esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimation of Time-Series Forest Leaf Area Index (LAI) Based on Sentinel-2 and MODIS
###### Abstract  
The LAI is a key parameter used to describe the exchange of material and energy between soil, vegetation and the atmosphere. It has become an important driving datum in the study of carbon and water cycle mechanism models at many regional scales. In order to obtain high temporal resolution and high spatial resolution LAI products, this study proposed a method to combine the high temporal resolution of MODIS LAI products with the high spatial resolution of Sentinel-2 data. The method first used the LACC algorithm to smooth the LAI time-series data and extracted the normalized growth curve of the MODIS LAI of forest and used this curve to simulate the annual variation of the LAI. Secondly, it estimated the LAI at the period of full leaf spread based on the traditional remote sensing statistical model and Sentinel-2 remote sensing data as the maximum value of the forest LAI in the study area and used it to control the LAI growth curve. Finally, the time-series LAI data set was created by multiplying the maximum LAI by the normalized forest LAI growth curve. The results indicate that: (1) the remote sensing statistical estimation model of LAI was developed using the atmospherically resistant vegetation index ARVI (\(R^{2}\) = 0.494); (2) the MODIS LAI normalized growth curve keeps a good level of agreement with the actual variation. This study provides a simple and efficient method for obtaining effective time-series forest LAI data for the scope of small- and medium-sized areas.  
Sentinel-2; leaf area index; time-series Article  
Estimation of Time-Series Forest Leaf Area Index on Sentinel-2 and MODIS  
2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
###### Abstract  
This research examines the performance of the affordable Xiaomi 11T smartphone in static positioning mode. Static Global Navigation Satellite System (GNSS) measurements are acquired over a two-hour period with a known reference point, spanning three consecutive days. The acquired data are processed, employing both real-time and post-processing Precise Point Positioning (PPP) solutions using GPS-only, Galileo-only, and the combined GPS/Galileo datasets. To correct the satellite and clock errors, the instantaneous Centre National d'Etudes Spatiales (CNES), the final Le Groupe de Recherche de Geodesie Spatiale (GRG), GeoForschungsZentrum (GFZ), and Wuhan University (WUM) products were applied. The results demonstrate that sub-30 cm positioning accuracy is achieved in the horizontal direction using real-time and final products. Additionally, sub-50 cm positioning accuracy is attained in the vertical direction for the real-time and post-processed solutions. Furthermore, the real-time products achieved three-dimensional (3D) position accuracies of 40 cm, 29 cm, and 20 cm using GPS-only, Galileo-only, and the combined GPS/Galileo observations, respectively. The final products achieved 3D position accuracies of 24 cm, 26 cm, and 28 cm using GPS-only, Galileo-only, and the combined GPS/Galileo measurements, respectively. The attained positioning accuracy can be used in some land use and urban planning applications.  
sustainable urban planning; land use; affordable smartphone GPS/Galileo; CNES
# Optimizing Charging Pad Deployment by Applying a Quad-Tree Scheme
## 3 Optimizing the Charging Pad Deployment Problem by Applying the Quad-Tree Scheme
### Proposed Method
The final set of pad placement locations should undergo a final check to identify any redundant pads. Here a redundant pad is one that can be removed without affecting the coverage of all sensors and maintaining connectivity between pads. The algorithms used in this article to perform this check are described below. ```
\(\mathit{Sset}\): the set all sensors \(\mathit{P}\): the set all pads for each pad \(p\) in \(\mathit{P}\) If removing \(p\) does not affect the connectivity between pads and coverage of sensors, then \(\mathit{P}=\mathit{P}-p\)
```  
**Algorithm 3** Removing Redundant pads  
This algorithm checks whether the removal of each pad affects connectivity and coverage. Assuming \(n_{s}=|S|,\ n_{p}=|P|\), according to the algorithm analyses below, the computational cost of this algorithm is \(\mathrm{O}\!\left(n_{p}\left(n_{s}n_{p}+n_{p}^{3}\right)\right)=\mathrm{O} \!\left(n_{s}n_{p}^{4}\right)\). ```
\(\mathit{P}\): the set all pads \(\mathit{SC}=\left[\mathrm{BS}\right]\) while\(\mathit{P}\) is not empty  Find the pad \(p\) closest to the element in \(\mathit{SC}\) from \(\mathit{P}\), if its distance is greater than \(\mathit{D}_{\mathit{maxpad}}\) return false else \(\mathit{SC}=\mathit{SC}+\mathit{p}\); \(\mathit{P}=\mathit{P}-p\); return true
```  
**Algorithm 4** Connectedness checking  
Let \(n_{p}=|P|\) represent the number of pads. Since the number of loops in the algorithm is dependent on \(n_{p}\), and the computational cost of finding the nearest pad in the loop is \(\mathrm{O}\!\left(n_{p}^{2}\right)\), the overall computational cost of this algorithm is \(\mathrm{O}\!\left(n_{p}^{3}\right)\). It is evident from the above algorithm that when \(n_{\text{s}}=|S|,\ n_{p}=|P|\), the algorithm computational cost is \(O(n_{s}n_{p})\).
