esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Random Forest  
The relationships between surface descriptors and backscatter coefficients may be more complex and non-linear, and the effects between explanatory variables on the dynamics of satellite signals can be combined. In this multifactorial context, an algorithm such as random forest appears to be appropriate [56], particularly when non-linear behaviors exist. Estimates of the target variable are obtained as follows: an ensemble of independent trees is constructed from a subset of bootstrap samples derived from the original dataset and aggregated through the weighted mean of the ensemble of estimates, providing an estimate of the targeted variable. The bootstrap aggregating procedure (also called bagging) provides a number of benefits, including reduced overfitting, the low influence of noise on the data and the high stability of the results. Furthermore, the algorithm provides a measure of the relative importance of the predictive variables, which can be used to rank, to select or to understand the influence of the considered explanatory variables [57].  
Figure 6: Synopsis of the methodology.
# Acupressure for Managing Osteoarthritis: A Systematic Review and Meta-Analysis
## 3 Results
### Outcome Assessment
#### 3.4.1 Primary Outcomes
_Acupressure vs. health education_  
The only RCT [42] comparing acupressure with health education did not show beneficial effects of acupressure in improving physical function (\(n\) = 35, MD: \(-\)0.85, 95% CI: \(-\)2.60 to 0.90, \(p\) = 0.34). WOMAC Total Score  
_Acupressure vs. sham acupressure_  
Two RCTs [38; 39] compared acupressure with sham acupressure for KOA, where one trial [38] reported that acupressure has positive effect in improving WOMAC total score and the other trial [39] reported otherwise. The meta-analysis showed that acupressure has equivalent effects on improving WOMAC total score (\(n\) = 135, MD \(-\)3.87, CI 95%: \(-\)8.41 to 0.67, \(p\)= 0.09, I\({}^{2}\) = 0%, Figure 3H), as compared to sham acupressure. _Acupressure vs. no intervention_  
Of the three RCTs [38; 39; 41] comparing acupressure with no intervention for KOA, one trial [38] reported that acupressure has positive effect in improving WOMAC total score and two trials [39; 41] reported otherwise. The meta-analysis showed that acupressure has no superior effects (\(n\) = 218, MD \(-\)10.03, CI 95%: \(-\)23.45 to 3.39, \(p\) = 0.14, I\({}^{2}\) = 89%, Figure 3I), as compared to no intervention.
###### Abstract
For example, [PERSON] et al. [15] indicated that the EVI-based SOS of boreal evergreen coniferous forest was much later (about a month) than the SIF-based SOS, but the EVI-based EOS was slightly advanced (about 1 to 2 weeks) to the SIF-based EOS. Although some studies have revealed differences in phenology derived from SIF and EVI among land cover types, the driving factors and underlying mechanisms are less known. Except for croplands, which could be largely affected by human activities, the dynamics of land surface phenology are driven by the physical characteristics of the vegetation itself and the external climate environment [22]. The external climate factors that affect vegetation phenology mainly include temperature, precipitation and radiation, which interact to promote or limit natural vegetation growth [23; 24]. For example, [PERSON] et al. [25] revealed that 80% of EVI-based phenology dynamics in dryland ecosystems are driven by the variability of annual precipitation. In contrast, recent studies have indicated that SIF has quicker responses to external environmental stress information (e.g., water stress) than EVI did [26; 27], as SIF contains additional information on stress conditions that reflects fluorescence efficiency [11]. However, climate controls on EVI-based phenology (greenness) and SIF-based phenology (photosynthesis) have not been compared, and a comprehensive analysis across different climatic conditions is still scarce. In this study, we defined the climatic limiting controls on vegetation growth as climatic limitations, which include temperature-limiting, water-limiting, and radiation-limiting factors. We focus on naturally vegetated areas in China and divide them into climate-limited areas (i.e., temperature, water, and radiation limitations). We then employed SIF and EVI to extract phenology from photosynthesis and greenness perspectives, respectively, and compared their characteristics across climate-limited areas. We further explored the underlying mechanisms by investigating the relationships between the differences in phenology derived from SIF and EVI and climatic limiting factors. This work can provide insights into the mechanistic differences between SIF and EVI in characterizing land surface phenology to improve our understanding of vegetation dynamics from greenness and photosynthesis perspectives and their interactions with climate conditions.
