esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Mapping Various Rice Disease Infested Areas
#### 2.4.3 The Sensitivity of the Identified Spectral Features to Rice Diseases  
We used statistical analyses in order to examine whether the proposed spectral features were sensitive to disease infestations, and to compare the performance of normalized two-stage VIs  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Definition** & **Related Bands and Equations** & **Sensitive to** & **Reference** \\ \hline Normalized difference vegetation index, NDVI & \((\text{R}_{\text{NIR}}-\text{R}_{\text{R}})/(\text{R}_{\text{NIR}}+\text{R}_{ \text{R}})\) & Green biomass & [47] \\ \hline Soil-adjusted vegetation index, SAVI & \((1+J)\times(\text{R}_{\text{NIR}}-\text{R}_{\text{R}})/(\text{R}_{\text{NIR}}+ \text{R}_{\text{R}}+L_{\text{J}};L=0.5\) & Canopy structure & [48] \\ \hline Triangular vegetation index, TVI & \(0.5\times[120\times(\text{R}_{\text{NIR}}-\text{R}_{\text{C}})-200\times( \text{R}_{\text{R}}-\text{R}_{\text{C}})]\) & Radiant absorption of chlorophyll & [49] \\ \hline Re-normalize difference vegetation index, RDVI & \((\text{R}_{\text{NIR}}-\text{R}_{\text{R}})/(\text{R}_{\text{NIR}}+\text{R}_{ \text{R}})^{0.5}\) & Vegetation coverage & [50] \\ \hline Modified Simple Ratio, MSR & \((\text{R}_{\text{NIR}}/\text{R}_{\text{R}})/(\text{R}_{\text{NIR}}/\text{R}_{ \text{R}})^{0.5}\) & Leaf area, Biomass & [51] \\ \hline Structural Independent Pigment Index, SIPI & \((\text{R}_{\text{NIR}}-\text{R}_{\text{R}})/(\text{R}_{\text{NIR}}-\text{R}_{ \text{R}})\) & Pigments content & [52] \\ \hline \hline \end{tabular}
\end{table}
Table 2: The vegetation indices used for classifications in this study, with red band, NIR band, and green band denoted as \(\text{R}_{\text{R}}\), \(\text{R}_{\text{NIR}}\), and \(\text{R}_{\text{G}}\), respectively, for the Planet Satellites.  
and single-date VIs for tracking individual biophysical and pathological progression. These priori knowledge-based statistical analyses were used based on two standards. Firstly, a threshold-based classification test was implemented to determine the classification capability of each spectral feature for healthy rice, and those infested with dwarf, blast, and glume blight [53]. The second criterion was to test the independence between variables. To further examine whether the satellite-derived vegetation indices are sensitive to disease infestations, a standard analysis of variance (ANOVA) was conducted to consider the impacts of information redundancy and multi-collinearity [23]. Here, ANOVA was used with a confidence level of 95% (\(p\)-value \(<\) 0.05) to ensure that the identified spectral features had sufficient independence and heterogeneity in subsequent analysis.
# Incorporating Spatial Autocorrelation into GPP Estimation Using Eigenvector Spatial Filtering
## 3 Methodology
### Accuracy Assessment
#### 3.4.2 Accuracy Comparison  
A comprehensive accuracy evaluation of the estimation results of the SA-LGBM model is conducted in our study using eddy covariance flux tower observations and two GPP products, MOD17 and GOSIF. We aggregated the original results obtained at a 500 m resolution from MOD17 and a 1 km resolution from SA-LGBM to a 5 km resolution for further analysis. This was carried out to accommodate the relatively limited spatial resolution of existing GPP products. In the experimental comparison, we fully considered the effect of the time scale and performed the comparative analysis at three different scales, from an 8-day scale, to monthly, to annual. Such multi-scale comparisons help us obtain a comprehensive awareness of the performance of the SA-LGBM model, while also capturing multiple aspects of its spatiotemporal generalization ability. We adopted the RMSE and \(R^{2}\) as metrics for quantitative analysis, which can effectively measure the error between model estimates and actual observed data, as well as the degree of fit.
