esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Generation of 3D Models of the Studied Columns and Finite Element (FE) Analysis
### FE Analysis Using ABAQUS  
Abaqus/Standard (implicit solver) was employed for FE analysis. A four-node shell element with reduced integration (S4R) was used. It allows transverse shear deformation, and accounts for finite membrane strains and arbitrarily large rotations. The number of integration points through the thickness is five (Simpson's rule). The load-displacement response was predicted using an incremental procedure based on arc-length methods. The modified Riks method [44], which is available in Abaqus, was used. On the basis of a mesh convergence study, at least ten elements across the plate widths were used. Stainless steels have considerable non-linear stress-strain response. To accurately predict the structural behavior of the studied columns, the adopted material property for each column was obtained from a corresponding uniaxial tensile stress-strain coupon test. Details of the parameters that describe the stress-strain curves can be found in the literature [21; 28; 29; 37; 38; 39]. For all the models, edge elements at both ends (top and bottom) were kinematically coupled and connected to two control points where the relevant degrees of freedom were constrained. Spread of plasticity through cross-section and along member length was traced by distributed plasticity approach. In the FE analysis, residual stresses have to be considered as they may have negative effects on the ultimate capacity of a structure. As the effect of through-thickness longitudinal bending residual stresses on the global behavior of stainless steel members with box sections is dominant [45], only longitudinal bending residual stresses were considered, and they were implicitly included in the stress-strain curves obtained from the tensile coupons test. For each model of the column with random \(\omega_{\text{max}}\), geometrically and materially nonlinear analysis with imperfections (GMNIA) was carried out to determine the ultimate compressive strength of the column. 6 Effect of Uncertainty in \(\omega\) on the Ultimate Compressive Strength of the Studied Columns  
A probabilistic study was carried out to investigate the effect on the uncertainty in localized imperfection (\(\omega\)) on the distribution of the ultimate strength of the stainless steel columns with cold-formed RHS and SHS. The experimental results and predicted results of the columns with random localized imperfection are shown in Table 3. Namely, P\({}_{\text{u-EXP}}\) is the ultimate compressive strength obtained from experiment; P\({}_{\text{u-rand}}\) is the predicted ultimate compressive strength for each model (each column have 50 models); \(\upmu\) and COV are the mean value and coefficients of variation, respectively; and \(|\epsilon_{\text{max}}|\) is the maximum value of relative error for each set of 50 models.
# Detection Method of Partial Discharge on Transformer and Gas-Insulated Switchgear: A Review
## 4 Partial Discharge Measurement Using Different Types of Sensors on the Power Transformer and GIS
### Mechanism in Acoustic Sensors
#### 4.3.2 PD Detection in Transformer  
The maximal temperature and insulation tolerance are crucial factors in determining a transformer's operational lifespan, which has a significant economic impact on the operation of an electrical power network. Any malfunctioning transformer reduces network dependability and increases maintenance expenses. Before analysis, the acoustic signals generated by partial discharges PD in oil-cooled transformers are detected using a decoupler and amplified with a low-noise amplifier to assure high sensitivity. In laboratory tests, PD must be simulated using three varieties of electrode structures: plane-plane, needle-plane, and wire-wire. When these structures are stimulated, they simulate partial discharges in the transformer's oil-filled walls. Increasing the alternating current (AC) voltage from 0 to 50 kV rms (refer to Figure 16) induces PD in the oil-immersed electrode system. On the outer surface of the transformer tank, an acoustic emission sensor is installed to detect the PD-induced acoustic signals. These signals are then transmitted to an oscilloscope via a low-noise amplifier with a frequency bandwidth of 1.6 kHz to 1.6 MHz and a 3 dB mid-band frequency bandwidth. Depending on the electrode system, the frequency ranges of the acoustic signals range from 45 kHz to 25 kHz for the plane-plane configuration, 60 kHz to 279 kHz for the needle-plane configuration, and 50 kHz to 180 kHz for the wire-wire configuration. Specifically, the detected frequencies for the needle-plane electrode were 145 kHz, and for the wire-wire electrode, they were 121 kHz. Using three acoustic sensors to capture the PD-induced acoustic signals, the location of the PD can be determined within a 1% margin of error [124].  
