esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Hardware Deployment
### Wireless Sensor  
Our proposal is based on the use of the Linksys WRT54 GL router, from Cisco Systems inc., as the core controller [28]. It is an embedded system that has a wireless IEEE 802.11 b/g interface, a FastEthernet interface in its board, so it meets our pre-requisites. In addition, Linksys WRT54 GL offers internally General Purpose Input/Output (GPIO), UART (JP2) and ETAJ (JP1) ports. Some extensions can be made to the router by using these ports. Figure 6 shows the embedded board and its hardware distribution.  
One of the main features that have caused the use of the Linksys WRT54 GL as a sensor node was the possibility of installing a Linux Kernel 2.4. On one hand, it is a known operative system, so we did not need to learn a new operative system and, on the other hand, we know all the possibilities that a Linux is able to provide us. So, at software level, this model is based on open source software, causing the development of different specific software applications for it and expanding the factory default capabilities.  
In order to connect two sensors directly to the board, we made an extension using the GPIO of the Linksys WRT54 GL router. It provided us two serial ports through the JP2 port. Figure 7 shows serial  
Figure 6: Hardware distribution on board.  
ports connected on board by welding pins on JP2. Then we added two DB9 Female DCE ports because we wanted flexibility in order to change the type of sensor connected to our device. To be able to connect a device with RS232 connection, it is needed a logical levels adapter based on MAX233 Integrated Circuit. The Linksys WRT54 GL router has TTL family logical levels so we had to adapt them to the RS232 family logical levels. Figure 7 shows the integrated circuit used to provide two serial ports. A RS232 line converter is needed to go from +3.3V to +5V.  
Also, we can connect an SD card reader to some of the GPIO pins found inside the Linksys WRT54 GL router and with the help of a little driver we can use as a block device from Linux. The SD card reader has been tested for a 1 GB SD card. It allows us to install applications for signal processing and store and manage acquired data from both sensors. Now, the sensor node is able to store and process data without the need of sending the measurements taken continuously. We developed a process that is running in the Linksys WRT54 GL router. It checks the values obtained by both serial ports. In one port we connected a smoke detector and in the other port we connected a fire infrared detector. The input variables of the multisensor are fire infrared radiation and smoke, but we are planning to add temperature (and the quick changes of temperature), humidity and CO\({}_{2}\). We programmed a software application that only gives a positive value if both sensors have values higher than a threshold; otherwise it is a false alarm. But this decision can be changed as desired using a combination of the measurements gathered. The system is able to gather data, process it internally, and send only alarms or statistical data spontaneously, saving energy.
# Unsupervised Anomaly Detection for Time Series Data of Spacecraft Using Multi-Task Learning
## 2 Related Work
### Deep Learning Models
#### 2.3.1 Lstm  
An RNN is a type of neural network with short-term memory capability. The neurons do not only accept information from other neurons but can also use their own information to form a network structure involving loops. Using such self-feedback neurons, the RNN can process time series data of any length. However, a problem of gradient explosion and disappearance arises for RNNs when the input sequence is relatively long; therefore, variants to overcome this problem, such as the LSTM network [35], have emerged.  
In an LSTM network, gates are introduced to control the information transmission and temporal memory paths; namely, the input \(i_{t}\), forget \(f_{t}\), and output \(o_{t}\) gates. In detail, \(f_{t}\) controls the amount of information on the previous-step internal state \(c_{t}\) that should be forgotten, \(i_{t}\) controls the amount of information to be saved regarding the current-step candidate state \(c_{t}\), and \(o_{t}\) controls the amount of information on the current-step internal state \(c_{t}\) that is output to the external state \(h_{t}\). The gates and cell updates and output are defined as follows:  
\[\begin{array}{c}i_{t}=\sigma(W_{i}x_{t}+U_{i}h_{t-1}+b_{i}),\\ f_{t}=\sigma\Big{(}W_{f}x_{t}+U_{f}h_{t-1}+b_{f}\Big{)},\\ o_{t}=\sigma(W_{0}x_{t}+U_{0}h_{t-1}+b_{o}),\\ \widetilde{c}_{t}=\tanh(W_{c}x_{t}+U_{c}h_{t-1}+b_{c}),\\ c_{t}=f_{t}\odot c_{t-1}+i_{t}\odot\widetilde{c}_{t},\\ h_{t}=o_{t}\odot\tan\h(c_{t}),\end{array} \tag{1}\]  
where \(x\) is the input vector, \(h\) is the output vector, \(\odot\) is the dot product operator, the matrix W contains the parameters to be trained, \(\sigma(.)\) is the sigmoid nonlinear function, and \(\tan\h\) is the hyperbolic tangent function.
