esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Running Time  
All above four methods were carried out on the same machine (Intel(r) Core(tm) i5-7300 HQ CPU 2.50 GHz, Memory 8.00 GB, Houston, TX, USA). In the first simulation consisting of 1000 repetitions, the total running time of HRePML, MLMM, FarmCPU, and GEMMA were 3.1419, 22.7274, 4.6653, and 2.4186 h, respectively. Compared with the other two multi-locus MLMM and FarmCPU methods, HRePML was the most computationally efficient, which was only slightly slower than the single-locus GEMMA method. In particular, HRePML achieved about seven times faster than the popular multi-locus method MLMM (Table 2 and Figure 4A). The second simulation also conducted with 1000 repetitions, and the same trend in total running time was observed, which was 3.2273, 27.4473, 4.9198, and 2.3855 h for the four methods, respectively. GEMMA was the fastest method, followed by HRePML, FarmCPU, and MLMM (Table S2 and Figure 4B). In the third and fourth simulations of 100 repeated experiments on the HRePML, the sample sizes and number of markers were investigated on the influence of running time. With sample sizes 500, 1000, 2000, and 4000, the running times were 0.3142, 1.1244, 3.9969, and 39.5439 h, respectively. The results showed that, as the sample size increased, the running time increased nonlinearly (Table 3 and Figure 5A). In the fourth simulation study, there were four datasets consisting of 10,000, 50,000, 100,000, and 200,000 SNP markers, respectively, and 500 individuals. With the number of markers 10,000, 50,000, 100,000, and 200,000, the running time was 0.3142, 1.5460, 3.2735, and 6.3574 h, respectively. Clearly, the running time increased almost linearly with the markers increasing (Figure 5B).  
Figure 4: Comparison of total running time using four GWAS methods (HRePML, MLMM, FarmCPU, and GEMMA). The descriptions in (**A**,**B**) are the same as those in Figure 1.  
Figure 3: Effect of the sample size on the statistical power using HRePML in the third simulation study.
## 4 Results and Discussion
### Simulation Results  
#### 4.2.1 Performance of First Eigenmode Transmission  
First we observe time variant characteristics within the CSI estimation period. Figure 4a shows average SINR fluctuation of each stream and corresponding throughput performance is shown in Figure 4b. The average values plotted include all subcarriers with various UE distributions. Here we compare Cases 1 and 2. Total transmission stream is set to \(NuN\)s = 16; 4 UEs with four streams for Case 1 and 16 UEs with single stream for Case 2. Maximum beamforming gain is calculated as \(10\text{log}_{10}[NtNr/(NuN\text{s})]\) = \(10\text{log}_{10}(256\times 16/16)\) = 24 dB and the 1 st eigenmode properly yields these gain values in Cases 1 and 2. However, higher eigenmodes for Case 1 exhibit lower SINR than the 1 st one and they rapidly decrease as time progresses. Their achievable throughput values are lower than 1 bps/Hz at \(t\) = 1.3 ms. As Case 2 allocates a single stream per UE, SINR values of all signal streams remain high so the throughput degradation is minimal.  
Note the achievable throughput values are directly converted from SINR in the evaluation. Practical MCS need to be determined with some margin considering the unpredictable SINR degradation possible with the time varying channel. If appropriate MCS cannot be selected, expected throughput cannot be achievable. The 1 st eigenmode approach can suppress such MCS selection error since it realizes stable SINR performance. Therefore, the superiority of the 1 st eigenmode transmission of Case 2 is expected to be demonstrated more clearly in actual use.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameters** & **Values** \\ \hline Tx/Rx Angular spread & \(5^{\circ}\)/\(5^{\circ}\) \\ \hline Precoding & BD/Eigenmode transmission \\ \hline Postcoding & MMSE \\ \hline Symbol duration & 6.67 μs \\ \hline CSI estimation period & 1.334 ms (200 symbol) \\ \hline UE speed; \(v\) & \(10\) km/h (\(f_{\text{D}}T_{S}=1.2\times 10^{-3}\)) \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**MCS Index** & **SINR (dB)** & **Throughput (bps/Hz)** \\ \hline
0 & - & 0 \\
1 & 1.00 & 0.1523 \\
2 & 3.21 & 0.2344 \\
3 & 5.43 & 0.3770 \\
4 & 6.36 & 0.6016 \\
5 & 8.14 & 0.8770 \\
6 & 9.93 & 1.1758 \\
7 & 11.71 & 1.4766 \\
8 & 13.50 & 1.9141 \\
9 & 15.29 & 2.4063 \\
10 & 17.07 & 2.7305 \\
11 & 18.86 & 3.3223 \\
12 & 20.64 & 3.9023 \\
13 & 22.43 & 4.5234 \\
14 & 24.21 & 5.1152 \\
15 & 26.00 & 5.5547 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Achievable throughput for signal-to-interference plus noise power ratio (SINR).
