esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials
### Study Sites
#### 2.1.1 Dinghushan (DHS)  
The study area in the DHS Nature Reserve (DNR) is located in the west-central part of Guangdong Province in southeast China. This study area is located in a southern subtropical monsoon humid climate; the average annual temperature of the DNR is 21.8 \({}^{\circ}\)C, and the mean annual rainfall is 1927 mm. The period from April through September represents the rainy season, and November to January represents the dry season [68; 69].  
The flux tower located in the DNR study area (23\({}^{\circ}\)10\({}^{\prime}\) N, 112\({}^{\circ}\)32\({}^{\prime}\) E, 240 masl) is surrounded by more than 70% of EBFs, and the remaining proportion is evergreen coniferous forest. The canopy height near the flux tower is approximately 17 m. Vertically, the forest structure can be divided into four layers: two tree layers, one shrub layer, and one herb and grass layer. The dominant species in the canopy layers are _Schima superba_, _Castanopsis chinensis_, and _Pinus massoniana_. Near the flux tower, the LAI values for the tree, shrub, and grass layers were 2.6 m\({}^{2}\)/m\({}^{2}\), 0.26 m\({}^{2}\)/m\({}^{2}\), and 0.92 m\({}^{2}\)/m\({}^{2}\), respectively, on 6 August 2005.  
The flux tower contained seven levels of meteorology instrumentation for measuring different variables above or within the canopy, such as temperature and humidity, with shielded and aspirated probes (HMP45C, Campbell Scientific, Inc., Logan, UT, USA) at 4 m, 9 m, 15 m, 21 m, 27 m, 31 m, and 36 m, and photosynthetic photon flux density (PPFD) instruments were installed at 36 m, 21 m, 9 m, and 2 m. For the above-canopy heights, the PPFD was measured by an LI190 SB quantum sensor (LI-COR Inc., Lincoln, NE, USA). An LQS7010-SUN sensor (APOGEE, Logan, UT, USA) was used to measure the PPFD at heights under the overstory layer.  
At 27 m, an open-path CO\({}_{2}\)/H\({}_{2}\)O gas analyzer (model LI-7500, LI-COR Inc.) and a 3D sonic anemometer/thermometer (model CSAT3, Campbell Scientific Inc., Logan, UT, USA) were installed on the tower at approximately two times the average canopy height (15 m) near the tower. These instruments measure carbon and water flux exchange between terrestrial ecosystems and the atmosphere [70; 71]. All of the data were recorded at 30-min intervals. At this site, the LAI values of the different layers were measured every three months from 2004 to 2005.  
Figure 1: Locations of the two study sites in East Asia.
## 3 LCI System Design Framework
### Lateral Resolution  
The lateral resolution of the system is controlled by the optics of the sensing arm of the interferometer. These fixed optics will allow for an axial field of view \((FOV_{axial})\) as shown in Equation (18) in which the lateral resolution \((\delta x)\) will be approximately equivalent to the focused spot size shown from Equation (19). Outside of this field of view, the lateral resolution will deteriorate:  
\[FOV_{axial}=\frac{0.221\lambda}{sin^{2}\left[\frac{sin^{-1}(NA)}{2}\right]}. \tag{18}\]  
\[\delta x=0.37\frac{\lambda_{0}}{NA}. \tag{19}\]  
Hence, from Equations (18) and (19), it can be seen that, as lateral resolution is increased due to a large NA focusing optic, the axial field of view over which the spot size is constant decreases.  
The refractive index of the operating medium may also impact the lateral resolution [34]. Additionally, for in situ measurements during manufacturing processes, tight spatial constraints may be in place along with environmental factors such as vibration and non-ideal operating regions containing dust and liquid contaminants which may affect optical components. In such a situation, the use of un-lensed optical fibres is a desirable solution due to the compact size and robustness nature. However, as highlighted above, the lateral resolution severely degrades as a result of the light diffraction upon exiting the fibre.
## 5 Attributes and Characteristics of FL
### Non-IID Data in FL
#### 5.8.2 Implications and Challenges of Non-IID Data  
* Model Convergence Issues: Navigating model convergence becomes challenging with non-IID data. [PERSON] et al. [48] shed light on this issue, highlighting that an uneven data distribution among nodes leads to slower convergence rates. This prolongs the training process and could diminish the effectiveness of the model.
* Performance Degradation: A model's real-world effectiveness is compromised when it is dominantly trained on non-representative data. In scenarios where such data are prominent, the model, when exposed to varied real-world scenarios, might underperform or yield unpredictable results.
