esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Sensory Analysis  
The hedonic quality assessment resulted in determination of the level of acceptability of the model soups and enabled analysis of individual sensory quality characteristics. As can be seen in Figure 9, the evaluators found the vegetable cream soup with beef balls to be more acceptable as it received higher ratings for its sensory characteristics.  
On the first day of the study, all characteristics of the cream soup with beef balls and carrots were rated higher by the representatives of the target group. The taste and appearance were rated as average (contingency coefficient values of 0.46 and 0.43, respectively). The consistency of the model soup with beef balls and carrots was rated higher. The contingency coefficient value of 0.33 indicated a weak correlation.  
Presumably, the beef balls increased the taste and aroma profile of the product due to the specific taste characteristics of beef. The beef balls also increased the attractiveness of the model soup. The evaluators found the soup with beef balls to be more attractive and more varied. They rated its colour better, probably because the beef juices (including colouring agents) partly penetrated the soup during heating. As the evaluators found the soup consistency acceptable, it can be concluded that elderly people will not have problems biting or swallowing beef balls. This fact was also confirmed by the low values of the texture parameters, which were determined instrumentally.  
The results of the semi-consumer evaluations made for two storage periods (1 and 21 days) enabled verification of the level of acceptability of the innovative soups within the assumed shelf life. There were similar values for the individual sensory quality characteristics of the model soups in both storage periods. The differences were not statistically significant (\(p>0.05\)). The only exception was the overall appearance of the model soups. There were statistically significant relationships between the storage time and the rating of the overall appearance. Although there were no statistical differences, the evaluators preferred the cream soup with beef balls to the vegetable cream soup in both storage periods. As can be seen in Figure 9, the quality characteristics of the innovative soups in the last storage period point to positive reception of the products by future consumers.  
Figure 8: Selected soup texture characteristics vs. the type of sample, preparation method (cold and hot), and storage time for principal component analysis and loadings.  
Soups need to have a moderate liquid consistency to be acceptable to consumers. Starch-based thickeners are usually used in the soup production process because they are readily available. The ideal soup should be thick enough. It cannot be watery or lose the desired viscosity when stirred. The most favourable sensory parameters apply to a soup that falls within the scope of consumers' general perception or acceptance. In other words, such a soup cannot receive lower sensory ratings for its aroma, taste, creaminess, consistency, and overall acceptability [44].
# An Improved Image Filtering Algorithm for Mixed Noise
## 2 The Filtering Algorithm for Mixed Noise with Salt and Pepper + Gaussian Noise
### The Improved Wavelet Threshold Denoising Algorithm
#### 2.2.3 The Selection of Wavelet Threshold  
It is important to construct a good threshold function, and it is crucial to select an appropriate threshold. The wavelet coefficient of the signal after wavelet decomposition is larger, the wavelet coefficient of the noise is smaller, and the wavelet coefficient of the noise is smaller than the wavelet coefficient of the signal. Therefore, by selecting an appropriate threshold, wavelet coefficients larger than the threshold are considered to be generated by the signal and should be retained, and wavelet coefficients smaller than the threshold are considered to be generated by noise and should be removed. This is the basic principle of wavelet threshold denoising. If the threshold is too large, some important features of the image will be filtered out. If the threshold is too small, there will be more residual noise and the denoising effect is not ideal. [PERSON] proposed a general threshold [12]:  
\[\lambda=\sigma\sqrt{2\ln(M\times N)} \tag{16}\]  
where \(\sigma\) represents the standard deviation of noise and \(M\times N\) represents the size of image. Equation (16) is a global threshold, which is a unified threshold used for denoising the whole image. However, the local threshold can determine the threshold of each image block according to its own noise level. Compared with the global threshold, the local threshold can be adjusted according to different noise blocks, and the value is more flexible.  
Noise components are mainly concentrated in the high-frequency coefficients and decrease with the increase of decomposition scale. Effective signal components are mainly concentrated in the low-frequency coefficients and increase with the increase of decomposition scale. Therefore, the threshold should gradually decrease as the decomposition scale increases.  
