esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussions, Conclusions, and Future Directions
### Future Directions  
For future research, there is a critical need to broaden the scope of experimentation by testing a more extensive range of channels with specifically tailored waveforms. This expanded testing approach will provide a deeper understanding of how different channelcharacteristics interact with various waveform designs, thereby enhancing the performance and adaptability of JCAS systems.  
Additionally, as MIMO communication and radar systems become increasingly prevalent, it is essential to consider factors such as the number of users and data streams. These factors significantly impact system performance and efficiency, necessitating thorough exploration and optimization to achieve optimal results.  
Addressing the challenges related to waveform selection, discrimination algorithms, and resource management is paramount for the successful implementation of joint systems. Researchers must develop innovative solutions to mitigate these challenges, ensuring the seamless integration and operation of communication and sensing functionalities.  
The effective management of datasets is crucial for training robust and generalizable DNN models. This involves the meticulous organization of training, validation, and test datasets, as well as the comprehensive analysis and optimization of hyperparameters, such as the learning rate, batch size, and optimizer settings.  
Furthermore, extending joint design systems to encompass MIMO downlink communication in advanced network generations like 5G or beyond 6G is essential for staying at the forefront of technological advancements. This expansion will facilitate the evaluation of the communication and radar sensing performance in realistic multi-user environments, enabling researchers to overcome the limitations of the conventional SISO systems and propel the field towards new frontiers of innovation and functionality.  
Conceptualization, P.N.L., A.T., K.K. and C.P.; software, P.N.L. and C.P.; validation, P.N.L.; formal analysis, P.N.L.; investigation, P.N.L., A.T. and K.K.; resources, K.K.; data curation, P.N.L. and C.P.; writing--original draft preparation, P.N.L.; writing--review and editing, A.T.; visualization, P.N.L.; supervision, A.T. and K.K.; funding acquisition, P.N.L. and K.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Electronics and Computer Technology Center (NECTEC) and National Science and Technology Development Agency (NSTDA).  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request due to the data being part of an ongoing study.  
The authors would like to express our appreciation to the Professors at the Faculty of Information and Communication Technology, School of Engineering and Technology, Asian Institute of Technology and the researchers at the National Electronics and Computer Technology Center (NECTEC) for their guidance and assistance in obtaining relevant information.  
The authors declare no conflict of interest.
# Around-View-Monitoring-Based Automatic Parking System Using Parking Line Detection
## 4 Experimental Results
### Experimental Environment
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parking Type** & **No.** & **X Offset** & **Y Offset** & **Heading Angle** \\ \hline \multirow{8}{*}{Perpendicular parking} & 1 & 0.053 & 0.204 & 0.1 \\  & 2 & 0.27 & 0.211 & 0.5 \\  & 3 & 0.075 & 0.197 & 0.4 \\ \cline{1-1}  & 4 & 0.11 & 0.023 & 0.2 \\ \cline{1-1}  & 5 & 0.104 & 0.024 & 0.2 \\ \cline{1-1}  & 6 & 0.064 & 0.01 & 0.2 \\ \cline{1-1}  & 7 & 0.028 & 0.173 & 0.4 \\ \cline{1-1}  & 8 & 0.072 & 0.162 & 0.3 \\ \cline{1-1}  & 9 & 0.083 & 0.074 & 0.2 \\ \cline{1-1}  & 10 & 0.053 & 0.204 & 0.1 \\ \hline \multirow{8}{*}{Angle parking} & 1 & 0.069 & 0.083 & 2.4 \\  & 2 & 0.143 & 0.004 & 1.7 \\ \cline{1-1}  & 3 & 0.2 & 0.016 & 1.1 \\ \cline{1-1}  & 4 & 0.178 & 0.053 & 1.4 \\ \cline{1-1}  & 5 & 0.112 & 0.002 & 1.3 \\ \cline{1-1}  & 6 & 0.191 & 0.067 & 2.1 \\ \cline{1-1}  & 7 & 0.136 & 0.048 & 2.2 \\ \cline{1-1}  & 8 & 0.105 & 0.009 & 1.2 \\ \cline{1-1}  & 9 & 0.254 & 0.007 & 1.0 \\ \cline{1-1}  & 10 & 0.154 & 0.023 & 1.6 \\ \hline \multirow{8}{*}{Parallel parking} & 1 & 0.073 & 0.108 & 0.8 \\ \cline{1-1}  & 2 & 0.096 & 0.114 & 1.2 \\ \cline{1-1}  & 3 & 0.058 & 0.099 & 0.7 \\ \cline{1-1}  & 4 & 0.041 & 0.104 & 0.6 \\ \cline{1-1}  & 5 & 0.109 & 0.089 & 1.1 \\ \cline{1-1}  & 6 & 0.172 & 0.104 & 0.7 \\ \cline{1-1}  & 7 & 0.138 & 0.098 & 1.2 \\ \cline{1-1}  & 8 & 0.077 & 0.087 & 1.4 \\ \cline{1-1}  & 9 & 0.048 & 0.112 & 1.0 \\ \cline{1-1}  & 10 & 0.04 & 0.097 & 0.9 \\ \hline \hline \multicolumn{2}{c}{Average} & 0.1027 & 0.0889 & 0.94 \\ \hline \end{tabular}
\end{table} 
Table 3: Results of automatic parking using the AVM-based system.
