esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Transmittance Test  
The transmittance test was conducted to observe the uniformity through emulsification process in this study. Although the hydrophobic octyl group does not react with chitin, the hydrophilic sulfonate substituting -OH group in the chitin still reacts with water to decrease the transmittance for using the modified chitin as an emulsifier, as shown in Figure 5. Since the remaining water are immiscible with the oil phase, the uniformity is lower than other two emulsifiers, referring to the higher transmittance. Figure 5 also shows that the transmittance of the modified chitosan as an emulsifier was lower than that of sodium acrylates copolymer, and the transmittance decreased with increases in the amount of emulsifier. The results showed that the emulsification effect of the amphoteric chitosan was better, and that a dose of 0.2 g is more suitable. In addition, the same transmittance by a smaller amount of amphoteric chitosan than that of sodium acrylates copolymer can be found in Figure 5.  
Figure 4: Structures of chitosan and the modified chitosan.  
Figure 5: Change of transmittance with amount of emulsifier.  
Figure 6 shows the change in transmittance over time for a 0.2 g dose of emulsifier. The emulsifiers include sodium acrylates copolymer, the modified chitin, and the modified chitosan. Since the acetyl group in chitin was not removed, only the replacement of -OH group by -SO\({}_{3}\) group occurred in the modification; that is, the modified chitin. The deacetylated chitosan was modified as amphoteric N-octyl-O-sulfate chitosan; that is, the modified chitosan. No emulsification behavior occurred when the modified chitin was used as an emulsifier, for it only the sulfonate substituting -OH group in the chitin reacted with water. Therefore, the inhomogeneous phenomenon was observed, and the transmittance was changed by the reaction of sulfonate and water. The transmittance changed from 83 to 59% when the modified chitin was used. Only the hydrophilic sulfonate reacts with water, meaning that the modified chitin cannot be an emulsifier, and leads to the higher transmittance. A lower transmittance indicates a faster emulsification effect. Figure 6 confirms that the emulsification effect was faster when the modified chitosan emulsifier was used, and the suitable time for the emulsification process was about 20 min.
## 3 Novel Vehicle Terminal Based on Acoustic Communication Technology
### Acoustic Demodulation Module
#### 3.2.2 Modulation/Demodulation Process for Acoustic Communication  
The acoustic communication model includes two parts: mobile phone and vehicle terminal. The modulation/demodulation process of acoustic communication includes software token, sine wave generation and coding, data buffer, speaker, microphone, data storage, decoding, and execution is as shown in Figure 5.  
Figure 4: Signal modulation waveform of integrated modulation mode.  
Figure 5: Communication process of acoustic wave.  
The software token is consisted of 6-bit decimal numbers called as TOTP (time-based one-time password) passwords. Using the integrated SDK package in the mobile phone, the generated sine wave is modulated into PCM (pulse code modulation) signal. The PCM signal is cached in the data buffer. Finally, the PCM signal is played through the speaker.  
The microphone obtains the recorded signal and stores the PCM signal in the data storage area. After power amplification and filtering noise reduction, the PCM signal is decoded by compound modulation, and the corresponding operation is performed according to the analytical results.
## 3 Materials and Methods
### FTIR Measurement of Aggregates and Mineral Powders Using Transmission Mode  
#### 3.2.1 Samples Preparation  
To investigate the crucial functional groups of aggregates and mineral powder, the FTIR spectra of fine aggregate, coarse aggregate, and mineral powder were collected by the transmission mode via a KBr squashing method [38]. After the samples were dried and ground to a particle size of less than about 2.5 um, the thin KBr plate was pressed by a manual tablet press at 8-10 Mpa for 30 s.
