esa-sceva/satcom-chunk-collection huggingface dataset:
# Negative Poisson's Ratio Lattice Structure with Chiral and Re-Entrant Properties
## 3 Results and Discussions
### Finite Element Numerical Simulation Result  
Figure 10a,b depict the longitudinal and transverse displacement diagrams acquired through finite element analysis. When the specimen underwent longitudinal tension, it experienced transverse expansion, leading to the occurrence of an NPR effect. The outcomes of this numerical simulation exhibited congruity with the findings of the experimental study.  
Figure 10c displays the outcomes of the static tensile experiments and finite element simulations, with the inclination of the curve indicating the Poisson's ratio. The simulation exhibited a negative Poisson's ratio of \(-0.09\), whereas the experimental average negative Poisson's ratio was \(-0.07\). The results obtained from the experiment and the simulation exhibited a high degree of similarity, thereby providing substantial evidence for the accuracy of the finite element simulation discussed in Section 3.4. It was observed that the strain-strain dependence in the experimental data was non-linear, in contrast to the linear dependence observed in the simulations. The presence of human error, experimental instrument error, and other factors during the experimental process can account for this phenomenon.  
In addition, we conducted an examination of the structure of individual cells while considering the effects of periodic boundary conditions. The lattice structure of an \(8\times 8\) unit cell was analyzed under identical boundary conditions and loads as the original lattice structure. The negative Poisson's ratio in the central region, as depicted in Figure 11a, was determined using the same methodology outlined in Section 2.5. The results of the calculations are presented in Figure 11b. The slope depicted in the figure indicates a negative Poisson's ratio, and our analysis revealed that the negative Poisson's ratios of the  
Figure 10: The cloud picture of y-direction (**a**) and x-direction (**b**), the comparison between the experiment and the simulation (**c**), and the deformation process of the window grill structure (**d**).  
two samples were closely aligned. However, under periodic conditions, the single-cell structure exhibited reduced stress and strain changes when subjected to the same load, which could be attributed to the larger scale of the lattice structure.
# Potentials for Pressure Gain Combustion in Advanced Gas Turbine Cycles
## 3 Results and Discussion
### Intercooled Gas Turbine  
The effect of intercooling on the thermodynamic cycle at a blade temperature constraint of 1100 K is presented in Figure 10 for pressure ratios on the primary axis. Compared to the simple cycle with isobaric combustion (\(\pi_{HPC}~{}=~{}24.78\)), intercooled cycles had overall pressure ratios (OPR) at least twice as high. The intercooled cycle with isobaric combustion shifted most of the compression to the HPC for optimum efficiency. The pressure ratios for the compressors were substantially reduced for the isochoric case. In the combustor, additional compression took place to reach the same OPR as with isobaric combustion. The realistic PGC models required OPR in excess of 57. Both LPC and HPC pressure ratio were increased. Comparing these models, LPC and combustor pressure ratios  
Figure 8: TS-diagrams of the simple cycle for Mix ref and [PERSON]’s PGC model at different blade temperature constraints.  
Figure 9: Relative changes of thermal efficiency for Mix models at different temperature constraints for the simple cycle.  
rose from the Mix ref to [PERSON]'s to [PERSON]'s models. The opposite happened with the HPC pressure ratios for optimum efficiency. Hence, the thermodynamic cycles were again very sensitive to the selected combustor model. In Figure 10, TET is also plotted for the different combustor models. Intercooling allowed for a reduction in TET for isobaric combustion. This was not the case with realistic PGC. The TET for realistic PGC models was similar to a TET in a simple cycle with isobaric combustion.  
Intercooling reduced the SAS mass flow for isobaric combustion from 24% of the engine mass flow to 11%. However, applying PGC required between 21% and 25% cooling air again. Compared to the simple cycle with isobaric combustion plotted on the left-hand side in Figure 11, intercooling combined with realistic PGC maintained the required SAS mass flows. The power requirement of the SAS compressor to overcome the pressure gain in the combustor was in the order of 5%. That was a result that was already found in [19] for the simple cycle with PGC. The intercooler inlet temperature of the hot side was also plotted in Figure 11. The LPC pressure ratios were higher for realistic PGC, leading to higher inlet temperatures into the intercooler. Hence, more specific heat was removed in the given setup with constant intercooler outlet temperature. This explains the efficiency gain of [PERSON]'s model compared to isochoric combustion in Figure 5.  
