esa-sceva/satcom-chunk-collection huggingface dataset:
# Atomic Layer Deposition (ALD) of Metal Gates for CMOS
## 1 Introduction
### High-\(\kappa\) Dielectric and Effective Oxide Thickness (Eot)
The decrease in conduction band offset will trade off the positive effect in leakage suppression due to the thickness increase. Therefore, an ideal high-\(\kappa\)should have a balance between the \(\kappa\)value and band gap. The films with ultra-high \(\kappa\)value and very small band offsets, such as TiO\({}_{2}\), are thus out of consideration. Furthermore, ultra-high \(\kappa\)value means a very thick dielectric and a large thickness/length ratio. An undesirable effect, the fringing effect of the gate stack, will become extremely high [18]. Besides the \(\kappa\)-value and band alignment, attention must be paid to the interfacial quality between the high-\(\kappa\) dielectric and Si. The high leakage has been found due to the degradation of the interfacial oxide between the high-\(\kappa\) and the Si [17]. The defects near the interface could reduce the barrier height \(\varPhi_{B}\), and induce carrier scattering, causing the degradation of the channel mobility. Thermal stability of the high-\(\kappa\) stack is another filter. In fact, plenty of metal oxides have higher \(\kappa\)-values than Si dioxide, from Si\({}_{3}\)N\({}_{4}\) with a k\(\sim\) 7 to PbLaTiO\({}_{\mathrm{x}}\) with a k-value as high as 1400 [19; 20], most of them in a stack in contact with Si shows poor stability at high temperatures, and thus eliminated from the list. Other properties, such as breakdown properties, adhesion with the top metal layer and bottom Si substrate, the temperatures of deposition and annealing, and readiness to be patterned with available lithography, should also be taken into account. After the elimination, very limited candidates are left in the list. The finally crowned high-\(\kappa\) dielectric by industry is HfO\({}_{2}\)[21; 22; 23; 24]. HfO\({}_{2}\) has a \(\kappa\)-value about 25, a bandgap around 5.7 eV, yielding large enough band offsets with Si. Its large formation heat (-271 kcal/mol, compared to that of SiO\({}_{2}\) --218 kcal/mol) guaranties good thermal stability in contact with silicon. The gate leakage current of the MOSFETs using a HfO\({}_{2}\) dielectric show several orders of magnitude reduction compared to that of SiO\({}_{2}\) with the same gate capacity [25; 26].
# Development of Dialogue Management System for Banking Services
## 4 Methods
### Intent Classification  
The next step of the NLP based chat-bot development is intent classification. Intent classification helps to identify customer intention and detect needs from the provided text message. For intent classification, we have applied several classic machine learning methods and the self-attention mechanism-based DIET Classifier.  
The DIET Classifier is an attention based dual intent and has entity transformer architecture [23]. The main advantage of the DIET architecture is its capability of processing words, character n-gram level sparse features, and pre-trained word embeddings at the same time. Both intent classification and entity recognition problems can be addressed inside the DIET Classifier. The architecture has three components: intent classification, entity recognition, and masked language model. Total loss of the model can be calculated with:  
\[L_{total}=L_{I}+L_{E}+L_{M}\]  
where \(L_{I}\) is intent classification loss, \(L_{E}\) is entity recognition loss, and \(L_{M}\) is masked language loss.  
Additionally, custom intent classification models were developed using classic machine learning techniques. In our work, we utilized and tuned NB, SVM, KNN, logistic regression, and neural networks.  
The NB classifier is a statistical learning algorithm based on Bayes theorem. The prediction of a sentence belonging to a certain category using the Naive Bayes method is based on the probability assigned to each term. These probabilities are found according to the occurrences of a term in the training data. The sum of the probabilities of all tokens is then calculated to detect the category of the text.  
SVM is a linear classification method that is currently considered to be stable. The objective of the support vector machine algorithm is to find a hyperplane in N-dimensional space (N--the number of features) that distinctly classifies the data points. To classify unknown points, it will be enough to look at which side of the hyper-plane they will be located.  
Moreover, KNN, logistic regression algorithm, and neural networks architecture were applied for the intent classification. The results of the Naive Bayes and KNN models have shown low performance for our dataset. Thus, we focused on DIET Classifier, SVM, LR, and NNs approaches.