# Genetic, Physiological, and Industrial Aspects of the
## 4 Industrial Application of _S. bacillaris_
### S. bacillaris as a Co-Starter in Grape Must Fermentations  
In mixed alcoholic fermentations, _S. bacillaris_ preferentially consume fructose, providing the evolving _S. cerevisiae_ cell population with the use of glucose at both middle and later fermentation stages [60; 73]. Thus, co-inoculation of _S. bacillaris_ and _S. cerevisiae_ strains can result in complete fermentation of the major sugars present in musts [59; 63; 73]. Moreover, mixed fermentations with _S. bacillaris_ can also alleviate osmotic stress for the prevailing _S. cerevisiae_ cells, improving fermentation kinetics and reducing acetic acid production [8; 65; 73; 74]. _S. bacillaris_ strains can also reduce the final contents of malic acid in wines [6; 72; 75; 76]. This phenomenon appears to be dependent on the use by _S. bacillaris_ of malic acid and/or on the stimulation of the malalactic activity of _O. oeni_, thus playing an indirect role in driving malolactic fermentation [75]. It has also been reported, however, that the inoculation of a _S. bacillaris_ strain inhibited malolactic fermentation, possibly by the presence of inhibitory compounds that negatively affected the yeast-bacteria interaction [77].  
_S. bacillaris_ can normally maintain relatively high cell population levels up to the middle [25; 60; 78] or even to the final [73; 79] stages of fermentation. This may have negative and/or antagonistic consequences of _S. bacillaris_ in _S. cerevisiae_ growth. In fact, mixed inoculations of _S. cerevisiae_ with _S. bacillaris_ can lead to a reduction in maximum _S. cerevisiae_populations in comparison with _S. cerevisiae_ single starter fermentations [59; 60; 63; 80]. This could be related to a decrease in nutrient concentrations in the must [25; 60; 78]. _S. bacillaris_ death in mixed inoculated fermentations has also been investigated. [PERSON] et al. [79] demonstrated that high ethanol concentrations (\(\sim\)11.4% \(v/v\)) did not influence viability loss of _S. bacillaris_. What is more, it has been shown that _S. bacillaris_ strains were able to grow at ethanol concentrations as high as 14% \(v/v\), which could contribute to the successful implantation and good performance of this species during fermentations [10; 17; 50]. Other _S. bacillaris_ isolates were reported to have low tolerance to alcohol levels (up to 5% \(v/v\) ethanol) [11; 12]. Because the production of ethanol and other toxic metabolites by _S. cerevisiae_ (such as killer toxins, SO\({}_{2}\), and short- to medium-chain fatty acids) have not resulted in a negative co-existence of _S. bacillaris_ populations [11; 78], cell-to-cell contact mechanisms may be associated with _S. bacillaris_ cell death [78]. Finally, it should be stressed that all reported _S. bacillaris_ and _S. cerevisiae_ interactions could be strain-specific more than species-specific [25; 59].
# A Forwarding Latency Optimization Method for Software Data Plane Based on Spin-Polling
## 6 Evaluation
### The Selection of Threshold  
On the other hand, the choice of the threshold \(H\) is also quite critical because the choice of the threshold \(H\) for CPU utilization determines when the data plane switches strategies. From Figure 5 in Section 3.2, we can see that most of the latency curves start to rise when the input rate exceeds 10 Gbps, so that we can choose the CPU utilization at this point as our threshold \(H\). Moreover, the CPU utilization of the data plane varies at different \(\theta_{Tx}\). Table 3 shows the CPU utilization of the data plane at an input rate of 10 Gbps for different \(\theta_{Tx}\). As seen in Table 3, when the input rate is at 10 Gbps, the CPU utilization is around 63%, except for the case \(\theta_{Tx}=4\). This is because the CPU utilization rises more significantly the closer the input rate is to the maximum throughput rate. In addition, the smaller \(\theta_{Tx}\) is, the smaller the maximum throughput rate of the data plane is. The starting point of our scheme is to make full use of the CPU to reduce the packet latency, so we should choose as small a threshold as possible. Thus, we can choose the CPU utilization at \(\theta_{Tx}=64\) as the threshold \(H\) of our scheme.  
