esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Basic Requirements and Primary Concepts
### Photodetector  
A photodetector is a semiconductor device that converts light's photon energy into an electrical signal by releasing and speeding current-conducting carriers within the semiconductors. The Positive-Intrinsic-Negative photodiode (PIN) and the Avalanche Photodiode (APD) are the two photodiodes that are most frequently utilized [17; 64]. Only two varieties are accessible because of their good quantum efficiency, semiconductor design, and widespread availability in commercial-off-the-shelf (COTS) [74].  
FSO receivers can generally be built with amplifiers for a variety of advantages, including the following:  
* The optical preamplifier can be used to increase optical signal strength that has been diminished by different atmospheric circumstances, to successfully increase receiver sensitivity,
* Overcome eye-limit restrictions on transmitted laser power, and
* Suppress the limiting effect of the receiver thermal noise generated in the electronic amplifier.  
In low-cost, low-data-rate FSO lines, PIN photodetectors are preferred since they are inexpensive, can function at low bias, and can withstand vast temperature fluctuations [17; 70]. This results in a significant internal electrical gain, which raises the receiver's Signal to Noise Ratio (SNR) [17; 29]. APD photodetectors outperform PIN, especially in systems with low ambient light noise. As a result, APDs are preferred in FSO systems with high data rates and good performance. APDs, on the other hand, are more expensive and have a temperature-dependent gain. In [64] several noise sources connected to PINs and APDs are analyzed.  
Recent breakthroughs in graphene, two-dimensional materials, and (nano)materials, such as plasmonic nanoparticles, semiconductors, and quantum dots, have opened the way for the development of ultrafast photodetectors that work over a wide wavelength range [75; 76; 77]. These photodetectors help optical communication systems with ultrahigh bandwidth and more incredible data speeds.
# Analysis of Airglow Image Classification Based on Feature Map Visualization
## 2 Structure of the Convolutional Neuron Network
Therefore, the entire CNN process can be represented as a function \(F\):  
\[F(m)=u, \tag{5}\]  
in which the input image of the CNN is \(m\) and the output is the prediction of \(u\)-th category of the input \(m\). The loss function is used to measure the discrepancies between the predicted category and the real category. In our experiment, the cross-entropy loss function is applied:  
\[L_{m}=-\text{log}p_{g}, \tag{6}\]where \(L_{m}\) is the loss of prediction about an input image \(m\). \(p_{g}\) indicates the possibility of the real category of the input image \(m\). The cross-entropy reflects the difference between the predicted probability distribution and the true category. A high probability of the true category leads to low loss and indicates the high accuracy of model prediction. Ideally, the predicted probability of the true category should be 1, and the loss is zero. Besides the loss, accuracy is also used to evaluate the classification model. The accuracy is defined as the proportion of correctly classified samples in the total samples:  
\[Accuracy=\frac{TP+TN}{Total}, \tag{7}\]  
where _TP_ (True Positive) represents the number of images of the true category that are predicted as the true category by the model, _TN_ (True Negative) represents the number of images of other categories that are predicted as other categories. The cross-entropy loss and accuracy are recorded after every training epoch to represent the overall performance of the training classification model. To visualize the feature maps of classification, we need to invert the scoring standard representing a category into a visual image. The procedure of visualization is to find an L2-regularised image \(I\) through a back-propagation algorithm, so that the value \(C_{u}\) is high:  
\[\underset{I}{\text{argmax}}C_{u}(I)-\lambda\|I\|_{2}^{2}, \tag{8}\]  
where \(\lambda\) is the regularization parameter and \(\|I\|_{2}^{2}\) indicates the square of 2-norm. Although the purpose of the model is to distinguish the airglow images of clear nights from unclear nights, we still subdivide the images of unclear nights into the other four types according to the environmental conditions, which reduces the complexity of the model in extracting the features of a single category and simplifies the structure of our CNN. A deeper network does not perform better, but worse. The added third convolutional layer introduces more convolutional kernels, which greatly expands the dimension of the data matrix and results in the overflow of GPU memory and interruption. More pooling layers will further reduce the size of the image, but the cost is the elimination of the features in the image. Our practice demonstrates that the current CNN architecture ensures a high accuracy while only requiring an affordable computation cost for personal computers.
