esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Systems and Technologies for Smart Homes and Smart Grids
## 5 Review of the Technologies for Smart Homes
### Home Automation Systems
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Product Name** & **Main Features** & **Strengths** & **Weaknesses** & **Reference** \\ \hline British Gas Smarter living \& Energy saving—Smart meters & It is a smart home energy monitoring and controlling system developed by British Gas for their customers in order to keep better control of the energy bills & (1) It allows users to interact with home appliances and works with a smart energy monitor that can be placed anywhere in the home; & It is a system developed by the British Gas company and therefore it is linked to a contact & [90] \\ \hline Control4 Home Automation & It is a HA system that allows the products and devices that the users already have and use every day, work together & (1) Compatible with any other smart technology; (2) User-friendly control by a dedicated app & (1) The system allows the users to control lights, audio and security in the house, but not monitoring the energy usage and production. & (2) The system must be installed professionally an authorized dealer; (3) It works only with internet connection and only with families of peripherals that are compatible with the controllers in this system are Bluetooth, Wi-Fi, KNK, Z-Wave and ZigBee. & (4) It offers limited mobile access functionality with its base system setup; (5) The Help \& Support section of the system is poorly performed & (1) The system allows the users to control lights, audio and security in the house, but not monitoring the energy usage and production; & (2) Many of the peripherals are specifically designed to work with Crestron hardware controllers. & (92) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Features, strengths, and weakness of the analyzed HA systems.
# The Microwave Temperature and Humidity Profiler: Description and Preliminary Results
## 3 Calibration Strategy
Note that this method differs from other three-point calibration methods as a quadratic function is used to fit the three points. In our case, the resulting regression may not intercept some of the points. Instead, it will look for the most probable gain and offset coefficients based on the three points provided. Further information on radiometric calibration is available at [23; 24; 25]. This calibration methodology was tested on the ground during MTHP integration with the TI3 GER GV airplane using an external hot calibration load of 355 K. The measurement setup and its results are shown in Figures 7 and 8, respectively. Note that for Figure 8, the spectral components have been averaged to a single value to ease the visualization. As can be seen in both channels, following the rotor angle, the estimated antenna temperature is 290 K (dark blue) when pointing inside the hangar at room temperature. The external heated load, located at 220\({}^{\circ}\), is at an antenna temperature of 355 K (red). The temperature of the internal ambient load is the light blue line at 300\({}^{\circ}\), and the thermally controlled internal target is the yellow line at 350\({}^{\circ}\) (T = 333 K). Note that in this plot, the zenith is \(\sim\)55\({}^{\circ}\).
# Computation Offloading and Resource Allocation Based on P-DQN in LEO Satellite Edge Networks
## 6 Conclusions and Future Work  
This work has investigated the application of P-DQN RL method to the joint computation offloading and resource allocation problem in LEO satellite edge networks. Unlike the discrete action space-based method (e.g., DQN) and the continuous action space-based one (e.g., DDPG), the P-DQN method takes effects in the mixed discrete-continuous space, without approximating the mixed space into the discrete one or relaxing it into the continuous one. Thus, this work considered the time-varying channel characteristics, and formulated the power control, computation offloading, and resource allocation to maximize the rate of satisfied tasks over the long run. To solve it, the PAMDP model was used to capture the dynamics in LEO satellite edge networks, using the parameterized continuous action. Finally,  
Figure 10: Rate of satisfied tasks in terminals, satellites, and cloud server.  
Figure 9: Proportion of offloaded tasks under different approaches.  
the effectiveness of proposed method was verified through simulations, showing that it not only has a faster convergence rate, but also outperforms existing methods in terms of the rate of satisfied tasks. In future work, we will consider issues such as unstable connections between terminals and satellites (i.e., satellite handovers), and long-run optimization of EE.  
Conceptualization, X.Y. and H.F.; methodology, X.Y. and Y.G.; software, X.W.; validation, X.Y., H.F. and K.W.; formal analysis, Y.G.; investigation, K.W.; resources, Z.L.; data curation, X.Y. and H.F.; writing--original draft preparation, X.W.; writing--review and editing, X.Y. and H.F.; visualization, Z.L.; supervision, K.W.; project administration, Y.G.; funding acquisition, X.Y. and H.F. All authors have read and agreed to the published version of the manuscript.  
This work was in part supported by the National Key Research and Development Program of China under Grant 2020 YFB1808003, in part by the National Natural Science Foundation of China under Grant 61801379, and in part by the Natural Science Foundation of Shaanxi Province of China under Grant 2020 JQ-647.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors would like to thank the editor and all reviewers for their valuable comments and efforts on this article.  
