esa-sceva/satcom-chunk-collection huggingface dataset:
# Laser Finishing of Ti6 Al4V Additive Manufactured Parts by Electron Beam Melting
## 3 Results
### Surface Roughness  
Figures 10 and 11 depict the comparison of 3D maps and surface profiles acquired by the profilometer respectively on the as-built and on the laser-polished samples. The analysis confirms the smoother surface when the lower focal distance was adopted (Fd = 5 mm). In order to better analyze the improvement, all the acquired roughness parameters are reported in Figure 12 vs. the laser ones. For the as-built samples, an arithmetic mean surface roughness (Ra) of about 27 \(\upmu\)m is observable; while for the treated sample, in the best conditions, a Ra value of about 5 \(\upmu\)m can be obtained, allowing an 80% reduction in the roughness surface.  
ANOVA was performed to assess the influence of the process parameters on sample roughness; the residual distribution was previously checked and a confidence level of 95% was adopted (\(\alpha\) = 0.05). The results for Ra are reported in Table 7, where the model summary is also reported in terms of R\({}^{2}\). It is worth noting that the R\({}^{2}\) value is very high, this means that the model has a good fit; the predicted R\({}^{2}\) is high, thus the model has a good predictive ability. Based on these assumptions, both the control factors (Fd and Ss) and their interaction are significant (i.e., \(p\)-value less than 0.05). However, the most affecting factor is surely the focal distance (Fd), since it shows the greatest F-value. Figure 13 depicts the main effect plot and interaction plot for Ra. Figure 13a shows that the effect of the control factors (i.e., the laser parameters). The roughness increases when the focal distance and scan speed increases (i.e., when energy density and laser-material interaction decrease); this result is consistent in literature. The effect of the interaction between the focal distance and scan speed can be seen in Figure 13b: at Fd = 7.5 mm the roughness parameter is not affected by a significant variation, while at Fd = 5 mm,  
Figure 12: Roughness parameters vs. laser parameters.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source** & **DF** & **Adj SS** & **Adj MS** & **F-Value** & \(p\)**-Value** \\ \hline Fd (mm) & 1 & 623.46 & 623.463 & 142.26 & 0.000 \\ Ss (mm/min) & 2 & 149.35 & 74.673 & 17.04 & 0.003 \\ Fd (mm) \(\times\) Ss (mm/min) & 2 & 194.40 & 97.199 & 22.18 & 0.002 \\ Error & 6 & 26.29 & 4.382 & & \\ Total & 11 & 993.50 & & & \\ \hline \multirow{2}{*}{**Model Summary**} & \multicolumn{2}{c}{**R2**} & **R2 (adj)** & **R2 (pred)** \\  & \multicolumn{2}{c}{97.35\%} & 95.15\% & 89.41\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Analysis of variance results for Ra.
# SROR: A Secure and Reliable Opportunistic Routing for VANETs
## 3 System Model and Routing-Related Metrics
### Relative Velocity  
As shown in Equation (1), the end-to-end delay depends on the distance \(D_{i,j}\) when the length of transmitted packet and transmission rate are given. In fact, in the same VANET system, the length of packet and transmission rate of different vehicles is a little difference. Relative distance can more accurately reflect their spatial location relationship due to the mobility of vehicles, while the relative distance depends on the relative velocity. If the mobility direction of two vehicles is the same, small relative velocity can make the distance unchanged for a long time. The unchanged distance is conducive to network connectivity, and this will improve the packet delivery ratio.  
As a consequence, the relative velocity is considered an important routing metric. Furthermore, it will be taken into account when the next-hop relay node is selected. For vehicle pairs \((\vartheta_{i},\vartheta_{j})\), their relative velocity is denoted by \(\upsilon_{i,j}\), which can be calculated as [9]:  
\[\upsilon_{i,j}=\sqrt{(\upsilon_{i})^{2}+(\upsilon_{j})^{2}-2\upsilon_{i} \upsilon_{j}cos\theta}, \tag{3}\]  
where \(\upsilon_{i}\) and \(\upsilon_{i}\) represent the velocity of \(\vartheta_{i}\) and \(\vartheta_{j}\), respectively, and \(\theta\) is the angle between \(\vartheta_{i}\) and \(\vartheta_{j}\), which can be defined as:  
\[\theta=\begin{cases}\text{arctan }\frac{Y_{i,j}}{X_{i,j}}&\text{if }X_{i,j}\geq 0,Y_{i,j}>0;\\ \text{arctan }\frac{Y_{i,j}}{X_{i,j}}+\pi&\text{if }X_{i,j}<0,Y_{i,j}\
eq 0;\\ -\pi&\text{if }X_{i,j}<0,Y_{i,j}=0;\\ \text{arctan }\frac{Y_{i,j}}{X_{i,j}}+2\pi&\text{if }X_{i,j}\geq 0,Y_{i,j}<0; \\ \end{cases} \tag{4}\]  
where \(X_{i,j}=x_{i}-x_{j}\) and \(Y_{i,j}=y_{i}-y_{j}\). Furthermore, \((x_{i},y_{i})\) and \((x_{j},y_{j})\) represent the coordinates of \(\vartheta_{i}\) and \(\vartheta_{j}\), respectively.
