esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Antennas and Antenna Related Technologies for Future Internet
### Antennas for Distributed Solutions Based on Network Structure  
#### 4.4.1 Antennas in Cell-Free Communications  
Cell-free communications is an emerging wireless technology where multiple distributed access points (APs) collaborate to serve all UEs in a given area without relying on cell boundaries. This technology aims to improve network performance by leveraging beamforming and interference management. Key advantages include improved coverage [295], as multiple APs are distributed across the area and serve all UEs simultaneously, significantly reducing dead zones and weak coverage areas. A better user experience is also achieved, since unlike traditional cellular systems where performance degrades at cell edges, cell-free systems ensure consistent data rates across the entire coverage area. Robustness to user mobility is another key benefit, as the absence of fixed cell boundaries minimizes handover procedures, reducing latency and service interruptions for moving users.  
Major drawbacks include complexity in signal processing; coordinating multiple APs for coherent transmission requires sophisticated algorithms and significant computational resources. Deployment costs are another key challenge, as cell-free architectures often require more APs and additional infrastructure and incur higher operational costs than traditional cellular systems [296; 297]. The coordination and communication between various APs in the infrastructure of cell-free communication systems also present significant challenges.  
The merits of cell-free communications are supported in numerous publications [295; 298; 299]. An example of a radio stripe system design is shown in Figure 18. Each radio stripe sends and receives data to or from one or multiple CPUs through a shared bus.  
Figure 18: Efficient radio stripe system [295].  
In [300; 301], a cost-efficient radio stripe system architecture is presented. In such a system, the antennas and the associated antenna processing units (APUs) are serially located inside the same cable, which also provides synchronization, data transfer, and power supply. This approach significantly improves performance by minimizing signal degradation caused by environmental obstacles. In [302], the cell-free concept is extended by integrating LEO satellites with terrestrial antenna systems. This hybrid model enables seamless connectivity in remote and underserved regions. The combination of satellite and terrestrial antennas enhances the reliability and scalability of cell-free networks. Radio stripe antennas are utilized for wireless energy transfer in [303]. This solution provides improved indoor coverage and supports energy-efficient designs, especially for IoT and smart building applications.
## 4 Conclusions
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Cars** & **Window** & **Wall** & **Building** & **Tank** \\ \hline \multirow{4}{*}{PARI} & SSIM = 0.9665 & SSIM = 0.9834 & SSIM = 0.9616 & SSIM = 0.9223 & SSIM = 0.9788 \\  & PSNR = 31.4232 & PSNR = 41.2151 & PSNR = 31.9594 & PSNR = 27.5346 & PSNR = 41.9182 \\  & RMSE = 0.0268 & RMSE = 0.0087 & RMSE = 0.0252 & RMSE = 0.0420 & RMSE = 0.0080 \\ \hline \multirow{4}{*}{NP} & SSIM = 0.9740 & SSIM = 0.9848 & SSIM = 0.9665 & SSIM = 0.9477 & SSIM = 0.9834 \\  & PSNR = 32.7683 & PSNR = 42.4306 & PSNR = 33.1721 & PSNR = 29.4241 & PSNR = 43.2909 \\  & RMSE = 0.0230 & RMSE = 0.0076 & RMSE = 0.0220 & RMSE = 0.0338 & RMSE = 0.0069 \\ \hline \multirow{4}{*}{PCDP} & SSIM = 0.9752 & SSIM = 0.9859 & SSIM = 0.9696 & SSIM = 0.9484 & SSIM = 0.9848 \\  & PSNR = 32.8784 & PSNR = 42.6716 & PSNR = 33.6649 & PSNR = 29.4233 & PSNR = 43.6527 \\  & RMSE = 0.0227 & RMSE = 0.0074 & RMSE = 0.0207 & RMSE = 0.0338 & RMSE = 0.0066 \\ \hline \multirow{4}{*}{MP} & SSIM = 0.9725 & SSIM = 0.9852 & SSIM = 0.9675 & SSIM = 0.9335 & SSIM = 0.9818 \\  & PSNR = 32.2656 & PSNR = 41.3038 & PSNR = 32.7580 & PSNR = 27.6061 & PSNR = 42.0923 \\  & RMSE = 0.0244 & RMSE = 0.0086 & RMSE = 0.0230 & RMSE = 0.0417 & RMSE = 0.0079 \\ \hline \multirow{4}{*}{This Work} & SSIM = 0.9845 & SSIM = 0.9892 & SSIM = 0.9810 & SSIM = 0.9757 & SSIM = 0.9918 \\  & PSNR = 34.7240 & PSNR = 46.0793 & PSNR = 37.4299 & PSNR = 33.1091 & PSNR = 46.8903 \\ \cline{1-1}  & RMSE = 0.0184 & RMSE = 0.0050 & RMSE = 0.0134 & RMSE = 0.0221 & RMSE = 0.0045 \\ \hline \hline \end{tabular}