Figure 11: Intercooler inlet temperature and SAS mass flow and SAS compressor power for the intercooled cycle.  
Figure 10: Pressure ratios and TET for the intercooled cycle. OPR, overall pressure ratio.
# Mobility-Multihoming Duality
## 1 Introduction
### Contributions  
The contributions of this paper are the following:* An analysis of the addressing mechanisms used for mobility and multihoming today. We use a different addressing architecture that better matches today's dynamic, multinetwork connectivity.
* A realisation of an Identifier Locator-based addressing architecture. We implement the Identifier Locator Network Protocol (ILNP) as a superset of IPv6 to support _mobility-multihoming duality_ at the network layer.
* A comprehensive testing and analysis of the performance our approach implemented in Linux. With a testbed using commercial network equipment, we demonstrate operation over IPv6 using standard TCP and UDP APIs via the iperf2 traffic generation tool.
* The demonstration that an end-to-end architecture with a simple control plane can provide mobility-multihoming duality. Our approach works without proxies, middleboxes, tunnels, or address translation, nor modification to existing routing protocols. We use only normal unicast addressing and routing for both mobility and multihoming provision.  
Overall, this presents an approach to harmonised mobility and multihoming for flexible connectivity at the network layer. Therefore, it is applicable to potentially any transport protocol, both current and future.
# Universally Composable Oblivious Transfer with Low Communication
## 6 Discussion  
We modified the codes on SABER's GitHub to simulate the protocol. The CPU is i7-9700f and the memory size is 16 GB. The simulation was repeated 100,000 times and the probability of success was 100%. The average communication cost is 24,576 bits and the average running time is 0.54509 ms. Therefore, the protocol can run efficiently with low communication costs.  
Considering that none of the currently available lattice UC-safe OT protocols are based on the Mod-LWR difficulty problem, this protocol is compared to other OT protocols based on the other lattice difficulty problems such as LWE, RLWE, and so on.  
The metrics used to judge the efficiency of UC-secure OT protocols are the number of rounds, communication cost, and computation cost. For the lattice OT protocols, the communication cost can be calculated from the size of intermediate amount and the computation cost can be calculated by operations on the lattice, such as multiplication and addition. Comparing the OT protocol proposed with other state-of-the-art protocols in the theoretical aspect, the results are shown in Table 6.  
The OT protocol proposed by [PERSON] is under the common reference string (CRS) model, which needs to interact with the ideal function to obtain the CRS before starting to run. The OT protocol proposed by [PERSON] is also under the CRS model, which uses dual-mode encryption to achieve statistical receiver security. The OT protocol proposed by [PERSON] needs rec(.) and dbl(.) operations, which leads to additional computational costs. Compared with the OT protocol proposed in [16; 24], the protocol has advantages for communication and computational costs. Compared with the OT proposed in [22], the protocol reduces the communication cost by reducing the scale and size of the polynomial. In summary, the protocol in this paper has the advantage of high efficiency and low communication.
## 3 Results
### Effect of Operation Conditions on HT Dechlorination Behavior
#### 3.1.2 Effect of NaOH Dosage on HT Dechlorination of PVC  
Figure 2 also presents the effect of the NaOH dosage on the DE. As indicated, the alkalinity was also an important factor affecting the DE of HT dechlorination. To remove the most chlorine from PVC, the optimum NaOH dosage varied with the HT temperature. With the increase of the NaOH dosage, three different trends in the DE were observed in this figure. For PVC dechlorinated at lower temperatures of 180 \({}^{\circ}\)C and 200 \({}^{\circ}\)C, only slight changes were observed with changes in the NaOH dosage, reaching a maximum dechlorination of 13.38% at 200 \({}^{\circ}\)C for 60 min with 5% NaOH. As the temperature rose from 200 \({}^{\circ}\)C to 220 \({}^{\circ}\)C, the DE was significantly increased. The DE of the sample hydrothermally dechlorinated at 220 \({}^{\circ}\)C in the absence of NaOH reached a DE of 16.73% for PVC, which was higher than the maximum DE for the PVC treated at lower temperatures. In addition, the DE increased with the increase of the NaOH dosage, approaching a maximum of 54.62%. When the NaOH dosage reached up to 3%, a slight and negligible increase was observed, indicating that the optimum NaOH dosage was 3% for HT dechlorination of PVC at the condition of 220 \({}^{\circ}\)C and 60 min.  
