esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Internet of Medical Things  
The IoMT refers to the interconnection of medical devices and healthcare systems through the internet infrastructure to collect, transmit, and analyze health data in real-time real. IoMT combines internet of things (IoT) technologies with medical applications and services, enabling remote patient monitoring, tracking, and diagnosis, and the improvment of healthcare processes. In the context of IoMT, medical devices such as sensors, monitors, and diagnostic devices connect through wireless or wired networks, sending health data to cloud platforms and systems. These data may include information on vital signs,health measurements, medical histories, and other parameters relevant to the diagnosis and treatment of patients. The collected data are processed and analyzed using artificial intelligence, machine learning, and big data analytic techniques to gain clinical insights and make informed medical decisions.  
The IoMT has the potential to revolutionize healthcare by improving the efficiency, quality, and accessibility of healthcare services. It enables the real-time monitoring of patients in hospital and home settings, facilitating the early detection of health problems, disease prevention, and chronic disease management. In addition, the IoMT allows for communication and collaboration between health professionals, facilitating telemedicine, remote consultations, and the secure exchange of medical information. However, the IoMT also poses health data security, privacy, and confidentiality challenges. The protection of patient privacy and safety and data integrity are critical issues that must be addressed in the design and implementation of IoMT solutions. It is necessary to guarantee the protection of sensitive medical data, the encryption of communication, the authentication of devices and users, and compliance with regulations and security standards in the health field.
## 4 Sensors Calibration Tests
### HS Calibration  
To calibrate the HS sensor, two types of cap/aluminum specimen arrangement were adapted. The first set (called reversed) consisted in putting the cavity free space in direct contact with the aluminum sample without using the plug (Figure 9a). This method guaranteed the passage of the confining fluid to the sensor inside the cap. The second set (called normal) was similar to the one used for pore pressure measurement, in which the cavity micro-holes met the longitudinal hole of the aluminum specimen (Figure 9b). Four hydrostatic calibration tests up to 500 MPa of confinement were  
Figure 8: Effect of confining pressure on the cap and sensor: (**a**) water collecting cap displacement for 400 MPa of pressure, applied pressure vs. cap inner displacement with and without counting the tolerance; and (**b**) sensor sensitivity after applying the tolerance, \(p_{c}\) vs. \(\epsilon<0\) in compression.  
conducted on the HS sensor, as described in Table 3. The cavity was filled by the confining fluid (DOZ) before the beginning of each experiment. Three tests were performed without any gauge protection while the gauge of the last one was protected by a protective mastic called _Dow Corning_. This test aimed to study the mastic effect on the overall behavior of the sensor/gauge system. This mastic was used to limit the water conductivity on the gauge wires system since some signal perturbation were detected during pore pressure measurement tests.  
Figure 10 depicts results of calibration tests where the confining pressure is plotted versus the gauge volumetric strain. A quasi-linear increase of the sensor deformation was recorded when the sensor was pressurized. The slope of each curve represents the sensor bulk modulus. The repeatability of the results seemed to be acceptable where a maximum difference of 3% between the theoretical value and the experimental ones was observed. This difference could be due to some mechanical effect during the experiments where the sensor outer side can slightly be in contact with the cap cavity wall and thus to limit its deformation. A mean value of 930 MPa for the bulk modulus could be deduced and was used to evaluate the concrete interstitial pore pressure.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test No.** & **Position** & **Protective Mastic** & **p (MPa)** & **Slope K (MPa)** \\ \hline HS-calib-1 & reversed & No & 500 & 940 \\ HS-calib-2 & reversed & No & 500 & 920 \\ HS-calib-3 & normal & No & 400 & 930 \\ HS-calib-4 & reversed & Yes & 400 & 930 \\ Theoretical & — & — & 500 & 900 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Description of the HS calibration test: sample/cap arrangement, condition, pressure applied and bulk modulus value.  
Figure 9: HS calibration test concept and results: (**a**) normal; and (**b**) reversed set.
