esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Design and Measurement of a Stacked Dual-Patch Antenna with Thick Quasi-Air Substrates
Next, two square-pipe resin frames are built to realize the thick quasi-air substrates using the SLA 3D printing method [17] that can provide high-durability structures with low manufacturing tolerances. The metal cavity wall with the ground plate is then manufactured using the SLM 3D printing method [18] that can provide full-metal body structures with low manufacturing tolerances at a low cost. Finally, the stacked dual-patch antenna, the square-pipe resin frames, and the ground plate are assembled with a metal cavity wall to form a complete antenna structure. To verify the antenna's feasibility, indicators of the antenna's performance such as reflection coefficients, bore-sight gains, and radiation patterns are measured in a full anechoic chamber. Figure 3 presents the reflection coefficient of the proposed antenna. The solid and dashed lines indicate measured and simulated results, respectively. The measured reflection coefficient has a bandwidth of 620 MHz (2.63 GHz to 3.25 GHz, \(\mathrm{\Gamma}\mathrm{\Gamma}\mathrm{\Omega}\mathrm{<-10}\) dB), with a fractional bandwidth of 20.7%. This result is in good agreement with the simulated fractional bandwidth of 24%. In addition, the average reflection coefficients of the measurement and simulation are \(-14.9\) dB and \(-14.2\) dB, respectively. Figure 4 illustrates the 2D radiation gain patterns (in a spherical coordinate system) of the proposed antenna in the E-plane (\(zx\)-plane) and H-plane (\(zy\)-plane). At 3 GHz, the bore-sight gains of the measurement and simulation are 7.9 dBi and 8.9 dBi, respectively. In addition, the measured half-power beamwidths (HPBWs) in the E- and H-planes are 48\({}^{\circ}\) and 55\({}^{\circ}\), respectively, while the simulated HPBWs are 56\({}^{\circ}\) and 52\({}^{\circ}\). Figure 2: Photographs of the fabricated antenna: (**a**) each component of the antenna; (**b**) assembled antenna.
## 3 Double Boomerang Watermarking  
In this Section, the principles of the _tracing_ watermarking procedure and the metrics employed for the QoS assessment are introduced. Although copyright protection was the very first application of watermarking, different uses have been recently proposed in the literature. Fingerprinting, broadcast monitoring, data authentication, multimedia indexing, content-based retrieval applications [17]-[19], are only a few of the new applications where watermarking can be usefully employed. When these techniques are used to preserve the copyright ownership with the purpose of avoiding unauthorized data duplications, the embedded watermark should be _detectable_. Usually, these techniques use a robust watermarking which means that the embedded watermark is supposed to be detectable (_i.e._ the watermark used for copyright protection should not allow the use of the watermarked content until it has not been correctly extracted). Indeed, there are a number of desirable characteristics that a watermark should exhibit [20]. It at least should comply with the following two basic requirements for image watermark: first, the digital watermark should not be noticeable to the viewer (_i.e._ transparency of digital watermark). Second, the digital watermark is still present in the image after distortion and can be detected by the watermark detector (_i.e._ robustness of digital watermark to image processing). The key to the watermarking technique is to compromise between the two aforementioned requirements. Recently, an _unconventional_ use of fragile watermarking has been proposed to blindly estimate the quality of service of a communication link [5], and for QoS provisioning and control purposes [2]. The rationale behind the approach is that the alterations suffered by the watermark are _likely_ to be suffered by the data, since they follow the same communication link. Therefore, the watermark degradation can be used to evaluate the alterations endured by the data, obtaining a QoS index of the communication link (as explained in principles in Fig. 1). At the receiving side the watermark is extracted and compared with its original counterpart. Spatial spread-spectrum techniques perform the watermarking embedding. In practices, the watermark (narrow band low energy signal) is spread over the image (larger bandwidth signal) so that the watermark energy contribution for each host frequency bins is negligible, which makes the watermark near imperceptible. Following the same methodological approach of [5] and referring to the block scheme of Fig. 2, a set of uncorrelated pseudo-random noise (PN) matrices (one per each frame and known to the receiver) is multiplied by the reference watermark (one for all the transmission session and known to the receiver): \(w_{i}^{(s)}[k_{1},k_{2}]\!=\!w[k_{1},k_{2}]\!\cdot\!p_{i}[k_{1},k_{2}]\!\), where \(\mathrm{w}[k_{1},k_{2}]\) is the original watermark, \(\mathrm{p}_{i}[k_{1},k_{2}]\) the PN matrices and \(\mathrm{w}_{i}^{(s)}[k_{1},k_{2}]\) the spread version of the watermark to be embedded in the \(i\)-th frame.
