esa-sceva/satcom-chunk-collection huggingface dataset:
# Quantum Key Distribution for 5G Networks: A Review, State of Art and Future Directions
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
3 GPP & 3 th Generation Partnership Project \\
5G & 5 th Generation Network \\ ABE & Attribute-based Encryption \\ AMF & Access and Mobility Management Function \\ AUSF & Authentication Server Function \\ CE & Channel Estimation \\ C-RAN & Cloud Radio Access Network \\ CSP & Cloud Service Provider \\ D2D & Device-to-Device \\ DoS/DDoS & Denial of Service \\ DPR & Distributed Phase Reference \\ EAP & Extensible Authentication Protocol \\ ECC & Elliptic Curve Cryptography \\ HIPPA & Health Insurance Portability and Accountability Act \\ HMAC & Hash-based Message Authentication Code \\ IBC & Identity-based Cryptography \\ ICC & Inter-Controller Communication \\ IIoT & Industrial Internet of Things \\ IMSI & International Mobile Subscriber Identity \\ INS & Industrial Network System \\ ISP & Internet Service Provider \\ LTE & Long-Term Evolution \\ LPWAN & Low-Power Wide-Area Network \\ MANET & Mobile Ad-Hoc Network \\ MEC & Multi-Access Edge Computing \\ MISO & Multiple-Input Single-Output \\ MIMO & Multiple-Input Multiple-Output \\ \end{tabular}
## 3 Materials and Methods
### Proposed Approach
#### 3.1.2 Predictive Module  
A long short-term memory (LSTM) model is adopted to constitute the predictive module. LSTM models are DL models designed to address the challenges of learning long-term dependencies in sequential data by capturing temporal dependencies over extended sequences. These features make them well-suited for tasks such as time series forecasting, and several studies have proved their advantages over other models in solving predictive tasks in the field of SCM [31; 32; 33; 34].  
One separate LSTM model for each supplier is proposed to be adopted in the predictive module to estimate the future delivery punctuality of suppliers. The dependent variable (label) predicted by each LSTM model is represented by the number of days of delay or advance that a specific supplier will deliver the purchased components with respect to the planned delivery dates. On the other hand, the historical record of supplier delivery punctuality has been adopted as independent variables (features) to estimate the value of the label.
## 1 Introduction
In the above studies, the impact velocities were less than 3000 m/s, but the velocity of space debris can reach more than 7000 m/s, which belongs to hypervelocity. At the end of the 20 th century, the National Aeronautics and Space Administration (NASA) carried out a series of hypervelocity impact tests to study the damage consequences of hypervelocity impact on spacecraft. In 1991, the White Sands Test Facility (WSTF) participated in a joint test program with the NASA Hypervelocity Impact Research Laboratory (HIRL) [16] where they used an aluminum projectile with the diameter of 0.318 cm moving at velocities from 6000 m/s to 6400 m/s to impact a vessel filled with liquid hydrazine. Circular perforations, petal-shaped bulges, and cracks were observed on the entry and exit walls. In the same year, the Science Applications International Corporation (SAIC) also performed a test in which a cylindrical projectile weighting 100 g was launched at a velocity of 5000 m/s against a 100-mm-diameter vessel filled with liquid hydrazine [17]. The results indicated that some reaction occurred in the liquid hydrazine, but not enough evidence was gathered to confirm a detonation. Hypervelocity impact is characterized by a short duration, high pressure, and strong nonlinearity. The hydrodynamic ram effect caused by debris hypervelocity impact on satellite tanks is unclear, and the influence of debris angular velocity on the hydrodynamic ram effect has also been rarely studied. In the present work, based on the ALE algorithm in the ANSYS/LS-DYNA software, a numerical simulation was carried out in which the spherical debris impacted the satellite tank at the velocity of 7000 m/s. The attenuation law of debris velocity, the propagation process of the shock wave, and the deformation of walls were investigated. The influences of the liquid filling ratio, magnitude, and direction of the debris angular velocity on the hydrodynamic ram effect were analyzed. The proposed method attempts to give a general modeling procedure that can be taken into account for similar numerical simulations. Attention is drawn to the material models and failure criteria for describing material behavior, to the connections of the debris/tank and debris/liquid, and to the geometrical meshing [18]. Moreover, numerical modeling of the satellite tank under hypervelocity impact can provide a beneficial reference for the structural design, and the need to validate the numerical models based on hypervelocity test data is emphasized.
