esa-sceva/satcom-chunk-collection huggingface dataset:
# Regularized Urdu Speech Recognition with Semi-Supervised Deep Learning
###### Abstract  
Automatic Speech Recognition, (ASR) has achieved the best results for English, with end-to-end neural network based supervised models. These supervised models need huge amounts of labeled speech data for good generalization, which can be quite a challenge to obtain for low-resource languages like Urdu. Most models proposed for Urdu ASR are based on Hidden Markov Models (HMMs). This paper proposes an end-to-end neural network model, for Urdu ASR, regularized with dropout, ensemble averaging and Maxout units. Dropout and ensembles are averaging techniques over multiple neural network models while Maxout are units in a neural network which adapt their activation functions. Due to limited labeled data, Semi Supervised Learning (SSL) techniques are also incorporated to improve model generalization. Speech features are transformed into a lower dimensional manifold using an unsupervised dimensionality-reduction technique called Locally Linear Embedding (LLE). Transformed data along with higher dimensional features is used to train neural networks. The proposed model also utilizes label propagation-based self-training of initially trained models and achieves a Word Error Rate (WER) of 4% less than that reported as the benchmark on the same Urdu corpus using HMM. The decrease in WER after incorporating SSL is more significant with an increased validation data size.  
speech recognition; locally linear embedding; label propagation; Maxout; low resource languages +
Footnote †: journal: Applied Sciences
## 4 Design Implementation
### System Design Architecture  
The system architectural design is depicted in Figure 6, which, above the light green arrow symbol, from the \"Stream Signal\" input to the \"Stream Signal\" output, encompasses multiple pipeline data processing units and associated control software programs, controlled by the internal micro-processor. Additionally, there is a \"Network Interface\" linked to a gateway for Internet data access. Several implemented hardware modules, including \"Phase-Locked Loop (PLL)\", \"Input/Output Buffers\", \"NIC Controller\", and \"Memory Controller\" are utilized functional components provided in the FPGA design library [50]. Additionally, the self-designed hardware modules are indicated by blue and red boxed sections. The blue boxes represent functions including implemented high-speed \"Signal Sampling\" and \"Signal Emitting\", and a \"Scatter-Gather Direct Memory Access (SG-DMA) Controller\" attached to the system bus, behind which the green box encompasses the \"Micro-Processor (\(\upmu\)P)\" executing the related software control programs. Next are the \"Configuration Status Registers (CSRs)\" providing functional setting and execution status for the software control required by the designed hardware modules. Furthermore, the red boxes include modules of \"Key Stream Generator\", stream data \"Encryption Encoding\", and \"Decryption Decoding\". In the subsequent sub-section, the system execution flow of data processing in the designed architecture of Figure 6 will be introduced.  
Figure 5: FPGA embedded-system development board [49].  
Figure 6: System architecture and pipeline data processing flow.
# An Innovative Information Hiding Scheme Based on Block-Wise Pixel Reordering
## 3 Proposed Scheme
### Codeword Table Formation  
After a data hider obtains the encrypted image \(I^{\prime}\), it is converted into a pixel stream. Depending on the block size desired, it is broken into pixel-blocks. An owner can set the number of blocks in a group to perform the index reordering technique which is derived from the codeword index reordering detailed in Background of the Work. We are using the same technique as the codeword index reordering to embed data into the block-groups generated from the encrypted image.  
