esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Number of Detected Trees and Merge Process  
The results of the two models regarding the detection of trees are shown in Table 3. The manual identification and delimitation resulted in 2117 canopies in the inference area, used as ground truth. The RGB model correctly identified 2086 canopies, incorrectly 190, and failed to identify 31 trees. The RGBC model had a higher performance in the three situations and correctly identified 2108 crowns, incorrectly 56, and failed to identify 9. All the evaluation metrics for the identification of the RGBC model were superior to those of the RGB model and, disregarding overall accuracy, the largest difference (4.98%) between the models was given by the Precision_det--which evaluates the influence of FP--and the lowest (0.4%) by recall_det--which assesses the influence of FN.  
After validating the identification, it was found that none of the FN occurrences were caused by the application of the result from the aggregation methodology (patch merge). The redundancies caused by the overlapping image patches were correctly identified and all were suppressed, as well as all non-redundant inferences were kept unchanged in the final result. Figure 8 summarizes some examples of the performance from the clustering process.  
The unidentified crowns (FN) by the RGB model were the result, in some cases, of the mistaken identification of only one crown when there were two or more dense trees (under-segmentation), while the RGBC model was able to perform, with some exceptions, the correct identification of the different crown units. This indicates that, in general, the RGB model did not correctly identify the separation line between two contiguous trees, which was not observed with the same frequency in the RGBC model.  
As for the FP, the biggest difference between the two models is due to the incorrect identification of crowns located between planting lines because of the vegetation cover. The canopy height information entered in the RGBC model provided a greater ability to identify between what is a real canopy and what is grass. Furthermore, cases of incorrect identification (FP) by the RGB model are also associated, at some level, with the incorrect detection of more than one canopy for the same tree when there are shadows that imply an over-segmentation (Figure 9) of the canopy detection process.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metrics** & **RGB Model** & **RGBC Model** \\ \hline Detections/Ground Truth & 2276/2117 & 2164/2117 \\ TP & 2086 & 2108 \\ FP & 190 & 56 \\ FN & 31 & 9 \\ Overall Accuracy & 90.42\% & 97.01\% \\ Precision_det & 91.65\% & 97.41\% \\ Recall\_det & 98.54\% & 99.57\% \\ F1-Score\_det & 94.97\% & 98.48\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of the crown detection process.  
Figure 8: Results of unsupervised merge process.  
Figure 9: Results of canopy detection and delineation process. (**A**–**E**) indicate different canopy scenarios of orange plantation.
# Lead Monoxide Nanostructures for Nanophotonics: A Review
## 2 Synthesis of PbO Nanostructures
### Two-Dimensional PbO NSs  
Two-dimensional NS materials, atomically thin sheets, have attracted tremendous interest due to their fascinating properties [52; 53; 54; 55; 56; 57; 58]. Inspired by the huge success of graphene and black phosphorus, a series of 2D materials have been exploited and demonstrated to have great potential in many applications, such as energy [8; 59; 60; 61], catalysis [62; 63; 64; 65; 66], sensors [67; 68; 65; 66; 67; 68], nanophotonics [9; 58; 69; 70; 71; 72], and biomedicies [73; 74; 75]. Although great progress in 0D and 1D PbO nanostructures has been achieved, much less is known about 2D PbO NSs, their synthetic strategies, and their fascinating performance so far. In 2020, [PERSON] et al. [26] developed a chemically clean route for synthesizing 2D PbO NSs by laser ablation. The SEM image of the as-fabricated PbO NSs shows a mean size of 1.5 \(\upmu\)m in the planar dimension (Figure 4a). The NS displays a hexagonal shape with a regular edge, as well as a smooth surface (left inset of Figure 4a), and the SAED pattern indicates that the NS shows a (002)-terminated surface with a single-crystal nature (right inset of Figure 4a). Moreover, in 2020, high-quality PbO NSs were also successfully synthesized by the thermal decomposition of lead carboxylate, the same as the abovementioned PbO NWs, without a higher HDA content (Figure 3c) [49]. The uniform PbO NSs, with a lateral size of \(\sim\)300 nm, were successfully obtained at the initial reaction time of 30 min (Figure 4b), and typical square-like NSs are clearly observed (Figure 4b inset), indicating the successful synthesis of the 2D PbO nanostructures. In addition, in 2018, our group employed an LPE method  
Figure 3: The structural characterizations of 1D PbO nanostructures. (**a**) TEM image of ultrathin PbO NWs prepared by an alkylamine-mediated thermolysis route (inset showing the HRTEM image). Reproduced with permission from Reference [49]. Copyright 2020, Elsevier B. V. (**b**) SEM image of PbO NWs prepared by an improved sol–gel method combined with porous AAO templates. Reproduced with permission from Reference [50]. Copyright 2007, American Chemical Society. (**c**) TEM image of PbO NRs prepared by a hydrothermal method in the presence of citrate (inset showing the corresponding SAED pattern). Reproduced with permission from Reference [51]. Copyright 2006, Elsevier B. V.  
