esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
To alleviate the problem of energy consumption in UWSNs, this paper presented an energy-efficient clustering routing algorithm based on the improved ACO algorithm. The contributions of the paper were as follows. Firstly, the improvement of the heuristic information was proposed in the paper based on the consideration of the residual energy of nodes and the distance factor. Secondly, this paper provided the improved adaptive strategy of the evaporation parameter for the pheromone update mechanism, which can be of help to the global search ability and the convergence rate of the algorithm. Thirdly, this paper proposed the ant searching scope. Fourthly, we optimized the CHN selection by  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithms** & **Packet Loss Ratio** \\ \hline ant colony optimization clustering routing (ACOCR) & 12.8\% \\ energy optimization clustering algorithm (EOCA) & 14.9\% \\ cluster-based underwater wireless sensor network (CUWSN) & 15.8\% \\ low-energy adaptive clustering hierarchy based on ant colony (LEACH-ANT) & 17.1\% \\ distributed underwater clustering scheme (DUCS) & 18.8\% \\ low-energy adaptive clustering hierarchy (LEACH) & 20.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The packet loss ratio for the six algorithms.  
Figure 8: The number of received packets versus the number of rounds for the six algorithms.  
considering the residual energy of nodes, the distance from the node to the SN, and the average distance between the node and the other nodes in the cube. Finally, simulation results demonstrated that the proposed ACOCR algorithm outperforms the LEACH, the DUCS, the LEACH-ANT, the CUWSN, and the EOCA in terms of the network lifetime, the energy consumption, and the packet loss ratio. The limitation of the paper is that the multipath effect of underwater channels was not considered. Therefore, we plan to study the multipath effect on the data packet transmission and design cross-layer protocols in the future. Moreover, in this paper, we employed a random method to generate the network node. In order to make the network model closer to the practical situation, we plan to use NS-3 to simulate our algorithm and call the function to generate the nodes as well as set attributes for them.  
Conceptualization, X.X. and H.H.; methodology, X.X.; software, X.X.; validation, X.X. and H.H.; formal analysis, X.X.; investigation, X.X.; resources, X.X.; data curation, X.X.; writing--original draft preparation, X.X.; writing--review and editing, X.X.; visualization, X.X.; supervision, H.H.; funding acquisition, H.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China, grant number 2018 YFC1405904.  
The authors declare no conflict of interest.
## 3 Prototype Description and Implementation
### Prototyping of Software Components
#### 3.4.2 UAV Software Prototyping  
The first software of major importance to consider for the development of the UAV prototype is ArduPilot [46]. ArduPilot is an open-source, firmware-based project that performs the information processing functions required by the vehicle that makes use of them. Additionally, due to the MAVLink protocol [47], the UAV offers two-way telemetry  
Figure 6: Laptop used as CGS during testing.  
in real-time. MAVLink (Micro Air Vehicle Link) is a protocol for communication with small, unmanned vehicles. It is designed as a \"header-only\" message classification library. It can be used to transmit the orientation of the vehicle, its position or speed. In addition to MAVLink, the Project implements the ROS system (Robot Operating System) through MAVROS. ROS is a framework for software development used in robots that provides the functionality of an operating system in a heterogeneous cluster. This system is licensed under BSD (Berkeley Software Distribution), so it can be used openly and freely. MAVROS is defined as an extensible communication node for ROS with a proxy for ground control stations. The ArduPilot software, on the other hand, works directly on a Raspberry Pi 2, 3 or 4 with Navio2 (the autopilot program can work directly on the Raspberry Pi as well). In this Project, a Raspberry Pi 3 Model B was used, which used Raspbian as the Operating System to work with ArduPilot. This software is not only free, but also has a large amount of teaching material, as well as ongoing projects. Its appearance once it was launched has been displayed in Figure 7. Note that neither the UAV nor the Raspberry Pi have any screen to display information; the latter must be connected to a monitor for data visualization.  
