esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Overview of the L-PBF Process
### Defects
#### 2.3.3 Thermally Induced Cracks  
The L-PBF process is governed by extremely fast cooling rates (around \(10^{8}\) K/s). Thus, a large temperature gradient occurs around the meltpool [68]. As a result, the meltpool size and the heat-affected zone are significantly smaller than the base plate and previously melted layers [69]. This leads to the creation of residual stresses, which affect the micro- and macroracks formation, as illustrated in Figure 5[70]. The crack formation depends on the L-PBF process parameters and the powder materials; in other words, different types of cracks may occur in different materials. For example, four different crack mechanisms were identified, similar to welding of Ni-based alloys [71]. [PERSON] et al. [11] identified three types of cracks that can be observed in metal AM parts: solidification cracking, liquid cracking, and delamination. The solidification crack is observed along the grain boundaries and is usually attributed to shrinkage and thermal contraction. Liquidation cracking is related to the presence of liquid films [56]. It is more common in alloys exhibiting high-temperature differences between the liquids and solidus, large meltpool size, or large thermal contractions. Delimitation occurs when the residual stresses at the layer interface exceed the yield strength of the alloy [72; 73]. It may occur between the part and the build  
Figure 4: Typical types of porosity formed at different process parameters [59].  
plate and may occur between two successive layers. Preheating the build plate might reduce the probability of crack and delimitation formation. However, sometimes the crack formation is attributed to the solidification sequence, not only L-PBF process parameters. Acoustic emission sensors can be beneficial in the online detection of cracks formation during the L-PBF of metals [74].
## 3 Results
### Resource Efficiency Metrics
#### 3.2.1 Model Size (Flash Memory Usage)  
According to Table 3, the original TF DNN model has a size of approximately 150 kB. After LiteRT conversion, the model size is reduced by 3.75 times to about 40 kB, requiring minimal free memory on the Flash card. This reduction enables the execution of non-quantized models on both the Raspberry Pi 4 and STM32F7 microcontroller boards. Further quantization, achieved by using 8-bit integer weights instead of 32-bit floating-point weights (Table 1), doubles the size reduction, shrinking the model to approximately 20 kB. This optimization facilitates deployment on memory-constrained microcontrollers.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Model Size** & **SRAM** & **SDRAM** & **Execution** & **Energy** \\  & **(Flash)** & **Usage** & **Usage** & **Time** & **Consumption** \\ \hline
**TensorFlow DNN model in GPU-based workstation** & & & & & \\ TF DNN model & 149,452 Bytes & NA & NA & 1.007 \(\pm\) 0.147 ms & NA \\ \hline
**LiteRT DNN model in Arm-based microcontroller** & & & & & \\ Non-Quantized & 40,372 Bytes & NA & NA & 1.064 \(\pm\) 0.025 ms & 3.91 mJ \\ \hline
**C++ LiteRT DNN models in Arm-based microcontroller** & & & & & \\ Non-quantized & 40,372 Bytes & 234,476 Bytes & 202,860 Bytes & 4824 \(\pm\) 0.612 ms & 4592 mJ \\ \hline DynQ & 20,712 Bytes & ERR & ERR & ERR & ERR \\ IntQ & 20,424 Bytes & 214,528 Bytes & 54,956 Bytes & 615.7 \(\pm\) 0.468 ms & 586 mJ \\  & & (40.92\% *) & (0.66\% *) & 614.6 \(\pm\) 0.483 ms & 585 mJ \\ \hline \hline \end{tabular}  
* *. % of available SRAM memory of 512 kB; *. % of available SDRAM memory of 16 MB.  
\end{table}
Table 3: Evaluation of resource efficiency for executing DNN models across three platforms: a GPU-based workstation, a Raspberry Pi 4, and STM32F7 microcontrollers. Execution time is reported as mean value \(\pm\) standard deviation for all ECG recordings in the test database. Energy consumption is measured based on power usage during the mean execution time. NA: not available or not applicable for GB-sized RAM memories in PC workstation and Raspberry Pi 4. ERR: error message by the debugger “Hybrid models are not supported on Lite RT for microcontrollers [86]”.