At a HT temperature of 240 \({}^{\circ}\)C, the DE was first increased and then decreased sharply when the NaOH dosage was increased from 0 to 5%. The maximum DE of 94.33% happened at 1% NaOH at this temperature. When the NaOH dosage was improved from 1% to 3%, the DE sharply decreased from 94.33% to 68.65%. This could be ascribed to the higher viscosity of the reaction system, which resulted in a loss of active sites in the chain [12]. Moreover, the surface poisoning through the formation of metal chloride species obtained from the reaction between the hydrogen chloride and sodium hydroxide might be another reason for the reduction in DE [24]. It is interesting to note that the DEs of PVC hydrothermally dechlorinated at lower NaOH dosages (0% and 1%) were higher than those treated at higher sodium hydroxide dosages (3% and 5%) at a HT temperature of 240 \({}^{\circ}\)C. It clearly indicates that the PVC decomposition in subcritical water was a temperature-dependent process. At a HT temperature of 220 \({}^{\circ}\)C, the NaOH could play as a catalyst and scavenger [24], which promoted the decomposition of PVC. However, as the decomposition rate also depended on the HT temperature, the maximum DE was around 55.5%. At higher temperatures (e.g., 240 \({}^{\circ}\)C), the increase of the sodium hydroxide would hinder the HT dechlorinating of PVC.
## 3 Validation Examples
### Example 2  
For this validation example, slug frequency, which was obtained via numerical simulation, is compared against existing slug frequency correlations published in the literature. The most relevant horizontal pipe correlation is the one reported by [PERSON] and [PERSON] (1993). According to their correlation, slug frequency can be calculated as:  
\[f_{\mathrm{s}}(\mathrm{Hz})=0.0226\left[\frac{U_{SL}}{gD}\left(\frac{19.75}{U_{M }}+U_{M}\right)\right]^{1.2} \tag{16}\]  
Another correlation based on experimental work was developed by [PERSON] and [PERSON] (1993). This correlation for was developed based on 42 mm diameter pipe and its expression is given as:  
\[f_{\mathrm{s}}(\mathrm{Hz})=0.0434\left[\frac{U_{SL}}{U_{M}}\left(\frac{2.02 }{D}+\frac{{U_{M}}^{2}}{gD}\right)\right]^{1.02} \tag{17}\]  
[PERSON] and [PERSON] (1993) developed a correlation to calculate slug frequency based on their experimental work, which was conducted for slug flow in pipes with diameters ranging from 32 mm to 152 mm.  
\[f_{\mathrm{s}}(\mathrm{Hz})=0.0434\left[\frac{U_{SL}}{U_{M}}\left(\frac{2.02} {D}+\frac{{U_{M}}^{2}}{gD}\right)\right]^{1.02} \tag{18}\]  
For validation, the purpose inlet flow conditions used were water superficial velocity, \(U_{SL}=1.1\) m/s, and air superficial velocity \(U_{SG}=1.7\) m/s. Table 3 represents the comparison of slug frequency obtained from numerical simulations and experimental correlations. The numerical results and experimental correlations were in good agreement.
## 3 Results and Discussion
### Quantitative Analysis  
The quantitative analysis was performed in two stages. In the first stage, a preliminary analysis of the basic repair-related data was performed in order to limit the area for further analyses. In the second stage, a detailed evaluation of the bin failure rate was performed on the basis of the determined indicators.
# A Seabed Real-Time Sensing System for In-Situ Long-Term Multi-Parameter Observation Applications
## System Design of SRSS/ILMO
### Seabed Observation Flow of SRSS/ILMO  
The seabed observation flow of SRSS/ILMO includes the placement process, seabed penetration process, seabed observation process, seabed pull up process, and recovery process. The detailed description of each work process is as follows.  
