esa-sceva/satcom-chunk-collection huggingface dataset:
# Satellite Network Security Routing Technology Based on Deep Learning and Trust Management
## 2 Related Research
In [32], an improved ant colony algorithm was used to optimize the dynamic path given the shortcomings of the dynamic path optimization method. The experimental results showed that the running time of the optimal path obtained by this algorithm was obvious. In [33], an ant colony optimization algorithm based on the small-window strategy was proposed to solve the routing and wavelength allocation problems in satellite optical networks. The results showed that, compared with [PERSON]'s algorithm, this algorithm improved the system resource utilization by 45%. To meet the quality of service (QOS) requirements of end-to-end delay, link utilization, and bandwidth, ref. [34] proposed a combination of heuristic algorithm and ant colony algorithm to provide a better QOS guarantee. Experiments showed that this algorithm can provide more QOS guarantees than the shortest path algorithm. The subject of this paper is secure routing in satellite networks. To provide a clearer explanation, the bullet points of the methods proposed in the literature for secure routing are presented in the form of a table below. In Table 1, Internet of Things represents algorithms applicable to the field of the Internet of Things. Satellite network represents algorithms applicable to the field of satellite networks. Traditional trust model indicates that the algorithm uses a trust model without employing artificial intelligence algorithms. Intelligent trust model signifies that the algorithm employs a trust model utilizing artificial intelligence algorithms. Energy factor signifies that the algorithm takes both security and energy consumption factors into account. Lastly, resource consumption signifies that the algorithm has a relatively higher consumption of computational resources. \(\surd\) indicates that the reference in the row of \(\surd\) matches the characteristics of the column in which it resides.
# Implementation of a Fast Link Rate Adaptation Algorithm for WLAN Systems
## 1 Introduction
RSSI-based link adaptation [29; 30], which does not require feedback from the receiver, is based on the measured received signal strength of received frames and the number of retransmission attempts, in order to determine the channel and receiver conditions. Hybrid automatic rate control [31] uses both SNR-based and statistic-based algorithms. In this approach, a signal-strength-indicator-to-rate lookup table is employed such that the three signal-strength-indicator thresholds of the table are dynamically adjusted at the end of each time window, according to the frame error rate. Opportunistic rate adaptation is used in [32; 33] and distributed cooperative rate adaptation is used in [34]. To summarize, the problems in ARF-like algorithms include:  
* No consideration of fails due to collision;
* Non-optimal rate selection (due to the rate fallback only in successive fails);
* Meaningless periodic rate upgrade in slow channel variations;
* Obscure number of consecutive transmission successes or fails before a rate change. On the other hand, the problems in PHY-measurement-based algorithms include:
* A large SNR variation in case of a short-term measurement;
* Weakness with mobile clients in case of a long-term measurement for smoothening. The system considered in this paper for fast link rate adaptation is a \(1\times 1\) WLAN chip having one transmitter (TX) antenna and one receiver (RX) antenna. In a certain system that allows null data packet transmission, the RX can utilize a null data packet (NDP) from the TX to decide an MCS feedback which will be sent to the TX. However, since the target system considered in this paper is a \(1\times 1\) WLAN chip, an NDP is not sent. Many APs in WLANs do not support sounding and accordingly an algorithm that does not use a sounding PHY protocol data unit is considered in this paper. As a consequence, in our target system, no MCS feedback is assumed from the RX. The proposed fast link rate adaptation algorithm in this paper is based on the preamble (instead of NDP or sounding). The target system in the proposed algorithm receives wireless channel coefficients (namely, channel state information or CSI, from PHY measurements) and previous packet information (ACKs), translates them to amended signal-to-noise ratios, and then, via the mean mutual information, selects the MCS with the maximum throughput. The proposed algorithm can be applied to IEEE 802.11a/b/g/n/ac WLAN systems. The paper is organized as follows. The basics of the link rate adaptation algorithm is addressed in Section 2, followed by the link adaptation firmware in Section 3 that deals with the overall flow of the proposed link adaptation and the functions in detail that constitute the proposed firmware. Analysis and optimization of the operating speed of the proposed link adaptation are covered in Section 4 and the link adaptation performance measurements and wireless tests are depicted in Section 5, followed by discussion and conclusion with an appendix.
