esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Energy-Aware Security Mechanisms for the Internet of Things
## 2 Energy-Efficient IoT
### Energy Consumption Factors in the IoT
#### 2.1.1 Data Centers  
IoT devices, equipped with sensors, cameras, and other components, generate a large amount of data collected from their environments [11], which is then transferred to data centers, becoming a critical source of input for servers, storage devices, and cooling systems. Such a process ultimately poses a huge strain on existing infrastructure, including cloud data centers [12]. Consequently, data centers must constantly undergo upgrades and expansions to meet the increasing demand for servers, storage devices, and cooling systems. Unfortunately, the constant upgrades not only lead to a high energy consumption in data centers but also cause additional environmental and economic costs. To address this issue, rethinking how to achieve higher energy efficiency and mitigate the over-reliance on power resources in data centers becomes indispensable.
## 2 Experimental Method  
### Principle Description  
The grinding wheel chips through the workpiece material as the workpiece passes underneath. Normal and tangential forces are generated between the grinding wheel and the workpiece, as seen in Figure 1. The forces cause the abrasive grains of the grinding wheel to penetrate the workpiece. Rubbing, ploughing, and cutting are the three stages of material removal. The extent of each stage strongly depends on the physical characteristics of the workpiece, its deformation characteristics, and its reactivity to the abrasive grains and the environment [18].  
**Grinding Force.** The normal grinding force acting upon the grits increases due to friction, workpiece shear fracture, and elastic and plastic deformation under grits. The normal grinding force operating on the cutting grits is given by the normal force on the sliding indenter and can be approximated by the following expression [13; 14].  
\[F_{n}=H_{w}A_{X\cdot Z} \tag{1}\]  
\(F_{n}\) is the normal grinding force.  
\(H_{w}\) is the material hardness of the workpiece.  
\(A_{X\cdot Z}\) is the cross-sectional area of the wheel grit on the workpiece surface.  
The dynamometer is used to ensure that grinding forces are correctly identified. The tangential force \(F_{t}\) is the vertical force \(F_{v}\), and the normal force \(F_{n}\) is the horizontal force \(F_{h}\) measured perpendicular to the contact point.  
\[F_{t}=F_{h}\text{cos}\theta-F_{v}\text{sin}\theta \tag{2}\]  
**Chip formation force.** The specific chip formation energy \(U_{ch}\) is divided into the static specific chip formation energy \(U_{s}\) and the dynamic specific chip formation energy \(U_{d}\)[18].  
Figure 1: Precision grinding machine and auxiliary facilities. (**a**) ESG-1020 CNC Grinding Machine. (**b**) The diamond wheel of the steel hub. (**c**) The diamond wheel of the CFRP hub.  
The specific chip formation energy \(U_{ch}\) is calculated using the following equation:  
\[U_{ch}=\frac{F_{t,ch}V_{s}}{V_{w}a_{p}b}=U_{s}+U_{d}. \tag{3}\]  
\(V_{s}\) is the grinding wheel velocity.  
\(V_{w}\) is the workpiece feed velocity.  
\(a_{p}\) is the grinding depth.  
\(b\) is the grinding width.  
The static specific chip formation energy \(U_{s}\) is a constant, which is determined by an experiment based on the element material and the grinding wheel material. On the other hand, the dynamic specific chip formation energy \(U_{d}\) is determined by the element material, grinding wheel material, and grinding processing parameters.
