esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards Domain-Specific Knowledge Graph Construction for Flight Control Aided Maintenance
## 2 Related Work
### Fault Diagnosis in Aircraft Systems  
Fault diagnosis methods in aircraft systems generally fall in three categories: (1) model-based method; (2) signal-based method; and (3) data-driven-based method. In model-based methods, fault diagnosis models are usually obtained by physical principles and systems identifications. Once a model is achieved, fault identification or a detection algorithm is designed to monitor the consistency between the measured outputs and the model outputs [1,2]. Signal-based methods utilize measured signals rather than input-output models. Faults are reflected in the measured signals. By extracting the features of mea  
Figure 1: System overview.  
sured signals, a fault diagnosis algorithm could be designed according to the symptoms observed and prior experiences [3; 4]. Recently, data-driven methods have received a lot of attention [5; 6]. This kind of method relies on historical data rather than establishing models or signal patterns. Generally, fault diagnosis is considered as a classification problem handled by deep neural networks using labeled or unlabeled data.  
The method leveraged in our work is also data driven. Different from [5; 6], we first build a knowledge graph from unstructured manuals and then design a maintenance prototype system to locate possible faults. We noticed that a few studies are conducted by the same knowledge-graph-based methodology [7; 8]. However, they mainly focus on structural data such as TSM (trouble-shooting manual) and maintenance records to construct a knowledge graph. According to our investigation, consulting TSM or maintenance records is convenient in the current digital system. However, when confronted with a new aircraft type or unusual problems, it is inefficient to consult other unstructured manuals. Our work pays more attention to this gap where effective knowledge is extracted from unstructured data to support a better query.
# Investigating Effects of Landfill Soil Gases on Landfill Elevated Subsurface Temperature
## 5 Results
### Effect of Variable Combinations on Temperature
These three pairs are: (temperature = 1, CH\({}_{4}\) = 1, CH\({}_{4}\):CO\({}_{2}\) = 1, RN\({}_{2}\) = 1, O\({}_{2}\) = 0) and (temperature = 1, CH\({}_{4}\):CO\({}_{2}\) = 1, RN\({}_{2}\) = 0, O\({}_{2}\) = 0), (temperature = 1, CH\({}_{4}\) = 1, CH\({}_{4}\):CO\({}_{2}\) = 1, RN\({}_{2}\) = 1, O\({}_{2}\) = 1) and  
Figure 7: Distribution of temperature in different combinations. (temperature = 1, CH\({}_{4}\) = 1, CH\({}_{4}\):CO\({}_{2}\) = 1, RN\({}_{2}\) = 1, O\({}_{2}\) = 0), (temperature = 1, CH\({}_{4}\) = 1, CH\({}_{4}\):CO\({}_{2}\) = 1, RN\({}_{2}\) = 1, O\({}_{2}\) = 1) and (temperature = 1, CH\({}_{4}\) = 1, CH\({}_{4}\):CO\({}_{2}\) = 1, RN\({}_{2}\) = 0, O\({}_{2}\) = 1). Furthermore, the probability of four temperature ranges (under 55 \({}^{\circ}\)C, 55-80 \({}^{\circ}\)C, 80-93 \({}^{\circ}\)C, 93-149 \({}^{\circ}\)C) were analyzed with respect to the different combinations of four gas parameters in the series of CH\({}_{4}\), CH\({}_{4}\)-to-CO\({}_{2}\) ratio, RN\({}_{2}\), and O\({}_{2}\). The