## 3 Results
### Impact of Rainfall Intensity on Splash Erosion  
The points deviating above the fitted linear regression line for the BK soil were mostly measured in Mistelbach (Figure 4). Considering the site-specific differences (Table 2), it was necessary to distinguish the erosive events with higher rainfall intensities from the ones with low intensities having the same \(KE_{sum}\). The example in Figure 5 shows two rainfall events with similar cumulative precipitation and \(KE_{sum}\) measured in Mistelbach (Event 1) and Prague (Event 2) with the corresponding splash erosion. The rainfall Event 1 was recorded on June 6, 2018, with a duration of 3.5 h. The rainfall Event 2 was recorded on August 8, 2018, with a duration of 43 min. Cumulative rainfall reached 19 and 20 mm for Event 1 and Event 2, respectively. Corresponding \(KE_{sum}\) values were 467 J m\({}^{-2}\) for Event 1 and 446 J m\({}^{-2}\) for Event 2. However, during Event 2, up to 86% higher splash erosion rates were measured. Therefore, to compare the effect of kinetic energies characterized by different rainfall intensities on splash erosion, the \(KE_{sum}\) was divided by rainfall duration (\(T\)) (defined in Equation (3)).  
Figure 5: Cumulative rainfall \(P\) (mm) and kinetic energy \(KE_{sum}\) (J m\({}^{-2}\)) with corresponding mean splash erosion for Zwerbach (ZW), Mistelbach (MI), and Bilkovice (BK) soil for (**a**) Event 1 and (**b**) Event 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Soil** & **Equation** & \(R^{2}\) & **RMSE** \\ \hline \multirow{3}{*}{\(KE_{sum}\)} & ZW & \(S=0.767\)-\(KE_{sum}-75.968\) & 0.50 & 349.25 \\  & MI & \(S=0.653\)-\(KE_{sum}+22.841\) & 0.45 & 326.10 \\ \cline{1-1}  & BK & \(S=0.825\)-\(KE_{sum}-1.299\) & 0.52 & 357.73 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Main outputs of the regression analysis between splash erosion \(S\) (g m\({}^{-2}\)) for Zwerbach (ZW), Mistelbach (MI), and Bilkovice (BK) soils and rainfall event cumulative kinetic energy \(KE_{sum}\) (J m\({}^{-2}\)). \(R^{2}\) is the determination coefficient of the regression model, and RMSE (g m\({}^{-2}\)) indicates the root mean squared error.
## 3 Finite Element Modeling  
### Module Selection and Meshing  
In this paper, the Midas FEA NX finite element analysis software is used to establish a local \"beam-shell-solid-contact\" finite element model of the steel channel beam under the maximum cantilever condition. The maximum cantilever condition is when the guide beam is about to reach pier 142 and the cantilever length reaches 100 m. The shell unit is selected to model the 8 m long girder section supported by the cantilever root on pier 143 in detail, and the geometric model is shown in Figure 3. The remaining main girders, guide girders, and hollow web truss-type diaphragm are selected to be modeled with beam units, and rigid connections are established in the part where the beam unit meets the shell unit to make their nodes coupled. The rigid connection is shown in Figure 4. Solid units are selected for modeling the pads. The material of the main girder and steel guide beam is Q345 qD steel with a density of 7850 kg/m\({}^{3}\), a modulus of elasticity of 210 GPa, Poisson's ratio of 0.3, and the pads are made of rubber pads with a modulus of elasticity of 30 MPa. The overall mesh size of the main beam is 100 mm. The whole beam is divided into 24,379 cells.