# Hardware Reliability Analysis of a Coal Mine Gas Monitoring System Based on Fuzzy-FTA
## 2 The Principle of the Fuzzy-FTA Analysis
### Fault Tree Analysis Method Process
Then, through the qualitative analysis of the FTA method, the confidence interval of the occurrence probability of top events is obtained at a certain confidence level. In summary, the Fuzzy-FTA analysis method is found capable of qualitatively and quantitatively analyzing the reliability of a gas monitoring system hardware and calculating its failure probability [16].
## 4 Experimental Results
### Chaotic Time-Series Prediction  
In this example, the Mackey-Glass chaotic time-series \(\alpha(t)\) was considered. The following delayed differential equation was followed.  
\[\frac{d\alpha(t)}{dt}=\frac{0.2(t-\tau)}{1+\alpha^{10}(t-\tau)}-0.1\alpha(t) \tag{22}\]  
where \(\tau\) = 17 and \(x\) (0) = 1.2. The proposed IT2 FNN has four inputs, each corresponding to \(\alpha(t)\), and one output represents \(\alpha(t+\Delta t)\) where \(\Delta t\) is a prediction of the future time. The first 500 pairs were used as the training data set, and the remaining 500 pairs were used as the test data sets for validating the proposed method.  
The learning phase involved using the DGCPSO method for parameter learning, and the relevant parameter setting is presented in Table 3. The learning was performed for 200 generations and repeated 30 times. Figures 12-14 and Table 4 present the results for the chaotic time-series. The results indicate that the proposed method achieves superior performance to other methods. The root mean square error (RMSE) results from the chaotic time-series with noise added were 0.106 and 0.009 in the T1 FNN and T2 FNN, respectively (Figure 15). Therefore, the performance of the T2 FNN was considerably inferior to that of the T1 FNN.  
Figure 14: Chaotic time-series prediction error rates of **(a)** DGCPSO, **(b)** DE [24], **(c)** PSO [25], **(d)** QPSO [26], and **(e)** CPSO [27].  
Figure 13: Chaotic time-series prediction results for **(a)** DGCPSO, **(b)** DE [24], **(c)** PSO [25], **(d)** QPSO [26], and **(e)** CPSO [27].
# [PERSON]: An HPC Job Scheduler Using Deep Reinforcement Learning and Remaining Time Prediction
## 4 Experimental Results
### Experiment Setup
#### 4.1.3 PPO Setup  
RLSchert adopts the proximal policy optimization algorithm. We implemented the policy network and training algorithm based on Python 3.6.12 and PyTorch 1.1.0. The hardware equipment includes a forty-eight-core Intel(R) Xeon(R) Gold 5118 CPU @ 2.30 GHz processors and four GeForce GTX-1080 Ti 12 GB GPUs. The policy network of RLSchert is a two-layer fully connected neural network, which contains 64 neurons at each layer. The input of the policy network is the status of the cluster simulator, which is a 120 \(\times\) 72 matrix. The output dimension of the policy is 14, including 5 select actions, 8 kill actions, and 1 None action. The number of parameters is 557,952. We also designed other types of policy networks and RL algorithms, such as a convolutional neural network (CNN). The scheduling performances of each network and RL algorithm are shown in Table 3.  
pg_bs128_mlp64_64 indicates that the policy gradient is used to train [PERSON], and bs128 indicates that the batch size during training is 128. mlp64_64 means that the policy function (mlp) is represented by a two-layer fully connected neural network, with 64 neurons in each layer. In the same way, ppo_bs128_mlp64_64 means using PPO to train [PERSON]. Both the first and second layers of the policy network have 64 neurons.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & simu\_len & num\_ex & max\_seq & num\_nw & time\_horizon \\ \hline
**Value** & 200 & 16 & 3000 & 5 & 120 \\ \hline
**Parameter** & job\_len & job\_size & res\_slot & backlog & job\_rate \\ \hline
**Value** & 100 & 10 & 12\(\rightarrow\)24 & 600 & 0.3\(\rightarrow\)0.95 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The parameters of the cluster simulator.  