# Special Issue on Light Communications: Latest Advances and Prospects
###### Abstract
The authors collected data on the channel gain with the increasing aperture sizes, confirming that the performance is initially improved. However, wave front aberrations appear in very large apertures, and an adaptive optics system needs to be installed so as to compensate for the wave-front aberrations. [PERSON], et al. [9] have discussed the detection of high-orbit targets, which has applications in the establishment of FSO links. They have developed a detection model based on the SNR of the target and verify it via the assembly of an infrared-K band photoelectric system. The detection range is in the order of \(10^{4}\) km, and the results indicate that the short-wave infrared bands have better imaging effects. The last two papers of this section also discuss important aspects of outdoor systems, albeit non-communication ones. [PERSON], et al. [10] have developed a novel mode-field matching model, motivated by the problem of efficiently coupling light from FSO systems to optical fibers. Their mathematical model is based on Zernike polynomials, and its predictions are validated through experimental results. Finally, [PERSON] and co-researchers [11] have presented the improvements on a tracking control system, which is based on a micro-electro-mechanical systems (MEMS) accelerometer. The authors proposed a modified disturbance observer structure to alleviate the limitations of the aforementioned tracking system at low frequencies and implement the corresponding controller to experimentally validate its performance.
## 1 Introduction
In [17], a global EMF exposure metric denoted \" Exposure Index (EI)\" is proposed to estimate the EMF exposure caused by a wireless cellular network, accounting for both the transmissions of the users' devices (UL exposure) and the BSs (DL exposure). However, all these researches focused on the previous wireless cellular network technologies (2G, 3G and 4G) rather than the 5G, which is the aim of our work. The main contribution of this study consists in proposing a simulation-based method to design massive MIMO 5G cellular networks such that the BSs' positions and their respective power levels are optimized while jointly complying with the constraints of lower power consumption, lower DL EMF exposure and lower UL EMF exposure. Furthermore, a realistic assessment of EMF exposure of the network based on the actual maximum power rather than the theoretical maximum power is considered here. To the best of our knowledge, no related works yet investigated a design of massive MIMO network in the context of 5G which simultaneously minimizes the power consumption, the DL EMF exposure and the UL EMF exposure, while guaranteeing a good coverage and required capacity to accommodate the user demand. The remainder of this paper is organized as follows. Section 2 describes the 5G system model considered for this paper; in Section 3 the methodology and the metrics dealing with the optimization objectives are investigated, while Section 4 discusses the results of the simulations based on the different scenarios used before concluding in Section 5.
## 1 Introduction
Some researchers have described a two-stage method where a statistical normalization of the solar power is first obtained, and then the forecasts of the normalized solar power are calculated to predict the PV power [15]. We chose to employ the extreme learning machine (ELM) algorithm because of the following advantages:  
1. The computation complexion of ELM is much lower than many other machine learning algorithms. 2. The learning speed of ELM is much faster than most feed forward network learning algorithms. 3. The generalization performance of ELM is better than many others. 4. The amount of hidden layer nodes is small and they do not need to be tuned [16]. The aim of our work was to develop a simple and accurate online model for forecasting the power produced by a PV power plant. In order to achieve this goal, we built the prediction model based on the online sequential extreme learning machine with forgetting mechanism (FOS-ELM), which predicts the PV power output for the next 15 min in a rolling manner. The main contributions of our work are summarized below:  
1. We introduced an online learning model with a Forgetting Mechanism to the area of photovoltaic prediction, which can update the data in real time. 2. We compared the ELM, OS-ELM and FOS-ELM prediction models in predicting PV power in different seasons. 3. The simulation results showed that the FOS-ELM model can not only improve the accuracy but also reduce the training time.