The second software of major importance used is OpenTX, the open-source firmware used by radio transmitters and implemented in the radio control station. To use it, it is necessary to install it in the station via a microSD card. One of the main advantages of this firmware is that the controls, switches and potentiometers of the transmitter are highly configurable by means of the \"LUA scripts\" which are text files not part of the firmware, yet they allow the customization of the mentioned components.  
The third and last major piece of software that will be described in this section is the TBS (the chosen Radio Controller transmitter module, as described before) Agent. It is used for the configuration and update of the radio control transmitter module, as well as the nano receiver integrated in the UAV. It is only necessary to connect the transmitter to the computer through the Universal Serial Bus interface (USB) and follow the steps indicated by the TBS Agent program.
## 4 Extended Hybrid Method
### Improved Strategy of Symmetric Rigid-Body Displacements  
In Figure 7, it is shown that the convergences in Test 2 after load step 21 and in Test 3 after load step 30 are not reproduced by the strategy of symmetric rigid-body displacements. Inaccuracies occur when the structures reach convergence-related serviceability class C. Hence, an improved strategy is needed. Additional symmetric rigid-body displacements are added such that the measured convergences are reproduced. The improved strategy of rigid-body displacements is described in the following. At first, \(\Delta\theta_{j=1,2,\ldots,6}^{rd}\) are calculated by means of the strategy of symmetric rigid-body displacements. Then, additional relative rotations are added to \(\Delta\theta_{j=1,2,\ldots,6}^{rd}\), resulting in  
\[\Delta\theta_{j}^{mod} = \Delta\theta_{j}^{rd}+\alpha\mathbf{e}_{sym}\,,\quad j=1,2,\ldots,6\,, \tag{51}\]  
where \(\mathbf{e}_{sym}\) denotes the orthonormalized vector of the relative rotations in the symmetric mode of rigid-body displacements. The vertical and horizontal convergences from the model simulation are expressed as  
\[C_{v}^{mod} = C_{v}^{rd}+\alpha C_{v}^{e}\,, \tag{52}\] \[C_{h}^{mod} = C_{h}^{rd}+\alpha C_{h}^{e}\,, \tag{53}\]  
because they are linear functions of the relative rotations in case of the mode of symmetric rigid-body displacements. In Equations (52) and (53), \(C_{v}^{e}\) and \(C_{h}^{e}\) are the vertical and horizontal convergences resulting from \(\mathbf{e}_{sym}\). \(C_{v}^{e}\) and \(C_{h}^{e}\) are calculated by inserting Equation (39) into the transfer relations (2). They read as  
\[C_{v}^{e} = -0.3466R\,, \tag{54}\] \[C_{h}^{e} = 0.3437R\,. \tag{55}\]  
Hence, \(C_{v}^{mod}\) and \(C_{h}^{mod}\) are the functions of the unknown \(\alpha\). To obtain the smallest error index, \(\alpha\) is calculated by the condition  
\[\frac{d\sqrt{\frac{1}{2}\left[(C_{v}^{exp}-C_{v}^{mod})^{2}+(C_{h }^{exp}-C_{h}^{mod})^{2}\right]}}{d\alpha} = 0\,. \tag{56}\]  
It is obtained as  
\[\alpha = \frac{C_{v}^{e}\left(C_{v}^{exp}-C_{v}^{rd}\right)+C_{h}^{e} \left(C_{h}^{exp}-C_{h}^{rd}\right)}{C_{v}^{e2}+C_{h}^{e2}} \tag{57}\]
## 5 Evaluation of the Auralization System  
A series of experiments to assess the accuracy of the sound field reconstruction given by the proposed method is presented. For this, the real-time implementation in Max was used to record the synthesized acoustic field at different positions of the enclosure. Two types of analysis were performed: monaural (based on omnidirectional signals) and a spatial evaluation based on first order B-format signals. The procedure consisted of recording the output signals from the real-time auralization system implemented in Max and comparing them to numerical references from the FE model.