After obtaining the encrypted image \(I^{\prime}\) which consists of \(M\times N\) pixels, it is denoted as \(I^{\prime}=\left\{p_{0},\ p_{1},\ \cdots,p_{(M\times N)-1}\right\}\). The encrypted image is cut into pixel-blocks, and each pixel-block contains \(n\) contiguous pixels. A current pixel-block can be denoted as \(b_{c}=\left\{p_{n(c-1)},\ p_{n(c-1)-1},\ \cdots,p_{n(c-1)-(n-1)}\right\}\), where \(c\) is the number of the current pixel-block. After the pixel-blocks are formed, \(m\) pixel-blocks are grouped tookinher to be a block-group \(g_{l}=\left\{b_{m(l-1)},\ b_{m(l-1)+1},\ \cdots,b_{m(l-1)+(m-1)}\right\}\), where \(l\) is the number of the current block-group and m is the length of a desired codeword table for the codeword index reordering detailed in Section 2. The total number of block-groups is \(K=\frac{(M\times N)}{(m\times n)}\) and the block-groups in the encrypted image \(I^{\prime}\) can be represented as \(G=\left\{g_{0},\ g_{1},\ \cdots,\ g_{K-1}\right\}\). A codeword table containing \(m\) n-dimensional codewords is generated for each corresponding block-group \(g_{l}\).  
Figure 6 is an example of how pixel-blocks and block-groups are created for a \(512\times 512\) image. When a pixel-block consists of 8 pixels and a block-group combines 255 blocks, there are 32,768 pixel-blocks and 128 block-groups are formed in total for the encrypted image.  
A codeword table for the block-group \(g_{0}\) of the example in Figure 6 is illustrated in Figure 7. The codeword table is treated as a codebook used in the codeword index reordering to hide data at a later stage.  
Figure 6: Cutting the pixel stream into pixel-blocks and forming block-groups.
# Design and Performance of a XBee 900 MHz Acquisition System Aimed at Industrial Applications
## 4 Experimental Tests of the Monitoring System
### Latency and Jitter for Distributed Values Collection Frames Using Different Sizes  
The length of the frames issued by the Smart Measuring Nodes may vary according to the configuration selected, i.e., how many sensors were selected for measuring and whether simple versus multiple sampling capabilities were used. The designed Smart Measuring Node prototype board may carry frames with the values measured by one to four sensors (from 23 to 29 bytes). In addition, in the case of using the multiple sampling, the number of values may be multiplied by the number of samples per measurement command. For example, a frame for measuring the values of four sensors, using simple sampling, carries four values, and the size of the frame is of 29 bytes. When multiple sampling is used with four sensors and sampling four times each between measurements, the commands would carry sixteen values, yielding a frame of 53 bytes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **SMN1** & **SMN2** & **SMN3** \\ \hline Average & 23.19 & 38.12 & 53.10 \\ Std Deviation & 0.42 & 0.42 & 0.42 \\ Maximum & 24.54 & 39.77 & 55.74 \\ Minimum & 22.76 & 37.69 & 52.67 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Reception timestamps of Smart Measuring Nodes at the Coordinator Node (in ms).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Distance (m)** & **Jitter (ms)** \\ \hline
0 & 0.76 \\
1 & 0.76 \\
5 & 0.72 \\
8 & 0.84 \\
12 & 0.96 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Reception jitter at three different Smart Measuring Nodes at different distances.  
This test is aimed at evaluating the latency of the distributed values collection frames sent by the Smart Measuring Nodes to the Coordinator Node. This information is required in order to design an adequate schedule. All tests lasted 5 min. Results in Table 5 prove that latency depends on the size of the frames, but even for the longest frames, 53 bytes carrying 16 values, the jitter is bounded to 1 ms.
## 5 Experiments and Results
### Comparison with Other Target Detection Models
The comparative experimental results are shown in Table 5. Compared with those of other models, the crack images generated by Real-ESRGAN have higher quality, with more detail and background information. The PIQE and ENIQA values for the generated crack region images are 2.7 and 0.092, respectively, which are 0.13 and 0.017 lower than those of ESRGAN, indicating better reconstruction effects. These results can provide technical support for the analysis of the formation mechanism of apparent cracks in metro tunnel linings and for maintenance and repair work. Figure 17: The comparison of super-resolution results of apparent cracks in subway tunnel linings of different methods.