Figure 16: The experimental setup for PD measurement by replicating the PD stimulate in a transformer using a PD test cell [124].  
Figure 15: The touch sensor practically used to detect PD in GIS.
# Testing Multi-Frequency Low-Cost GNSS Receivers for Geodetic Monitoring Purposes
## 3 Results and Discussions
### Displacement Detection Results
The 3D case is just a combination of 1D and 2D cases, so the results in Table 10 are very similar to the 2D case. However, a slight drop (3%) in detected displacements for the L-baseline of 10 mm displacement is due to the error in height component (see Table 8). Figure 7 represents graphically results from Tables 8-10, where differences between true and estimated displacement in all three dimensions, i.e., 1D, 2D, and 3D, are shown. True displacements were always zero for 1D case and in steps of 5 mm for 2D and 3D cases. There are 720 calculated displacements in the case of 1D and 270 calculated displacements for 2D and 3D cases. In 1D cases, the variation in differences is larger, while for 2D and
# A Survey on Hardware Accelerators for Large Language Models
## 3 FPGA-Based Accelerators
### Multi-Head Attention  
In 2020, [PERSON] et al. presented an FPGA-based architecture for the acceleration of the most computationally intensive parts of Transformer networks [11]. In their work, they propose a novel hardware accelerator for two key components, i.e., the multi-head attention (MHA) ResBlock and the position-wise feedforward network (FFN) ResBlock, which are the two most complex layers in the Transformer.  
Firstly, an efficient method is introduced to partition the huge matrices in the Transformer, allowing the two ResBlocks to share most of the hardware resources. Secondly, the computational flow is well-designed to ensure high hardware utilization of the systolic array, which is the largest module in the design. Thirdly, complicated nonlinear functions are highly optimized to further reduce the hardware complexity and the latency of the entire system.  
The proposed framework is implemented on a Xilinx FPGA. Based on the performance evaluation, the proposed design achieves a speedup of 14.6\(\times\) compared to a V100 GPU.
## 5 Results and Discussion
### Predictive Models Developed Using DA  
We developed two DA models. The first model that we developed was Linear Discriminant Analysis (LDA) model, and the second one was Quadratic Discriminant Analysis (QDA) model. Below were the table of performance measurements of both models.  
1. LDA  
Based on Table 3, it is shown that the LDA model with 70% training data yielded the highest accuracy (45.45%), sensitivity (45.73%), and specificity (72.89%).  
Based on Table 4, it is shown that the QDA model with 80% training data yielded the highest accuracy (60.87%) and sensitivity (59.72%).  
Based on the obtained results, QDA model with 80% training data yielded the highest accuracy (60.87%) and sensitivity (59.72%). Therefore, QDA model with 80% training data was the proposed predictive model so far. This model was then compared with the other predictive models developed using ANN.
# Plum Tree Algorithm and Weighted Aggregated Ensembles for Energy Efficiency Estimation
## 1 Introduction
The evaluation and the validation of the PTA using 24 benchmark functions and the comparison of the results to the results obtained by using the Chicken Swarm Optimization (CSO) algorithm [56], the PSO algorithm, the Grey Wolf Optimizer (GWO) [57], the CS algorithm, the CSA, and the HOA;
5. The application of the PTA to weight optimization for an ensemble of four algorithms for energy efficiency data prediction using the Energy Efficiency Dataset from the UCI Machine Learning repository [58; 59];
6. The comparison of the results obtained using the PTA ensemble approach to the ones based on other nature-inspired algorithms and to the results obtained in other research studies. The article has the following structure. Section 2 presents the research background. Section 3 presents the Plum Tree Algorithm (PTA), including its sources of inspiration and its main steps. Section 4 presents the results obtained after the comparison of the PTA to six nature-inspired algorithms using 24 benchmark objective functions. Section 5 presents the application of the PTA to weight optimization for an ensemble of four regressors used for energy efficiency estimation. Section 6 presents a discussion and compares the results to the ones obtained using other methods. Finally, Section 7 presents conclusions and future research directions.