# Field Study on the Waterstop of the Rodin Jet Pile Method in a Water-Rich Sandy Gravel Stratum
## 5 Test Results and Analysis
### Monitoring Data Analysis
#### 5.2.1 Horizontal Displacement of the Deep Soil  
The horizontal displacements of the strata at different distances were monitored during test pile construction. The monitoring analysis was as follows:  
1. The three monitoring points exhibited the same trend: The maximum horizontal displacement of the deep soil occurred from -12 to -13 m (the fine sand and silty clay interface), and the cumulative maximum change value was 16.7 mm (see Figure 9a). The reason may have been that the shear strength of the interface was low and easily destroyed under the action of the ultra-high-pressure shear, resulting in a large change in the horizontal displacement of the formation.
2. The change values of the free ends of the three monitoring holes were small, and the horizontal displacement of the monitoring holes remained almost unchanged as the monitoring holes entered the gravel stratum. The reason may have been that the gravel stratum had large porosity, and under the action of the ultra-high pressure, the slurry and water replaced the fine particles in the pores of the gravel, and the coarse particles in the gravel were not disturbed; hence, the horizontal displacement perturbation was very small.
3. Measurement point A could not be monitored due to the equipment occupancy during construction of JP2. From the monitoring curve analysis of measurement points B and C, the cumulative change trend of each measurement point under the construction of JP1, JP2 and JP3 was consistent, but the cumulative growth change value was very small. For example, the horizontal displacement of measurement point B reached 8.9 mm at a position of -13 m during construction of JP1, but the cumulative change values were 9.8 mm and 10.8 mm during the construction of JP2 and JP3, respectively, and the variation was very small (see Figure 9b); the behavior at measurement point C was similar (see Figure 9c). The reason was that JP1 was located between measurement point B and JP2 and JP3. After solidification of JP1, measurement point B was isolated. Therefore, the construction of JP2 and JP3 had little effect on the horizontal displacement of measurement point B. During the construction of JP2, the cumulative value of the horizontal displacement of measurement point C increased greatly, but during the construction of JP3, the cumulative increase in the horizontal displacement was very small. The reason was that JP2 was closest to measurement point C and was located between measurement point C and JP3. Therefore,  
Figure 8: Variation curves and occlusion distributions of the test piles in the different strata.  
the construction process of JP2 would have the largest influence on the horizontal displacement of measurement point C, and the solidification of JP2 occurred; measurement point C was isolated. The construction of JP3 had little effect on the horizontal displacement of measurement point C.
## 5 Results  
This section presents and analyses the results of Experiments 2 and 3. Due to the nature of the results having a small sample size, any statistics derived from the data are susceptible to being influenced by extreme values within the data sets. From our collected measurements within Experiment 2, it can be seen that, during experimentation, there is a low point of data before the attack commences and, afterwards, including such data would heavily skew the accuracy of our results. This also applies to data within Experiment 3 as the small sample size will be susceptible to the same skewing. Steps were taken to consider this, most notably the trimming of the data set which is an important consideration from a statistical viewpoint to ensure that inferences from data are as accurate as possible. For comparison, we define the following attack levels depending on the percentage of the power draw increase: level-1 20%, level-2 40%, level-3 60%, level-4 80%, and level-5 100% or more.  
In summary, the results of Experiment 2 show that DoS attacks are incredibly taxing on small, embedded-level systems. We observe a power draw increase for every attack and sometimes the attack even rendered the nodes non-responding. The most efficient system during the attack was Buildroot with only one attack reporting as a level-1 severity attack. This is a similar outcome to [48] where a similar concept of power draw analysis per timing interval was utilised, with the smoothing of the resulting data (normalisation). In particular, they utilised Arduino UNO devices with Dht22 sensors to investigate the potential decrease of power consumption if paired with machine learning algorithms. However, the resulting experimentation suggested that the more computational stress was placed on small-bandwidth nodes the higher their power consumption was, which was very similar to the outcome of this experiment.  
