esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results and Discussion
### Parameters Setting  
In order to verify the optimization performance of EChOA-SVM, this paper compares the algorithm with different meta-heuristic algorithms such as ChOA [31], GWO [35], WOA [36], ALO [37], GOA [38], MFO [39] and SSA [40].  
All settings and parameters that are used in the experiments for all algorithms are presented in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Set** & **Features** & **Instances** & **Classes** \\ \hline Balance & 4 & 625 & 3 \\ Bupa & 6 & 345 & 2 \\ Coimbra & 9 & 116 & 2 \\ Dermatology & 34 & 358 & 6 \\ Diagnostic & 10 & 683 & 4 \\ Divorse & 18 & 170 & 2 \\ Glass & 10 & 214 & 6 \\ Heart & 13 & 270 & 2 \\ Iris & 4 & 150 & 3 \\ Knowledge & 5 & 258 & 4 \\ Liver & 6 & 345 & 2 \\ Lymphography & 18 & 148 & 8 \\ Sonar & 60 & 208 & 2 \\ Teaching Assistant Evaluation (TAE) & 5 & 151 & 3 \\ Transfusion & 4 & 748 & 2 \\ Wine & 13 & 178 & 3 \\ Zoo & 16 & 101 & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of data sets.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Parameters** & **Value** \\ \hline  & \(\eta\)m & 1 \\  & C & 2 \\ EChOA & K & 0.95 \\  & f & \([0,2.5]\) \\  & m & Gauss chaotic \\  & f & \([0,2.5]\) \\ ChOA & m & Gauss chaotic \\ GWO & \(\alpha\) & \([0,2]\) \\ WOA & \(\alpha\) & \([0,2]\) \\  & a2 & \([-2,-1]\) \\ ALO & Number of search agents & 30 \\  & Number of iterations & 100 \\  & cMin & 0.00001 \\ GOA & cMax & 1 \\  & Number of search agents & 30 \\  & Number of iterations & 100 \\  & \(\alpha\) & \([-2,-1]\) \\ MFO & b & 1 \\  & t & \([-2,-1]\) \\  & c1 & \([2\times 10^{-16},2]\) \\ SSA & c2 & \([0,1]\) \\  & c3 & \([0,1]\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter settings of different algorithms.
# Three Diverse Applications of General-Purpose Parameter Optimization Algorithm
## 5 Application to Face Contour Approximation Model
### Score Function  
To optimize the parameters of the human face contour approximation, the score function \(S(P)\) is calculated by the following procedure:  
1. Calculate the Euclidean distance between each of the 17 keypoints (\(0{\sim}16\)) and keypoint 33 in the _OpenPose_ result in Figure 8 respectively.
2. Find the corresponding coordinate on the function of the proposed model to each of the 17 keypoints by calculating the \(y\) coordinate on the function that has the same \(x\) coordinate.
3. Calculate the Euclidean distance between each corresponding point to the 17 keypoints and the keypoint 33 respectively.
4. Calculate the score function \(S(P)\) by: \[S(P)=\sum_{i=0}^{16}\lvert E_{i}^{k}-E_{i}^{s}\rvert\] (4) where \(E_{i}^{k}\) represents the Euclidean distance between keypoint \(i\) for \(i\) = 0\(\sim\)16, and keypoint 33, and \(E_{i}^{s}\) denotes the Euclidean distance between the corresponding coordinate on the model function and keypoint 33. In the parameter optimization algorithm, tabu \(t_{i}\) = 10, and \(\Delta p_{i}\) = 1 are used.
## 3 Data Assimilation Strategy
### Lund-Potsdam-Jena Dynamic Global Vegetation Model  
The LPJ-DGVM [5] is a process-based model dealing with the exchange of carbon and water between terrestrial vegetation and land-atmosphere in a modular framework, and inherits many characteristics from the biome family of models [5; 47; 48; 49]. The key features primarily involve feedback through canopy conductance between photosynthesis and water balance, and close coupling among these \"fast\" processes and other ecosystem processes, including resource competition and production, tissue turnover, growth, carbon cycle in soil and litter, and disturbance regime [5]. For each grid cell, ten plant functional types (PFTs) were defined clearly in terms of FPC, including eight woody and two herbaceous types, with the potential occurrence determined by the bioclimatic limits as a standard for determining whether it can survive and/or regenerate in the simulation. Daily photosynthesis is a function of several abiotic factors including Absorbed Photosynthetically Active Radiation (APAR), day length, biochemical pathway (C\({}_{3}\) or C\({}_{4}\)), atmospheric CO\({}_{2}\) concentration, and temperature [50; 5]. For each PFT, the GPP is calculated by applying the coupled photosynthesis and water balance. The entire simulation is derived by monthly climate data, soil texture, and atmospheric CO\({}_{2}\) concentration in every grid cell.  