Figure 5: The distribution of the runtime and CPU cores of VASP.  
pop_bs128_cnn uses a CNN to represent the policy function, and each layer of convolution has 16 kernels. The results showed that pop_bs128_mlp64_64 is the best configuration, and the experimental results were based on this configuration.  
The hyperparameters of PPO are shown in Table 4. The epochs represent the maximum number of training iterations. \(\tau\) is the parameter of the advantage function. The KL target controls the early stopping of PPO. \(\gamma\) is the discount factor in DRL, which makes the training stable. The learning rate is the learning step of PPO. Specifically, we used the Adam optimizer to train the policy network. \(\epsilon\) is the clip threshold. \(\alpha_{v}\), \(\alpha_{t}\), and \(\alpha_{e}\) represent the weight of the value loss, mimic learning, and entropy, respectively.
## 5 Conclusions  
The methodologies for assessing the accuracy of SBAS orbit, clock, and ionospheric corrections are presented in this contribution. The orbit and clock references are based on IGS final orbit and clock products. The orbit inconsistency by which SBAS-derived orbits are referred to satellite APC and IGS final precise products are referred to satellite COM are corrected before comparison. The clock inconsistency between SBAS and IGS final clock products caused by different signal combination and underlying realizationis also removed. The ionospheric reference is obtained with code-leveled phase ionospheric measurement.  
Satellite orbit, clock, and ionospheric corrections from five currently available SBAS systems during the period, from 1 January to 30 June 2018, are assessed and analyzed. At the same time, GPS broadcast orbit, clock and ionospheric correction are also evaluated as a comparison. Among the five SBAS systems, the SISRE and ionospheric correction accuracy of EGNOS are 0.645 and 0.491 m, respectively, which outperform other SBASs assessed. The accuracy of SDCM corrections is very similar to that of EGNOS corrections, which is 0.650 m for the SISRE and 0.523 m for the ionospheric corrections. The WAAS system provides ionospheric corrections with comparable accuracy of 0.505 m, but SISRE is close to 1 m. However, the accuracies of MSAS and GAGAN corrections are significantly worse than other systems with SISRE of 1.931, 1.325 m and the ionospheric correction accuracy of 0.795 and 0.858 m. The SISRE of the GPS broadcast orbits and clocks during the evaluation period is 0.695 m, and the accuracies of GPS broadcast ionospheric corrections at the WAAS, EGNOS, MSAS, GAGAN and SDCM service areas are 1.482, 1.556, 1.626, 1.333 and 1.896 m, respectively. It is found that the benefits of applying SBAS corrections to GPS broadcast orbits and clocks are marginalized in terms of SISRE. However, the accuracy of SBAS ionospheric corrections is still much better than that of GPS broadcast ionospheric corrections, which could be useful for single-frequency users.  
[PERSON] did literature review, collected data, processed data and prepared the paper draft. [PERSON] analyzed the processing results and wrote result part of the paper. [PERSON] checked the software and validated the processing results. [PERSON] and [PERSON] contributed to the revision of the paper.  
This study was funded by Key Program of National Natural Science Foundation of China (Grant No.: 41631073), National Natural Science Foundation of China (Grant No.: 41604027) and Qingdao National Laboratory for Marine Science and Technology (Grant No.: QNLM2016 ORP0401).  
**Acknowledgments:** The authors acknowledge CNES for providing SBAS RINEX data and IGS for providing observation data.  
**Conflicts of Interest:** The manuscript has not been published elsewhere, and it has not been submitted simultaneously for publication elsewhere. We also have no conflicts of interest to disclose. All authors have seen and approved the manuscript.