# Multi-Player Tracking for Multi-View Sports Videos with Improved K-Shortest Path Algorithm
## 4 Experiments  
### Experiment Data  
To verify our method, two datasets were prepared for testing. One is a publicly available sequence named APIDIS dataset [53]. The other was captured by our own at Shantou University in China and we called it STU dataset. The basic information of the two datasets is described as follows.  
APIDIS dataset: The 1500-frame sequence was acquired by seven cameras, with five ground cameras around the court and two fish-eye cameras overhead of the court, as shown in Figure 6. Each camera captures the videos at a rate of 25 _fps_ in 800 \(\times\) 600 resolution. There are 12 people on the court (two referees and two five-player teams). In this dataset, the overall brightness is relatively low and thus the identity of the player is a bit challenging to be recognized.  
STU dataset: The dataset was collected by eight synchronized cameras with a size of 1280 \(\times\) 720 and the frame rate is also 25 _fps_, as shown in Figure 7. The cameras were evenly distributed around the basketball court. The dataset has a better illumination condition compared with the APIDIS dataset. However, the occlusion is more serious since our cameras were installed at a lower position, although this kind of installation can help us recognize more jersey numbers. Thus, this dataset is also challenging for tracking. The STU dataset was collected by our own in a basketball match and we divided the data into 10 periods of sequences. There is not much difference between them except the frame length which varies from 600 to 4400.  
Figure 6: APIDIS basketball dataset is captured by five ground cameras around the court and two fish-eye cameras overhead of the court: (**a**) images captured from seven views; (**b**) the schematic diagram of camera installation.
# A Software Defined Radio Based Anti-UAV Mobile System with Jamming and Spoofing Capabilities
###### Abstract  
The number of incidents between unmanned aerial vehicles (UAVs) and aircrafts at airports and airfields has been increasing over the last years. To address the problem, in this paper we describe a portable system capable of protecting areas against unauthorized UAVs, which is based on the use of low-cost SDR (software defined radio) platforms. The proposed anti-UAV system supports target localization and integrates effective jamming techniques with the generation of global positioning system (GPS) spoofing signals aimed at the drone. Real-life tests of the implemented prototype have shown that the proposed approach is capable of stopping the reliable reception of radionavigation signals and can also divert or even take control of unauthorized UAVs, whose flight path depends on the information obtained by the GPS system.  
jamming; GPS spoofing; radionavigation; software defined radio; unmanned aerial vehicles +
Footnote †: journal: Sensors
# A 60-GHz Ultra-Thin and Flexible Metasurface for Frequency-Selective Wireless Applications
## 3 Results and Discussion
### Simulated Results of the 60-GHz Metasurface  
Two flexible substrates, i.e., Melinex and Kapton have been used, thus the material properties of both of the substrates have been incorporated in the simulation. The designed structure in the CST software is illuminated by a plane wave at a normal incidence angle of the inward travelling plane wave, where the polarization of the electric field is in the x-direction. Figure 4a shows the simulated S-parameter plots of the reflection (S\({}_{11}\) and S\({}_{22}\)), and transmission coefficients (S\({}_{12}\) and S\({}_{21}\)). The transmission is fully blocked at 60 GHz with a \(-41.5\) dB magnitude and the complete reflection of 0.06 dB is observed. While the transmission is isolated across the metasurface, it acts like a guided medium and allows the signal to propagate along the surface. Simulated results show that a bandwidth of about 6.7 GHz (56.6-63.3 GHz) is achieved at \(-20\) dB reference. Figure 4b shows the phase profile where the phase of transmission coefficients coincides with \(0^{\circ}\) at the designed frequency of 60 GHz along with a phase coverage of \(\pm 180^{\circ}\).