results of this analysis help us to identify possible gas combinations linked to SETs. Figure 9 shows a gradual upward trend for the 80-93 \({}^{\circ}\)C range in the four gas combinations of 1\(1\)0_1, 1\(1\)0_0, 1\(1\)1_0, and 1\(1\)1\(1\)1; whereas a decreasing trend is observed for the \"under 55 \({}^{\circ}\)C\" range. The 1\(1\)1_0 combination has a likelihood of only 3% in the 93-149 \({}^{\circ}\)C range. Hence, the graph shows that the 1\(1\)1_0 combination has the greatest potential to associate with high temperature ranges, rather than the 1\(1\)1_1 combination in which all the gas parameters fall in the unsafe range. Temperatures in the 55-80 \({}^{\circ}\)C range occur with a 15% probability in the combinations with 1\(1\)1_0, 1\(1\)1_1, 1\(1\)0_0, 1\(1\)0_1, 1\(0\)1_0, 1\(0\)1_1. Therefore, gas combinations with 1\(1\)0_0, 1\(1\)1\(0\)1, 1\(1\)1_0, 1\(1\)1_1 should be considered high risk; 1\(0\)1_0, 1\(0\)1_1 combinations correspond to a medium risk that indicates a tendency to proceed to a high risk level. Table 5 summarizes the potential risk levels (low, moderate, or high) corresponding with different gas combinations based on the 80 \({}^{\circ}\)C safe limit discussed in Section 5.1, which could be informative for the assessment of an SET risk. Figure 8: Median values of the difference between a pair of events resulted from the Wilcoxon rank-sum tests shown in a matrix of events (note: *** represents an insignificant \(p\)-value \(<\) 0.05 for that pair of events). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Gas Combinations (CH\({}_{4}\)\({}_{-}\)CH\({}_{4}\)-to-CO\({}_{2}\) Ratio\({}_{-}\)RN\({}_{2}\)\({}_{-}\)O\({}_{2}\))** & **Risk Levels** \\ \hline
0\_0\_0\_0, 0\_1\_0\_0, 1\_0\_0, 0\_1\_0\_0, 1\_0\_0\_1, 0\_0\_0\_1, 0\_0\_1, 0\_0\_1\_0 & Low \\
1\_0\_1\_0, 1\_0\_1\_1 & Moderate \\
1\_1\_0\_0, 1\_1\_0, 1\_1\_1\_0, 1\_1\_1\_1 & High \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Potential risk levels for different gas combinations.
## 3 Retrieval Method
alternative way of processing the first retrieval result directly to achieve satisfactory estimations, which is discussed in detail in Section 4. For the first iteration, we regard \(F_{n}(k)\) as zero and consider Equation (21) in the matrix form  
\[\mathbf{Y}=\overline{\mathbf{Q}}\cdot\mathbf{w}, \tag{22}\]  
where \(\mathbf{w}\) is \(\{w_{n}\}_{n=1}^{N}\) and \(\overline{\mathbf{Q}}\) is the \((M-\Delta)\times N\) differential matrix:  
\[\overline{\mathbf{Q}}=\left[ \begin{array}{cccc}q_{1+\Delta,1}-q_{1,1}&q_{1+ \Delta,2}-q_{1,2}&\cdots&q_{1+\Delta,N}-q_{1,N}\\ q_{2+\Delta,1}-q_{2,1}&q_{2+\Delta,2}-q_{2,2}&\cdots&q_{2+\Delta,N}-q_{2,N}\\ \vdots&\vdots&\vdots&\vdots\\ q_{M,1}-q_{M-\Delta,1}&q_{M,2}-q_{M-\Delta,2}&\cdots&q_{M,N}-q_{M-\Delta,N} \end{array} \right] \tag{23}\]  
and for \(\mathbf{Y}\)  
\[Y(k)=V(k)-V(k+\Delta),\;k=1,2,\cdots,\;M-\Delta. \tag{24}\]  
We then solve Equation (22) by formulating the following linear least-squares problem  
\[\hat{\mathbf{w}}=\text{argmin}S(\mathbf{w}),\text{ subject to }w_{n}\geq 0,\;1 \leq n\leq N, \tag{25}\]  
where  
\[S(\mathbf{w})=\left\|\mathbf{Y}-\overline{\mathbf{Q}}\cdot\mathbf{w}\right\| ^{2}. \tag{26}\]  