In this algorithm, the threshold is set as:  
\[\lambda=\frac{\sigma\sqrt{2\ln(M\times N)}}{m\times j-1} \tag{17}\]  
where \(m\) is an adjustable parameter, \(j\) (\(1\leq j\leq n\)) is the corresponding wavelet decomposition scale, \(\sigma\) is the noise standard deviation, and \(\sigma\) is defined as:  
\[\sigma=\frac{median\big{(}|W_{1,k}|\big{)}}{0.6745} \tag{18}\]  
where \(median(x)\) is the median operation, \(median\big{(}|W_{1,k}|\big{)}\) is the median value of absolute value of the wavelet decomposition coefficient of the first layer, and 0.6745 is the adjustment coefficient of the standard deviation of Gaussian noise.  
The proposed threshold can be adjusted according to the value of the current decomposition scale, so as to obtain an adaptive threshold for different decomposition scales, and the threshold meets the condition of \"gradually decreasing with the increase of the decomposition scale\".
# A Geostationary Instrument Simulator for Aerosol Observing System Simulation Experiments
## 2 Methodology and Data
### GEOS-5 Nature Run Aerosol Simulation
#### 2.2.2 Evaluation of G5 NR Aerosols

Table 3: Global 550 nm AOD Scaling Factors. Figure 5: Vertical profiles of aerosol attenuated backscatter coefficient at 532 nm over the African dust region (\(60^{\circ}\) W \(-20^{\circ}\) E, \(0^{\circ}\) N \(-30^{\circ}\) N). On the left are time-averaged retrievals from CALIOP for July 2011. On the right is the G5 NR simulation July 2006.
## 5 Fuel-Consumption Assessment Results
### FPGA Implementation  
In Section 4, the SOM training as well as a classification method were explained. Additionally, the classification was carried out on the Uyanik dataset completed by the GT-Suite simulation data, obtaining the weights and clusters to which the neurons belong. With that data, the SOM network was implemented in the FPGA in order to classify the Uyanik drivers by storing the neuron weights and their corresponding clusters into the internal ROM of the architecture.  
The FPGA of the Xilinx ZynQ-7000 family (XC7Z045-2 FFG900 PSoC) [60] is used in order to implement the SOM accelerator. This FPGA, derived from the Xilinx Kintex-7 family, has the following logic resources:  
* 54 650 logic blocks, each one conformed by four six-input LUTs and eight flip-flops;
* 19.2 Mbits of high-speed RAM blocks;
* 900 digital signal processing (DSP) blocks; and,
* an analog-to-digital converter (ADC).  
The architecture described in Section 6.1 has a total of \(M=121\) neurons and \(N=4\) input features. We used fixed-point binary arithmetic to represent data. Both input data and neurons' weights have been represented with 8 bits, with all the 8 bits representing the decimal part, because both are unsigned positive numbers. The intermediate operations' bitwidths were selected such that neither overflows nor rollovers could occur.
# Rank and Wormhole Attack Detection Model for RPL-Based Internet of Things Using Machine Learning
## 3 Methodology
### Network Model Setup, Simulation, and Network Scenarios for Data Collection
#### 3.2.4 SN-Inherited (WHA) Attack Model Simulation  
The first scenario consists of a network with partially mobile nodes. The first scenario consists of  
mobile. The first use case consists of all static nodes, while the second use case consists of a network with partially mobile nodes.  
The wormhole attack is simulated from the SN-inherited attack category. The attack model is presented in Figure 3, where two nodes, 16 and 26, form a tunnel between each other by probing using DIS messages and then sending and receiving DIO, DAO, and acknowledgment messages. In the attack model, malicious nodes are placed near the victim nodes to observe the attack effect. Consequently, the neighbor nodes 17-25 join the attacker node 16 by selecting it as a parent due to its illegitimate preferred parent characteristics, such as decreased rank value and shortest path to the root node. Similarly, nodes 27-30 also join the attacker node 26 for the same reasons mentioned earlier. Algorithm 2 demonstrates the simulation of SN-inherited wormhole attack in the RPL-based IoT network in the Cooja simulator.