## 3 Results and Discussion  
In Figure 2, we present the AFM (atomic force microscope) topography scan of the n-GaN film deposited on the Ammono-GaN substrate. The GaN surface was atomically flat, characterized by straight atomic steps, with a root-mean-square (RMS) roughness measured for a 3 \(\times\) 3 \(\upmu\)m\({}^{2}\) square below 0.14 nm. This was significantly lower than the RMS roughness of over 0.4 nm typically reported for GaN films grown by MOVPE on Al\({}_{2}\)O\({}_{3}\) substrates [22]. These results clearly demonstrate the superior surface morphology of the GaN layers studied in this paper. Additionally, the high structural quality of GaN and the Al\({}_{x}\)Ga\({}_{1-x}\)N films and crystals grown on Ammono-GaN was previously validated by XRD (X-ray diffraction) measurements as well. These results can be found in our other papers referenced in refs. [23; 24]. The n-GaN film investigated in this study was grown on a highly conductive substrate. Therefore, neither the electron concentration nor mobility could be determined  
Figure 2: AFM topography of the n-GaN films grown on Ammono-GaN substrate for a square of 3 \(\times\) 3 \(\upmu\)m\({}^{2}\). through standard Hall effect measurements typically used for evaluating the electrical transport parameters of semiconducting materials. To address this issue, we employed capacitance-voltage (C-V) measurements at 300 K on processed Ni SBDs to assess the electrical characteristics of the n-GaN film. Our analysis revealed a net donor concentration, N\({}_{\text{D}}\)-N\({}_{\text{A}}\), of approximately 2 \(\times\) 10\({}^{16}\) cm\({}^{-3}\). A comprehensive examination of the electrical properties of the semitransparent Ni SBDs, using both the C-V and current-voltage (I-V) measurements, has been discussed in detail in our recent publication [15] and, therefore, is not repeated here. Before characterizing the deep traps using SSPC and DLOS measurements, the Ni/GaN SBDs were investigated with the DLTS technique to identify relatively shallow trap levels. The corresponding DLTS spectrum for the Ni SBDs, recorded at a rate window of 20 s\({}^{-1}\) with a filling time (t\({}_{\text{P}}\)) of 1 ms, is shown in Figure 3. Two trap levels were identified at energies of Ec-0.25 eV and Ec-0.59 eV, with concentrations of 2 \(\times\) 10\({}^{12}\) cm\({}^{-3}\) and 6.7 \(\times\) 10\({}^{13}\) cm\({}^{-3}\), respectively. These trap levels are frequently reported in n-GaN films grown by MOVPE [20; 25]. Notably, the origin of the deeper trap state at Ec-0.59 eV has been previously identified as being associated with Fe\({}_{\text{Ca}}\) (0/ \(-\)) [25], which is an acceptor. In contrast, the potential source of the trap level at Ec-0.25 eV has been discussed in detail in reference [20], but its origin remains unknown.
# Research on Short-Term Traffic Flow Combination Prediction Based on CEEMDAN and Machine Learning
## 3 Methods and Principles  
### Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)  
[PERSON] et al. [22] first proposed the empirical modal decomposition algorithm of adaptive noise-assisted sets, which continuously averages new signals by adding adaptive Gaussian white noise on the basis of the EMD algorithm. It not only solves the problem of modal aliasing but also avoids noise interfering with the original signal, making the signal decomposition more thorough. The decomposition process of CEEMDAN is as follows:  
(1) The new signal sequence \(x_{i}(t)\) is obtained by adding the adaptive noise of n times the same length to the original signal sequence \(x(t)\). The new sequence is decomposed \(N\) times with EMD, and the average value is the first eigenmodal function IMF\({}_{1}\). The formula is described as follows:  
\[x_{i}(t)=\ x(t)+\varepsilon\omega_{i}(t) \tag{1}\]  
\[\text{IMF}_{1}(t)=\frac{1}{N}\underset{i=1}{\overset{N}{\sum}}\text{IMF}_{1i} (t) \tag{2}\]  
where \(\varepsilon\) is the adaptive coefficient and \(\omega_{i}(n)\) is the noise sequence.  
