esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Natural disasters are a major source of significant damage and costly repairs around the world. After a natural disaster occurs, there is usually a significant amount of damage, and with that, there are also a lot of costs involved with repairing and aiding all the people involved. In addition, the occurrence of natural phenomena has increased significantly in the past decade. With that in mind, post-disaster damage detection is usually performed manually by human operators. Taking into consideration all the areas one has to closely look into, as well as the difficult terrain and places with hard access, it becomes easy to understand how incredibly difficult it is for a surveyor to identify and annotate every single possible damage out there. Because of that, it has become essential to find new creative solutions for damage detection and classification in the case of natural disasters, especially hurricanes. This study focuses on the feasibility of using a Visual Question Answering (VQA) method for post-disaster damage detection, using aerial footage taken from an Unmanned Aerial Vehicle (UAV). Two other approaches are also utilized to provide comparison and to evaluate the performance of VQA. Our case study on our custom dataset collected after Hurricane Sally shows successful results using VQA for post-disaster damage detection applications.  
Keywords:visual question answering; post-disaster; damage detection; aerial footage +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 2 Methods and Data
### Data Source and Pre-Processing
#### 2.2.2 Satellite- and Flux-Based GPP Datasets  
Here, this study employed three satellite-observed GPP datasets to quantify the interannual changes in vegetation productivity, including GLASS GPP, NIRv_GPP, and EC_LUE_GPP. GLASS GPP was publicly released by Beijing Normal University with a spatial resolution of 5 km and an 8-day temporal interval ranging from 1981 to 2018. The dataset is freely obtained from the official website of NESSDC ([[http://www.geodata.cn/](http://www.geodata.cn/)]([http://www.geodata.cn/](http://www.geodata.cn/)), (accessed on 15 September 2022)). The NIRv_GPP dataset was developed by using a new proxy of the Near-Infrared Reflectance vegetation (NIRv) index ranging from 1982 to 2018. It has a spatial resolution of 5 km and a temporal resolution of monthly and is available from the spatial-temporal Three-pole Environmental Big Data Platform (TEBDP, [[http://poles.tptc.ac.cn/zh-hans/](http://poles.tptc.ac.cn/zh-hans/)]([http://poles.tptc.ac.cn/zh-hans/](http://poles.tptc.ac.cn/zh-hans/)), (accessed on 14 March 2019)). EC_LUE_GPP was generated by using the Light Use Efficiency (LUE) model; it has a spatial resolution of 5 km and a monthly interval, and it is freely downloaded from the website of ZONADA ([[https://zenodo.org/](https://zenodo.org/)]([https://zenodo.org/](https://zenodo.org/)), (accessed on 28 April 2017)). In addition, this paper also used the Flux station-observed GPP to verify the satellite-observed GPP in this study. The Flux station-observed GPP was obtained from the FLUXNET 2015 dataset. This paper extracted the annually averaged GPP values of 37 flux stations within global drylands, and the spatial distribution of 37 flux stations is presented in Figure S1.
## 6 Field Test of the WSN Platform
### Long-Run Test of the WSN Platform
#### 6.3.2 Sensor Node Issues during the Long-Run Test  
Thermal expansion mismatch between the PCB and the NTC leads generated repeated mechanical strains due to cyclic temperature variations of 15-20 \({}^{\circ}\)C between day and night. These eventually led to fatigue cracking of NTC solder joints on some sensor nodes, which, in turn, led to intermittent contacts, especially during morning and evening hours, when the temperature changed most rapidly. The sensor node decoded the imperfect contacts as fast ambient temperature changes, and notified these right away to the gateways as wildfire warnings (see Section 5.1). The gateways handled these warnings the same way as fire alerts, forwarding them immediately to the server to promptly notify the operators.  
Since the GPRS modem is one of the highest energy drains of the gateway, keeping it powered up for a few hours in the morning and evening every day to forward these warnings eventually consumed more energy than the harvesting capacity of the solar panels and discharged the battery of the only gateway left. Its failure required a maintenance operation to restore both gateways to full functional state and to replace the faulty sensor nodes.  