As an effective method for describing plant community structure, FPC is defined as the vertically-projected percentage cover of photosynthetic foliage of all strata [51], which has an essential relationship with canopy transpiration and radiation interception. Both LAI and FPC can be used to describe the leaf area, and based on the Lambert-Beer Law [52]:  
\[FPC_{ind}=1-e^{(-0.5 LAI_{ind})}, \tag{1}\]  
Figure 2: Schematic diagram of the Lund-Potsdam-Jena-data assimilation (LPJ-DA) modeling framework.  
where _ind_ represents average individual values. In a grid cell, the entire FPC is calculated by multiplying \(FPC_{ind}\) by the crown area (\(CA\)) and population density (\(P\)):  
\[FPC_{total}=CA\cdot P\cdot FPC_{ind}, \tag{2}\]  
\(CA\) is obtained by applying the stem diameter (\(D\)) based on the inversion of [PERSON]'s rule [53]:  
\[CA=k_{allom1}D^{k_{rp}}, \tag{3}\]  
where \(k_{allom1}=100\), \(k_{rp}\approx 1.6\), and \(CA\) is prescribed below a constant \(CA_{max}\) (15 m\({}^{2}\)). The daily GPP for each PFT is calculated by combining the FPC from Equation (2), the daily phenological status (have leaves or not), and climate data. The entire calculation of GPP flux is performed for the middle day of each month, and the daily values are derived by interpolation of the mid-month values. In our scheme, the state variable is FPC, and the observed variable is LAI obtained from remote sensing data (GLASS LAI). We selected Equation (1) as the observation operator for integrating the simulation and observation to update the FPC values.
# GNSS and LiDAR Integrated Navigation Method in Orchards with Intermitt GNSS Dropout
## 3 Results
### Navigation Test with Intermittent GNSS Dropout  
An anti-bird net was laid over a part of the road between the third and fourth rows of apple trees in the orchard experimental area to test the effect of satellite differential signal blocking. The positioning trajectory during navigation is shown in Figure 15. Figure 16 shows the positioning deviation of the laser odometer and the GNSS/INS positioning deviation of the occluded part of the satellite differential signal. The positioning deviations of the laser odometer and the GNSS/INS under obstruction conditions are shown in Table 1. The average positioning deviation of the laser odometer is 0.14 m, the maximum  
Figure 12: Experimental scenario orchard environment. (**a**) Unshaded orchard environment; (**b**) orchard environment under bird-proof netting.
## 2 Related Work
### Privacy-Preserving Technique  
Researchers have presented a variety of methods such as privacy policy [5; 6], distortion, and encryption [7; 8; 9; 10; 11]. Among these methods, distortion has been the most widely used method of protecting users' privacy. It mainly contains three main methods: Pseudonym, obfuscation, and dummy. Pseudonym [12; 13; 28] protects users' privacy by replacing or directly deleting users' identity identifiers with a false or temporary pseudonym. In [12; 28], users did not communicate with the LBS server directly but communicated with an anonymizer proxy which replaced the user's real identity to pseudonymous before the user's message was submitted to the LBS server. In [13],for MNAME and REGP, a user stored several user names, selected randomly one user name from the name set as the current user name in every time LBS query; for SNAME, anonymizer changed the user name to the same and sent the query to LBS server in every time LBS query. However, only using pseudonyms is not enough to protect users' privacy, and adversaries can still infer the user's real identity by analyzing the spatial-temporal correlation of continuous queries [13].  
