esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods and Data
### Data Source and Pre-Processing  
#### 2.2.1 Multisource Satellite-Observed LAI Datasets  
In this paper, five LAI datasets were used to detect the interannual trend of vegetation greenness over the past several decades. The Global LAnd Surface Satellite (GLASS) LAI was developed by Beijing Normal University with a spatial resolution of 5 km and an 8-day temporal interval, and it ranges from 1982 to 2018. The GLASS LAI C6 dataset is freely available at the National Earth System Science Data Center of China (NESSDC, [[http://www.geodata.cn/](http://www.geodata.cn/)]([http://www.geodata.cn/](http://www.geodata.cn/)), (accessed on 25 August 2023)). The GLOBMP LAI has an 8 km spatial resolution, a half-month interval before 2000, and an 8-day time interval after 2000. This dataset is obtained from the Resource and Environment Science and Data Centre, CAS (RESDC, [[http://www.geodata.cn/](http://www.geodata.cn/)]([http://www.geodata.cn/](http://www.geodata.cn/)), (accessed on 25 August 2023)). Global Inventory Modelling and Mapping Studies (GIMMS) LAI3g has a spatial resolution of 8 km and half monthly intervals ranging from 1981 to 2016, and it can be obtained freely from the website of NASA Earth Exchange (NEX, [[https://ecocast.arc.nasa.gov/](https://ecocast.arc.nasa.gov/)]([https://ecocast.arc.nasa.gov/](https://ecocast.arc.nasa.gov/)), (accessed on 14 March 2022)). Additionally, the GIMMS LAI4g product (version 1.2) ranging from 1982 to 2020, the updated product of the GIMMS LAI3g dataset, has a 15-day temporal interval and \(\sim\)8 km (near-equatorial region) spatial resolution. The dataset can be freely downloaded from the ZENODO website ([[https://zenodo.org/records/8281930](https://zenodo.org/records/8281930)]([https://zenodo.org/records/8281930](https://zenodo.org/records/8281930)), (accessed on 7 March 2019)). The modified Moderate-resolution Imaging Spectroradiometer (MODIS) LAI has a spatial resolution of 5 km and a temporal interval of a half month, and it was  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Index** & **Positive Contributions** & **Negative Contributions** & **Lower Negative Values in VPD and SW** \\ \hline VPD & Non-VPD-stressed & VPD-stressed & VPD-stressed dominated ecosystems \\ SW & Non-SW-stressed & SW-stressed & SW-stressed dominated ecosystems \\ \hline \hline \end{tabular}
\end{table}
Table 1: Contribution-based identification of water-stressed ecosystems.  
freely obtained from the website of the land-atmosphere interaction research group at Sun Yet-sen University.
# Semantic Lidar-Inertial SLAM for Dynamic Scenes
## 5 Conclusions  
Most SLAM systems work on the assumption that the environment is static. In this paper, we have proposed a semantic lidar-inertial SLAM system for dynamic scenes. We imported an attention mechanism to the PointConv network to elevate the feature-learning capability of the network. The LIO-mapping system was combined with an attention PointConv semantic segmentation network, and the points belonging to dynamic objects were removed in each lidar frame. The stable static feature points were extracted, and a static local map was built to perform ego-motion estimation. The experiments demonstrate that our method can decrease the errors of the estimated trajectories and build a clear global map of dynamic scenes.  
Conceptualization, Z.B.; methodology, Z.B.; software, Z.B.; validation, Z.B.; formal analysis, Z.B.; writing--original draft preparation, Z.B.; writing--review and editing, C.S. and P.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
Figure 11: Detailed parts of global map created using LIO mapping and our method: (**a**) corresponds to LIO-mapping method; (**b**) corresponds to our method.