## 5 Data Imbalance
### Data Imbalance Handling Techniques  
#### 5.3.1 Traditional Oversampling Methods  
Traditional oversampling methods balance datasets by generating minority types of samples, such as the Synthetic Minority Oversampling Technique (SMOTE) [88]. Although SMOTE effectively improves the detection capability for minority classes in many cases, it has drawbacks, including the potential introduction of noise, particularly when the dataset's quality is not high [89]. These noisy samples can lead to model overfitting, adversely affecting the model's generalization ability on real-world data [90].
## 1 Introduction
Its strong performance was compared to the other metaheuristic techniques [21]. The SOS algorithm is considered a new algorithm, hence, its ability to solve different types of a complex problem should be further investigated and explored. Furthermore, only a few studies have been carried out to explore the potential of SOS in solving a discrete problem such as the CSLP problem. Hence, there is space for research and experiments for enhancing the performance of SOS in exploiting and exploring the discrete solutions. Choosing a useful hybrid approach is a key determinant for achieving the best performance in solving a difficult problem [22; 23]. This study proposes a novel hybrid metaheuristic algorithm called hybrid symbiotic organisms search with local operators (HOSOS-LO) for solving the CSLP problem. The HOSOS-LO presents the two-fold improvement in searching mechanism over the basic SOS. First, HOSOS-LO transforms the original real-valued individual positions of SOS into a discrete-valued representation by sorting the values of the solutions and memorizing the index as discrete CSLP-based solutions. Second, HOSOS-LO adopts three dynamic neighborhood local search mechanisms to refine the solution quality after the SOS searching routine is conducted. For validating the performance of HOS-LO, three actual case studies from the construction CSLP problem are employed, which are a single floor facility layout problem and a multi-floor facility layout problem. Subsequently, the results received from other heuristic methods introduced by previous studies are examined and compared with the proposed method. The remainder of this paper is structured as follows. Section 2 introduces the basic concept of the SOS algorithm. Section 3 presents the review of CSLP problem. Section 4 introduces the basic components and the procedure of the HSOS-LO for solving the CSLP problem. Section 5 shows the comparative performance analysis of the proposed algorithm and other methods. Section 6 presents the conclusion and future direction.
## 3 Results and Discussions
### Analysis of Nutritional Components and Total Flavonoids in V. kulingiana Leaves
#### 3.2.2 Trace Elements  
Wild edible plants have long been an excellent source of trace elements, and their consumption contributes to meeting nutritional requirements while overcoming micronutrient deficiencies at minimal costs [37; 38]. The current study also measured the mineral content in _V. kulingiana_ leaves, as shown in Table 2.  
_V. kulingiana_ leaves are remarkably rich in several trace elements, including copper, potassium, phosphorus, and calcium. The copper content is particularly noteworthy at 2.6 mg/100 g, its potassium content reaches 3650 mg/100 g, its phosphorus content is 1110 mg/100 g, and its calcium content is 565 mg/100 g. To meet the recommended daily intake of these minerals for the average adult, one would only need to consume approximately 31 g, 55 g, 65 g, and 142 g of _V. kulingiana_ leaves, respectively. Furthermore, iron and zinc contents are also abundant, with 100 g of _V. kulingiana_ leaves meeting the daily iron and zinc requirements for an individual. For comparison, spinach, known for its iron content, contains approximately 2.9 mg of iron per 100 g of edible portion [35], whereas _V. kulingiana_ leaves contain 22 mg. Similarly, oysters, a zinc-rich food, contain 9.39 mg of zinc per 100 g, while beef contains only about 4 mg/100 g, yet _V. kulingiana_ leaves provide 11.5 mg/100 g of zinc. Nuts, in general, are known for their magnesium content, with almonds being one of the highest sources, containing around 178 mg of magnesium per 100 g. Remarkably, _V. kulingiana_ leaves contain 360 mg of magnesium [35], surpassing the content of that in almonds. Additionally, the sodium content in _V. kulingiana_ leaves is relatively moderate. The leaves of _V. kulingiana_ are rich in mineral elements, especiallysome that are relatively difficult to obtain from regular foods, such as calcium, copper, iron, zinc, magnesium, and more. Their content far exceeds that in other common vegetables, requiring only a small intake to meet the human body's nutritional needs.
