esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The well-appreciated MUD algorithms simultaneously detect the transmitted symbols of all active user terminals. However, they know which terminals are active and which user must be inactive. The existing algorithms only detect the active users in MIMO-OFDM when the user activity factor is known and small. However, this user activity factor is usually unknown and could be large in practice, which makes it difficult to employ the multiuser detection (MUD). In the frequency-selective channel, the resource splitting between pilot and data symbols within the channel's coherence bandwidth causes a trade-off between the channel estimation quality and bandwidth available for data transmission. Sparsity has been used for estimating the parameters of communication systems. Basically, noise and interference are the major issues in the MIMO OFDM systems. To overcome these issues, the multiuser detection method along with channel estimation is used in MIMO-OFDM. The main aim of the paper is to develop the fast MUD and CE algorithm in MIMO-OFDM for high-speed connectivity and interference cancellation. The proposed method aims to provide high reliability with a focus on high-speed connectivity fora small number of user equipment per BS. The analysis for the multipath Rayleigh fading channel in the multi-input multi-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems has an issue of frequency selective channel estimation. MIMO-OFDM is the dominant air interface for 4G and 5G broadband wireless communications. In order to avoid interference and concentrate on the active users of the network, MIMO-OFDM has more advantages in multiuser detection. The detection of the active users in the multi-access system is performed by the machine learning approach when the activity factor is unknown. To evaluate the unknown activity factor at a high data rate, the sparse based \(k\)-nearest neighbor (SKNN) classifier is used. The BS receiver explores the active users and decodes their data. In activity detection, both pilot and data symbols are applied. For optimal pilot placement, a novel pilot allocation method is projected, which decreases the common relation of the measurement matrix. The optimal selection of pilot patterns with the help of cat swarm optimization are used to ultimately verify the sparse channel evaluation and to reduce the computational complexity. The effect of both noises and MAI is controlled by both the CE and turbo/decoder technique. It decodes the received signal properly without any errors from the channel. The reduced BER and SER are calculated to compare the performance in the proposed framework. The description of the nomenclature for the entire paper is described in Table 1.
## 4 Discussion
Regarding the relationship between lens dose and the number of procedures performed, a positive correlation was identified in this study for PET nurses (Figure 5) but not for PET or RI RTs (Figure 6). The number of PET examinations involving \({}^{18}\)F-FDG is increasing in Japan; the demand for such examinations is expected to continue increasing. Accordingly, concerns have been expressed regarding eye-lens doses among nurses who supervise PET scans. The number of PET procedures per nurse can be reduced by rotating their shifts. In contrast, we observed no clear relationship between lens doses and the numbers of procedures performed by PET and RI RTs; this difference potentially occurred because, unlike nurses, these staff members do not directly assist patients [60]. Many of the patients in this study had cancer with severe symptoms; thus, they required more assistance. Therefore, the time spent in close proximity with patients may have been more extensive than is usual for nurses. Similar to the case with the number of procedures, although we observed a positive correlation between the eye-lens doses of nurses and the amounts of PET drugs administered to patients, this relationship was not observed in PET RTs. Among RTs, excessive lens exposure is unlikely if they are not in close proximity to patients, even if a high radiation dose is used. Future studies could perform Monte Carlo simulations using GEometry ANd Tracking 4, GATE, and Monte Carlo N-Particle Transport Code to facilitate this examination [61; 62; 63; 64]. These techniques would facilitate an in-depth analysis of \(\gamma\)-ray behavior during drug administration and examination work, leading to enhanced radiation protection strategies for nurses and RTs in NM examinations. In summary, radiation-dose control and lens protection are important in reducing occupational exposure [65]. The International Atomic Energy Agency classifies NM examinations as being high risk with respect to lens exposure [15]. When working with radiopharmaceuticals, a protective shield should be used. We found that lens-dose limits are unlikely to be exceeded for staff involved in NM examinations when protective equipment is used appropriately. Moreover, in PET nurses, the estimated lens doses differed between those measured with personal dosimeters attached to the neck and with lens dosimeters attached near the lens. Depending on the height/shape of the protective shield used during NM examinations, standard/conventional lens dose-management measures may not be adequate when using the glass badge dosimeter; DOSIRIS is needed. The small number of participants was a limitation of this study. In addition, we did not consider the clinical experience or body size of the nurses or RTs; both factors may have affected the eye-lens doses. Furthermore, the use of real-time dosimeters may help to elucidate the factors that contribute to lens exposure among NM department staff [66; 67].