## 1 Introduction
They do not cover the entire product life cycle, the integration and automation of tool-chains and tools, or the cooperation between different engineering teams and stakeholders. These aspects are crucial for the life cycle engineering of digitalisation products, applications, and end-to-end solutions in the domains of Industry 4.0, energy, transportation, healthcare, and smart cities. This paper addresses the automation and digitalisation of the engineering process (EP) used to manage the entire systems life cycle (single products, services, complete systems, and systems of systems) in the Industry 4.0 domain. We propose the _Arrowhead Engineering Process_ (_Arrowhead-EP_), a service-oriented EP model, and its associated ontology designed to support and simplify EP automation in conjunction with an SOA/Microservice oriented framework. The new _Arrowhead-EP_ model enables the description of complex multistakeholder systems offering efficient and automated life cycle management, both for native and legacy products in the domains of the Internet of Things (IoT) and the System of Systems (SoS). The _Arrowhead-EP_ (i) further addresses the management of both design-time and run-time automation and digitalisation solutions, (ii) supports the integration of different EPs adopted by multiple stakeholders in the product value chain, and (iii) provides a comprehensive and simple map of the product life cycle and of the associated value chain. The adoption of the new _Arrowhead-EP_ model and the automation of EP can reduce the costs that companies bear for managing the product life cycle, frequently characterised by a low level of integration, interoperability, and automation of the phases composing EP [17; 18]. With a service-oriented model, a simple and expressive multilayer representation, and specific tools for model management, service-oriented tool chains and tools, the _Arrowhead-EP_ is attractive to fast-evolving businesses in the era of complex IoT and SoS ecosystems, which already rely on rapid and agile engineering practices [19]. The rest of the paper is organised as follows: Section 2 briefly discusses the motivations for engineering process model innovation and the gaps and difficulties of adopting state-of-the-art reference models in real industry 4.0 use cases. Section 3 presents the _Arrowhead-EP_ model with a focus on the main engineering phases and the description of the ontology defined to identify the interaction between the various components of the _Arrowhead-EP_. In the same section, the _Value Chain Engineering Process Map (VCEP-map)_ is introduced, proposing a solution to represent the system life cycle and to summarise various information of the associated value and supply chain in a single diagram. Section 4 proposes a use case, a smart heating system, inspired by a real business case and intended to demonstrate the advantages of adopting the _Arrowhead-EP_. Section 5 presents our discussions on the advantages of adopting the _Arrowhead-EP_ model and _VCEP-map_ in the Industry 4.0 domain. Section 6 concludes the paper.
# An Asymmetric Encryption-Based Key Distribution Method for Wireless Sensor Networks
## 3 The Key Management Scheme for Cluster-Based WSNs
### Static Sensors Subscheme for Hierarchical WSNs
#### 3.3.2 Session Key Update Process  
To protect the nodes against long-term attacks, a periodic key update mechanism is designed. The steps of the key update are given as follows.  
1. The new session key \(SK_{\_}i^{\prime}\) is generated via the cluster head \(CH_{\_}i\) at a certain moment.
2. \(CH_{\_}i\) notifies the base station to update the session key.
3. Using the proposed handshake operation, the new session key \(SK_{\_}i^{\prime}\) is distributed between the BS and the \(CH_{\_}i\). After that step, the \(CH_{\_}i\) notifies all sensors to update their session key in its cluster with a broadcasting message. Sensors will stop encrypting sessions until they receive the new session key \(SK_{\_}i^{\prime}\).
4. After the establishment of \(SK_{\_}i^{\prime}\), the \(CH_{\_}i\) distributes \(SK_{\_}i^{\prime}\) encrypted using the original session key \(SK_{\_}i\) to all sensors by broadcasting cipher5, which is denoted as follows:  
\[\text{Cipher5}=E(SK_{\_}i,SK_{\_}i^{\prime}). \tag{5}\]  
Figure 2: Flowchart of authentication and key agreement in the static scenario.  
Each sensor in the cluster decrypts the cipher5 using the old session key \(SK_{i}\) and substitutes it for the \(SK_{i}\)'. The subsequent dialog is decrypted using the new session key.