## 3 Results and Discussion
### Alternation of Sediment Humic Substances in SMFCs  
Humus is the supramolecular association and relatively stable and recalcitrant fraction of organic matter [33]. HA and FA are the major components of the humic substances, and they have different solubility in water under different pH values [14]. Contents of HA and FA in sediments under different treatments on Day 700 are shown in Figure 4. The original values of HA were 2.32 and 1.13 mg-C g\({}^{-1}\) from site TH1 and TH2, respectively. It was found that the HA content in sediments with SMFCs increased (10.43 mg-C g\({}^{-1}\) from site TH1 and 4.93 mg-C g\({}^{-1}\) from site TH2); moreover, the HA content was substantially higher than the HA content in the control sediments (6.13 mg-C g\({}^{-1}\) from site TH1 and 2.92 mg-C g\({}^{-1}\) from site TH2) (one-way ANOVA, \(p<0.05\)). FA content showed a similar tendency (Figure 4B), which markedly increased from 4.0 and 3.75 mg-C g\({}^{-1}\) respectively from sites TH1 and TH2 of the control sediment to 8.0 and 6.4 mg-C g\({}^{-1}\) respectively from sites TH1 and TH2 with SMFCs (one-way ANOVA, \(p<0.05\)). However, both of the HA and FA contents from site TH1 were higher than those from site TH2 at the end of the experiment. From this, when compared with the control sediments without SMFC, it is clear that SMFCs stimulated the humification of organic matter in sediment,  
Figure 3: Removal efficiencies of total organic carbon (TOC) in TH1 and TH2 sediments by control and SMFCs during the 700 days of incubation.  
especially for the macrophyte-dominated area. The phenomenon was also found elsewhere [34], which researched the degradation of the polycyclic aromatic hydrocarbons in freshwater sediments.
## 3 Material and Test Procedures
### FEM Procedures  
Three-dimensional FEM analysis was conducted using the commercial software program ABAQUS. To perform PFA, a UMAT code was developed and applied to the FEM procedure. The material properties obtained from the experimental results were introduced, and the stacking sequences were arranged according to the specimen type as depicted in Tables 1 and 2. The linear hexahedral C3D8 element was used to construct the model, which is displayed in Figure 5. For the tensile test, the left side of the model was fixed, and a 2 mm displacement was applied to the right side of the model. For the compressive test, the out-of-plane displacement was constrained while following ASTM D6484. In addition, the applied displacement at the right edge was changed to \(-2\) mm. To verify the mesh dependency of the PFA results, the mesh numbers were controlled near the open hole, as shown in Figure 6.  
Figure 4: Test configuration for tensile and compressive tests of open-hole composite laminate.  
Figure 5: 3D FEM model for open-hole tensile specimen.  
Figure 6: Controlled mesh number near the open hole to verify the mesh dependency of PFA.
## 4 Results
### The Maintenance Worker 5.0 Definition and Characteristics
#### 4.2.2 Connected Maintenance Worker 5.0  
Worker 5.0 has wearables that help to promptly detect an unfavorable situation that leads to lower performance. Through an IIoT Architecture and the levels of the ISA-95 standard [125], the issue of maintenance and resilience of physical assets can be addressed from different levels (Figure 10): business level, manufacturing level, supervisory level, control level, and field level [126]. While the information technology (IT) worker connects and gives advice to the business and manufacturing levels, the operations technology (OT) worker connects and gives feedback to monitoring, control, and data acquisition systems.  
Some wearable models that are used for emotion recognition like stress include the Empatica E4, Samsung Gear S, BodyMedia Sense Wear Armband, Neurosky MindWave, and XYZlife Bio-clothing [39]. All the data obtained from the wearable sensor is concentrated in a large data cloud, and big data can be transformed from personal situational awareness into collective awareness [127]. To establish communication in a collaborative or cooperative wireless sensor network (WSN) [107] and the wearables, [PERSON] [33], points out that the most used protocol for IoT in the application layer is MQTT (Message Queuing Telemetry Transport); however, the Constrained Application Protocol (CoAP), the AMQP (Advanced Message Queuing Protocol) or the Extensible Messaging and Presence Protocol (XMPP) can also be used. For the transport layer, the User Datagram Protocol (UDP) protocol is used, while for the Network layer the IPv6, 6 LoPAN, RPL, and thread protocols are used, and for the physical layer Wi-Fi, ZigBee, LoRa, BLE, and cellular. Signals can be pre-processed by using filtration, normalization, and winsonization methods; and features can be extracted by time or frequency domain methods, as well as statistical indices and nonlinear methods [39]. Finally, the security and privacy implications of implementing these solutions must be considered [128].