## 3 Physical Layer Improvement Proposals for PRIME and G3-PLC
### G3-PLC: Tone Map
#### 3.3.2 Reallocation of the Power Assigned to Inactive Tones  
The specification of G3-PLC tone map defines that all the subcarriers transmit either data or a pseudo-random sequence. In this work, an alternative approach will be analyzed. P1901.2 [21] supports, for FCC and ARIB bands, the option of not introducing energy in inactive subcarriers. This nulling technique will be applied to G3-PLC CENELEC A band and will be extended reallocating that power within the rest of subcarriers. The total transmission power will be maintained constant, but the transmission power spectral density of the active carriers changes with the number of inactive subcarriers. The expected increase of the power spectral density for these subcarriers can be represented in dB as:  
\[PSD_{TX}=10\cdot\log\frac{Number\;of\;inactive\;subcarriers}{Number\;of\;active\; subcarriers} \tag{1}\]  
The impact of this power reallocation is depicted in Figure 5. It represents the spectral evolution of the payload for different tone map combinations, while the total power is kept constant to 120 dB\(\mu\)V.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Modulation Scheme** & **SNR Threshold** \\ \hline Robo Coherent & 2 dB \\ \hline Robo Differential & 3.5 dB \\ \hline BPSK & 5.5 dB \\ \hline DBPSK & 7.5 dB \\ \hline QPSK & 9 dB \\ \hline DQPSK & 11 dB \\ \hline
8 PSK & 14 dB \\ \hline D8 PSK & 16 dB \\ \hline \hline \end{tabular}
\end{table}
Table 1: SNR thresholds for tone map detection.
## 7 Conclusions  
In this paper, we study the impact of using SCHC over NB-IoT in terms of the latency and energy efficiency of a device under different scenarios. We consider real DLMS metering traffic optionally secured by DTLS. The experiments are carried out considering both IP and non-IP data delivery, with measurements conducted in a controlled environment. The measurements show that transmitting on NIDD is more efficient and induces less delay than on the IP. We also observed that SCHC is very effective at reducing latency and energy consumption for DTLS handshakes and normal transmission, on both NIDD and IP. This is especially significant in bad radio conditions where energy consumption becomes very high. SCHC was able to reduce the energy consumption by up to 40%. As such, the study shows that SCHC can effectively extend the lifetime of a battery-powered smart meter. Consequently, we recommend implementing SCHC in NB-IoT devices, specifically for metering applications. As for future work, we intend to study the impact of using SCHC in applications other than metering. In addition, it would be interesting to study the impact of SCHC on the capacity of the NB-IoT network.  
Conceptualization and project administration, M.D., H.A.H.H., D.B. and A.P.; Investigation, M.D., H.A.H.H., P.S. and T.A.; Methodology, M.D. and H.A.H.H.; Software, P.S. and T.A.; Validation, M.D., A.P. and H.A.H.H.; Writing--original draft, M.D. and H.A.H.H.; Writing
## 4 Results
### Vgg-16  
We trained the VGG-16 model on both the training and validation datasets in our third experiment, which evaluated the model. The model's accuracy, precision, AUC, and loss training history curves are displayed in Figure 9. With accuracy and AUC averaging 80% throughout the training process, VGG-16 produced very smooth training curves. The accuracy curve, however, stayed mostly stable. Despite the lack of gain in precision, VGG-16 outperformed preceding models. The model exhibited not only consistent and noteworthy accuracy, but also outstanding accuracy of over 80% for both training and validation data. These figures demonstrate that both accuracy and AUC were around 80% throughout training (Figure 9a).  
Figure 8: Training history curve of AlexNet model on training and validation data. (**a**) Accuracy, precision, and AUC. (**b**) Loss.  
Figure 9: Training history curve of VGG-16 model on training and validation data. (**a**) Accuracy, precision, and AUC. (**b**) Loss.  
While the loss curve (Figure 9b) shows a smooth decline for both training and validation data, more research into potential overfitting is needed. Similar to the preceding models, the existence of high data volatility and class imbalance is likely to impede optimal performance. Addressing these data concerns might improve the VGG-16 model's ability to handle classification tasks.