From the second iteration, \(F_{n}(k)\) is updated using Equations (8) and (10), \(Y(k)\) is updated by \(Y(k)=V(k)-V(k+\Delta)+F_{n}(k)-F_{n}(k+\Delta)\), and the least-squares problem (25) is run again to update \([w_{n}]_{n=1}^{N}\). Two types of basis functions are investigated in this paper. The first is a two-dimensional rectangular function obtained by  
\[\tau_{n}(x,z)=\left\{ \begin{array}{ll}1\;(\text{dB}/\text{km}),&\text{for} \;|x-X_{n}|<\frac{u}{2}\;\text{and}\;|\;z-Z_{n}|<\frac{u}{2}\\ 0\;(\text{dB}/\text{km}),&\text{otherwise}\end{array} \right., \tag{27}\]  
where \(u\) is the width of the rectangular function and \((X_{n},\;Z_{n})\) is the center coordinate of the \(n\)-th function. Essentially, the rectangular basis functions offer a way to cover the field of interest with a regular grid. The integral \(q_{k,n}\) is simplified to the length of the \(k\)-th link within the \(n\)-th unit square. The second type of basis function is a Gaussian basis function defined by [22]  
\[\mu_{n}(x,z)=\exp(-\frac{\delta_{x,z,n}^{2}}{2\sigma^{2}})\,(\text{dB}/\text{ km}), \tag{28}\]  
where \(\delta_{x,z,n}\) is the distance from \((x,z)\) to the center of the \(n\)-th basis function located at \((X_{n},\;Z_{n})\)  
\[\delta_{x,z,n}=\sqrt{(x-X_{n})^{2}+(z-Z_{n})^{2}}, \tag{29}\]  
and \(\sigma\) is the width of the Gaussian functions. Note that no matter which basis function is applied, the differential matrix \(\overline{\mathbf{Q}}\) can be calculated offline before using the least-squares.
# Efficient FPGA Implementation of an ANN-Based Demapper Using Cross-Layer Analysis
## 5 Results and Discussion
### Interaction of Design Layers  
The proposed work of designing an FPGA-based AE for communication can be divided into three main design layers: communication systems, ANN design and hardware implementation. Those layers are not independent but highly influence each other; therefore, a joint analysis is of high importance for efficient cross-layer design. We go one step further towards this design methodology as shown in Figure 12, by relating metrics of all design layers: SNR (communication layer), training latency (ANN design layer, hardware implementation layer) and DOP (hardware implementation layer).  
Figure 12 shows the training latency needed to reach a target BER of 0.25 over different SNRs for multiple DOPs. The latency to obtain the target BER increases with lower DOP and lower SNR. With higher SNRs, the number of iterations needed to get to a target BER will be reduced. Therefore, we can see in Figure 12 that for any given DOP, higher \(E_{b}/N_{0}\) reduces the training latency for the fine-tuning. Nevertheless, this latency reduction is not linear and is dependent on the DOP used. By having a closer look at the region when the \(E_{b}/N_{0}\) is smaller than 0 dB, the training latency for the fine-tuning increases exponentially when the DOP is reduced from 32 to 1. The difference in latency between different DOPs decreases for higher \(E_{b}/N_{0}\) regimes (from 0 to 5 dB) and becomes much smaller in the regime when \(E_{b}/N_{0}\) is  
The data visualized in Figure 12 are also one main building block of our cross-layer framework. Based on those evaluations, the framework is able to calculate the optimal DOPs for the application constraints such as target BER and training latency.