## 1 Introduction
The static noise data can be interpolated for different thrusts, thus providing noise level SPL as a smooth function of frequency, azimuthal angle, and thrust. In conjunction with a simple interpolation of the engine thrust as a function of flight speed and altitude at takeoff, it allows for significant simplification of the optimization procedure. The decoupling approach thus relies on experimental data for engine noise that are obtained independently of the flight parameters of aircraft. Although the noise sources of aircraft engines in static conditions can be different from those in flight conditions [28],  
Figure 1: Static engine noise tests: (**a**) at the open test rig [26], (**b**) with the engine installed on an aircraft [27]. it is the static condition data that are used at the later stages of aircraft design. As a result, the methods of estimating certification noise levels based on such kind of data are very reliable and mature. The present study intends to capitalize on this experience of calculating certification noise of subsonic aircraft at the later design stages and implement it for assessment of VNRS efficiency. At takeoff, the ICAO defines the certification noise at two measurement points, lateral and flyover [1]. All the above studies consider the lateral certification point the point located on a line 450 m parallel to the runway centerline; this measurement point is used for the certification of subsonic jet aircraft. However, there is another position for the lateral certification point, which is used for the certification of propeller-driven subsonic aircraft. It is located on the extended runway centerline below the departure flight path, where the aircraft altitude at full thrust is 650 m. As an example of the SST concept with turboprop buster engines [4] shows, it is not immediately obvious which position of the lateral point should be preferred. The current efforts of researchers and the industry aim at achieving compliance of next-generation SST with the current ICAO regulations for subsonic aircraft noise (so-called Chapter 14 norms). If the certification noise levels for SST with or without VNRS prove to be lower at one position of the lateral point than at the other, this can be used to demonstrate lower certification noise levels without additional effort. Therefore, it is of interest to consider SST noise levels for both allowable positions of lateral certification points, as well as the efficiency of VNRS for noise reduction in both positions. Thus, the contribution of the paper is twofold: firstly, it provides an assessment of VNRS efficiency for SST noise reduction at takeoff with the decoupling method, and secondly, it compares these results for two possible choices of the lateral certification point. It has to be stressed that the study focuses on VNRS efficiency and does not involve an investigation of the aircraft's compliance with Chapter 14 norms.
## 1 Introduction
### Organization of the Paper  
The remainder of this paper is organized as follows. Section 2 provides a comprehensive review, beginning with an exploration of dynamic spectrum access, focusing on both signal processing techniques and machine learning/deep learning approaches, followed by a discussion on radio astronomy and radio frequency interference. Section 3 delves into the approximate discrete Fourier transform, offering insights into the theoretical background and the specific implementation of the ADFT. In Section 4, we present a detailed system overview, covering the proposed architecture, the analog front-end, the ADFT core and digital ADFT spectral estimation, digital FFT spectrometer, and digital high-speed connectivity. Section 5 outlines experimental results including calibration and beam measurements,showcasing the practical performance of the proposed method. Finally, Section 6 discusses the implications of our findings and outlines potential directions for future research.
## 5 Discussion and Recommendations
### Limitations of the Study  
There are limitations to this study. Some of the constraints were that we could not access some types of customer data, such as billing and credit information, due to telecom data categorization and confidentiality restrictions. This was a significant research constraint. Another constraint in conducting this study was the lack of demographic information on the clients. Therefore, it was not possible to include such criteria in the classification process, which would have improved the classification accuracy and interpretability.
# Study on the Evolution Law of Overlying Rock Collapse Induced by Mining Based on BOTDR
###### Abstract  
Based on Brillouin optical time-domain reflectometry (BOTDR) technology, this study integrates laboratory tensile tests and similarity simulation experiments to systematically investigate the relationship between overlying strata collapse and fiber strain during coal seam mining. An analytical expression was established to describe the correlation between overlying strata displacement and fiber strain. The horizontal fiber monitoring results indicate that fiber strain accurately captures the evolution of overlying strata collapse and exhibits strong agreement with actual displacement height. When the working face advanced to 115 m and 155 m, the rock strata primarily underwent stress adjustment with minimal failure. At 195 m, the collapse zone expanded significantly, resulting in a notable increase in fiber strain. By 240 m, severe roof failure occurred, forming a complete caving zone in the goat. The fiber strain curve exhibited a characteristic \"double convex peak\" pattern, with peak positions closely corresponding to rock fracture locations, further validating the feasibility of fiber monitoring in coal seam mining. Vertical fiber monitoring clearly delineated the evolution of the \"three-zone\" structure (caving zone, fracture zone, and bending subsidence zone) in the overlying strata. The fiber strain underwent a staged transformation from compressive strain to tensile strain, followed by stable compaction. The \"stepped\" characteristics of the strain curve effectively represented the heights of the three zones, highlighting the progressive and synchronized nature of rock failure. These findings demonstrate that fiber strain effectively characterizes the collapse height and evolution of overlying strata, enabling precise identification of rock fracture locations. This research provides scientific insights and technical support for roof stability assessment and mine safety management in coal seam mining.  