## 2 Materials and Methods  
### SLA Resin  
SLA uses a variety of materials with distinct properties, including flexible, transparent, stable and heat-resistant materials [30]. Photosensitive resin is used in the photocuring process for 3D printing. The curing procedure depends on the light wavelength and printing method [31]. Common materials used in SLA are conventional epoxy, acrylate resins, thermoplastic elastomers and water-washable or soluble resin [32]. In this work, water-washable resin from the manufacturer Creality was used. The water-washable resin was a new type formulated explicitly for use in stereolithographic (SLA) 3D printing technology. It is designed to be easily cleaned and postprocessed with water, making it more convenient and efficient than other types of resins that require harsh chemicals or solvents for cleaning. Most of the water-washable resin compositions include inert dyes for light absorption and improved polymerization control. Resins have additional additives like acrylic acid, methacrylic acid, acrylamide, dimethyl-acrylamide, dimethylamino ethyl methacrylate, vinylpyrrolidone, etc. These resins have good solubility, low viscosity, high photosensitivity, shrinkage during polymerization and controlled mechanical properties,and are relatively unaffected by temperature and humidity changes [33]. The physical properties of water-washable resin are given in Table 1.
# Process Development of Low-Loss LPCVD Silicon Nitride Waveguides on 8-Inch Wafer
## 4 Device Characterization and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Waveguide Height \(\times\) Width (\(\upmu\)m)** & **Wafer Size** & **Propagation Loss (dB/cm)** & **Deposition** \\ \hline
[2] & 0.4 \(\times\) 0.7 & 8 & 2.1 & PECVD \\
[12] & 0.4 \(\times\) 1.0 & 8 & 0.62 & LPCVD \\
[21] & 0.7 \(\times\) 1.65 & 4 & 1.0 & LPCVD \\
[31] & 0.6 \(\times\) 1.5 & 8 & 3.75 & PECVD \\
[32] & 0.9 \(\times\) 0.8 & 4 & 0.4 & LPCVD \\
[33] & 0.77 \(\times\) 1.75 & 4 & 0.05 & LPCVD \\
**This work** & **0.4 \(\times\) 1.0** & **8** & **0.157** & **LPCVD** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of propagation losses at 1550 nm for Si\({}_{3}\)N\({}_{4}\) waveguides. Figure 13: The measurement result of the Si\({}_{3}\)N\({}_{4}\) waveguide with a cross-sectional size of 0.4 \(\upmu\)m \(\times\) 1 \(\upmu\)m (**a**) the transmission loss of the cutback structure in the wavelength range of 1490–1580 nm, (**b**) the fitted propagation loss in the wavelength range of 1490–1580 nm, (**c**) the fitting line of bending loss @1550 nm per 90\({}^{\circ}\) bending waveguide for a radius of 50 \(\upmu\)m, (**d**) the fitting line of bending loss @1550 nm per 90\({}^{\circ}\) bending waveguide for a radius of 80 \(\upmu\)m.  
Table 4 summarizes the results of other groups and this work. It is clear that the Si\({}_{3}\)N\({}_{4}\) waveguide with LPCVD has a lower loss than that of PECVD. Compared to other works with LPCVD, this work is state of the art, especially on an 8-inch wafer.
###### Abstract  
The internet of things (IoT) is becoming more indispensable in modern society as the further development and maturity of information technology progresses. However the exponential growth of IoT devices leads to severe energy consumption. As a technology with broad application prospects, simultaneous wireless information and power transfer (SWIPT) enables IoT devices to harvest energy from receiving radio frequency (RF) signals while ensuring information transmission. In this paper, we investigate the transmission rate optimization problem for a dual-hop multi-relay IoT system, where a decode-and-forward (DF) relay supports the SWIPT technique. We jointly optimize the resource including power and subcarrier allocation, to maximize the system transmission rate. The time-sharing strategy and Lagrange dual method are used to solve this optimization problem. Simulation results reveal that the proposed algorithm has a larger transmission rate than other benchmark algorithms when ensuring each relay has no additional energy supply. Specifically, the proposed algorithm improves the information transmission rate by 2.8%, 3.4% and 43% compared with other algorithms in the case of five relays when the source's power is equal to 0.5 W, respectively.  