## 5 The Proposed Protocol
### Communication Overhead of the Proposed Protocol
#### 5.6.1 Data Fusion Management in Building Occlusion Impairment  
Building occlusion presents a significant challenge in urban environments, particularly for applications requiring accurate spatial data. The presence of tall structures obstructs line-of-sight measurements, complicating the acquisition of complete and reliable sensor data. To address this issue, advanced data fusion techniques that integrate information from diverse sources, such as ground-based SNs, are essential. In urban IoT networks, building occlusion, particularly at varying heights, has a substantial effect on wireless communication performance and reliability. Buildings of different heights block line-of-sight pathways, which results in multipath propagation and signal attenuation. Such conditions raise packet loss and lower data rates. The degree of occlusion increases with building height; that is, higher buildings can withstand greater signal blockage, dead zone creation, and interference with the connection of SNs behind or at lower levels. Signal transmission is further complicated by the complexity of metropolitan surroundings with a mix of low- and high-rise structures. Effectively addressing building occlusion in the proposed protocol requires a multifaceted strategy that combines advanced technology with strategic planning. One key approach is optimizing the placement of PDFHs at elevated sites where they are less likely to encounter signal blockage. In this work, by employing data fusion algorithms and considering a novel RM named ASBO in the design of the DFFF and RDFCF, data gaps caused by occlusions can be mitigated. Accordingly, these methods enable the synthesis of multi-sensor inputs to reconstruct occluded areas and improve the robustness of urban datasets. Additionally, the use of data fusion grouping, supported by a robust backup policy, enhances the network's ability to handle disruptions. Multipath routing is another vital component, as it ensures uninterrupted data flow by enabling alternate transmission paths when primary routes are blocked. The proposed protocol also features adaptive algorithms that dynamically regroup and adjust routing pathways in response to real-time network conditions, thereby significantly mitigating the effects of occlusion. Furthermore, signal amplification using the two-ray ground-reflection model enhances signal penetration and intensity, reducing the impact of physical barriers. Such approaches not only enhance communication and data reliability but also support real-time decision-making in dynamic and cluttered urban environments.
# A Compendium of Parameterized Problems at Higher Levels of the Polynomial Hierarchy
## 3 Propositional Logic Problems
### Minimization for DNF Formulas  
Let \(\varphi\) be a propositional formula in DNF. We say that a set \(C\) of literals is an _implicant of \(\varphi\)_ if all assignments that satisfy \(\bigwedge_{l\in C}l\) also satisfy \(\varphi\). Moreover, we say that a DNF formula \(\varphi^{\prime}\) is a _term-wise subformula_ of \(\varphi^{\prime}\) if for all terms \(t^{\prime}\in\varphi^{\prime}\) there exists a term \(t\in\varphi\) such that \(t^{\prime}\subseteq t\). The following parameterized problems are natural parameterizations of problems shown to be \(\Sigma^{\mathrm{P}}_{2}\)-complete by [PERSON] [46].  
```
Shortest-Implicant-Core(core size) Instance: A DNF formula \(\varphi\), an implicant \(C\) of \(\varphi\), and an integer \(k\). Parameter: \(k\). Question: Does there exist an implicant \(C^{\prime}\subseteq C\) of \(\varphi\) of size \(k\)? Complexity: \(\Sigma^{\mathrm{P}}_{2}[k*]\)-complete [6; 7]. Shortest-Implicant-Core(reduction size) Instance: A DNF formula \(\varphi\), an implicant \(C\) of \(\varphi\) of size \(n\), and an integer \(k\). Parameter: \(k\). Question: Does there exist an implicant \(C^{\prime}\subseteq C\) of \(\varphi\) of size \(n-k\)? Complexity: \(\Sigma^{\mathrm{P}}_{2}[k*]\)-complete [6; 7].
```  
```
DNF-Minimization(reduction size) Instance: A DNF formula \(\varphi\) of size \(n\), and an integer \(k\). Parameter: \(k\). Question: Does there exist a term-wise subformula \(\varphi^{\prime}\) of \(\varphi\) of size \(n-k\) such that \(\varphi\equiv\varphi^{\prime}\)? Complexity: \(\Sigma^{\mathrm{P}}_{2}[k*]\)-complete [6; 7].
```  
```
DNF-Minimization(core size) Instance: A DNF formula \(\varphi\) of size \(n\), and an integer \(k\). Parameter: \(k\). Question: Does there exist an DNF formula \(\varphi^{\prime}\) of size \(k\), such that \(\varphi\equiv\varphi^{\prime}\)? Complexity: para-co-NP-hard, in FPTNP[few], and in \(\Sigma^{\mathrm{P}}_{2}[k*]\)[6; 7].
```
# Detection of Cracks in Arch Dams Using Monitoring Data and Numerical Models
## 2 Methods
### Study Case
#### 2.3.2 Monitoring Devices  
This paper analyzed the synthetic results of radial and vertical displacements modeled numerically. Circumferential displacements were not discussed, given that such displacements do not play a significant role in the behavior of a cracked arch dam.  
Displacements are recorded at 19 points (Table 2) corresponding to the location of the installed monitoring devices. Among them, the five points corresponding to direct pendulums are located at short distances from the monitoring points of inverse pendulums. Two monitoring points at close distance do not provide useful information; therefore, direct pendulums were not considered.  