# Education and Training in Applied Remote Sensing in Africa: The ARCSSTE-E Experience
## 1 Introduction
On 24 th November 1998, a tripartite agreement was signed among the United Nations Office for Outer Space Affairs (UNOOSA), the Federal Ministry of Science and Technology (on behalf of the Nigerian government), and the Obafemi Awolowo University (OAU), Ile-Ife, Nigeria (as the host). OAU was chosen due to the availability of university faculty who have expertise in the different courses, as well as the availability of relevant facilities and resources. The earlier citing of the Regional Centre for Training in Aerospace Surveys (now called the African Regional Institute for Geospatial Information Science and Technology) also contributed to making OAU a suitable choice. The university additionally granted a large expanse of land, where the Centre currently operates from. Students have access to the medical and library facilities of the university. The university has also benefitted from the presence of ARCSSTE-E on its campus. For example, it commenced a Masters and PhD programme in RS/GIS after relevant faculty interaction with ARCSSTE-E. Presently, it is developing a programme on Aeronautical Engineering with a contribution from ARCSSTE-E, especially in the area of data and staff. The Centre annually awards a full scholarship to international participants to facilitate the participation of foreign students in the postgraduate programmes of ARCSSTE-E. This scholarship is sponsored by UNOOSA and the Nigerian government. UNOOSA provides travel tickets for foreign participants on the Postgraduate Diploma (PGD) programme, while the Nigerian government makes a provision for living allowances, which includes accommodation and monthly stipends. This arrangement enables participants to travel to Nigeria and live there for the nine months of study. The Nigerian government also funds the salaries of staff and the operational cost of running the Centre. Publicity for the programme has largely been through alumni and word-of-mouth due to the quality of the programme. Self-funded participants are gradually increasing.
# Can We Survive without Labelled Data in NLP? Transfer Learning for Open Information Extraction
## 4 ROIE: A Recurrent Neural Network Model for Open Information Extraction
### Neural Model Architecture  
Throughout the back-propagation process, RNNs are prone to vanishing and exploding gradient descent complications, making RNN training challenging. Thus, LSTMs and gated recurrent units(GRUs) were established to address the issues related to the unstable gradient. When the gradient becomes too big or simply disappears, killing the learning process, LSTMs and GRUs aid by using the relevant gates to allow the gradient to flow backward through time, freely and effectively keeping long-term dependencies [33].  
Both LSTMs and GRUs are able to train on long word contexts and connect information using cell states. LSTM has three gates (_input_, _output_, and _forget_), contrary to GRU, which couples _input_ and _forget gates_ in one gate--_update gate_, in addition to _reset gate_, which determines how to incorporate previous memory with the current input. As a result, our model employs GRUs instead of LSTMs, since GRUs are less complex with only two gates, and hereby they require less training parameters and utilize less memory, effectively making GRU faster than LSTM.  
The default operation in RNN captures context in a single direction, which may lead to comprehending issues; for instance, consider the following two sentences:  
\"Second place is not as prestigious as first place.\"  
\"Second is the standard international unit of time.\"  
In these sentences, the word \"second\" carries different meanings, which traditional RNNs will not be able to comprehend, since it is the first word in the sentence; nevertheless, bidirectional RNNs support learning from both ends. A bidirectional GRU (Bi-GRU) was employed in our model to learn forward and backward lexical semantics of each word in a given sentence. There are two different methods to implement a bidirectional network; either by having two RNNs operating in opposite directions or within the internal architecture of the RNN itself. In our ROIE framework, we implemented the latter approach.