# A Novel Strategy for VNF Placement in Edge Computing Environments
## 3 Problem Statement and Modeling
### ILP Formulation
\[R_{s,u}^{\text{C}m}=\sum_{v_{inst}^{\phi_{s}^{b}}\in I_{v}}\sum_{l_{ij}\in L} \frac{y_{l_{ij}}^{v_{inst}^{\phi_{s}^{b}}}}{b(l_{ij})},\forall v\in V,\forall s \in S_{u},\forall u\in U \tag{6}\]  
\[D_{s,u}^{prc}=\sum_{v_{inst}\in I_{v}}\sum_{h\in H}d_{prc}(v_{inst},h)\times \chi_{h}^{suv_{inst}},\forall v\in V,\forall s\in S_{u},\forall u\in U \tag{7}\]  
\[D_{s,u}^{q}=\sum_{v_{inst}\in I_{v}}\sum_{h\in H}d_{q}(v_{inst},h)\times\chi_{ h}^{suv_{inst}},\forall v\in V,\forall s\in S_{u},\forall u\in U \tag{8}\]  
\[D_{s,u}^{p}=\sum_{v_{inst}^{\phi_{s}^{b}}\in I_{v}}\sum_{l_{ij}\in L}d_{p}(l_{ ij})\times y_{l_{ij}}^{v_{inst}^{\phi_{s}^{b}}},\forall v\in V,\forall s\in S_{u}, \forall u\in U \tag{9}\]  
\[D_{s,u}^{tm}=\sum_{v_{inst}^{\phi_{s}^{b}}\in I_{v}}\sum_{l_{ij}\in L}d_{tm}(l _{ij})\times y_{l_{ij}}^{v_{inst}^{\phi_{s}^{b}}},\forall v\in V,\forall s\in S _{u},\forall u\in U \tag{10}\]  
Finally, the multi-objective function is defined in Equation (11), which aims to minimize the sum of the queuing and transmission delays, the energy consumption of processing VNFs, and transmitting VNF data from edge nodes to their outgoing links. The processing and propagation delays are omitted, since they are embedded in the energy consumption calculation. \[minimize\sum_{u\in U}\sum_{s\in S_{u}}D_{s,u}^{q}+D_{s,u}^{tm}+E_{s,u}^{p}+E_{ s,u}^{prc}+R^{\text{C}p}+R^{\text{C}m} \tag{11}\]  
Equation (11) is subject to the following constraints:  
* VNF instances \(v_{inst}\in I_{v}\) will be placed only on a node \(h\) that can offer the VNF type \(v\in V\), as described in Equation (12): \[\sum_{h\in IM_{v}}\chi_{h}^{suv_{inst}}=0,\forall v_{inst}\in I_{v},v\in V,s \in S,u\in U\] (12)
* The resources of edge nodes cannot be over-allocated, as defined in Equation (13); \[\sum_{v\in V}TR_{v}\times\chi_{h}^{suv_{inst}}\leq TR_{h},\forall h\in H\] (13)  
The proposed ILP model for the VNF placement is an NP-hard problem. Therefore, to solve this problem, we propose a heuristic-based algorithm, as described in the following section.
