esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Efficient Communications for Reliable IoT Multicast 5G/Satellite Services
###### Abstract  
Satellites can provide strong value-add and complementarity with the new cellular system of the fifth generation (5G) in cost-effective solutions for a massive number of users/devices/things. Due to the inherent broadcast nature of satellite communications, which assures access to remote areas and the support to a very large number of devices, satellite systems will gain a major role in the development of the Internet of Things (IoT) sector. In this vision, reliable multicast services via satellite can be provided to deliver the same content efficiently to multiple devices on the Earth, or for software updating to groups of cars in the Machine-to-Machine (M2M) context or for sending control messages to actuators/IoT embedded devices. The paper focuses on the Network coding (NC) techniques applied to a hybrid satellite/terrestrial network to support reliable multicast services. An energy optimization method is proposed based on joint adaptation of: (i) the repetition factor of data symbols on multiple subcarries of the transmitted orthogonal frequency division multiplexing (OFDM) signal; and (ii) the mean number of needed coded packets according to the requirements of each group and to the physical satellite links conditions.  
IoT domain; satellite communications; reliable multicast; network coding +
Footnote †: journal:  
_Future Internet_
## 4 Validation and Discussion of Land Subsidence Results
### Correlation Analysis  
#### 4.4.1 Correlation Analysis between Land Subsidence and Rainfall  
According to previous studies [57], there is a certain relationship between urban precipitation and urban surface subsidence. With increased rainfall, the surface rock  
Figure 13: Distribution of annual vertical settlement along metro lines in Shanghai; (**a**) shows the subway distribution through significant settlement areas, and (**b**) is the relatively stable subway distribution.  
pore water increases so that the surface is stable or slightly uplifted. Here, we selected the monthly cumulative rainfall and subsidence period deformation of the PS02 and PS06 feature points in typical subsidence areas and analyzed the correlation between the subsidence points and rainfall based on the SSA singular analysis model.  
Firstly, the time delay matrix was established; then, the singular value decomposition was carried out. Then, the target signal component and other signal components were separated, and the corresponding singular values were reconstructed according to the grouping results. The singular spectrum analysis window length L of the feature point was set to 10. Figure 14 shows the original time series trend deformation and periodic term time series deformation of the two feature points. From the trend diagram, it can be concluded that the displacement shows a continuous downward trend, which is an unstable time series diagram. From February to May of each year, the surface shows an upward trend, and the settlement cycle term is unstable.  
The rainfall infiltration process has a certain process, and due to the evaporation of surface runoff water and other reasons, the infiltration amount is smaller than the rainfall. Therefore, the deformation caused by rainfall has a certain lag. Figure 15 shows the relationship between the periodic deformation of the PS02 and PS06 points and the monthly average rainfall of adjacent meteorological stations. The rainfall data are seasonally distributed, and the rainfall is basically more than 250 mm from June to August every year. However, with the gradual increase in rainfall, the surface deformation shows different states: sometimes uplift, sometimes subsidence, with instability. From the above modeling process, it can be concluded that the correlation between land subsidence in Shanghai and seasonal rainfall in the region is not strong.  
Figure 14: The time series deformation decomposition and reconstruction of PS02 and PS03 with the SSA method; (**a,b**) are the time series deformation and fitting trend term of PS02 and PS06, respectively, and (**c,d**) are the periodic term deformation of PS02 and PS06, respectively.
## 4 Empirical Study
### Performance Evaluation
Prediction models used for evaluation are Bagging-based Random Forest, Boosting-based Light GBM (LGBM), commonly used in time series prediction, and LSTM-based neural networks. To measure the model's performance, classification metrics such as accuracy, precision, recall, F1 Score, AUC, and specificity were calculated and averaged.