(2) The first margin sequence \(r_{1}(t)\) is calculated as follows:  
\[r_{1}(t)=x(t)-\text{IMF}_{1}(t) \tag{3}\]  
(3) On the basis of (2), the second eigenmodal function IMF\({}_{2}\) is calculated as follows:  
\[\text{IMF}_{2}(t)=\frac{1}{N}\underset{i=1}{\overset{N}{\sum}}\text{IMF}_{1} (r_{1}(t)+\varepsilon_{1}\text{IMF}_{1}(\varepsilon\omega_{i}(t))) \tag{4}\]  
(4) Repeat the calculation until the \(k+1\) stage and the margin sequence \(r_{k}(t)\) of the \(k\)th stage and the \(k+1\) eigenmodal component IMF\({}_{k+1}\) are obtained as follows:  
\[r_{k}(t)=r_{k-1}(t)-\text{IMF}_{k}(t) \tag{5}\]  
\[\text{IMF}_{k+1}(t)=\frac{1}{N}\underset{i=1}{\overset{N}{\sum}}\text{IMF}_{1} (r_{1}(t)+\varepsilon_{k}\text{IMF}_{k}(\omega_{i}(t))) \tag{6}\]  
(5) Repeat the above steps until the extreme point of the margin sequence is not greater than 2, then stop the EMD algorithm. Then the original sequence can be decomposed into \(K\) eigenmodal functions and a margin sequence through the above steps [23]:  
\[x(t)=\underset{i=1}{\overset{K}{\sum}}\text{IMF}_{K}(n)+R(n) \tag{7}\]
## 3 Results and Discussion
### Consumer Evaluation
#### 3.1.3 USA Consumers  
For overall appearance and color liking, USA consumers scored FS-CLVR and FS-TMR butters significantly (\(p<0.017\)) higher than FS-GRSS butter (Hedonics Table 1). When attempting to elucidate the drivers for liking of butter, [PERSON] et al. 2007 [25] identified from a focus group that USA consumers found a light yellow color desirable in butter, which likely explains why USA consumers in this study rated their liking of appearance and color of FS-TMR butter the highest (Hedonics Table 1), promoting the theory of familiarity dictating preference [36; 37]. However, it is difficult to interpret why consumers liked the FS-CLVR butter similarly to FS-TMR butter for appearance and color, yet rated the FS-GRSS butter significantly lower for these same attributes. In the JAR Evaluation, USA consumers also scored FS-CLVR and FS-TMR butters significantly (\(p<0.05\)) higher for 'just about right' for yellow color. However, these same consumers also scored FS-TMR butter significantly (\(p<0.05\)) higher for 'not enough yellow', compared to FS-GRSS and FS-CLVR butter. Interestingly, FS-GRSS butter flavor was perceived as the most favorable by USA consumers. However, it was not significantly (\(p>0.017\)) different from FS-TMR butter. This may reflect the trend in the USA for value added grass fed milk  
Figure 1: Average results (\(n=20\)) from ranking descriptive analysis evaluation of FS-GRSS, FS-CLVR and FS-TMR butters by Irish (**a**) and German (**b**) assessors. * \(p<0.05\).  
and other dairy products [38]. In terms of texture liking, USA consumers liked the FS-CLVR butter significantly (\(p<0.017\)) more than FS-TMR butter. [PERSON] et al. 2007 [25] also identified a cluster of USA consumers, referred to as'margarine lovers', who were also butter users, but preferred the sensory attributes of margarines, i.e., the soft texture. This result corresponds to that of the JAR Evaluation, where consumers ranked FS-CLVR and FS-GRSS butters higher for 'just-about-right' texture, with FS-CLVR butter significantly (\(p<0.05\)) higher compared to FS-TMR butter. In addition, USA consumers ranked FS-TMR butter significantly (\(p<0.05\)) higher for'much too firm'.
# A Privacy-Preserving Scheme for a Traffic Accident Risk Level Prediction System
###### Abstract
Considering those adversaries, our proposal involves processing encrypted data. In other words, our proposal requires performing operations on encrypted data without decrypting it before using it in the learning model. Additionally, data must be encrypted end-to-end and kept encrypted at any time. Thus, we chose Homomorphic Encryption, specifically Fully Homomorphic (FHE), because it offers unlimited operations of additions and multiplications over encrypted data, unlike other encryption methods. Lastly, our contribution is a privacy-preserving scheme based on fully Homomorphic Encryption for a system that includes a learning model that predicts risk levels of suffering a traffic accident. It includes a circuit for key generation and inference, where a secret key is generated for the encryption and decryption of data and results, an encrypted model for encrypted inference, and an evaluation key for performing operations on encrypted data. This circuit guarantees that the data and the model are always protected, improving their privacy and security. In summary, the main idea is a PPML model that can infer directly from encrypted data and produce encrypted results. Our proposal prevents adversaries described in the adversary model from replicating models, identifying statistical characteristics of data, or inferring inputs or outputs of the learning model [8; 9]. The rest of this paper is organized as follows. Section 2 presents the state of the art of this topic. Section 3 presents definitions, concepts, and ideas on Homomorphic Encryption and security notions. Section 4 describes the initial system, problem definition, adversary model, our solution, and secrecy proofs. Section 5 presents the design of the experiments that validate our proposed scheme. Section 6 presents the results of the evaluation of our privacy-preserving security schema. Finally, Section 7 discusses the most relevant thoughts about this topic, and Section 8 presents the conclusions of this work.