Keywords:internet of things; SWIPT; orthogonal frequency division multiplexing (OFDM); multi-relay; decode-and-forward +
Footnote †: journal: Journal of the Acoustical Society of America
## 3 Proposed Method
### Embedding Process  
1. Divide the cover image into nonoverlapping blocks, each of which contains \(n\times m\) pixels.
2. Use [PERSON] et al.'s parity assignment to assign a \(k\)-bit parity to each color in palette \(P\).
3. Take one block \(B\) with pixels (\(B_{0},\ldots,B_{n\times m-1}\)) and \(k\) secret data bits \(w\) sequentially.
4. Let \(c_{B_{j}}\) be the color of \(B_{j}\), and \(q_{j}\) be the parity of \(c_{B_{j}}\), \(0\leq j\leq n\times m-1\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(w\) (3 Bits) & \(t_{0}\) & \(t_{1}\) \\ \hline
7 (111) & 0 & 3 \\
6 (110) & 0 & 2 \\
5 (101) & 0 & 1 \\
4 (100) & 0 & 0 \\
3 (011) & 1 & 0 \\
2 (010) & 1 & 1 \\
1 (001) & 1 & 2 \\
0 (000) & 1 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Relation between secret data \(w\) and (\(t_{0},\ t_{1}\)).  
* Calculate \(r\) using the following expression: \[r=\sum\
olimits_{j=0}^{n\times m-1}q_{j}\bmod 2^{k}.\] (12)
* If \(r=w\), then go to Step 10.
* For each pixel \(B_{j}\), parity \(q^{{}^{\prime}}_{j}\) is calculated using the following equation: \[q^{{}^{\prime}}_{j}=\left(q_{j}+w-r+2^{k}\right)\bmod 2^{k},j=0,\dots,n \times m-1.\] (13)
* For each pixel \(B_{j}\), find a new color \(c^{*}_{B_{j}}\) with parity \(q^{{}^{\prime}}_{j}\) according to the following equation: \[c^{*}_{B_{j}}=\underset{c_{i}\in S_{j}^{{}^{\prime}}}{argmin}d_{c_{B_{j}}c_{i }},\] (14) where \(S_{q^{{}^{\prime}}_{j}}\) is the set of all colors with parity \(q^{{}^{\prime}}_{j}\).
* Consider pixel \(B_{\alpha}\) satisfying the following expression, and set the color of \(B_{\alpha}\) to be \(c^{*}_{B_{\alpha}}\). \[\alpha=\underset{j\in[0,\dots,n\times m-1]}{argmin}d_{c_{B_{j}}c_{B_{j}}},\] (15)  
Step 10: Repeat Steps 3 to 9 until all blocks are processed.  
In Step 2, [PERSON] et al.'s parity assignment is used; it makes each color be able to find a closer color in each set with a different parity. In Step 8, for each pixel, we always select the color with the required parity and the minimal embedding distortion to replace the original color. In Step 9, at most one pixel is modified with the minimal embedding distortion. Through these three steps, each block is used to embed secret data, and the embedding quality is also improved.
## 4 Numerical Results and Discussion
### BER Performance over an AWGN Channel  
Figure 3 shows the theoretical and simulation results of the MSSS-I/Q-IM and MSSS systems over an AWGN channel. Signal-noise-ratio (SNR) in the figure represents the chip-energy-to-noise-ratio. The shift step size is \(L+1=101\), and the number of sequences being superimposed is \(J=3,6,8\). In this circumstance, the number of transmitted bits by the two systems within each block is shown in Table 1. As illustrated in the figure, the simulation curves and the theoretical curves are basically fitted, which verifies the correctness of the theoretical BER formula deduced in the previous section. According to Table 1, when \(J=3\), the two systems transmit the same number of bits within a spreading sequence duration, which is 18. As shown in Figure 3, compared with the MSSS system, the MSSS-I/Q-IM system can obtain an SNR gain of about 1 dB at a BER level of \(10^{-5}\). When \(J=6\), the data rate of the proposed system exceeds \(1/3\) of that of the MSSS system, and the BER gradually approaches that of the MSSS system with the increasing of SNR.