# The 3D Product Model Research Evolution and Future Trends: A Systematic Literature Review
## 4 Discussion
### The 3D Product Model Research Evolution
#### 4.1.6 2010s  
From the 2000s to the 2010s, the research on 3D product models continued to accelerate. Key focus areas of that research included enhancing the ability of the 3D product model data to be processed by computers, the need to catalogue 3D product model parts in a library for reference-and-reuse versus recreation, and a larger focus on augmented reality applications. To enhance 3D product model data processing, compression algorithms were developed, mesh segmentation of the 3D model was utilized, and programs were developed to convert 3D product model file formats to standardized formats [62; 207; 285]. Model-based definition structures and lightweight 3D models were also developed to enhance data processing [94; 95; 233; 234]. As more 3D models are developed, the need began to arise for a library of 3D product models that could be referenced and reused rather than recreated. Research on ways to achieve the reuse of existing 3D product models from 3D product model libraries included assembly-based modeling, 3D CAD assembly model matching, and a significant subassemblies approach [54; 137; 290; 138]. In the 2010s, the application of augmented reality also spread across and intertwined with other research categories of the 3D product model, including design for assembly, assembly planning, and disassembly [55; 186; 268].
# A Self-Powered UHF Passive Tag for Biomedical Temperature Monitoring
## 2 Architecture of Proposed Self-Powered Passive Tag
### High Frequency (HF) Section
#### 2.3.2 Matching Network  
The used matching network is shown in Figure 9. As the matching network is connected with the antenna through an off-chip bonding wire and an on-chip pad, their inductance \(L_{bond}\) and capacitance \(C_{pad}\) must be included and used for matching purposes. The matching network includes an on-chip inductor \(L\) and a parasitic capacitance \(C_{par}\) that models the capacitance provided by the ASK backscatter modulator and the interconnection lines.  
Figure 9: Schematic circuit of the HF section.  
To obtain the maximum power transfer condition between the antenna and the differential rectifier, Equation (35) must be fulfilled. After solving Equation (35) for the antenna impedance \(Z_{a}\), the real \(R_{a}\) and imaginary \(X_{a}\) part of the antenna can be computed in Equation (36).  
\[\frac{\partial P(Z_{REC})}{\partial Z_{REC}}=\frac{\partial\Big{(} \frac{V_{RF}^{2}}{Z_{REC}}\Big{)}}{\partial Z_{REC}}=0 \tag{35}\]  
\[Z_{a} = R_{a}+jX_{a}\] \[R_{a} =\frac{3Z_{REC}(1+\omega^{2}C_{pad}L)}{(3Z_{REC})^{2}+[\omega(C_{ pad}+C_{par})]^{2}}\] \[X_{a} =\frac{\omega\Big{\{}(C_{pad}+C_{par})\big{[}1-\omega^{2}[(C_{pad }+C_{par})L_{bond}+C_{par}L]\big{]}-(3Z_{REC})^{2}(L+L_{bond})\Big{\}}}{(3Z_{ REC})^{2}+[\omega(C_{pad}+C_{par})]^{2}} \tag{36}\]
## 5 Numerical and Simulation Results  
In this section, we present numerical evaluations of the derived performance metrics and validate their accuracy through comparison with Monte Carlo simulation results. Simulations are conducted for both the single-relay SWIPT AF system described in Section 2 and the multi-relay scenario presented in Section 4. All simulations are carried out using MATLAB software (version R2024a) under the following assumptions. For the single-relay system, BPSK modulation (i.e., \(M=2\)) is assumed, and all wireless channels \(\{h_{\text{sd}},h_{\text{sr}},h_{\text{rd}}\}\) are modeled as mutually independent Rayleigh fading. Using MATLAB, the channel gains \(\{h_{\text{sd}},h_{\text{sr}},h_{\text{rd}}\}\) are independently generated \(10^{8}\) times. AWGN with variance \(\sigma^{2}\) is assumed at all receivers. Using MATLAB, the noise terms \(\{n_{\text{sd}},n_{\text{sr}},n_{c},n_{\text{rd}}\}\) are also independently generated \(10^{8}\) times. These assumptions are similarly extended to the multi-relay case. In addition, the average SNR is set to \(\text{SNR}=\bar{\gamma}_{\text{sd}}\). In all figures presented in this section, the blue and red lines represent the derived numerical results for the indirect and combined links, respectively.