## 3 Materials and Methods
### Dataset Description
#### 3.1.2 Sensor Configuration and Parameters  
Each sensor node in the network recorded multiple environmental parameters at predefined intervals, capturing the temporal and spatial variations essential for anomaly detection. The recorded features include the following:  
* Temperature (\"C)--measures the ambient temperature variations.
* Smoke density--indicates the concentration of smoke particles.
* Light intensity--measures variations in natural and artificial light sources.
* Sound levels (dB)--monitors abnormal noise patterns that may indicate external disturbances.  
Each sensor node was strategically placed to ensure comprehensive coverage of the emulated environment. The collected data were timestamped, enabling temporal analysis and sequence-based learning models.
# Experimental Study on Flexural Properties of Polyurethane-Cement Composites under Temperature Load
###### Abstract
Compared to cementitious materials, polyurethane cement composites, as a homogeneous body made of polymeric elastomer combined with inorganic cement particles, have good tensile and tear strengths, can withstand significant stresses without fracture damage, and have higher flexural strength than ordinary concrete materials [34]. (3) The density of polyurethane cement composites is about 1.45\(\sim\)1.62 g/cm\({}^{3}\)[13], while the density of cement concrete is about 2.4 g/cm\({}^{3}\)[35], which is 1.58\(\sim\)1.65 times the density of polyurethane cement composites. As a new lightweight construction material, polyurethane cement composites can reduce the stress on the structure caused by the material's gravity and can be used in building structures with high seismic intensity levels. (4) The chemical properties of the polyurethane composite material are stable, and the chemical substances in the surrounding environment only have a minimal reaction to them. Polyurethane composites have strong anti-corrosion and anti-aging properties. The components or structures of the polyurethane cement composite material can easily adapt to areas with seawater erosion, soil microbial corrosion, or chemical pollution [36,37]. (5) There is no hydration reaction process in the preparation process of polyurethane cement composites, which can be solidified and formed in 60 min at room temperature under the action of a catalyst, and the performance requirements for regular use can be achieved within 24 h. The curing and setting times are much less than cement concrete, and no maintenance treatment is required at a later stage, effectively saving construction time and reducing construction costs [38]. Polyurethane cement is a new organic-inorganic composite material--its performance should meet the requirements of the use of the environment, inevitably affected by the external environment in the actual use of the process, the impact of the external environment on its mechanical properties cannot be ignored. Temperature is an essential factor in the external environment. The properties of materials at different temperatures are different. Therefore, the mechanical properties of polyurethane cement at different temperatures play an essential role in its application in practical engineering. In this paper, in order to explore the flexural properties of polyurethane cement composites under temperature loads, bending tests were carried out in strain control mode. The samples' reaction characteristics, stress processes, and failure modes under different temperature loads were obtained. The polyurethane cement composites were analyzed for flexural strength in the actual working environment, further verifying the feasibility of polyurethane cement composites to supplement and replace traditional materials.
# Wi-GC: A Deep Spatiotemporal Gesture Recognition Method Based on Wi-Fi Signal
## 3 Related Theory  
### Basic Principles of Wi-Fi Human Perception  
Wi-Fi signals will have multiple transmission paths in the process of propagation. The direct propagation path between the transmitter and the receiver is called a Line of Sight (LOS). When Wi-Fi signal propagation encounters obstacles such as ceiling and floor, reflection, scattering, etc., will occur. The propagation path is called Non-Line of Sight (NLOS). The influence model of human gestures on Wi-Fi signals is shown in Figure 1.  
Figure 1: The effect of the human body on Wi-Fi signals.  