## 2 Technical Architecture and Solution
### RAG Integration
#### 2.2.2 Querying Pipeline  
The querying pipeline is a critical component of our system, designed to generate responsive answers to user queries. This pipeline employs RAG and integrates GPT-3.5 and GPT-4o models to provide accurate and relevant information from various sources, including research documents from the OECD library (PDFs, word, txt formats), Press Corner releases accessible from Press Corner, and Web search results, retrieved real-time under user query and in-memory documents (PDFs, word, txt formats), which the user can upload real-time and be embedded.  
The querying pipeline begins with an embedding retriever that searches the vectorized database for the most relevant documents based on the user's query. For all pipelines except  
Figure 1: Diagram of Document indexing and Web search processes.  
Web Search, the retriever identifies the most relevant documents, which are then passed to a prompt node. This prompt node, configured with a predefined template, synthesizes a comprehensive answer using the GPT-3.5 [31] or GPT-4o [32] models. The prompt extracts relevant information from the retrieved documents and generates a concise, contextually accurate answer.  
For web search results, the querying pipeline incorporates additional components to manage the dynamic nature of web data. It starts with a web retriever that performs a comprehensive search to gather relevant web pages using Serper (modular). The retrieved web pages are then sampled and ranked based on diversity and relevance. We used the Lost in the Middle ranker, which further refines this set based on content length and relevance, as presented in Figure 1. Finally, the prompt node generates a synthesized answer from the top-ranked web snippets. The main components of the abovementioned RAG query and pipeline processes are presented in Figure 2.  
In addition, highlighting the modularity of this system, the models are under each preference due to the available modularity; different open-source models or other models can be used, such as Hugging Face, OpenAI, Anthropic Claude, Llama, Azure ChatGPT (OpenAI), Cohere, and SageMaker.  
Figure 2: RAG and Conversational pipelines process diagram.
###### Abstract  
The recent seismic activity in Croatia has inflicted significant damage upon numerous buildings, with masonry structures being particularly affected. Consequently, experimental investigations and structural condition assessments' have garnered increased attention, as they have become integral to the renovation process for such buildings. Additionally, assessing the structural condition prior to seismic events is vital for determining the extent to which earthquakes impact the stiffness of systems, such as masonry structures. This paper presents the results of experimental investigations and numerical analysis conducted on a damaged high school building in Sisak, Croatia. The experimental investigation involved shear testing, flat jack analysis, and operational modal analysis. Utilizing the available drawings and mechanical properties determined experimentally, an initial numerical model was developed. Subsequently, through the iterative process of finite element model updating, the initial numerical model was refined based on the structural dynamic properties. The updated numerical model was then employed to assess the structural condition prior to the earthquake event. This study contributes to the field by providing insights into the post-earthquake estimation of dynamic properties in intact masonry buildings, utilizing a comprehensive approach that combines experimental investigations and finite element model updating. By quantifying the changes in dynamic parameters, such as natural frequencies and mode shapes, the study provides valuable insights into the response characteristics of damaged masonry building. The observed differences in natural frequencies between the damaged and undamaged states are as follows: 9% for the first mode shape, 6% for the second mode shape, and 2% for the third mode shape.  
seismic activity; masonry buildings; structural condition assessment; finite element model up-dating; structural dynamic properties +
Footnote †: journal: Applied Sciences
## 3 Proposed HEMOGWO for SCOS  
This section first introduces the original MOGWO and then proposes an enhanced method, HEMOGWO. Although MOGWO demonstrates strong search capabilities in solving multi-objective optimization problems, it still suffers from several limitations, including poor initial population quality, susceptibility to local optima, and insufficient solution diversity. These issues become more pronounced in practical applications such as industrial software resource composition, which often involve high-dimensional and highly constrained environments. To address these challenges, two key improvement strategies are proposed: (1) an initialization method based on Tent chaotic mapping is employed to enhance the uniformity of the initial population distribution, and (2) a hybrid mechanism combining Levy flight and differential evolution is integrated to improve the algorithm's global exploration and local exploitation capabilities during the later stages of the search, thereby enhancing population diversity and convergence speed.