# GPU Accelerated PIC and SIC for OFDM-NOMA
## 4 Numerical Results and Discussion
The time gradually increased to 157 ms and 330 ms for 350 users in a cell respectively for FFT sizes of 2048 and 4096; whereas, PIC on GPU was the fastest. The execution time of PIC on GPU was almost 90- and 138-times faster than on the CPU for different OFDM FFT sizes and less sensitive to the number of users. It took only 2.54 ms for 50 users and 6.88 ms for 350 users with an FFT size of 4096 and approximately 2 ms for any number of users for an FFT size of 2048. Furthermore, it was observed that PIC was nearly 75-times faster than SIC on GPU for a large number of users with a 4096  
Figure 4: Bit error rate (BER) versus the number of users (\(K\)) for PIC and SIC. FFT size and 220-times faster for a 2048 FFT size. This was because SIC had mutual data dependency within users during execution iterations and had to be executed serially on both CPU and GPU.
# Towards Client Selection in Satellite Federated Learning
## 3 System Model  
This section begins with the model assumptions and, secondly, the communication model is introduced. Then, an overview of the general framework of FL algorithms is given. Finally, we introduce the CS in SFL.
## 1 Introduction
### Contribution and Innovation of the Paper  
In this work, for the first time we investigate the applicability of the same methods to the co-channel coexistence of IEEE 802.11p and NR-V2X, which is the latest standard defined by the 3 GPP for connected vehicles. We consequently also used IEEE 802.11 bd; as clarified in the following, IEEE 802.11 bd adds new features to IEEE 802.11p but shares the same access mechanism, so results would be very similar if IEEE 802.11 bd was considered with a similar duration of the transmission. Furthermore, although NR-V2X shares similar principles with LTE-V2X, there are two different characteristics that should be taken into account when looking at the co-channel coexistence with IEEE 802.11p/bd:  
* NR-V2X introduces numerology; different numerologies lead to different subcarrier spacing (SCS) and transmission time interval (TTI);
* In NR-V2X, the resource selection process is mainly based on reservations indicated in the control information, rather than on the average measured power over the last time interval.  
How much these differences impact the performance of technologies under co-channel coexistence has never been addressed in the previous literature, thus deserving further studies. To fill this gap, we here consider several of the coexistence mitigation methods, namely methods A, B, C, and F, discussing their applicability to the new standard and comparing their performance in realistic scenarios. As will be clarified further, the methods that are not analyzed, i.e., D and E, require the implementation in LTE-V2X or NR-V2X stations of the full IEEE 802.11p stack and are therefore not compliant with the assumption that each station is equipped with only one of the technologies.  
As part of the contribution of this work, the results are obtained by specifically modifying the open-source simulator WiLabV2 Xsim [15], thus providing a platform for the development and testing of new ideas from any researcher interested in this topic. (The simulator is available at [[https://github.com/V2](https://github.com/V2) Xgithub/WiLabV2 Xsim]([https://github.com/V2](https://github.com/V2) Xgithub/WiLabV2 Xsim) (accessed on 25 April 2023) and will include the mitigation methods in its next release).  
Results provide a detailed analysis of the performance of both IEEE 802.11p and NR-V2X under several metrics, in scenarios with variable density and variable proportions of vehicles equipped with either technology, and for both periodic and non-periodic traffic. As it will be shown, Methods A and C appear to be the only two able to show an overall positive impact on the performance of the two technologies, although they also imply modifications to the standards and drawbacks that are further clarified in the following. All results are discussed also in comparison with the case of coexistence between IEEE 802.11p and LTE-V2X, highlighting the few but relevant differences.