The influence of human behaviour on Wi-Fi signal propagation will be presented in the Wi-Fi signal at the receiving end. Assuming that the LOS distance of the transceiver is \(d\), the distance from the reflection point between the ceiling and the ground to the LOS is \(h\), combined with the Friis free space propagation equation [33], the expression of the received power is as follows:  
\[P_{ry}(d)=\frac{P_{ty}G_{ty}G_{ry}\lambda^{2}}{(4\pi)^{2}(d+4h+\eta)^{2}} \tag{1}\]  
In the formula, \(d\) is the direct distance between the two ends of the transceiver. \(P_{ty}\) is the transmit power of the transmitter. \(P_{ry}(d)\) is the received power of the receiver. \(G_{ty}\) is the transmit gain, \(G_{ry}\) is the receive gain, \(\lambda\) is the wavelength of the Wi-Fi signal and \(\eta\) is approximated path length variation caused by human interference, due to the different scattering paths caused by various human behaviours to the propagating Wi-Fi signal. According to Equation (1), it can be seen that distinct human actions lead to differences in the received power at the receiver. These differences can be used to differentiate between different human behaviours.
# An Adaptive Buffering Scheme for P2P Live and Time-Shifted Streaming
## 3 Adaptive Buffering Scheme
### Adjustment Cycle of Buffer Area Ratio  
Since peers frequently join and leave P2P networks and perform time-shifted viewing, membership of each group changes dynamically in the adaptive buffering scheme. When a peer joins a group, it determines the ratio of the caching and prefetching areas in its buffer after receiving the necessary information from its super peer. However, a peer that has stayed in the same group for a long time periodically needs to adjust the ratio of its buffer areas to reflect changed group membership, since some peers may have joined and left the corresponding group.  
In our adaptive buffering scheme, we determine the cycle for adjusting the buffer area ratio of each peer so that, as membership in a group changes more frequently, its buffer area ratio can be adjusted more often. When the previous buffer ratio adjustment period ends, the next buffer ratio adjustment cycle is determined depending on how many peers have joined and left the group in the previous period.  
Figure 4: A comparison of ratios of caching and prefetching areas between (**a**) fixed buffering scheme and (**b**) adaptive buffering.  
\[F_{i}=AF_{i-1}\times\frac{UNi_{i-1}}{UNAi_{i-1}}\quad\left(AF_{i-1}=\frac{\sum_{k=1 }^{i-1}F_{k}}{i-1},\quad UNA_{i-1}=\frac{\sum_{k=1}^{i-1}N_{k}}{\sum_{k=1}^{i-1} F_{k}},\quad UN_{i-1}=\frac{N_{i-1}}{F_{i-1}}\right) \tag{3}\]  
As shown in Equation (3), the buffer ratio adjustment cycle (i.e., \(F_{i}\)) for the \(i\)-th time period is determined by multiplying the ratio of the number of peers that have joined and left per time unit during the previous time period (i.e., \(UNi_{i-1}\)) to the number of peers that have joined and left per time unit during all previous time periods (\(UNAi_{i-1}\)) by average cycle during all previous time periods (i.e., \(AF_{i-1}\)). That is, \(F_{i}\) is obtained by reflecting the degree of ratio fluctuation in the previous time period in \(AF_{i-1}\). Therefore, in our proposed adaptive buffering scheme, the buffer ratio adjustment cycle in each time period is dynamically determined according to the frequency of member change in each group.
# Automated Clinical Impression Generation for Medical Signal Data Searches
## 1 Introduction  
Medical retrieval systems have garnered interest among clinicians [1]. However, commercial retrieval systems like Apache Lucene [2] and Elasticsearch [3], which hinge on term-based indexing, lack efficacy when dealing with continuous medical data, such as electroencephalography (EEG) data. Moreover, the processes of annotating, classifying, and interpreting medical data are resource intensive and demand expert domain knowledge. Conversely, the cost of data recording systems has become increasingly affordable [4].  