# Visible Light Communication: A System Perspective--Overview and Challenges
## 5 Discussion and Open Research Challenges  
There has been increasing innovation in solid-state lighting which has resulted in the availability of efficient and low-cost devices available in the form of LED bulbs. This LED-based lighting infrastructure is now increasingly being used in indoor environments such as homes, offices and shopping malls [63], and in the outdoor environment such as for street and roads lighting [95]. Moreover, it has been shown that LED-based communication (which has been standardised by IEEE as 802.15.7) [33; 96] offers higher data rates. According to some researchers, theoretical data rates for LED-based lighting communication is more than 15 Gbps [30]. While this inherent data communication capability is available on the downlink because of the readily available lighting infrastructure and user devices, equipped with photoreceivers and cameras, capable of detecting light. However, using VLC for the uplink has some issues. First, most modern devices do not have the transmitter for VLC and providing new ones will not only add to cost but also increase the device size. Second, using VLC for the uplink results in unusual lighting conditions from a user perspective which are aesthetically not pleasing. To provide seamless integration of VLC, researchers have focused on developing a hybrid system for seamless integration of VLC. In such systems, VLC is used for the downlink transmission and RF or IR for the uplink. Unlike VLC, IR and RF are already supported by most of the current networking devices. RF has a wider coverage range compared to VLC though susceptible to interference. Radio waves can penetrate objects and as a result can be a better substitute for the uplink transmission.  
VLC is in the research and development phase. There are many research gaps need to be addressed before its commercial availability for home and enterprise users. Some of the research gaps is discussed as follow.
# The Use of Smart Rings in Health Monitoring--A Meta-Analysis
## 4 Discussion
### Challenges and Future Directions  
Despite the potential of SRs to promote patient engagement and healthy lifestyle choices, more research is needed to understand their influence on behaviour and treatment adherence. Further studies may focus on standardizing the methodology of the SR accuracy assessment and measuring different sleep and cardiovascular parameters across diverse patient populations. In particular, by aligning study designs, we can better evaluate SR reliability and accuracy. Further research with diverse and larger populations is critical to validate SRs as a consistent and reliable tool in clinical practice.  
Ensuring the security and reliability of SR technology is another challenge [82]. Continuous connectivity requires fault-tolerant networks with backup systems. Cybersecurity is crucial to protect patient data and maintain the digital health ethical standards [82]. Additionally, evaluating the cost-benefit and financial sustainability of SRs in healthcare systems is crucial for understanding their potential scalability and accessibility. As the technology matures and gains approval from regulatory bodies, continued investment in research and development will be necessary to maximize the potential of SRs.
# Method to Characterize Potential UAS Encounters Using Open Source Data
## 1 Introduction
### Objectives and Contribution  
We focused on one of the many objectives identified by the aviation community to support integration of UASs into the airspace, quantitatively define a close encounter between UASs. Satisfying this objective could lead to or enhance existing risk-based minimum operating performance standards (MOPS) for a DAA system [12]. In response, the primary contribution was an analytical method that uses freely available open source data to estimate range and azimuth between potential UAS operations. We applied this method to calculate the distance between various realistic short and medium term UAS inspection operations. We focused on inspection of long linear infrastructure and point obstacles due to the industry demand and to demonstrate that a variety of data sources can be used with this method. The results can easily be extended with other use cases or enhanced using digital elevation models to estimate potential vertical separation.  
These results directly informed the scoping and generation of simulated Monte Carlo encounters of two UASs to support DAA development within RTCA SC-147 ([[https://www.rtca.org/sc-147/](https://www.rtca.org/sc-147/)]([https://www.rtca.org/sc-147/](https://www.rtca.org/sc-147/))). However, discussion of these Monte Carlo encounters were out of scope for this paper. Additionally, while DAA systems can be obstacle aware [10], our research does not directly support a UAS capability to avoid obstacles. Rather, these results can inform if obstacles should be represented as part of simulated UAS encounters when designing or evaluating DAA capabilities to mitigate the risk of a midair collision between aircraft.  