## 3 Results and Discussion
### Kinetic Model for Water Aromaticity Loss  
Figure 3 shows the evolution of water aromaticity expressed in normalized values, which represents the loss of aromaticity during SMX degradation by the photo-Fenton process and allows for the evaluation of the degradation of the aromatic rings contained in the SMX molecular structure. Although SMX disappears rapidly, the loss of aromaticity is more gradual, suggesting that the intermediate by-products still contain aromatic structures before their complete mineralization (carbon dioxide and water) takes place [34]. The aromaticity of water decreases over time, but more slowly than the SMX concentration, suggesting that the aromatic rings persist in the by-products before being completely destroyed. At higher Fe(II) concentrations, the degradation of aromaticity is faster, similar to the behavior observed in Figure 2. Higher Fe(II) concentrations accelerate the loss of aromaticity, favoring the conversion of intermediates into final products consisting of CO\({}_{2}\) and water.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**[Fe(II)]0** & **k\({}_{\mathrm{SMX}}\)** & **k\({}_{\mathrm{Arom}}\)** & **[Arom]0** & **[Arom]\({}_{\mathrm{final}}\)** \\
**(mg/L)** & **(I/min)** & **(I/min)** & **(AU)** & **(AU)** \\ \hline
0.5 & 0.3661 & 0.050 & 2.054 & 0.066 \\
1.0 & 0.6498 & 0.052 & 1.920 & 0.222 \\
2.0 & 0.6695 & 0.060 & 1.998 & 0.184 \\
3.0 & 0.9790 & 0.075 & 2.250 & 0.219 \\
4.0 & 0.9859 & 0.075 & 2.153 & 0.052 \\
5.0 & 1.  
The photo-Fenton process not only oxidizes SMX but also attacks aromatic structures through hydroxyl radicals. SMX undergoes hydroxylation, bond cleavage, and partial opening of the aromatic rings. The aromatic rings gradually open, forming compounds such as dicarboxylic acids. The final products of the process are carbon dioxide and water, indicating the complete degradation of the pollutant. Based on this approach, a first-order kinetic model for the loss of aromaticity in water is proposed (Equation (8)), where the aromatic rings would open according to a kinetic constant \(\mathrm{k}_{\mathrm{Arom}}\) (1/min). A rate equation (Equation (9)) has been proposed, including the parameter \(\mathrm{[Arom]}_{\mathrm{final}}\) (AU), because the reagents (hydrogen peroxide) are consumed
## 3 RSSI Signal Based MLT Location Estimation
### RSSI Measurement and Acquisition  
A network made up of all the nodes was created to find the MNs. A beacon packet was created by the RSN and sent to the MN. After receiving the packet, the MN gathered the FN ID, location, and packet number from it while measuring the FN's RSSI readings. Additionally, the MN sent a report packet containing the data it had gathered to the RSN, which then sent it on to the computer. Using the identity, RSSI, and values, location, and packet number of each FN, the MN's position was ascertained.
## 3 Discrete Event Simulation Model
### Equipment Modeling
#### 3.1.3 Quality Costs  
Quality costs are obtained considering the effects on PM and upgrading activities on the component age based on the PAR model. The developed model assumes that: (1) The equipment only produces non-conforming items with constant rate, \(\alpha\), while the process is out-of-control. (2) The time to the system swaps out-of-control follows a Weibull distribution, which depends on the age of the equipment. (3) The preventive maintenance and the process inspection are executed simultaneously. (4) Inspections do not have errors. (5) The process is restored to the in-control state when the preventive maintenance activity is carried out. To model the quality costs, it is crucial to determine the fraction of time during which the process is under control (UC) state named \(\kappa_{m}(w)\).  
\[\kappa_{m}(w)=\int_{w_{m-1}^{+}}^{w_{m}^{-}}w_{m}\cdot f(w_{m})\cdot dw_{m}, \tag{15}\]  
where \(f(w_{m})\) is the density function, which can be obtained using the conditional hazard function of the component, as detailed in the following equation:  
\[f(w_{m})=\lambda\cdot\gamma\left(\lambda\cdot w_{m}\right)^{\gamma-1}\exp \left(-\left(\lambda\cdot w_{m}\right)^{\gamma}\right), \tag{16}\]  
being \(w_{m-1}^{+}=w_{c}\left(1-\varepsilon\right)\sum_{k=1}^{m-1}e^{k-1}\) and \(w_{m}^{-}=w_{c}\).  