* Biased Outcomes: A skewed data source, if dominant during training, can lead to models that are biased towards particular user groups or scenarios. Such bias not only reduces the model's broad application but also raises ethical concerns. As [PERSON] et al. note, data biases can inadvertently promote certain groups while marginalizing others, thus compromising the fairness and ethical nature of the model [80].
# Intermitt FOCV Using an I-V Curve Tracer for Minimizing Energy Loss
## 2 Conventional MPPT Algorithms  
### Perturb and Observe (P \(\&\) O)  
The procedure to track the MPP in the P & O algorithm is explained as follows: the P & O algorithm operates by periodically incrementing or decrementing the voltage of a PV cell. At first, the MPPT controller measures the voltage and the current of the PV cell. Then, the present power is compared to the previous power of the PV cell. Depending on whether the present power or the previous power is larger, the controller changes the PV voltage. If the present power is higher than the previous power, the P & O algorithm keeps increasing the input voltage of the PV cell, and vice versa. It is very simple to implement, but it is not appropriate for low power and low energy environments because of overall power loss caused by the fluctuation of its operating point around the MPP. If there are multiple MPPs, it cannot detect the GMPP because of the oscillation.
## 3 Materials and Methods  
### Materials  
Individual standards of each VMSs (L3-L5 and _D3-D6_; purity > 97%) and tetrakis(trimethylsilyloxy)silane (M4Q), used as internal standard, were purchased from Sigma-Aldrich (St. Louis, MO, USA). For SPME, two types of fiber, namely 100 \(\upmu\)m polydimethylsiloxane (PDMS) and 65 \(\upmu\)m PDMS/Divinylbenzene (DVB), were also obtained from Sigma-Aldrich. A Hei-Standard magnetic stirrer with heating from Heidolph Instruments (Schwabach, Germany) was also used in the SPME extraction. Helium (99.999%) used in the GC-FID system, was supplied by Air Liquide (Maia, Portugal).  
Individual stock solutions of each siloxane, including the internal standard M4Q, were prepared in \(n\)-hexane at 1 g/L. From those individual stock solutions, a 5 mg/L mix stock solution containing all the target analytes were prepared in acetone. A diluted M4Q individual stock solution, with a final concentration of 5 mg/L, was also prepared in acetone. From those stock solutions, new mix solutions with 50 \(\upmu\)g/L and 500 \(\upmu\)g/L were prepared in acetone. Eight calibration standards in acetone, with concentrations of each analyte ranging from 0.125 \(\upmu\)g/L to 5 \(\upmu\)g/L (internal standard concentration of 3 \(\upmu\)g/L) were also prepared. All the solutions were stored protected from light and at \(-\)22 \({}^{\circ}\)C.  
Figure 4: Global uncertainty of \(D5\) (**a**) and variation of the relative weight of each individual source of uncertainty for \(D5\) (**b**).
# Low-Computational Extended Orthogonal Matched Filter Structure for Multiuser Detection
## 3 Proposed EOMF Structure
### Computational Complexity of the Proposed EOMF Structure  
First, the total computational complexity of the NLMS algorithm is considered, which requires the antenna weights for beamforming for each desired user, the antenna weights for null steering all desired users, and the LC weights for each desired user. Hence, the sum that expresses the computational complexity of the NLMS algorithm is \(2 KM_{d}+2K+2(N-1)M_{d}\). Next, both \(M_{d}\) weight multiplications and additions are required to obtain the beamforming output and the interference replica. On the other hand, the calculation amount of the correlation value in a null steering output and an MF is equal to the conventional EOMF. Thus, their sum is \(KM_{d}+K+N^{2}+(N-1)M_{d}\). From the above, the computational complexity of the proposed structure, \(C_{\text{Prop}}\), is as follows in Equation (30):  
\[C_{\text{Prop}}=2 KM_{d}+2K+2(N-1)M_{d}+KM_{d}+K+N^{2}+(N-1)M_{d}=N^{2}+3(K+N-1) M_{d}+3K. \tag{30}\]  
On the other hand, the computational complexity when using \(M_{d}\) conventional EOMFs can be expressed as \(M_{d}C_{\text{EOMF}}\). Figure 3 shows a comparative example of the computational complexity of the conventional EOMF and the proposed structure. From this figure, the conventional EOMF increases the computational complexity in proportion to the number of desired users, while that of the proposed structure does not increase significantly even if the number of desired users increases. Specifically, \(C_{\text{Prop}}\) is approximately 40% of \(M_{d}C_{\text{EOMF}}\) when \(M_{d}=2\). In addition, \(C_{\text{Prop}}\) is less than 25% of \(M_{d}C_{\text{EOMF}}\) when \(M_{d}=8\). In other words, it can be seen that the proposed structure can significantly reduce the increase in the computational complexity. The reason for that will be considered from Equations (16) and (30). When \(N\) is large enough, \(M_{d}C_{\text{EOMF}}/C_{\text{Prop}}\) is expressed as follows:  
\[\lim_{N\rightarrow\infty}\frac{M_{d}C_{\text{EOMF}}}{C_{\text{Prop}}}=\lim_{N \rightarrow\infty}\frac{M_{d}\Big{(}N^{2}+3N-3+6K\Big{)}}{N^{2}+3(K+N-1)M_{d} +3K}=\lim_{N\rightarrow\infty}\frac{M_{d}\Big{(}1+\frac{3}{N}+\frac{-3+6K}{N^ {2}}\Big{)}}{1+\frac{3M_{d}}{N}+\frac{3(K-1)M_{d}+3K}{N^{2}}}=M_{d}. \tag{31}\]  
Hence, it is derived that the computational complexity of the conventional EOMF increases linearly as the number of desired users increases compared with the proposed structure. In other words, this means that the proposed structure reduces the sum of the correlation value calculations of each MF in multi-user detection.