# A New Internet of Things Hybrid VLC/RF System for m-Health in an Underground Mining Industry
## 1 Introduction  
The International Labor Organization (ILO) states on its website that every 15 s, 153 workers have an accident at work, and annually more than 317 million accidents occur at work, many of which result in absenteeism. In the same way, ILO affirms that every 15 s a worker dies from accidents or work-related illnesses, that is, every day 6300 people die from that cause, equivalent to more than 2.3 million deaths per year. There are work environments that are, by nature, more dangerous than others. This is the case for the mining industry, where 12,000 workers die every year in mining accidents around the world [1].  
Accident rates within the mining industry persist at elevated levels, underscoring the need for a stringent legal framework that requires employers to proactively institute comprehensive measures aimed at preventing workplace setbacks. Recognizing the urgency of addressing this issue, the United States Congress took decisive action in summer 2006. Responding to a poignant series of tragedies that occurred earlier in that year, the legislative body enacted the Mine Improvement and New Emergency Response (MINER) law [2].  
The MINER law is a crucial piece of legislation that places a compelling obligation on mine managers not only to implement safety protocols, but also to submit a meticulously designed emergency response plan. This multifaceted strategy seeks to mitigate the occurrence of accidents while ensuring a rapid and effective response in the unfortunate event of an emergency [3]. The emphasis of the law on proactive planning and preparation reflectsa paradigm shift towards a more comprehensive and preventive approach to occupational safety within the mining sector. Consequently, it serves as a critical milestone in ongoing efforts to improve workplace safety standards and protect the well-being of those employed in this highly risky industry [3].  
In this sense, this work proposes a new Internet of Things system to monitor the health of mobile workers in the underground mining industry. The Wireless Body Area Network (WBAN) placed in the mining workers uses an optical receiver that obtains a cell ID sent by the infrastructure through a Visible Light Communication (VLC) link based on optical cells, which is used for joint positioning and communication within the mining tunnel and Radio Frequency (RF) for the transmission of primary vital signs to the cloud. Once the data are obtained, a medical professional located outside the mine can observe and control the previously processed data.  
The manuscript has the following organization. Section 2 presents a review of wireless communication technologies for m-Health, Section 3 presents the state of the art in m-Health technologies, Section 4 establishes the network architecture, and Section 5 shows the simulation results. Finally, Section 6 is dedicated to conclusions based on the results obtained.
## 1 Introduction
[8] used MTL to improve the performance of SSVEP-based BCIs by jointly performing signal enhancement and classification tasks. However, MTL frameworks typically require a large amount of training data. To reduce the need for training data, [PERSON] et al. [29] proposed an MTL block that performs group convolutions. The results showed that this proposed MTL block outperforms fully connected and traditional convolutional layers. Therefore, integrating an MTL block can increase the accuracy of an SSVEP-based BCI. In this study, an SSVEP-based BCI with MDFs and MTL is develop to help people communicate with others. To precisely represent the characteristics of the SSVEP signal, the SSVEP signals in the time and frequency domains are selected as the multi-domain features and used as inputs for the neural network. To effectively extract the embedding features, convolutional neural networks are separately used for the time and frequency domains. The embedding features for the time and frequency domains are then fused using an element-wise addition operation and batch normalization. To find the discriminative embedding features for classification, a sequence of convolutional neural networks is adopted. Finally, to correctly detect the corresponding stimuli, the MTL block is used. The rest of this paper is organized as follows: The proposed SSVEP-based BCI with multi-domain features and MTL is described in Section 2. Section 3 then conducts a series of experiments to evaluate the performance of our approach. Section 4 presents the conclusions and provides recommendations for future research.