## 4 Experiment and Analysis
### Datasets and Settings  
The dataset used in the test includes three categories: MNIST handwritten numeral sets, HCL2000 handwritten Chinese character datasets, and EnglishHand handwritten alphabet datasets, all of which are grayscale images, as shown in Figure 7 (Table 1). MNIST was established by the National Institute of Standards and Technology for the study of handwritten numeral recognition and contains 60,000 training samples and 10,000 test samples, each with 28 px \(\times\) 28 px BMP images. HCL2000 is a national standard database of off-line handwritten Chinese characters established by the Beijing University of Posts and Telecommunications, funded by the National 863 Program, and it collects 1300 written handwritings for 3755 first-level Chinese characters to form 1300 \(\times\) 3755 samples. Each Chinese character has a sample of a 64 px \(\times\) 64 px two-valued pixel. In this experiment, 3000 Chinese characters are randomly selected to form the training or test set, which is divided into 10 categories. The dataset is then adjusted to 28 px \(\times\) 28 px size, and the pixel is normalized to [0,1]. EnglishHand is a public, handwritten alphabet dataset downloaded from the network ([[https://pan.baidu.com/s/ijPtPSa](https://pan.baidu.com/s/ijPtPSa)]([https://pan.baidu.com/s/ijPtPSa](https://pan.baidu.com/s/ijPtPSa))). It mainly consists of 2860 lowercase letters and 2860 uppercase data samples, which are divided into 26 categories.  
The 60,000 test samples and 10,000 training samples selected from the MNIST dataset are divided into 10 categories, i.e., 0-9 for a total of 10 characters corresponding to the training and test sets. The 2000 test samples and 1000 training samples selected from the HCL2000 dataset are divided into 10 categories, i.e., each Chinese character corresponds to the training and the test sets. The 1520 test samples and 5200 training samples selected from the EnglishHand dataset are divided into 26 categories, i.e., each alphabet character corresponds to the training and test sets. The volume kernel size of the convolution layer is 7 \(\times\) 7 and 5 \(\times\) 5, the pool factor is 3 \(\times\) 3, the maximum number of iterations is 20,000, and the number of experiments is 10 times. The dropouts in the training and test phases have probabilities of \(p\) = 0.5 and \(p\) = 1, respectively.  
Figure 6: Convolution neural network structure model of comparison algorithms. (a) WCNN; (b) MLP-CNN; (c) SVM-ELM.
# Physical Layer Authentication in Wireless Networks-Based Machine Learning Approaches
## 3 Background
### Signal Classification
#### 3.4.2 Specific Emitter Identification  
Specific emitter identification (SEI) is a method for identifying individual emitters by signal features extraction and analysis [64; 65]. The SEI defines the individual emitter by distinguishing it from a group of emitters by obtaining emitter operating parameters and performance information by comparing the characteristic parameters of signals intercepted by receivers. The SEI is often utilized in intrusion detection systems to improve the security of radar, satellite communication, and radio frequency systems. The SEI procedure generally comprises four steps [66; 67]: signal acquisition and preprocessing, extraction of features from the transmitted signal, and classification recognition by matching the features with database reference, assigning the best matching aggregate to these features. The signal acquisition and preprocessing step eliminates uncertain factors that affect the signal. To obtain better identification performance, extracting features that can effectively reflect the subtle differences between devices is necessary. Existing feature extraction methods involve transient and steady features. For identification, current state-of-the-art SEI systems rely on measuring pre-determined and expert-defined signal features clustered by emitter [68]. However, expert-defined signal features require a lot of the raw signal data to be preprocessed, for example, synchronization, carrier tracking, demodulation, signal-to-noise ratio (SNR) estimation, and the computational cost of measuring or estimating the expert features [67]. Moreover, their different domains extract features in signal processing, including the time domain, frequency domain, and time-frequency domain [13]. As well, these domains are based on five signal parameters pulse repetition interval (PRI), the direction of arrival (DOA), pulse frequency (PF), pulse width (PW), pulse amplitude (PA); all these parameters are combined in a pulse descriptive word (PWD) [16; 69].
## 4 Review of Existing Methods and Capabilities
### Path Planning
#### 4.1.1 Fixed Target
[PERSON] _et al._[64] proposed a hybrid algorithm that utilized GA and ACO algorithms for Multi-UAS mission planning which provided performance improvement over the two independent methods.
