esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Investigation of Task Performance and Human Vigilance in Different Noise Environments
## 2 Materials and Methods
### Experimentation
#### 2.2.4. Pvt  
PVT metrics have been shown to be significantly correlated with vigilance [15; 25]. To investigate whether noise quality affects the vigilance of participants, PVT was performed on the participants under three noise conditions. The stimulus was presented on the center screen. Participants immediately pressed the \"J\" button in response when the center screen changed from \"+\" to an increased figure. If the subject responded correctly, the number would stop increasing and change to a \"+\" to proceed to the next trial. In this study, four PVT performance metrics were calculated to explore the impact of different noise exposure conditions on objectively measured human alertness, as shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**PVT Metrics** & **Description** \\ \hline PVT performance (-s) & The ratio of accuracy to average response times. \\ Response time (ms) & The average response times for all trials. \\ Fastest 10\% response time (ms) & The fastest 10\% response times for all trials. \\ Slowest 10\% response time (ms) & The slowest 10\% reciprocal response times for all trials. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Description of PVT performance metrics.
## 2 Materials and Methods
### Data Processing  
Figure 2 explain our data processing architecture in real time. The data collection step follows Figure 1 directly. The position of the user is monitored during n seconds, when n is the value of the time parameter, in this paper, we used a time window of 5 s. The transmission frequency of our BLE emitter is 1 Hz. We collect data during 5 s and then we apply the following steps of the data preprocessing. Using only BLE signal in an indoor positioning system expose us to use data subject to imprecision and outliers. We then need to collect a certain amount of data to overcome them.
# Management of Obstructive Sleep Apnea in Hospitalized Patients
## 4 Treatment Options in Hospitalized Patients
### Auto Titrating CPAP (Auto-CPAP)  
Auto-CPAP therapy has been used successfully in the inpatient population [60] (Figure 1). Auto-CPAP devices have built-in feedback mechanisms to assess and titrate pressure to reduce obstructive events or respond to detected snoring [61; 62]. After titrating pressure to control events, the devices use feedback to titrate the pressure down until the obstructive events, lower flow, or snoring reappear. They can then titrate the pressure up to eradicate these events [62]. This helps prevent continuous exposure to high pressure. Auto-CPAP is usually set up in a pressure range with upper and lower pressure limits. A pressure range of 4 cm to 15 cm can be a good starting point [63; 64]. Auto-CPAP pressure titration has been demonstrated to be similar to CPAP titration in the sleep lab with comparable outcomes [64; 65]. Outcome studies in outpatient populations have shown improvements in symptoms of sleepiness, hypercapnia, blood pressure control, and mortality benefits [64; 65; 66].
# Minimum-Cost-Based Neighbour Node Discovery Scheme for Fault Tolerance under IoT-Fog Networks
## 2 Related Works  
The most important challenges addressed in this paper are how to identify the faulty Fog access nodes using a priority task-based fault-tolerance algorithm and reassign the task-processing to the new Fog access node with a minimum-cost-based neighbouring candidate node discovery algorithm. The paper also addresses how to select a Fog access node in a neighbouring server for task-processing via replication and a pre-emptive forwarding algorithm. Multiple techniques were suggested to ensure fault tolerance in Fog computing; some FT techniques employ task replication, which reduces the cloud's utilization of resources. Some other methods improve the mean response time by using checkpoint recovery to overcome faults.
## 5 Performance Evaluation
Therefore, one can derive the approximate relation between the false probability of integer CFO estimation \(P_{f,\text{IntCFO}}\) and the false probability of signaling detection \(P_{f,\text{sig}}\), as given by  
\[P_{f,\text{Sig}}=1-(1-P_{f,\text{IntCFO}})^{2}. \tag{18}\]  
From the simulation results in Figure 8, one can find that the simulation results are consistent with the theoretical analysis. From the simulation results, we can see that the proposed scheme has superior performance in the accuracy of preamble detection in the presence of NBI compared with the benchmark scheme of [PERSON]'s method. It should also be noted that, the performance gain of the proposed method is achieved at the cost of the increase in computational complexity. As mentioned in Section 4, the proposed timing scheme requires three pairs of cyclic parts in the calculation of the SACs, which is more than that of [PERSON]'s method. The scrambling operation also brings extra complexity to the scheme. Hence, the proposed scheme is more suitable for the PLC system with a moderate computational capability but requires more accurate timing performance. Figure 7: Timing correlation peaks for two methods in the presence of NBI.