## 4 Model Validation and Results Analysis
### Evaluation Indicators  
For the problem of ship trajectory prediction, the predicted results consist of latitude and longitude data that have actual physical significance. Therefore, this paper selected the Haversine distance as the metric for measuring the accuracy of the model's predictions. The Haversine distance provides an intuitive evaluation of the prediction results by calculating the distance between each predicted point and its corresponding target value. In this paper, a smaller Haversine distance is deemed to indicate a more accurate model prediction. The formula for calculating the Haversine distance is as Equation (22):  
\[d_{k}=2 Rarcsin\left(\sqrt{sin^{2}(\bar{\phi})+cos(\phi_{1})cos(\phi_{2})sin^{ 2}(\bar{\lambda})}\right), \tag{22}\]  
where \(R\) is the radius of the Earth and \(\bar{\phi}=(\phi_{2}-\phi_{1})/2\), \(\bar{\lambda}=(\lambda_{2}-\lambda_{1})/2\), where \(\phi_{1}\) is the predicted longitude, and \(\phi_{2}\) is the target longitude, and \(\lambda_{1}\) is the predicted latitude, and \(\lambda_{2}\) is the target latitude.  
Meanwhile, Mean Absolute Percentage Error (\(MAPE\)), Mean Absolute Error (\(MAE\)), and Root Mean Square Error (\(RMSE\)) are also calculated in this paper as the evaluation indicators of the ship trajectory prediction model. \(MAPE\), \(RMSE\), and \(MAE\) are calculated as Equations (23)-(25):  
\[MAPE=\frac{1}{N}\sum_{i=1}^{N}\lvert g_{i}-y_{i}\rvert, \tag{23}\]  
\[RMSE(X,h)=\sqrt{\frac{1}{m}\sum_{i=1}^{N}(h(x_{i})-y_{i})^{2}}, \tag{24}\]  
\[MAE=\frac{1}{N}\sum_{i=1}^{N}\lvert\frac{\hat{y}_{i}-y_{i}}{y_{i}}\rvert. \tag{25}\]  
where \(y_{i}\) is the target value of the data set, and \(\hat{y}_{i}\) is the predicted value of the model. \(MAE\) and \(RMSE\) show the difference between the predicted value and the target value, and the range of their values is [0, \(\infty\)), and the value is 0 when the predicted value matches the target value. MAPE also shows the degree of difference between the predicted value and the target value and is suitable for problems with a large difference in the magnitude of the target variable.
## 5 Overview of 5G-Enabling Technologies
### Key 5G-Enabling Technologies
#### 5.1.6 Network Function Virtualization (NFV)  
The purpose of 5G network virtualization is to allow programmable networks for 5G applications by enabling customized network slicing over-dispersed clouds. By abstracting network capabilities and implementing them in software, telecommunication businesses can develop features that meet the criteria of their Service Level Agreements (SLAs) and service models [93]. NFV is accomplished by splitting up a single physical network into several virtual ones, allowing the user equipment to be modified to create as many virtual networks as necessary to meet the needs of each application's QoS. Internally, different blades used for particular tasks are dispersed across the network architecture in a typical NFV [94; 95].  