Thus, it is possible to obtain the time with the process under control between two maintenance activities \((M_{m}-\kappa_{m})\), as well as the quality costs, \(C_{q}\) as:  
\[C_{q}=n_{c}\cdot C_{\alpha}\cdot\sum_{m=1}^{c}V_{m}^{*}\cdot(M_{m}-\kappa_{m} )\cdot A(\mathbf{x})\cdot\alpha, \tag{17}\]  
being \(C_{\alpha}\) the cost of the non-conforming unit.
## 2 Materials and Methods
### Instrumentation  
Acceleration signals were measured with Shimmer3 (from here on Shimmer) sensor platform (Shimmer Research, Dublin, Ireland). While sensor fusion between accelerometers and gyroscopes has shown to increase classification performance in some studies [23], then others have found that gyroscope information does not contribute to activity recognition performance [22]. Due to the emphasis on designing physical activity classifier with low power consumption, gyroscope data were disregarded in this study.  
The Shimmer sensor system has two built-in triaxial accelerometers: low noise accelerometer with the dynamic range of \(\pm 2\) g and a wide range accelerometer with the dynamic range switchable between \(\pm 2\) g to \(\pm 16\) g (where 1 g equals to about 9.81 m/s\({}^{2}\)). Since acceleration values during human motion surpass \(\pm 2\) g [24], the data from wide range accelerometer was used with the dynamic range set to \(\pm 16\) g. The wide range accelerometer uses STMicroelectronics LSM303 AHTR sensor (Geneva, Switzerland), which has a numeric resolution of 16-bit. Acceleration was measured with a sampling rate of 512 Hz.
###### Abstract  
Surface soil moisture is an important hydrological parameter in agricultural areas. Periodic measurements in tropical mountain environments are poorly representative of larger areas, while satellite resolution is too coarse to be effective in these topographically varied landscapes, making spatial resolution an important parameter to consider. The Las Palmas catchment area near Medellin in Colombia is a vital water reservoir that stores considerable amounts of water in its andosol. In this tropical Andean setting, we use an unmanned aerial vehicle (UAV) with multispectral (visible, near infrared) sensors to determine the correlation of three agricultural land uses (potatoes, bare soil, and pasture) with surface soil moisture. Four vegetation indices (the perpendicular drought index, PDI; the normalized difference vegetation index, NDVI; the normalized difference water index, NDWI, and the soil-adjusted vegetation index, SAVI) were applied to UAV imagery and a 3 m resolution to estimate surface soil moisture through calibration with in situ field measurements. The results showed that on bare soil, the indices that best fit the soil moisture results are NDVI, NDWI and PDI on a detailed scale, whereas on potatoes crops, the NDWI is the index that correlates significantly with soil moisture, irrespective of the scale. Multispectral images and vegetation indices provide good soil moisture understanding in tropical mountain environments, with 3 m remote sensing images which are shown to be a good alternative to soil moisture analysis on pastures using the NDVI and UAV images for bare soil and potatoes.  
soil moisture; andosols; remote sensing +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Point Set Mapping and Point Cloud Geometric Segmentation
#### 2.2.2 Geometric Segmentation of the Point Cloud via Spatial Clustering  
The 3D mesh data from oblique photography were converted to a point cloud model (Section 2.2.1). To reduce the computational time during subsequent machine learning and improve the model's scalability, the point cloud was divided into different subsets via  
Figure 2: Conversion of oblique photography into 3D data. (**A**) An oblique photography 3D model. (**B**) Point cloud data of triangular mesh vertices.  
clustering, with points in the same regional subset possessing homogeneous semantics. Following the terminology of [PERSON] [27; 28], each subset was labeled a \"superpoint\". Compared to the unprocessed point cloud data, the data after clustering contained more information, such as cluster size and the relationship between classes. Considering the large number of planar regions in a 3D urban landscape, this study adopted a plane detection method based on region growth to conduct a coarse segmentation clustering of point clouds, in which each point set is regarded as belonging to a specific plane as a cluster. This method assumes that points in the same plane region of clustering have similar normal vectors [29]. The following iterative approach was adopted:  
* Step 1: An available seed point is selected from the dataset, and its neighborhood space is determined;
* Step 2: The neighborhood that meets the regional plane detection rules is included in the region;
* Step 3: Steps 1 and 2 are repeated for all neighborhoods;
* Step 4: A new region is created until no neighborhoods meet the 3D rules of regional plane detection.  
Our plane detection based on 3D points adopted a plane-fitting method based on principal component analysis. When the normal vector of a point is less than the threshold value, and the distance from the plane is less than a certain value, that point is included in the regional plane. Each planar region is represented by a superpoint element.