## 3 System Design and Implementation
### Software Design
#### 3.3.2 Implementation of the AUDP Mechanism
Referring to the Transaction ID verification mechanism of Modbus TCP, the MBAP of Modbus TCP is transplanted to Modbus UDP (as shown in Figure 11), the request and response matching mechanism is added to Modbus UDP at the application layer, an incremental Transaction ID is generated before each request to ensure the uniqueness of the request, and the request is sent to the gateway. When sending to the gateway, the gateway will record the Transaction ID, and then append it to the Modbus UDP response frame. When the host computer parses the response, it will extract the Transaction ID and verify it. If the request frame is consistent with the Transaction ID in the response frame, it means that it is a valid response; otherwise, it enters the retransmission mechanism. * Analysis of Transaction ID Matching Mechanism  
To reduce the network load and increase the probability of retransmission, the program waits for a period before each retransmission, with an initial waiting time 'base_delay', and doubles the waiting time after each retry. By integrating the analysis of the UDP validation mechanism, retransmission mechanism, Transaction ID matching mechanism, and exponential backoff mechanism, and to reduce the computational load on the gateway, the reliability mechanism is implemented through collaboration between the host computer and the gateway. Figure 13 shows the schematic diagram of the Modbus AUDP reliability mechanism.
# Optimization Dubins Path of Multiple UAVs for Post-Earthquake Rapid-Assessment
## 3 HGSA Algorithm
### Initialization
#### 3.1.4 Fitness Evaluation  
The fitness of a chromosome represents the expected profit of a routing scheme. According to the objective function of the MURARP, Equation (6), the higher the expected profit, the better the routing scheme. However, UAVs have multiple optional heading angles when they access a target, and chromosomes with the same fitness may have different task completion times. The routing scheme shown in Figure 4a and the routing scheme shown in Figure 4b are exactly the same in both the targets to be accessed and the number of times that targets are accessed, so the expected profits of the two schemes are also the same. However, the total flight time of the routing scheme shown in Figure 4b is shorter, which helps make room in UAV routing, thus allowing UAVs to visit more potential targets [39]. According to the above analysis, the quality of the routing scheme is related to both the total expected profit and the total flight time. Therefore, the paper designs the chromosome evaluation mechanism of a double fitness function, that is, the total expected profit fitness function \(Fit1\) and the total flight time fitness function \(Fit2\). The calculation formulas are  
\[Fit1=\sum_{i=1}^{N}w_{i}P_{i}=\sum_{i=1}^{N}w_{i}\times[1-\prod_{k=1}^{K}{(p_{ k})^{k}}] \tag{13}\]  
\[Fit2=\sum_{k=1}^{K}t_{R_{k}}=\sum_{k=1}^{K}\frac{d_{q_{D_{k}}}^{k}{q_{V_{k}^{k }}}}{\frac{+\sum\limits_{l=1}^{r-1}d_{q_{V_{k}^{k}}}^{k}{q_{V_{l+1}^{k}}}^{k} +d_{q_{V_{k}^{k}}}^{k}{q_{D_{k}}}^{k}}{v_{k}}} \tag{14}\]  
The value of \(Fit1\) is the expected profit of the routing scheme represented by the chromosome. The bigger value of \(Fit1\), the higher is the fitness of the chromosome. The value of \(Fit2\) is the total flight time of the routing scheme represented by the chromosome. The smaller the value of \(Fit2\), the higher is the fitness of the chromosome. In this paper, \(Fit1\) is used as the main fitness function for crossover and SA operations, and \(Fit2\) is used to evaluate two chromosomes with the same expected profit, i.e., the values of \(Fit1\) are the same. \(Fit2\) is adopted in the mutation operation.  
Figure 4: \(Cont\).