###### Abstract  
In response to the practice of rescue channel blocking and a shortage of emergency materials in the event of sudden significant disasters, a multi-UAV collaborative distribution scheme was designed based on the demand for rapid and accurate distribution of materials. This paper constructed a multi-UAV collaborative task assignment and routing problem with simultaneous delivery and pick-up and time windows (MVTARPSDPTW), considering the factors of UAV load, energy consumption, cargo quality, and volume to minimize the total cost of UAV distribution and the full penalty of the task, as well as optimizing the balance of UAV efficiency. This paper proposes a multi-objective, improved brainstorming optimization algorithm based on Pareto dominance (MIBSO) to solve the MVTARPSDPTW problem. With DTL24, DTLZ5, and DTL26 benchmarks, this work tests the algorithm performance according to the characteristic attributes of the model sought, selecting the four indicators of GD, the Spacing metric, HV, and ICD, concerning convergence, solution distribution, and comprehensive performance. Case validation is based on a COVID-19 scenario in Changchun, China, and the results show that the model algorithm designed in this paper has good performance and feasibility in convergence and distribution of reconciliation. Finally, the multi-UAV emergency material distribution solution provides practical, theoretical support for rescue tasks in sudden significant disasters.  
emergency material distribution; multi-UAVs; task assignment; multi-objective optimization; Pareto dominance
# Personalized Federated Multi-Task Learning over Wireless Fading Channels
## 2 System Model and Problem Formulation  
### Federated Learning (FL)  
FL [3] is a distributed machine learning approach that enables training on decentralized data in devices such as smart phones, IoT devices, and so on. FL can be described as an approach that brings the training model to the data, instead of bringing data to the training model [30]. In FL, edge devices collaboratively learn a shared model under the orchestration of a PS without sharing their training data.  
The generic form of FL with \(N\) clients is  
\[\min_{\omega}\left\{F(\omega)\triangleq\frac{1}{N}\sum_{i=1}^{N}p^{(i)}F^{(i)} (\omega)\right\} \tag{1}\]  
where \(p^{(i)}\) is the loss weight for client \(i\) such that \(\sum_{i=1}^{N}p^{(i)}=N\), and \(F^{(i)}\) is the local loss function for client \(i\).
## 3 Methods
### Target Broadside Radiation Pattern  
3.1.1. Target Broadside Radiation Pattern with the Null Information  
Assuming the spacing between neighboring loudspeakers to be much smaller than the wavelength, the ideal 2\(N\)th-order broadside differential beampattern can be expressed as  
\[\widetilde{B}^{(2N)}(\theta)\approx\prod_{n=1}^{N}\biggl{(}1-\frac{\cos^{2} \theta}{\beta_{n}^{2}}\biggr{)} \tag{10}\]  
where the superscript \((\cdot)^{(2N)}\) denotes the 2\(N\)th-order; \(N\) is a positive integer; the parameters \(\beta_{n}\), \(n=1\), 2, \(\ldots\), \(N\), determine the \(N\) null directions in the range of 0 to \(\pi/2\), where \(\theta_{N,n}^{\text{null}}=\arccos\beta_{n}\) and \(0<\theta_{N,1}^{\text{null}}\leq\theta_{N,2}^{\text{null}}\leq\ldots\leq \theta_{N,N}^{\text{null}}<\pi/2\). Note that to ensure the effectiveness of the target broadside beampattern, \(\theta_{N,N}^{\text{null}}\) must be less than \(\pi/2-\pi/(4N)\)[30]. It should be also pointed out that in order to generate the 2\(N\)-th target beampattern, at least \(2N+1\) loudspeakers are required.  