We intend to release the software used for this analysis under a permissive open-source license. These contributions are intended to support current and expected UAS DAA system development and evaluation, specifically estimating the probabilities associated with encountering a low-altitude aircraft based on geography or defining total allowable systems latency of a safety critical system that satisfies performance-based requirements. The results inform how to generate representative UAS trajectories [3] and pair trajectories together to simulate encounters [2]. This paper is complimentedby another effort to propose a quantitative metric to assess the performance of a smaller UAS safety system [8; 9].
## 4 Results and Discussions
#### 4.1.3 Experiment with Three Hardware and Three Software Routers  
For a more accurate assessment of packet processing time by a real router and to determine the packet delay from end to end an experiment using three routers via which the same information flow created by the generator passed and in previous experiments was carried out. The experimental scheme for determining the packets delay by three hardware Cisco 2801 routers is depicted in Figure 19. The experimental scheme for determining the packets delay caused by three software routers is depicted in Figure 20.  
Comparison of the average packets delay caused by the three Cisco 2801 hardware routers and three software routers are shown in Figure 21. Comparison of probability density functions of packets delay caused by three hardware and three software routers are shown in Figure 22.  
Figure 19: Experimental scheme for determining the packets delay caused by three hardware Cisco 2801 routers.  
Figure 20: Experimental scheme for determining the packet delays by three software-based routers.  
The average packets delay caused by the three hardware router \(D_{HR(A-B-C)_{3}}\) is 930.97 us, and through the software \(D_{SR(A-B-C)_{3}}\) it is 927.46 us when processing the same information flow. As we can see by the comparison of average packets delays, the measured difference between the hardware and software routers is 3.57 us. The probability density function of packets delay of the three software routers approaches that of the three Cisco 2801 series hardware routers.
# Efficient Algorithms for Computing the Inner Edit Distance of a Regular Language via Transducers
## Notation, Background and Preliminary Results
### Sets, Words, Languages, Channels  
The set of positive integers is denoted by \(\mathbb{N}\). Then, \(\mathbb{N}_{0}=\mathbb{N}\cup\{0\}\). If \(S\) is any set, the expression \(|S|\) denotes the cardinality of \(S\). We use standard basic notation and terminology for alphabets, words and languages--see [22], for instance. For example, \(\Sigma\) denotes an alphabet, \(\Sigma^{+}\) the set of nonempty words, \(\lambda\) the empty word, \(\Sigma^{*}=\Sigma^{+}\cup\{\lambda\}\), \(|w|\) the length of the word \(w\). We write \(u\leq_{p}w\) to indicate that the word \(u\) is a _prefix_ of \(w\), that is, \(w=uv\) for some word \(v\). Then, \(u<_{p}w\) means that \(u\) is a _proper prefix_, that is, \(u\leq_{p}w\) and \(u\
eq w\). We use the concepts of (formal) language and concatenation between words, or languages, in the usual way. We say that \(w\) is an \(L\)_-word_ if \(w\in L\) and \(L\) is a language.  
A binary word _relation_\(\rho\) on \(\Sigma^{*}\) is any subset of \(\Sigma^{*}\times\Sigma^{*}\). The _domain_ of \(\rho\) is \(\{u\mid(u,v)\in\rho\text{ for some }v\in\Sigma^{*}\}\). A _channel_\(\gamma\) is a binary relation on \(\Sigma^{*}\) that is _input-preserving_; that is, \(\gamma\subseteq\Sigma^{*}\times\Sigma^{*}\) and \((w,w)\in\gamma\) for all words \(w\) in the domain of \(\gamma\). When \((u,v)\in\gamma\), we say that \(u\) can be received as \(v\) via the channel \(\gamma\), or \(v\) is a possible output of \(\gamma\) when \(u\) is used as input. If \(v\
eq u\), then we say that \(u\) can be received (via \(\gamma\)) _with errors_. Here, we only consider the channel \(\operatorname{sid}(k)\), for some \(k\in\mathbb{N}\), such that \((u,v)\in\operatorname{sid}(k)\) if and only if \(v\) can be obtained by applying at most \(k\) errors in \(u\), where an error could be a deletion of a symbol in \(u\), a substitution of a symbol in \(u\) with another symbol, or an insertion of a symbol in \(u\)--see further below for a more rigorous definition via edit-strings.
