esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 ZnO-Based Nanostructures Preparation
### Microwave-Assisted Method  
The microwave-assisted method is not only an energy-saving, environmentally friendly, and heat-free method, but also with many advantages such as fast synthesis speed and the ability to tune the particle shape [44; 62]. ZnO nanoparticles with different morphologies can be synthesized by the microwave-assisted hydrothermal method by adjusting the time and power of microwave irradiation [63; 64]. Through microwave-assisted chemistry techniques, ZnO nanostructures with different morphologies were synthesized in different pH reaction mixtures (acidic, basic, or neutral). Furthermore, nanomaterials are synthesized without any heating and addition of surfactants. Hence, obtaining ZnO particles with oxygen vacancies and defects is expected to improve their pollutant degradation behavior due to the fast reaction process and non-stoichiometric synthesis [65]. As shown in Figure 3, the microscopic morphology of the microwave-synthesized ZnO nanostructures was observed by SEM. It can be seen intuitively that the appearance of ZnO nanoparticles changes dramatically with the pH change. In addition, from the XRD analysis in Figure 4, it was demonstrated that the change in intensity and peak width of the two sets of samples (prepared with NaOH and KOH as pH control agents) can be observed as the solution pH changes. The above results showed that the shape of synthesized ZnO nanoparticles is affected by pH changes, which has important implications for the directional synthesis of nanoparticles with diverse morphologies. In addition, several studies have shown that the power of the microwave is the most important factor affecting the synthesis of ZnO-based nanomaterials by microwave-assisted method [66; 67]. Moderate-power microwave have been shown to be suitable for the synthesis of ZnO-based nanomaterials with stronger antibacterial capabilities [68; 69]. However, the exact power influence mechanism needs to be further explored.  
Figure 3: Typical SEM images of synthesized ZnO nanostructures at different reaction pH values. Reprinted/adapted with permission from Ref. [65]. Copyright © 2022, Elsevier Ltd and Techna Group S.r.l.
# Inverse Solution Error Analysis and Correction of Beam Steering System Based on Risley Prisms
## 1 Introduction
This method is based on an analytical model of achromatic Risley prisms. The genetic algorithm was used to fit the actual parameters of the physical model, which can achieve higher accuracy than the approximate model. However, not all errors are included in the error fitting of the article. The Risley prism used in the article has a small pointing range (\(\pm 3^{\circ}\)), so errors that have little effect on pointing accuracy are ignored. In a system with a large pointing range, the beam-pointing deviation caused by these errors cannot be ignored. However, the above studies all focus on the impact of errors on the forward solution; there are few studies on the impact of errors on the inverse solution. Considering the accuracy and the real-time and data volume requirements of laser communication, the two-step method is the most suitable inverse solution method. Since the solution of the two-step method is based on the ideal motion trajectory of the spot, the offset of the actual motion trajectory caused by the systemic error of the prism will cause the pointing error of the inverse solution. This paper investigates the inverse solution error of Risley prisms induced by systemic error and proposes a method based on pointing-field transformation for correction. The outline of this paper follows. A more accurate error model is established in Section 2. In Section 3, the causes of the inverse solution error are explained, and simulations are performed to analyze the impact of the inverse solution error on pointing accuracy. Furthermore, the correction algorithm is proposed. In Section 4, the feasibility of the correction algorithm is verified by simulation and experiment. Conclusions are drawn in Section 5.
## 3 Network Model  
In this study, the WSN system comprising \(N\) static sensor nodes and one sink node. The sink is placed in the center of the sensing region \(R\), and sensor nodes are randomly scattered over \(R\) to monitor the environment. Sensor nodes can be grouped into several clusters, with one sensor node being appointed as the CH to aggregate data from the remaining cluster members within the same cluster. The set of sensor nodes is defined as \(\mathbf{S}=\{s_{1},s_{2},\ldots,s_{N}\}\), and the set of CHs is defined as \(\mathbf{CH}=\{ch_{1},ch_{2},\ldots,ch_{k}\}\), where \(k\) is the number of clusters. Each CH creates a time division multiple access (TDMA) communication message for its cluster members, which is used to inform cluster members to send data to the corresponding CH according to the schedule. The data collection, processing, and communication functions of sensor nodes are determined by the architecture of the nodes (Figure 2).  