## 1 Introduction
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Characteristics** & **Body-Worn Sensor Networks** & **Implantable Body Sensor Networks** \\ \hline Communication range & Up to 50 m & Up to 3 m \\ \hline Number of nodes & Up to 10 nodes & More than 10 nodes \\ \hline Node functionality & Non-critical, Entertainment, Relays & Life-Critical \\ \hline Sensor accuracy & Not very accurate & Very accurate and reliable \\ \hline Size of the node & Wearable size, but not limited in dimensions and bio-compatibility & Should be implantable and very small compared to BSN \\ \hline Environment & Outside the body, Electromagnetic properties being influenced by the environment & Inside the body, either shallow or deep implant. Electromagnetic properties vary significantly \\ \hline Event detection & Events are not life-critical, and detection algorithm can be offline and complex & Events are life-critical and detection algorithm should be simple and online \\ \hline Heterogeneity & Medium in terms of devices, sensing and actuation capabilities & Medium in terms of devices, sensing and actuation capabilities \\ \hline Security & Security is required but not critical & Security is crucial since the life-critical operations can be fatal is security is threatened \\ \hline Energy efficiency & Can be recharged. Hence energy constraints can be recharged & Replacement of the battery is not an option and lifetime of the nodes is in the order of months to years. It has to be highly energy-efficient \\ \hline Energy availability & Energy is abundant & Energy is scarce and needs to be used with efficiency \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Differences between the requirements of BSN and IBSN. Inspired from [7].
## 5 Conclusions  
In this paper, a general NFLE transformation, FKNFLE, for HSI classification has been proposed. Kernelization and fuzzification in NFLE were both considered in order to extract non-linear and non-Euclidean structures. Three state-of-the-art algorithms, NFL, NRS and NRS-LFDA, were compared with the proposed FKNFLE. Three land-cover benchmarks, IPS-10, Pavia University, and Pavia City Center, were tested for performance evaluation. From the experimental results, algorithm FKNFLE outperformed the other algorithms. More specifically, using the 1-NN classifier, the rates of FKNFLE were higher than those of NFLE to the value of 5.75%, 3.01%, and 7.51% for datasets IPS-10, Pavia City Center, and Pavia University, respectively. Though FKNFLE had high classification rates using the features on a single pixel, there was some speckle-like noise in the image segmentation results. In the future, the features of spatial neighbors will be adopted for better classification and segmentation.
## 2 Overview of Measurement Setup and Used Data-Extraction Method
### Case 2: Indoor Corridor Environment at 28 GHz and 38 GHz  
In [7], a campaign is conducted for channel measurements in indoor corridor environment for mmWave frequencies at 28 GHz, and 38 GHz. The experiment is conducted in Malaysia on the second floor of the Menara Tun Razak Building of Universiti Teknologi Malaysia (UTM). The corridor size where measurements are taken is 67 m \(\times\) 1.80 m. The height of roof is 3 m. The corridor has glass door, concrete walls, gypsum boards and plywood. Glazed ceramic tiles have covered the floor and the fiberglass material is used in the ceiling. While conducting measurements, Tx remained unmoved and Rx is moved along different directions. The height of Tx and Rx from the floor are 1.7 m and 1.5 m, respectively. The measurements extended here are taken for non-LoS (NLoS) scenarios. Three different scenarios are taken, and the results are recorded by changing the distance between Tx and Rx from 3 m, to 5 m, and 6 m, at 28 GHz. Same setup is repeated by changing the frequency band to 38 GHz. The results of AoA distributions are presented with respect to received power.