# Survey of Network Coding Based P2P File Sharing in Large Scale Networks
## 4 Network Coding Based P2P File Sharing Systems
### Generation Based Coding  
After the appearance of Avalanche and the associated issues, generation-based network coding [61], also known as grouped [23], chunked [62], segmented [63], or clustered [64], is proposed. In such a scheme, the file is segmented into groups where each group contains a mutually exclusive subset of the overall pieces; afterwards, network coding is applied per group only. The two major benefits of this scheme are: (1) reducing the required computations and disk operations, and (2) minimizing the size of encoding vectors. For the rest of the paper, we use the terminology 'generation based coding' or 'generation coding' for simplicity. [PERSON] et al. [65] propose a so-called practical network coding which splits a file into generations with each generation containing a subset of the file's pieces. Subsequently, network coding is applied on each generation separately. The authors suggest that the transmission of encoded pieces can be done sequentially, i.e., generation by generation. Control messages among nodes are exchanged to request a generation and then to inform that the generation is fully completed and thus decoded. [PERSON] et al. [66] suggest that, in order to avoid the overhead of the control messages, a generation can be selected randomly and then the generation based encoded piece is sent. The authors argue that this method achieves a good level of the performance without the need for feedback. However, redundant and unrequired pieces could be pushed to the network, and thus yielding even more overhead. The former two studies are general and not intended for P2P content distribution networks. To address the overhead problems of the aforementioned studies [65; 66], [PERSON] et al. propose Swifter [67], a scheme that mixes local-rarest-first scheduling policy with generation coding algorithms so as to achieve a high scheduling efficiency. The authors start by studying the trade-off between the network coding overhead and the scheduling overhead. To make sure that the pieces are shared
## 3 MAC Protocols for HCWSNs
In terms of energy consumption, MD-SMAC still manages to outperform S-MAC and DS-MAC [23] even though it employs mechanisms for mobility-handling and delay-handling. The superior performance is due mainly to the modifications made to S-MAC and DS-MAC [23] described above. [PERSON] _et al._[26] do not provide any simulation results to evaluate DS-MAC [26]. However, the key detail to extract from DS-MAC [26] is the use of traffic type characterization and prioritization. For health care applications used in environments such as hospitals, it is very important to differentiate data under life threatening conditions from data under stable normal operating conditions. Therefore, traffic type characterization should be considered for MAC protocols designed for a HCWSN. \begin{table}
\begin{tabular}{|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|} \hline
**Criteria** & **S-MAC [22]** & **DS-MAC [23]** & **MD-SMAC [24]** & **DS-MAC [26]** \\ \hline Key Feature & Message-passing & Dynamic duty cycle & Dynamic duty cycle and adaptive & Prioritized channel provisioning for differentiated service \\  & & & & (+) Prioritizes energy efficiency by & No discussion presented in [26] \\  & & listening & based on current & reducing duty cycle & \\  & (–) Poor under variable traffic loads & energy consumption level & when energy level & \\  & traffic loads & level & threshold has been exceeded & \\ Timeliness & (–) Unfairness in the sharing of the medium, leading to extended transmission delays & (+) Autonomous duty cycle adjustment & (–) Aims to extend network lifetime at expense of increased latency & (+) Service preemption and shorter back-off duration allows emergency data to win channel access when competing with normal data \\  & & & & \\ Robustness to Mobility & (–) Designed for stationary scenarios. Nodes are disconnected from network for 10 s every 2 min & (+) Multiple duty cycles and dynamic duty cycling allows for adjustment to different update & (+) Adaptive neighbor discovery frequency keeps the mobile node \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \end{tabular}
\end{table} 
Table 2: Summary of potential CSMA-based protocols for HCWSN.
## 1 Introduction
### Background and Motivation  
To solve the shortcomings of the existing studies, we propose an SF-partition-based clustering and relaying scheme to address the near-far unfairness problem caused by the low success probability, long channel occupancy time (also known as time on air (ToA)), redundancy, low coverage, capture effect challenge, and collisions in IoT LoRa networks. For this, the proposed SFPCR scheme has three methods: SF-based network partitioning, density-based subspace clustering, and relay LD selecting. The SF-based network partitioning method is used to determine the appropriate partitioning threshold point for bridging the packet delivery success probability gap between different SF regions in an IoT LoRa network. Then, the packet delivery success probability is evaluated based on the hop distance of each hop in multihop LoRa communications. By the partitioning threshold point, the IoT LoRa network is separated into two SF zones: the lower SF zone (LSFZ) and the higher SF zone (HSFZ). To avoid long-distance transmissions with low reliability from individual LDs in the HSFZ of the GW, the density-based subspace clustering method creates arbitrary-shaped clusters by grouping adjacent LDs in the HSFZ and selects the CH of each cluster for gathering and transmitting data packets of cluster members. For clustering the arbitrary-shaped clusters and choosing CHs based on the CH conditional probability, a combination of the DBSCAN algorithm and the naive Bayes classifier is used. To support reliable data transmissions from each CH in the HSFZ to the GW by multihop communications, the relay LD selecting method chooses the best relay LD in the LSFZ to extend uplink transmissions from the CH to the GW. Then, the best relay LD is determined by the harmonic mean of the packet success probability and the remaining energy selection strategy of all candidate LDs in the LSFZ.