Figure 11: The maximum throughput rate of data plane at different observation times.
# Intra-Articular Bone Marrow Aspirate Concentrate Injection in Patients with Knee Osteoarthritis
## 4 Discussion
### Study Limitations and Strengths  
In the current study, patients with K-L Grade III or IV showed a lower rate of improvement in clinical outcomes and a higher rate of secondary operations, such as a conversion to TKA and treatment failure, than those with K-L Grade I or II. Thus, it can be concluded that BMAC injections would not be useful in patients with K-L Grade III or IV. Despite the meaningful results, the following limitations of this study need to be considered. First, the study had a retrospective study design with no control group. Prospective randomized controlled trials are needed to understand the effects of the BMAC injection. Secondly, radiological and/or other imaging information, including a follow-up MRI or arthroscopy, would be needed to obtain objective data regarding the efficacy of this injection. Thirdly, a female predominance was observed in this study because women in South Korea show a higher rate of knee OA than women in Western countries [36]. Fourth, this was a mid-term follow-up study. Thus, it is possible that long-term follow-up might reveal different outcomes with regard to clinical efficacy. Therefore, long-term studies are needed in the future. Fifth, adverse events of this procedure should be considered, including joint pain, swelling and skin rash or itching [15]. Although most of the reported complications for BMAC might be self-limited and generally resolved without specific intervention, they should be explained and recognized prior to administration. Nevertheless, the strengths of this study are as follows. First, to our knowledge, this is the first study to report on the 5-year follow-up after an intra-articular BMAC injection. Secondly, a variety of clinical assessments were performed; therefore, the patient-reported outcomes can be considered sufficiently reliable. A BMAC injection can be a relatively simple and less invasive alternative treatment for knee OA.
## 2 Materials and Methods
### HS-SPME-GC-MS Analysis of Soil Validime Profile  
Five grams of sieved soil samples were placed in 20 mL headspace vials containing 1 mL of 20% saltwater (0.2 g NaCl/1 mL double-distilled water). The inclusion of salt in this step of the HS-SPME procedure facilitated the extraction of a complex array of compounds, ranging from polar to non-polar ones, with diverse physicochemical properties and varying vapor pressures. The headspace vials were sealed with crimp-top caps with PTFE-silicone headspace septa (Thermo Fisher Scientific, Waltham, MA, USA). The headspace vials were incubated at 45 \({}^{\circ}\)C for 30 min with the preconditioned (10 min at 45 \({}^{\circ}\)C) 1 cm length three-phase divinylbenzene/carboxen/poly-dimethylsiloxane (50 \(\upmu\)m (DVB layer), 30 \(\upmu\)m (CAR/PDMS layer)) extraction fiber. The captured volatile organic compounds were then thermally desorbed for 5 min onto the GC-MS (Trace GC 1310 TSQ 9000 MS, Thermo Fisher Scientific, Waltham, MA, USA) injection port, which was set at 250 \({}^{\circ}\)C in splitless mode. The volatile organic compounds were separated using a DB-WAX capillary column (30 m \(\times\) 0.25 mm i.d. \(\times\) 0.25 \(\upmu\)m film thickness; J & W Scientific Inc., Folsom, CA, USA). Ultra-high-purity helium was used as the carrier gas at a linear velocity of 1 mL-min\({}^{-1}\). The oven temperature program was as follows: an initial temperature of 35 \({}^{\circ}\)C, heated to 180 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C-min\({}^{-1}\), increased to 230 \({}^{\circ}\)C at a rate of 15 \({}^{\circ}\)C-min\({}^{-1}\), and then held there for 1 min. Mass spectra were recorded in electron impact (EI) ionization mode at 70 eV using a mass spectrometer (TSQ 9000 MS, ThermoFischer Scientific, Bremen, Germany). The quadrupole mass detector, ion source, and transfer line temperatures were set at 150, 230, and 280 \({}^{\circ}\)C, respectively. The mass spectra were scanned in the range of m/z 50-550 amu. VOCs were identified by comparing the mass spectra with the NIST 14 database system library and linear retention index. The criterion for compound identification was a mass-spectrum matching score of \(\geq\)80%. The results were expressed as a percentage of the relative peak area of a peak for each soil sample, which was calculated by dividing the peak area by the total peak area of all identified peaks in each chromatogram. Each sample's total ion chromatogram (TIC) was used for peak-area integration.