In response to these challenges, researchers have suggested cross-modal retrieval systems. However, these proposals predominantly focus on images and texts [5; 6; 7; 8; 9], leaving time-series medical data like EEG data, which documents brain activity, largely overlooked [10]. Analyzing and diagnosing EEG signals necessitates the expertise of a neurologist, which makes it the costliest step. Thus, a proficient retrieval system that can use text to locate corresponding signals without requiring expert analysis is in high demand.  
The aim of this work is to aid the creation of indexing systems that utilize EEG signals for report generation. The proposed system in this work is designed for the following scenario: A medical center collects EEG signals from patients and stores them in a dedicated database. However, the accompanying reports are yet to be produced by a neurologist. If a neurologist or researcher requests specific signals using a query, such as \"abnormal EEG due to: Asymmetric EEG background with left arrhythmic delta activity\", as depicted in Figure 1, the signals need to be characterized with their respective impressions for storage in a term-based indexing system.  
The proposed method in this work incorporates an encoder that employs convolutional neural networks (CNNs) from DeepSleepNet [11] to extract EEG signal features, along with a transformer [12] that learns the signal's auto-correlation and the relationship between the signal and the target report. Experimental evaluation with real-world data showed the proposed approach in this work surpasses baseline methods.  
The contributions of this work can be summarized as follows.  
* Development of a method supporting indexing systems for EEG signals in the medical domain, specifically tailored for report generation purposes.
* Introduction of a cost-effective system that minimizes the requirement for extensive neurologist involvement in the EEG indexing process.
* Demonstration of superior performance and effectiveness through rigorous experimentation using real-world data, surpassing baseline approaches.
## 3 Datasets
### OSi Digital Mapping  
A typical example of vector mapping such as the OSi digital mapping of Maynooth University (Figure 4a) provides any potential user with a quick method of acquiring building footprint polygons and was incorporated into this project. The vector mapping was edited using Bentley's MicroStation V8 [41], a popular CAD environment. For this project, all mapping layers (e.g., water, infrastructure, legal boundaries) with the exception of the building footprints were removed, as illustrated in Figure 4b where the single building layer has been isolated. If this process was expanded to a larger area involving hundreds of digital mapping tiles, this process could be streamlined by specifying a single layer and running a batch process to extract the relevant layer. These building polygons were created from aerial photography and have a quoted accuracy of approximately \(\pm\)0.60 m [42].
# An Unsupervised Anomaly Detection Based on Self-Organizing Map for the Oil and Gas Sector
## 4 Results and Discussions
### Summary  
Self-Organizing Maps are generally used to solve clustering problems such as image classification [53] or customer segmentation in the economic-financial domain [54]. In the case of this study, the SOM algorithm is applied to anomaly detection instead. The proposed algorithm can ensure reliable performance in detecting and classifying anomalies in the experimental two-phase system.  
The readings with which the algorithm was trained describe six distinct macro-areas on the output map: the steady-state area, the transient area, the water anomaly area, the air anomaly area, the air-water two-phase fluid anomaly area, and the reservoir leakage anomaly area. Given that the algorithm has an overall accuracy of 90%, the network efficiently classifies the readings on the output map.  
Once the algorithm is trained using steady-state test data and level 3 anomalies, it maps the output for data obtained from level 1 and level 2 anomalous tests. However, the accuracy of these tests is lower compared to the training data since the pipe obstructions created during these tests are of lower intensity. As a result, in most cases, the readings from level 1 and 2 anomalous states are projected into the transient area.  
Apart from its ease of implementation, the algorithm generates an easily comprehensible output map, even for individuals not well versed in artificial neural networks. Moreover, if the algorithm is connected to the Digital Twin of the experimental system, it would enable real-time projection of the system data onto the output map. Consequently, if the system were to be in a condition different from the steady state, it would be easy to identify the anomaly type, as the readings would be mapped onto the relevant anomaly area.  
Two conclusions can be drawn from the output map if an anomaly is detected. Firstly, the more severe the obstruction, the further the map will project the reading towards the centre of the region of interest. Secondly, if the obstruction is minimal, the data will be displayed at the border of the area.  
Figure 18: Outlet air flow rate of steady state, anomaly 9 level 1 and 2 samples.  