distributed optical fiber; overlying rock collapse; optical fiber strain; similar simulation +
Footnote †: journal: Applied Sciences  
Article
## 2 Results and Discussion
### Optimisation of SPME Extraction Conditions
observed, preventing thermal oxidation or degradation of the compounds of interest. The optimum HS-SPME/GC-MS conditions were applied in the profiling method, facilitating the identification and semi-quantifitation of 92 VOCs of different chemical classes, as shown in Table 2. A representative gas chromatogram of a Quality Control (QC) sample analysed with the optimal HS-SPME/GC-MS conditions is shown in Figure 3. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Compound Name**} & \multirow{2}{*}{**Rt**} & \multirow{2}{*}{**Rlexp**} & \multirow{2}{*}{**RI**} & \multirow{2}{*}{**Chania**} & \multirow{2}{*}{**Mean**} & \multirow{2}{*}{\(\pm\)SD**} & \multirow{2}{*}{**Mean**} & \multirow{2}{*}{\(\pm\)SD**} & \multirow{2}{*}{**Mean**} & \multirow{2}{*}{\(\pm\)SD**} & \multirow{2}{*}{**Mean**} & \multirow{2}{*}{\(\pm\)SD**} \\  & & & & **(n = 17)** & & & **(n = 26)** & & & **(n = 10)** & & **(n = 10)** \\ \hline \hline
**Alcohols** & & & & & & & & & & & & \\ \hline Isobutanol & 2 & 626 & 622 & 0.06 & 0.05 & 0.06 & 0.05 & 0.1 & 0.04 & 0.07 & 0.06 \\ \hline
1-Penten-3-ol, C5 & 2.54 & 683 & 686 & 1.96 & 0.38 & 1.69 & 0.49 & 1.45 & 0.49 & 1.05 & 0.37 \\ \hline
3-Methylbutan-1-ol & 3.33 & 738 & 744 & 0.12 & 0.05 & 0.14 & 0.05 & 0.15 & 0.05 & 0.13 & 0.07 \\ \hline
1-Pentanol & 3.89 & 771 & 766 & 0.09 & 0.08 & 0.08 & 0.06 & 0.04 & 0.05 & 0.17 & 0.05 \\ \hline (Z)-2-Penten-1-ol, C5 & 3.92 & 774 & 767 & 1.39 & 0.25 & 1.05 & 0.36 & 0.86 & 0.35 & 0.85 & 0.19 \\ \hline (Z)-Hex-3-en-1-ol, C6-LnA & 5.53 & 857 & 858 & 0.12 & 0.04 & 0.12 & 0.05 & 0.09 & 0.05 & 0.19 & 0.06 \\ \hline (E)-Hex-2-en-1-ol, C6-LnA & 5.82 & 871 & 866 & 1.99 & 1.75 & 2.23 & 2.49 & 0.72 & 2.36 & 7.96 & 3.45 \\ \hline Hexan-1-ol, C6-LA & 5.9 & 875 & 862 & 1.77 & 0.9 & 1.62 & 0.88 & 0.99 & 0.9 & 4.31 & 1.12 \\ \hline
1-Octanol & 9.76 & 1076 & 1074 & 0.08 & 0.02 & 0.08 & 0.03 & 0.07 & 0.02 & 0.07 & 0.04 \\ \hline
1-Nonanol & 11.48 & 1176 & 1172 & 0.06 & 0.03 & 0.1 & 0.05 & 0.1 & 0.04 & 0.05 & 0.04 \\ \hline (Z)-11-Hexadecen-1-ol & 12.31 & 1228 & - & 0.05 & 0.01 & 0.05 & 0.02 & 0.05 & 0.02 & 0.03 & 0.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Chromatographic data (retention time, retention index) and mean values (mg/kg) relative to internal standard (Eucalyptpol) and standard deviation for identified volatile compounds in Cretan EVOO samples. Figure 2: Contour plot for factors extraction temperature (Ex.temp) and extraction time (Ex.time). Green colour indicates lower response values and pink colour higher response values. Contours include corresponding response levels. The contour plot is a slice at the centre points for the rest of the remaining factors (Conditioning time (Co.time), Desorption temperature (Des.temp), and Desorption time (Des.time)). (e + 08 = \(\times\) 108).