# Device-Free Indoor Location Estimation System Using Commodity Wireless LANs
## 3 DF Indoor Location Estimation
### Signal Processing
#### 3.2.2 Proposed Method  
To address the problem mentioned above, we proposed a method that treats the multi-path components separately in the delay time domain and beam space. Here, beamforming with three orthogonal beams was used to separate the multipaths in the three beamspaces. The output signal obtained at multiple delay taps and multiple beamspaces is expressed as:  
\[y_{m,n}(t)=\mathbf{w}_{m}^{H}\mathbf{x}_{n}(t)=\gamma_{m,n}(t)\mathrm{e}^{\mathrm{i} \theta m,n}, \tag{34}\]  
where \(\mathbf{w}_{m}\in\mathbb{C}^{3\times 1}\) denotes the weight vector for the \(m\)th orthogonal beam (\(m=1,2,3\)) and \(\mathbf{x}_{n}(t)\in\mathbb{C}^{3\times 1}\) denotes the \(n\)th delay tap of the channel impulse responses (\(n=1,2\)). The weight vector is obtained by (18) where \(\phi\in\{-42^{\circ},0^{\circ},42^{\circ}\}\), and the beam patterns are as shown in Figure 11a. The target space was divided into three parts to receive multiple waves. \(\gamma\) and \(\theta\) denote the resulting amplitude and phase, respectively. Figure 11b shows the condition when the single-bounce reflection wave is blocked by a human body. Here, because the multipath components were treated separately and received, its fluctuation can be detected when blocked, as seen in Figure 11c. Finally, the amplitude and phase of the received signal obtained at multiple delay taps and multiple beamspaces were used as spatio-temporal feature values for machine learning.
# An Improved Rectifier Circuit for Piezoelectric Energy Harvesting from Human Motion
## 2 EH Circuits
### Equivalent Circuit of PD  
To evaluate the internal characteristics of the given PD, a vibrating PD attached to a cantilever beam with proof mass is shown in Figure 5a. When a typical PD is harmonically  
Figure 4: Voltage generated by piezoelectric device (PD) from walking motion.  
vibrated, it can be modelled as a sinusoidal current source (ip (t)) connected in parallel with its internal capacitance (C\({}_{\text{P}}\)) and resistance (R\({}_{\text{P}}\)) [31; 34; 35; 36; 37; 38; 39], as shown in Figure 5b. The PD can also be alternatively modelled as a voltage source (V\({}_{\text{P}}\) (t)), as shown in Figure 5c [21].  
A vibrating PD generates an AC voltage that contains both positive and negative half cycles. In both cycles, the internal capacitor of the PD is first charged before the actual power flows out from the PD terminals. The fluctuated characteristics of voltage and current with the mechanical excitation, which contains both positive and negative half cycles is shown in Figure 6. The charging period of the internal capacitor (u), denoted as Interval 1 is the non-harvesting period. Interval 2 represents the harvesting period of the PD.  
The generated current needs to charge and discharge the internal capacitor, as depicted in Figure 5b. During this time, the rectifier circuit is in turn off condition. Thus, there is no output through the circuit, and the external load capacitor (C\({}_{\text{load}}\)) does not charge. Interval 1 continues until the magnitude of the PD voltage (V\({}_{\text{P}}\) (t)) equates to the load capacitor voltage. Interval 2 will then begin, and the load capacitor will be charged. A similar process occurs in the negative half cycle.  
The generated current by the PD can be expressed as  
\[\text{ip (t)}=\hat{\Gamma_{\text{P}}}\ \sin(\omega\text{t}) \tag{1}\]  
where \(\hat{\Gamma_{\text{P}}}\) is the magnitude of current, \(\omega\) is the angular frequency, and t is time. On the other hand, the PD voltage can be expressed as  
\[\text{V}_{\text{P}}\ \text{(t)}=\text{V}_{\text{P}}\sin\biggl{[}\frac{2\pi \text{t}}{\text{T}_{\text{in}}}\biggr{]} \tag{2}\]  
where T\({}_{\text{in}}\) is the period of the voltage source, V\({}_{\text{P}}\) is the amplitude of the voltage.  
Figure 5: A vibrating PD (**a**) excited by mechanical shaker, and its equivalent circuit model as (**b**) Current source, (**c**) Voltage source.  
Figure 6: The fluctuated characteristics of current and voltage of a vibrating PD.