Sensor nodes can convert their sensed information into digital signals in the data collection module. Then, the data processing module processes and stores these data. Sensor nodes can interact with each other via a wireless link within their communication range. The information they exchange includes collected data and some control information. Each Sensor node can locate its position through the global positioning system (GPS). The power module can provide energy to the above mentioned functional modules. Most of the available energy is consumed during data transmission. Unreasonable data transmission can lead to high and unbalanced energy consumption of sensor nodes, thereby reducing the lifetime of the network. The energy consumption model and network lifetime are described in the following subsections based on the following assumptions.  
* All sensor nodes have the same communication range and equipment with the same initial energy.
* Sensor nodes can know their location via the GPS.
* The sink node has enough energy; the battery of sensor nodes cannot be replaced or recharged.  
Figure 2: The main architecture of sensor nodes comprising four modules: data collection module, data processing module, wireless communication module, and power module.  
* Each sensor node has data of the same size to transmit in each round.
## 2 Related Work
The results show that when the average number of packets per message is one, the performances of R-CRDSA and RFSA are very similar to CRDSA and slotted-ALOHA, respectively. As the average number of packets increases, R-CRDSA achieves the highest throughput. In addition, the differences in throughput between R-CRDSA and RFSA decrease as the mean number of packets per message increases. To the best of our knowledge, existing performance evaluations of RFSA have been done in steady-state conditions, assuming that devices generate long messages following a random distribution, typically Poisson. However, the performance of RFSA in the case of delta traffic conditions remains an open research question.
## 1 Introduction
Existing source node location privacy protection schemes protect source location privacy by increasing path length or path complexity, such as cyclic entrapment [11], dummy data sources [6; 7; 12; 13; 14; 15; 16], phantom routing [6; 7; 16; 17; 18; 19; 20; 21; 22], etc. These technologies mainly improve security performance by sacrificing network performance (such as increased communication overhead and decreased network stability). However, the communication overhead is the largest energy consumption and is far greater than the computation overhead [23]. For example, the energy consumed to send 1-bit data by a sensor node can be used to perform 3000 calculation instructions [24]. Therefore, how to save energy in the process of network operation to maximize the network life cycle is critical in the design of security strategy. Worse, the sensor node is powered by a battery and its computational capability, storage capacity and energy resources are limited, so the existing schemes are not applicable to such resource-constrained application scenarios. How to save energy in the process of protecting the source location privacy is critical when designing the security strategy. In addition, in order to make the real source node difficult to be traced by the attacker, the PN should be as far as possible from the real source node [18], and the PN should be replaced periodically for the sake of security. However, in the existing schemes, the selected PNs are mainly concentrated near the SoN and remain unchanged, and the location privacy of the source node cannot be well protected.
## 4 Results and Discussion
This result implies fewer atmospheric impairments at lower frequencies. Generally, the lower the frequency of radio signal transmission, the higher the communication distance due to higher wavelength. Total propagated electromagnetic signal attenuation losses over various atmosphere components, including cloud, gases, rain, and free space versus varied transmission distances, are shown in Figure 11. The graphs reveal that attenuation loss due to free space is more dominant from the total computed loss values. This result is followed by rain rate attenuation, then atmospheric gases and cloud values. These results also revealed that attenuation intensity levels induced by atmospheric gases, rain, and cloud effects are less than levels induced by free space. For example, while moderate and heavy rain depths achieved 3 dB and 4 dB attenuations, the attenuation due to light rainfall attained a 2.5 dB  
Figure 7: Rain attenuation plot versus elevation angle and rainfall depth levels. level. Even at a lower T-R distance, the acquired attenuation values are as high as 78.23 dB in free space. Figure 8: Rain attenuation plot versus tilt angle and rainfall depth levels. Figure 9: Attenuation loss plot versus frequency of wet and dry air. As stated earlier, the resultant effects of totally natural and artificial blockades are high attenuation, slow /fast deterioration and steady losses in the intensities of the transmitted waves in space. For a carrier frequency of 3.5 GHz, a transmission power of 43 dB, receiver/transmitter cable losses of 5 dB, and a transmitter and receiver gain of 15 dB, the high attenuation and slow deteriorating power attained due to the natural and manufactured blockades, particularly atmospheric impairment-induced ones, versus distance plot is revealed in Figure 11. For example, slow fluctuations (attenuation) occur when atmospheric precipitation particles, particularly rainwater droplets, absorb, disperse, and scatter the amplitude and energy of transmitted electromagnetic signals in the Earth-satellite and terrestrial communication links. The received signal power quality at the user equipment reduces when the attenuation losses over the communication link also increase. The attenuated signal power plot versus T-R propagation distance is depicted in Figure 3:0  
Figure 3:1 Plot of propagation loss due to attenuation from atmospheric variables. Figure 3:2 Attenuation due to cloud plot versus frequency.