# An Enhanced Distributed Data Aggregation Method in the Internet of Things
## 5 Performance Evaluation in Practical Tests
### Routing Packets and Loss Packets and Aggregation Evaluation  
In this test, in order to evaluate the performance of the proposed method and investigate its success in the delivery of produced data packets throughout the network, the difference between the network nodes' transmitted packets and root node's received packets postulated as the evaluation criterion. The number of packets being retransmitted because the noise is not calculated, withstands the fact that the connection type of this test considered as user datagram protocol (UDP).  
Table 3 shows the number of packets sent/receive/drop, packet delivery ratio (PDR), and Aggregation rate for all evaluated methods are in two indoor and outdoor environments. RPL protocol shows the packet delivery ratio from indoor (91%) and outdoor (95%) environment in the network perspective. Due to the use of graph-grade constraints and the formation of a balanced graph, the delivery rate has risen to 96% in BD-RPL protocol. Using a single objective function in M-RPL method, the percentage of packet delivery ratio (PDR) reaches to 93% in an indoor environment and 95% in outdoor space. The A-RPL method has performed more efficiently than expected in its predecessor's expectations and reach to 96% of the packets in the network in both indoor and outdoor space, but the proposed method has not yet been able to overtake it. According to the results of the proposed LA-RPL approach, around 98% of packets sent to the network reached in an indoor environment.. On the other hand, the PDR reaches 99%. Thus, the reliability of the packet delivery in the network in the proposed method is far higher than the comparable methods.  
Figure 19: A perspective of nodes distribution in an indoor environment.  
Figure 20: A perspective of nodes distribution in the outdoor environment.  
Based on results in Table 3 noted that the packet aggregation rate in RPL and BD-RPL is zero because both methods have not any aggregation process in their protocol. In M-RPL aggregation rate is 6.78 and 9.83 in the indoor and outdoor environment respectively, but these rates in A-RPL are increased about 11.03 and 13.26 in each experimental environment. Finally in the proposed method (LA-RPL) aggregation rate is 34.11% in the indoor environment, and 37.3% in the outdoor environment. In other words, for each hundred transmitted packets in the network, approximately 34 packets in the indoor environment and 37 packets in the outdoor environment are received by the root node in an aggregated format.
## 1 Introduction
### Challenges and Motivation  
Table 2 shows some evidence that the use of EMSs caused a reduction in energy consumption by about 5-15% [25]. In 2003, a smart home equipped with solar panels and energy-efficient systems in Japan was built, and it was demonstrated that the home could decrease one-third of energy consumption in comparison with a typical home. It was discussed that the annual amount of energy consumption that a zero-utility cost house decreased from 10,750 kWh to 7167 kWh [26]. In 2004, a zero net smart home model was presented in Japan called 'Grand To You', and it offered increases in thermal performances of exterior walls, ceilings, and floors, by 32%, 17%, and 18%, respectively compared to their previous one [26]. In 2015, the results of experimentation on a proposed smart home energy management (SHEM) algorithm in Turkey showed a reduction of 17.5% in peak demand by means of voltage control and a reduction of 38% by means of using both voltage control and shifting in loads of appliances [27]. In 2016, a survey of 156 families in Sweden living in smart homes showed an average of 9.5% reduction in electricity consumption [28]. The case study also proved that smart homes contributed to a reduction of 30% in the amount of heat energy and greenhouse gas emissions [29]. Smart technologies are forecasted to enable energy consumption reduction, especially in commercial and residential buildings, by up to 10% between 2017 and 2040 globally. This would lead to a considerable reduction in energy consumption, which is equivalent to all the energy used in non-OECD countries in 2015 [30].  
\begin{table}
\begin{tabular}{c c} \hline  
It is predicted that globally new power plants will see an investment of USD 270 billion to supply electricity to 1 billion households with interconnected electricity systems by 2040 [30]. Table 2 presents some evidence of the use of smart systems globally and its impact on different aspects of the smart homes in different contexts.