## 1 Introduction
### History of Existing Research Work
The current work addresses the identification of pores and fractures that do not have a fixed geometric shape; hence, before discussing the principal findings of the present work, a brief discussion of the recent noteworthy findings of machine learning regarding the identification of irregularly shaped objects would be useful. In an early work, [PERSON] and [PERSON] [19] presented a face detection framework capable of detecting faces from an image and hence, suitable for detecting objects with near-fixed geometric structures. This framework mainly focuses on collecting Haar-like features along with the histograms of oriented gradients (HOGs) of the objects with fixed geometric shapes for their possible detection. However, these Haar and HOG features were found to be relatively inefficient in detecting irregularly shaped objects, which is highly required in the current context as the shapes of pores and fractures are highly irregular. [PERSON] and [PERSON] [20] introduced limited and arbitrary rotations for object identification, which are relatively inefficient in identifying objects of irregular shapes. [PERSON] et al. [21] proposed a pedestrian classifier based on Haar wavelets () and edge orientation histograms (EOHs) with AdaBoost; which is compared to SVMs using HOGs. The results show that the HW + EOH classifier achieves comparable accuracy but at a much lower processing time than the HOG. In another contemporary work, [PERSON] et al. [22] introduced a ray feature set that considers image characteristics at distant contour points to gather information about an object's shape. However, this strategy is often computationally expensive and may often be challenging for identifying the necessary context-specific ray features [22]. Modern deep learning and other machine learning classifiers require many training samples to correctly extract shape-related information necessary for identifying a specific object. However, the scenario becomes more complex in the presence of a low-quality, tiny training image set of extremely deformable objects (such as pores and fractures in the current case) since identifying the shape-related features necessary for their unique identification often becomes extremely difficult. Hence, a low-level pixel intensity-based image analysis approach could be more computationally efficient and accurate if used to identify irregular objects instead of the deep learning and machine learning counterparts.
## 3 Methodology
### Elm-Rbf-Saga  
Due to the lack of effective optimization for ILW and HLB, conventional ELM-RBF is often unstable and prone to falling into local optimal solutions [14]. Considering SAGA's extraordinary optimization ability as shown in Section 3.1.3, we introduce SAGA into ELM-RBF to adjust ILW and HLB for improving model's prediction accuracy. Specifically, ILW and HLB are firstly mapped to individual vectors in SAGA. Then, the overall error function of training set and test set is taken as SAGA's fitness function, which can be described by Equation (18). In addition, the prediction error of ELM-RBF-SAGA will be gradually reduced through iterative optimization operations, such as crossover, mutation, and selection. As a result, the optimal ILW and HLB are used to minimize error:  
\[Fitness(S)=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(f(x_{i})-y_{i})^{2}} \tag{18}\]  
where \(S=\{(x_{i},\,y_{i})\mid 0<i\leqslant n\}\) is the dataset. \(x_{i}\) is the input instance as shown in Equation (7), \(y_{i}\) is the real label corresponding to \(x_{i}\), and \(n\) is the number of instances. \(f(.)\) is the output of ELM-RBF as shown in Equation (8).  
The detailed steps of ELM-RBF-SAGA are described as below.  
1. Build ELM-RBF network model.
2. Initialize population in SAGA. ILW and HLB of ELM-RBF are encoded in individual genes as shown in Figure 13, and the evolutionary population is initialized accordingly. Figure 14 demonstrates the variation trend of population before and after evolution. It can be clearly seen from Figure 14a that the initial individuals are aimlessly distributed. However, after iterative optimization, the population is consciously close to the global optimal position as exhibited in Figure 14b.
3. Set fitness function of SAGA. The error function of ELM-RBF is taken as the fitness function of SAGA, and then the fitness values of initial population are calculated.
4. Perform selecting operation. With the continuous advancement of evolution, the calculation error will gradually be reduced. As a result, it is necessary to take the reciprocal of fitness function to select the individuals with \"high fitness\".