# HCTC: Hybrid Convolutional Transformer Classifier for Automatic Modulation Recognition
## 2 Received Signal and Proposed HCTC Model
### Proposed HCTC Model
#### 2.2.3 Stage C--Feature Mapping  
In the final stage (Stage C), several dense layers were applied, followed by batch normalization to enhance the performance of the proposed model. The first dense layer, referred to as fc1, contained 256 units and utilized the scaled exponential linear unit (SELU) activation function, which helped to stabilize the learning process and promote faster convergence. L2 regularization was used with a weight decay factor of \(1\times 10^{-4}\).  
The subsequent dense layer, fc2, had 128 units and employed the SELU activation function. This was accompanied by L2 regularization to maintain consistency throughout the network. This layer was followed by another batch normalization step to further stabilize the activation.  
Finally, the output layer was a dense layer with the number of units equal to the number of target classes and used the softmax activation function. The softmax function converted the output to a probability distribution over the classes, allowing for the model to produce class probabilities that summed to one. This configuration was essential for classification tasks, because it enabled the model to predict the most likely class for each input. The details of the configuration of stage C are presented in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer (Name)** & **Output Shape** & **Description** \\ \hline multiply & (124, 16) & Multiplies the range tensor for adjustment of positional encoding \\ sin & (124, 16) & Computes the sine of the angle in radians for positional encoding \\ cos & (124, 16) & Computes the cosine of the angle in radians for positional encoding \\ cast & (1, 124, 32) & Casts the positional encoding tensor \\ transformer\_encoder (Function) & (1, 124, 32) & to tf.float32. \\ global\_average\_pooling1d (GlobalAveragePooling1D) & (32) & Applieds multihead self-attention and feed-forward layers with positional encoding \\ \hline \hline \end{tabular}
\end{table}
Table 2: Configuration of the transformer layer (Stage B) of the proposed HCTC model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer (Name)** & **Output Shape** & **Description** \\ \hline  & & Number of units: 256 \\ fc1 (Dense) & (256) & Activation function: SELU \\  & & Kernel regularizer: L2 (\(1\times 10^{-4}\)) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Configuration of the feature mapping (stage C) of the proposed HCTC model.
# Flavour Compensation Role of Yeast Strains in Reduced-Salt Dry Sausages: Taste and Odour Profiles
## 3 Results and Discussion
### Sensory Evaluation  
Table 6 summarizes the results of the sensory evaluation, including colour, hardness, aroma, sourness, saltiness, and umami. There was no significant difference in the colour score (_p_ > 0.05); however, this was not consistent with the instrumental colour measurement results, which are more accurate than sensory evaluation. In terms of hardness, the PK treatment score was significantly lower than that of the controls (_p_ < 0.05), which was in line with the shear force analysis results. Moreover, the aroma scores of the inoculated treatments were significantly higher than those of the controls (_p_ < 0.05). This may be associated with the higher abundance of volatiles detected in the inoculated treatments. In terms of taste, the DH and PK treatments had higher saltiness scores, and the PK treatment presented higher umami scores which were consistent with the electronic tongue analysis results. In contrast, there were no significant differences in the sourness of the dry sausages (_p_ > 0.05), which may be because the change in the acidity of the dry sausages was not enough to affect their perceived sourness. Combining these results, we concluded  
Figure 3: Partial least squares-discriminant analysis score plot (**A**) and the top 15 variables important in the projection (VIP) values (**B**) of all volatile compounds for the control and reduced-salt dry sausages with and without yeast inoculation on days 0 and 12. V9-V58: the numbers of the corresponding volatile compounds in Table 5. CT: 2.50% NaCl; CR: 1.75% NaCl; PK: 1.75% NaCl + _P. kudriwzewii_; TD: 1.75% NaCl + _T. delbrueckii_; DH: 1.75% NaCl + _D. hansenii_.  