## 3 Results and Discussion
### Properties of the Nanocomposite Films
#### 3.2.2 Morphology and FTIR  
Figure 3 shows the FESEM surface and cross-section micrographs of gelatin-based films. The control gelatin film was integral and smooth, deprived of cracks or defects. The nanocomposite film with a low content of CuSNP formed a well-dispersed and homogeneous film without any aggregation. However, when a high amount of CuSNP (1.0 and 2.0 wt %) was added, the film showed an accumulation of nanoparticles. The cross-section image also showed good compatibility of nanofillers in the polymer matrix. The neat gelatin film has a relatively soft morphology, but with the addition of nanofillers, it became somewhat rough. Previously, an analogous accumulation of particles at higher CuSNP content was reported for agar/CuSNP and carrageenan/CuSNP nanocomposite film [40,41].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Films** & \(\mathbf{L}\) & \(\mathbf{a}\) & \(\mathbf{b}\) & \(\mathbf{\Delta E}\) & \(\mathbf{T_{280}}\) (\%) & \(\mathbf{T_{660}}\) (\%) \\ \hline Gelatin & 91.1 \(\pm\) 0.1 d & \(-\)0.7 \(\pm\) 0.1 a & 6.6 \(\pm\) 0.2 a & 2.3 \(\pm\) 0.3 a & 18.2 \(\pm\) 0.1 d & 89.5 \(\pm\) 0.3 d \\ Gel/CuS\({}^{0.5}\) & 83.7 \(\pm\) 1.0 c & \(-\)1.3 \(\pm\) 0.2 b & 9.5 \(\pm\) 0.6 b & 10.0 \(\pm\) 1.1 b & 11.0 \(\pm\) 0.9 c & 78.1 \(\pm\) 0.5 c \\ Gel/CuS\({}^{1.0}\) & 66.5 \(\pm\) 3.0 b & \(-\)2.1 \(\pm\) 0.3 c & 18.2 \(\pm\) 1.6 c & 29.2 \(\pm\) 3.4 c & 3.9 \(\pm\) 0.8 b & 61.3 \(\pm\) 1.8 b \\ Gel/CuS\({}^{2.0}\) & 55.7 \(\pm\) 4.0 a & \(-\)1.35 \(\pm\) 0.3 b & 19.1 \(\pm\) 0.7 c & 39.3 \(\pm\) 4.0 d & 1.0 \(\pm\) 0.1 a & 39.5 \(\pm\) 2.1 a \\ \hline \hline \end{tabular}  
* The matching superscript letters in each column indicate that they are not significantly dissimilar (\(p\) > 0.05) from [PERSON]’s multiple range tests.  
\end{table}
Table 2: Optical properties of gelatin/CuSNP films.  
The FTIR spectra of the gelatin and gelatin/CuSNP films are presented in Figure 4a,b. Due to the absence of characteristic peaks in the range of 2000-2700 cm\({}^{-1}\), a break was added. The peak detected in all films at 3283 cm\({}^{-1}\) was due to the O-H stretching vibration of gelatin [45]. The peak around 2934 cm\({}^{-1}\) was owing to the C-H stretching vibrations of alkane groups in the gelatin polymer chain. The peaks at 1625 and 1542 cm\({}^{-1}\) were attributed to the carbonyl stretching amide I and the N-H stretching of amide-II of gelatin [46]. The peaks at 1451 and 1239 cm\({}^{-1}\) corresponded to the C-N and N-H stretching of gelatin, respectively [27]. A peak at 1030 cm\({}^{-1}\) was ascribed to the -OH group of glycerol. The gelatin/CuSNP composite film's peaks are similar to the gelatin film, apart from a slight variation in the peak magnitude. Overall, the addition of CuSNP did not alter the chemical structure of the film matrix. The slight changes in peak magnitude (3283, 1625, 1239, and 1030 cm\({}^{-1}\)) were mainly owing to the weak interactions between CuSNP and gelatin, such as Van der Waals force [19].
## 3 Methods
### Comparison of Phenological Patterns between In Situ and MOD17A2H  
The phenological patterns in ecosystem GPP are important in the terrestrial carbon cycle and have significant ecological implications. To understand if the MOD17A2H satellite GPP product can capture the functional phenology, which has been defined as the interaction and close association between plant functional traits and phenology [42], of in situ GPP, we first filled the data gap in the original 8-day GPP time series using a linear interpolation method. The gap-filled GPP time series was further smoothed using the Savitzky-Golay (SavGol) filter with a window size of 9 time steps and a second-order polynomial, which not only eliminated noise but also preserved the basic phenological attributes [43]. Finally, we extracted the timing of maximum GPP (day of year, DOY) during the photosynthetically active period for each site from both the in situ and MOD17A2H data. Agreement between the peak timings extracted from the in situ GPP and those extracted from the MOD17A2H was used as an indicator of the performance of the MOD17A2H GPP product in representing the functional phenology patterns of arctic ecosystems.
## 3 Related Works
### Applications of Reinforcement Learning in Link Quality Estimation
An alternative approach is the Max Min power control scheme, for which the main idea is to find an optimal power allocation strategy that maximizes the minimum SNR among all users [32]. However, transmission power control is fundamentally more challenging in a multihop mesh network with the presence of a routing protocol.