5. Adjust individual fitness according to Equations (10) and (11).
6. Update the \(P_{c}\) and \(P_{m}\) in SAGA according to Equations (12) and (13).
7. Perform crossover and mutation according to new \(P_{c}\) and \(P_{m}\).
8. Compute the fitness value of new individual based on Equation (18). These individuals with superior performance are preserved to compose the next evolutionary group.
9. Iterate steps 4-8 until the termination condition is satisfied, for instance, the number of iterations or the minimum error.
10. Decode the individual gene with the best fitness value. ELM-RBF is initialized with the decoded ILW and HLB to obtain the optimal network structure, which is used for multi-label classification as shown in Section 2.2.  
Figure 13: The illustration of the encoding process.
# Scenario-Based Emergency Material Scheduling Using V2X Communications
###### Abstract  
Vehicle-to-everything (V2X) communications can be applied in emergency material scheduling due to their performance in collecting and transmitting disaster-related data in real time. The urgency of disaster depots can be judged based on the disaster area video, and the scenario coefficient can be evaluated for building a fairness model. This paper presents a scenario-based approach for emergency material scheduling (SEMS) using V2X communications. We propose a SEMS model, with the objectives of minimum time and maximum fairness in the cases of multiple supply depots, disaster depots, commodities and transport modes for logistics management of relief commodities. We design the SEMS algorithm based on the artificial fish-swarm algorithm to obtain an optimized solution. The results demonstrate that the SEMS model can enhance the fairness of relief scheduling, especially for disaster depots with small demands compared to the Gini and enhanced Theil fairness models. Moreover, the acquired vehicle speed via V2X communications updates the SEMS model in real time, which approaches a solution closer to reality.  
emergency; scheduling; vehicle-to-everything; optimization; intelligent vehicle +
Footnote †: journal:  
Article
# Cooperative Spectrum Sensing Based on Convolutional Neural Networks
## 4 Main Contribution
### Dataset Construction  
The simulation band is conducted at very high frequency (VHF), where the carrier frequency is set as \(f_{c}=1.0\times 10^{8}\) Hz and sampling frequency is \(f_{s}=3.0\times 10^{8}\) Hz. Orthogonal Frequency Division Multiplexing (OFDM) signal is chosen as the test signal (PU) for its generality and popularity in wireless communication networks. In the simulation, the OFDM signal is first generated, and then white Gaussian noise with with the mean zero and the variance one is added into the OFDM signal.  
In this paper, the sensing nodes are fixed to 5 without the description because the sensing node number determines the dataset construction. If the number of sensing nodes is not fixed, the required dataset is very large. On the other hand, the sensing node number determines the size of the two-dimensional matrix, and the sensing performance with the same CNN network will be relatively positive with the sensing node number. As a result, fixing the sensing nodes does not influence the performance validation of the proposed network.  
For each sensing node, a random integer between \(-8\) and 5 is used as the signal-to-noise ratio (SNR). There are two kinds of samples in the constructed dataset, namely the \(H_{0}\) sample and the \(H_{1}\) sample, where the \(H_{0}\) sample denotes the absence of PU and the \(H_{1}\) sample denotes the presence of PU. The \(H_{0}\) sample and the \(H_{1}\) sample are in equal proportion. For each sample (the \(H_{0}\) sample or the \(H_{1}\) sample), 6000 sets of the complex signal are obtained, with the sampling points of with each sampling points 1000 and then the modulus value is calculated for each sampling point. As a result, a modulus sequence matrix is obtained with the size 5 \(\times\) 1000. According to the operations in (9) and (10), the updated covariance matrix is obtained. Then, the dataset construction is finished, where the first 2000 groups are taken as the test set and the rest work as the training set.  
Figure 2 exhibits the obtained covariance matrix when SNR = \(-8\) dB, where the left figure denotes the \(H_{0}\) case and the right one denotes the presence of PU. In Figure 2, much difference exists, which helps to determine the presence or absence of the PU.