Such sensor node faults can happen also in large deployments that should run unattended for many years. Thus, the system should be able to exclude the network effects of faulty nodes before they can create larger problems that may lead to (partial) loss of service. For this purpose, we decided to provide the gateways with a list of sensor node IDs that should be ignored and which can be set remotely by the operators when needed. Such a list would have helped us avoid a costly field trip for maintenance during the test.  
Last, but not least, all systems powered by harvested energy (which may fluctuate widely reaching very low levels) should have brown-out detection (BOD) enabled to avoid entering non-functional states when the battery voltage is too low. Because the BOD was not enabled on the gateway prototypes, Gateway 1 failed several times to return to normal operation after periods of insufficient energy.  
Figure 12: Gateway temperature can exceed 45 \({}^{\circ}\)C, the maximum allowed for lithium-ion battery charging (**a**), while the internaloscillator drift can break the synchronous serial communication with the GPRS modem at low temperatures (Days 6–13 and 43–45) (**b**).
## 4 Numerical Experiments
### Accuracy Comparison for Different Binary Variables  
We compare the Mean Squared Error (MSE) of predictions for three types of binary variables (\(u\) for commitment variables, \(s\) for startup variables, and \(d\) for shutdown variables) under three different system sizes (50 units, 100 units, and 1080 units), and the lower the better. As shown in Table 4, startup and shutdown maintain substantially lower MSE values than commitment across all system scales, indicating that GNN predicts the startup and shutdown variables with much higher accuracy than the commitment variable.  
To validate the benefits of including additional variables and eliminate the influence of fixed strategies and branching strategies, we designed experiments where stable variables were simply fixed using a soft fix. Based on the selection of binary variables, the methods can be categorized into three types: only commitment variables (the approach used in most existing papers) as \"M1\", only startup and shutdown variables as \"M2\", or all three types of binary variables as \"M3\", and we use the state-of-the-art solver Gurobi as the baseline. We use the primal gap [29] to evaluate the performance of different methods, which defined \(\frac{v-v^{*}}{v^{*}}\), where \(v^{*}\) is the best objective value obtained by any approach in the portfolio for comparison and \(v\) is the primal bound.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable Type** & **50** & **100** & **1080** \\ \hline \(u\) & 0.04773 & 0.02801 & 0.06902 \\ \hline \(s\) & 0.00951 & 0.00765 & 0.02248 \\ \hline \(d\) & 0.00923 & 0.00646 & 0.02100 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Variable loss comparison.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Case Size** & **Variable** & **Integer** & **Constraint** & **Nonzero** \\ \hline
50 & 6000 & 3600 & 10,848 & 50,678 \\ \hline
100 & 12,000 & 7200 & 21,648 & 101,349 \\ \hline
1080 & 129,600 & 77,760 & 233,328 & 1,109,492 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Test system summary.  
Figure 5 shows the primal gap as a function of runtime. In all methods applied to the three systems, a soft fix for all three variables shows the best performance. Especially in the 100-unit system, the M3 gap exhibits the fastest decline, while the other methods take more than 100 s to reach the gap achieved by M3 in just 50 s.
# Recognizing Urban Functional Zones by GF-7 Satellite Stereo Imagery and POI Data
## 2 Study Area and Data  
### Study Area  
Beijing is the capital of China and serves as the political, economic, and cultural center, with a high urbanization rate of approximately 86.5%. For our study, we specifically selected the area within the Fourth Ring Road of Beijing, as shown in Figure 1, which spans an approximate area of 300 km\({}^{2}\). The study area encompasses significant commercial zones, residential zones, and numerous administrative agencies in Beijing. The diverse and mixed functions within this area pose challenges for accurately identifying UFZs.  
Figure 1: Location of the study area.