# Clinical Assessment of Customized 3D-Printed Wrist Orthoses
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Study** & **[PERSON], 2017**[21] & **[PERSON], 2020**[11] & **[PERSON], 2022**[17] & **[PERSON] 2020**[20] & **[PERSON] 2020**[12] & **[PERSON], 2021**[18] & **[PERSON], 2019**[19] & **Our Study** \\ \hline \hline Scan site & Affected wrist & Affected wrist & Affected wrist & Confralateral wrist & Dominate/non- & Affected wrist & Affected wrist & Affected wrist \\ \hline Scanning & CT/MR & 3D scanner & 3D scanner & 3D scanner & 3D scanner & 3D scanner & 3D scanner & 3D scanner \\ \hline Software & Self-developed & Autodesk inventor & T3 DDV-Oplh & Blender & NS & NS & Rhinoceros v5.0 & Self- developed \\ \hline Structure & Two halves & Two halves & Two halves & Three brace pads & Two halves & Two halves & Two halves & Two halves \\ \hline Clearance & NS & 3 mm & \(\leq\)3 mm & NS & NS & NS & \begin{tabular}{c} Regional ROIs \\ 0.3-2 mm \\ \end{tabular} & \begin{tabular}{c} Regional ROIs \\ 0.3-2 mm \\ \end{tabular} \\ \hline \hline \begin{tabular}{c} Closure \\ mechanism \\ \end{tabular} & Strap & Strap & Plastic ties & Rods and screws & NS & Tamper-proof locker & Fixation locker & Strap \\ \hline \hline \begin{tabular}{c} Immobility test \\ \end{tabular} & None & None & None & None & \begin{tabular}{c} JFFT/PRWE \\ \end{tabular} & None & None & \begin{tabular}{c} Pressure \\ sensor \\ \end{tabular} \\ \hline \hline \begin{tabular}{c} Subjects \\ \end{tabular} & \(10\) patients & None & \begin{tabular}{c} 10 pediatric \\ patients \\ \end{tabular} & \begin{tabular}{c} 10 healthy \\ volunteers and \\ 5 patients \\ \end{tabular} & \begin{tabular}{c} 12 healthy \\ volunteers \\ \end{tabular} & \begin{tabular}{c} 22 pediatric patients \\ volunteers \\ \end{tabular} & \begin{tabular}{c} 18 pediatric \\ patients \\ \end{tabular} & \
\begin{tabular}{c} 28 healthy \\ volunteers and \\ 33 patients \\ \end{tabular} \\ \hline
\begin{tabular}{c} Clinical \\ effectiveness \\ assessment \\ \end{tabular} & Y & N & Y (High-rate & & & & \\ \hline \hline \end{tabular}  
* NS: not specify; JFFF), Jensen Hand Function Test; PRWE: Patient-Rated Wist Evaluation.  
\end{table} 
Table 9: Comparisons of design method for 3D-printed brace applying on wrist fracture patients.
# Anomaly Detection in Autonomous Deep-Space Navigation via Filter Bank Gating Networks
## 3 Materials and Methods
### Cost Function Definition and Solution Derivation
\[\vec{p}_{k} =\Big{(}G_{k}+\tilde{W}^{-1}\Big{)}^{-1}\Big{[}\tilde{W}^{-1} \vec{p}_{k-1}-\lambda\vec{\mathrm{I}}_{N}\Big{]} \tag{8}\] \[\lambda =\frac{\vec{\mathrm{T}}_{N}^{T}\big{(}G_{k}+\tilde{W}^{-1}\big{)} ^{-1}\tilde{W}^{-1}\vec{p}_{k-1}-1}{\vec{\mathrm{I}}_{N}^{T}(G_{k}+\tilde{W}^ {-1})^{-1}\vec{\mathrm{I}}_{N}}\]  
Having obtained an analytical solution that minimizes our designed cost function, we will next focus on this algorithm's strengths and weaknesses, how it may be slightly modified to improve its performance in detecting filter mis-modelings, and how it may be applied practically.
# A Novel Cross-Layering Power Control Mechanism for AODV +
## 4 DPCM (Density Power Control Mechanism)
### AODV Routing Protocol Modification  
The modification that we made considers three main stages: access to the physical layer from the network layer, obtaining the number of neighbors of the routing protocol, and implementing a periodic update process. For that, it is necessary to establish a link between AODV and the physical layer, which is shown in Figure 1.  