# Harmonic Detection Method Based on Parameter Optimization VMD-IWT Combined Noise Reduction
## 4 Simulation Signal Verification
### Detection Accuracy Analysis  
After denoising using wavelet thresholding, the instantaneous amplitude and instantaneous frequency of each component are computed via the Hilbert transform. As presented in Table 3, the proposed method exhibits high detection accuracy for simulated signals. Specifically, in a noise background with an SNR of 10 dB, the average detection errors  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Denoising Method** & **SNR/dB** & **RMSE** \\ \hline WT (Hard) & 16.36 & 0.3364 \\ WT (Soft) & 17.53 & 0.3182 \\ EMD-WT (Soft) & 21.94 & 0.2904 \\ VMD-WT (Soft) & 22.59 & 0.2512 \\ RIME-VMD-IWT & 27.64 & 0.2137 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Noise reduction index of simulation signal.  
Figure 7: Simulation signal waveform comparison.  
for amplitude and frequency are 4.6 \(\times\) 10\({}^{-4}\) and 1.1 \(\times\) 10\({}^{-3}\), respectively. These results indicate the capability of accurately detecting simulated harmonic signals and possessing robustness against noise.  
In order to quantitatively evaluate the detection effect and anti-noise ability of the proposed RIME-VMD-IWT method for harmonic signals, the detection accuracy of three existing harmonic detection methods under different noise levels was analyzed, and the average error of amplitude and frequency was used to measure the detection effect of each method. As shown in Figure 8, the overall performance of the proposed method is superior to that of the other algorithms. Specific data indicate that the average errors of detection amplitude and frequency for the EMD method are 16.729% and 11.460%, respectively, while those for the VMD method are 6.549% and 4.017%, respectively. However, the detection error of RIME-VMD-IWT is significantly lower than that of the other three methods. As can be seen from Figure 8, under the background of a low signal-to-noise ratio, the proposed method has high detection accuracy, minimal average errors in detected amplitude and frequency, and it can still achieve significant noise reduction results for environmental noise components under different noise intensities while retaining more signal details. It also exhibits good robustness. Combining the above simulation results, it is proven that the RIME-VMD-IWT algorithm proposed in this paper exhibits high accuracy and superiority in the processing of power harmonic signals.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Frequency True** & **Frequency** & **Anplitude True** & **Amplitude** & **Amplitude Error** \\  & **Value/Hz** & **Detection Value/Hz** & & **Value/A** & **Detection Value/A** & **Amplitude Error** \\ \hline \multirow{4}{*}{RIME-VMD-IWT} & 50 & 50.0026 & \(5.1\times 10^{-4}\) & 5 & 4.9984 & \(3.2\times 10^{-4}\) \\  & 150 & 150.4950 & \(3.3\times 10^{-3}\) & 10 & 9.9
###### Abstract  
Headspace solid-phase microextraction (HS-SPME) coupled to gas chromatography-mass spectrometry (GC-MS) is widely employed for volatile analyses of plants, including mapping populations used in plant breeding research. Studies often employ a single internal surrogate standard, even when multiple analytes are measured, with the assumption that any relative changes in matrix effects among individuals would be similar for all compounds, i.e., matrix effects do not show Compound \(\times\) Individual interactions. We tested this assumption using individuals from two plant populations: an interspecific grape (_Vitis_ spp.) mapping population (\(n=140\)) and a tomato (_Solanum_ spp.) recombinant inbred line (RIL) population (\(n=148\)). Individual plants from the two populations were spiked with a cocktail of internal standards (\(n=6\), \(9\), respectively) prior to HS-SPME-GC-MS. Variation in the relative responses of internal standards indicated that Compound \(\times\) Individual interactions exist but were different between the two populations. For the grape population, relative responses among pairs of internal standards varied considerably among individuals, with a maximum of 249% relative standard deviation (RSD) for the pair of [U\({}^{13}\)C]hexanal and [U\({}^{13}\)C]hexanol. However, in the tomato population, relative responses of internal standard pairs varied much less, with pairwise RSDs ranging from 8% to 56%. The approach described in this paper could be used to evaluate the suitability of using surrogate standards for HS-SPME-GC-MS studies in other plant populations.  
breeding population; internal standards; matrix effects; plant volatiles; SPME; odorant analysis +
Footnote †: journal: molecules