Similarly, Experiment 3 shows that RPL-based attacks are also a significant danger to WSNs with most attacks increasing the overall power usage by more than 100%. Experiment 3 also focused on the up-scaling of networks to see if an increase in power draw was visible. This was proven to be true to a certain extent, with an average increase of power draw being larger in 13 sensor networks compared to 10 sensor networks. The data suggest that there is a certain ratio of sensors to malicious nodes where the most efficient attacks are possible; using data from the study, this ratio is likely 13 sensor nodes to 1 malicious node. A paper that analysed a similar conceptual model to the one utilised within Experiment 3 from an endpoint security perspective was [49], which revolved around different propositions to DDoS attack detection and recovery mechanisms. The paper analysed multiple propositions and found that the suggested FBDR method of DDoS attack mitigation outclassed other examples currently in use. The study in question also utilised the method of plotting time and power consumption to represent power draw over a set time period, whilst performing a set task. This closely relates to the methodology used within Experiment 3.
# Advances in Emerging Memory Technologies: From Data Storage to Artificial Intelligence
## 2 Market and Trends of Memory Technologies
### General Context and Evolution of Nonvolatile Memories
#### 2.2.2 Exponential Increase in Required Energy  
This data deluge leads to greater energy consumption. It is forecasted that in 2025, data centers' consumption will represent 5% of the globe's energy production [29]. The International Energy Agency [30], \"which aims to promote energy efficiency as the key to ensuring safe, reliable, affordable, and sustainable energy systems, provides technical analysis and policy guidance to its members and other governments concerning energy using equipment and systems\". In particular, in the frame of the Technology Collaboration Program (TCP), it delivers detailed reports on device usage and energy consumption in the Electronic Devices and Networks Annex (EDNA).  
EDNA studies analyze energy consumption due to devices becoming connected to the network, and identify three main areas: upstream energy use of the communications, data networks, and data centers; network standby energy use of the edge equipment; and network active energy use of the edge and LAN equipment. Then, each device (TV, smart phone, tablet, etc.) has two energy consumption contributions, one coming from the device itself and one from the upstream network and data centers. Energy use will increase significantly from 75 TWh in 2010 to 220 TWh in 2030 for LAN devices. The largest increase in device energy use is due to machine-to-machine devices. The TV category exhibits the second largest increase in network-connected energy use at the device level, essentially due to video on demand. Smartphones, one of the largest contributors, use more upstream energy in comparison with their device energy [28].
## 3 Security and Privacy-Preserving Solutions for IoE
### Integration of Different Security Layers
#### 3.5.1 Security Layer Combinations According to IoE Use Cases  
Table 15 presents selected examples where these technologies are used together across various application domains, showing which security layers target which problems in each scenario and which technological tools support them. This table serves as a structural foundation that justifies the integrated layered security architecture proposed in the subsequent sections of this study.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Real-World Problem** & **Sector** & **Risk/Impact** & **Suitable Technology Stack** & **References** \\ \hline Unauthorized access to EHRs in hospital networks & Healthcare & Data breach, misdiagnosis & Blockchain + ABAC + AI & [49; 69; 88; 127; 128; 129] \\ \hline Fake IoE devices injecting false data in smart homes & Smart Home & Security breach, manipulation & IoE + Blockchain + DID & [74; 84; 85] \\ \hline Counterfeit medicines in pharmaceutical supply chains & Supply Chain & Health risk, loss of trust & Blockchain + RFID + Traceability & [9; 48; 97] \\ \hline Sensitive data leakage due to cloud-only processing & Healthcare/IoT & Privacy loss, regulatory violation & Edge AI + FL + Differential Privacy & [23; 87; 133; 134] \\ \hline Delayed anomaly detection in traffic systems & Smart Cities & Accidents, congestion & Edge AI + IoE + Real-time ML & [31; 59; 136; 135; 137] \\ \hline Incompatibility in data sharing across cross-domain systems & Healthcare & Fragmented data, incomplete medical records & Interoperable Blockchain + Semantic AI & [82; 83; 84; 138; 82; 83; 139] \\ \hline Vulnerability of digital signatures to quantum attacks in long-term records & Legal/Medical & Future forgery risk & Quantum-Resilient Signatures (Lattice, Hash-Based) & [33; 35; 54; 122; 127; 128] \\ \hline Impersonation attacks in IoE infrastructure & Critical Infrastructure & Unauthorized command execution & Blockchain + AI + IoE Trust & [7; 20; 130; 139] \\ \hline Data tampering in emergency disaster response coordination & Disaster Management & Wrong routing, loss of aid & Blockchain + Edge + Secure Access Control & [86; 97; 102; 140; 141] \\ \hline \hline \end{tabular}
\end{table}
Table 14: Mapping of real-world problems to suitable enabling technologies for effective and sustainable solutions.