Obfuscation techniques [14; 15; 16] protect the users' privacy by generalizing or perturbing the time and locations in LBS queries, and then the users' precise locations are not recognized. In those methods, the anonymizer submitted an area containing at least \(k-1\) other subjects in every time LBS query, instead of submitting the user's exact location; this is called spatial cloaking. During the above process, the LBS query would be delayed until \(k\) vehicles have visited the area chosen for the requestor, which reduces the accuracy in time; this is called temporal cloaking. However, submitting inaccurate locations or time will reduce the quality of the service in some specific services. Dummy techniques [13; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27] to protect the privacy of a user add dummy queries to the real query and do not reduce the quality of the service.  
In this paper, we focus on how to hide a user's locations or query content among dummies in the scenario of a continuous query. Different from existing dummy techniques, such as [13; 27], in our method, each selected dummy location evenly distributes across different organizations in a continuous query and locations in each query are distributed across different organizations. In addition, we also replace the real user's location with a historical location that is in the same organization as the real user's location.
## 4 Enabling Technologies for FSO Communication
### Cooperative Relaying
#### 4.8.1 Relay-Assisted Transmission
The electrical amplifier gain at each relay is established based on the CSI of the previous hop, necessitating continuous estimation of the channel fading amplitude. As with all AF systems, the noise introduced at each relay accumulates along the transmission path, limiting the maximum transmission distance [331; 363; 364; 366; 367]. DF FSO systems, on the other hand, involve additional electrical processing steps after OE conversion. To mitigate the propagation of background noise, the received electrical signal at each relay is decoded and re-encoded before retransmission in DF systems. While DF significantly enhances performance, it introduces considerable encoding/decoding delays and requires more complex hardware [88; 332; 366; 367; 368; 369]. The need for more processors results in higher component error rates and increased costs [370; 371]. The modified versions of the DF strategy, such as Bit Detect and Forward, Adaptive Bit Detect and Forward, and Adaptive Decode and Forward, have been considered [372]. Additionally, FSO DF relaying techniques have progressed from buffer-free (BF) relaying to buffer-aided (BA) relaying [333; 324]. These FSO BA relaying methods are inspired by their RF counterparts [373; 374; 375]. In mixed RF/FSO systems, buffers play a significant role by queuing data collected from mobile RF users before they are multiplexed along the backhaul FSO link [376]. Implementing BA relay selection strategies requires the acquisition of CSI and/or buffer state information (BSI). CSI knowledge can be either full or partial [373; 375; 377]. BSI involves acquiring the number of packets stored in the relays' buffers, which enhances performance but increases system complexity. Recently, deep reinforcement learning (DRL) has been applied to maximize the throughput of RF BA networks, and a relay clustering approach has been suggested to improve performance in asymmetrical RF networks [373; 374; 375].
# Multi-Scale CNN-Transformer Dual Network for Hyperspectral Compressive Snapshot Reconstruction
## 3 Materials and Methods
### DualCT Module
#### 3.2.2 Spectral Domain Transformer Branch  
Due to the effectiveness of the Transformer in capturing long-range dependencies, we utilize the Transformer branch to capture the global correlation information between spectral bands, and name it the spectral Transformer. As in [25], it treats each spectral band as a token and performs self-attention calculations between each token. Figure 4 shows the diagram of the spectral Transformer. The input \(X^{l}\in R^{H\times W\times C}\) of the spectral Transformer is reshaped into \(X\in R^{HW\times C}\), and each column of X is taken as a token. X is then linearly projected to obtain query \(Q\in R^{HW\times C}\), the key \(K\in R^{HW\times C}\), and the value \(V\in R^{HW\times C}\). They are defined as  
\[Q=XW^{Q},K=XW^{K},V=XW^{V}, \tag{9}\]  
where \(W^{Q},W^{K},W^{V}\in R^{C\times C}\) are learnable projection matrices. We split Q, K, and V into N heads along the channel dimension, and each head independently calculates self-attention within its channel group which is defined as follows:  
\[\begin{split} S_{a_{j}}&=softmax(\mu_{j}K_{j}^{T}Q _{j}),\\ V_{j}^{{}^{\prime}}&=A\odot V_{j},\\ head_{j}&=V_{j}^{{}^{\prime}}S_{a_{j}},\end{split} \tag{10}\]  
where j is the index of head number, \(\mu_{j}\in R\) is the learnable parameter. In order to introduce the complementary feature from the spatial-domain CNN branch, we use the channel interaction to weight the value matrix V with channel attention A. Channel attention A is calculated according to Equation (6). Then the outputs of N heads are concatenated along the spectral dimension to form a larger feature matrix. Considering the spatial location relationship within each token, we also add positional embeddings to integrate positional information into the feature representation. The output of spectral Transformer can be calculated as  
\[Spe-Trans(X^{l})=\begin{pmatrix}N\\ \cup&(head_{j}))W+f_{p}(V^{{}^{\prime}}),\\ j=1\end{pmatrix} \tag{11}\]  
where \(W\in R^{C\times C}\) is the linear projection matrix, and \(\cup\) is the concatenation of N heads. The positional embeddings function \(f_{p}(\cdot)\) consists of two \(3\times 3\) convolutional layers, a GELU activation layer, and a reshaping operation. Finally, after a reshaping operation, we can obtain the output feature map \(X_{out}\in R^{H\times W\times C}\).