With network virtualization, services may be operated flexibly and deployed in many places, hence lowering network load. Numerous virtual tools, including virtual machines, hypervisors, and network operations that require security, are present in the NFV interface. The performance of the system will be impacted if attacks such as cyber- and physical attacks compromise any one tool. The network can be protected from cyber-attacks by intrusion detection systems and firewalls, but trusted computing can shield hypervisors from physical assaults [96].  
In order to ensure that only trusted software is running on the NFV interface, trusted computing can be used to ensure data security and the detection of malicious software in virtual functions. Virtual network functions (VNFs) integrated into a service function chaining (SFC) architecture may be quickly deployed and scaled thanks to NFV, which gives network operators the environment they require [97]. Because it provides scalable and adaptable network capabilities, 5G NFV will significantly influence the development of the 5G Internet of Things [98]. To assess the importance of NFV deployment in enabling scalable, resilient, and high-performance communication in 5G networks, the work [7] conducted an overview of the most recent NFV and SFC implementation frameworks as well as major open research challenges. NFV helps network operators by providing the environment required for rapidly deploying and scaling virtual network functions linked in a service function chaining architecture.  
Figure 7: Vehicle-to-Anything Communication.
## 3 The SensoTube Architecture
In addition, particular techniques for energy saving in WSAN nodes have already been studied and have shown remarkable results. Some of them have focused on the wakeup and idle states of MCUs [99; 108; 109], or on the behavior of MCUs as normally-off devices [110; 111]. The realization of functional layers in autonomous shields can help the designers to decide which and how many layers are needed to build a particular WSAN system, to work with discrete functional building blocks, to focus on specific systems' features, to isolate other functional layers from changes at a particular layer, and to work on an add-and-remove basis, in order to adapt to the specific requirements of implementations. In order to handle the functional layer shields as autonomous functional blocks, which can occasionally be added and removed from the main system, particular provisions have to be in place, so as to avoid anarchy in the expandability and scalability. All the functional shields have some common characteristics regarding their operation. In particular, they have to share their electrical signals with other shields, they demand either a single or a multi-value voltage source, they have to be in-system programmed and updated, their MCUs must easily communicate with other MCUs from other shields. The proposed SensoTube architecture establishes the necessary mechanism to support these uniformity and openness needs by the introduction of four inter-layer services:  
* Signals management
* Communications
* Programming and debugging
* Energy management  
Since the four service layers cannot be implemented as distinct plug-in shields, specific provisions have been made in the form of electrical channels in the BECs of the expansion mechanism of the SensoTube architecture, as explained in the implementation reference model in Section 4. Actually, the establishment of the inter-layer services is the key enabler for the realization of the proposed functional abstraction. Without the inter-layer services provision, the elimination of the Arduino-like OSH expandable platforms' constraints could not be avoided at all. Furthermore, the inter-layer services can ensure the building of a sound, expandable and scalable system. For instance, it is possible to have a system comprised of several OSH main-board shields sharing the very same expansion mechanisms, but at the same time each one of them can be self-expandable and autonomous. A complete representation of the SensoTube architecture is given in Figure 8. At a conceptual level, the presented architecture can satisfy the sub-aims (a) to (g), posed at the beginning of this section. In the following sub-sections, the usage and benefits of the proposed seven functional layers are described, with particular emphasis on the advantages of the novel mechanisms of the inter-layer services. Additional emphasis is given on the facilitation of challenging WSAN research aspects, and on the solutions to existing design constraints. At the same time, the target is to explain how the WSAN designers can use the SensoTube architecture to adapt their particular requirements.