## 5 Conclusions  
This paper proposed an effective method for predicting the internal leakage rate of natural gas pipeline valves. The proposed method combines the advantage of factor analysis in reducing the dimensionality of AE signal sharply and the advantage of wavelet-BP neural network in improving the prediction accuracy and in resisting interference. The extraction rate of the three common factors obtained by factor analysis based on dimensionality reduction for the acoustic emission data reached more than 75%, and the load between the eight original sample parameters and the common factor almost reached more than 90%. This proves the effectiveness of factor analysis. The comparison of models with dimensionality reduction and without dimensionality reduction proves the advantage of factor analysis. The comparison of models with wavelet-BP neural network and BP neural network proves the advantage of wavelet decomposition. The prediction results show that the wavelet-BP neural network quantitative model based on the sample feature set after dimensionality reduction by factor analysis has a high prediction accuracy and a strong anti-interference ability. It can be used as a quantitative model for the prediction of the internal leakage rate in gas pipeline valves, which provides technical support and guarantees the safe operation of valves in gas transmission stations.  
Conceptualization, H.Z., Z.L., S.Z. and Y.Y.; data curation, H.Z., Z.L., S.Z. and Y.Y.; formal analysis, H.Z., S.Z. and Y.Y.; funding acquisition, Z.L.; investigation, H.Z., Z.L., S.Z. and Y.Y.; methodology, H.Z., Z.L., S.Z. and Y.Y.; project administration, H.Z. and Z.L.; resources, Z.L.; software, H.Z., S.Z. and Y.Y.; supervision, H.Z. and Z.L.; validation, H.Z. and Z.L.; visualization, H.Z., S.Z. and Y.Y.; writing--original draft, H.Z. and Z.L.; writing--review and editing, H.Z. and Z.L. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Models** & **MAE** & **RMSE** & **R** \\ \hline Wavelet-BP neural network model & 0.476255 & 0.581045 & 0.994024 \\ after dimensionality reduction & 1.0261 & 1.1005 & 0.9764 \\ BP neural network model after dimensionality reduction & 1.1916 & 1.2974 & 0.9501 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Index scores of three models.  
Figure 10: Comparison of the influence of wavelet decomposition on model prediction error: (**a**) prediction error of the wavelet-BP neural network model after dimensionality reduction and (**b**) prediction error of the BP neural network model after dimensionality reduction.  
**Funding:** This research was funded by the National Key R&D Program of China, Grant No. 2017 YFC0805800; and the Science Foundation of China University of Petroleum, Beijing (Grant No. 2462018 YJRC018).  
**Conflicts of Interest**: The authors declare no conflict of interest.
## 6 Results and Discussion
### Metamaterial Unit Cell Property Extraction Analysis  
The transmission coefficient (S21) and reflection coefficient (S11) obtained from the CST studio suite 2019 are plotted in Figure 11a. The results were analyzed using MATLAB code from Equations (6) and (13) to determine the relative permittivity, relative permeability, refractive index, and impedance. The graphs are plotted in Origin Pro 2018 using the exported data from MATLAB. Figure 11b shows the negative permittivity in four separate range frequencies of 2.42-2.71 GHz, 5.64-6.15 GHz, 8.9-9.1 GHz, and 11.57-12.64 GHz, respectively. Another resonance frequency is seen near 6.2 GHz, whereas the other four indicate a strong contribution to negative permittivity characteristics. The positive permittivity imaginary obtains the metamaterial behavior criteria. In Figure 11c, the permeability graph is plotted where three district areas are identified with negative permeability of near 2.4 GHz, 5.5 GHz, and 11.2 GHz, which strongly established itself as a double negative metamaterial. To confirm the results from the CST simulator, another popular simulator, the Ansys High-Frequency Structure Simulator (HFSS), was utilized to extract the transmission coefficient (S21) and reflection coefficient (S11) data under the simulator's proper setup conditions. The compared results of the S21 and S11 from the CST and HFSS simulator are illustrated in Figure 11e. They have almost the same results, which validates the design structure of the metamaterial unit cell.  
Figure 10: Surface current distribution at (**a**) 2.4 GHz; (**b**) 5.6 GHz; (**c**) 8.9 GHz; (**d**) 11.5 GHz.  