## 7 Simulation Results
### Normal Operation Scenario  
In the first scenario (Figure 12), the simulation started by considering fully charged batteries (100\(\%\)\(SOC\)). This mission was the simplest one and required the vehicle to run at a constant speed equal to 200 km/h for about 200 km. In Figure 12a, the reference and actual speeds of the vehicle are represented with a blue line and the battery \(SOC\) values are represented with a red one. Figure 12b shows the total power of the motor drives and the total power delivered by the batteries. These quantities represent the sum of the powers of the two motor drives and the two battery packs, respectively.  
Figure 12: Simulation results of the first scenario in terms of the speed of the vehicle and \(SOC\) (**a**) and the total power of the motor drives and total power of the batteries (**b**).  
In Figure 12b, the difference between the power of the motor drives and the power of the batteries represents the additional power which is provided by the range extender (RE). In any time, in fact, the total power of the motor drives \(P_{drives}\) is given by the following equation:  
\[P_{drives}=P_{ batteries}+P_{RE} \tag{39}\]  
The on-board energy management control algorithm allowed the batteries to operate with an almost constant _SOC_ rate, thus allowing the vehicle to reach the end of the mission without any restriction in the performance and without excessive usage of the range extender (and hence fuel).
## 2 Materials and Methods
### Remote Sensing Data
#### 2.4.4 Derivation of Phenological Metrics  
Seasonal phenological metrics from 8-day NDVI MODIS time series were derived on a pixel basis for the study area whenever TIMESAT recognised full seasons. Figure 5 shows an example of two seasons detected from a three-year MODIS NDVI dataset from 2011 to 2013. These metrics define the phenological profiles and enable interpretation of results [23]. They also reveal the relationship between NDVI and the phenology of the rubber, where NDVI values in the study reflect the canopy cover and hence the tree health of the rubber plantation. In this process, model input variables of 460 NDVI values of each pixel were reduced to nine phenological metrics with a maximum of nine seasons identified.  
Using these parameters, three important seasonal parameters were identified. These are the start of season (SOS), end of season (EOS), and length of season (LOS). The SOS indicates the beginning of the season, and normally refers to the transition date in which there is a significant increase in NDVI values. EOS indicates the end of the season where there is a significant decrease in NDVI values [29]. LOS is defined by the duration from start (SOS) to end of season (EOS) [64]. In TIMESAT, SOS and EOS dates are determined  
Figure 5: Three important seasonal parameters: (**a**) start of season; (**b**) end of season; and (**c**) length of season; were extracted using TIMESAT after smoothing MODIS NDVI data. The red dashed boxes indicate the rubber wintering period (defoliation and refoliation). The blue dotted points represent the NDVI value. The blue lines connect adjacent data points only when data are available, and the black curve line represents the smoothing curve.  
using thresholds of seasonal amplitude [61]. In this study, SOS and EOS refers to the day of year (DOY) when 20% of the seasonal amplitude was reached from the left (start of season) or right (end of season) of a particular seasonal profile.  