to successfully fabricate circular PbO NSs in an isopropanol (IPA) solvent [19]. The TEM image of the circular 2D PbO NSs shows that the diameter of these as-prepared PbO NSs range from \(\sim\)260 nm to \(\sim\)400 nm (Figure 4c). The HRTEM image exhibits that a clear fringe lattice of \(\sim\)0.263 nm can be indexed to the (200) plane of the \(\beta\)-PbO crystal [19]. It should be pointed out that the preparation conditions in the LPE, such as the sonication power and time, as well as the solvent and sonication temperature, have a crucial effect on the size and morphology (for NSs or NPs) of the final PbO product.
## 3 Results  
### Data Verification  
In this paper, the 8 day and month-by-month PML remote sensing products (GPPP\({}_{\text{PML}}\), ET\({}_{\text{PML}}\)) and flux site observation data (GPP\({}_{\text{OBS}}\), ET\({}_{\text{OBS}}\)) were fitted, and the results showed good agreement for both datasets. For the 8 day scale data, the \(R^{2}\) of GPP was 0.94 and the RMSE was 0.74 g C m\({}^{-2}\)/8 days, while the \(R^{2}\) of ET data was 0.82 and the RMSE was 0.29 mm/8 days (Figure 2a,c); for the monthly scale data, the \(R^{2}\) of GPP was 0.92 and the RMSE was 28.47 g C m\({}^{-2}\), while the \(R^{2}\) of ET data was 0.74 and the RMSE was 8.56 mm (Figure 2b,d). The results show that the fitting accuracy between the two datasets was high; hence, it was feasible to use PML data to study the carbon-water coupling process in the Loess Plateau.
# Post-Quantum Authentication in the MQTT Protocol
## 1 Introduction
It is also expected that the number of organizations working in the area of quantum computing will nearly double in the coming years ([[https://www.techrepublic.com/article/6-experts-share-quantum-computing-predictions-for-2021/](https://www.techrepublic.com/article/6-experts-share-quantum-computing-predictions-for-2021/)]([https://www.techrepublic.com/article/6-experts-share-quantum-computing-predictions-for-2021/](https://www.techrepublic.com/article/6-experts-share-quantum-computing-predictions-for-2021/)) (accessed on 7 February 2023)). It has been found that using an algorithm that simulates a quantum algorithm on a classic computer is up to 10 million times slower than when the algorithm is run on a quantum computer [11]. Exact differences in computing power are yet to be known, but it is expected that quantum computers will change computing as we know it at present. The Internet of Things (IoT) is an incredibly versatile term and has many possible applications, including industrial and domestic implementations, security, logistics and transport. It is expected that the uses for IoT will increase, as will the number of connected IoT devices [12]. This makes it imperative that these new cryptographic standards be considered specifically for IoT devices. Due to the prevalence that IoT devices have, and are projected to continue having, it is imperative that there be security options when quantum computers become available [13]. It is especially crucial to be considering postquantum security options for IoT devices, because one of the major concerns for users of these devices is the possibility of security issues [12]. Message Queue Telemetry Transport (MQTT) ([[https://docs.oasis-open.org/mqtt/mqtt/v5.0/mqtt-v5.0.html](https://docs.oasis-open.org/mqtt/mqtt/v5.0/mqtt-v5.0.html)]([https://docs.oasis-open.org/mqtt/mqtt/v5.0/mqtt-v5.0.html](https://docs.oasis-open.org/mqtt/mqtt/v5.0/mqtt-v5.0.html)) (accessed on 26 January 2023)) is a lightweight application layer protocol commonly used for IoT communication. MQTT has no in-built security features, and usually runs over the TCP transport layer protocol. It is designed to be efficient and reliable, even if the network is unreliable. It is also easily scalable, so can be used for many connected devices ([[https://mqtt.org](https://mqtt.org)]([https://mqtt.org](https://mqtt.org)) (accessed on 26 January 2023)). MQTT can be configured to run over the secure TLS protocol [1] to make the MQTT communication secure. However, this comes with the overheads associated with TLS, losing many of the advantages that MQTT has as a lightweight protocol.