The refractive index is plotted in Figure 11d, which indicates the negative refractive index (real) in the frequency range of 2.4-2.74 GHz, 5.6-6.1 GHz, 8.9-9 GHz, and 11.5-12.6 GHz. The imaginary part of the refractive index indicates positive values in the frequency range of 2.4-2.7 GHz, 5.6-6.1 GHz, 8.9-9 GHz, and 11.6-12.6 GHz, respectively. The incident wave within the metamaterial structure decreases when the refractive index has a positive imaginary value. For the validation of the suggested design, the extracted results from the HFSS simulation software are also compared with the CST simulator, which is illustrated in Figure 11e. The magnitude negative values of the S21 from the HFSS are slightly higher than in the CST simulator, where the resonance frequencies are placed at almost the same points. The S11 values from the HFSS simulator are slightly shifted right than the CST results. This comparison establishes the validity of the design structure based on different simulation software, which is mostly popular for microwave simulations.  
Figure 11: (**a**) Transmission coefficient and reflection coefficient, the real and imaginary part; (**b**) relative permittivity; (**c**) relative permeability; (**d**) refractive index; (**e**) transmission coefficient reflection coefficient results between CST and HFSS simulator; (**f**) simulated S21 phase of unit cell and arrays.  
The simulated S21 phase of the metamaterial unit cell and different size array is shown in Figure 11f.
# Small-Scale Foreign Object Debris Detection Using Deep Learning and Dual Light Modes
## 2 Methods
### Benchmark Network  
The single-stage object detection network YOLOv5 proposed by [PERSON] [25] in 2020 combines the advantages of the previous YOLO series algorithms, resulting in more robust detection accuracy and speed. This paper selects YOLOv5, which has a faster speed and smaller network model, as the benchmark network. The model structure and detailed structure of each component are shown in Figure 2.  
The backbone of YOLOv5 uses CSPNet (Cross Stage Partial Network), which enhances the learning ability of the network, maintaining accuracy while being lightweight enough [26]. The backbone network mainly comprises the CBS module, C3 module, and SPPF (SPP-Fast) module. The CBS module includes a Convolutional Layer (Conv), a Batch Normalization Layer (BN), and a SiLU activation function to extract information features from images at different levels. The C3 module includes three Conv and multiple residual components, optimizing the path of gradient backpropagation and improving network learning ability while reducing computational costs and memory overhead. The SPPF module draws inspiration from spatial pyramid pooling (SPP) [27], expands the receptive field, and extracts the essential contextual features with a faster detection speed. The prediction head is a module that generates features of different scales on images of different scales, enabling the detection of multi-scale targets. It comprises two components: a Feature Pyramid Network (FPN) [28] and a Path Aggregation Network (PAN) [29]. FPN is a feature extractor that outputs proportionally sized feature maps at multiple levels, while PAN is a feature enhancer that boosts information flow between different levels of the feature pyramid. Due to the combination of semantic information conveyed by FPN from top to bottom and positional information transferred by PAN from bottom to top, the YOLOv5 network has strong feature fusion ability.
## 2 Related Works
### Gesture Recognition Using EMG Signals  
EMG signal data can be measured easily on hand gestures; therefore, studies using hand gestures are actively being conducted [4; 5; 6; 7]. Features of the EMG signal are extracted from the time and frequency domains. [PERSON] [4] used the time domain features of EMG signals and conducted a study to classify ten hand gestures using eight LSTM layers by extracting the features of the EMG signal in the time domain. [PERSON] [5] studied the time-frequency domain features of EMG signals. To classify five hand gestures, features were extracted using seventeen feature extraction methods such as root mean square (RMS) and zero crossing (ZC), and classification was conducted using the k-nearest neighbor (KNN). [PERSON] [6] classified three hand motions and three hand gestures that express intention. Data were converted into a continuous wavelet transform (CWT), and biometrics were performed using CNN for time-frequency analysis in EMG signals. [PERSON] [7] extracted features  
Figure 1: Electromyogram (EMG) signal measurement using the surface electrode method.  
from the time-frequency domain to classify nine hand gestures such as open palm and hand flexion, and performed normalization to match the range of each feature. Subsequently, the data dimension was reduced using principal component analysis (PCA), and each gesture was classified using a general regression neural network (GRNN). [PERSON] [8] used sliding windows to partition the data and preprocess EMG signals via CWT conversion. Seven hand gestures were classified using CNN-based EMGNet, which consists of five convolutional layers. [PERSON] [9] used a 5-500 Hz band pass filter (BPF) to denoise EMG signals and conducted a study to classify ten hand gestures using a 1D CNN consisting of three convolutional layers.  