# Titanium Dioxide in Chromogenic Devices: Synthesis, Toxicological Issues, and Fabrication Methods
## 2 Synthetic Routes and Fabrication Techniques for TiO\({}_{2}\)
### Chemical Vapor Deposition (CVD) and Physical Vapor Deposition (PVD)
Thermal evaporation is an approach mostly used in industrial fields to deposit a thin film by the formation and growth of a specific material under High Vacuum (HV) pressure and through different steps [62]. The target material is vaporized by applying high temperature, and successively the vapor is transported to the substrate by vacuum. This last step is characterized by the condensation of the material on a substrate, forming a thin film [63]. Ion plating technique is based on intermittent bombardment of a film by energetic flux ions, which controls the properties of the depositing film. The process takes place inside an inert gas discharge source where the gas pressure ranges from 1 to 0.1 Pa. The final product is a dense film suitable for depositing a hard-thin film on compound materials [64]. Contrary to thermal evaporation, generally the sputtering route does not require substrate heating to vaporize the solid material (Figure 2b) [65]. In fact, the atom escapes from the target materials by an atomic collision caused by argon or nitrogen plasma bombardment, and the substrates are placed in front of the target at an appropriate distance. Figure 2: (**a**) Schematic representation of Chemical Vapor Deposition (CVD) and (**b**) Physical Vapor Deposition (PVD) processes. Finally, the target is vaporized and the vapors are deposited on the substrate, creating a thin atomic layer. The entire procedure takes place in a vacuum chamber in the presence of low pressure plasma (0.67 Pa) [66]. A largely used physical approach is reactive electron-beam evaporation, producing amorphous TiO\({}_{2}\) films (1 nm grain size) if the substrate temperature is kept below 300 \({}^{\circ}\)C during the fabrication process; substrate temperatures higher than 400 \({}^{\circ}\)C allow for the fabrication of anatase TiO\({}_{2}\) thin films. Sputtering has been widely used as to produce TiO\({}_{2}\) films. Reactive dc magnetron sputtering was used by [PERSON] et al. [67]. In their work, several 200 nm thick films were fabricated to fully assess the effects of the deposition parameters (i.e., the argon sputter gas pressure or oxygen to argon ratio) on their EC figures of merit. Sputtering allowed for the fabrication of highly performing films at room temperature. The same group also investigated the effect of the film thickness on EC properties, finding that the best CE (26.3 cm\({}^{2}\)/C) can be observed in 400 nm thick films, fabricated at 25 \({}^{\circ}\)C with an oxygen/argon ratio of 0.04 [68]. Albeit these methods are consistent for obtaining good TiO\({}_{2}\) NPs, without the use of toxic chemicals their use is often unfeasible due to the limitations caused by large energy consumption, the need of expensive instrumentation, and long times required to reach thermal stability.
## 3 Test Results
### Load-Displacement Curves
#### 3.2.3 Discussion of Bending Performance  
Following the comparison, three similarities between the load-displacement curves of basalt fiber-reinforced sea-sand concrete beams and BFRP grid-reinforced beams are found:  
(1) Both kinds of beams' load-displacement curves have two segments, each of which represents a different behavioral stage. At first, the relationship between load and displacement is linear. The grid-reinforced concrete beam cracks at the point of inflection on the load-displacement curve, which causes a large drop in stiffness. Following cracking, the lower concrete component begins to break, and the curve's slope becomes much steeper than it was in the earlier stage.  
(2) The load-displacement curves display a two-stage linear relationship. Before the tensile zone concrete starts to fail, the concrete is in an elastic stage. After the tensile zone concrete reaches its cracking strain, the tensile zone concrete fails, and the tensile forces are transferred to the composite material grid. Composite materials react linearly under stress because of their innate linear elastic characteristics.  
(3) Brittle failure: After reaching their peak, the load-bearing capacity curves of BFRP grid-reinforced concrete beams show an abrupt decline. Grid-reinforced beams with 3 mm and 1 mm thickness exhibit bottom grid bar rupture. In the case of the 5 mm thick grid-reinforced beam, local crushing of the upper concrete occurs, and the bottom grid bars rupture.
## 6 Results and Discussion
### Conventional Boost Converter  
The thermal behavior of different components within the boost converter is provided in Figure 11. It can be seen that the junction temperatures start to rise from the ambient temperature (25 \({}^{\circ}\)C), and after 1 s, they almost reach their constant values in accordance with the fixed output load. It should be noted that the diode chips always have smaller cross-sectional areas and higher thermal resistance; thus, at the same power losses, the diode would have a higher temperature than an IGBT switch.  
Figure 10: IGBT temperature variations under power cycling test.  
Figure 9: Power cycling lifetime curve for the Fuji U-series IGBT module.  
The lowest temperature rise is related to the anti-parallel diode (D1) which has a temperature increase of about 41 \({}^{\circ}\)C. Here, the steady-state junction temperature for the switch is undesirably high while supplying an output power of 25 kW, and its final temperature rises to approximately 110 \({}^{\circ}\)C. Although this converter has a quite simple topology with only three semiconductor devices in its structure, its components' temperature seems not to be entirely compatible with the fuel cell applications.