## 4 Results and Discussion
### Governance and Policy Barriers  
Despite the promising advancements, several governance and policy barriers hinder the widespread adoption of MCDA and AI in RSCSW:  
* Regulatory fragmentation: The lack of cohesive regulatory frameworks across regions and sectors creates challenges in implementing standardized practices for waste management. [PERSON] et al. [14] identified regulatory inconsistencies as a major barrier to the circular economy, which aligns with the findings of [PERSON] et al. [62], who highlighted the need for accurate information and policy adjustments to improve reverse logistics networks.
* Technological challenges: The integration of AI and MCDA requires significant technological infrastructure, including data collection systems, computational resources, and skilled personnel. [PERSON] et al. [19] emphasized the need for technological innovations to support the integration of MCDA and AI in RSCSW, which is further supported by [PERSON] et al. [52], who identified \"Technology Integration\" as a critical factor for adopting AI in supply chains.  
Collaboration among stakeholders: Effective governance of RSCSW requires collaboration among diverse stakeholders, including governments, private sectors, and communities. [PERSON] et al. [19] highlight the role of Information and Communication Technologies (ICT), such as blockchain and the Internet of Things (IoT), in improving governance by providing greater visibility over material and waste flows.
* The adoption of AI and MCDA in waste management may have significant social and ethical implications, such as the displacement of low-skilled workers. Public policies and training initiatives must be implemented to ensure a fair and inclusive transition to more advanced technologies.  
Thus, it is concluded that fragmented policy frameworks do exist, and many studies advocate for AI-driven governance, but [PERSON] et al. [14] emphasize that without standardized policies, these technologies will remain underutilized. Moreover, there are also financial barriers; unlike high-income economies that lead AI implementation, emerging economies face limited public and private funding for waste management technologies, a concern echoed by [PERSON] et al. [61].
## 2 Materials and Methods
### Statistical Analysis  
Only satellite detections with location classes of 1 to 3 were included in the analyses (i.e., classes A, B, Z, and 0 were excluded), so that all the locations had a maximum error radius of <1500 m. All locations were reprojected to an equal-area projection system (GDA 94/Australian Albers; EPSG: 3576).  
A shapefile of the coastline of Australia was downloaded from the Geoscience Australia data repository ([[https://data.gov.au/dataset/geodata-coast-100k-2004](https://data.gov.au/dataset/geodata-coast-100k-2004)]([https://data.gov.au/dataset/geodata-coast-100k-2004](https://data.gov.au/dataset/geodata-coast-100k-2004)), accessed on 17 July 2023). Data were derived from a 1:100,000 scale topographic map, and coastline features were defined from the mean high-water mark. This shapefile was reprojected into the same datum as the satellite tag locations. The 'near' tool in ArcGIS 10.5 (Environmental Systems Research Institute (ESRI), Redlands, CA, USA) was used to calculate the distance from (i) the coast for each of the locations for the first ten days post-tagging and (ii) the post-tagging release location to the first detection. The Geodesic method was used for both, as this accounts for the curvature of the earth to correctly measure the distance between two features. The location of the VR4G acoustic receivers was used as the shark location for all locations from acoustic tags.  