# New and Efficient Algorithms for Producing Frequent Itemsets with the Map-Reduce Framework
## 4 Incremental Frequent Itemset Mining with Map-Reduce
### Experimental Evaluation
#### 4.3.2 Setup  
We ran all the experiments on the Google Compute Engine Cloud (GCE) by directly spanning VMs with Hadoop version 1 (old numbering 0.20.XX). We used different cluster sizes with 4 cores, 5 cores, 10 cores and 20 cores, and GCE instance types of n1-standard-1 or n1-standard-2.
# SatEC: A 5G Satellite Edge Computing Framework Based on Microservice Architecture
## 3 System Framework  
### System Design  
The design of the SatEC framework needs to consider the characteristics of the satellite system, such as power consumption, reliability, scalability, and security. A specially designed hardware resource platform based on embedded computing platforms will be the best choice. Energy source limitation and heat dissipation of space-based systems limit the power consumption of on-orbit devices. [PERSON], [PERSON] et al. compared the power of nonembedded platform with that of embedded platform, the power consumption ratio is 14.375 [33]. Based on this result, we can conclude that the power consumption of SatEC is much lower than that of terrestrial edge computing platform. At the same time, compared with terrestrial 5G, services on satellite nodes are very different. It is necessary to design a new service system for 5 GsatEC. The core idea of the system is the embedded high reliability hardware platform and the software architecture based on microservices. Furthermore, lightweight virtual mechanisms will be key to minimize the resources that the infrastructure requires.  
The proposed design described in Figure 1 can be divided into two layers: the hardware platform and the software framework. The hardware platform, which is the infrastructure of the system, provides computing resource, storage resource, and network resource for services and users. The software framework is based on microservice architecture [18; 34; 35; 36; 37]. Using resource virtualizationtechnology (virtual machine (VM), container, etc.), this layer shields the bottom hardware information and provides a uniform interface for users [38; 39]. All the functions will be provided in the form of microservices. User's services will be comprised by basic services.
## 5 Discussion
### Controlling Factors on Acoustic Velocity
#### 5.2.2 Bioturbation Impact on Acoustic Velocity  
The impact of bioturbation on the acoustic wave velocities is not widely documented in literature. However, earlier studies discussed the relationship between bioturbation and sediment acoustic and geotechnical properties [67, 68, 69, 70]. Those studies demonstrated, based on recent sea-bottom derived box cores, that different bioturbation activities (i.e., dwelling, grazing, feeding, etc.) have a different impact on the acoustic behavior of sediments, where the individual processes can either increase or decrease the acoustic velocity. Our data show a weak relationship between compressional wave velocity (V\({}_{\rm P}\)) and bioturbation intensity (Figure 13). Therefore, bioturbation intensity alone does not explain the velocity variation, but the combination with the type of burrow filling material may explain the variation in the petrophysical properties (porosity and permeability) as well as the acoustic velocity.  
However, the grain-dominated samples in our case show higher velocities (\(\mu\) = 5.02 km/s \(\pm\) 0.62 km/s) and lower porosity (\(\mu\) = 9.99% \(\pm\) 4.2%) and permeability values (\(\mu\) = 0.24 md \(\pm\) 0.32 md). Such observations can be attributed to the burrow infill of the grain-dominated samples, by fine material in addition to the extensive calcite cementation of the host matrix. In addition, samples that have open burrows (vugs) have higher velocities relative to the samples with filled burrows (Figure 12). These results agree well with the findings of [22, 24, 29, 64, 71] who demonstrated that carbonates with vuggy pores have higher velocities relative to the other pore types.  
Figure 15: Micrite microstructure impact on petrophysical and acoustic properties. (**A**) SEM photomicrograph of porous, subhedral micrite of a mud-dominated sample. (**B**) SEM photomicrograph of tight, compact micrite of a grain-dominated sample.  
