esa-sceva/satcom-chunk-collection huggingface dataset:
# Quantum Modular Adder over \(GF(2^{n}-1)\) without Saving the Final Carry
## 3 Quantum Modular Adder over \(\boldsymbol{GF(2^{n}-1)}\)
The second module, SZ, sets the register qubits \(|b_{i}>(i=0,...,n-1)\) to zero when \(|c_{n}>\) is also 0 but all \(|b_{i}>\) is 1 according to the second specification. The third module, AT, cuts out \(|c_{n}>\) and adds 1 to \(|b_{0}>\) when \(|c_{n}>\) is 1 following the third specification. As a result, the output of the two numbers \(a\) and \(b\) over \(GF(2^{n}-1)\) through the three-level modules is \(s\) over \(GF(2^{n}-1)\) with \(|s_{i}>\) measuring register \(|b_{i}>\). Figure 10 shows the specific quantum circuit of the proposed quantum modular adder when \(n\) is 5. Figure 8: The difference by 1 bit in \(s\) as the interim calculation result when \(s_{n}=1\) as the flagship and truncated bit. Figure 11 shows the optimized circuit for Figure 9, which is an intuitive configuration of the proposed quantum circuit. Quantum circuits can be converted very effectively by an equivalence rule [33]. If a circuit, C, turned into an equivalent circuit, C', by the rules, size(C') is the same as O(size(C)). The converted circuit, C', consists of the number of qubits and the result is the same as C, while having fewer gates and a different depth. Reconstruction of this quantum circuit by the equivalence rules increases the efficiency of the quantum circuit by reducing the number of gates and the depth. The optimized quantum modular adder over \(GF(2^{n}-1)\) in Figure 11 consists of the two submodules AT and FA with additional two NOT gates and two CNOT gates. The transposition of the two submodules and the four additional gates has the effect of replacing the second module SZ in Figure 9. Figure 12 shows a more detailed quantum circuit of the optimized quantum modular adder over \(GF(2^{n}-1)\) at the unit gate level. Figure 13 shows a simple example of the quantum circuit when \(n=5\) at the elementary gate level. This optimized quantum modular adder will be very useful for quantum operations that require a full adder over \(GF(2^{n}-1)\). For example, [PERSON] et al. proposed an efficient classical quantum and quantum-quantum modular multiplication circuit over \(GF(2^{n})\) and \(GF(2^{n}-1)\)[34]. Their multiplication circuit can be applied to any full adder, and they used QCLA focused on speed in their simulation. As described in the third paragraph of the introduction, our quantum modular adder was designed based on RCA for simplicity and cost. Applying our quantum modular adder to [PERSON]'s quantum modular multiplication enables more efficient multiplication operations over \(GF(2^{n}-1)\) than other RCA-based quantum additions. Figure 10: An example of the referenced quantum circuit for the proposed quantum modular adder over \(GF(2^{n}-1)\) in the elementary gate level, where \(n=5\). Figure 9: The referenced quantum circuit for the proposed quantum modular adder over \(GF(2^{n}-1)\) with the sub-modules \(FA\) (full adder), \(SZ\) (set zero), and \(AT\) (adder truncated).
# ADAL-NN: Anomaly Detection and Localization Using Deep Relational Learning in Distributed Systems
## 3 Methodology
### Anomaly Detection  
We create an already trained Att-Bi-LSTM method for offline anomaly identification via historical log-based data as our data source. Log parsing and sentence embedding are processes that are carried out whenever a new batch of log messages is received. After that, the vectors that were generated are used to train a model that has already been constructed. In conclusion, the Att-Bi-LSTM has the capability of identifying anomalies. Take this into consideration whenever this method is going to make assumptions by using the session of log-based data that are connected by a similar identifier, such as a block identifier. Because of this, an unusual occurrence can be reliably reported until the end of the session. In other words, this approach, similar to LogRobot [9], operates in a fast mode. Algorithm 2 is used for anomaly detection using BERT embedding and LSTM-based neural network. Our log parser has the worst complexity, \(O(n2)\), where \(n\) is the length of the input sequence.  