This process includes the idea of cross-layering so that the transmission power is configured based on information that the routing protocol has sent.  
Considering that the mechanism will act based on the number of neighbors that detect AODV, it is necessary to obtain all the information from neighbors to include it in the mechanism. Finally, a periodic detection process is included to get neighbor information updated constantly.
# Investigation on Ship Hydroelastic Vibrational Responses in Waves
## 2 Ship Hydroelasticity Theory in Regular and Irregular Waves
### Hydroelastic Equation of Motion  
Based on the modal superposition formulation [2], the hydroelastic equation of motion of a large vessel advancing in regular or irregular waves can be expressed as follows:  
\[[\textbf{m}][\textbf{\dot{p}}]+[\textbf{c}][\textbf{\dot{p}}]+[\textbf{k}][ \textbf{p}]=[\textbf{F}_{I}]+[\textbf{F}_{D}]+[\textbf{F}_{R}]+[\textbf{F}_{H} ]+[\textbf{F}_{SL}] \tag{2}\]  
where **[m]**, **[c]** and **[k]** are called generalized mass, damping, stiffness matrices, respectively. [**p**] denotes the principal coordinate matrix. All the five terms of the right-hand side of Equation (2) are called generalized force matrices, where \([\textbf{F}_{I}]\), \([\textbf{F}_{D}]\), \([\textbf{F}_{R}]\), \([\textbf{F}_{H}]\) and \([\textbf{F}_{SL}]\) are the Froude-Krylov force matrix, diffraction force matrix, radiation force matrix, hydrostatic restoring force matrix, and slamming force matrix, respectively.  
Even though the structural part of Equation (2) is based on the linear theory of solid mechanics, we can still obtain the nonlinear structural responses by practically taking into account the nonlinear effects of some of those five generalized force matrices. Those nonlinear effects should appropriately reveal the real physics of the fluid structure interaction whilst balancing this requirement against the  
Figure 1: Mathematical description of ship-fluid interaction.  
consideration of time efficiency. Therefore, [**F\({}_{I}\)**], [**F\({}_{H}\)**] and [**F\({}_{SL}\)**] are calculated on the instantaneous wet surface of the ship to include nonlinearities associated with hull geometry. In contrast, [**F\({}_{D}\)**] and [**F\({}_{R}\)**] are remained linear and computed based on the mean wet elastic ship surface in calm water. The following paragraphs will elaborate on each of those five generalized force matrices.
## 3 Experimental Results
### Measurements of Mat Pressure Sensor
#### 3.1.2 Second Experimental Test  
In the second test experiment, the measurements of the pressure sensor mat were carried out using as pressure force the calibration weights cylinders. Calibration weights based on four cylinders of diameter \(d\) equal to 9.75 mm and a foam base were applied to the mat on different sensitive cells of the sensor matrix mats to measure the resistance. The variation in mat resistance (\(\Omega\)) in the function of pressure force expressed in Newtons for different sensitive cells (B1, E1, B4, E4) of the mat is reported in Figure 7.  
The rows and columns of the mat matrix are numbered, respectively, with letters (A, B, C...) and with numbers (1, 2, 3...), as shown in Figure 3b. The weights of the load applied on the mat are 50 g of plate, 221 g of board, and 220 g of steel cylinder. The supply voltage \(V_{g}\) is 4 V. The repeatability of the sensor mat experiment was tested, providing the same result of resistance under the same measurement procedure for the same weights, as shown in Figure 8.  
Figure 8: Resistance in function of the weight applied on the mat. By applying some weights to the mat, the resistance changes.  
Figure 7: Resistance in the function of the force applied on the mat for different active cells.  
The additional pressures were obtained by increasing weights from 500 g to 5000 g and reading the current value from the multimeter with a constant voltage excitation (\(V_{g}\) = 5 V), and then, the opposite measurements were achieved for decreasing weights from 5000 g to 500 g.