## Materials and Methods
### Methods
Morphology and composition characterization:  
The resulting nanostructures, then, had their macro and nano topology characterized through Scanning electron microscopy (SEM) and their chemical composition characterized through Energy-dispersive X-ray spectroscopy (EDX) and X-ray photoelectron spectroscopy (XPS). The substrates were first observed under a scanning electron microscope (FEI Inspect S50) with no tilting at an operating voltage of 25 kV and photographed at a magnification of 20,000. EDX single spectrum was obtained at an accelerating voltage of 8 kV and 5000 magnification and the mapping was obtained at 2.5 kV and 2500 magnification. Due to the fact that the XPS instrument had a hemispherical analyzer, the electrons, which are allowed to pass and arrive to the detector slits, had a given energy called pass energy. Spectral resolution depends on the values of: pass energy (lower values give higher resolution), number of scans (increased number of scans increases the peak signal) and the duration of each scan (dwell time). For the XPS survey spectra, the parameters used were: pass energy 50 eV, 10 scans, dwell time 50 s, while for high-resolution spectra: pass energy 10 eV, 50 scans and dwell time 200 s.  
SERS characterization experiments:  
The SERS effect on the Raman signal of the R6G on the LIPSS generated was assessed by single point Raman microspectroscopy. The fluorescence of R6G is considered noise during Raman Spectroscopy and attempting to amplify the Raman signal through an increase in exposure time will also amplify the fluorescence intensity. In order to test the Raman signal enhancement, a diluted R6G solution was pipetted onto every substrate, in drops of 25 \(\upmu\)L. Considering that the volume of R6G excited by the laser spot is the same for every substrate, it is safe to assume that the same number of R6G molecules are excited by the Raman spectroscopy laser. The drops were then left to dry onto the surface. All Raman spectra of R6G solution with 10\({}^{-4}\) M molar concentration were collected using 10 s of laser exposure time per acquisition and by calculating an average over 10 spectral acquisitions, with a total of 100 s of exposure time and a spectral resolution of 3.4 cm\({}^{-1}\). A 632.8 nm HeNe laser with 2.2 mW average power was used for all Raman spectroscopy experiments and was focused onto the substrates using a 100\(\times\) objective, which reduced the laser spot diameter to 2 \(\upmu\)m.
## 4 WEAMR Overview
### Data Transmission
The sender will try the secondary path if valid, and start a path discovery process if required to replace the expired path with a new path. Figures 6-9 provide an insight to the overall mechanisms for the sender and the intermediate nodes to send data packets. Since WEAMR is based on AOMDV so the path discovery method DiscoverPaths(m.Destination_id) takes equal amount of time as taken by AOMDV, The methods such as SelectBestPath(m), DecrementLocalEnergy(m) and ForwardData(m) complete their execution in linear time O(n). In case of delays or error the send timeout of 30 seconds ensures the execution is not delayed indefinitely for a process. Figure 6: Algorithm to send data packet. RcvDataPacket(m)  
1. DecrementLocalEnergy(m); **if** (( \(E_{i}-\mathrm{m.}LE_{p}<0\)) && (\(\mathrm{m.}LE_{p}-E_{i}<E_{t}\))) // The node energy is less than energy value sent in data so it should send back Update RRep msg
2. **then Begin** SendUpdateRREPToSender( \(E_{i}\), m.Destination_id, m.Sender_id); //send to sender to update entry energy value for destination to recalculate path cost. m.\(LE_{p}=E_{i}\); //update energy value in data packet with energy of current node, as it is the least energy value node now. UpdatePathEnergyValue(m.\(LE_{p}\), m.Destination_id); //update energy value and recalculate cost. 3. **End** if (m.Destination_id == this.Destination_id) //The receiving node is the destination node. 4. **then Begin** ProcessMsg(m); //Process the message. 5. **End**
6. **Else Begin**
7. **ForwardData(m); //Forward the message to next hop. ** End**  
Figure 8: Algorithm to receive data packet. Figure 7: Overall procedure for the sender node to send data packet. The methods DecrementLocalEnergy(m), SendUpdateRREPToSender(\(E_{i}\), m.Destination id, m.Sender_id), UpdatePathEnergyValue(m.\(LE_{p}\), m.Destination_id) all execute in linear time O(n) in normal case.