## 6 Conclusions  
To summarize, this work presents an active HSI classification system with a coherent SC laser illumination and a hybrid DenseNet classifying model. It is an exploration of the applications of SC lasers and provides a possible alternative to the halogen lamps as illuminators for active HSI. The experimental results indicate that the reflected spectral signatures illuminated by the SC laser are suitable for applications of active HSI in the dark. Moreover, the accurate object classification results indicate that most parts of objects can be rightly classified based on their different spectral fingerprints and spatial information, even when using a small number of samples to train the hybrid DenseNet classification model. What is more, the experimental results indicate the superiority of the proposed hybrid DenseNet model, which uses double-branch structure to extract discriminative spectral and spatial features in parallel. Although this system is still a proof-of-concept and needs more explorations before it is used in complex real-life conditions, such as imaging from low-flying unmanned aerial vehicles or high towers, the correct active HSI classification of different objects supports the viability for camouflage detection, biomedical alteration detection and so on. In the future, more attention  
Figure 14: Spectra of the Class 6 in active HSI data. A total of 11 spectra are randomly selected.  
will need to be paid to improving the deep-learning-based classification model and accelerating the classification algorithms. Further, working distance of the active HSI classification system should be extended to kilometers to take full advantage of SC lasers after considering the effect of atmospheric turbulence and absorption [7; 8; 10].  
Conceptualization, J.H. and T.J.; data curation, Y.L.; formal analysis, Y.L. and Z.T.; funding acquisition, T.J.; investigation, Y.L., Z.T., J.Z. and H.H.; methodology, T.J.; project administration, Y.P. and T.J.; supervision, J.H. and T.J.; visualization, Y.L.; writing--original draft, Y.L.; writing--review and editing, J.H. and T.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation (NSF) of China under Grant numbers 11802339, 11805276, 61805282, 61801498 and 11804387; Scientific Researches Foundation of the National University of Defense Technology under Grant numbers ZK16-03-59, ZK18-01-03, ZK18-03-36 and ZK18-03-22; NSF of Hunan Province under Grant number 2016 JJ1021 and The Youth talent lifting project under Grant number 17-JCJQ-QT-004.  
We acknowledge editors and reviewers for their valuable suggestions and corrections.  
The authors declare no conflict of interest.
## 1 Introduction
Since HIXs may contain a considerable amount of inorganic phase (which can exceed 30 wt%) [32], causing pore narrowing and blockage and grain deformation and cracking during drying and so hindering gaseous phase access to the inside of the ion exchanger, we proposed to subject also freeze-dried samples retaining their open internal structure (which they had in the aqueous phase, i.e., in the natural operating conditions) to thermal analysis [30]. A similar phenomenon may occur in the case of inorganic composite materials when Cu\({}^{+}\) ions penetrate inside the metal matrix, and the formation of Cu clusters causes a high strain of the host atoms [33]. In order to broaden and systematize our knowledge about the thermal properties of HIXs, including the effect of the oxidation state of the copper atom contained in the deposit on the properties, we investigated materials based on the same anion exchangers as previously, but containing fine particles of Cu\({}_{2}\)O [34] and Cu\({}^{0}\) (zero valent copper, ZVC) [35]. The characteristic of the composite with Cu\({}^{0}\), discovered in HIX for the first time, is the anomalous loss of the anion exchanger's original porosity and strong volume contraction. Typically, macroreticular anion exchangers and the HIXs based on them have been known to exhibit (including when dried) porosity, which is an important parameter characterizing their performance (determined using the N\({}_{2}\) adsorption-desorption method and mercury intrusion porosimetry) [32]. The materials investigated in this study differed from the most popular hydroscopic, porous materials containing hydrated oxides of polyvalent metals in the anion exchanger's matrix. The aim of this study was to examine the effect of dispersed Cu\({}_{2}\)O and Cu\({}^{0}\) particles on the thermal decomposition of both macroreticular (M) and gel-like (G) anion exchangers in different atmospheres (under respectively air and N\({}_{2}\)) and to identify the solid products of the processes. More precisely, we wanted to answer the following questions: (a) What is the effect of the deposits on the hygroscopic water content in HIXs? (b) Do the deposits differ in their catalytic activity in decomposition of the anion exchanger? (c) Does the pyrolysis of the HIX with reducible Cu\({}_{2}\)O yield more char than the pyrolysis of the HIX with unreducible Cu\({}^{0}\)? and (d) Does the pyrolysis of the HIX with Cu\({}^{0}\) yield a similar amount of char as the pyrolysis of the anion exchanger with no copper deposit?The study is of both theoretical and practical importance as it shows that it is possible to sequentially (in stages) reduce the volume of spent HIXs in two different processes, presents a way of obtaining activated (doped with metallic copper) carbon sorbents through pyrolysis, and gives an insight into the catalytic properties of fine cuprous oxide and metallic copper particles deposited in the polymeric phase. Polymeric composites with copper-based additives (e.g., Cu, Cu\({}_{2}\)O, or CuO) are prospective materials for catalysis [36; 37].