## 4 Modelling of Reflectance and Transmittance Spectra
The transmittance and reflectance of the samples QWR, HBR and SUB obtained by FTIR spectroscopy were calculated in a similar way. For these samples the transfer matrices \(\mathbf{M}=\mathbf{D}_{6/5}\mathbf{P}_{5}\mathbf{D}_{5/4}\mathbf{P}_{4} \mathbf{D}_{4/3}\mathbf{P}_{3}\mathbf{D}_{3/2}\mathbf{P}_{2}\mathbf{D}_{2/0}\), \(\mathbf{M}=\mathbf{D}_{6/5}\mathbf{P}_{5}\dots\mathbf{P}_{3}\mathbf{D}_{3/0}\) and \(\mathbf{M}=\mathbf{D}_{6/5}\mathbf{P}_{5}\mathbf{D}_{5/0}\) were used, respectively. It should be noted that the modelling algorithm of the THz spectra obtained with THz TDS spectrometer was slightly different from above. It was used for a short time window of 10 ps for measurement of single pass THz pulse characteristics [9]. Consequently, the optical transmittance of the whole structure can be written as  
\[T=T_{m}\frac{n_{5}}{n_{0}}e^{-a5d5}(1-r_{65}), \tag{14}\]  
where the optical transmittance \(T_{m}\) of all the epitaxial layers were simulated using their total transfer matrix,  
\[\mathbf{m}=\mathbf{D}_{5/4}\mathbf{P}_{4}\mathbf{D}_{4/3}\mathbf{P}_{3} \mathbf{D}_{3/2}\mathbf{P}_{2}\mathbf{D}_{2/1}\mathbf{P}_{1}\mathbf{D}_{1/0} \tag{15}\]  
and Equation (5) replacing \(M_{ij}\) with \(m_{ij}\). In Equation (14), \(\alpha_{5}=2\frac{\omega_{i}}{c}k_{5}\) and \(r_{65}=\left(\frac{n_{5}-1}{n_{5}+1}\right)^{2}\) denote the absorption coefficient of the substrate and the reflection coefficient of the substrate/vacuum interface, respectively.
###### Abstract  
The upcoming fifth- and sixth-generation (5G and 6G, respectively) communication systems are expected to deal with enormous advances compared to the existing fourth-generation communication system. The few important and common issues related to the service quality of 5G and 6G communication systems are high capacity, massive connectivity, low latency, high security, low-energy consumption, high quality of experience, and reliable connectivity. Of course, 6G communication will provide several-fold improved performances compared to the 5G communication regarding these issues. The Internet of Things (IoT) based on the tactile internet will also be an essential part of 5G-and-beyond (5 GB) (e.g., 5G and 6G) communication systems. Accordingly, 5 GB wireless networks will face numerous challenges in supporting the extensive verities of heterogeneous traffic and in satisfying the mentioned service-quality-related parameters. Optical wireless communication (OWC), along with many other wireless technologies, is a promising candidate for serving the demands of 5 GB communication systems. This review paper clearly presents how OWC technologies, such as visible light communication, light fidelity, optical camera communication, and free space optics communication, will be an effective solution for successful deployment of 5G/6G and IoT systems.  