# Research Progress on Machine Learning Prediction of Compressive Strength of Nano-Modified Concrete
## 4 The Application of Machine Learning in Nano-Modified Concrete
### Decision Tree (DT) Algorithm  
The decision tree (Figure 7) constructs classification rules through feature threshold partitioning (e.g., nano-CaCO\({}_{3}\) dosage \(\leq\) 1.5%). Leaf nodes output strength prediction values (e.g., 45.3 MPa), forming an explicit decision path from material parameters to mechanical properties.  
The advantages of decision tree are that it has low computational complexity and are easy to interpret; the data preprocessing stage is relatively simple and can handle missing data; and decision trees can be constructed for datasets with many attributes.  
By comparing the performance of depth AutoEncoder (AE), DT, Extreme Learning Machine, and hybrid deep learning models for predicting compressive strength of nano-silica modified Engineered Cementitious Composites (ECC) at elevated temperatures, [PERSON] et al. [53] found that the accuracy of the hybrid depth AE-DT model for estimating the CS was 94.28%, so it is recommended to use the hybrid depth learning model of AE-DT for predicting the ECC strength of concrete. [PERSON] et al. [23] built a model of compressive strength of concrete modified using carbon nanotubes (CNTs) by individual predictions of DT, multilayer perceptron neuron network, and SVM, as well as an integrated learning model, Ada-boost-based DT, and ultimately finds that the integrated learning model has the smallest error and good overall performance. A single decision tree is prone to overfitting due to excessive subdivision of nanomaterial interactions (e.g., training set R\({}^{2}\) = 0.98, test set R\({}^{2}\) = 0.72). As shown in Figure 8, the random forest model improves the test set R\({}^{2}\) to 0.89 and reduces variance by 62% through Bootstrap sampling (80% of samples) and random subspace selection to construct 300 subtrees.
## 2 Materials and Methods  
### Theoretical Safety Stock Estimation  
Safety stock can be calculated using different approaches. The most appropriate method depends on the circumstances of an organization. As the stock-out cost can be very difficult to estimate in practice, calculating the SS based on an ordering policy that does not require knowledge of cost is recommended. Customer service is frequently used in the literature.  
The safety stock dimensioning depends on the levels of demand uncertainty and the corresponding forecast errors when the forecast errors are assumed independent and identically distributed (i.i.d). In other words, the forecast errors follow a normal distribution with zero mean and constant variance; safety stock is computed using Equation (1).  
\[\text{SS}=\text{k}\sigma_{\text{L}}, \tag{1}\]  
where:  
\(\sigma_{\text{L}}\): standard deviation of the forecasting error for a certain lead time LT,  
k: the safety factor with a chosen cycle service level (CSL). It is estimated using Equation (2).  
\[\text{k}=\text{$\phi$}^{-1}(\text{CSL}), \tag{2}\]  
\(\text{$\phi$}(.)\) is the standard normal cumulative distribution function.  
The lead time (LT) is considered to be known and constant. In this case, the problem is how to calculate \(\sigma_{\text{L}}\). An initial theoretical approach is considered in which the standard deviation of the forecasting error \(\sigma_{\text{L}}\) is estimated on the basis of the demand forecasting model.  
\(\sigma_{\text{L}}\) is estimated using the standard deviation \(\sigma_{1}\) which is based on forecasting the mean squared error (MSE).  
\[\sigma_{1}=\sqrt{\text{MSE}}, \tag{3}\]  
The cumulative variance forecasting error is calculated and then the safety stock is computed using the theoretical well-known formulas as mentioned in Equation (4).  
\[\text{SS}=\text{k}.\sigma_{1}.\sqrt{\text{LT}}\,, \tag{4}\]Equation (4) is used when the forecast errors are independent and do not vary for longer forecast horizons [34]. However, when demand can be expressed by a single exponential smoothing (SES) model method [35]. The safety stock will be expressed in Equation (5).  
\[\text{SS}=\text{k.}\sigma_{1}.\sqrt{\text{LT}}\sqrt{1+\alpha^{\prime}(\text{LT} -1)+\frac{1}{6}\alpha^{\prime 2}(\text{LT}-1)(2\text{LT}-1)}\, \tag{5}\]  
where:  
\(\alpha^{\prime}\): is the SES parameter that is constant and varies between 0 and 1.  