## 6 Simulations Results and Discussion
For the higher number of task size in Figure 4b, the proposed methods exhibit slightly better performance compared to existing studies that have energy cost near optimum for the offline solution. The proposed methods maintain energy consumption below the energy budget \(\rho\) while achieving relatively low time cost. Figure 5 displays the trade-off between the average time cost and the total energy cost for \(\alpha\) from \(10^{-4}\) to \(10\). The \(\alpha\) is the controlling parameter in the optimisation solution. We must therefore ensure it is optimally selected. The interception points indicate the optimum \(\alpha\) values, as presented in Figure 5.  
In Figure 5a, the BS-side information is presented. In Figure 5b, the proposed algorithm learns the total cost throughout observations. As can be seen, the interception points are almost the same, varying between \([-2.3,-2.1]\). To conveniently evaluate all algorithms in the same condition, the controlling parameter is set to be \(\log(\alpha)=-2.2\). Due to the HO and learning process in Figure 5b, the time and energy costs are slightly higher than those in Figure 5a. Figure 6 reveals the performance of the algorithms for various energy budgets \(\rho\). The figures indicate that by increasing the energy budget \(\rho\), all algorithms will achieve similar performance as the time greedy algorithm. Users will have enough energy budget to neglect cost and stick to the BS with the lowest average time cost. It must be noted that if users do not have BS-side information they will experience performance loss (see Figure 6). Therefore, the EMM-LSI and proposed BS-learning algorithms are not going to reach the optimum offline cost level. As a result, EMM-LSI and the proposed BS-learning algorithms will not reach the optimum offline cost level. The next important point is the slope of the lines, as shown in Figure 6. The results indicate that our proposed algorithms are near optimum for the offline solution compared to the algorithms in [43]. Figure 5: Effect of optimization parameter \(\alpha\) (\(\rho=410\) J, \(J=\{5,15\}\), \(k_{s}=20\) s, \(\sigma^{2}_{observe}=0.3\)): (**a**) online UE-BS algorithm; (**b**) online BS-learning algorithm.
# Identification of a Human-Structure Interaction Model on an Ultra-Lightweight FRP Footbridge
## 3 Experimental Campaign
### Tests with a Walking Pedestrian  
The dynamic behaviour of the footbridge was assessed under the action of a single pedestrian walking at a step frequency of 1.9 Hz. Six test subjects, which mass is presented in Table 2, were asked to cross the bridge 3 times, walking along the center lane. Figure 5a shows the third trial of the Test Subject 2. An accelerometer attached to the bottom of the central stringer was employed to collect the footbridge response at midspan, as displayed in Figure 5b. The step frequency was controlled using a metronome and chosen aiming to synchronise the fourth harmonic of the human action with the fundamental frequency of the structure. Additionally, the human velocity in each trial was measured, obtaining a mean value of 1.3 m/s.  
The eighteen recorded signals (three per person) were processed using a band pass filter with an upper and lower cut-off frequencies at 1 Hz and 10 Hz, respectively. Figure 6a,b shows the acceleration response in the time and frequency domain when the Test Subject 2 crossed the bridge once. Also, the 1s running root-mean square (RMS) acceleration is included in the graph together with the peak response and the maximum transient vibration value (MTVV). Both results exceeded 0.50 m/s\({}^{2}\), which is the acceleration limit for a maximum degree of comfort stated in HIVOSS guideline [13] and Eurocode 0 [16]. Clearly, the analysed footbridge shows a significant response due to the fourth harmonic of the dynamic pedestrian action.  
From the experiments carried out by the six pedestrians, the mean 1s RMS was computed using the sets of collected data. The outcome is displayed in Figure 6c, in which the colored band represents the dispersion of the results obtained from the eighteen records. The width of this band corresponds to the minimum and maximum values.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test Subject** & **Mass (kg)** & **Test Subject** & **Mass (kg)** \\ \hline
1 & 71.8 & 4 & 68.0 \\
2 & 68.8 & 5 & 71.9 \\
3 & 78.2 & 6 & 65.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mass of the tests subjects.  
Figure 5: Experiment setup: (**a**) Third trial of Test Subject 2 crossing the bridge. (**b**) Accelerometer attached at the midspan of the central stringer.