## 2 Project Background
### Design of Freezing Scheme  
According to the bidding documents, the freezing depth is determined to be 305 m. Based on the well wall structure diagram and the geological data of the well inspection holes provided in the bidding documents, after the optimization of the plan, the comprehensive freezing construction scheme is adopted in the Dongfeng Well, where the main freezing holes, auxiliary freezing holes, and prevent piece holes are operated together with the three circles of holes. The differential freezing scheme is employed in the main freezing holes, while the \"plum blossom pile\" arrangement is adopted for the auxiliary and prevent piece holes. To monitor the real-time development of the freezing temperature field, temperature measurement holes (C1-C3) and hydrological holes (S1-S2) are set in the freezing area. Among them, the S1 hole (hole depth: 40 m) is designed with four flower tube parts at a depth of 6.5-11.5, 25.5-30.5, 31.5-33, and 35.0-39.0 m. A partition at a vertical depth of 25 m is established, and the casing is lowered to obtain the change in the water level layer by layer (the casing size is \(\Phi\)38 \(\times\) 3.5 mm). For S2 hole (depth: 125 m), a flower tube at a position of 118.0-123.0 m is designed. The parameters of the freezing holes are listed in Table 1, and their layout and cross-section are shown in Figure 1. Based on the depth of the wellbore alluvial layer and the control formation (\(-\)216 m sandy clay), combined with the relevant specification [26], the brine temperature during the active freezing period is determined to be \(-\)25 to \(-\)30 \({}^{\circ}\)C. The average design temperature of the effective frozen wall of the Neogene control horizon (\(-\)216.45 m sandy clay) is \(-\)12 \({}^{\circ}\)C, and the design thickness is calculated with Domke formula [31].  
\[E=R\times\left[0.29\times\left(\frac{P}{R}\right)+2.3\times\left(\frac{P}{K} \right)^{2}\right] \tag{1}\]  
where \(E\) is calculated frozen wall thickness, m; \(R\) is shaft radius, \(R\) = 4.053 m; \(P\) is calculated horizon pressure, MPa; \(P\) = 0.013 H = 2.81 MPa; \(K\) is calculated strength of frozen soil, \(K\) = 4.93 MPa.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Freezing Holes** & **Circle Diameter (m)** & **Hole Spacing (m)** & **Depth (m)** & **Freezing Method** \\ \hline Main freezing holes & 13.2 & 1.296 & 305/255 & Differential freezing \\ Auxiliary freezing holes & 10.1 & 2.115 & 240 & Full depth freezing \\ Prevent piece holes & 9.5 & 1.990 & 150 & Full depth freezing \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of the freezing holes.  
Figure 1: Plan layout and cross-section of the freezing holes. Z: main freezing hole; F: auxiliary hole; P: prevent piece hole; C: temperature measurement point; S: hydrological observation hole.  
After calculation, \(E\) = 3.70 m. According to the calculation results and combined with the construction unit's experience in freezing construction of vertical shafts with similar geological conditions, the thickness of the frozen wall \(E\) = 4.2 m is determined.
## 4 Methods (ASMAG Algorithm)
### Data Pre-Processing
#### 4.1.2 Topographic Normalization with the Ekstrand Correction  
Sensor data in the original digital numbers (DNs) are converted into physical values (radiance) and normalized values (reflectance) with band-specific radiometric calibration constants. The retrieval of brightness temperature in degrees kelvin (used for cloud cover mapping) and TOAR follows a two-step process. First, the at-satellite radiance (W/(m\({}^{2}\) sr \(\upmu\)m)) is calculated for all bands based on the solar zenith angle at the time of satellite overpass. In a second step, the satellite radiance is converted once into kelvin (thermal band only) and into TOAR once [46; 47; 48]. The TOAR values are influenced by topography that alters the local incidence angle of solar radiation. This causes differences in reflectance between otherwise similar surface cover types on a pixel-by-pixel basis. As the selected glaciers are located in high mountain regions with partly steep topography, a topographic correction of the TOAR  
Figure 2: Generalized data processing flow chart of ASMAG.  
values is applied. Several methods have been developed [49; 50; 51] and a previous study [52] concluded that the Ekstrand correction provides the best results especially in scenes with SC or glaciers in steep mountain terrain. To perform the Ekstrand correction, the slope, aspect and a hillshade of the DEM for the Landsat acquisition date and time are calculated. This allows the assimilation of pixels considering (a) different illumination conditions, and (b) atmospheric propagation [49].
## 2 Study Area and Data
### Remote-Sensing Datasets
#### 2.2.3 GF-3 Data  
The GF-3 satellite is the first C-band multi-polarization SAR imaging satellite in China, with a resolution of 1 m and 12 imaging modes [67]. The spatial resolution of GF-3 ranges from 1 to 500 m, and the width ranges from 10 to 650 km, suitable for distinguishing and identifying objects on the ground and at sea.  