## 2 Numerical Simulation of Forced Vibration
### Dynamic Grid Technology  
The grid division method of \"rigid moving region + dynamic grid region + static grid region\" is adopted to ensure the high quality of the near wall grid when the sectional model is moving. The rigid moving region and the static grid region consist of structured quadrilateral grids, and the dynamic grid region is composed of unstructured triangular grids, as shown in Figure 3. After independent verification of grid number and time step by [PERSON] et al. [22], the final grid number is determined to be 505,334, and the time step is selected to be 0.0001 s. Local details of the grid around the section are shown in Figure 4. According to the requirements of the SST \(k\)-\(\omega\) turbulence model for the near wall grid, the \(y^{+}\) value is taken as 1 [32]. The height of the first-layer grid near the wall surface is calculated as \(\Delta y\) = \(6\)\(\times\)\(10^{-5}\) m by Equation (5), and the normal growth rate is set as 1.01.  
\[\Delta y=\frac{\mu Y^{+}}{\rho u_{\tau}} \tag{5}\]  
where \(u_{\tau}=\sqrt{\frac{\tau_{\omega}}{\rho}}\) is the wall friction velocity; \(\tau_{\omega}\) is the wall shear stress; \(\frac{\mu}{\rho}\) is the kinematic viscosity coefficient.  
Figure 1: Deck section dimensions and shape (m).  
Figure 2: Schematic diagram of model computing domain.  
The pitching and heaving motions are combined (see Figure 5), and they are simple harmonic vibrations with the same frequency. Point E is the shear center, located 0.393\(D\) from the top of the sectional model, and it is also the center of motion. The lift force \(L(t)\) and the moment \(M(t)\) at the center of plate, defined in Figure 5. The corresponding lift and moment coefficients are defined as:  
\[C_{L}=\frac{L(t)}{0.5\rho U^{2}B} \tag{6}\]  
\[C_{M}=\frac{M(t)}{0.5\rho U^{2}B^{2}} \tag{7}\]  
where \(\rho\) is the air density, \(U\) is the speed of uniform inflow, and \(B\) is the width of deck section.  
Figure 4: Grid of Local details. (**a**) Rigid moving region (**b**) Front edge near wall.  
Figure 5: Schematic diagram of the pitching and heaving combined motion of the sectional model.  
Figure 3: Grid of the whole computational domain.  
Forced vibration is realized by loading UDF, and the grid update of forced vibration is implemented by assigning the motion form to the DEFINE_CG_MOTION macro. Two dynamic grid updating methods, smoothing and remeshing, are combined to ensure the efficiency and quality of grid updating. The motion expression of coupled motion is calculated as:  
\[h(t)=h_{0}\sin(2\pi ft) \tag{8}\]  
\[\alpha(t)=\alpha_{0}\sin(2\pi ft) \tag{9}\]  
where \(h_{0}\) and \(\alpha_{0}\) are the heaving forced amplitude and pitching forced amplitude, respectively; \(f\) is the forced frequency.  
A second-order implicit scheme and a second-order upwind scheme are adopted to solve the pressure and momentum equations, respectively. The decoupling of pressure and velocity is treated by SIMPLEC algorithm.
# Using IoT for Cistern and Water Tank Level Monitoring
## 2 Related Work
### IoT Solutions for Rural and Urban Environments  
In addition to industrial and rural applications, some studies have focused on urban applications. [PERSON] et al. presented a solution that employs commercial water dispenser modules and clamp meters to collect consumption data and automatically adjust system parameters in urban environments such as universities [9]. [PERSON] et al. proposed a water consumption monitoring system for residential tanks using IoT technology, which measures household water consumption and sends the data to a cloud server for real-time analysis [15]. [PERSON] et al. proposed a solution for a remote water management device for agriculture. A Tank Toad automates the task of data collection and remote control, reducing workload and drive time. Livestock producers must check water storage levels and operate pumps to effectively care for livestock [16]. In the paper [17], the authors proposed a monitoring method for detecting critical levels of ozone concentrations. They used an automatic process of analyzing plant electrical signals for ozone critical level detection based on the fundamentals of correlation theory. The authors segmented time series measurements of the electrical activity of plants before, during, and after the stimulation. Finally, other work described an innovative monitoring technology used to detect ground-level ozone pollution by deploying a network of wireless devices connected to a collection of plants and used as biosensors. These devices collect and transmit the electrical activity signals generated within the plants and are used to monitor environmental conditions [18].  