# Adaptive Emergency Call Service for Disaster Management
## 4 Adaptive Multi-Hop Emergency Call Protocol: 5G-SOS
### 5G-SOS Adaptive Parameters
#### 4.1.3 Waiting Time for Re-Transmission, \(T_{0}\)  
As seen in Algorithm 1, the source of the emergency call waits a period of time \(T_{0}\) to check if sufficient devices have relayed emergency calls. The computation of the \(T_{0}\) parameter follows the same behavior of \(T_{max}\) since the retransmission delay has to align with the \(T_{max}\) of the neighbor devices in order to prevent losing the retransmitted call in the neighborhood. Consequently, \(T_{0}\) is modeled as (7):  
\[T_{0}=T_{\text{max}}(n_{k},n_{c}). \tag{7}\]
# A Novel Approach to EEG Speech Activity Detection with Visual Stimuli and Mobile BCI
## 3 An Experimental Database
### Acquisition Protocol  
An acquisition protocol for EEG signals recording was partly inspired by the experiment in Reference [7]. The acquisition protocol was designed to activate the speech, visual and imaginary areas of the brain. EEG signals recording was performed in two sessions, each lasting about 10 min. In the first session specific colours were displayed on the screen as pictures, while in the second session, the colours were displayed as text on the screen. The recorded subject was tasked to focus on the displayed colours and to name them. In the first session the focus was on the activation of the subject's imaginary areas of the brain. These areas help to create the image form of the word or to transform the seen image into a verbal form. The second session of the experiments was focused on activating the area of the brain responsible for understanding the written text, where the written text is transformed into a verbal form and vice versa. In the above exercise, 10 easily distinguishable colours were displayed randomly one by one with short breaks represented by a plain black colour. The colours spectrum selected for this protocol were yellow, green, red, blue, orange, violet, white, pink, grey, and brown.  
Figure 1: (**a**) Electrode configuration according to 10/20 international system used in the experiment. (**b**) Lateral view of the electrode configuration, compared with the brain areas (motor, auditory, visual, and speech).
_Article_  
**Gait Classification Using Mahalanobis-Taguchi System for Health Monitoring Systems Following Anterior Cruciate Ligament Reconstruction**  
**[PERSON] 1,2\({}^{\text{\textregistered}}\), [PERSON] 2,3,*\({}^{\text{\textregistered}}\) and [PERSON] 1,*\({}^{\text{\textregistered}}\)**  
Footnote 1: footnotemark:  
Received: 22 July 2019; Accepted: 8 August 2019; Published: 12 August 2019  
**Required Application: This novel method can be implemented by orthopaedic doctors or physiotherapists to provide a clinical assessment of their Anterior Cruciate Ligament Reconstruction patients with more objective, efficient and effective way.**  
**Abstract:** In this paper, a gait patterns classification system is proposed, which is based on Mahalanobis-Taguchi System (MTS). The classification of gait patterns is necessary in order to ascertain the rehab outcome among anterior cruciate ligament reconstruction (ACLR) patients. (1) Background: One of the most critical discussion about when ACLR patients should return to work (RTW). The objective was to use Mahalanobis distance (MD) to classify between the gait patterns of the control and ACLR groups, while the Taguchi Method (TM) was employed to choose the useful features. Moreover, MD was also utilised to ascertain whether the ACLR group approaching RTW. The combination of these two methods is called as Mahalanobis-Taguchi System (MTS). (2) Methods: This study compared the gait of 15 control subjects to a group of 10 subjects with laboratory. Later, the data were analysed using MTS. The analysis was based on 11 spatiotemporal parameters. (3) Results: The results showed that gait deviations can be identified successfully, while the ACLR can be classified with higher precision by MTS. The MDs of the healthy group ranged from 0.560 to 1.180, while the MDs of the ACLR group ranged from 2.308 to 1509.811. Out of the 11 spatiotemporal parameters analysed, only eight parameters were considered as useful features. (4) Conclusions: These results indicate that MTS can effectively detect the ACLR recovery progress with reduced number of useful features. MTS enabled doctors or physiotherapists to provide a clinical assessment of their patients with more objective way.  
**Keywords:** Mahalanobis-Taguchi system; ACLR; return to work; spatiotemporal; gait classification