The authors declare no conflict of interest.
# Hard-Decision Coded Modulation for High-Throughput Short-Reach Optical Interconnect
## 4 Numerical Analysis for an Optical Transmission System
### Multi-Span Fiber System Setup
#### 4.2.2 Post-FEC BER Results  
In order to evaluate the reach increase for the proposed CM for multi-span transmission, we implemented SCCs for multiple transmission distances and compared the post-SCC BER of \(M\)QAM and \(M\)-GS modulation formats (\(M\in\{64,128\}\)). Two code rates, \(R_{s}^{1}=233/252\) and \(R_{s}^{2}=95/114\), are used for SCCs. Therefore, the line rates of 450 Gbit/s, 500 Gbit/s, 525 Gbit/s, and 583 Gbit/s can be obtained. In addition, we concatenated LDPC codes with code rate \(R_{l}=0.9\) (as inner code) and a SCC with code rate \(R_{s}^{1}\) (as outer code) as a SD-FEC CM scheme baseline. The LDPC codes are the ones from the DVB-S2 standard, with a block length of \(N=64800\) bits, and 50 decoding iterations. The decoded bits from the LDPC decoder are used as input to the staircase decoder. The overall concatenated code rate is \(R=R_{s}^{1}\cdot R_{l}\approx R_{s}^{2}\), which is the code rate as SCC with \(R_{s}^{2}=95/114\).  
Figure 9: AIRs as function of the transmitted power for different modulation formats over multi-span SSMF.  
In Figure 10, post-FEC BER performance of central WDM channel at the optimum launch power is shown for _M_QAM and _M_-GS modulation formats (\(M\in\{64,128\}\)) at different line rates. The transmission reach extension of 64-GS with HD-FEC (SCC) at 500 Gbit/s and 450 Gbit/s are measured to be 80 km (13%) and 140 km (17%), respectively. Considering the higher complexity of the CM system combining 64 QAM and SD-FEC (LDPC+SCC) as a baseline at the date rate of 450 Gbit/s, we observe only a gain of 75 km compared to the proposed scheme with 64-GS and SABM decoding. Therefore, the combination of HD-FEC and GS constellation offers a good complexity-gain trade-off. For a transmission range between 300 km and 500 km, the reach extension of 128-GS with HD-FEC (SCC) at 583 Gbit/s and 525 Gbit/s, are measured to be 70 km (24%) and 80 km (20%), respectively.
# Reliability Analysis of CFRP-Packaged FBG Sensors Using FMEA and FTA Techniques
## 5 Conclusions  
CFRP-packaged FBG sensors are widely used in WTB full-scale structural testing, and their quality and reliability have attracted a lot of attention. In this paper, FMEA and FTA techniques are used to complete the reliability analysis of CFRP-packaged FBG sensors. The failure modes, failure causes, and failure effects are analyzed for each of the FBG sensors, PL, BL, CC, and the FBG demodulator. The three key faults (\"FBG-FM7\", \"BL-FM15\", and \"CC-FM17\") are defined and their corresponding FTAs are built. Recommendations to mitigate the identified key failures are discussed. However, the FMEA and FTA methods used in this paper are based on precise probability theory without considering cognitive uncertainty, such as the assessment of severity (S), occurrence (O), and detection (D), or the definition of system or component failures, etc. Specifically, the RPN calculation method in this paper does not consider the weight of each index. Thus, in the future, other alternative methods for risk evaluation such as action priority [13,18], fuzzy sets [25,26], Bayesian network [26,27], interval analysis [28], etc., will be implemented.  
Conceptualization, Z.L. ([PERSON]), Y.L., N.Z., Z.L. ([PERSON]) and F.L.; methodology, Z.L. ([PERSON]) and Y.L.; software, Z.L. ([PERSON]) and Y.L.; validation, N.Z., Z.L. ([PERSON]) and F.L.; investigation, Y.L.; data curation, Y.L.; writing--original draft preparation, Y.L.; writing--review and editing, Z.L. ([PERSON]) and F.L.; supervision, Z.L. ([PERSON]); project administration, Z.L. ([PERSON]); funding acquisition, Z.L. ([PERSON]) and Z.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 51905116; National Key R&D Program of China, grant number 2018 YFB1501200, and National Key R&D Program of China, grant number 2018 YFB2000501.  