## 2 Methods
### Lighting Settings  
We used a multi-channel LED lighting system (Thouslite) in a cube shape of 30 \(\times\) 30 \(\times\) 21 cm. The LED lighting contained eleven independently controlled LED channels. The system was controlled with LED Navigator-LC V6.3.2 software for spectral tuning, and it was switched on 30 min before starting the experiment. For experiment 1 and experiment 2, a Gossen Mavolux 5032C Meter GO 4056 was used to measure the illuminance on the work surface. A factorial between-subject design was employed with two independent variables: the CRI (CRI < 80 vs. CRI \(\geq\) 80) and CCT (3000 K vs. 6500 K). Conditions involving LED lights with a CRI < 80 and LED lights with a CRI \(\geq\) 80 were applied to evaluate the color rendition properties of the LED lights, and CCTs of 3000 K and 6500 K were applied to evaluate the objects the lights reflected off. Previous studies used an IES Rf in the range of 60-95 for fidelity and an IES Rg in the range of 80-120 for gamut [39; 40; 41]. The National Electrical Manufacturers Association (NEMA) [42] used ten levels to specify the chromaticity of solid-state lighting (2200-6500 K). The two levels for the CRI and CCT selected in this study were thus based on prior research and industry recommendations. The CRI < 80 (LEDs A, C, E, and G) and CRI \(\geq\) 80 (LEDs B, D, F, and H) conditions had two levels of relative gamut area, presenting those that could saturate or de-saturate different colors. The CRI < 80 condition had higher Rg values and the CRI \(\geq\) 80 condition had lower Rg values. To investigate the recommendation of illuminance levels, an illuminance of 300 lux was selected for experiment 1 and of 500 lux for experiment 2. The CIE R\({}_{\text{a}}\) is a color fidelity measure to characterize light source color rendition. It quantifies how similarly a source can render eight test color samples in comparison to a reference illuminant. The IES Rf is a measure of color fidelity. It quantifies how similarly a source can render ninety-nine test color samples in comparison to a reference illuminant. The IES R\({}_{\text{g}}\) is a measure of the color gamut. It quantifies the average increase or decrease of the chroma of objects in comparison to a reference illuminant. The IES Rf and IES Rg combination values were used to give a detailed understanding of hue and chroma shifts in this study. D\({}_{\text{uv}}\) is the distance from the test chromaticity coordinates to the Planckian  
Figure 1: Experimental setup: (**a**) plan; (**b**) section.  
locus. Table 1 shows the LED lighting conditions for the experiments. Figure 2 shows the spectral power distribution of each LED light.
## 2 Results and Discussion
### Principal Component Analysis  
The datasets containing volatile constituents determined via GC-MS analyses of the fractions obtained using hydrodistillation (EO) and HS-SPME were subjected to PCA. The data in both cases demonstrated some natural clustering into three groups (Figures 3 and 4). In the case of the data obtained for the EO, the first two PCA factors explained 67. 3% of variance between the samples and the data obtained for the headspace extracts; the main two factors explained 65.0% of variance in the dataset. The variables providing the greatest contribution to the first two principal components were the following for EO: carvone (accounting for more than 45% and 35% of components 1 and 2, respectively) and piperitone oxide (accounting for more than 50% and more than 40% of factors 1 and 2, respectively), along with \(p\)-menthone, linalool, and menthol (accounting in total for about 20% of factor 2). In the case of HS-SPME data, the variables providing the greatest contribution to the first two principal components were carvone (accounting for more than 70% and 15% of components 1 and 2, respectively) and piperitone oxide (accounting for more than 20% of component 1 and nearly 60% of component 2), along with linalool, accounting for more than 10%, and \(p\)-methone, _cis_-dihydrocarvone, and menthol, accounting for less than 5% each of component 2. In Figures 3 and 4, the first five compounds exhibiting major contribution to the variance are presented. The samples naturally formed three groups: chemotypes differentiated by the abundance of piperitone oxide, carvone or linalool, and menthol, \(p\)-menthone, and other related compounds. The clustering was similar based on both EO and HS-SPME data. The first group characterized by the abundance of piperitone oxide contained _M. longifolia_ taxa (_M. longifolia_ var. _schimperi_**M6**, _M. longifolia_**M7**). The second group consisted of _M. \(\times\) piperita_ f. _citrata_ 'Grapefruit' (**M2**) and _M. \(\times\) carinthhaica_ (**M4**), which are hybrids of _M. spicata_ and _M. aquatic_ or _M. arrensis_ and _M. suavelens_, respectively. The last group consisted of _M. \(\times\) piperita_ var. _officialis_ f. _pallescens_ [PERSON] 'Swiss' (**M1**), _M. \(\times\) piperita_ f. _citrata_ (**M3**) two _M. piperita_ (_M. spicata_\(\times\)_aquatica_) cultivars, and _M. dulcia citreus_ Hillary's Sweet Lemon **M5** (_M. aquatic_\(\times\)_saucoleens_) [36].  