## Abbreviations  
The following abbreviations are used in this manuscript:  
LASSOLeast Absolute Shrinkage and Selection Operator  
SCADSmoothly Clipped Absolute Deviation)  
MCPMinimax Concave Penalty  
GMCGeneralized Minimax Concave  
GMEGeneralized Moreau Enhancement  
LiGMELinearly involved Generalized-Moreau-Enhanced (or Enhancement)  
SRCSparse Representation-based Classification  
GSCGroup Sparse Classification  
WGSCWeighted Group Sparse Classification  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Training Set Size (\(\alpha=\text{max}_{i}\{n_{i}\}\), \(\beta=\text{min}_{i}\{n_{i}\}\))**} \\ \cline{2-7}
**Method** & \(\alpha\) & **4** & **6** & & **8** & \\ \cline{2-7}  & \(\beta\) & **4** & **4** & **6** & **4** & **6** & **8** \\ \hline GSC & 86.3\% & 85.0\% & 91.3\% & 85.0\% & 92.5\% & 93.8\% \\ Proposed (\(\mathbf{W}=\mathbf{I}_{n}\)) & 88.8\% & 86.3\% & 93.8\% & 86.3\% & 93.8\% & 95.0\% \\ WGSC & 90.6\% & 87.5\% & 95.0\% & 88.8\% & 93.8\% & 96.3\% \\ Proposed (\(\mathbf{W}\) by (9)) & 91.3\% & 89.4\% & 95.6\% & 91.9\% & 94.4\% & 96.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Recognition results on the ORL database.
# A Comprehensive Survey on the Societal Aspects of Smart Cities
## 3 Smart Cities' Technologies
### Machine Learning and Artificial Intelligence
ML and AI can be used to more efficiently handle waste management and treatment [277], track pollution levels or serve as early warning systems [278], aid in preparing for extreme weather phenomena caused by climate change [279], and fight climate change directly through helping research, weather prediction, and carbon emissions reductions [280]. As an example, the project I4C--Intelligence for Cities, which uses AI models to predict thermal stress in the city of Freiburg down to the level of individual streets, can be used to automatically determine the best places to plant trees to achieve maximum reduction of thermal stress [281]. However, it must be noted that ML and AI also present new challenges that must be addressed. Security is important, as there are several ways to attack these algorithms (e.g., data poisoning, trojan attacks, or model stealing), which can make them not work entirely or to make them work erroneously [282]. It is also important to define the ethics of ML and AI in how humans create and use those tools, such as, for example, their impact on jobs or individuals' privacy [283,284]. Furthermore, the black box problem exists, where the ML and AI algorithms make decisions, but there is uncertainty as to how they operate and reach their decisions. This has implications for what ML and AI algorithms can be introduced since their decisions are trustworthy, which requires new tools to adequately evaluate these algorithms [285].
# Research on Ensemble Learning-Based Feature Selection Method for Time-Series Prediction
## 4 Experimental Results and Analysis of Feature Selection Based on Ensemble Learning
### Experimental Preparation
#### 4.1.2 Experimental Dataset  
In terms of dataset selection, to ensure diversity and representativeness, this paper chose stock time-series data from three distinct industries: finance, power, and technology. Specifically, the stock data from three datasets, namely, China Industrial and Commercial Bank (ICBC), GD Power Development (GD Power), and China Unicom, were selected. This approach enabled us to comprehensively explore and evaluate the applicability of ensemble learning-based feature selection across different industry datasets.  
Each stock dataset encompasses a total of 716 trading days, spanning from 1 January 2020 to 1 January 2023. Specifically, each dataset is composed of 20 features from the initial feature set in Table 1, along with the closing prices used for prediction, forming a 716 \(\times\) 21 dimensional data matrix. The datasets were sequentially split into training and testing sets in an 8:2 ratio. Descriptive statistics for the closing prices in each dataset, including mean, standard deviation, minimum, quartiles, and maximum values, are presented in Table 2.
## 3 NDN: The Networking Paradigm for Fog Computing
### NDN as Enabler of Fog Computing  
Recently, NDN has been considered an enabler of edge and fog computing because it can support provider-agnostic in-network computing and caching services [34; 35]. According to this new vision, interest packets can be overhauled to let a consumer request by name not only a content but also a specific computation service. Such named service requests can be processed directly at the network layer. Services can be executed by any node in the network that owns the required capabilities. The named service can process contents already available in-network (i.e., in the CS of a router) or produced on demand by another source or even produced by the consumer itself. Data packets can be overhauled to carry computation results that can be stored in the CS of NDN nodes to satisfy future requests. This is a radical departure from IP-based fog computing approaches, which usually rely on specific entities to resolve an application-layer request into the IP address of a node able to perform the computation [34].  
Besides overhauling interest and data packets, further modifications are required to let NDN support in-network computation. So far, the related literature has focused on the following main design aspects:  
* _Naming schemes_ for identifying both contents and services [6; 36; 37].