## 3 System and Threat Models
### Threat Model Based on the Bacon Semantic Analysis  
By intercepting the beacons transmitted within the VANET, the attacker conducts beacon semantic analysis using the beacons emitted by the targeted vehicle as it enters the mix zone (indicated by the black solid line in Figure 2), with the intention of predicting sensitive data, like GPS position, speed, and timestamp, once the targeted vehicle exits the mix zone (indicated by the blue solid line in Figure 2). The attacker then compares the beacons obtained outside the mix zone with the anticipated trajectory, identifying the targeted vehicle based on the closest match. It is important to note that there are two common assumptions regarding mix-zone vehicle beacon semantic analysis [30]: First, the attacker can access the beacons outside the mix zone (indicated by the solid line), while the beacons inside the mix zone (indicated by the dotted line) remain invisible due to encryption. Second, the attacker operates remotely instead of resorting to hacking cameras or tailing vehicles, as it requires a higher level of attacker capability and greater costs. In other words, the adversary relies solely on collecting beacons to gather information such as speed, position, and timestamps to uncover traces.  
This study will take the multi-hypothesis-tracking (MHT) method originated from [48] and simplified in [24] as an example to illustrate how beacon semantic analysis can effectively unveil vehicle trajectories. In brief, the MHT method aims to match the new measurement driving outwards from a mix zone to a piece of trace that leads into the same junction, as detailed below.
## 3 Results and Discussion
### Droplet Homogeneity  
Significant differences (\(p<0.001\)) were found in both nozzles for every single variable and their interactions. In general, it could be stated that the distance increase to the CP generates an increase in the droplet heterogeneity (Figure 10). This result is in line with the results obtained in previous works for the CP and the AP [55]. RSF values were slightly lower in the hand spout than in the cannon one (1.63 vs 1.78), especially in the surroundings of the CP.  
In the cannon spout, the hose position change, although statistically significant, did not generate major differences in the mean RSF values (Figure 10a). Thus, values for every single position were near 2.00. The observed trend was a reduction in the droplet population homogeneity with the distance increase from the CP. The increase in AS also increased the RSF in general, especially in the case of the lower LFR in the CP (Figure 10a).  
There was a much higher influence of the hose insertion position in the case of the hand spout (Figure 10b). In this case all values were around 2.00, but with important differences among the tested hose positions. There was a significant particularity in the hand spout case: values presented a higher RSF in the OP than in the XP, while the opposite would have been the most predictable scenario (Figure 10b). It should be pointed out that in this nozzle both LFR and AS had a higher impact on the RSF than they had in the cannon spout.  
The comparison between both nozzles regarding the droplet homogeneity follows a trend similar to that observed in previous works [53]. In those, a positive influence of both LFR and AS was observed on the RSF in both nozzles. Similarly to the present findings, the cannon spout had a higher RSF than the hand-type one did. A marked increase in the droplet heterogeneity was also observed in the maximum AS and minimum LFR combination (Figure 10a). This could be explained by the fact that with a very low LFR the amount of sprayed liquid might be insufficient for the high air current to produce an optimal water division into homogeneous droplets, thus increasing the amount of very fine ones, altering their normal distribution and increasing the droplets heterogeneity by affecting the kurtosis.  
Figure 10: Relative SPAN Factor (RSF) per liquid hose position (HP), liquid flow rate (LFR) and airflow speed (AS) for both the (**a**) cannon-type and (**b**) hand-type nozzles. Bars show the mean \(\pm\)Standard Error. CP, conventional insertion of liquid hose position; AP, alternative position; OP, out position; XP, extreme position.