that _D. hansenii_ and _P. kudriavzewii_ inoculation can compensate for the flavour deficiencies of reduced-salt dry sausages.  
Based on the above analysis, it was speculated that the complex microbial enzymes involved in relevant the enzymatic breakdown of carbohydrates, proteins and lipids that affect colour, flavour and texture may be important to improve the quality characteristics of reduced-salt dry sausages. Previous studies have purified a variety of endo- and exo-peptides from yeast which facilitate the production of FAAs and peptides in meat products [63], which were the flavour compounds and the precursors of flavour compounds. Particularly, some FAAs and peptides can provide saltiness and umami and thus may enhance the taste properties of reduced-salt dry sausages [23]. In addition, a glutaminase that purified from _D. hansenii_ was able to neutralize the acid pH of fermented sausages and generated L-glutamate that can act as a flavour enhancer [64].
# Mobile Robot for Security Applications in Remotely Operated Advanced Reactors
## 9 Introduction to Mobile Dog Robots
### Operational Capabilities
#### 9.1.3 Battery Capabilities  
The battery type of all the robots is a Li-ion battery. The running time on a full charge of a SPOT robot at a normal speed is 17 km, that for the Ghost Robotics robot is 12.8 km, and that for the ANYmal Robotics robot is 4 km. The standby time for the SPOT robot is one and a half hours, while that for the Ghost Robotics robot is 21 h. The recharge time of the ANYmal robot is 3 h for a full charge and 100 min for a 70% quick charge. The SPOT robot does not have ingress protection whereas Ghost Robotics and ANYmal Robotics robots have ingress protection (IP67). The Ghost Robotics robot has overall better battery capabilities, and the SPOT robot has the longest range.
## 2 Materials and Methods  
### Study Sites  
Two sugarcane sites were selected for this study, based on EC flux data availability and quality: one sugarcane site at Pirassununuga, State of Sao Paulo, Southeastern Brazil [31] and the other in Schriever, Louisiana, USA at the Ardoyne farm [77] (Figure 1). The Louisiana site is under the management of the USDA-ARS Sugarcane Research Unit, and the Sao Paulo site is under the management of Embrapa Meio Ambiente.  
The Louisiana EC flux tower site (Chachoula) (29.6341\({}^{\circ}\)N, 90.8349\({}^{\circ}\)W) had an annual mean temperature of 23.6 \({}^{\circ}\)C and an annual precipitation of 1200 mm. The site had Cancienne silty clay loam (Fluvacquentic Epiapapetts) type soil. The fields were graded with a 0.2% slope towards the south, and the elevation of the fields ranged between 2.40 m and 0.61 m over 700-900 m. Sugarcane cultivation in Louisiana dates back to 1850 [78], and the study site had experienced more than 50 years of continuous sugarcane production. The field was cultivated with sugarcane variety HoCP 04-838\({}^{\prime}\), reg. no. CV-181, PI 687221, and the sugarcane plants were spaced at 1.83 m intervals for single-planted rows and 2.44 m for double-planted rows (<1% total field area). Sugarcane crop green up typically occurs in April, while it is harvested between October and December. The EC tower on the site was in a 60 m\({}^{2}\) unplanted area surrounded by commercial sugarcane crops. The sugarcane annually received 89 kg ha\({}^{-1}\) of nitrogen fertilizer in April.  