```
Data:\(inputSequence=[]\) Result:\(anomalies=[]\) \(embeddings\gets BERT(inputSequence,model_{\mathbf{BERT}})\) \(outputSequence\gets LSTM(embeddings,model_{\mathbf{LSTM}})\) \(anomalyScores\leftarrow computeAnomalyScores(inputSequence,outputSequence)\) \(anomalyThreshold\leftarrow computeAnomalyThreshold(anomalyScores)\) for\(i\gets 1\)to\(length(inputSequence)\)do if\(anomalyScores_{i}>anomalyThreshold\)then \(anomalies\gets anomalies\cup\{i\}\) return anomalies
```  
**Algorithm 2**The BERT and LSTM approaches to anomaly detection
# Detection Method of Partial Discharge on Transformer and Gas-Insulated Switchgear: A Review
## 2 Method of PD Detection Based on Different Types of Signals Emitted during PD Activities
### Electrical Method  
One of the electrical measurement approaches for detecting PD is the pulse capacitive coupler method. In a pulse capacitive coupler arrangement, the PD-induced current is gathered and measured in the detecting coil, which is coupled in a loop with some impedance to the ground line [67]. Quantitative approaches are highly sensitive and simple to implement. However, because of its high sensitivity, it is prone to false alarms, making it unsuitable for continuous transformer monitoring.  
EE detection techniques utilize the existing pulse-produced signal to detect PD. The circuit is connected to the spotted areas, allowing it to detect current pulses that indicate PD activity [68]. The International Electrotechnical Commission (IEC) and the Institute of Electrical and Electronics Engineers (IEEE) both utilize similar techniques [69]. This method may be used to determine the PD charge and evaluate the insulation's state. The assessment of PD detection systems in power transformers has been advised by the International Council on Large Electric Systems (CIGRE) [70]. Many variations can be derived from the basics of PD test circuits. The electrical detection techniques most often used for power transformer status monitoring are shown in Figure 3a,b, which include the test circuit for measuring self-excited test objects as well as the test circuit for measurement at a tapping of a bushing, respectively [71]. A coupling capacitor (\(C_{\text{k}}\)) is connected in parallel with the capacitance of the insulation system being tested (\(C_{\text{a}}\)) in the measurement setup shown in Figure 3a. The PD measurement device that is connected to a measuring input impedance (\(Z_{\text{m}}\)) will compute the apparent charges.  
On the other hand, Figure 3b illustrates an arrangement that is suitable for testing objects equipped with capacitance-graded bushings in which the bushing capacitance is used in place of the coupling capacitor, \(C_{\text{k}}\). When the bushing is equipped with a tapping and it is connected to the coupling device, in this case, a relatively large capacitance (\(C_{\text{m}}\)) appears across the input impedance (\(Z_{\text{m}}\)) of the coupling device which potentially impacts the measurement's sensitivity. The existence of that large capacitance makes this technology impractical for online testing. However, online testing is feasible if the power transformer has available bushing taps, as depicted in Figure 3b.  
Currently, there are limitations in identifying PD activities using this online testing. The presence of electromagnetic interference during online testing poses challenges [72; 73]. Therefore, offline testing is preferred, although it may not fully represent the conditions of online operation. For regular tests of produced goods or pre-commissioning routine tests, offline testing is still useful. Even with these drawbacks, this method offers useful insights into the comprehension of the insulation performance.