## 3 Results
### Electro-Optical Measurements of the Optical Links
#### 3.3.2 Data Transmission Parameters of the Visible Link
To achieve a distance of 110 cm in the set-up depicted in Figure 27, the maximum without video errors, an error correction factor of \(R=\frac{1}{2}\) was required, as the number of errors per packet exceeded 30, which could not be corrected using other encoding methods. For distances less than 92.5 cm, an error correction factor of \(R=\frac{2}{3}\) was used, addressing the errors per packet exceeding 15. For distances less than 82.5 cm, an error correction factor of \(R=\frac{3}{4}\) was sufficient, where the errors per packet were fewer than 5. All these measurements were conducted at the center of the beam to obtain the characteristics of the system in optimal conditions. It is clear then that when the detector moves from the center of the beam, the performance in terms of data transmission is degraded, as demonstrated in [56].
## 3 Applications of Game Theory in WSN
### Game Theory for WSN Security
#### 3.3.2 Intrusion Detection  
The game types for intrusion detection include non-cooperative game [177; 178; 179; 180; 181] and Markov game. [PERSON] [179] discussed currently available intrusion detection techniques; attack models using game theory, and then propose a framework to detect malicious nodes using zero sum game approach for nodes in the forward data path. [PERSON] _et al_. [39] modeled the interaction of nodes in WSN and IDS as a Bayesian game formulation and use this idea to make a secure routing protocol. By this approach nodes are motivated to act rationally and gain reputation, and IDS can perform better by using the history of the game. This game approaches to NE and leads to a defense strategy for the network. [PERSON] and [PERSON] [33] developed a framework to detect malicious nodes using Zero-Sum game approach and selective node acknowledgements in the forward data path. A game theory-based scheme is introduced for finding out the vulnerable areas in WSN [174, 177]. Non-cooperative game theory-based schemes could assist detection schemes in improving their performance as well as efficiency though they are not concerned with detection directly [182]. [PERSON] _et al_. [15] proposed an active defense model for WSN where nodes have limited rationality of evolutionary learning abilitybased on evolutionary game theory. The nodes can achieve the most effective defense by adjusting their defensive strategies active and dynamic. [PERSON] and [PERSON] [16, 17, 18, 19] envisioned a WSN as an entity analogous to a biological population with individual nodes mapping to individual organisms and the network architecture mapping to the biological population. The interactions between individuals, either insects or WSN sensors, can be captured with evolutionary game theory models, in which individuals are the players and reliability are the fitness (payoff) of the players. The evolutionary stable strategy (ESS) forms the basis of network survivability. [PERSON] and [PERSON] [20] introduce a layered modeling architecture consisting of dynamic hybrid fault modeling and extend evolutionary game theory for reliability, survivability, and fault tolerance analyses. The architecture extends traditional hybrid fault models and their relevant constraints in the agreement algorithms with survival analysis, and evolutionary game theory. Selfish nodes refuse to forward packets for other nodes in order to save energy which causes the network to malfunction. At the same time, some nodes may be malicious, whose aims are to damage the network. In [67], [PERSON] and [PERSON] analyzed the cooperation stimulation and security in self-organized WSN under a game theoretic framework.
## 1 Introduction
### Occurrence Probability Calculation
#### 2.2.2 Data Bus Inversion (DBI) Applied  
When buffers are correlated to each other (data coding), the impacts of data coding can be reflected by calculating the occurrence probability of each coding. In this sub-section, DBI-coding is given as an example, since this coding has been widely adopted in memories to suppress power/ground noise and crosstalk as a standard.  
When DBI coding is applied, the case when \(N_{01}+N_{10}>N_{T}/2-1\) should be eliminated by the bit inversion. In this case, occurrence probability of \(P_{00\text{ and }11}(N_{01},N_{10})\) and \(P_{01\text{ and }10}(N_{01},N_{10})\) must be separately calculated, since the latter one, the victim buffer, is already making a bit transition. When the DBI coding is occurred, \(P_{\text{m}}(N_{01},N_{10},N_{00},N_{11})\) becomes \(P_{\text{m-complimentary}}(N_{00},N_{11},N_{01},N_{10})\) and should be added up to the corresponding occurrence probability when DBI coding has not occurred. In Table 1, occurrence probabilities are calculated and summarized.  