## 4 Extending M2M Transformations with NLP
### Implementation Architecture  
The core of our implementation, along with the requirements for other CASE tools to support the approach, is described in detail in [14]. As stated in [14], the main requirements are support of the UML extension mechanism via UML profiling and extensibility of both the CASE tool functionality and the DSL engine via proper API. _MagicDraw_ by No Magic Inc. [61] fully meets these requirements, which was the base factor to select this tool as a platform for the implementation of our M2M transformation method.  
The solution architecture, along with the newly introduced extension enabling NLP-based processing, is presented in Figure 4. To enable the NLP functionality, we extended our _M2M Transformations Core_ API with additional components for natural language processing (_NLP Components_). As in the initial version, this API fully abstracts M2M transformation processing and can be used to develop similar M2M transformation solutions for other compliant CASE tools. NLP components are also abstracted from specific implementations and include the NLP processing component, which performs defined NLP tasks such as named entity recognition, part-of-speech tagging, or relation classification, together with required natural language resources (such as WordNet thesaurus [62]). Currently, only the English language is supported by NLP operators in the engine. At this stage of development, we also simplified the requirements for resolving synonyms and abbreviations by limiting it mainly to the processing of nouns and noun phrases. For practical implementation, Porter stemmer or lemmatization functionality from a package like Apache OpenNLP [63] can be considered due to their compatibility with our implementation platform. Other frameworks, like Stanford Stanza and Spacy, are being experimented with by utilizing their part-of-speech (POS) tagging and named entity recognition (NER) functionality; as discussed further in Section 7.2, the Stanford Stanza tool provided the best results with NLP-related tasks relevant to our research. Interfaces with supporting external semantic services and linguistic resources, like Linked Data, FrameNet [64], or BabelNet [65], are also considered, but research on their actual applicability is still in progress.
# Stability-Aware Geographic Routing in Energy Harvesting Wireless Sensor Networks
## 3 System Model
### Wireless Link Model  
In this paper, the received signal power at distance \(d\) from the transmitter can be expressed by [25; 27]:  
\[P_{r}=\frac{P_{t}G_{t}G_{r}\lambda^{2}}{\left(4\pi\right)^{2}d_{0}^{2}L}\times \left(\frac{d_{o}}{d}\right)^{\beta}\times 10^{0.1X_{\sigma}} \tag{7}\]  
where \(P_{t}\) is the transmitted signal power, \(G_{t}\) and \(G_{r}\) are the antenna gains of the transmitter and the receiver, respectively, \(L(L\geq 1)\) is the system loss, and \(\lambda\) is the wavelength, the mean of \(X_{\sigma}\) is zero, reference distance \(d_{0}\) is 1 m, \(\beta\) is the path loss exponent with fixed value between 2 and 6.  
A packet can be successfully received at the receiver if the received power is greater than or equal to a threshold. The probability of this event happening is denoted as \(p_{S}(d)\). Therefore, we have  
\[p_{S}(d)=P\left(P_{r}\geq P_{th}\right)=\left(\frac{P_{t}G_{t}G_{r}\lambda^{2 }}{\left(4\pi\right)^{2}d_{0}^{2}L}\times\left(\frac{d_{0}}{d}\right)^{\beta} \times 10^{0.1X_{\sigma}}\right)\geq P_{th} \tag{8}\]  
Figure 3 presents \(p_{S}(d)\) as a function of the distances from a transmitter to receivers with different \(\beta\), \(P_{t}=1\) mW, \(f=914\) Mhz, \(P_{th}=10^{-12}\) W, the mean of \(X_{\sigma}\) is zero, standard deviation \(\sigma\) is 12. As we can see in Figure 3, when the receiver is far from the transmitter, the \(p_{S}(d)\) is lower, and _vice versa_. It should also be noticed that \(p_{S}(d)\) is not linearly proportional to the distance \(d\).  