The seabed observation system placement process: The FPGA of the seabed observation system controller and the observation sensors connected to FPGA do not work. The ARM processor works and sleeps periodically. The underwater acoustic communication module is continuously powered, and the seabed observation system communicates with the ship-borne operation monitoring system periodically. The pressure sensor does not work, and the altimeter and azimuth attitude sensor collect data periodically.  
Seabed observation process: The seabed in-situ long-term observation sensors, altimeter, pressure sensor, and azimuth attitude sensor work periodically, and the non-operating state is powered down. The controller of the seabed observation system works in the working mode and the standby mode periodically. The underwater acoustic communication module is continuously powered, and the sea surface relay transmission buoy system, ship-borne remote monitoring system, and land-based remote monitoring system communicate with the seabed observation system periodically.  
Seabed pull up process: The FPGA of the seabed observation system controller and the observation sensors connected to FPGA do not work. The ARM processor works and sleeps periodically. The underwater acoustic communication module is continuously powered, and the seabed observation system communicates with the ship-borne operation monitoring system periodically. The pressure controller and the observation sensors connected to FPGA do not work. The ARM processor works  
Figure 7: Remote monitoring system.  
and sleeps periodically. The underwater acoustic communication module is continuously powered, and the seabed observation system communicates with the ship-borne operation monitoring system periodically. The altimeter, pressure sensor, and azimuth attitude sensor connected with the ARM processor collect data periodically.
## 3 Materials and Methods
### Model Design
#### 3.6.4 CNN-LSTM  
Figure 9 shows the architecture of the method used in creating a predictive model for energy consumption by combining two techniques: 1D-CNN and LSTM [46; 47; 48]. The modeling process consists of three main stages: input, processing, and output. In the input stage, the process begins by using the CNN method, which consists of several conventional layers with 50 neurons each. The input data consist of the fields: T_1, H_1, T-2, T_3, H_3, T_4, H_4, and electricity. The CNN is used to extract features from the input data. Next, the results from the CNN are used as input for the LSTM method, which consists of two layers with 50 neurons each. LSTM is used to understand the sequential patterns of the data in the future period. Each layer uses the ReLU activation function. Furthermore, before generating the output, the flattening process is performed, followed by the dense layer stage with 10 neurons. This dense layer is responsible for processing the previously extracted data representations and generating energy usage predictions. Furthermore, during the model training process, optimization is performed using the Adam algorithm. A summary of the properties of each layer can be seen in Table 6.  
Figure 8: Architecture of CNN.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Layer** & **Properties** \\ \hline
1 st Convolutional & filter = 55, kernel size = 3, activation = ReLU \\
2 nd Convolutional & filter = 55, kernel size = 2, activation = ReLU \\ Flattening & - \\ Dense & unit = 10 \\ \hline \hline \end{tabular}
\end{table}
Table 5: CNN properties of the proposed model.
# Double-Layer Compressive Sensing Based Efficient DOA Estimation in WSAN with Block Data Loss
## 3 Double-Layer CS Framework-Based DOA Estimation
### Improved DOA Estimation by DCS-DDOA  
To avoid the error propagation from signal recovery to DOA estimation in DCS-DOA, we develop a new scheme DCS-DDOA, where a joint frequency and spatial domain sparse representation of the sensor array data is constructed and leveraged. Therefore, we can directly conduct DOA estimation at the FC based on the received data samples without the need for signal recovery.
## 6 Conclusions, Limitations, and Future Work  
### Concluding Remarks  
This study provides a comprehensive review of the evolving landscape of OHS in manufacturing through the lens of advanced digital technologies, particularly DTs, and IM technologies. The findings highlight a significant transformation in safety management, propelled by the integration of immersive technologies, real-time monitoring tools, and data-driven decision-making systems enabled by the Industry 4.0 and 5.0 paradigms.  
DTs offer predictive insights and virtual simulations to proactively identify and mitigate risks. IM technologies enhance this by enabling immersive collaborative spaces for training and safety evaluations without real-world exposure. Together, these technologies foster a paradigm shift from reactive safety management to proactive and predictive strategies that prioritize human-centric design and well-being.  