We then used the values of SOS, EOS, and LOS to develop boxplots for the purpose of portraying the dynamics of these values during a 10-year time window. The fluctuations that occurred in these seasonal parameters were analysed and related to events that were documented during that particular year so as to understand and predict the causes.
# Classification and Fast Few-Shot Learning of Steel Surface Defects with Randomized Network
## 3 Proposed Methods
### Classification of Defects with EfficientNet-B7  
There is a large number of DNNs for object recognition or classification and to compare their performance different benchmark datasets are created, such as ImageNet [38], MNIST [39], CIFAR-10, and CIFAR-100 [40]. If one network has high performance on some dataset it has high-probability for good performance on others, however, the different characteristics of data from different domains does not guarantee this.  
In 2019, Google Brain published open source EfficientNet [28] as a family of image classification models to achieve state-of-the-art accuracy, yet still being an order-of-magnitude smaller and faster than previous models. The members of the family are the differently scaled versions (from B0 to B7) of a base model. The largest variant (B7) achieved state-of-the-art top-one accuracy on ImageNet in 2019, and it could reach the same performance as the previous state-of-the-art model but being 8.4 times smaller and 6.1 times faster during inference. In ref. [28], a compound scaling method was introduced to scale the depth (number of layers), the width (number of kernels in a layer), and resolution (size of input image) of an existing model and a baseline network with fine-tuned layers in a balanced manner considering the computation limits. They followed the idea that a small model can be easily fine-tuned on a small problem, and that a systematic scaling, changing only the dimensions but not the main structure and operation of layers, should result in an efficient network. Compound scaling proposes the right ratio between dimensions, while the baseline network (EfficientNet-B0) was designed with the help of optimization [41], considering both accuracy and computational cost on ImageNet.  
The building blocks of the base EfficientNet model, obtained through a neural architecture search, are the so-called mobile-inverted bottleneck [42] layers, originally developed to be used as mobile-size networks (networks able to operate in mobile and embedded devices). These blocks use well-proven existing techniques, such as residual connections and depth-wise separable convolutions, and on the other hand also apply the special-techniques-inverted residuals and a linear bottleneck. The interested readers can find details about these in ref. [42].  
Since its introduction, there is a very wide range of applications where EfficientNet or its variants showed very good accuracy, e.g., mushroom recognition [43], skin disorder recognition [44], iris recognition [45; 46], forest fire detection [47], steganalysis [48], or plant disease detection [49], just to mention a few recent articles. Our experiments, detailed in Section 5, show that EfficientNet-B7 outperforms all previously published architectures for both NEU and X-SSD datasets. Moreover, in the next subsection we use EfficientNet-B7 (pre-trained on ImageNet) as the backbone of our proposed architecture for few-shot learning.
# Harnessing Digital Twins for Sustainable Agricultural Water Management: A Systematic Review
## 4 Results
### Key Concept and Applications
#### 4.5.1 Digital Twins in Irrigation
Notable advancements include the deployment of 5G networks for uninterrupted data transmission and the capacity for expansion through commercial cloud technologies. [PERSON] et al. [96] proposed a smart irrigation framework utilizing DTs, IoT, and AI to address water usage inefficiencies in agriculture. The framework integrates physical sensors and actuators with virtual counterparts to create a DT that simulates the irrigation system's behavior. The goal of this system is to enhance decision-making and water conservation by processing data collected from IoT devices to determine daily irrigation needs. Key benefits include evaluating system performance before field implementation and comparing various irrigation methods, ultimately improving farm operations and reducing water consumption by providing real-time information on soil, weather, and crops.
## 3 Turbo Equalization Concept  
The concept of turbo equalization was created in the laboratories of ENST (Ecole Nationale Superieure des Telecommunications), Bretagne, in the early 1990s, with the support of the spectacular results obtained with turbo codes. Turbo equalization consists of a reciprocal and iterative exchange of probabilistic information (extrinsic information) between the equalizer and the decoder. The first turbo-equalization scheme was proposed in 1995 by [PERSON] et al. [9]. This diagram implemented a weighted input and output Viterbi equalizer according to the soft-output Viterbi algorithm (SOVA). The principle was then taken up in 1997 [32] by replacing the SOVA equalizer with an optimal SISO equalizer using the BCJR algorithm developed in [10].