## 3 Methodology  
In this paper, frequency-wave number (F-K) [17; 18] and polarization analysis [19] are used to study the excitation mechanism of the microseisms generated by TOMAS and its source area.  
The F-K method's fundamental principle is to use [PERSON]'s high-resolution methodology [16] to extract information from the covariance matrix to estimate the slowness and back azimuth of the signal arriving at the array. In order to reduce the effect of the array response on the F-K spectrum by applying station weights. This algorithm is performed under the plane wave assumption by operating on the slowness vector at a time and measuring the energy output. Since different types of seismic waves have different slowness and different source locations can have different back azimuth, F-K analysis is a grid search method in the slowness space. The basic idea is to find the best combination of slowness and back azimuth to maximize energy.  
Polarization analysis uses a single-station algorithm to obtain frequency-dependent information from three-component seismic data. Our frequency domain polarization technique has been extensively discussed before [20]. The approach gives information on the noise power and the polarization properties as a function of time and frequency based  
Figure 1: The location of YL array, green triangles, and four AU stations, red triangles. The circles represent the best tracks of the TOMOS cyclone from JTWC. Inset is the configuration of 8 YL array stations used in the study. TD stands for tropical depression with maximum wind speed 0–34 km/h. TS stands for tropical storm with maximum wind speed 34–63 km/h. TY stands for typhoon with maximum wind speed 63–129 km/h.  
on the singular value decomposition of the spectral covariance matrix. The polarization properties include the degree of polarization and the direction of arrival at the seismometer. The polarization approach, in contrast to F-K or beamforming analysis, only calculates between different components of a single station.
## 2 Bounds Determination of Time-Invariant and Time-Variant Uncertainties under Insufficient Samples
### Feasible Implementation  
Considering that the value of the Grey constant coefficient \(c_{i}\) in Equation (13) is indeed determined by uncertainty samples themselves, a feasible parameter estimation method via limited data set \(X_{i}\) has to be implemented. The motivation procedures are summarized as:  
(i) Extraction of data \(x_{i}(1),\;x_{i}(2),\ldots,\;x_{i}(l)\) from original sample set \(X_{i}\), and definitions of the following characteristic quantities by  
\[\sigma_{i}(l-1)=\sqrt{\frac{1}{l-1}\left[x_{i}(l^{*})-\frac{1}{l}\sum\limits_ {l^{*}=1}^{l}x_{i}(l^{*})\right]^{2}},\;l=2,3,\ldots,m_{1} \tag{15}\]  
and  
\[\left\{\begin{array}{l}\Delta_{i}(l^{*})=\frac{x_{i}^{(2)}(l)}{l}\cdot l^{* }-x_{i}^{(1)}(l^{*})\\ \Delta_{i}^{\max}(l-1)=\max(\Delta_{i}(l^{*}))\\ d_{i}(l-1)=\frac{\Delta_{i}^{\max}(l-1)}{l-1}\end{array}\right.,l^{*}=1,2, \ldots,l \tag{16}\]  
where \(\sigma_{i}(l-1)\) is the sample standard deviation based on the statistical theory.  
(ii) Traversal of each value of \(l\) from 2 to \(m_{1}\), and establishment of two vectors related to the data sequences by  
\[\mathbf{\Sigma}_{i}=\left[\sigma_{i}(1),\;\sigma_{i}(2),\ldots,\;\sigma_{i}(m _{1}-1)\right]^{T}\text{ and }\mathbf{D}_{i}=\left[d_{i}(1),\;d_{i}(2),\ldots,\;d_{i}(m_{1}-1)\right]^{T} \tag{17}\]  
(iii) Acquirement of the accumulating sequences of Equation (17) referred by Equations (11) and (12), i.e., \(\mathbf{\Sigma}_{i}\rightarrow\mathbf{\Sigma}_{i}^{(2)}\) and \(\mathbf{D}_{i}\rightarrow\mathbf{D}_{i}^{(2)}\).  