As we have seen in this brief literature review, there are many solutions for water management, with or without the IoT. Works include automatic water level monitoring systems, energy consumption optimization in water systems, water quality monitoring, wireless sensor networks for water monitoring, and IoT solutions for rural and urban environments.  
Table 1 summarizes the main differences of the current proposal compared with other water monitoring solutions.
# C-SAR/02 Satellite Polarimetric Calibration and Validation Based on Active Radar Calibrators
## 3 Experimental Procedures and Methods
### SAR Image Processing Procedure  
After obtaining the experimental data, firstly, the corresponding position on the image was located according to the actual deployment coordinates of the equipment. The magnitude and phase of each ARC and CR were extracted and normalized. Then, the received distortion matrix R and the transmitted distortion matrix T were calculated from the observed scattering matrix M of the ARCs. Finally, the observation matrices of each CR were corrected using the distortion matrices calculated in the previous step to obtain polarization isolation, amplitude imbalance, and phase imbalance metrics.
## 4 Solution Methodology
### Validation Study  
The results obtained from the grid independence analysis were used to validate the simulation against measurement data for the validation study. As shown in Figure 11, the findings confirmed that the chosen grid type, numerical settings, boundary conditions, and initial conditions were appropriate and reliable for the subsequent investigation. This study developed two validation cases based on (1) academic testing and (2) onsite validation testing--shown in Table 2 as Case 1 (Validation). The academic testing served as a benchmark for assessing the accuracy of the numerical model, while the onsite validation ensured that the model performed effectively under real-world conditions. These complementary approaches verified the robustness and suitability of the computational framework for further analysis.
# Review and Evaluation of MAC Protocols for Satellite IoT Systems Using Nanoatellites
## 5 MAC Protocols for Satellite IoT and M2M
### Interference Cancellation-Based Protocols
#### 5.3.3 Coded Slotted Aloha (CSA)  
A protocol inspired by IRSA and CRDSA in which the nodes divide packets before transmission into \(k\) parts of the same length. Each one of these sub-packets is then encoded with an error correction code and sent through the discrete channel [66]. Upon reception, if all the sub-packets from a sender are received with no collisions, the recovery of other packets coming from the same sender can be achieved by applying a maximum-a-posteriori (MAP) decoding scheme. In addition, the receiver also employs the interference cancellation scheme for reception from other senders. The work in [66] identifies the satellite network as a potential application for coded slotted Aloha (CSA).  
The communications performance of the CSA protocol indicates a normalized throughput of \(S\simeq 0.8\) for a channel offered load of \(C\simeq 0.84\). The normalized load supported for a \(PLR=10^{-3}\) is not reported in the literature.  
Similar to CRDSA and other interference cancellation-based protocols, CSA is also energy efficient due to its low \(PLR\) for high channel loads. In terms of complexity, CSA is very similar to CRDSA, but it adds the difficulty of coding and decoding each packet with the model introduced in [66].
# Off-Cloud Anchor Sharing Framework for Multi-User and Multi-Platform Mixed Reality Applications
## 3 Design and Implementation of the System
### Communication Architecture  
The architecture of the proposed system is depicted in Figure 1. As it can be observed, the system consists of the following modules:  
* Network Manager, which uses the Mirror library, is in charge of exchanging messages among the clients and the server, while it is also responsible for keeping all the objects synchronized.
* Network Discovery, which also utilizes the network library, is the subsystem in charge of managing the automatic connection of the users to the local servers.
* Shared Anchor Manager: this module handles all aspects related to the sharing and synchronization of anchors between MR devices.
* Network Transmitter: combined with the Shared Anchor Manager, is used to divide the anchors into smaller segments and to send them through the network.