5G; 6G; IoT; heterogeneous networks; optical wireless communication; small cell +
Footnote †: journal: Applied  
_Applied_
## 6 Sixth-Generation (6G) Testbeds and Platforms
### Testbeds for 6G Technologies
#### 6.2.2 THz Testbeds  
To address 6G testing challenges with up to 10 GHz bandwidth, instrument manufacturers like Keysight and National Instruments introduced sub-THz testbed instruments. Northeast University's TeraNova [243] is an integrated testbed for ultra-broadband wireless communications at true THz-band frequencies. Researchers characterized the THz channel and identified bottlenecks in physical layer research. Photonics-aided THz-wave has garnered attention, with Purple Mountain Laboratories presenting a 352 Gbps THz wired transmission experiment at 325 GHz [244]. Another real-time transparent fiber-THz-fiber 2 \(\times\) 2 Multiple Input-Multiple Output (MIMO) transmission system showcased a 100 GbE (103.125 Gbps) streaming service platform at 370 GHz [56].
# Fast 50 Hz Updated Static Infrared Positioning System Based on Triangulation Method
## 1 Introduction
### Trilateration Positioning Method  
The trilateration method is a technique used to determine the location of an object based on distance measurements from at least three known points. These points are base stations with known coordinates. Assuming the distances are radii of circles, their intersection determines the common point, which is the location sought. This method is commonly used in surveying, cartography, and navigation. Although the trilateration method is simple to apply, there are various techniques available for determining positions, which are constantly evolving. A variety of wireless techniques are used for different purposes [10]. For instance, Bluetooth technology is used for locating people in office spaces [11], while Wi-Fi technology combined with received signal strength index (RSSI) analysis is used for locating pedestrians [12] or static storage tanks [13]. UWB technology, with some computational simplifications, can be used to calculate the location of robots on the production hall [14]. A two-stage trilateration algorithm using UWB works well in determining the position of drones, particularly in areas where GPS signals are not received [15]. Trilateration can also be used to locate RFID receivers [16]. Linear chirp signals are transmitted to base stations at RF frequencies to estimate the location of a tag. A trilateration system based on UAV assumptions can successfully locate and track flying insects with radio frequency tags [17]. In addition to the classical approach to the trilateration problem, many studies have proposed interesting simplifications [18] and extensions to the simultaneous and delayed algorithm [19], as well as the positioning of dynamic objects with a imprecisely known base point [20]. Other developments include an optimal trilateration algorithm based on the reptile search algorithm (RSA) for underwater wireless sensor networks [21] and K-Means clustering [22]. The algorithms' effectiveness has been verified even in unfavorable measurement conditions, such as ambient noise, environmental interference, or uncertainty in base station coordinates.
## 3 Urban Water System Issues and Potential Smart Solutions
### Water Quality  
Water quality monitoring in the water distribution system and end-use pipe system is usually very limited. At the same time, 30 to 60 percent of contamination events occur in the water distribution system. These events are often detected by consumers who have already been exposed. Once the existence of a problem is discovered, it may take days to identify the source of the event in order to fix it [33].  
The water quality in the drinking water distribution system can be affected by several factors including disinfect depletion (water age), contaminant intrusion from pressure differentials and pipe work (_i.e._, installation, repair, and replacement), biofilms, pipe corrosion, accidents, and terrorism [7,8,34,35,36]. In a smart water grid, biosensors and multi-contaminant sensors could alert authorities to potential problems and their location immediately, while smart meters could detect leaks and pressure differentials that cause contaminant intrusion. Smart meters could also help in the monitoring of water age. Lastly, smart valves can isolate contaminated water to prevent its spread through the distribution system.
# Evaluation of the Effects of Hidden Node Problems in IEEE 802.15.7 Uplink Performance
## 5 Simulation Results Analysis  
In this section, we present and analyze the results obtained from simulations made with our implemented model. The effects of the hidden node problem are evaluated by comparing simulation results when there are not hidden nodes with results when there are hidden nodes in the network. To ensure the reliability of the results, several independent simulations have been replicated for the same set of input parameters, averaging output result values. Simulation time was at least 400 s per replica.