# Advancing Data Privacy in Cloud Storage: A Novel Multi-Layer Encoding Framework
## 5 Discussions
### Various Examples of the Described Cloud Storage Privacy-Protecting Model  
The following are a few examples of the proposed model:  
1. _Data Encryption:_ Privacy-preserving models in cloud storage commonly utilize encryption techniques to safeguard data. Encryption ensures that data remains confidential, even if unauthorized access occurs.
2. _Access Control:_ Privacy-preserving models integrate access control mechanisms to regulate and restrict data access and modification in cloud storage. This prevents unauthorized users from viewing or altering sensitive information.
3. _Anonymization:_ Some privacy-preserving models in cloud storage employ anonymization techniques to remove personally identifiable information from data. This makes it challenging to link specific individuals to their stored information.
4. _Differential Privacy:_ Differential privacy techniques add noise or randomness to data before storing it in the cloud. This ensures that individual data points cannot be accurately identified, preserving individuals' privacy while allowing meaningful data analysis.
5. _Secure Multi-Party Computation (MPC):_ MPC enables various parties to collectively perform computations on their data without revealing sensitive information to each other or the cloud storage provider. This protects data privacy during collaborative analysis.  
* _Homomorphic Encryption:_ Homomorphic encryption is a groundbreaking advancement in the field of data security and privacy. It introduces a transformative capability to perform computations on encrypted data without the need for decryption, paving the way for a multitude of opportunities in secure and privacy-preserving data processing. It maintains data privacy throughout the computation process.
* _Attribute-Based Encryption (ABE):_ ABE is an advanced cryptographic technique designed to offer precise control over access to data stored in cloud environments. It enables data encryption with specific attributes, allowing only users with matching attributes to decrypt and access the data.
* _Privacy-Preserving Auditing:_ Privacy-preserving models may include auditing mechanisms that enable users to verify the integrity and authenticity of their cloud-stored data without revealing the actual content. This ensures data integrity while maintaining privacy.
* _Data Residency and Jurisdiction Controls:_ Privacy-preserving models can incorporate features that allow users to specify the geographic location where their data should be stored.
* _Secure Data Deletion:_ Privacy-preserving models address the secure deletion of data from cloud storage. This ensures that deleted data is permanently removed and cannot be recovered, protecting the privacy of individuals whose data was stored in the cloud.  
These examples (_example 'i'_ to _example 'x'_) illustrate the various privacy-preserving models used in cloud storage. Different combinations of these techniques, along with additional methods, may be employed to ensure the privacy of user data in the cloud.
# Middleware for Plug and Play Integration of Heterogeneous Sensor Resources into the Sensor Web
## 6 Use Cases
### EMSODEV Project
Therefore, in order to fulfil the architecture requirements, the SWE Bridge functionality was expanded by implementing a specific module to send instrument data to a Zabbix monitoring server using its specific format. This module, called Send to Zabbix, sends an UDP frame to a Zabbix server for each new value arriving from the sensors. Except the hydrophone, all the sensors including the EGIM itself are configured in streaming mode. Thus, each SDF use the same process chain for the SWE Bridge's mission scheduler, shown in Figure 20.  
A first field test of the EGIM developments at the OBSEA underwater observatory was conducted from 1 December 2016 to 15 April 2017 [48]. The test showed that the cyber-infrastructure was robust and interoperable as new sensors can be easily deployed, just plugging a new sensor to an EGIM's empty slot and writing a new SDF. Figure 19: EGIM cyber-infrastructure in a cabled mode scenario. Figure 20: Mission scheduler configuration used by the SWE Bridge instances deployed at the EMSODEV acquisition server. The data coming from each sensor (all of them configured in stream mode) is acquired using an Instrument Command process, which then passes the data to a Field Selector process. This process filters the useful data and discards the undesired variables, depending on the communications protocol of each sensor. Later on, two different branches are created, one that stores the data into Observations and Measurements (O&M) files using the Insert Result process, and another one that sends the data to the Zabbix Monitoring System.