[PERSON] [4], who conducted the gesture recognition study, conducted the experiment using EMG signals from similar gestures, such as 'hi gesture' and 'raising hand gesture'. However, other related works have conducted gesture recognition experiments without considering EMG signals when performing similar hand gestures. In addition, the number of gestures used in the experiment in all related works is less than ten, which leads to the problem of fewer gesture classes being classified.
# Bar-Wave Calibration of Acoustic Emission Sensors
## 3 Results and Discussion
### AE Sensor Response
#### 3.2.10 PAC R15 Sensor  
This sensor is a 12.7 mm element diameter, 150 kHz design for routine AE applications. Results of bar-wave calibration tests are given in Figure 18. The blue curve is from the present displacement sensitivity test exhibiting the maximum sensitivity of 15.8 dB at 146 kHz, while the red curve was reanalyzed from the previous test data for Figure 11 in reference [8]. The dashed green curve is the normally incident wave displacement sensitivity (Figure 10a in reference [4]), which shows peaks of similar values (approximately 7.6 dB) at 152 and 390 kHz. Another peak at 1130 kHz is only 2 dB lower. The dotted blue curve is for the bar-wave velocity calibration. The red curve showed a low-frequency drop below 120 kHz, but otherwise followed the blue curve well. Dips for the blue curve are at 460, 810 and 940 kHz and are comparable to the case for V103, another 12.7 mm diameter sensor. No corresponding decreases exist for the dashed green curve for normally incident waves.  
Figure 17: Receiving sensitivity of PAC HD50 (NL91). The displacement receiving sensitivities are shown in blue curves and velocity sensitivities are in red. Solid lines are for the bar-wave data, while dashed curves are for the sensitivities to normally incident waves.
# Reversing File Access Control Using Disk Forensics on Low-Level Flash Memory
## 5 ACRecovery
### Main Design of ACRecovery
#### 5.1.3 Rollback  
At a certain point, the user may want to undo recent changes to access control data. This generally follows Algorithm 2. The isPermissionByte function takes in the byte desired, the whole page being questioned, and the filesystem type, and returns true if the byte in question is related to access control data. Noting that the isPermissionByte function is highly dependent on the filesystem type, and detailed discussion can be found in Section 5.2. As the loops run, the permissions data in the new page is replaced with that of the old page without editing any other data. Following this, the page is written to its original location, effectively replacing any newly written permissions data with the oldest version present in the cache. Once each page in the cache has been written, it is cleared to prepare for new writes. After the rollback has run, the access control data should be rolled back to its state at the beginning of the cache, with all other data preserved. If desired, code could easily be written to allow the user to select individual writes to roll back, allowing for a more powerful tool. An example of this is shown in Figure 4. Malicious ACL data is written to page 12, causing its physical address to shift to page 73. Because monitoring detects this, the user can then issue a rollback command. This causes the access control data data from page 73 to be overwritten with the ACL data from page 72, and the final result is written to a new page 77.  
Figure 4: Example of rolling back changes to a page.  
```
1:DATA* data, oob* OOB // Used for reading in the page data (data) and out-of-bounds area (OOB).
2:\(isRollingBack\gets TRUE\)
3:for page in cache do
4:FTL_Read(page, &data, &OOB)
5:FTL_Read(OOB[2], &data2, &COB)
6:for byte in page do
7:if isPermissionByte(byte, data, filesystem_type) && data[byte]!= data2[byte] then
8:\(data[byte]\gets data2[byte]\)
9:endif
10:endfor
11:FTL_Write(page, data2)
12:endfor
13:CLEAR_CACHE(pages)
14:Return
```  
**Algorithm 2** Rollback() /* roll back all recent access control changes */