A mixed effects model was used to test if the sex, size (total length--cm), or time since release (in days) influenced the distance from the coast that each shark was detected following tagging. The response variable (distance from the coast) was positively skewed, so it wasn't appropriate to use a linear model. Mixed models were used to account for the repeated measurements on individual sharks (with the unique shark tag code used as a random effect). A Generalised Additive Mixed Model (GAMM) and a generalized linear mixed model (GLMM) with a Gamma link function were tested, and the GLMM had the lowest Akaike information criterion corrected for sample size (AICc), so it was used for subsequent modeling. The GAMM was implemented using the mgcv package [58] and the GLMM using the lme4 package [59] in R [60]. Generalized variance inflation factors (VIF) were checked for collinearity between the explanatory variables. All variables had a VIF of \(\sphericalangle\)3 [61] and were assumed to not be collinear. The inclusion of the variables in the \"best\" model was based on AICc. All models with an AICc of <10 were averaged using the MuMIn [62] package in R, and permutations with each possible combination of variables, including the null model, were tested. The relative importance of each variable was calculated as the sum of the AICc weights over all the models in which it was included. Model adequacy was checked using standard residual plots (Figure S1--Supplementary Materials).
# Singular Value Decomposition Channel Estimation in STBC MIMO-OFDM System
## 3 Conventional Channel Estimation Methods  
### LS Channel Estimation  
LS channel estimation is widely used in OFDM systems and has low calculation complexity [9]. This method requires pilots to obtain the channel coefficients. The CFR of the LS channel estimation in pilot positons \(\hat{\mathbf{H}}_{\text{LS}}(k_{\text{p}})\) which can be represented as:  
\[\hat{\mathbf{H}}_{\text{LS}}(k_{\text{p}})=\frac{\mathbf{Y}(k_{\text{p}})}{\mathbf{X}(k_{ \text{p}})}, \tag{5}\]  
where \(\mathbf{X}(k_{\text{p}})\) represents the transmitted pilot symbols on the \(k_{\text{p}}\)-th subcarrier. \(\mathbf{Y}(k_{\text{p}})\) represents the received pilot symbols on the \(k_{\text{p}}\)-th subcarrier. The spline interpolation method utilized in the MIMO-OFDM system can improve the channel estimation accuracy significantly. Hence, this paper uses spline interpolation to obtain the channel coefficients of all data symbols in the frequency domain.
# A Secure and Efficient Authentication Scheme for Fog-Based Vehicular Ad Hoc Networks
## 5 Security Analysis
### _Informal Analysis_
#### 5.2.4 Stolen Smart Card Attack  
The attacker can steal the vehicle's smart card to obtain the stored data \((N_{i}^{*}\), \(M_{i},s_{0})\). Based on these parameters, the attacker may attempt to impersonate the vehicle user and calculate the session key. However, all parameters are masked with the ID, password, and \(s_{0}\) value of \(V_{i}\). The attacker must guess the ID and password simultaneously, which is computationally infeasible. Therefore, the proposed protocol is resistant to stolen smart card attacks.
## 4 Results and Discussion
### Groundwater Drawdown
For the three hydraulic conductivities of the colluvium, the maximum drawdown of the transient analysis approximates 1.84, 5.31, and 6.89 m for a 20-day subsurface drainage and it can reach a final drawdown of 9.50 m=(22.5 m\(-\)13.0 m) for a steady-state calculation. In general, the drawdown rate (or drainage efficiency) is promoted with an increasing permeability of the colluvium and estimated to be 2.67 m/day (within 3 days for \(K_{S}\) =1.0 \(\times\) 10\({}^{-3}\) cm/sec), 1.50 m/day (within 6 days for \(K_{S}\) =1.0 \(\times\) 10\({}^{-4}\) cm/sec), and 0.27 m/day (within 13 days for \(K_{S}\) =1.0 \(\times\) 10\({}^{-5}\) cm/sec). For the colluvium with lower permeability (\(K_{S}\) \(\leq\) 1.0 \(\times\) 10\({}^{-5}\) cm/sec), the drainage efficiency of subsurface drainage can be comparatively uneconomical. Table 6 summarizes the variations of the groundwater depth of observation well _B12-W_, \(H_{w}(t)\) and the factor of safety, _FS_(\(t\)) of the landslide with elapsed time in response to the subsurface drainage. For the three hydraulic conductivities of the colluvium, the variation tendency of \(H_{w}(t)\) and _FS_(\(t\)) are similar and only the calculation results of \(K_{S}\) = \(1.0\times\) 10\({}^{-4}\) cm/sec are presented. It is found that the factor of safety increases steadily with the drawdown of groundwater level. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Time (day)** & **1** & **2** & **3** & **4** & **5** & **10** & **20** \\ \hline \(h_{w1,max}\) (m) & 5.96 & 7.76 & 8.59 & 8.84 & 8.97 & 9.85 & 10.19 \\ \hline \(h_{w2,max}\) (m) & 7.96 & 9.40 & 10.24 & 10.66 & 10.93 & 11.88 & 13.00 \\ \hline \end{tabular}
\end{table} 
Table 5: Maximum groundwater drawdown in subsurface drainage system (\(K_{S}\) =1.0 \(\times\) 10\({}^{-4}\) cm/sec).