## 5 Results
We tried to calculate the overall terrain roughness in the vicinity of each BSM to understand its relation with rainfall decay time. The digital terrain model (DTM) of 6 m resolution was taken to calculate the terrain roughness in the vicinity of 500 m radius of the BSMs, and the results are shown in Table 2. From the results, there seemed to exist a linear relation between the terrain roughness and rainfall decay time. However, we also found some BSMs in high roughness areas showing a relative short decay time. There were six BSMs (including BMMT, PFMT, CINT, DARB, RST, and RNT) located near rivers within a distance of 500 m in the mountain area, and rainwater could easily flow into the surrounding rivers. Thus, the quick dissipation of rainwater will result in the shorter decay time of the rainfall response (see Table 2 and Figure 6). For example, PFMT is located at a smooth slope of high altitude among mountains, which is conductive to concentrate the rainwater eastward to an existing river beside. Considering low terrain roughness and conduction of existing river, the rainfall decay time in PFMT took only 33 h to down to 10% of the initial response. Similar phenomena can also be observed in BMMT, RST, and RNT, which are expected having long-persisting decay time. On the other hands, the influence of rivers on runoff collection in plain/low areas seems to be smaller than that in hilly/mountain areas. For SANS, the terrain roughness is not high, and there are rivers passing nearby. The rainfall decay time of this station is expected to be short, but in fact its decay time was relatively long. The reduction of river pooling seemed to be not effective on the plains, or it might be affected by other factors. In addition, JING, which had the shortest decay time, was located on the plain and had a low terrain roughness. Sitting at the edge of the urban area and better artificial drainage facilities might be beneficial to shorten the decay time. There are many factors affecting surface runoff flow rate, including slope, surface vegetation, biological activity, soil distribution, particle composition, soil saturation, and topographic fluctuations [54]. More studies are required to understand the mechanism of generation and dispersion of runoff.
## 3 Problem Statement and Model Construction
### Model Construction
#### 3.3.2 Constraint Handling  
When addressing combinatorial optimization problems with constraints, a common approach is to convert constraints into penalty functions, which are then incorporated into the objective function [34]. According to the penalty function method principle, when a solution fails to meet the constraints, the presence of the penalty term causes the objective function value to become exceedingly large or small, effectively eliminating the solution. The penalty function below is designed to address the two primary inequality constraints in this paper: (1) resource sites shall not be allocated resources over their reserves, and (2) disaster sites shall not receive fewer resources than needed:  
\[g_{1}=\sum_{i=1}^{M}\sum_{k=1}^{K}\left[\min\left(0,\ S_{ik}-\sum_{j=1}^{N}w_{ ijk}\right)\right]^{2} \tag{10}\]  
Figure 4: The relationship between the rescue waiting time and the risk of casualties.  
\[g_{2}=\sum_{j=1}^{N}\sum_{k=1}^{K}\left[\min\left(0,\sum_{j=1}^{N}w_{ijk}-D_{jk} \right)\right]^{2} \tag{11}\]  
Finally, the penalty term is set to \(\theta(x)=g_{1}+g_{2}\), and \(L_{0}\)\(\sim\)\(L_{4}\) are used as equilibrium coefficients to standardize the following quantities: \(L_{0}\)\(\cdot\)\(T_{transit}+L_{1}\)\(\cdot\)\(C_{dispatch}+L_{2}\)\(\cdot\)\(f_{risk}+L_{3}\)\(\cdot\)\(g_{1}+L_{4}\)\(\cdot\)\(g_{3}\).
# Experimental Study and Parameter Optimization of a Magnetic Coupled Piezoelectric Energy Harvester
## 4 Results and Discussion
### Experimental Results
#### 4.1.5 Different Acceleration Test  
In order to show the properties of the structure more comprehensively, this section compares the different excitation accelerations and presents them with \(1\) m/s\({}^{2}\) and \(10\) m/s\({}^{2}\). Using a method similar to Section 4.1.3, this section simulates the optimal spacing using the condition of B = 0.12 T, as shown in Figure 12.  
Figure 11: Time series for the output voltage, acceleration in experiment and corresponding phase portraits curve in simulation: (**a–c**) 11 Hz; (**d–f**) 13 Hz; (**g–i**) 15.3 Hz; (**j–l**) 17 Hz; (**m–o**) 19 Hz.  
Figure 12: Output voltage at different \(D\) with \(B_{r}\) = 0.12 T based on simulation and experiment.  