# Cleaner Processes for Making Laundry Soap from Vegetable Oils and an Essential Oil
## 2 Materials and Methods
### Experimental Protocol  
The choice of saponification method for soap formulation takes into account the properties, the time required to obtain the soap, and the destination (toilet or laundry). For a less harsh soap, which will retain its scent for a long period of time, cold saponification is preferable because the low temperature does not accelerate the volatilization of some compounds from the essential oils included in the soap. In contrast, harder soaps, made faster, can be formulated by hot saponification. By practicing cold (30 \({}^{\circ}\)C, 30 min with continuous stirring) and hot saponification (100 \({}^{\circ}\)C, 1 h with continuous stirring), several ecological soaps are formulated using olive pomace oil (50 g), coconut oil 76 degrees (21 g) and an essential oil: Neem or Tea tree (ratio 31), or Thyme (ratio 16.9). In soap formulation, the perfume ratio (31 and 16.9) refers to the mass of essential oil added to 1000 g of the base oil mixture (olive and coconut). In Table 1, the essential oil concentration in the saponification recipe, expressed as a percentage, actually refers to the mass of essential oil added to 100 g of base oil mixture. Throughout the saponification process, strong and continuous stirring is practiced. The addition of the essential oil is carried out at the end of the saponification process and to obtain good homogeneity of the mixture of saponified oils, mixing is continued for another 5 min. The recipes used to obtain the soaps are shown in Table 1.  
Other information regarding the formulated soap recipes: mass of the oil mixture for formulating a soap = 71 g; water as percent of oil weight = 38.00%; lye concentration = 28.112%; water: lye ratio = 2.5572;1; fragrance ratio =16.9-31 g/1000 g base oil mixture (olive and coconut). Super Fat/Discount = 0%; Super Fat/Discount of 0% is specific to the formulation of laundry soap meaning zero excess of fat/triglycerides in the recipe. This means that there are no additional oils left unreacted at the end of the soap-making process. With these recipes, the soaps will be drier, harder and less smooth on the skin, but have a very good cleansing effect. Due to the strong fragrance of Thyme essential oil, only a percentage of 1.69 is used in the formulation of CS4 and HS4 soaps as a precautionary measure so that these soaps can also be used by people sensitive to odors, such as those with bronchial asthma. The concentrations of Thyme essential oil (1.69%) and Neem (3.1%) could give the laundry soap mild and gentle antimicrobial capacities, a pleasant, non-irritating fragrance, and a certain cleaning power. In contrast, the concentration of 3.1% for Tea Tree essential
## 5 Discussion
### Spatiotemporal Variation in Grassland AGB during 1982-2018  
In this study, the spatial distribution of grassland AGB confirmed previous findings that the grassland AGB decreased gradually from southeast to northwest in the TRHR [31]. We found a strong spatial correlation between the distribution of Ta and P in the TRHR.  
Figure 13: Probability density distributions of the predictive errors of MODIS-derived reference and AVHRR-derived estimated AGB in three AGB upscaling models, respectively.  
This was also documented by [PERSON] et al. [82]. Figure 9 presents that the grassland AGB increased 0.37 g C m\({}^{-2}\) per year during 1982-2018 in the TRHR. However, the main cause of the interannual increasing trend of grassland AGB is still controversial [37; 40; 41]. [PERSON] et al. [40] suggested that the interannual increasing trend of grassland AGB in the TP was significantly and positively correlated with temperature during 2000-2014. [PERSON] et al. [41] suggested that the national conservation policies of returning cultivated land to forests or grasslands since 1999 and grazing ban laws since 2004 may have promoted grassland restoration [83], which caused the increasing trend of grassland AGB in the TP during 2000-2017. [PERSON] et al. [37] presented that the primary factors influencing the increasing trend of grassland AGB in the TP were anthropogenic activities and climate warming during 2000-2012. Reflected by our results, NDVI and P are the two primary factors influencing the interannual variation in grassland AGB.  
Our study also discussed the turning point in the long-term temporal dynamics of grassland AGB in the TRHR during 1982-2018 (Figure 9). The turning point of AGB was identified as occurring in 1998 by the sequential Mann-Kendall test (Figure 9a). Before 1998, the increasing trend of AGB was mainly attributed to the increasing NDVI. The positive relationship between NDVI and AGB in the TRHR was also documented by [PERSON] et al. [30]. The AGB increased rapidly in 1998, coincident with the El Nino event, when the rising P was matched by an increasing available soil water content [82; 84]. After 1998, the increasing AGB coincided with an increasing P. The annual average AGB was relatively higher in the years with higher mean annual P (e.g., 2012 and 2014) than in the surrounding years (Figure 9b). Previous studies demonstrated that AGB increased with P in the TRHR or TP [30; 34], which provides evidence for our results. Therefore, P was the main contributor to the increasing trend of AGB during 1998-2018.
