymium magnets (internal permanent magnets, IPM). Then, we manually control the APM to generate the desirable torque and, consequently, the LEM robot's motion. One of the goals of this paper is to produce repeatable motion patterns using this actuation method.
## 4 Results and Discussion
### Differences per Sector before and after FSMS Implementation  
The processed fruit and vegetables, snacks and baked goods, and ingredients sectors presented a significant improvement in International FSMS implementation. The dairy, meat, and ingredients sectors expanded the rates of certifications. The snacks and baked goods and dairy sectors increased the rates of upgrades and changes in standards. Quality standards application has increased in the processed fruit and vegetables, snacks and baked goods, and dairy sectors, while GFSI implementation has increased also for ingredients and meat sectors. Moreover, all sectors presented significant increases in the implementation of Food safety culture, Manager leadership, and Training adequacy. Implementation of workers' training, Sustainability, waste reduction programs, Traceability, Crisis Management, Food Defense, and Food Fraud systems has increased for all sectors as well. Significant large improvements were also observed in prerequisites and HACCP systems applications for sectors that presented lower integration before FSMS implementation, and smaller non-significant improvements have also been detected for the ingredients and meat sectors, even though prerequisites and HACCP integration was sufficient even before the FSMS (Figure 8).  
No significant change in the incentives to implement FSMS Standards was detected within different sectors before and after FSMS implementation. The sector of snacks and baked goods, as well as the ingredients sector, perceived the FSMS after implementation as less expensive and complicated, compared to their attitudes before implementation. Yet, the sectors of processed fruit and vegetables, snacks and baked goods, food service, ingredients, and meat have reported greater constraints of non-familiarity with FSMS standards to consumers and customers after FSMS implementation. These findings well agree with the study of [PERSON] et al. [PERSON] et al. (2018) who indicated that the main motivating factors for HACCP implementation were the likelihood of future regulation, the value of HACCP for marketing, and avoiding food safety problems. They also mentioned that successful implementation depended heavily on management commitment. They also used semi-structured in-depth interviews from different food sectors, including meat, wine, baked goods, flavors, and minimally processed fruits and vegetables. Finally, the implementation of HACCP led to increased food safety and product quality.  
All indicators are very good as shown in Table 3 (>0.7). A low [PERSON]'s alpha index appears for Constraints, although it is still marginally acceptable because it is >0.5.  
\begin{table}
\begin{tabular}{l l l} \hline  & \multicolumn{2}{c}{**Cronbach’s Alpha**} \\ \hline  & **Before** & **After** \\ \hline
**FSMS Element Details** & 0.740 & 0.718 \\ \hline
**Prerequisites** & 0.712 & 0.827 \\ \hline \end{tabular}
\end{table}
Table 3: Cronbach alpha for all parameters tested.
# Lithological Discrimination of Khyber Range Using Remote Sensing and Machine Learning Algorithms
## 3 Materials and Methods  
### Datasets  
The two types of multispectral satellite imagery, ASTER and Landsat 8 OLI, utilized in this research were downloaded from the USGS EarthExplorer website (accessed on 12 January 2023) for free. In this investigation, the Khyber range, Pakistan, was covered by a single cloud-free ASTER Level 1 Precision Terrain Corrected Registered at Sensor Radiance (AST_L1T) picture. ASTER is an advanced multispectral sensor that was installed on the Terra spacecraft in December 1999. It has 14 spectral bands, including three VNIR bands with a 15 m spatial resolution, six SWIR bands with a 30 m spatial resolution, and five TIR bands with a 90 m spatial resolution (Figure 3). In order to provide stereoscopic capabilities, a further telescope is employed to look backward in the near-infrared spectral range (range 3B) [47]. The AST_L1T output is generated by the NASA Land Processes Distributed Active Archive Centre once the original AST_L1A image has undergone geometric,radiometric, and cross-talk corrections [48]. The level-1B ASTER data that were used in this study were collected on 15 May 2007.  
Landsat 8 OLI (with OLI and TIRS sensors) of the Landsat series utilized in this study was launched by NASA on 11 February 2013. In comparison to Landsat 7 (ETM+), the OLI has two extra bands, including a new deep blue band for observing coastal areas and aerosols and an SWIR band for detecting cirrus (Figure 3). For lithological differentiation in this investigation, one OLI scene taken on 16 October 2022 was used. Even though these images were obtained in different seasons, the study area location is a perfectly exposed place for lithological classification with little to no plant cover. So, the effects of vegetation, clouds, or snow are not present in this study area.