The studied monitoring points were increased to include at least two points per block (Figure 2). These additional monitoring points were placed at the crest of the dam or at an average height, selecting as the average height that of the pendulum PI-B7-GP.  
\begin{table}
\begin{tabular}{l c c} \hline
**Material** & **Foundation** & **Concrete** \\ \hline Density (kg/m\({}^{3}\)) & 2620 & 2350 \\ Modulus of elasticity (MPa) & 51,500 & 19,600 \\ Poisson’s ratio & 0.165 [18] & 0.2 \\ Coefficient of Thermal Expansion (\({}^{\circ}\)C\({}^{-1}\)) & 8.43 \(\times\) 10\({}^{-6}\)[19] & 10\({}^{-5}\)[20] \\ Friction coefficient & - & 0.8 [20, 21, 22] \\ \hline \end{tabular}
\end{table}
Table 1: Material properties.  
Figure 2: Position of monitoring points. Green: pendulum. Blue: leveling screw. Red: additional monitoring points.
## 2 Results
### Optimization for EPS Production  
The medium composition is a crucial factor that influences the yield of EPS [7]. Therefore, the optimization was performed for strain ZH27 to improve the EPS titer in this study. Figure 2a reveals that strain ZH27 using sucrose achieved the highest EPS titer of 40 g/L and simultaneously the lowest dried cell weight (DCW) among all the carbon sources (sucrose, glucose, fructose, maltose, and glycerol). Moreover, the EPS titers increased with sucrose concentrations rising up to 15%, after which they declined (Figure 2b), while the corresponding DCWs of strain ZH27 showed insignificant changes within the range of sucrose concentrations (13-17%) (Figure 2b). Clearly, the results indicate that sucrose emerged as the optimal carbon source for EPS production by strain ZH27, yielding the highest EPS titer of 49 \(\pm\) 1.17 g/L at 15% sucrose.  
Ammonium sulfate ((NH4)2 SO4) depletion is known to induce EPS synthesis in microorganisms [7]. The EPS titers decreased continually with increasing (NH4)2 SO4 concentrations, while the DCWs exhibited an increase followed by a decrease beyond 0.12% (NH4)2 SO4 (Figure 2c). This suggests nitrogen catabolite repression (NCR) appeared in EPS synthesis by strain ZH27 after 0.12% (NH4)2 SO4, and 0.01% (NH4)2 SO4 was identified as the preferred concentration for EPS production. Figure 2d shows that higher EPS titers were achieved at 0.3-0.7% K2 HPO4 compared with 0.1% K2 HPO4 and remained relatively stable, mirroring biomass changes. This suggests that 0.3% K2 HPO4 is optimal for EPS synthesis by  
Figure 2: Optimization for EPS production. (**a**–**g**) represent the optimization of carbon source, sucrose, (NH4)2 SO4, K2 HPO4, NaCl, temperature, and initial pH, respectively. “**” represents the probability value below 0.05, and “**” means the probability value below 0.01.  
strain ZH27, indicating a relative insensitivity to changes in K\({}_{2}\)HPO\({}_{4}\) concentrations. Strain ZH27 from a mangrove ecosystem exhibited a degree of salt stress tolerance [21]. Improved growth of strain ZH27 was observed at 0.1-0.5% NaCl compared with no NaCl, with DCWs declining below 0.4% NaCl but peaking at 0.5% NaCl (Figure 2e). Simultaneously, the highest EPS titer of 67.8 \(\pm\) 2.5 g/L was achieved at 0.4% NaCl (Figure 2e).  
The cell growth and cellular metabolic capacity of strain ZH27 were influenced by the culture conditions. Figure 2f shows that EPS titers and biomass of strain ZH27 all exhibited upward trends as the temperature increased to 28 \({}^{\circ}\)C and then declined, indicating that 28 \({}^{\circ}\)C was the most favorable temperature for strain ZH27 to produce EPS (68.5 \(\pm\) 0.63 g/L). Additionally, strain ZH27 achieved the maximum EPS titer (108 \(\pm\) 2.2 g/L) at an initial pH of 5, associating with insignificant changes in biomass beyond the optimal pH value (Figure 2g).  
Consequently, the final enhanced medium (EM) comprised 15% sucrose, 0.3% yeast extract, 0.3 % K\({}_{2}\)HPO\({}_{4}\), 0.02% MgSO\({}_{4}\).7H\({}_{2}\)O, 0.4% NaCl, and 0.01% (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\), with an initial pH of 5 at 28 \({}^{\circ}\)C.