Equation (4) of the safety stock is the most useful in the literature, although Equation (5) offers an accurate relationship. One of the major drawbacks of the theoretical approach is that it disregards the fact that the forecast errors are not i.i.d in reality. In addition, the true model of each item is not found and also the choice of the forecasting error model cannot be under the control of the firm. To overcome the i.i.d problem of the forecast errors and the problem when the distribution of forecast errors is unknown, a second empirical approach is considered in which neither the point forecast model nor its parameters must be known. This is mainly beneficial in practice, especially when such information is not provided to users.
# AI-Driven Pilot Overhead Reduction in 5G mmWaveMassive MIMO Systems
###### Abstract  
The emergence of 5G technology promises remarkable advancements in wireless communication, particularly in the realm of mmWave (millimeter-wave) massive multiple input multiple output (m-MIMO) systems. However, the realization of its full potential is hindered by the challenge of pilot overhead, which compromises system efficiency. The efficient usage of pilot signals is crucial for precise channel estimation and interference reduction to maintain data integrity. Nevertheless, this requirement brings up the challenge of pilot overhead, which utilizes precious spectrum space, thus reducing spectral efficiency (SE). To address this obstacle, researchers have progressively turned to artificial intelligence (AI) and machine learning (ML) methods to design hybrid beam-forming systems that enhance SE while reducing changes to the bit error rate (BER). This study addresses the challenge of pilot overhead in hybrid beamforming for 5G mmWave m-MIMO systems by leveraging advanced artificial intelligence (AI) techniques. We propose a framework integrating k-clustering, linear regression, random forest regression, and neural networks with singular value decomposition (NN-SVD) to optimize pilot placement and hybrid beamforming strategies. The results demonstrate an 82% reduction in pilot overhead, a 250% improvement in spectral efficiency, and a tenfold enhancement in bit error rate at low SNR conditions, surpassing state-of-the-art methods. These findings validate the efficacy of the proposed system in advancing next-generation wireless networks.  
h _Applied system_  
19 November 2024  
Revised: 22 January 2025  
Accepted: 8 February 2025  
Published: 13 February 2025
## 4 Algorithm Design
### Loop Structure and Complete Algorithm Process  
Since the overall coding is unknown to a single UAV, it can only adjust its own position singly in one loop, so we added a nested loop to the original iterative structure. We use the order in which the ABS emits signals as the rule for iteration. As shown in Figures 2 and 3, the slice of the formation is divided into two parts: polygons (with corners) and circles (with arcs), and circles and polygons are analyzed using circles and equilateral triangles, respectively. For an equilateral triangle (Figure 3), the launch sequence is to start from the top corner and move to the opposite side according to the scale of three UAVs. The UAV that is adjusted after each signal transmission is the positioning point for the next transmission signal so that the positioning points are optimized before use to ensure the accuracy of positioning. And after a complete iteration, all UAVs in the formation have adjusted their positions three times, which is very even. The same is true for the circles (Figure 2). The framework of the IGA Algorithm 1 is:  
```
input :The number of elements to be moved: \(num\) output :The obtained best solution: \(\pi^{*}\)
1\(\pi\leftarrow\mathit{Initialization}()\);
2\(\pi\leftarrow\mathit{LocalSearch}()\);
3\(\pi^{*}\leftarrow\pi\);
4 whileNotTerminationdo
5 whileNotTerminationdo
6\(\pi^{\prime}\leftarrow\mathit{Construction}(\pi^{D},\pi^{C})\);
7\(\pi^{\prime}\leftarrow\mathit{LocalSearch}(\pi^{\prime})\);
8 if\(\mathit{Evaluation}(\pi^{\prime})<\mathit{Evaluation}(\pi)\)then
9\(\pi^{*}<\pi^{\prime}\);
10
11 end if
12 if\(P\)then
13\(\pi^{D},\pi^{C}\leftarrow\mathit{Destruction}(\pi,num)\);
14
15 end if
16
17\(\pi\leftarrow\mathit{AcceptanceCriterion}(\pi,\pi^{\prime})\);
18
19 end while
```  
**Algorithm 1**Enhanced Iterative Greedy Algorithm  
Figure 2: Polygon array signal reception strategy.