## 4 Results and Discussion
### Experimental Validation  
An experimental study was carried out for validating the proposed decision tree model. As per the proposed design, the laminated and delaminated composite plate was fabricated. Both laminated and delaminated sheets were fitted on a workbench and the frequency response in the time domain was acquired. From the time-domain response, the statistical information was extracted. Then feature selection process was carried out using the effect of the number of feature study. Table 7 shows the effect of number of features study on the classification accuracy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **LAM** & **DLAM** \\ \hline LAM & 40 & 0 \\ DLAM & 0 & 40 \\ \hline LAM: Laminated; DLAM: Delaminated plate. & \\ \end{tabular}
\end{table}
Table 6: Confusion matrix for decision tree with simulated data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No. of Features** & \multicolumn{3}{c}{**Classification Accuracy (\%)**} \\ \cline{2-4}  & **C4.5 Decision Tree** & **Random Forest** & **Naive Bayes** & **Bayes Net** \\ \hline
1 & 62 & 60 & 56 & 64 \\
2 & 80 & 80 & 86 & 84 \\
3 & 92.5 & 89 & 91 & 90 \\
4 & 96 & 92 & 94 & 92 \\
5 & 98 & 96 & 96 & 95 \\
6 & 98 & 97 & 96 & 95 \\
7 & 97 & 97 & 96 & 94 \\
8 & 96 & 96 & 95 & 93 \\
9 & 96 & 96 & 93 & 93 \\
10 & 95 & 95 & 93 & 92 \\
11 & 94 & 93 & 92 & 91 \\
12 & 92 & 93 & 91 & 90 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Effect of the number of features on classification accuracy—Experimental data.  
Referring to Table 7, DT produced 98% maximum accuracy with the experimental data. Table 8 shows the confusion matrix for the experimental data. Among the total 100 experimental data, 98 data were correctly classified with an overall accuracy level as 98%. The decision tree uses a confidence factor of 0.25.  
Summary of the classifier results:  
Correctly classified instances  
Incorrectly classified instances  
2 (2%)  
Kappa statistic  
0.96  
Mean absolute error  
0.0296  
Root mean squared error  
0.1422  
Relative absolute error  
5.913%  
Root relative squared error  
28.4309%  
Coverage of cases (0.95 level)  
98%  
Mean rel. region size (0.95 level)  
50%  
Total number of instances  
100  
The random forest algorithm produced a maximum accuracy of 96% with the top six features. The random forest randomly uses 10 trees as a forest and the maximum accuracy was predicted. Similarly, the selected naive Bayes and Bayes net algorithms were also used for the classification processes. The naive Bayes algorithm produced 96% as its maximum accuracy, whereas the Bayes net produced a maximum accuracy of 95%. Both algorithms produced the maximum accuracy with the top five contributing features.
## 3 Survey of Techniques for Updating
### Dissemination
#### 3.1.1 Trickle  
Trickle [13] acts as a service to continuously propagate single-packet code updates throughout the network. The packet size is system dependent, with the original example being 30 bytes under TinyOS, enough to hold a Mate capsule (see Section 3.3.1). Periodically (with a _gossiping interval_) using the maintenance algorithm every node broadcasts a code summary if it has not overheard a certain number of neighbours transmit the same information. If a recipient detects the need for an update (either in the sender or in the receiver) then it brings everyone nearby up to date by broadcasting the needed code. Trickle dynamically regulates the per-node, Trickle-related traffic to a particular rate, thus adjusting automatically to the local network density. This scales well, even with packet loss taken into account. A listen-only period is used to minimise the short-listen problem (where unsynchronised nodes may cause redundant transmissions due to a shift in their timer phases). The CSMA hidden-terminal problem does not lead to excessive misbehaviour by Trickle, as long as the traffic rate is kept low. By dynamically changing the gossip interval, Trickle can propagate changes rapidly, while using less network bandwidth when there are no known changes. Trickle provides code dissemination support for Mate, with code _capsules_ fitting into a single packet.  
Trickle was one of the original code update protocols, and is still used as the basis for other protocols. Apart from its scalability it contains no special features related to autonomous updates.