# Recent Progress in On-Chip Erbium-Based Light Sources
### Er-Based Light Sources  
Compared with the III-V light sources, rare earth element based light sources have better CMOS technology compatibility, which may have more potentials in monolithic integration application. Among them, the spontaneous emission of erbium ions (Er\({}^{3+}\)) covers 1524-1574 nm, thulium ions (Tm\({}^{3+}\)) covers 1680-2020 nm, holmium ions (Ho\({}^{3+}\)) covers 1930-2130 nm [34; 35; 36]. For the on-chip optical communication at C band (1525-1565 nm), Er\({}^{3+}\) is more suitable, with the advantages of long luminescence lifetime, small noise, and low process manufacturing cost. Consequently, Er-based materials have inspired significant research to silicon-based on-chip light sources, especially since the erbium-doped waveguide amplifier (EDWA) was first realized in 1985 [37]. For the compact size integration requirements, the Er\({}^{3+}\) concentration of on-chip Er-based light sources need to be more than two orders higher than EDFA [38]. Therefore, it is crucial to choose the proper host materials.  
In 2017, [PERSON] et al., achieved giant optical gain in a single-crystal Er chloride silicate nanowire with Er concentration up to 1.62 \(\times\) 10\({}^{22}\) cm\({}^{-3}\)[39]. A net material gain over 100 dB\(\cdot\)cm\({}^{-1}\) at wavelengths at approximately 1530 nm is possible due to the nanowire's single-crystalline material quality and its high Er concentration. In 2019, [PERSON] et al. realized an Al\({}_{2}\)O\({}_{3}\):Er slot waveguide amplifier by atomic layer deposition (ALD) [40]. By combining low-loss silicon nitride waveguides with high-quality Al\({}_{2}\)O\({}_{3}\):Er films, up to 20 dB/cm net modal gain was achieved. In 2022, [PERSON] et al. demonstrated over 145 mW on-chip output power and >30 dB of small-signal gain based on Si\({}_{3}\)N\({}_{4}\):Er, which greatly expands the application prospects of the Si\({}_{3}\)N\({}_{4}\) platform [41].  
With the broadening of the selection of host materials and the progress of fabrication technology, Er-based light sources have developed rapidly in recent years. Based on the advantages above, the application prospects of on-chip Er-based light sources are broader and more conducive to the future development of the silicon photonics industry.  
In this paper, the recent progress and the state of the art in on-chip Er-based light sources are reviewed. We begin with comprehensive theoretical analysis of Er\({}^{3+}\) luminescence model, followed by recent advances in on-chip Er-based light sources of different host materials. The advantages and disadvantages of different host materials are compared and analyzed. Finally, the future directions and existing challenges towards higher-level integration are discussed.
# Blockchain-Driven Intelligent Scheme for IoT-Based Public Safety System beyond 5G Networks
### Applications of Blockchain in 6G Network
#### 1.1.1 Smart Healthcare  
The healthcare industry is an essential sector in the nation's economic system that offers medical services to treat patients, promote patients' health, and increase the quality of life of individuals. The emerging 6G technology revolutionizes healthcare by changing the perception of lifestyle [8]. A smart healthcare system offers better connectivity between patients and healthcare providers, medical organizations, and IoT-based medical devices. It provides a health information platform by adopting 6G-based services, such as augmented and virtual reality. In addition, healthcare systems require the blockchain to securely store medical records and update patient data across different platforms in real-time [9; 10].  
Researchers enabled blockchain technology in 6G-based applications and proposed many solutions for smart healthcare. For example, during the COVID-19 pandemic, patients suffering from chronic diseases are more at risk than ordinary people because patients with diabetes and asthma are more susceptible to coronavirus infection. Therefore, there is a requirement to treat these types of patients remotely and offer them emergency facilities such as temperature checks, pulse, and respiratory and O\({}_{2}\) monitoring. However, remote diagnosis and treatment can be lured by the attacker and cause patient data security issues. Thus, to overcome these issues, blockchain is integrated with a healthcare system that remotely monitors the health data of patients suffering from a chronic disease. In [11], the authors facilitated a private blockchain and a solidity-based smart contract that keeps track of patients' health information. The role of blockchain in their proposed work is to securely analyze patients' details and store patient health records from and to the sensors. Further, in [12], the authors investigated AI with healthcare systems and proposed a parallel gout diagnosis and treatment system (PGDTS). Their proposed system enhances the diagnosis accuracy and the effectiveness of medical treatment. After the diagnosis and treatment process, their adopted scheme was analyzed and evaluated based on diverse therapeutic regimens, and they established parallel execution for real-time optimization of healthcare processes. Their integrated blockchain-based PGDTS scheme offers integrity, scalability, and security of patients' data.