## 3 Proposed Approach  
The flowchart of the proposed two-stage approach is shown in Figure 1. In the first stage, our approach first applies pixel multiplication, image upsampling, and downsampling to convert an input 8-bit LDR image into a 12-bit EDR image. For the EDR image, we calculate the cumulative distribution function (CDF) of the intensity histogram. Subsequently, to learn the intensity distribution of HDR images, 16-bit ground-truth HDR images are converted to their 12-bit representations and used as ground truths for EDR histogram reshaping. We use a deep neural network to learn the differences between cumulative histograms of EDR-HDR image pairs and thus predict target histograms of HDR images from EDR images. Using the prediction as a reference for histogram matching, we can obtain a 12-bit HDR image with intensity distribution updates. In the second stage, the obtained 12-bit HDR image is converted to a 16-bit HDR image with intensity scaling (multiplied by 24). Then, reinforcement learning and pixel rewards are used to fine-tune intensity values for reconstructing the final 16-bit HDR image.
# Effects of Different InGaN/GaN Electron Emission Layers/Interlayers on Performance of a UV-A LED
## 4 Conclusions  
We demonstrated the effects of InGaN/GaN-based ILs and EELs with different GaN barrier layers grown using TEGa and TMGa sources under the active layer. UVLED chips were fabricated and evaluated by light output power (LOP) measurements. In UVLED-III, the ILs and EELs grown with the TEGa MO source showed better epi-structural and electrical properties than those grown with the TMGa source. UVLED-III showed 1.5 times greater light output power at 300 mA current injection compared to UVLED-I. This indicates that insertion of EELs/ILs with a GaN barrier layer grown with TEGa is effective to improve device performances of UVLEDs. In addition, PL and AFM measurements clearly showed that the introduction of the GaN barrier layer with a TEGa MO source significantly improved the crystal quality of the UVLED structure. The growth of the GaN layer with TEGa increased growth time and had a slower growth rate compared with TMGa, resulting in reduction of dislocation density at the grain boundary. As a result, UVLEDs with ILs and EELs consisting of a GaN barrier layer grown with TEGa showed enhanced optical and electrical performances.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and K.M.S. performed the growth and fabrication experiments; U.J., S.K., and D.S.S. conducted optical and electrical measurements and analyzed the data; [PERSON] and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript  
This research was funded by the Ministry of Education, Science, and Technology (NRF-2018R1D1A1B07048382).  
[PERSON] is grateful for the financial support from the Basic Science Research Program of the National Research Foundation of Korea (NRF).  
The authors declare no conflicts of interest.  
Figure 6: (**a**) Typical forward current–voltage (I–V) characteristics of UV-A LED (a semi-log plot is shown in the inset), (**b**) light output power as a function of injection current for three samples of UV-A LEDs, (**c**) PL, and (**d**) EL spectra of the UV-A LEDs.
###### Abstract  
Polyethylene cables, as important transmission equipment of modern power grid, would inevitably be slightly damaged, which seriously threatens the safety of the power supply. This paper has pioneered the preparation and typical performances of a self-healing polyethylene insulating composite. The self-healing performance to structural damage was verified by tests of electrical and mechanical damage. The effect mechanism of doping microcapsules on the electrical performance of polyethylene was emphatically analyzed. The results show that in appropriate conditions (such as 60 \({}^{\circ}\)C/30 min), the composite can not only repair the electrical tree and scratches, but also restore the insulation strength of damaged area. The effect of doping microcapsules on the electrical performances of polyethylene, such as breakdown strength, volumetric resistivity, dielectric properties, and space charge characteristics, are mainly related to impurity and the interface of microcapsule. Polarization and ionization of impurities can reduce the electrical performance of polyethylene. The interface not only improves the microstructure of polyethylene (such as how the heterogeneous nucleation effect increases the number of crystal regions, and the anchoring effect enhances the stability of amorphous regions), but also increases the charge traps. Moreover, the microstructure and charge trap can affect the characteristics of carrier transport, material polarization, and space charge accumulation, thus improving the electrical performance of polyethylene. In addition, the important electrical performance of the composite can meet the basic application requirements of polyethylene insulating material, which has good application prospects.  