## 4 Results and Discussion
### Building of Stress-Strain Relationship Model
#### 4.2.2 Error Evaluation of the Proposed Model  
For additional validation, it has been suggested that the model's estimate ability should be calculated using the relative error distribution [46]. Therefore, the absolute relative error (ARE) percentage is calculated as follows:  
\[\text{ARE}=\left|\frac{SA_{i}-SE_{i}}{SA_{i}}\right|\times 100 \tag{13}\]  
With a high R-value, the PSO model accurately predicts the required values with high accuracy, as shown in Figure 13. As a result, the proposed model has a good ability to predict (i.e., low values) and to generalize (i.e., similar values).  
Figure 12: Taylor diagrams of predicted versus observed standardized stress capacity using the PSO model (for 80% of the data).  
In an ideal scenario, the frequency of ARE should decrease as the amount of ARE increases. This model has highest frequencies at low absolute relative error (ARE 10%) and lowest frequencies at high absolute relative error (ARE > 15%). Accordingly, the proposed model has an acceptable error distribution. As a result of errors, it is possible to conclude that the proposed model can serve as an excellent design guide for future research projects.
## 1 Introduction
Convolutional neural network (CNN) is a commonly used deep learning model, mainly utilized to analyze and extract features from input data. A multi-scale convolutional network divides the network into blocks and limits the number of output channels in each layer to reduce the number of parameters and the computational complexity, and to prevent over-fitting. [PERSON] et al. used stacked automatic encoders to extract layered output-related features layer by layer and then applied the SVR model to accurately predict the water content of _Pinus massoniana_ leaves [21]. In another study, [PERSON] et al. used variable-weighted CNN to improve the one-dimensional CNN model's generalization ability and predicted the nitrogen content of _Pinus massoniana_ leaves. Here, we proposed a method named multi-scale short cut convolutional neural network (MS-SC-CNN) for measuring moisture and nitrogen content in leaves of _Pinus massoniana_ seedlings based on the residual network and CNN's characteristics with NIRS. First, hyperspectral reflectance data of _Pinus massoniana_ seedling leaves were collected by NIRS, after which two MS-SC-CNN prediction models were established. The MS-SC-CNN was applied to extract features layer by layer, while the short cut structure was used to make the features generated by the neural network spread across layers accurately, after which the measurement of moisture and nitrogen content was achieved. The main contributions of this study are as follows: (1) We constructed a new MS-CNN structure to estimate the moisture and nitrogen content in _Pinus massoniana_ seedling leaves with NIRS; (2) the short cut was used to transfer data by skipping multiply layers in the proposed MS-CNN network to improve the estimation accuracy and reduce model complexity. The remainder of this paper is organized as follows: Section 2 describes the collection and processing of the experimental materials and the proposed MS-SC-CNN method to estimate the moisture and nitrogen content in _Pinus massoniana_ seedling leaves. Section 3 discusses and analyzes the performance of the MS-SC-CNN model based on several comparative experiments. Finally, Section 4 summarizes the conclusions and future work.
# An IoT Transfer Learning-Based Service for the Health Status Monitoring of Grapevine
## 2 Materials and Methods
### Transfer Learning
#### 2.2.2 Training through Transfer Learning  
These DL models have been trained on the ImageNet dataset, a benchmark dataset that consists of over one million images for training and validation purposes (almost 90% of the total data), along with a hundred thousand images for testing (almost 10% of the total data). In this study, a transfer learning (TL) approach was employed, utilising the inherited training weights from the models' prior training on the ImageNet dataset. TL, by definition, is a deep learning technique that uses previously trained models as a foundation for new tasks. Instead of starting from zero when training a model, the information and features gleaned from a sizable dataset are used for a new, related task. This method saves time and resources because the pre-trained model is already well-versed in broad patterns and may be adjusted to excel at the given task. Even with the limited amount of data available for the new task, it enables faster training and better performance.  
TL can involve changing either only part of the convolutional base or the whole base, depending on the specific requirements of the task. The approach where only a portion of the convolutional base is updated is called \"Feature Extraction\" as the pre-trained weights are used as a fixed feature extractor in a high layer level, while the lower layers are trained in the target task. Meanwhile, the approach where the weights of the whole convolutional base are updated is called \"Fine Tuning\". In this approach, the weights of the base are adjusted along with the newly added layers in order to have a model that is more flexible model in terms of adaptation to the new task as it makes modifications to the learned representations at different levels of abstraction.  