# Pose Tracking and Object Reconstruction Based on Occlusion Relationships in Complex Environments
###### Abstract
Finally, the tracking algorithm is optimized based on the occlusion relationship, using a novel adaptive key-frame maintenance strategy to detect changes in the strength of occlusion relationships as a basis for retaining effective frames in the system. The proposed method enables the model to accurately estimate the target pose in frequent occlusion scenarios. Comparative studies are conducted by changing the baseline key frame maintenance strategy, demonstrating that the proposed method significantly improves the accuracy and robustness tracking in scenarios with frequent occlusions. Our main contributions can be summarized as follows:  
* We propose the concept of dual-layer masks and utilize depth relationships to compute the dual-layer mask of the current frame, which enables effective detection of foreground occluding objects while segmenting the target object. * We propose a novel key-frame selection strategy. This strategy detects changes in occlusion relationships and uses them as the basis for retaining effective frames in the system. Additionally, since this new strategy is highly efficient, our method can still achieve real-time performance. * Besides conducting experiments on the hand-object interaction dataset HO-3D [18], we also created some indoor interaction data for tracking experiments. The results of experiments on both the HO-3D dataset and our custom dataset demonstrate that our key-frame selection strategy significantly improves the robustness and accuracy of object tracking, leading to improved reconstruction results.
# A Comprehensive Study of the Use of LoRa in the Development of Smart Cities
## 4 Results of the Analysis of the Selected Works
### Analysis of the Network and Transport Capabilities
#### 4.2.2 Coverage of LoRa in IoT Applications
The location of the LoRa gateway in indoor or outdoor scenarios can impact the coverage area [61].
# Evaluating Packaging Design Relative Feature Importance Using an Artificial Neural Network (ANN)
## 2 Methods
### Methods for Evaluating Input Feature Importance
#### 2.2.3 Permutation Method  
The permutation method measures a feature's importance by observing how model accuracy changes when the values of that feature are randomly shuffled while keeping other feature values intact. A feature with higher importance will have a greater effect on the model's accuracy when its values are shuffled [29]. As detailed in Equation (5) and Table 1, to determine the feature importance of feature \(f_{j}\), the column corresponding to \(f_{j}\) is randomly shuffled to generate a corrupted dataset \(Dk_{j}\). The ANN model is then trained on \(Dk_{j}\), and its accuracy is compared with the accuracy of the original models. The difference in accuracy scores indicates the importance of feature \(f_{j}\).  
\[i_{j}=s-\frac{1}{K}\sum_{k=1}^{K}sk_{j} \tag{5}\]where  
\(ij\)--Importance for feature \(f_{j}\).  
\(s\)--Reference score of the model m on dataset \(D\).  
\(K\)--Repetition times for randomly shuffling column j of the dataset \(D\) to generate a corrupted version of the data named \(Dk_{j}\).  
\(sk\)--Score of the model on corrupted data \(Dk_{j}\).  
In this study, the np.random.permutation() function from the NumPy library in Python 3.11.5 was utilized to randomly shuffle the input features in the ANN.
## 4 Conclusions  
In order to accurately analyze volatile sulfur compounds, despite matrix parameters, a suitable internal standard must be found that will be affected in the same manner as the analyte of interest. Three fibers and five internal standards were used to analyze volatile sulfur compounds in synthetic wine matrices in order to determine the best method to compensate for said effects. The Carboxen-PDMS fiber showed the greatest sensitivity compared to the other analyzed fibers. The effects of ethanol on sulfur analysis were very substantial, for instance, a great decrease in sensitivity was seen between 0.0 and 0.5%. The ratios of analyte to internal standard were in most levels between 2-4% ethanol, suggesting a dilution of 2 mL wine to 8 mL salt water is satisfactory for analysis. Both H\({}_{2}\)S and MeSH, traditionally analyzed with EMS as an internal standard, showed poor resemblance to EMS with increasing ethanol content. DIDS was found to be a more suitable internal standard, despite their dissimilarities, based on their response to the matrix and their behavior on the fiber.  
P.M.D. performed the data collection, data analysis, results explanation, and prepared the original manuscript. M.C.Q. secured the funding, designed the experiment, supervised the research, explained data, edited and revised the manuscript. This research was partially funded by E&J Gallo Winery as a gift. The authors thank Agricultural Research Foundation, Oregon Wine Research Institute and Oregon State University for their supports. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.