The paper was partially supported by the National Natural Science Foundation of China (51905116), the National Key R&D Program of China (2018 YFB1501200), and the National Key R&D Program of China (2018 YFB2000501).  
The authors declare no conflict of interest.
# Energy-Efficient IoT-Based Light Control System in Smart Indoor Agriculture
## 4 Grow Lights Monitoring System
### RPL Routing Protocol  
RPL is a routing protocol for the networks constrained by their power consumption and packets loss. It constructs the routes towards a single destination node based on a distance vector algorithm minimizing the path length [65]. In addition, it can also use different objective functions (OF) to construct the routes based on other metrics like energy consumption and data packets delivery. RPL supports both upward and downward routing corresponding to sending packets from source nodes to the root node and sending them from the root node to a specific source node, respectively.  
Three objective functions are tested in the studied case: OF0 (Objective Function 0), MRHOF-ETX (minimum rank with hysteresis objective function-expected transmission count), and MRHOF-energy (minimum rank with hysteresis objective function-energy). OF0 corresponds to the shortest path, MRHOF-ETX corresponds to the minimum number of transmission attempts per successful transmission (minimum data loss), and MRHOF-energy corresponds to the minimum energy consumption of the nodes. In Section 5, we present the evaluation results of the network performance in terms of data loss, latency, and energy consumption while using these objective functions (OF0, MRHOF-ETX, and MRHOF-energy).
# Reliable Augmentation and Precise Identification of EPG Waveforms Based on Multi-Criteria DCGAN
## 2 Materials and Methods
### Data Reading and Segmentation  
The original EPG signals were manually inspected and annotated using Stylett++ software (version 1.30), which exported the annotations into an (.ANA) file containing the starting points of each segment. This tab-delimited text file, along with the original EPG data in ASCII format, was processed using MATLAB 2023A. To eliminate annotation errors, the first and last 10 data points of the waveforms were removed. The label information from the (.ANA) file was then used to slice the EPG data, and the results were saved as plain text files for further analysis. Then, different types of waveforms were stored as a dataset with a unified length of 100 data points. This length was chosen because the frequencies of waves A, E1, and E2 were generally 10-15 Hz, 5-8 Hz, and 4 Hz [20], respectively. This ensured that all features of each waveform type were included while facilitating processing. The E1 waveforms were stored as a separate dataset. All data were normalized within a specific range, then expanded by 2\({}^{16}\) times and converted to TIFF format for data augmentation. Table 1 lists the number of each waveform sample, with the training and testing sets divided in a ratio of 8:2 for subsequent modeling.
# Malware Detection Issues, Challenges, and Future Directions: A Survey
## 4 The Taxonomy of Malware Analysis and Detection Approaches
### Malware Detection Approaches
#### 4.2.1 Signature-Based  
Several studies have been undertaken to improve malware detection and classification models by relying on a unique signature that has been previously extracted statically or dynamically and stored to compare it with the collected investigated file signature. Those signatures include but are not limited to a set of API calls, opcodes or byte-code series, and entropy quantity. Static string-based signatures have been generated by [35; 60] to detect VBasic malicious software by representing the obtained strings using frequency vectors, while [61] generated static signatures based on n-grams and binary vectors. Additionally, [38] formed malicious static signatures using the statistical values of opcodes. On the other hand, behavioral signatures have been constructed based on the dynamically collected data. The authors of [48; 62; 63] created behavioral signatures using API calls invoked by malware during their running time. Specific sets of API calls are identified as reflecting malicious activities, and thus the behavioral malicious signatures are constructed utilizing those API calls. Static and behavioral signature-based malware detection models suffer from low detection rates when classifying unknown signatures that may be linked to unknown malware or different variants of known malware.
# Hybrid Sensing Platform for IoT-Based Precision Agriculture
## 3 Materials and Methods
### Data Analysis for Improving the Quality of Aerial Sensor Observations
#### 3.2.1 Techniques for Improving the Quality of Aerial Sensor Observations  
The HSP provides the following novel capabilities:  
* Techniques for validation of aerial data based on the analysis of related ground-based data, which allows the HSP to determine which aerial sensor observation data should be excluded from consideration (due to lack of sufficient data quality) or calibrated. Data validation is the process of determining the degree to which remote/aerial sensor observations provide an accurate representation of the real world from the perspective of the intended PA application.
* Techniques for calibration of aerial data based on related ground-based data (which we refer to as ground truth) to improve aerial data quality. Data calibration refers to the process of preforming accuracy and uncertainty calculation based on observation context variables and applying corrections on the aerial data to reduce their distance from the ground truth.  