# The Security of Big Data in Fog-Enabled IoT Applications Including Blockchain: A Survey
## 5 Fog Computing Security Challenges
### Core-Network and Service Level Challenges
#### 5.1.6 Privacy-Conserving Packet Forwarding  
Data packets that are received either from IoT devices or other fog nodes are forwarded through fog nodes, hence it is mandatory to preserve privacy of forwarded packets [8]. Resource constrained and mobile nature of IoT networks cause distinct packet forwarding features in fog-enabled IoT system. End-to-end connectivity is difficult, thus cooperation of other nodes is required for message delivery. Selfish nodes may become reluctant to be a part of message forwarding due to consumption of limited resources such as energy. Moreover, malicious nodes may collude to be the part of the route to perform malicious activities on data packets [94]. Therefore, privacy-conserving packet forwarding is a must. There are many security solutions such as remote data integrity verification, which allows the user or other trusted party to verify the data integrity. Data encryption before uploading on fog node by IoT devices is a fundamental requirement that secures the data from privacy leakage. Encryption of data by IoT devices causes an obstacle to share data with trusted parties. An atomic proxy cryptography scheme was proposed by [PERSON] et al. [77], in which semi-trusted proxy convert cipher-text by a sender is decryptable by a receiver without watching the original plain text through proxy encryption key. [PERSON] et al. [77] proposed chosen cipher-text-based secure proxy re-encryption. [PERSON] et al. [95] proposed a conditional proxy re-encryption that meets various data sharing requirements. For secure data sharing, the authors of [96] proposed attribute-based re-encryption in which secret key assignment is based upon attributes of the user.  
The authors of [97] proposed Ciphertext-Policy Attribute-Based Encryption (CP-ABE) and Key-Policy Attribute-Based Encryption (KP-ABE) schemes with a difference of associated access policies. Intermediate networks are utilized for IoT devices and cloud communication to build two-way communication by fog nodes. End-to-end security is a basic requirement to prevent sensitive data to disclose during transmission. It degrades properties of data sharing such as aggregation, search, and sharing.
## 4 Concluding Remarks  
In conclusion, we have proposed a novel approach to further enhance the near-field intensity in the terahertz dipole antenna. It is achieved by introducing HMM with periodic hole arrays between the terahertz antenna and the substrate, where the HMM is composed of InSb-SiO\({}_{2}\) multilayers. Fully vectoral simulation results have shown that by using this approach, the near-field intensity of a conventional dipole antenna can be further enhanced by more than three times. The greatly enhanced near-field intensity can improve the sensitivity of terahertz detectors or sensors. Simulations have also revealed that this enhancement originates from doubled \(|E_{x}|^{2}\) and significantly enhanced \(|E_{z}|^{2}\). We have showed that the smaller gap width, the larger the enhancement, and that the hyperbolic characteristic of the multilayers is vital for achieving large enhancement. Although we have focused on terahertz dipole antennas, we believe this approach should be applicable for other types of antennas. The proposed approach should also be applicable for other spectral regimes. For example, in the visible or near-infrared regime, one can shrink the size for such short waves according to the electromagnetic scaling law, which requires that the ratio of the geometric length to the wavelength is the same for different electromagnetic wave regimes. Furthermore, the semiconductor InSb used for the terahertz should also be replaced with noble metal gold for the visible. This is because InSb at THz frequencies and gold in the visible have similar permittivities [29]. A possible problem could be the structure size is so small that it may pose difficulties in fabrication. Therefore, we expect this work will advance the design of terahertz antennas, as well as antennas the visible and near-infrared regimes, which are widely used in biosensing and detecting applications.  
Figure 5: **(a)** Field enhancement factor versus the gap width for the dipole antenna on top of the PI substrate directly or sandwiched by the HMM with periodic hole arrays. **(b)** Field enhancement factor versus the ratio of the InSb and SiO\({}_{2}\) thicknesses, \(t_{\text{m}}/t_{\text{d}}\). **(c,d)** Effective relative permittivities of the InSb-SiO\({}_{2}\) multilayers with **(c)**\(t_{\text{m}}/t_{\text{d}}\) = 1:2 or (d) \(t_{\text{m}}/t_{\text{d}}\) = 2:1.  
**Author Contributions:** Conceptualization, C.C. and G.L.; methodology, C.C., W.C., Y.L., and G.L.; software, C.C., W.C., and G.L.; investigation and data analysis, all authors; writing-original-draft preparation, C.C. and G.L.; writing--review and editing, all authors; supervision, Y.L. and G.L.  