* _Discovery schemes_ for allowing NDN nodes to identify services and resources supported by their neighbours, e.g., CPU, energy, and storage [8; 34].
* _Service orchestration_ strategies to identify the best in-network service executors [6; 7; 8; 9; 10].  
* _Security support_ in order (i) to authenticate the consumers asking for a service and (ii) to allow the executor to sign the computation result to make it verifiable by the consumer [37; 38].
* _Privacy-preserving schemes_ ensuring that results of computation inherit the same confidentiality of the input contents [39].
* _Forwarding schemes_ for managing long and/or interactive computation [37] and for managing live stream processing [40].  
In this paper, we consider a much less investigated topic, which is the problem of IoT service orchestration via NDN in a fog context. Basically, given a service request with known requirements, such as the associated CPU demand and processing deadline, the network has to find an NDN node that can perform the service matching its requirements. This is a challenging objective since NDN nodes have usually limited and rapidly changing computing and storage resources and the allocation decision should be taken dynamically. Ad hoc designed mechanisms, closely tied to the NDN forwarding fabric, are therefore required that are different from the traditional cloud resource orchestration strategies conceived so far [41].
# Sequence-to-Sequence Video Prediction by Learning Hierarchical Representations
## 4 Experiments  
In our experiments, we use \(3\times 3\) convolutional kernel, Batch Normalization, and ConvLSTMs. The generator network \(G\) is trained to predict \(K=10\) future frames conditioned on the first \(T=10\) input frames. We compare HRPAE with state-of-the-art baselines for video prediction: DRnet, MCnet and PredRNN++ by using either the codes released by the authors or re-implementations. We use the MSE, PSNR (PSNR computes the peak signal-to-noise ratio between the ground-truth and corresponding predicted video frame. The higher the PSNR, the better the quality of the predicted video frame) and SSIM (SSIM measures the structure similarity index between the ground-truth and corresponding predicted video frame from three visual impacts: luminance, contrast and structure, similar to PSNR, higher SSIM scores represent better predicted image quality) [26] as the evaluation metric.  
We consider three datasets in order to demonstrate the capability of HRPAE in automatically capturing dynamics of features at multiple levels of hierarchy, but to varying degrees depending on the dataset:  
* **Bouncing Balls dataset** contains videos of 4 balls randomly bouncing off the walls and the other balls. The emphasis is mostly on capturing the complex dynamics of _high_-level features in an interactive environment, e.g., the center positions and velocity of the balls.
* **Moving MNIST (MMNIST) dataset** contains 2 hand-written digits moving independently. The focus is on tracking the dynamics of both _high_-level (digit position) and _mid_- to _low_-level features (digit shapes).
* **KTH action dataset** contains several classes of human actions. For predicting future frames, one needs to capture high-level features such as the person's positions; but the focus is largely on _low_-level features representing details of body parts.
## 3 Results and Discussion
### Characterization of AgNPs by High-Resolution Transmission Electron Microscopy (HRTEM)  
The extracellular biosynthesis of AgNPs using microalgal cultural filtrates was confirmed by HRTEM (Figure 5). The HRTEM analysis was performed for the silver nitrate precipitates that had been reduced by the extracellular exudates from the _S. obliquus_ cultivated for 18 days with KNO\({}_{3}\), NaNO\({}_{3}\), NH\({}_{4}\)NO\({}_{3}\), and (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\). Such samples were selected based on the preliminarily positive visible and UV-Visible spectroscopy data indicative of AgNP biosynthesis. Figure 5 shows the variations in particle size, distribution, and aggregation among the preformed AgNPs for the cultivation nitrogen source. The smallest AgNP formations were obtained by supernatants containing potassium nitrate and sodium nitrate (5-10 and 4-10 nm, respectively). AgNPs produced using ammonium sulfate and ammonium nitrate cultural filtrates, by contrast, were significantly larger (25-50 and 30-50 nm, respectively). This might be related to differences in the metabolic compositions of the culture supernatants due to the use of different nitrogen sources. [PERSON] et al. [60] also reported changes in filtrate metabolites by alteration of N-sources. The results in Figure 5 are in agreement with those obtained via spectrophotometric characterization (Figures 1 and 4). Based on these results, the effect of the nitrogen source in the cultivation media was reflected not only in the effectiveness of nanoparticle biosynthesis but also in the properties of the produced AgNPs. The N-source played a crucial role in reducing silver nitrates to the silver nano-form. This might have been due to the reduction of metal silver ions mainly by the action of \"nitrate-dependent reductase\" and other microalgae-reducing extracellular metabolites [61]. In any case, the role of reductase enzymes seems to influence the formation of AgNPs, as noted also by [PERSON] et al. [56].