In Figure 5, the occurrence probabilities are plotted for the case \(N_{T}=8\) with DBI coding. When data coding is applied, \(P_{01\ (\text{or }10)-\text{DBI}}(N_{01},N_{10})\) and \(P_{00\ (\text{or }11)-\text{DBI}}(N_{01},N_{10})\) must be different. For the case without data coding, \(P_{01\ (\text{or }10)}(N_{01},N_{10})\) and \(P_{00\ (\text{or }11)}(N_{01},N_{10})\) should be equal. When all occurrence probabilities are summed up, for the case without data coding, both \(\sum P_{01\ (\text{or }10)}(N_{01},N_{10})\) and \(\sum P_{00\ (\text{or }11)}(N_{01},N_{10})\) should also be equal to \(\frac{1}{2}\). This is natural, since each pattern is occurring randomly. However, when the data coding is applied, it has now changed. In case of the DBI coding, buffers remaining in the steady states have higher occurrence probabilities (\(\sum P_{01\ (\text{or }10)}(N_{01},N_{10})<\sum P_{00\ (\text{or }11)}(N_{01},N_{10})\)),  
Figure 4: Occurrence probabilities for \(N_{T}=16\) are plotted in (**a**). Occurrence probabilities for \(N_{T}=32\) are plotted in (**b**). For both cases, the probability around \(N_{01}=N_{10}=(N_{T}-1)/4\) is the largest.  
since the purpose of the DBI coding is to suppress the aggressor SSO buffer transitions below \(N_{01}+N_{10}<N_{T}/2\).
# Computational Acceleration of Topology Optimization Using Deep Learning
## 1 Introduction
### Current Work  
In the current work, we propose new ML-based TO methods that allow obtaining of highly accurate results without using computationally intensive FEA studies. In particular, we studied CNN, U-Net and Res-U-Net based ML approaches and tested them for the optimal generation of topology structures. The key scientific design point for the CNN model is to develop an architecture that can be applied for both optimized topology generation in 2D and 3D domains. On the other hand, design points for U-Net and Res-U-Net models reduce not only the computational time to generate optimized topologies, but also the training time, compared to similar models that are used to apply in TO. Thus, three different datasets that contain various optimized topologies for given initial and boundary conditions are used: (i) dataset #1 is generated using SIMP method [17]; (ii) dataset #2 is based on six parameters--volume fraction, displacement boundary condition, load on X axes, load on Y axes, strain energy density and von Mises stress [18]; and (iii) dataset #3, similar to the second one, uses physical fields as input parameters and corresponding output images of optimized topologies [19]. It has been shown that the performance of machine learning models which are based on convolutional mapping (CNN, U-Net, and Res-U-Net) are superior among deep learning algorithms. The performance of the ML algorithms trained using the above-listed datasets are compared in terms of accuracy and timing. According to experimental results, the convolutional mapping is effective not only for image-type data, and also for input data that takes into account the main parameters of the TO problem.  
Such ML models with reduced training times and higher accuracies are highly promising in accelerating the computational time of TO problems. Previous models such as TopologyGAN [18] and SILONET [19] require much longer training times per epoch (one complete pass of the ML algorithm through the training data) and larger networks with millions of parameters for accurate results. With the proposed modifications and improvements, the new models compared to their predecessors are much faster, robust, and reliable.  
Thus, the current work is structured in the following way: firstly, the brief theory of topology optimization and its computational complexity are introduced. In the main section, the proposed ML methods, including datasets, are considered, which are followed by the description of numerical experiments and results. Finally, conclusive remarks are provided.
# Evaluation of Structural and Functional Behavior of Battery
## 3 Test Results of Shaking Table Test
### Seismic Test  
The acceleration response spectrum was computed from the measured acceleration time histories, and a 5% damping level was adopted. The input motions were simultaneously applied in three orthogonal directions: side to side (X), front to back (Y), and in the vertical direction (Z). The acceleration response spectra for the different input seismic motions are compared in Figure 8. The test response spectrum has higher acceleration values than those of the required response spectrum.