In this study, a fine-tuning approach was considered, and thus, the training process involved keeping the models' convolutional architecture intact and updating the model weights in all the base layers. The weights were updated, using the grapevines subset data taken by the Eden Library dataset.  
Specifically, the base models were instantiated, having all of the base layers' weights unfrozen, and the fully connected (FC) layer with the 1000 classes from the ImageNet with a dense layer was replaced with six outputs, where each output corresponded to the probability of the identified health status label on the grapevine. The grapevines subset from the Eden Library dataset was used for the initial training of the models and for updating the weights. The validation of the model was performed online on the vineyard of the Chatzivarity winery after having deployed the model on a Raspberry Pi 4 (RPi) platform, with a custom RGB camera module mounted on it.
###### Abstract  
GNSS technology utilizes satellite signals to determine the position of a point on Earth. Using this location information, the GNSS velocities of the points can be calculated. GNSS velocity accuracies are crucial for studies requiring high precision, as fault slip rates typically range within a few millimeters per year. This study employs machine learning (ML) algorithms to predict GNSS velocity accuracies for fault slip rate estimation and earthquake hazard analysis. GNSS data from four CORS stations collected over 1-, 2-, and 3-year intervals with observation durations of 2, 4, 6, 8, and 12 h, were analyzed to generate velocity estimates. Position accuracies, observation intervals, and corresponding velocity accuracies formed two datasets for the East and North components. ML models, including Support Vector Machine, Random Forest, K-Nearest Neighbors, and Multiple Linear Regression, were used to model the relationship between position and velocity accuracies. The findings reveal that the Random Forest, which makes more accurate and reliable predictions by evaluating many decision trees together, achieved over 90% accuracy for both components. Velocity accuracies of \(\pm\)1.3 mm/year were obtained for 1-year interval data, while accuracies of \(\pm\)0.6 mm/year were achieved for the 2- and 3-year intervals. Three campaigns were deemed sufficient for Holocene faults with higher slip rates. However, for Quaternary faults with lower slip rates, longer observation periods or additional campaigns are necessary to ensure reliable velocity estimates. This highlights the need for GNSS observation planning based on fault activity.  
machine learning; GNSS velocity accuracy; GNSS position accuracy; slip rate +
Footnote †: journal: Applied Sciences  
1  
H.I.
# Policy-Based Composition and Embedding of Extended Virtual Networks and SFCs for IIoT
## 4 Methodology
### Transformations
#### 4.1.3 Security VNFs  
In this transformation, we assume that the service provider defines certain security policies. For example, for a cross-location or cross-domain VLi, a SFC with certain security VNFs must be attached to this VLi. Our exemplary transformation policies presented here consider both cases. For cross-location VLi, encryption, firewall, deep packet inspection, and monitoring VNFs are added to the VNF demand of the VLi. We assume that encryption is not needed for cross-domain VLis (in the same location). Except for the network monitoring function, each type of VNF is attached to the source or destination location/domain. Specific location/domain demands are attached to the added VNFs based on the location/domain demands of the AR. However, the general dependency graph forces the order of these VNFs in the FG. The VNFs in the link FG are assigned the node type \"VNF\".  
\[\begin{split} P:\{e_{AR}^{ij}|n_{AR}^{i}.& location\
eq n_{AR}^{j}.& location\}\\ T(SN,AR):\{e_{AR}^{ij}.& vfnDemand.add(& ENC,FWS,FWD,NM,DEC,DPI)\}\\ P:\{e_{AR}^{ij}|n_{AR}^{i}.& location=n_{AR}^{j}.& location\wedge n_{AR}^{i}.& domain\
eq n_{AR}^{j}.& domain\}\\ T(SN,AR):\{e_{AR}^{ij}.& vnfDemand.add(& FWS,FWD,NM,DPI)\}\end{split} \tag{4}\]