As an independent test of the model's accuracy, the scene covered by the Gaofen-3 satellite (L2A level, 5 m resolution, HH and HV polarization) in the Ji'An site was selected.
# A Data-Driven Model for Predictive Modeling of Vortex-Induced Vibrations of a Long-Span Bridge
###### Abstract  
Vortex-induced vibration (VIV) of long-span bridges can be of large amplitude, which can influence serviceability. Therefore, it is important to predict the response of vortex-induced vibration to aid the management of long-span bridges. A novel data-driven model is proposed to predict the time history of the dynamic response of VIV events. Specifically, the proposed model consists of gated recurrent unit (GRU) neural networks and the Newmark-beta method. GRU neural networks can perform accurate sequential prediction, and the Newmark-beta method can complement the physical meaning of the middle output of the proposed model. To aid the accurate prediction of the amplitude of VIV events, the proposed model employs weighted mean square error as the loss function, which can put more emphasis on the amplitude. The proposed model is validated on measured VIV events of a long-span suspension bridge. The weighted mean absolute percentage error and Pearson correlation coefficient of the trained model indicate the effectiveness of the proposed model.  
vortex-induced vibration; machine learning; long-span bridge; field monitoring
## 5 Proposed Models
### Architecture Overview
#### 5.3.3 Feature Extraction Stage  
The feature extraction stage of our model emulates the CNN-RNN structure from our prior research. Initially, input data undergoes processing via a CNN to extract spatial features. Given the grid-like structure of digital images, the CNN proves adept at identifying geospatial-based features, as demonstrated in the data illustrated in Figure 1. We employ a CNN with multiple layers to account for spatial dependencies within each region. A single convolution operation addresses only adjacent grid dependencies, which might be insufficient for comprehensive traffic forecasting. While temporal features are paramount in traffic forecasting, standard 2D CNN models typically do not capture them. Conversely, LSTM excellently identifies relationships between temporally contiguous data points. In Figure 3, multiple INT inputs (specifically \(X_{t-6}\) to \(X_{t-1}\)) are processed through the CNN stage before being sequenced into the recurrent neural network (RNN) stage. The CNN output is then fed into the LSTM, which captures temporal features among the six data points within an hour.  
Figure 6: Correlation coefficient between Social Pulse and Milano Today. (**a**) Correlation of INT and Social Pulse, (**b**) Correlation of INT and Milano Today.  
This structure is a deep hierarchical feature extractor, employing a CNN for spatial domain learning and an RNN for temporal domain learning. The approach has succeeded in activity recognition and video description tasks [7; 13; 44]. Notably, our method distinguishes itself from that described in [45] by incorporating a more comprehensive array of data, including Milano Today, Social Pulse, and various weather data, thus enhancing the LSTM model's comprehensiveness and adaptability.
## 2 Single-Channel Feedforward ANC System and Causality Constraint  
### Single-Channel Feedforward ANC System  
A principle of the ANC system is based on the superposition between the unwanted noise and the anti-noise, which is generated by the noise controller. The configuration of the ANC system depends on the noise properties. A feedforward ANC system can reduce broadband noise and consists of two microphones, one loudspeaker, and one noise controller. The microphone near the noise source is called the reference microphone and is used to pick up the unwanted noise. The other microphone near the noise control region is called the error microphone and senses the residual noise \(e(n)\), expressed as:  
\[e(n) =d(n)-y^{\prime}(n)\] \[=d(n)-s(n)*y(n)\] \[=d(n)-s(n)*\left[\mathbf{w}^{\mathrm{T}}(n)\mathbf{x}(n)\right], \tag{1}\]  
where \(d(n)\) is the unwanted noise through the primary path (In general, the primary path is defined as the path between the reference microphone and the error microphone. In this paper, we define the primary path as the path between the noise source and the error microphone for the discussion in Section 3.) \(P\), \(s(n)\) is the impulse response of the secondary path \(S\) at sample time \(n\), \(\mathbf{w}(n)=[w_{0}(n)\ w_{1}(n)\ \cdots w_{N-1}(n)]\) is the filter coefficient vector of the noise control filter \(W\), \(\mathbf{x}(n)=\left[x(n)\ x(n-1)\cdots x(N-1)\right]\) is the reference signal vector obtained by the reference microphone, and \(N\) is the filter length of the noise control filter.  