* Multiplatform Manager, which is responsible for managing the platform-dependent components, enables and disables the components specific to each runtime environment. Specifically, this is implemented using conditional compilation directives (e.g., #if UNITY_WSA, #if UNITY_STANDALONE) and runtime logic, ensuring that only the necessary elements are loaded and active on each platform. This approach allows for a clean separation of platform-dependent resources while maintaining a unified codebase.
* Lastly, the Interaction Manager, which uses MRTK on MR devices, is in charge of managing the different user inputs depending on the platform on which the application is running.  
Furthermore, the system server is responsible for managing all the communications exchanged among the clients, as well as for transmitting the information necessary to keep the position of all the objects in the scene up to date. It should be noted that the different de  
Figure 1: Proposed communication architecture.  
vices can act as servers or clients, so that, if necessary, two or more devices can be connected directly through a local network, without the need for an external Internet connection.
# Military Equipment Entity Extraction Based on Large Language Model
## 2 Related Works
[2018] reformulated the entity extraction task as a Machine Reading Comprehension (MRC) task, addressing both simple and nested entity recognition tasks uniformly, and further improved MRC model performance using dice loss. Compared to machine learning methods, deep learning-based methods significantly improve the ability to automatically learn language features and capture deep semantics, without requiring manual design of complex features. However, traditional deep learning methods have limited contextual understanding capabilities in entity extraction tasks, and their performance in extracting entities from complex texts heavily depends on the quality and scale of domain-specific training data. With the development of Large Language Models (LLMs), significant performance improvements have been achieved across various natural language processing tasks [2018, 2019, 2020]. Compared to traditional deep learning methods, LLMs are pre-trained on large-scale corpora, providing rich contextual understanding and language generation capabilities. They excel in capturing lexical and syntactic information and can be fine-tuned with minimal task-specific data to quickly adapt to specific tasks. However, entity extraction tasks are fundamentally sequence labeling tasks, while LLMs are text generation models, resulting in a mismatch of task objectives. [PERSON] et al. [2019] addressed this gap by using special tokens surrounding entities in LLM prompts to generate tagged sequences, thereby bridging the gap between sequence labeling tasks and generation tasks. To further enhance LLM performance under data scarcity, [PERSON] et al. [2018] prompted LLM to generate a list of potential entities and provided explanations demonstrating their compatibility with provided entity type definitions, thereby improving LLM performance in low-sample entity extraction tasks. While effective within specific domains, this method exhibits poor generalization across multiple domains. [PERSON] et al. [2018] utilized ChatGPT to automatically generate large volumes of data, enabling fine-tuning of the base LLM model to extract deep semantic knowledge and identify entities across diverse domains. The above methods leverage large-scale LLMs with their extensive parameters and rich training data, demonstrating outstanding performance in capturing complex contextual relationships and better generalizing across different domains. However, these models require substantial computational resources and storage space, with slower inference speeds, making them less suitable for military scenarios requiring rapid response. In contrast, small-scale LLMs fine-tuned via supervised learning require fewer computational resources, enabling quick deployment and integration into various applications. Yet, constrained by parameter size, their performance in complex data entity extraction remains limited. Thus, ensuring high performance and efficiency of LLMs in military equipment entity extraction tasks, characterized by complex entity names and diverse terminology, poses a challenging problem.
## 4 Static Network Performance of a TFBN
### Cost Parameters
#### 4.2.1 Node Degree  
The maximum number of links required to connect all surrounding nodes is called the degree of a node. Modern MPC systems have wiring between nodes. As they require large amounts of wiring, it is necessary to keep wiring to a minimum to save costs. Network topology has a pre-defined constant degree, and the higher it is, the higher the cost of a single node is, as the network requires much more connecting wires and static power. Further, considering the required scalability of MPC systems, it would be ideal to have a constant degree of a node because a variable degree will increase the complexity of network scalability, which is costly. Hierarchical networks are specially designed to support the present configuration of MPC system; many research analyses show that hierarchical interconnection network is more preferable than the flat or conventional networks such as torus and mesh.  
It is outlined in Table 1 that the degree of a node of the TFBN is constant at 8, which is higher than that of TESH, mesh, torus, and TTN networks. The high node degree will produce the message traffic congestion vs. packing density trade-off factor (TCPDTF) of the proposed hierarchical interconnection network TFBN as portrayed in Section 5.