## Materials and Methods
### Data-Driven Free-Form Modelling
#### 2.3.3 Phase 3: Generation of the Mesh Model  
The original point cloud, the \"dummy\" point cloud, and the point cloud of the contour line 2 were merged (CloudCompare: Merge--Figure 11a).  
In this case, the contour line 2 was used as a connection between roofs and facades. In general, the use of a connector is not mandatory and must be evaluated according to the case study.  
The resulting point cloud was imported into Geomagic Design X. A \"Cluster Filter\" was applied to the point cloud in order to eliminate initial noise; for this reason, a value of 20 was set in \"Max vertex count per cluster\" (Geomagic Design X: Remove Noise). Subsequently, the triangular mesh model (tri-mesh) was generated by means of surface reconstruction (Geomagic Design X: triangulate) and after several tests, suitable values for the process of \"HD mesh model\", \"High definition filter\", \"Noise reduction level\", \"Extend contours to fill holes\", and \"Shape of Filled area\" were used.  
The model generated in this way showed some imperfections; therefore, to improve its quality, the model was imported into Geomagic Wrap and corrected by removing non-manifold edges, self-intersections, high-fold edges, spikes, small components, small tunnels, small holes (Geomagic Wrap: Mesh Doctor), larger holes (Geomagic Wrap: Fill All), and smoothing the surface (Geomagic Wrap: Quicksmooth). The correct tri-mesh model is shown in Figure 11b.  
Subsequently, the model was textured in Meshlab (Parameterization + texturing from registered raster) using the nadir and oblique images (Figure 11c).  
Finally, the tri-mesh model was automatically simplified into a quadrangular mesh (quad-mesh) model with Instant Meshes; in this way, a lighter and more manageable 3D model for possible further processing was obtained (Figure 11d).  
Figure 11: Final models created: merged point cloud (**a**), reconstructed tri-mesh model (**b**), tri-mesh model textured (**c**), quad-mesh model created in Instant Meshes (**d**).
# A New Mobility Model for Multi-UAVs Reconnaissance Based on Partitioned Zone
## 3 Proposed Model  
### UAV Model  
In this paper, we assume a fixed-wing aircraft, which is more efficient than rotary-wing aircraft because it searches for a wide area. We assume the simple UAV model used in [9], as shown in Table 1. For realistic motion of fixed-wing UAV's turn, we set rotation radius of 500 m. The UAVs can communicate with others in the range of 8000 m. Let us note that we use _area_ for the whole scanning area, _zone_ for the unit area of managing waypoints, and _unit region_ for the unit area of scanning count throughout this paper.  
In this paper, we do not consider packet loss in communicating with each other. The UAV is equipped with a camera which can scan a row of 2000 m and a column of 1000 m. The speed of a UAV is fixed at 150 km/h. Lastly, we assume there are no collisions among UAVs by changing altitude.