The noise controller calculates the filter coefficients of the noise control filter. Many ANC systems use the filtered-x algorithm [13; 14] to update the filter coefficients. A block diagram of the feedforward ANC system is shown in Figure 1. In general, the anti-noise propagates either the error microphone and the reference microphone. The acoustic path from the secondary loudspeaker to the reference microphone is called a feedback path. The feedback path cannot be ignored when the ANC system is adopted to the duct or the secondary loudspeaker generates large anti-noise. Fortunately, in some ANC applications like a head-mounted ANC system, the feedback path can be ignored because the secondary loudspeaker is located near the user's ears and does not require generating large anti-noise. In this paper, the filter coefficients of the noise control filter are updated by the filtered-x normalized least mean square (FXNLMS) algorithm, whose update equation is given by:  
\[\mathbf{w}(n+1)=\mathbf{w}(n)+\frac{\alpha}{\beta+\|\mathbf{u}(n)\|^{2}} \mathbf{u}(n)e(n), \tag{2}\]  
where \(\alpha\) is the step size parameter, \(\beta\) is the regularization parameter, \(\mathbf{u}(n)=[u(n)\ u(n-1)\cdots u(N-1)]\) is the filtered reference signal vector obtained as:  
\[u(n)=\mathbf{\hat{s}}^{\mathrm{T}}\mathbf{x}(n), \tag{3}\]  
\(\mathbf{\hat{s}}=[\hat{s}_{0}\ \hat{s}_{1}\ \cdots\ \hat{s}_{L-1}]\) is the impulse response of the secondary path model \(\hat{S}\), and \(\|\cdot\|\) denotes the norm.
## 2 Operation and Control
### Advanced Electrical Equipment
#### 2.1.2 Incorporation of Power Electronics Control Equipment  
The SG, in its natural evolution, must incorporate new equipment. Mainly the SG must integrate power electronics control equipment. This new equipment will perform new actions, or it will use your measured data to serve them to other applications.  
The monitoring and control of energy are fundamental according to [PERSON] [8], and due to the proliferation of IEDs in the SG, the data originated by events in the system will trigger the volume of traffic to be managed. It is necessary a new concept, called Translational Knowledge, which comprises: fault locating, alarm processing, and protective relaying. The improvement of the permittivity in the elements of the electrical system is important, and [PERSON] et al. [9] propose an improvement by means of a cylindrical metal cell.  
Some works are based on linear equations or differential equations, which are applied to the information of the equipment. [PERSON] et al. [10] focus on the design, realization and validation of the prototype of an innovative relay (SmartID) based on Kalman. These prototypes are fundamental to achieve the development of the next generation of electrical equipment.  
Some authors, such as [PERSON] et al. [11], consider it of high importance to solve an optimal control in situations of unbalanced voltages for the photovoltaic converters, using differential equations as a solution. As shown by [PERSON] et al. [12], the increase in the number of EVs and plug-in hybrid electric vehicles (PHEVs) is inevitable, and this fact will lead to imbalance conditions in the network, so the authors propose a new EV battery charger, based on linear equations and with perfect harmonic cancellation (PHC) in the control.  
[PERSON] et al. [13] analyze the development trend of uninterruptible power supplies (UPS) and they present an intelligent UPS system, where the control employs linear equations for the best solution.  
As shown [PERSON] and [PERSON] [14] for a Taiwan Power Company, it is necessary to take into account the trips of switches due to false positives, for which reason they propose linear programming to optimize the coordination between protections.  
[PERSON] et al. [15] develop a voltage VAR optimization (VVO) and a complete optimization program in a real environment, which is based on the Sequential Mixed Integer Linear Programming(SMILP--mathematical optimization problem where the variables are restricted to integers, the objective function are linear). Continuing with the above, [PERSON] et al. [16] propose a strategy similar to the previous one but for a problem of photovoltaic connection to the grid.  
Through the control of VVO it is possible to receive attacks (through the communications layer) In this sense, [PERSON] et al. [17] propose an integral solution to mitigate these attacks. The solution combines a controlled adjustment of the voltage regulator and efficient and robust management of communications data.