## 3 System Model
### Service and Traffic Models  
We consider a multi-layer NR service with four layers, one base layer, and three enhancement ones. The rate of the base layer is \(d_{M}\). Each user is assumed to be subscribed to the base layer. All the users also want to receive enhancement layers which are associated with the rate \(d_{UJ}\), \(l=1,2,3\). The intensity of user requests for service is assumed to be Poisson with parameter \(\Lambda=\lambda\delta\pi R^{2}\), where \(\lambda\) is the parameter of exponentially distributed intervals between two consecutive requests and \(\delta\) is the density of users. Recalling the PPP assumption of the human distribution in \(\Re^{2}\), the geometric location of a new session request is distributed uniformly within the coverage area of a BS [24]. Utilizing this observation, one may estimate the amount of resource required for base and enhancement layers as shown in, e.g., [25; 26]. The VR session holding times are assumed to be exponentially distributed with the parameter \(\mu\).  
Upon arrival of a new session from the UE located in the area with no multicast service provisioned, a multicast session is initiated and the user is always allocated a base layer. Otherwise, if the new session is established in the area that is already covered by multicasting service, it joins the corresponding multicast group and proceeds without additional resources. Then, if there is a sufficient amount of resources available at BS, it allows a user to be also provided with the enhancement layers. However, if the amount of resources is insufficient the enhancement layer is dropped.
## 3 Results
### Cross-Comparison to HYDE and Hind-Casted GHSL Trajectories  
While the extraction results seem to be geographically plausible, how do they compare with the GHSL- and HYDE-based trajectories of urban areas over time? Figure 10 suggests that the extracted urban areas are largely in agreement with the trends of urban area estimated by the HYDE model, especially in the Birmingham and Sao Paulo study areas. We observe higher levels of dispersion of the extracted urban areas in London, where the extracted areas seem to be highly sensitive to the chosen post-processing parameters. Results for Lahore show higher levels of systematic deviation from the HYDE area estimate, in particular for the scenarios involving spatial constraints. This could be attributed to lower levels of data quality of the GHSL in 1975 in this area resulting in higher levels of omission of built-up areas as compared to HYDE.  
Lastly, we visualized the hind-casted GHSL trajectories of built-up area and overlaid them with the HYDE trajectories extracted for the same areas. Figure 11 suggests that for most cities, the hind-casted trajectory exhibits high levels of steadiness, except in the London study area. A higher temporal density of historical maps would probably mitigate this effect and produce a smoother curve. Importantly, the uncertainty of these hind-casted trajectories due to the different post-processing parameters is relatively small, and appears to be smallest in the Lahore study area (Figure 11, yellow bands). As a side note, we observe high levels of discrepancies between the GHSL built-up area and HYDE urban area estimates in some study areas, such as Birmingham. This is likely an effect of different definitions, as the GHSL includes all detected settlements (including rural settlements), whereas the urban areas in HYDE are likely to exclude those areas, but can also be attributed to the general difficulty of global models such as HYDE to estimate historical land use patterns at the regional or local level [92]. In the specific case of the  
Figure 9: Extracted historical urban extents for all study areas. (**a**) Boston metro area, (**b**) Atlanta metro area, (**c**) London (dashed rectangle shows the study area extent), (**d**) Birmingham and surroundings, (**e**) south-east part of greater Sao Paulo area, and (**f**) Lahore-Amritsar area with an inset map of Lahore.  
London study area, this discrepancy could also be the result of edge effects due to the small study area in relation to the HYDE grid cells (i.e., 5\({}^{\prime}\)), which may exclude partially overlapping grid cells from the study area.
# Resilient Green Cellular IoT for Landslide Monitoring Using Voice Channels
## 4 Evaluation of VRTHI  
In this section, we evaluate the effectiveness of VRTHI in achieving increased protection, efficient real-time communications, and reduced energy consumption of the nodes. Then, we also compare its performance with a number of popular IoT protocols.