# Complete Subhedge Projection for Stepwise Hedge Automata +
## 7 Congruence Projection
### Algorithm
#### 7.2.2. Initial Difference Relation
: We show that \(\mathit{nw}(h)\) is subhedge relevant for \(L\) and \(\mathbf{S}\) if and only if \(q\) is subhedge relevant for \(D^{A,\text{F_{S}}}_{init}\) wrt. \(\Delta\). \"\(\Rightarrow\)\" Let \(\mathit{nw}(h)\) be subhedge relevant for \(L\) and \(\mathbf{S}\). Then, there exist hedges \(h^{\prime},h^{\prime\prime}\in\mathcal{H}_{\Sigma}\) and a nested word \(w\in\mathcal{N}_{\Sigma}\), such that  
\[\mathit{nw}(h)\cdot\mathit{nw}(h^{\prime})\cdot w\in L\wedge\mathit{nw}(h)\cdot \mathit{nw}(h^{\prime\prime})\cdot w\in\mathbf{S}\setminus L.\]  
Since \(A\) is deterministic and \(q_{0}\xrightarrow{h}q\ \text{wrt}\ \Delta\) for the unique \(q_{0}\in I\), it follows that there exist states \(p^{\prime},p^{\prime\prime}\in\mathcal{Q}\), \(q^{\prime}\in F\), and \(q^{\prime\prime}\in F_{\mathbf{S}}\setminus F\), such that  
\[q\xrightarrow{h^{\prime}}p^{\prime}\xrightarrow{\mathit{hdg}(w)}q^{\prime}\ \text{wrt}\ \Delta\wedge q \xrightarrow{h^{\prime\prime}}p^{\prime\prime}\xrightarrow{\mathit{hdg}(w)}q^{ \prime\prime}\ \text{wrt}\ \Delta.\]  
From \(q^{\prime}\in F\) and \(q^{\prime\prime}\in F_{\mathbf{S}}\setminus F\), it follows that \((q^{\prime},q^{\prime\prime})\in D^{A,\text{F_{S}}}_{init}\). Since \(D^{A,\text{F_{S}}}_{init}\) is a difference relation, \(p^{\prime}\xrightarrow{\mathit{hdg}(w)}q^{\prime}\ \text{wrt}\ \Delta\) and \(p^{\prime\prime}\xrightarrow{\mathit{hdg}(w)}q^{\prime\prime}\ \text{wrt}\ \Delta\), this implies that \((p^{\prime},p^{\prime\prime})\in D^{A,\text{F_{S}}}_{init}\), too. Hence, \((p^{\prime},p^{\prime\prime})\in acc^{\Delta}(\{q\})^{2}\cap D^{A,\text{F_{S}}}_{init}\), i.e., \(q\), is relevant for \(D^{A,\text{F_{S}}}_{init}\) wrt. \(\Delta\). \"\(\Leftarrow\)\" Let \(q\) be subhedge relevant for \(D^{A,F_{\mathsf{S}}}_{init}\) wrt. \(\Delta\). Then, there exist a pair \((p^{\prime},p^{\prime\prime})\in acc^{\Delta}(\{q\})^{2}\cap D^{A,F_{\mathsf{S}}} _{init}\). So, there are hedges \(h^{\prime}\) and \(h^{\prime\prime}\), such that  
\[q\xrightarrow{h^{\prime}}p^{\prime}\text{ wrt }\Delta\wedge q\xrightarrow{h^{\prime \prime}}p^{\prime\prime}\text{ wrt }\Delta.\]  
Since \(D^{A,F_{\mathsf{S}}}_{init}=ldr^{\Delta}(F\times(F_{\mathsf{S}}\setminus F))\) by Lemma 10, there exist a nested word \(w\) and a pair \((q^{\prime},q^{\prime\prime})\in\mathcal{Q}^{2}\) so that either \((q^{\prime},q^{\prime\prime})\) or \((q^{\prime\prime},q^{\prime})\) belongs to \(F\times(F_{\mathsf{S}}\setminus F)\) and  
\[p^{\prime}\xrightarrow{hdg(w)}q^{\prime}\text{ wrt }\Delta\wedge p^{\prime\prime} \xrightarrow{hdg(w)}q^{\prime\prime}\text{ wrt }\Delta.\]  
Hence, \(nw(h)\cdot nw(h^{\prime})\cdot w\) in \(L\) and \(nw(h)\cdot nw(h^{\prime\prime})\cdot w\in\mathbf{S}\setminus L\) or vice versa. This shows that \(nw(h)\) is relevant for \(L\) and \(\mathbf{S}\).