Figure 4: Principal component analysis (PCA) scores (**left**) and loadings (**right**) (only first five variables with the highest contributions are shown) plots based on headspace volatiles dataset.  
Figure 3: Principal component analysis (PCA) scores (**left**) and loadings (**right**) (only first five variables with the highest contributions are shown) plots based on essential oil volatiles dataset.
## 2 Model and Theory
It is a key factor combining full width at half maximum (FWHM) and signal-to-noise ratio (SNR), which is described by  
\[FOM\ =\ \frac{m}{FWHM} \tag{7}\]  
where \(m\) denotes the slope of two adjacent resonance points, and FWHM denotes the full width at half the maximum peak of each detected signal [39]. For performance analysis, commercial COMSOL 5.2 software integrating an FEM solver was utilized to study transmission and coupling properties in PCFs, combining a perfect match layer (PML) [40]. Here, PML thickness was 5 um. The electromagnetic-mode patterns of the sensor fiber could be achieved quickly and precisely. The cross-section was discretized as depicted in Figure 1b, and divided into 106,026 mesh elements in total for computing. A mobile workstation with an Intel Core i7 CPU and 8 G random access memory (RAM) was also utilized to support software operation.
# Tightly-Coupled GNSS/Vision Using a Sky-Pointing Camera for Vehicle Navigation in Urban Areas
## 5 Algorithm Development: GNSS/Vision Integration
### Tightly-Coupled GNSS/Vision
#### 5.4.2 Vision Observables  
The vision observables that are added to the tight integration filter are the rotation and translation around and along the axes, as depicted in Figure 11.  
However, as presented in [42], it is also possible to consider the distance and azimuth rate as the vision measurement, by considering the elapsed time between corresponding frames. The vision system measurement vector is given as:  
\[\delta\mathbf{z}_{Vision}=\begin{bmatrix}\mathbf{r}_{c_{b},k}\\ \mathbf{t}_{c_{b},k}\end{bmatrix} \tag{36}\]  
where \(k\) corresponds to the image frame number.
# An Optimal Resource Allocation Scheme with Carrier Aggregation in 5G Network under Unlicensed Band
## 3 System Model
### Reserved Unlicensed Spectrum Minimization Problem with QoS Satisfying Rate-Sensitive UE
\begin{split}\textbf{P1:}\text{min}_{\gamma,\xi}& \sum b_{w}(\gamma,\xi)\\ \text{s.t.}&\sum b_{w}\leq B,w\in\textbf{W}\\ & E[C_{w}](\gamma,\xi)\geq C_{t},w\in\textbf{W}\\ & Pr_{w}^{c}\leq Pr_{thr,w}^{c},w\in\textbf{W}\end{split}  \tag{20}\]  
where the summation of unlicensed band used by the Wi-Fi users should not be greater than the provided system spectrum bandwidth \(B\). The mean achievable rate for rate-sensitive UE associated with Wi-Fi APs is \(E[C_{w}]\). The \(Pr_{w}^{o}\) and \(Pr_{thr,w}^{o}\) are the outage probability and outage probability threshold for rate-sensitive UE associated with Wi-Fi APs, respectively. The pre-reserved unlicensed spectrum minimization problem for rate-sensitive UE can be solved by utilizing the Gradient descent method with the Bisection method [33]. Numerical results for the optimization problem (20) are shown in Figure 2, which represents the mean achievable rate of rate-sensitive UE \(E[C_{w}]\) as a function of unlicensed spectrum allocation ratio \(\gamma\) with different power ratios \(\xi\). In Figure 2, with \(\gamma\) increasing from 0.05 to 0.95 when the power ratio is 0.5, \(E[C_{w}]\) decreases due to the decrease in the allocated unlicensed spectrum. With power allocation ratio \(\xi\) increasing, \(E[C_{w}]\) increases in the same spectrum allocation ratio. Specifically, with \(\xi\) increasing, optimal \(\gamma\) for maximum \(E[C_{w}]\) increases due to inter-channel interference between UE. The QoS threshold of the Wi-Fi system shown in the figure guarantees the performance of rate-sensitive UE by restricting the maximum unlicensed spectrum allocation ratio \(\gamma\) for the corresponding power allocation ratio \(\xi\), which also implies that the minimum spectrum resources need to be served to guarantee the performance of rate-sensitive UE associated with Wi-Fi APs.