In the following section, we discuss the data validation and calibration techniques employed in the HSP.
###### Abstract  
The propagation performance of a free-space optical (FSO) communication system in an atmospheric environment is restricted and degraded due to the influence of atmospheric turbulence. In this paper, both the lognormal and Gamma-Gamma channel models are employed to characterize this turbulence under weak-to-strong conditions. In addition, the average bit error rate and average channel capacity of an FSO communication system under the influence of background noise, thermal noise and quantum noise (resulting from the environment, the device, manual operation, etc.) are considered. Moreover, the comparison of system performance under different turbulence conditions and various noises are conducted. Simulation results reveal that thermal noise has a dominant effect on the FSO system. In addition, both the channel parameters and the system parameters have a significant influence on the performance of an FSO communication system.  
free-space optical communication; noise; lognormal model; Gamma-Gamma channel; BER; channel capacity +
Footnote †: journal: Sensors
## 2 Results and Discussion
### Optimization of HS-SPME Procedure
#### 2.1.3 Effect of Salt Addition  
Addition of sodium chloride was tested to check its effect on the HS-SPME procedure. The salting-out method is known to increase the distribution constant between the fiber and the  
Figure 1: Effect of extraction time on hexanal, 4-methyl-3-penten-2-one, heptane, benzaldehyde and 1-octen-3-ol.  
Figure 2: Effect of extraction temperature on hexanal, 4-methyl-3-penten-2-one, heptane, benzaldehyde and 1-octen-3-ol.  
analytes since it minimizes the interaction of the analytes with water and helps the analytes to go to the headspace [22]. An addition of 0.75 g and 1.5 g of sodium chloride, as well as absence of salt, were tested. Figure 3 shows the effect of salt addition in the extraction procedure. It was found that there was no significant effect of sodium chloride addition. As a result, no salt addition was required.
## 2 System Model
### Parameter Estimation Based on Carrier Phase  
The N phase measurements of the \(i\)th satellite received by the antenna during the rotation process at different times are:  
\[\Phi_{i}(k)=\rho_{i}(k)+\varepsilon_{i}(k),\quad i=1,2,\cdots,I;k=0,1,2, \cdots,N-1 \tag{12}\]  
where \(\rho_{i}(k)\) is the true carrier phase value of the \(i\)th satellite, and \(\varepsilon_{i}(k)\) is the carrier phase measurement noise of the \(i\)th satellite. Due to the rotation of the antenna at angular velocity \(\omega\), \(\rho_{i}(k)\) can be expressed as [16]:  
\[\rho_{i}(k)=\rho_{i,sat}(k)+r\cos\theta_{i}\cos(\omega T_{0}k+\varphi_{i}+ \varphi_{0}) \tag{13}\]  
where \(\rho_{i,sat}(k)\) is the true value of the carrier phase at the stationary origin. Its change is mainly caused by the satellite's motion relative to the origin. \(\varphi_{i}\) and \(\theta_{i}\) are the azimuth and elevation angles of the \(i\)th satellite's signal incident into the coordinate system, and \(\varphi_{0}\) is the initial rotation phase. \(r\) is the horizontal projection distance from the center of the antenna to the center of rotation, \(\omega\) is the rotational angular velocity of the antenna, and \(T_{0}\) is the sampling period of the receiver connected to the rotating antenna. By performing forward and backward differentiation on the carrier phase measurement value in Formula (12), (14) can be obtained [16]:  
\[ \begin{array}{l}d\Phi_{i}(k)=\Phi_{i}(k+1)-\Phi_{i}(k-1)\\ =\{\rho_{i,sat}(k+1)+r\cos\theta_{i}\cos[\omega T_{0}(k+1)+\varphi_{i}+\varphi _{0}]+\varepsilon_{i}(k+1)\}-\\ \{\rho_{i,sat}(k-1)+r\cos\theta_{i}\cos[\omega T_{0}(k-1)+\varphi_{i}+\varphi _{0}]+\varepsilon_{i}(k-1)\}\\ =d\rho_{i,sat}(k)-2r\sin(\omega T_{0})\cos\theta_{i}\sin(\omega T_{0}k+\varphi _{i}+\varphi_{0})+\gamma(k),k=1,2,\cdots,N-2\end{array}  \tag{14}\]  
where \(\gamma(k)\) is the new noise term obtained from the differential operation of the noise term. For receivers in a stationary state, satellite navigation spoofing signals also need to simulate the changes in signal characteristics caused by navigation satellite motion; that is, the real signal and spoofing signal in Equation (14) have the same carrier phase change \(d\rho_{i,sat}(k)\) caused by satellite motion, and the change in satellite motion can be calculated based on ephemeris data to obtain \(d\rho_{i,sat}(k)\). Therefore, \(d\rho_{i,sat}(k)\) can be used as a known quantity to obtain a differential sequence that only includes errors and carrier phase changes caused by the antenna rotation motion:  
\[d\Phi_{i,rcv}(k)=-2r\sin(\omega T_{0})\cos\theta_{i}\sin(\omega T_{0}k+\varphi _{i}+\varphi_{0})+W_{\Phi,i}(k) \tag{15}\]  
Continuing of the above equation, one can obtain the following:  
\[d\Phi_{rcv}(t)=M\sin(\omega t+\gamma)+W_{\Phi}=s(t;M,\gamma)+W_{\Phi} \tag{16}\]  
where \(M=-2r\sin(\omega T_{0})\cos\theta_{i}\), \(\gamma=\varphi_{1}+\varphi_{0}\), \(W_{\Phi}\) is the zero mean additive white Gaussian noise with variance \(\sigma_{\Phi}^{2}\).