Under the acceleration excitation of 1 m/s\({}^{2}\), the optimal spacing is 19.5 mm. In the case of 10 m/s\({}^{2}\), 33.5 mm is obtained. Combined with 23 mm at 4 m/s\({}^{2}\), it can be seen preliminarily that as the excitation increases, the optimal spacing will increase. Experiments and simulations are carried out with the two accelerations under the parameters of the optimal spacing. The experimental procedure is consistent with Section 4.1.4, and the results are shown in Figure 13. It can be seen from the figure that the structure exhibits similar properties at different accelerations, and the model can still predict the voltage within a certain error.
# Study on Load Transfer Mechanism of Pile-Supported Embankment Based on Response Surface Method
## 2 Numerical Simulation of Pile-Supported Embankments
### Boundary Conditions and Mesh Division  
In this paper, a three-dimensional finite element model was established using the finite element software ABAQUS. As shown in Figure 3, four piles were selected as the research units to reduce the influence of artificial boundaries on the internal lateral deformation of the model. Considering the mesh division and the symmetry of the results, under the principle of an equivalent area, square piles were used instead of circular piles. The diameter of the circular pile was 0.4 m, so the side length of the square pile was about 0.35 m. The cushion thickness of the model was 0.3 m, and the embankment height was 5 m. It was filled in 21 layers, and the filling height of each layer, except the cushion, was 0.25 m. In order to explore the internal load transfer law of the embankment, the influence of the embankment slope was ignored, and only the internal filler part was established in the model. In order to improve the calculation efficiency of the model, the soil between piles was simulated by elastic materials, and the pile length was equal to the subgrade height. The bottom constrained the displacement in three directions, x, y, and z, and the horizontal displacement was constrained around the model. In the numerical model, the element type of the embankment, the pile, and the subgrade was C3D8, and the element type used by the geogrid was M3D4.
# Fast Face Tracking-by-Detection Algorithm for Secure Monitoring
## 4 Experimental Results
### Quantitative Evaluation  
This work evaluates the FFTD and KCF algorithms with 31 trackers using OTB2015. Figure 8 shows the one-pass evaluation results in terms of the success rate and precision. The proposed FFTD performed well, with a precision of 88.1% and an overlap success rate of 86.2%. The CCT and Struck were the top two methods among the 31 trackers [43], with 84.6%/83.1% and 77.6%/78.2% for the precision score (PS)/success rate (SR) metrics. Compared with other trackers, FFTD was an improvement.  
The KCF method performed well, with a precision of 82.7% and an overlap success rate of 81.6%. However, it usually failed when an object moves out of view, which is a fatal problem. Figure 9 shows the tracking performance in the out-of-view tracking problem. Compared with KCF, the proposed FFTD method performed better by 20.9/20.5 in terms of PS/SR. The face detection module and new tracking strategy of the FFTD algorithm solved this problem. Overall, the FFTD algorithm performed well on the OTB2015 dataset.  
1. Attribute-Based Analysis  
Figure 8: Precision and success plots over the 14 sequences using one-pass evaluation on the OTB2015 dataset. The top 10 trackers are shown in the figure. The legend shows the success rate with an overlap threshold of 0.5 and precision score for a threshold of 20 pixels for each tracker.  
Figure 9: Tracking performance in the out-of-view challenge over the 14 sequences from the OTB2015 dataset.  
Table 1 reports the success rate and precision data of the FFTD and KCF algorithms and the other four trackers (CXT, CCT, Struck, TLD) for some challenging tracking attributes. The attributes were scale variation (SV), out-of-view (OV), out-of-plane rotation (OPR), low resolution (LR), in-plane rotation (IPR), illumination (IV), motion blur (MB), background clutter (BC), occlusion (OCC), deformation (DEF), and fast motion (FM). They are helpful to test the performance of the algorithms in terms of different aspects.  
The FFTD algorithm performed well in terms of the attributes of scale variation, out-of-view, out-of-plane rotation, in-plane rotation, background clutter, occlusion and fast motion. According to the experimental results, the FFTD algorithm did not perform well in motion blur. In motion blur, the target region is blurred due to the motion of target or camera. The fuzzy face features lead to the inaccurate detection result. In the future work, we will improve the performance in the challenging attributes. The CXT method had the highest success rate in terms of scale variation, out-of-plane rotation and in-plane rotation. However, in most cases, FFTD algorithm performed better than other algorithms.