With the help of the \(n\)th elementary symmetric function definitions [31], (10) can be written in a sum form:  
\[\widetilde{B}^{(2N)}(\theta)=\sum\limits_{n=0}^{N}\alpha_{N,n}\cos^{2n}\theta \tag{11}\]  
where  
\[\alpha_{N,n}=\left\{(-1)^{n}\sum\limits_{1\leq i_{1}<i_{2},\ldots<i_{n}\leq N }\frac{1}{\beta_{i_{1}}^{2}\ldots\beta_{i_{n}}^{2}},\quad n=1,\ldots,N.\right. \tag{12}\]
# Experimental Evaluation of Display Field Communication Based on Machine Learning and Modern Design
## 2 Related Works
The flicker and the display pattern are separated by capturing two images with different exposures. Some works also deal with specific challenges of D2C communications. For instance, the authors of [29] tackled a key challenge of imperfect frame synchronization over D2C links. LightSync achieved frame synchronization, which features in-frame color tracking to decode imperfect frames and a linear erasure code across frames to recover lost frames. Reference [30] created data patterns by considering the color space for lightness modifications. The work of [31] that introduced D2C in the form of visual MIMO encoded the information by modulating the video frame with bright or dark intensity in a differential manner. The information can be decoded by retrieving the residuals obtained by subtracting the alternate frames.
## Materials and Methods
### Solid-phase Extraction (SPE Method)  
A SPE robot RapidTrace+ (Biotage, Uppsala, Sweden) was used for _conditioning_ and _eluting samples_, while a SPE manifold (Agilent, Santa Clara, CA, USA) was used for _loading/extracting_ the samples. Samples were concentrated by an automatic evaporator TurboVap LV (Biotage, Uppsala, Sweden) using clean air produced by an Atlas Copco SF2 Oil-free air system (Atlas Copco Airpower n.v. B, Wilrijk, Belgium), delivering certified 100% oil-free air, complying with ISO 8573-1 CLASS 0 certification. Both the PHWE extracts and the Espresso extracts were concentrated and purified using 200 mg Oasis HLB SPE cartridges (Waters). The HLB SPE cartridges were conditioned with 5 mL of MeOH followed by 5 mL of reagent water prior to extraction. HLB SPE cartridges were mounted in the sample manifold together with a 70 mL plastic syringe container on top. Thereafter one volume of PHWE-or Espresso extract was added, together with 50 \(\upmu\)L FoA (10%) and 50 \(\upmu\)L saturated EDTA solution. Samples were then passed through the SPE cartridges at a rate of ca. 5 mL/min. Afterwards, the cartridges were air-dried under a positive pressure for 15 min, a step which proved crucial for both recovery results and evaporation time reproducibility, since drying the cartridges on the manifold was insufficient. SPE cartridges were mounted in the Rapid Trace+ SPE robot and analytes were eluted with 6 mL MeOH into disposable borosilicate glass tubes (PYREX(r), 16 \(\times\) 100 mm, Corning Incorporated, Corning, NY, USA). The extracts were evaporated to complete dryness (22 min) at 40 \({}^{\circ}\)C. The dry extracts were reconstituted to a total volume of 1 mL by adding 100 \(\upmu\)L MeOH to the dry borosilicate glass tube, followed by a rapid swirling (vortexing) for 15 s. Thereafter 885 \(\upmu\)L water was added followed by 15 s of rapid swirling, and transferred to a vial. Finally, 10 \(\upmu\)L of _instrumental standard solution_ (Thiacloprid-d4, 250 pg/\(\upmu\)L, Table S1) was added along with 5 \(\upmu\)L saturated EDTA solution. A volume of 1 \(\upmu\)L of the final sample was injected into the UPLC-ESI-MS/MS system. The same SPE procedure was used for both extraction techniques.