The mud-dominated samples (coarse-filled burrows) show lower compressional velocities relative to those with fine-filled burrows (Figures 11A and 14B). This is mainly attributed to the higher porosity of coarse-grained infill of the burrows within mud-dominated units, and dissolution of micrite, resulting in a porous subhedral micrite texture (Figure 15). These results agree to some extent with findings of [68], who reported an inverse relationship between rigidity and bioturbation intensity of A. marina and _C. arenarium_ trace fossils (open burrows), in sandy sediments of the coast of North Wales. Our data also agree with the study of [69] for the Venezuela basin carbonate sediments, who documented that an increase of _Mulinia lateralis_ decreased the compressional velocity significantly. On the other hand, ref. [69] attributed the increase of bulk modulus and Lame's constant in sediments dominated with _Heteromusus filiformis_ to the dewatering process by bioturbation, which increased sediment compaction.
# Drilling Condition Identification Method for Imbalanced Datasets
## 5 Experiment and Analysis of Drilling Condition Identification
### Data Processing and Model Design
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Condition**} & \multicolumn{2}{c}{**Original Data**} & \multicolumn{2}{c}{**Processed Data After Oversampling**} & \multirow{2}{*}{**Label**} \\ \cline{2-2} \cline{4-6}  & **Sample Size** & **Percentage** & **Sample Size** & **Percentage** & \\ \hline Rotary drilling & 40,159 & 20.70\% & 40,159 & 11.11\% & 0 \\ Sliding drilling & 18,977 & 9.78\% & 40,159 & 11.11\% & 1 \\ Trip out & 21,423 & 11.04\% & 40,159 & 11.11\% & 2 \\ Trip in & 25,378 & 13.08\% & 40,159 & 11.11\% & 3 \\ Making a connection & 34,851 & 17.96\% & 40,159 & 11.11\% & 4 \\ Stationary (drilling) & 23,740 & 12.23\% & 40,159 & 11.11\% & 5 \\ Drilling break cycle & 14,571 & 7.51\% & 40,159 & 11.11\% & 6 \\ Drilling break cycle + rotation & 11,947 & 6.16\% & 40,159 & 11.11\% & 7 \\ Bottom hole spinning & 2989 & 1.54\% & 40,159 & 11.11\% & 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Proportions of each condition and label settings before and after class imbalance handling. Figure 9: The relationship between model classification \(F1\) score and sliding window size. Estimation) optimizer and the _F1_ score as the evaluation metric, with the learning rate set to 0.001. Since drilling condition classification is a multi-class classification task, sparse categorical cross-entropy is chosen as the loss function, and the predicted values input into the loss function are the probabilities processed by the softmax activation function. The dataset is divided into a training set and a test set in a 7:3 ratio, with the number of samples input into the model for each iteration set to 64.
## 4 Discussion
The security analysis of a scheme is conducted to assess its resilience against potential vulnerabilities and various attack scenarios. Weaknesses in key generation, storage, or distribution may lead to the compromise of the confidentiality of encrypted logistics data. Additionally, vulnerabilities in encryption algorithms can render encrypted data susceptible to potential attacks, such as brute force attacks, chosen ciphertext attacks, or key recovery attacks. To mitigate these risks, the scheme should incorporate robust encryption key algorithms, adhere to key rotation practices, and implement stringent access controls to ensure that only authorized users have access to encryption keys. Moreover, the encryption algorithm should adhere to recommended key lengths and enforce secure encryption and decryption processes. It is imperative to conduct regular security audits and encryption algorithm evaluations to identify and rectify any potential encryption weaknesses or vulnerabilities. To bolster security measures, the incorporation of secure communication channels, deployment of end-to-end encryption protocols, and robust authentication mechanisms are essential in thwarting man-in-the-middle attacks. Essentially, this scheme is suitable for a multi-party communication network including trusted third parties and identity verification nodes, and it provides identity privacy protection services for the communication process of the network. Some existing IoT systems such as smart cities and the Internet of Vehicles also work in multi-party communication networks, including trusted third parties and identity verification nodes, so the network structure they work in is similar to that in this scheme. Therefore, this scheme can be transplanted to those IoT systems with a similar network structure and provide them with protection. This scheme is compared with other privacy protection technologies based on several evaluation metrics, as shown in Table 4.