# A Neural Network Approach for the Analysis of Reproducible Ribo-Seq Profiles
## 2 Related works  
The Ribosome profiling approach offers a promising method for developing unbiased translation models from data, but the quantitative analysis of ribosome profiling data is challenging, because of high measurement variance and the inability to distinguish the ribosome rate of translation. The Ribosome profiling strategy based on deep sequencing of ribosome-protected mRNA fragments enables genome-wide investigation of translation at the codon and sub-codon resolution [7].  
In recent years, techniques based on machine learning have been employed with increasing frequency and intensity in many different fields, ranging from computer vision [8; 9; 10; 11] to natural language processing [12; 13; 14] and bioinformatics [15; 16]. The popularity of these approaches stems from their success in the automatic inference of complex functions directly from the data. In particular, both statistical and ML approaches have been successfully applied to non-biological _sequential data classification_ tasks [17; 18; 19], in which each sequence is associated with a class label and the classification is performed on the whole sequence. Within bioinformatics, examples of ML applications include the prediction of splicing patterns and protein secondary structures, protein-protein interface prediction, protein subcellular localization, drug side-effect prediction, and DNA/RNA motif mining, to name just a few [20; 21; 22; 23]. Moreover, ML and deep learning approaches have been used to process Ribo-seq data for gene annotation in prokaryotes [24], to predict ribosome stalling [25] and for micropeptide identification [26]. In particular, in [27], a deep learning based approach, called RiboMIMO, was proposed, based on a multi-input and multi-output framework, for modeling the ribosome density distributions of full-length mRNA Coding Sequence (CDS) regions. Through considering the underlying correlations in translation efficiency among neighboring and remote codons, and extracting hidden features from the input full-length coding sequence, RiboMIMO can accurately predict the ribosome density distributions along with the whole mRNA CDS regions, a problem strictly correlated with the one we intend to face in this paper. Indeed, we propose a machine learning-based approach to validate the extraction procedure of highly reproducible profiles, by classifying the translation speed of the extracted regions, which deeply depends on the ribosome density along mRNA.
# Physical-Layer Security in Power-Domain NOMA Based on Different Chaotic Maps
## 4 Simulation Results
### Numerical Results  
#### 4.2.1 Bit Error Rate (BER)  
Figure 9 shows the BER versus the transmission SNR (dB) for each user in the four C-DL-NOMA schemes. Notably, the closest user (User 3) had the worst performance because two levels of SIC were performed for User 3, whereas User 2 outperformed User 3 because one level of SIC was performed at User 2. Otherwise, since User 1 demodulated the data directly without performing SIC, this user provided the best performance. In addition, we can see that the logistic scheme had the best BER performance and that the BERs of the Henon and Arnold's cat schemes were semisimilar, as they used the same method to generate key values. Finally, the hyperchaotic DNA scheme had the worst BER performance among the rest of the schemes because of the number of layers in the encryption and decryption processes. Compared with the 3 UE's SUI-6 model in [14], we obtained better BER performance.  
In Table 3, we chose the BER to be \(10^{-3}\) to compare the achieved SNR (dB) per user in the four C-DL-NOMA schemes and the 3 UE's SUI-6 model in [14]. The closest user (User 3), with the lowest power factor and two levels of SIC, achieved a BER of \(10^{-3}\) at SNRs of 30 dB for NOMA with no encryption, 30.1 dB for the logistic scheme, 33.3 dB for the Henon and Arnold schemes, and 33.6 dB for the hyperchaotic DNA scheme; the second-nearest user (User 2), with a single level of SIC, achieved a BER of \(10^{-3}\) at SNRs of 26.8 dB for NOMA with no encryption, 26.9 dB for the logistic scheme, 29.5 dB for the Henon and Arnold schemes, and 29.6 dB for the hyperchaotic DNA scheme; and the farthest user (User 1) achieved a BER of \(10^{-3}\) at SNRs of 19.6 dB for NOMA with no encryption, 19.8 dB for the logistic scheme, 22 dB for the Henon and Arnold schemes, and 22.18 dB for the hyperchaotic DNA scheme.