## 3 Results
### Usability and Satisfaction  
The data from the satisfaction and usability questionnaires, expressed in mean \(\pm\) SD, were SEQ: 57.64 \(\pm\) 5 out of 65 points and QUEST 2.0 (only five questions): 19.29 \(\pm\) 3.89 out of 25 points. This indicates that users had a high level of satisfaction and good handling and use of the mixed-reality device. There were no adverse events during the use of the program and the setup.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Measurement** & **Pre (Mean \(\pm\) SD)** & **Group** & **Post (Mean \(\pm\) SD)** & **Residual Effect (Mean \(\pm\) SD)** \\ \hline DCFET (s) & 16.46 \(\pm\) 3.76 & MRG & 24.83 \(\pm\) 7.96 ** & 24.45 \(\pm\) 6.13 ** \\ Fatigue & & CEG & 22.17 \(\pm\) 5.78 ** & 24.45 \(\pm\) 6.13 ** \\ DCFET (cm) & 3.29 \(\pm\) 2.13 & MRG & 3.26 \(\pm\) 2.47 & 3.67 \(\pm\) 2.17 \\ CJPET & & CEG & 3.20 \(\pm\) 2.27 & 3.67 \(\pm\) 2.17 \\ Flexion (\({}^{\circ}\)) & 3.28 \(\pm\) 1.52 & MRG & 3.12 \(\pm\) 1.72 & 4.80 \(\pm\) 3.65 \\ CJPET & & CEG & 3.51 \(\pm\) 1.74 & 4.80 \(\pm\) 3.65 \\ Extension (\({}^{\circ}\)) & 5.15 \(\pm\) 1.81 & MRG & 3.64 \(\pm\) 1.08 ** & 4.18 \(\pm\) 1.49 \\ CJPET & & CEG & 4.20 \(\pm\) 2.48 & 4.18 \(\pm\) 1.49 \\ Rotation Left (\({}^{\circ}\)) & 4.57 \(\pm\) 2.07 & MRG & 3.56 \(\pm\) 1.29 & 3.44 \(\pm\) 1.88 \\ CJPET & & CEG & 3.49 \(\pm\) 1.23 & 3.41 \(\pm\) 1.88 \\ Rotation Right (\({}^{\circ}\)) & 4.04 \(\pm\) 1.56 & MRG & 3.37 \(\pm\) 1.81 & 4.10 \(\pm\) 1.72 \\ \hline \end{tabular}  
* \(p\) < 0.05; ** \(p\) < 0.01. DCFET, deep cervical flexor endurance test; s, seconds; cm, centimeters; CJPET: cervical joint position error test; \({}^{\circ}\), degrees; SD, standard deviation; MRG, mixed-reality group; CEG, conventional exercise group.  
\end{table}
Table 1: Measurements comparing pre- and postintervention in both groups and residual effect.
## 1 Introduction  
With the widespread application of automation technology in commercial aircraft, the complexity of aircraft automation systems has been increasing, highlighting the importance of studying the interaction between pilots and aircraft automation systems. Currently, aircraft automation has reached an advanced level. Modern commercial aircraft are equipped with sophisticated automatic flight systems, including autopilot, auto-throttle management, automatic navigation, and automatic landing capabilities. These automation systems provide a high level of autonomy and precision during various flight phases such as takeoff, cruise, descent, and landing. This high level of automation allows pilots to focus more on monitoring and decision-making tasks.  
However, despite the convenience brought by automation, there have been numerous instances where aircraft automation failures occur and pilots fail to correctly identify the failure and promptly take over to prevent accidents [1; 2; 3]. A notable recent example is the Boeing 737-MAX8 accident, where the Maneuvering Characteristics Augmentation System (MCAS) misjudged the aircraft's state during flight, continuously providing erroneous nose-down commands without adequately considering the pilots' reactions and understanding of the system. The captain of the involved aircraft failed to fully recognize the system error and engaged in a continuous struggle by pulling up against the downward commands from the flight control system. After a series of struggles, the aircraft ultimately lost control and crashed [1].  
Airline pilots are given ultimate responsibility and final authority over their aircraft to ensure the safety and well-being of all its occupants [4; 5]. In today's airline operations, pilots continue to serve as supervisors and operators, requiring an understanding and adaptation to the workings and limitations of automation systems, as well as the capability to handle system failures and unpredictable circumstances. Human-automation interaction issues persist as a prevalent problem nowadays, thus calling for extensive research into various aspects of pilot behavior in unexpected situations to enhance the synergy between pilots and automation systems and seek effective approaches to problem solving.