**Funding:** This research was funded by the Shenzhen Research Foundation (Grant No. JCYJ20160531174039457, JCYJ20150925163313898, and JCYJ20160510154531467), the National Key Research and Development Program of China (No. 2017 YFC0803506), and the Youth Innovation Promotion Association of the Chinese Academy of Sciences (No. 20160320).  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
Future deployment of 5G NR base stations in the 6425-7125 MHz band raises numerous concerns over the long-term impact on the satellite transponders located in geostationary orbit. To study this impact and understand whether 5G NR may cause adverse effect to the spaceborne receivers, the research which estimated the interference levels to the satellite bent pipe links was done. The study presents the evaluation of aggregate interference from 5G NR base stations located inside the victim satellites' footprints using Monte-Carlo analysis and calculation of signal-to-noise degradation and bit error rates of the fixed-satellite service (FSS) bent-pipe transponders for each scenario. The results of the study showed the feasibility of co-existence between 5G NR and satellite systems in the 6425-7125 MHz bands, and that no negative impact on the performance of the satellite links is expected.  
5G; Monte-Carlo analysis; fixed satellite service; bent-piper transponder; 6425-7125 MHz; interference analysis; frequency management; spectrum sharing; 5G digital beamforming; BER Article
# Effects of Water-Reducing Agents on the Mechanical Properties of Foamed Phosphogypsum
## 3 Results and Discussion  
Effect of the Water-Reducing Agents on the Mechanical Properties of the Foamed Phosphogypsum Samples  
It can be seen in Table 5 that, under the action of both FDN and PCE, the development trends of the 2 h strength and absolute dry strength of the phosphogypsum A samples were almost similar. With the increase in the water-reducing agent content, the strength of the phosphogypsum A samples first increased and then gradually decreased. When the content of FDN or PCE was 0.4%, the 2 h strength and absolute dry strength of the phosphogypsum A samples reached a maximum. However, the 2 h strength and absolute dry strength of the foamed phosphogypsum samples mixed with FDN were higher than those of the samples mixed with PCE under the same water-reducing agent content. It is evident from Table 6 that under the action of both FDN and PCE, the strength development law of the phosphogypsum B samples was similar to that of the phosphogypsum A samples. However, in comparison to PCE, FDN yielded a more significant effect on the strength of the foamed phosphogypsum samples; when the FDN content was 0.4%, the strength of the corresponding sample was much higher than that of the sample with the same amount of PCE. These results indicate that there was an optimal dosage of the water-reducing agents for the foamed phosphogypsum samples. In this experiment, the optimal dosage of the PCE and FDN was selected as 0.4%. The foamed phosphogypsum samples mixed with FDN had a higher strength than the samples mixed with PCE.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**Dosage of Water Reducer (\%)**} & \multirow{2}{*}{**Two-Hour Flexural Strength (MPa)**} & \multirow{2}{*}{**Absolute Flexural Strength (MPa)**} & \multirow{2}{*}{**Two-Hour Compressive Strength (MPa)**} & \multirow{2}{*}{**Absolute Compressive Flexural Strength (MPa)**} \\  & & & & & \\ \hline Phosphogypsum A doped with FDN & 0.1 & 0.9 & 1.88 & 1.42 & 2.95 \\ \hline Phosphogypsum A doped with PCE & 0.1 & 0.89 & 1.73 & 1.36 & 2.90 \\ \hline Phosphogypsum A doped with FDN & 0.2 & 0.92 & 1.89 & 1.43 & 3.00 \\ \hline Phosphogypsum A doped with PCE & 0.2 & 0.9 & 1.82 & 1.38 & 2.95 \\ \hline Phosphogypsum A doped with FDN & 0.3 & 0.96 & 1.94 & 1.49 & 3.15 \\ \hline Phosphogypsum A doped with PCE & 0.3 & 0.92 & 1.88 & 1.44 & 3.05 \\ \hline Phosphogypsum A doped with FDN & 0.4 & 0.98 & 1.96 & 1.52 & 3.25 \\ \hline Phosphogypsum A doped with PCE & 0.4 & 0.94 & 1.92 & 1.47 & 3.15 \\ \hline Phosphogypsum A doped with FDN & 0.5 & 0.94 & 1.92 & 1.46 & 3.10 \\ \hline Phosphogypsum A doped with PCE & 0.5 & 0.91 & 1.85 & 1.41 & 3.00 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mechanical properties of the foamed phosphogypsum A samples with different water-reducing agent dosages.