self-healing; polyethylene; microcapsule; crystallinity; electrical tree; breakdown strength; volume resistivity; dielectric properties; space charge Article
## 4 Security Benefits of Directional Antennas in WSNs
### Eavesdropping  
Eavesdropping is the attack in which a malicious entity intercepts private communication in an unauthorized real-time manner. The attacker, analyzing the stolen information packets can obtain contextual and targeted information (e.g., sensing data, network routing paths, _etc._) that later can be used in more destructive attacks. In WSNs, two categories of eavesdropping attacks have been identified [55; 56]: (a) passive eavesdropping where malicious nodes intercept the information by simply listening to the wireless broadcast messages; and (b) active eavesdropping in which malicious nodes pretending to be friendly nodes gather the information by sending queries to the network nodes or access points. In the case of sensor nodes equipped with directional antennas, efficient eavesdroppers are those placed inside the antennas' radiation regions (Figure 3).  
In [41; 42], [PERSON] _et al._ proved that when using directional antennas, the eavesdropping probability is drastically decreased compared to the case of omnidirectional antennas. These studies make three important contributions: (i) they establish eavesdropping models for both omnidirectional and directional antennas in the context of wireless sensor networks; (ii) they prove that in the case of directional antennas the eavesdropping probability is diminished due to two factors: the number of hops to route a message is reduced and the exposure region from where malicious nodes may listen is smaller; and (iii) they validate the two eavesdropping models (in omnidirectional and directional case) and the corresponding values of eavesdropping probability through extensive simulation studies.  
Another analysis of the effects of using directional antennas upon eavesdropping probability in wireless networks, but this time from the attacker's perspective, is presented by [PERSON] _et al._[43]. The proposed framework enables the theoretical evaluation of the node density and antenna model on eavesdropping possibility, furthermore laying the foundation for cost-effective and practical eavesdrop attacks prevention mechanisms.  
An interesting approach to mitigate eavesdropping attacks in wireless networks is proposed in [44] and employs defensive jammers. These devices are meant to confine the network's wireless coverage into a spatially limited zone by increasing the interference level outside that particular area. By this, a potential adversary located outside the coverage zone will be blocked from illegitimately gathering the sent messages. The results of this defense strategy are substantially improved, even in the case of advanced attackers that use anti-jamming countermeasures, if these defensive jammers are placed in optimal locations and use directional antennas.
# An Image Processing Method for Extraction of the Stress Wave Reflection Period
## 2 Algorithm for the Automatic Extraction of the Reflection Period Based on Image Processing
### Step 4: Edge Detection  
The Sobel operator is a discrete differentiation operator mainly used for edge detection. It combines Gaussian smoothing and differential derivation to calculate the approximate gradient of the image. Using this operator at any point in the image will produce the corresponding gradient vector or its normal vector. Since the bottom reflection signal appears periodically in time corresponding to the \(X\)-axis (time axis) of the time-frequency spectrogram, it is only necessary to detect edge information that changes horizontally on the image. When the Sobel operator kernel size is 3, the kernel matrix in the X direction is  
\[\textit{Kernel}_{X}=\left[\begin{array}{ccc}-1&0&1\\ -2&0&2\\ -1&0&1\end{array}\right]. \tag{3}\]  
Using the kernel matrix to convolve with the original image matrix, the X-direction edge information of the original image is obtained. \(I_{src}\) represents the original image matrix, and \(I_{X}\) represents the result of the X-direction edge detection on the original image. The formula is as follows:  
\[I_{X}=\textit{Kernel}_{X}\ast I_{src}. \tag{4}\]
## 3 Related Work on Correcting Typographical Errors
### Non-Word Detection  
The dictionary is the most popular error detection method, sometimes called a lexicon or a unigram language model. The dictionary detects non-words, that is, the ones that cannot be found in it. The first system [50] used exactly this method with some additional heuristics. Modern spell checkers, such as GNU Aspell [51] and Hunspell [52] also compare each word of a text to their large lists of words. In Hunspell's case, the dictionary is compacted by keeping only the main word forms with transformation rules, prefixes, and suffixes, thus supporting many languages with rich morphologies.  