Moreover, enabling technologies such as IoT, AI, XR, CPS, and COBOTS have been shown to play critical roles in this advanced digital ecosystem, contributing to real-time hazard detection, personalized safety interventions, and seamless human-machine interaction. These innovations support the broader vision of Industry 5.0, which emphasizes sustainability, resilience, and worker empowerment in manufacturing environments.  
However, despite their promise, the implementation of DT and IM technologies faces several barriers, including technical limitations, high costs, data security concerns, and organizational and human-centric resistance. Addressing these challenges is crucial for enabling large-scale adoption and realizing the full potential of smart, immersive safety systems.  
To make these technologies more accessible, especially in places with limited resources, such as SMEs, it is recommended to employ scalable cloud-based DTs and lightweight XR systems. This approach will allow organizations to benefit from digital safety and training tools without the need for expensive equipment requiring large investments in infrastructure, making Industry 5.0 more inclusive.
## 6 Applications of VSNs
### Healthcare  
VSNs are envisioned to be heavily used in healthcare environments. Although hospital scenarios can be handled differently, VSNs provide great potential to ease the development of new services by getting rid of cumbersome wires and simplifying patient care in hospitals and for home care as the World is rapidly graying. The worldwide population of elderly people over age 65 is expected to be more than double from 357 million to 761 million by 2025 [6]. The speed with which this age-structural change is taking place implies an urgent need for solutions that will relieve the mounting pressure on our health-care systems as well as support a better quality of life and quality of care for our aged. Let us consider the following scenario for further clarification: an old citizen who lives alone wears one to a few wearable sensor nodes to measure heartbeat, pulse rate, _etc._ Dozens of sensor nodes are densely installed at home for movement detection. A WSN edge router at home will send the sensed information to a connected healthcare center. Portable base stations with LCDs may be used to check the data at home, as well. The different roles of devices have different duty-cycles, which affect node management. This scenario can be better handled by the concept of sensor network virtualization.
# MOIRA-UNIMORE Bearing Data Set for Independent Cart Systems
## 7 Preprocessing Methodology
### Feature Ranking Methods
#### 7.1.3 Laplacian Score  
The Laplacian score [47] is a feature ranking method designed to evaluate the relevance of individual features based on their ability to preserve the intrinsic geometric structure of the data. Unlike methods that rely on labeled data, the Laplacian score is particularly effective for unsupervised learning, as it identifies features that maintain the locality information in the feature space. This makes it highly suitable for applications such as clustering, dimensionality reduction, and other tasks where preserving the data's manifold structure is critical. Given a data set \(X\in\mathbb{R}^{n\times d}\), where \(n\) is the number of samples and \(d\) is the number of features, the method begins by constructing a similarity graph, which represents the relationships between the data points. For two data points \(x_{i}\) and \(x_{j}\), the similarity \(W_{ij}\) is computed using a Gaussian kernel, defined as  
\[W_{ij}=\begin{cases}e^{-\frac{\left\|x_{i}-x_{j}\right\|^{2}}{t}},&\text{if }x_{i}\in N(x_{j})\\ 0,&\text{otherwise}\end{cases} \tag{5}\]where \(t\) is a scaling parameter, and \(N(x_{i})\) denotes the neighbors of \(x_{i}\). The diagonal degree matrix \(D\) is then calculated as \(D_{ii}=\sum_{j}W_{ij}\), and the graph Laplacian matrix \(L\) is derived as \(L=D-W\). For a given feature \(F_{j}\), the Laplacian score is mathematically expressed as  
\[S_{j}=\frac{\sum_{i,j}(x_{j,i}-\bar{x}_{j})(x_{i,j}-\bar{x}_{j})W_{ij}}{\sum_{i }(x_{j,i}-\bar{x}_{j})^{2}D_{ii}} \tag{6}\]  
where \(x_{j,i}\) is the value of the \(j\)-th feature for the \(i\)-th sample and \(\bar{x}_{j}\) is the mean of the \(j\)-th feature. Features with lower Laplacian scores preserve the locality of the data better and are ranked higher, making them more suitable for tasks such as clustering or dimensionality reduction.