## 3 Related Work
### AUVs  
Multiple AUVs move around the survey area to collect data from GWs and forward it to the sink when it passes by the sink. Each AUV periodically broadcasts a beacon signal to show its presence to GW nodes and the sink node. AUVs communicate with GWs and the sink node using a separate short-range, high rate data channel. As a result, AUVs, GWs, and the sink form a separate collision domain for the medium access from the remaining network (e.g., U-sensors and GWs), which significantly reduce the packet collisions in a multi-hop network.  
If an AUV has failed (e.g., due to communication or mechanical malfunction) the data it carries can be lost. One possible way of recovering from this loss is that the GW keeps the data, after it sends data to an AUV, until it receives acknowledgement (ACK) from sink via the AUV at the next AUV's visit. If the GW does not receive ACK from the AUV, it assumes that the data was lost and retransmits it to the AUV. Another point to be considered is how fast the failure can be detected to minimize the recovery delay. Fast failure detection can be achieved since the mothership usually keeps track of the AUV.
## 3 Suggested Techniques
### Suggested Routing Protocol
#### 3.3.3 Collection Data Transmission  
The nodes belonging to the cluster transmit the collected data to the CH of the corresponding cluster, and the CH performs the process of collecting and integrating the received data. According to Reference [11], more than 50% of the total energy consumed by WSN occurs during data transmission. Therefore, the process of integrating aggregated data to reduce the amount of data transmitted from CH to BS is recognized as an important function. The CH transmits the integrated data to the BS, and the BS collects the data received from each CH and sends it to the database/web server to perform the service.
## 3 The Related Work: Decoding Framework of an MIM-QSMS Decoder  
The MIM-QSMS decoder [15] is one type of FAIDs that exchange the messages represented by symbols from finite alphabets within the decoder. The precision of the MIM-QSMS decoder is predetermined according to system constraints, which can be represented by the tuple \(\left(q_{m},\,q_{v}\right)\). More specifically, \(q_{m}\) is the bit width of the exchanged messages and \(q_{v}\) refers to the bit width of the a posteriori message. Figure 1 demonstrates the decoding framework of a \(\left(q_{m},\,q_{v}\right)\) MIM-QSMS decoder with respect to the node \(v_{n}\) of degree \(d_{n}\) at the \(t\)th iteration. Suppose that the MIM-QSMS decoder proceeds based on the base matrix \(\mathbf{H}_{b}\) as described in Section 2.3. Therefore, we have \(j=\lfloor n/Z\rfloor\) and \(m_{k}\in\mathcal{N}(v_{n})=\left\{m_{1},m_{2},\ldots,m_{d_{n}}\right\}\).  
* CN update: The MIM-QSMS decoder adopts the min operation [15] for the CN update, where we denote the CN update function by \(\Phi_{c}^{\text{MS}}\). Assume that \(R_{n^{\prime}m_{k}}\in\mathcal{R}=[2^{q_{m}}]\) for all \(n^{\prime}\in\mathcal{N}(c_{m_{k}})\backslash n\) are the V2C message symbols received at a neighboring node \(c_{m_{k}}\) connecting to the node \(v_{n}\). Let \(f(\cdot)\) be a function mapping the V2C message symbols \(0,1,\ldots,2^{q_{m}}-1\) to integers \(2^{q_{m}-1},\ldots,1,-1,\ldots-2^{q_{m}-1}\), respectively. At the \(t\)th iteration, the C2V message symbol \(S_{m_{k}n}^{(t)}\in\mathcal{S}=[2^{q_{m}}]\) is computed by \[S_{m_{k}n}^{(t)}=\Phi_{c}^{\text{MS}}\Big{(}\{R_{n^{\prime}m_{k }}^{(t-\{\lfloorlfloor n^{\prime}/Z\rfloor>j\})}:\,n^{\prime}\in\mathcal{N}( c_{m_{k}})\backslash n\}\Big{)}\] \[=f^{-1}\left(\prod_{n^{\prime}\in\mathcal{N}(c_{m_{k}}) \backslash n}\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!  