## 3 Results and Discussion
### Aging Resistance  
The CAI and PAI of the HCRMB with different amounts of sulfur after RTFOT aging are presented in Figure 10. Based on Figure 10a, the CAI of the HCRMB with different amounts of sulfur is lower than that of 70# below 70 \({}^{\circ}\)C and the ranking of the CAI at 46 \({}^{\circ}\)C and 52 \({}^{\circ}\)C is 40 TB_0.45 ul \(<\) 20 TB_0.45 ul \(<\) 40 TB_0.25 ul \(<\) 20 TB_0.25 ul \(<\) 70# after RTFOT aging, and the ranking of the CAI at 6 \({}^{\circ}\)C is 20 TB_0.45 ul \(<\) 40 TB_0.45 ul \(<\) 40 TB_0.25 ul \(<\) 20 TB_0.25 ul \(<\) 70# after RTFOT aging. Moreover, in accordance with Figure 10b, from 70 \({}^{\circ}\)C to 88 \({}^{\circ}\)C, the PAI values of 40 TB_0.45 ul are higher than those of 40 TB_0.25 ul and the PAI values of 20 TB_0.45 ul are higher than those of 20 TB_0.25 ul. The above data indicate that the RTFOT aging resistance of HCRMB is better than that of conventional RMB, and the increase in the amount of sulfur can improve the RTFOT aging resistance of HCRMB.  
Figure 6: G* and \(\delta\) of HCRMB with different amounts of sulfur after aging: **(a)** 70#, **(b)** 20 TB_0.2 SuI, **(c)** 20 TB_0.4 Sul, **(d)** 40 TB_0.2 Sul, and **(e)** 40 TB_0.4 Sul.  
Figure 11 shows the CAI and PAI of the HCRMB with different amounts of sulfur after PAV, respectively. As described in Figure 11, compared with 70#, the CAI of the HCRMB with different amounts of sulfur after PAV decreases from 40 \({}^{\circ}\)C to 88 \({}^{\circ}\)C, and the ranking of CAI from 70 \({}^{\circ}\)C to 88 \({}^{\circ}\)C is 40 TB_0.45 ul < 40 TB_0.25 ul < 20 TB_0.45 ul < 20 TB_0.25 ul < 70# after PAV. In addition, the ranking of PAI of HCRMB with different amounts of sulfur after PAV is similar to that of PAI ranking in Figure 10b, indicating that the PAV aging resistance of HCRMB is better than that of conventional RMB and that the addition of sulfur improves the PAV aging resistance of HCRMB. This is because as the amount of CR increases, substances such as sulfur and silica from the CR enter the bitumen colloid system and play a role in improving the aging resistance of HCRMB.  
Figure 8: R of HCRMB with different amounts of sulfur: (**a**) R0.1 and (**b**) R3.2.  
Figure 7: Jnr of HCRMB with different amounts of sulfur: (**a**) Jnr0.1 and (**b**) Jnr3.2.  
Figure 9: Jnr3.2 and R3.2 of HCRMB with different amounts of sulfur after RTFOT and PAV: (**a**) 70#, (**b**) 20 TB_02 SuI, (**c**) 20 TB_0.4 Sul, (**d**) 40 TB_0.2 SuI, and (**e**) 40 TB_0.4 Sul.