# Intelligent Radio Frequency Identification for URLLC in Industrial IoT Networks
## 3 RFI Based on Semisupervised Learning For URLLCs
### Novel Architecture Illustration of Semisupervised Learning Model
\begin{split}\
abla_{w_{t}}L_{l}&=\eta_{s}\cdot \underbrace{\frac{\partial\mathcal{C}(y_{t},S(x_{l};w_{s}^{\prime}))}{\partial w _{s}}}_{\text{labeled+student network}}\cdot\underbrace{\left(\frac{\partial \mathcal{C}(\hat{y}_{u},S(x_{u};w_{s}))}{\partial w_{s}}\bigg{|}_{w_{s}=w_{s}} \right)^{T}}_{\text{unlabeled+student network}}\cdot\underbrace{\frac{\partial \mathcal{C}(\hat{y}_{u},T(x_{u};w_{t}))}{\partial w_{t}}}_{\text{unlabeled+ teacher network}}\\ &=\eta_{s}\cdot\
abla_{w_{s}^{\prime}}\mathcal{C}\Big{(}y_{t},S \big{(}x_{l};w_{s}^{\prime}\big{)}^{T}\Big{)}\cdot\
abla_{w_{s}}\mathcal{C}( \hat{y}_{u},S(x_{u};w_{s}))\cdot\
abla_{w_{t}}\mathcal{C}(\hat{y}_{u},T(x_{u} ;w_{t}))\end{split}  \tag{18}\]  
On the other hand, the student's training still relies on the objective in (7), except that the teacher parameter is not fixed anymore. The teacher's optimization process involves changing. More interestingly, we also used SGD to update the weight, and the updated student's parameter can be reused in the one-step approximation of the teacher's objective. This design naturally gives rise to an alternating optimization procedure between student and teacher network updates, which can be denoted as  
\[w_{s}^{\prime}=w_{s}-\eta_{s}\cdot\bigtriangledown_{w_{s}}L_{u}(w_{t},w_{s}) \tag{19}\]  
\[w_{t}^{\prime}=w_{t}-\eta_{t}\cdot\bigtriangledown_{w_{t}}L_{l}(w_{s}^{ \prime}) \tag{20}\]  
As shown in Figure 6, we constructed the one-dimensional RFIResNet by ID and conv blocks. We used two convolutional layers and an additional add module to form ID blocks. For conv blocks, there exists a dimensional transformation to realize vector length matching. First, the convolutional operation of the input signal is realized by 64 linear filters with a size of \(7\times 1\). All those kernels were designed to obtain characteristic information at different time scales. ReLu activation functions reduce the general gradient vanishing [30]. Furthermore, the proposed RFIResNet contains 42 layers to learn RF characteristics, and the total number of parameters was 11,304,907, which approximately equals to that in the original ResNet18. Lastly, we utilized a full connection layer and softmax function to realize dimension reducing and probability mapping. Cross entropy is used to guide the learning and convergence of the network model [37]. Furthermore, our proposed semisupervised learning framework with meta pseudo labels is shown as Algorithm 1. Figure 5: Main workflow of machine learning. (**a**) supervised learning. (**b**) semi-supervised learning.