With the given above, the probability \(p_{S}(d)\) that a node at distance \(d\) to the transmitter can successfully receive a packet from the transmitter, the number of receivers that successfully receive a packet within the distance \(R\) of the transmitter is calculated as  
\[N_{tx}(R)=\int_{0}^{R}2\pi d\eta p_{S}(d)dd \tag{9}\]  
Figure 3: Illustration of the relationship between distance and \(p_{S}(d)\) between two nodes with \(\beta=2\), \(\beta=4\), and \(\beta=6\).  
where \(\eta=(N-1)/a^{2}\) is the density of receivers, \(N\) is the number of sensor nodes in the network, and \(a\) is the network size.  
The average number of receivers within distance \(R\) of the transmitter is given by  
\[N_{n}(R)=\eta\pi R^{2} \tag{10}\]  
As an important reliability index of communication among sensor nodes, packet reception rate (PRR), is defined as the percentage of nodes that successfully receive a packet from the transmitter among the receivers. Specifically, the PRR is taken as the ratio between the number of nodes within distance \(R\) of the transmitter that successfully receive packets from the transmitter and the average number of receivers within distance \(R\) of the transmitter; _i.e._,  
\[PRR(R)=\frac{N_{tx}(R)}{N_{n}(R)} \tag{11}\]  
Our proposed SAGREH uses the above \(PRR\) as a metric to select the most stable multi-hop routing path. The details of route selection will be presented in Section 4.2.
## 2 Materials and Methods
### Statistical Analysis  
Statistical analysis was performed utilizing SPSS software (Version 20 IBM Corp, Armonk, NY, USA). The Shapiro-Wilk test of normality was used to test the normality hypothesis of all quantitative variables. Most of the variables were found normally distributed allowing the use of parametric tests. One-Way ANOVA was used for comparing the SBS means of all groups and Bonferroni method was used for pairwise comparison. A General Linear Model (GLM) was applied to study the effect of the independent factors (priming agent and cement type) and their interaction. Chi-squared test was used for fracture mode analysis. Significance level of (\(\alpha\) = 0.05) and two-tailed tests were assumed throughout the analysis for all statistical tests.
## 1 Introduction
monitored the damage of carbon fiber-reinforced polymer composites using the acoustic emission technique and deep learning. They classified the AE Spectrograms from four damage modes--matrix cracking, delamination, debonding, and fiber breakage--which were obtained in the Mel scale. The overall accuracy of their prediction is 97.9%. However, they made a special observation that the fiber breakage and delamination events could be predicted with 100% accuracy [44]. [PERSON] et al. also used AE and CNN for the damage prognostics of fiber-reinforced composite laminates [39]. This paper proposes a deep learning framework based on two types of CNN architecture (alexNet and squeezeNet) and a new model. First, tensile tests were performed, and AE signals were recorded from these tests. The CNN models were used to classify the acoustic emission signals according to the stages of damage (elastic and plastic deformation), the acoustic emission signals of the building configuration of the specimens (Tx, Ty Tz, and T45), and the AE signals with similarities in these specimens. Certain parameters were varied to see their impact on the accuracy of the constructed CNN models--notably, the size of the input data and the activation function and pooling layer. A comparison of three CNNs has been based on the training time and achieved accuracy.
# Security Protection of 3D Models of Oblique Photography by Digital Watermarking and Data Encryption
## 1 Introduction  
With the rapid development of oblique photogrammetry technology based on unmanned aerial vehicle (UAV), 3D models of oblique photography (3 DMOP) are playing an increasingly significant role in the establishment of digital cities [1, 2] and twin watersheds [3], due to their huge advantages such as high accuracy, clear texture information, virtual simulation capability, etc. Despite their significant utility, the security issues they face are becoming increasingly prominent [4]. Compared with other types of spatial data, 3 DMOP have higher positioning accuracies of clearer texture information, in which classified elements (e.g., prisons and barracks) can be accurately located and sensitive information (e.g., car numbers and human portraits) may be exposed thoroughly [5]. When a leakage of this classified or sensitive information occurs, irreparable losses will be caused to data sharing, commercial interests, and even national security. Against this background, it is particularly important to take effective protective measures to prevent data theft and illegal dissemination. Digital watermarking [6] and data encryption [7], two representative technologies of data security protection, have been considered as effective means for security protection of 3 DMOP [8].  