However, the analysed case study highlighted a limitation in characterizing the transient region. The algorithm can alert us that the system is no longer in steady-state conditions but cannot classify the detected anomaly type. To describe the transient area more thoroughly, one could study how the network organizes readings recorded on the system when multiple anomalies are combined simultaneously. In addition, these new tests could make a more detailed analysis of the macro-output areas boundaries.
# Properties of Emulsion Paint with Modified Natural Rubber Late/Polyvinyl Acetate Blend Binder
###### Abstract  
The direct use of natural rubber latex (NRL) as a binder for emulsion paints did not produce emulsion paints with good opacity, washability resistance, and regulated touch drying time, even when mixed with polyvinyl acetate (PVAc). This study aimed to study the properties of opacity (hiding power), washability resistance, and set drying touch time of emulsion paint with a binder added from a mixture of modified natural rubber latex (NRL) and PVAc. NRL modifications included UV photodepolymerization with TiO catalyst and grafting copolymerization of methyl methacrylate and styrene (NRL-g-(MMA-co-Si)). NRL was mixed with PVAC at ratios of 0/100; 15/85; 25/75; 35/65; 50/50; 100/0% \(w/w\) before being used as a binder for emulsion paint. Emulsion paint samples had different binder contents, namely 2, 4, 6, and 8% \(w/w\). Tests on paint samples included opacity using a UV-Vis spectrophotometer (EASYSPEC safas Monaco), washability using the Digital BGD 526 Wet Abragion Scrub Tester, and drying time set using the ASTM STP500 procedure. The results showed that the opacity (hiding power), washability resistance, and set drying touch time met the emulsion paint standards for all binder levels, except the 100% \(w/w\) modified NRL composition. The higher level of NRL in the binder causes these properties to decrease and become unstable. The best opacity (hidden power), washing resistance, and drying touch time were obtained on modified NRL with a concentration of 15% \(w/w\). The binder content in the paint was around 4% \(w/w\), with an opacity of about 1.78% abs, washing resistance of 12 times, and the set drying touch time to 80 min.  
emulsion paint binder; grafted natural rubber; liquid natural rubber; natural rubber latex; polyvinyl acetate +
Footnote †: journal: Applied
## 5 Conclusions  
The performance of a proposed transmission strategy for MIMO-WCDMA networks based on eigenvalue analysis was evaluated in this work. The goal was to select the appropriate transmission matrices per active user in order to maximize system throughput in a multiuser/multipath environment. As it was shown in the results section, the selection and processing of all multipath components via RAKE receivers can improve system performance in terms of mean BER. Moreover, the adoption of the proposed approach can reduce the overall complexity of transmit weight formulation. Results were also validated for the case of a realistic four-element antenna. In this case, correlation coefficients values among all four elements were less than 0.1. Hence, BER deterioration was only 10% compared to the ideal case of uncorrelated channels among all transmit and receive antennas.  
Motivated by the above results, we aim that future work will include the extension of the presented results in cellular networks, where fundamental trade-offs among spatial multiplexing and diversity transmission mode will be investigated.  
Conceptualization, P.K.G. and G.A.K.; Methodology, P.K.G., D.I.K. and K.S.; Software, I.S.V.; Validation, P.K.G., M.T.C. and K.S.; Formal Analysis, D.I.K. and K.S.; Writing: Original Draft Preparation, P.K.G. and C.T.D.; Writing: Review and Editing, P.K.G., C.T.D., D.I.K., I.S.V., G.A.K., M.T.C. and K.S.  
This research has been co-financed by the European Union (European Social Fund--ESF) and Greek national funds through the Operational Program \"Education and Lifelong Learning\" of the National Strategic Reference Framework (NSRF)-Research Funding Program: THALIS-DUTH, \"Design Techniques for Digitally Controlled RF-Microwave Structures Appropriate for Software Defined-Cognitive Radio\" (MIS 379521).  
The authors declare no conflicts of interest.  
Figure 12: BER comparisons for a MIMO–WCDMA orientation for varying number of eigenvector/ eigenvalues for signal reconstruction (_SNR_ = 10 dB, _PG_ = 32, Cases A and B).