# Predicting the Evolution of Syntenies--An Algorithmic Review
## 5 Accounting for Gene Trees
### Evolution through Segmental Duplications and Losses
#### 5.3.3 Unordered Super-Reconciliation  
As ignoring rearrangements is usually much too restrictive and asking for consistent gene orders is not very realistic, a variant of the above model is to allow for rearrangements, yet only consider minimizing duplications and losses. Alternatively, this can be seen as a variant of the Super-Reconciliation problem, ignoring gene orders. For example, Figure 5(4) reflects a history for the syntenies \(\{A_{3},A_{4},B_{3},B_{4}\}\) that are not order-consistent, with three duplications and two losses. However, rearrangements are still required on some branches, for example on the branch leading to \(B_{3}\) or the one leading to \(v\).  
Reducing each synteny \(X\) to its set \(Set(X)\) of genes (i.e., ignoring the order of genes), an _unordered evolutionary history_ of a set of syntenies can be represented as a partially binary tree where each internal node \(x\) corresponds to an event \(e(\text{Set}(X))\) with \(X\) being the synteny at \(x\) and \(e\in\{Spe,Dup,pLoss\}\) such that if \(e\) is:  
1. \(Spe\), then \(x\) is a binary node with two children corresponding to syntenies \(Y\) and \(Z\) such that \(\text{Set}(X)=\text{Set}(Y)=\text{Set}(Z)\) and \(s(Y)\) and \(s(Z)\) are the two children of \(s(X)\) in \(S\).
2. \(Dup\), then \(x\) is a binary node with two children corresponding to syntenies \(Y\) and \(Z\) such that \(\text{Set}(Y)=\text{Set}(X)\), \(\text{Set}(Z)\subseteq\text{Set}(X)\) and \(s(X)=s(Y)=s(Z)\).
3. \(pLoss\), then \(x\) is a unary node with a child corresponding to a synteny \(Y\) such that \(\text{Set}(Y)\subsetneq\text{Set}(X)\) and \(s(X)=s(Y)\).  
An Unordered Super-Reconciliation (USR) is a labeled synteny tree representing a valid unordered evolutionary history for \(\mathcal{X}\). The Unordered Super-Reconciliation problem then consists of inferring the USR of minimum cost. As for the ordered version of the problem, the USR Problem reduces to a small phylogeny problem which consists of inferring internal node gene contents of a tree \(\widehat{T}\) leading to a minimal duplication and loss cost. As duplications are already determined by the node labeling of \(\widehat{T}\), only loss events remain to be minimized, which is done by a programming algorithm running in time \(O(|V(T)||\mathcal{G}|)\).
## 2 Operation and Control
### Operation Support Tools
#### 2.5.3 Catastrophes in the Electrical Grid  
Relating to catastrophe, disasters caused by electrical parameters in the grid topology are detected by means of a new method, according to [PERSON] et al. [114].  
[PERSON] et al. [115] build a catastrophe predictor with a tool called wide-area-severity indices (WASI), which is based on random-forest (RF) learning. Following the theme, [PERSON] and [PERSON] [116] build a fault sequence detection algorithm, and in this way the algorithm detects the origin of the fault in the system.  
[PERSON] et al. [117] present a model based on GIS where the model includes a monitoring and management system. [PERSON] and [PERSON] [118] define a catastrophes detection system based on three algorithms. The first algorithm monitors the nominal value of the current, the second algorithm monitors the value of the energy in each substation and the third algorithm compares the initial and final line current. The system has a direct application and has been validated in a real scenario.
## 1 Introduction
This method is useful for observing a two-dimensional display of the delay (dispersion) and the spectrum, allowing the full characterization of the chirp of the pulse. However, the use of a crystal for SHG limits the spectral range to the NIR-visible region and also makes the on-site measurement of the pulse width difficult. In this study, we propose a simple method to evaluate the widths of pulses from a DUV femtosecond laser without use of an interferometer. This technique is based on measurement of a non-resonant two-photon ionization signal in mass spectrometry by translating the prism in the laser's pulse compressor. This method was applied to femtosecond optical pulses emitted at 241 and 219 nm, in addition to pulses emitted at 267 nm, which were generated by four-wave Raman mixing in hydrogen gas. The results obtained herein were compared with the transform-limited pulse widths that were calculated from the spectral bandwidths of the laser used in the experiments.
## 2 Materials and Methods
### Performance Metrics  
The error metrics that are used to validate the atmospheric models using observational data from the closest meteorological stations to the computational grid, employing the nearest neighbor algorithm, are presented. Additionally, the same metrics are used for the comparative analysis of the DA model and its control case counterpart, which is the downscaled model without any data used to correct it.  