Digital watermarking, an important branch of data hiding, has played an important role in security protection of digital products, such as digital images [9, 10], CAD graphics [11, 12], remote sensing images [13, 14], vector maps [15, 16], etc., and a huge amount ofresearch results have been obtained. However, few works have been carried out focusing on the security protection of 3 DMOP.  
While 3 DMOP have played an important role in various fields, they are facing significant security issues. Traditional security protection methods of 3 DMOP still have limitations, especially in the security protection of secret information in texture images. To make a breakthrough in this field, a novel security protection scheme of 3 DMOP is proposed in this paper, the novelty of which can be summarized as follows:  
1. To enhance the robustness of the proposed watermarking scheme, point clouds are clustered and watermarks are embedded cluster-by-cluster, using slightly adjusted distances between centroids and feature points.
2. Watermarks are embedded into both coordinates of point clouds and texture coordinates of 3 DMOP, and double assurances of security of 3 DMOP can be thus realized.
3. Arnold transformation is carried out on texture images of 3 DMOP, and secret and sensitive information in texture images can be prevented from being exposed.
## 3 Modeling Methodology
### Validation Methods  
To assess and compare the ability of the six models proposed in this study, the following quantitative statistic measures were used: positive predictive value (PPV), negative predictive value (NPV), sensitivity (SST), specificity (SPF), accuracy (ACC), Kappa index, root mean square error (RMSE), and area under the receiver operating characteristic (ROC) curve (AUC). A full description on these methods and their corresponding formula can be found in the literature [89; 90; 91].
## 3 System Description
### Problem Definition  
WSNs' main objective is to balance energy usage and lengthen network longevity. The data transfer procedure from SNs to sinks in WSNs uses the greatest amount of energy. In a WSN deployment, locating the BS far from the SNs leads to reduced network lifespan and significantly higher energy consumption. To address these challenges, the BS must be situated closer to the SNs. Optimizing the BS location can minimize energy consumption, lower operational costs, and reduce network complexity. Given the increasing energy costs and the growing challenges in managing modern IoT, UAV, and WSN systems, efficient energy management is crucial. Our study proposes a novel approach that utilizes a virtual BS points grid to identify the optimal BS location. The grid is designed to minimize the distance parameter, and the best location is dynamically chosen from among the grid points.  
Figure 3: BS virtual points grid.  
Figure 2: The network model.  
As SNs begin to deplete their energy and die, the optimization process is repeated to update the BS location, introducing the concept of BS mobility.  
In computer science, a mobility model calculates the variations in position, speed, and acceleration of nodes over time. Mobility models can be classified as individual or group-based, depending on whether the nodes move independently or in a correlated manner. In WSNs, mobility models are critical for simulating sensor movement, helping to extend network lifetime, improve coverage, and maintain connectivity. By incorporating these principles, our proposed model aims to enhance the efficiency and sustainability of WSN deployments, with potential applications in other network types. In our model, while all the sensor nodes are stationary, it is assumed that the BS can move in the sensor deployment area based on the calculated grid point.  
Furthermore, for large-area WSNs, SNs that are far from BSs will use a lot of energy and eventually die, early; so, to address this issue, the addition of an RN is explored for improving scalability. For large-area WSNs, the data sender and data receiver components have a distance of \(d\). The impact of path loss among the SNs and the base station was examined in the network's large area. The path loss in the network's large area grows considering the distance between the SNs and the BS.
## 5 Evaluation
### Evaluation Result  
#### 5.2.1 Inference Latency Comparison  
Our research goal is to shorten inference latency using edge-distributed inference, so inference latency is the most reflective indicator of our method's performance. Based on the image classification service, we obtained inference latencies of VGG16 and AlexNet using our method and three benchmark methods.  
As shown in Figure 8, compared to the other three benchmark algorithms, we can see that our method EDIJP can obtain a shorter inference latency. For the execution of AlexNet, compared with the local approach, the inference latency of EDIJP has decreased by 56%, and compared to EdgeFlow [48] and CoEdge [37], the inference latency of EDIJP has decreased by 33% and 50%, respectively.