## 6 Conclusions  
Traditional GNSS positioning error estimation methods rely on prior assumptions about observation noise and motion models, and have difficulty handling the complex  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Gating Operation** & **MHA** & \
\begin{tabular}{c} **RMSE on the First,** \\ **Dataset** \\ \end{tabular} &
\begin{tabular}{c} **RMSE on the Second,** \\ **Dataset** \\ \end{tabular} \\ \hline \multirow{3}{*}{\(\checkmark\)} & & 0.449 & 0.042 \\  & & 0.427 & 0.041 \\  & \(\checkmark\) & 0.398 & 0.038 \\ \(\checkmark\) & \(\checkmark\) & 0.239 & 0.029 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation experiment results of the proposed model (unit: m).  
nonlinear relationships between different error factors. However, the GNSS positioning error estimation method based on deep learning does not require prior assumptions. It can extract effective information from a large amount of historical data, and capture the complex nonlinear relationship between different error factors, thereby improving the accuracy of positioning error estimation. Therefore, a GNSS positioning error estimation method based on deep learning is explored. The nonlinear modeling ability and feature selection ability of a MLP model are improved by introducing a multi-head attention mechanism and gating operations into the MLP model. Experimental results show that the presented method has better performance than that of the extended Kalman filter, linear regression, decision tree, support vector machine, and other methods for two datasets, and the presented method is also better than the current state-of-the-art long short-term memory network and convolutional neural network considering comparative experiments. This shows that the presented method can reduce the influence of errors on the positioning results to a certain extent, and has more accurate positioning error estimation capabilities. Currently, the proposed model only estimates the positioning error based on GNSS data. However, other sensors, such as IMU and visual sensors, also provide valuable information in practical applications. In the future, we will explore how to fuse data from different sensors to further improve the error estimation accuracy of the model in complex environments.  
Conceptualization, X.L., Z.T. and J.W.; methodology, X.L.; validation, X.L., Z.T. and J.W.; formal analysis, X.L.; investigation, X.L. and Z.T.; writing--original draft preparation, X.L., Z.T. and J.W.; writing--review and editing, X.L. and J.W.; visualization, X.L.; supervision, X.L. and Z.T.; All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China (Nos. 62171191 and 62271223).  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
We thank the editor and reviewers for their time and energy in reviewing our manuscript and providing insightful comments, which greatly improved our manuscript.  
The authors declare no competing interests.
## 4 Discussion  
### LULC Classification Performance  
The object-based supervised classification in this study achieved high accuracy (Overall Accuracy > 0.94) for all classified maps in each year in both areas (Tables 4 and 5). This is comparable to previous LULC change studies in the Amazon using Random Forest [55] and object-based image classification [19]. [PERSON] et al. used Random Forest to classify land cover and simulate forest dynamics in an area inside Para state, in the Brazilian Amazon, with an overall accuracy reaching 0.97 for the classified map using Sentinel 2 [55]. [PERSON] et al. used object-based classification, and Landsat and Sentinel 2 mosaics for LULC classification of a watershed, also located in Para state, reaching an overall accuracy higher than 0.94 for all classified maps [19].  
Regarding the accuracy of each class, despite the object-based classification approach generally aiming to reduce spectral confusion compared to pixel-based approaches, some land cover classes still faced misclassification issues [56; 57; 58]. This challenge is common in tropical forest studies, where dense canopy cover can complicate the differentiation of forest types. Similar difficulties were encountered with Agriculture/Pastureland classification, leading to the combination of these two land use types into a single class. Additionally, some Burned Areas were incorrectly classified as Agriculture/Pastureland, possibly due to the prevalence of slash-and-burn practices in the Amazon region [59].  
Elevation data and Principal Component Analysis (PCA) proved particularly useful in enhancing the model's performance. The inclusion of topographic information has been shown to improve land cover classification accuracy in complex terrains [43], while PCA can effectively reduce data dimensionality while retaining essential information for classification [60].