Based on the decoding architecture in Figure 1, the main idea of designing the MIM-QSMS decoder in [15] is to construct the LUTs by tracking the probability mass functions (pmfs) of exchanged messages via DE [4]. To be more specific, the reconstruction LUTs are generated by scaling the LLR values associated with each message symbol in \(\mathcal{L}\) and \(\mathcal{S}\), respectively. The quantization LUTs are designed to be an optimal sequential deterministic quantizer (SDQ) by using dynamic programming (DP) [21].
## 2 Materials and Methods
### Manual Pesticide Residue Coverage Area Determination  
Prior to the development of the AI-powered software, a proof-of-concept study was performed to determine the feasibility of the proposed work. Firstly, a manual pesticide residue coverage area was determined to assess the correlation between pixel areas of residue and the concentration of the applied pesticide. The coverage of the OTC and Kocide 3000 residue on the citrus leaves was performed following previously reported protocols and with some modifications [41; 42; 43]. The digital images were acquired following the previously described data acquisition protocol, followed by image processing using open-source software (Image J) version 1.53t to determine the area of coverage of both pesticides on the leaf surface. Initially, the image was split into RGB colors, and then the green image was used to determine the OTC signal through a set threshold analysis. Conversely, for Kocide 3000, the blue image was used to determine the Kocide signal through a set threshold analysis. This analysis generated a binary mask, and the OTC coverage area was measured. Similarly, the red image was processed under threshold analysis to determine the total leaf surface area. The percentage of pesticide coverage was calculated with the following equation:  
\[\textit{Pesticide Coverage area }(\%)=\frac{\textit{Pixels in green or blue threshold area}}{\textit{Pixels in red threshold area}}\times 100\]  
Lastly, the OTC coverage area (%) was calculated with the aforementioned equation using the pixels from the green threshold area, and the Kocide was calculated with the pixels from the blue threshold area.
## 4 Simulation Results  
To validate the experimentally observed evolution of the CNR of a ghost image with the FWHM and the peak positions of speckles' pixel intensity distribution from the nonuniform pseudothermal light, a numerical model was developed to simulate the observed behavior. The numerical framework simulated the speckle pattern with a defined average speckle size and Gaussian-like distributions with a freely selectable width and position of maximum. The so-gathered speckle field \(\Phi_{in}\) represented the speckle field at the object's position, shown in Figure 1. The speckle field was then multiplied elementwise by the object \(O\) and numerically propagated to the photodiode's position, where the field was integrated to generate the bucket value \(b=\int\Im(\Phi_{in}*O,d)\), where \(\Im(\Phi,d)\) is a Fourier based complex field propagator for a field \(\Phi\) over a distance \(d\). The reconstruction itself was then done with the reconstruction code from [25]. The simulation framework then used a set of parameters for the width \(\omega_{h}\) and the peak position of the maximum \(p_{h}\) of the speckles' pixel intensity distribution to create a simulated ghost image \(G(\omega_{h},p_{h})\) dependent on the distribution parameter set with a fixed average speckle size of 30 \(\upmu\)m. As a parameter set, a width from 60 to 160 pixel intensity value and a position from the pixel intensity from 60 to 160 were chosen to include the experimental parameters. After generating the ghost image, the CNR was measured and plotted as a function of the parameter set to produce the CNR matrix shown in Figure 4. It was shown that the CNR rose with the increased width of the speckle field distributions, which corresponded to experimental observations. However, the dependence of the CNR on \(p_{h}\) fell without showing a decent local maximum, which is explained by the shape of the distribution. The simulated field had a slightly asymmetrical Gaussian-like distribution of the histogram. When the pixel intensity distribution is simulated, there is a higher probability of such asymmetry. Shifting the maximum to brighter values led to saturation of more and more pixels because of the limited dynamic range and, therefore, to a loss of pixel intensity variety. A loss of pixel variety decreased the usable spatial information for image reconstructions and lowered the CNR. The local maximum of the experimental CNR-position dependence is explainable similarly. At low positions, zero-valued pixels became saturated. With an increasing position, this saturation decreased, and the pixel brightness variety rose. If the position increased further, the pixel saturation rose to high values again, leading to a lower CNR.  
The simulation validates the experimentally found correlation between the quality of a ghost reconstruction and the distribution parameters of the input speckle field. Concerning the slightly deviating role of position dependence, an explanation involving the shape of the distributions, as they are not perfectly Gaussian, could be found.