# Perception of Familiar Second Language Accents and the Role of Linguistic Background
## 1 Introduction  
Identifying talkers plays a crucial role in everyday life, helping people adapt to social expectations and enhancing community interaction. While both visual and auditory cues contribute to recognizing talkers, it is possible to rely solely on auditory cues [1]. Listeners can detect certain features in speech to infer details like social class [2], regional accents [3], age [4], gender [5], health status [6], and more. This highlights the complexity of the human voice and demonstrates its ability to provide essential information for both voice recognition and speech perception [7]. The former helps identify the speaker's identity, while the latter allows for understanding the speaker's linguistic content among others. In recent years, there has been a growing interest in examining the categorization of accents. This research expands on previous studies by focusing on non-native accents and investigates how listeners' linguistic backgrounds affect their ability to categorize accents in a condition including a second language (L2) accent and first language (L1)-accented L2 speech.  
Listener performance in accent categorization is influenced by language familiarity. Previous research suggests that experience with a language impacts how accurately listeners categorize accents. The \"language-familiarity effect\" posits that listeners are more adept at categorizing accents when hearing speech in their L1 compared to a foreign or less familiar language [8, 9, 10, 11, 12]. Language familiarity can be understood in different ways. For instance, it can relate to a person's residential history, which includes factors like their region of origin, geographic mobility, and travel experience [13]. [PERSON] and [PERSON] [14] found thatlisteners who had lived in multiple U.S. states were better at categorizing different varieties of American English than those who had only lived in one state. However, familiarity with accents does not always stem from travel experiences. Acents or dialects frequently seen in the media, for example, might also seem more familiar to listeners [15]. Familiarity extends to non-native accents as well and can be acquired through either naturalistic or non-naturalistic exposure (e.g., see [9]).
## 4 Proposed Watermarking Scheme  
Generally, the SVD transform can be applied to an image with two different techniques: on the whole image and on blocks of the image. The former tends to spread the watermark all over the image, whereas the latter only affects local regions of the image. The watermarking scheme we propose in this paper is block-based, i.e., it splits the original image into a number of blocks and applies the SVD transform to each of them, producing a matrix of SVs (_S_) and two matrices of vectors (_U, V_) for each block.  
The basic idea of the proposed algorithm is to act on some of these vectors, rotating them so to embed the watermark into angles related to such vectors and, at the same time, maintain the orthonormality of the matrices \(U\) and \(V\). The angles considered here are the ones formed by the projection of the chosen vectors on selected planes with respect to the axes defining the planes. The watermarking algorithm is based on the idea of [PERSON] _et al_. [53], a feature-based watermarking scheme which embeds the watermark, i.e., an independent identical distributed pseudo-random sequence \(W\), into a set of features \(F\) obtained from the original image _I_\(o\).  
We consider that _I_\(o\) is a gray-scale image of size _N\(\times\)N_ and the watermark \(W\) is a pseudo-random sequence, with zero mean and unitary variance, of size _n\({}^{2}\)_, where _n=N/M_ and _M\(\times\)M_ is the size of a single block. Our feature set _F  
To perform the rotation, we use a unitary transformation matrix constructed as presented in [54]. The orthonormality property of the matrix \(V\) is maintained by rotating a second vector \(v_{j}\) together with vector \(v_{i}\).  
To improve the security of the algorithm, a secret key \(K\) is used to choose the right singular vectors, which will be rotated to embed the watermark, and the corresponding coefficients that determine the secret plane. The secret key, which allows the watermarking algorithm to be public, also contains a value which is used to initialize the pseudo-random generator to produce the watermark sequence \(W\). For the specific purpose of content protection, note that such a value must be univocally associated with the copyright owner identity, e.g., applying a standard hash function to the string containing his name.
# Routing Protocols for Mobile Internet of Things (IoT): A Survey on Challenges and Solutions
## 3 Issues Faced by RPL during Mobility
### Lack of Positioning Information  
To make the best routing decision for mobile nodes, the routing protocols should consider the position of nodes. We note that RPL does not take into account the positioning information of mobile nodes during routing decisions [54].