The Sao Paulo EC flux tower site (FAYS) (21.9506\({}^{\circ}\)S, 47.3394\({}^{\circ}\)W) had an annual mean temperature of 21.4 \({}^{\circ}\)C, annual precipitation of 1410 mm, and gentle slope of <2%. Sugarcane new stem cutting (IAC-5000 variety) was completed on 10/2015 (DOY 275), and the distance between the plotting rows was 1.5 m, with a canopy height of ~5 m during the growing season. The soil type was clay (65% clay, 21% sand, silt 14%), and the site was managed under regular tillage, receiving superphosphate (28% P\({}_{2}\)O\({}_{5}\)) and 100 Mg ha\({}^{-1}\) dry matter of filter cake (sugar production residue) [31]. The flux tower at this site was installed 24 days after planting. Sugarcane is a multi-year ratoon crop; typically, in Brazil, the crop cycle includes one plant crop and four ratoon crops [30]. The first harvest took place in late October 2016, and the ground trash was left on the soil; nitrogen fertilizer (80 kg N ha\({}^{-1}\)) and potassium (180 Kg K\({}_{2}\)O ha\({}^{-1}\)) were applied two days later [31].
## 5 Proposed Methodology
INCA stands out for its ability to significantly reduce computational costs while simultaneously improving classification performance. This step ensures that only the most relevant and influential features are retained, minimizing the overhead associated with large datasets and complex models. The Iterative Neighborhood Component Analysis (INCA) technique offers distinct advantages over more traditional feature selection and dimensionality reduction methods such as Principal Component Analysis (PCA) and Minimum Redundancy Maximum Relevance (mRMR). Unlike PCA, which performs unsupervised dimensionality reduction by projecting data onto directions of maximum variance regardless of class labels, INCA is a supervised method that directly optimizes class separation by maximizing the classification accuracy. This makes INCA particularly effective in tasks where discriminative power is more important than variance preservation, such as defect detection or fine-grained classification. Compared to mRMR, which focuses on selecting features that are most relevant to the target variable while minimizing redundancy between features, INCA iteratively refines the feature subset based on its impact on classification performance. This iterative refinement process allows INCA to dynamically adapt to the dataset structure and learn an optimal feature subset tailored to the classifier used, which often leads to a higher accuracy and better generalization. In summary, while PCA and mRMR are powerful and widely used, INCA provides a more targeted and performance-driven approach to feature selection by integrating label information and classifier feedback during feature optimization. Finally, in the fifth and final stage, this study implements seven popular classification algorithms, including Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naive Bayes (NB), Linear Discriminant (LD), Subspace KNN, Subspace Discriminate, and RUSBoosted Trees. Through extensive testing, the SVM algorithm demonstrated the best performance, highlighting the efficiency and accuracy of this method in classifying surface defects. This comprehensive methodology not only demonstrates significant advancements in pre-processing and model design but also introduces innovative techniques in feature selection and classifier evaluation, making it a substantial contribution to the field of surface defect detection and classification.
###### Abstract  
Although conventional denoising filters have been developed for noise reduction from digital images, these filters simultaneously cause blurring in the images. To address this problem, we proposed the fast non-local means (FNLM) denoising algorithm which would preserve the edge information of objects better than conventional denoising filters. In this study, we obtained thoracic computed tomography (CT) images from a male adult mesh (MASH) phantom modeled by computer and a five-year-old phantom to perform both the simulation study and the practical study. Subsequently, the FNLM denoising algorithm and conventional denoising filters, such as the Gaussian, median, and Wiener filters, were applied to the MASH phantom image adding Gaussian noise with a standard deviation of 0.002 and practical CT images. Finally, the results were compared quantitatively in terms of the coefficient of variation (COV), contrast-to-noise ratio (CNR), peak signal-to-noise ratio (PSNR), and correlation coefficient (CC). The results showed that the FNLM denoising algorithm was more efficient than the conventional denoising filters. In conclusion, through the simulation study and the practical study, this study demonstrated the feasibility of the FNLM denoising algorithm for noise reduction from thoracic CT images.  
noise analysis; computed tomography thoracic image; fast non-local means approach; denoising algorithm; MASH phantom +
Footnote †: journal: Applied Sciences