# Design of an Underwater Acoustic Waveform and Integrated System for Communication and Detection
## 5 Conclusions  
This paper considered the design of a waveform for integrated communication and detection in underwater acoustic environments. Data are transmitted via CPM on an LFM waveform. An underwater acoustic system was also developed. The detection and  
Figure 23: BER comparison of the proposed waveform and integrated system.  
Figure 22: Output with a Hamming window and zero Doppler frequency.  
communication performances were evaluated to verify the effectiveness of the proposed design. It was shown that the proposed waveform provides good velocity ambiguity, and the range ambiguity degradation is acceptable, considering the communication capability.  
The frame structure employs a symbol suffix frame structure with a SS-CPM-LFM signal. This suffix is used for frequency estimation and signal frame compensation to adapt to complex underwater acoustic channels. Simulation results were obtained which show that the corresponding velocity ambiguity is consistent with the LFM signal and the sensitivity of the range ambiguity to the data signal is reduced, which improves the ranging performance. The proposed frequency offset and phase offset estimation methods provide BER performance approaching that of an AWGN channel. This verifies the effectiveness of the proposed integrated underwater system.  
Conceptualization, T.L. and Y.W.; methodology, Q.T. and Y.W.; software, Y.W. and J.W.; validation, T.L., Q.T. and Y.W.; formal analysis, T.L., Q.T. and Y.W.; investigation, Y.W.; resources, T.L.; data curation, T.L., Q.T. and Y.W.; writing--original draft preparation, T.L., Q.T., Y.W. and J.W.; writing--review and editing, J.C., Y.Z., Y.Y. and T.A.G.; visualization, Y.W. and J.W.; supervision, T.L.; project administration, T.L.; funding acquisition, T.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 2 Related Works
REAP-IIoT establishes a session key (SK) for encrypted communication while ensuring privacy preservation and resistance against attacks such as replay and man-in-the-middle. The protocol's security is validated using the real-or-random model, Scyther verification, and informal analysis, demonstrating robust protection with low computational, communication, and storage overheads--making it highly suitable for resource-constrained IIoT environments.
## 3 Results
### Improvement of GPP Estimation Using a Combination of SIF and PRI for Corn and Soybean
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Crops** & **Timescale** & **Multi-Variable Linear Model** & **R\({}^{2}\)** & **RMSE** & \(p\) \\ \hline \multirow{3}{*}{Corn} & Half-hourly & \(GPP=6.32\times SIF_{t}+389.88\times PRI+32.71\) & 0.78 & 6.60 & \textless{}0.01 \\  & Daily & \(GPP=4.89\times SIF_{t}+287.46\times PRI+27.01\) & 0.84 & 3.56 & \textless{}0.01 \\ \cline{1-1} \cline{2-6}  & Half-hourly & \(GPP=2.87\times SIF_{t}+230.13\times PRI+19.00\) & 0.78 & 6.60 & \textless{}0.01 \\ \cline{1-1}  & Daily & \(GPP=0.95\times SIF_{t}+218.85\times PRI+19.39\) & 0.82 & 4.34 & \textless{}0.01 \\ \hline \end{tabular}  
\end{table} 
Table 3: Multiple-variable GPP regression model based on SIF and PRI.
## 2 Results and Discussion
### Method Validation
#### 2.3.2 LLOQ and Sensitivity  
The LLOQ obtained for piperacillin was 0.02 mg/L, with a mean accuracy and precision of 91.45% and 9.98%, respectively. For impenem, the LLOQ was determined at 0.05 mg/L, with the mean accuracy and precision of 116.32% and 2.27%, respectively. The experimental results confirmed sufficient sensitivity for both analytes. The blank:analyte response at LLOQ ratio for impenem was \(\leq\)16.95%, with a mean accuracy of 105.93% and precision of 16.08%, while for piperacillin it was \(\leq\)1.14%, with a mean accuracy of 91.14% and precision of 13.57%, for all of fifteen replicates (five in each of the three runs).