There are some downsides to the dictionary method. As noted in [53], about 40% of spelling errors are real-word errors (i.e., \"from\" \(\rightarrow\) \"form\") and cannot be detected by the dictionary. The study by [54] showed that GNU Aspell corrects only 51% of errors and performs best on non-word errors. Secondly, the dictionary cannot cover rare words, such as proper names, country and region names, technical terms, and acronyms. This issue could be dealt with by enlarging the dictionary. However, [53] argues that, eventually, most of the misspellings would match rare words and would, therefore, fail to be spotted.
## 2 Materials and Methods
### Proposed Model Architecture
#### 2.6.1 Feature Extractor  
In a feature extractor block, kernels or filters are applied to project input features onto another meaningful dimension to produce hidden feature maps. Using hidden feature maps as the input for the following layers, the networks can accurately extract or comprehend the original input's semantic meaning. With a convolutional layer, the kernels (filters) scan multiple cells at once, revealing hidden meanings and effects between them. Convolutional layers are often used because of their ability to extract adjacent cells and faster inference times compared to RNNs.  
The conv1D extracts features from a one-dimensional sequence of acceleration and gyroscopic data. The model learns to extract features from sequences of observations and map the internal features to the LSTM or transformer encoder. In order to extract dense feature representations of our input signals for effective learning, three different representations of features with varying filters sizes are computed.  
Figure 3: Overall model architecture, transformer encoder block, and LSTM encoder block.
# Influence of Soil Heterogeneity on the Contact Problems in Geotechnical Engineering
## 4 Illustrative Example  
In this paper, a simple plane-strain contact problem is analysed as an example. Figure 2 shows the flowchart of using the proposed method to analyse the illustrative example. The LAS is first used to generate multiple realisations of the random field, and then each realisation is imported to the contact analysis using the penalty method. Finally, all the results from all the realisations are collected and analysed. In this example, an elastic block is pressed against a rigid foundation, which is extracted from the contact between the overlying soil and the concrete lining in tunnels. The elastic block is 4 m long and 2 m high. On the top of the elastic block, there is a uniformly distributed load. The uniformly distributed load is 200 \(\rm{kN/m}\). The finite element mesh for this problem is shown in Figure 3, and the size of the mesh is 0.2 m by 0.2 m. Each element has 4 nodes. In this example, the Poisson's ratio \(\
u\) is 0.3. Young's modulus is taken to be a random variable, which follows a normal distribution.
# Rainfall Induced Landslide Studies in Indian Himalayan Region: A Critical Review
## 3 Results
### Extreme Events and Climate Change  
The effect of global warming and the corresponding changes to climate and geohazards is expected to affect landslide events [66]. However, forecasting and understanding the impact of climate change on landslide activity still poses a challenge. [PERSON] and [PERSON] [66] in their review article on climate change studies related to landslides emphasized the need for more studies as large parts of the world suffer from a few to no studies. Although it is predicted that the Indian Himalaya region will be affected by climate change at the time of compiling this article, we could not find any article in which the effect of climate change has been considered in the Indian Himalaya for any type of rainfall-induced landslide study. However, some works exist that describe some of the most recent extreme rainfall events: Jammu and Kashmir Himalaya for the 2014 rainfall event [51], Uttarakhand Himalaya for the 2009 event [67], and Leh region for the 2010 event [68]. One of the main reasons for the lack of inclusion of climatic variation in landslide studies for the Indian Himalayan region is the unavailability of data both in spatial and temporal context, which has been highlighted by several researchers. [PERSON] et al. [51] estimated that the pattern of rainfall intensity along the Himalayas varies from west to east, thereby precipitation patterns being affected by western disturbances and summer monsoon. Such lack of studies in a region such as the Himalayas, which is prone to severe landslides and other geohazards, is a matter of concern. Hereon, studies should include climate aspects using remote sensing products, which could reconstruct events and provide a better understanding of climate impact.