## 3 Results  
### Evaluation Metrics  
In our dataset, the terms true positives (TP) and true negatives (TN) denote the count of cases accurately identified as sick and healthy, respectively. Conversely, false positives (FP) refer to the instances wrongly labeled as sick, while false negatives (FN) pertain to the cases mistakenly classified as healthy.  
Accuracy represents the proportion of correct predictions made by a classification model, compared to the overall number of predictions. It is calculated by dividing the count of accurate predictions by the dataset's total number of predictions. Mathematically, accuracy can be expressed as:  
\[Accuracy=\frac{TP+TN}{TP+FP+TN+FN} \tag{1}\]  
Sensitivity, or recall, measures a classification model's effectiveness in correctly identifying positive cases. It is calculated as the fraction of true positives out of the total positive instances, which includes both true positives and false negatives. It is particularly important in applications where identifying positive instances is critical, such as in medical diagnosis or fraud detection. Sensitivity can be expressed as:  
\[Sensitivity=\frac{TP}{TP+FN} \tag{2}\]  
Specificity assesses a classification model's accuracy in identifying negative cases. It is determined by the proportion of true negatives relative to the combined count of true negatives and false positives. Mathematically, specificity can be expressed as:  
\[Specificity=\frac{TN}{TN+FP} \tag{3}\]  
Precision is a metric that gauges the accuracy of a model's positive predictions in classification tasks. It is expressed as the ratio of true positive predictions to the overall number of positive predictions made:  
\[Precision=\frac{TP}{TP+FP} \tag{4}\]  
The F1 score represents a balanced metric that combines precision and recall through their harmonic mean, offering a unified measure that equally weighs both aspects. It can be expressed as:  
\[F1=\frac{2\;*\;Precision\;*\;Recall}{Precision+Recall} \tag{5}\]  
The Matthews correlation coefficient (MCC) serves as an evaluative metric for the effectiveness of binary classification. It considers every quadrant of the confusion matrix. The MCC is mathematically formulated as follows:  
\[MCC=\frac{TP*TN-FP*FN}{\sqrt{(TP+FP)(TP+FN)(TN+FP)(TN+FN)}} \tag{6}\]The area under the curve (AUC) is a metric for assessing binary classification models' performance. It quantifies the area beneath the receiver operating characteristic (ROC) curve--a plot that depicts a binary classifier's diagnostic capacity as the discrimination threshold changes. The ROC curve plots the true positive rate (TPR) against the false positive rate (FPR) across different thresholds. The AUC reflects the likelihood that the model will assign a higher score to a randomly selected positive instance over a negative one. AUC values range from 0 to 1, where 0.5 signifies no discriminative power--equivalent to random chance--and 1.0 signifies flawless discrimination, with all positive and negative instances correctly identified.
# Ground Target Detection and Damage Assessment by Patrol Missiles Based on YOLO-VGGNet
## 5 Experimental Validation and Results
### Traditional Damage Assessment Method Based on Image Change Detection  
The simulation platform is divided into the server and the client. The server sends the RGB images collected by the missile swarm to the client through the UDP protocol. The flight trajectory of the missile swarm is randomly initialized. Due to the missile swarm mission requirements, we assume that the nodes in the swarm can send image information of the ground targets to each other in real-time and detect the changes before and after the strike.  
As we know, image-change-detection-based methods are greatly affected by the surrounding environment. The results of the PCA-k-means are shown in Figure 16, and the states below are the evaluation results. Due to the uncertainty of the image change factors, the smoke and sparks around the target negatively impact the evaluation. In addition, the degree of an image change in the target area is not necessarily related to the target's damage level, which increases the difficulty of damage assessment based on image change detection.  
Figure 15: The changes in the activation intensity of the clustered convolution kernels.
## 2 Materials and Methods
### Numerical Model  
The governing equation of the two-dimensional tidal model is the Navier-Stokes equation integrated along the water depth [22]. The numerical simulation of tidal currents provides a reliable dynamic field for the calculation of range and tidal prism. The finite volume method is used to discretely solve the equation, and the explicit Euler format is used for time integration [23]. Ocean tides possess short-period characteristics as well as long-period characteristics such as semi-monthly, monthly, and annual ones [24]. In accordance with the requirement that the open boundary condition of the model should not be affected by the bay's coastline, the tidal data of a certain year can be selected as the open boundary condition. This enables accurate simulation of tidal change results under the coastline conditions of different years in the study area. Consequently, it is reasonable to run the model for one year.