A strong statistical framework was created to back up the analysis mentioned above. The following error metrics have been summarized in [31] and are the most regularly utilized in a variety of various disciplines (for example, weather prediction and hydrology) where models are validated using observational data.  
(a) **The bias and absolute bias metric**  
\[BIAS=\frac{1}{N}{\sum}_{i=1}^{N}\left(M_{i}-O_{i}\right)=\overline{M}- \overline{O}, \tag{4}\]  
where N denotes the number of records, M the model prediction, and O the observation records. This statistical parameter provides information in a standard way about systematic errors, i.e., the tendency of the model to overestimate or underestimate the predicted values. BIAS serves as a measure of differences between observations and model predictions, with ideal values closer to zero. Note that bias can be positive or negative, indicating overprediction or underprediction. Sometimes it is of more use to calculate the absolute value of the difference of measured and observed values, which will henceforth be referred to as absolute bias.  
(b) **Root mean squared error (RMSE)**\[\mathbf{RMSE}=\sqrt{\frac{\sum_{i=1}^{N}\left(\mathbf{M}_{i}-\mathbf{O}_{i}\right)^{2}}{N}}, \tag{5}\]  
RMSE (root mean squared error) is utilized as a measure of the variability of the error and offers an indication of the efficacy of the model to predict the general behavior of the system. The ideal value is 0, and the lower the better applies [32].
3. **The correlation coefficient:** \[\mathbf{r}=\frac{\sum_{i=1}^{N}\left((\mathbf{x}_{i}-\overline{\mathbf{x}})(\mathbf{y}_{i}- \overline{\mathbf{y}})\right)}{\left(\sum_{i=1}^{N}\left(\mathbf{x}_{i}-\overline{\mathbf{ x}}\right)^{2}\!\!\sum_{i=1}^{N}\left(\mathbf{y}_{i}-\overline{\mathbf{y}}\right)^{2} \right)^{1/2}},\] (6)  
which measures the linear correlation between forecasts (y\({}_{\text{i}}\)) and observations (x\({}_{\text{i}}\)) and takes values lower or equal to 1, with the higher values being the better ones [31; 32].
# A Critical Examination of the Beam-Squinting Effect in Broadband Mobile Communication: Review Paper
## 4 Studies Related to the Beam Squint Exploitation
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Year** & **Method** & **Results** & **Remarks** \\ \hline
[46] & 2012 & TTD instead of phase shifters &
\begin{tabular}{c} TDD provided good result in \\ overcoming beam squint \\ \end{tabular} & Increasing complexity and cost \\ \hline
[47] & 2012 & Negative Group Delay Circuits & Reduced the beam squint & Hardware complexity \\ \hline
[49] & 2013 & Three beamforming codebook design schemes & Reduced the beam squint &
\begin{tabular}{c} Increases the number of phase \\ shifters, which increases power \\ usage and complexity \\ \end{tabular} \\ \hline
[50] & 2016 & \
\begin{tabular}{c} Algorithm that ensures minimum gain for \\ all frequencies and angles \\ \end{tabular} & Reduced the beam squint &
\begin{tabular}{c} Creatly increases the size of \\ codebook, which leads to high \\ latency \\ \end{tabular} \\ \hline
[51] & 2018 & Alamuti-based beamforming scheme & \
\begin{tabular}{c} May successfully fix the beam \\ squint and increase throughput \\ performance. \\ \end{tabular} &
\begin{tabular}{c} Proposed beam pattern \\ optimization involves \\ eigenvalue and -vector \\ computation, the computation \\ complexity of this scheme grows \\ with the number of antennas \\ \end{tabular} \\ \hline
[52] & 2019 & \begin{tabular}{c} Proposed subcarrier-to-beam allocation \\ scheme \\ \end{tabular} & \
\begin{tabular}{c} Exploited beam squint to \\ enhance system performance \\ \end{tabular} &
\begin{tabular}{c} Hardware complexity since it is \\ applied in digital domain \\ \end{tabular} \\ \hline
[53] & 2019 & \begin{tabular}{c} Frequency-division duplex (FDD) massive \\ MIMO-OFDM systems with hybrid \\ analog/digital precoding using a channel \\ estimation scheme \\ \end{tabular} & \
\begin{tabular}{c} Provided more accurate channel \\ estimation than other conventional \\ methods \\ \end{tabular} &
\begin{tabular}{c} The assignment of pilot \\ subcarriers and the design of \\ training beams were not \\ considered \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison among various studies conducted in the field of beam squint from 2012 to 2022. Figure 17: Near-field system model [67].