(iv) Construction of the least-square estimation model for fitting the equation shaped like \(\mathbf{\Sigma}_{i}^{(2)}=c_{i}\cdot\mathbf{D}_{i}^{(2)}\), and determination of the optimal estimation \(\mathbf{\zeta}_{i}\) by  
\[c_{i}\rightarrow\hat{c}_{i}=\left(\left(\mathbf{D}_{i}^{(2)}\right)^{T}\cdot\mathbf{D} _{i}^{(2)}\right)^{-1}\cdot\left(\mathbf{D}_{i}^{(2)}\right)^{T}\cdot\mathbf{\Sigma}_{ i}^{(2)} \tag{18}\]  
As discussed earlier, the Grey constant coefficient \(c_{i}\) can be finally confirmed by the insufficient sample data to provide necessary input condition for solving \(s_{i}\), as is shown in Equation (13). Moreover, considering that in some typical cases, the errors in measurement or from an inherent system may be inevitable during the sample extraction process; therefore, the way to eliminate the gross error needs to be explored as well (referred by [36]). To sum up the foregoing comprehensive analysis, the uncertainty bounds for interval variable \(x_{i}\) are available. Similarly, the processes of the lower and upper bounds subjected to the time-varying uncertain parameter \(y_{k}(t)\) can be rationally represented.
## 2 Methodology
### Census Block Groups (CBG)  
Census block groups (CBGs) were used to aggregate the total area of canopy cover (UTC) data and socioeconomic variables. CBGs are statistical division areas used by the US Census Bureau that closely resemble the sizes of neighborhoods containing between 600 and 3000 people [19]. The neighborhood boundaries for Chattanooga are arbitrary and often contested. A GIS layer of CBGs was obtained from the US Census TIGER/Line Files (Topologically Integrated Geographic Encoding and Referencing) program and clipped to the city boundary of Chattanooga. Demographic data were obtained from the American Community Survey and Esri's demographic data program (Table 2). Heat island data were sourced from the Trust for Public Land Heat Severity Dataset. These data variables were aggregated to the CBGs along with the total area and percentages of land cover types derived from the thematic land cover layer using the ArcGIS Pro geoprocessing tools \"Summarize Within\" and \"Data Enrichment\" (Figures 4 and 5). The final layer resulted in a dataset containing CBGs with their unique FIPS codes, socioeconomic variables for population demographic variables, a heat severity index for the identification of heat islands, and percent canopy cover area for statistical and spatial analysis (Table 2). These variables, described in Table 2, were selected from a combination of past studies identified in the literature [34] and stakeholder input from the City of Chattanooga and the nonprofit Green I Spaces, which focuses on sustainability and equity issues for Chattanooga neighborhoods.  
Figure 3: Subsets showcasing the true color SkySat imagery (**top**) and refined land cover dataset (**bottom**) over Coolidge Park/North Shore (**left**) and the Chattanooga National Cemetery (**right**). Green areas represent forest canopy, beige areas represent non-forest vegetation, and red areas represent impervious surface land classes.  
Figure 4: Mean household income for Chattanooga, TN. Source: American Community Survey, 2020.  
Figure 5: Bivariate map showing mean percentage tree canopy and mean household income for Chattanooga, TN. Lighter colors indicate areas with a lower mean income and lower percentage of UTC and darker colors indicate a higher mean income and higher percentage of UTC. Source: American Community Survey 2020 and UTC IGTLab.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Attribute** & **Source** \\ \hline Mean Household Income & American Community Survey (2020) \\ Population Density & American Community Survey (2020) \\ Educational Attainment: Bachelor’s Degree & American Community Survey (2020) \\ Race White & American Community Survey (2020) \\ Race Black & American Community Survey (2020) \\ Race Hispanic & American Community Survey (2020) \\ Heat Severity (Heat Islands) & Trust for Public Land (2021) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Variables used for this study.
# An Improved RAIM Availability Assessment Method Based on the Characteristic Slope
## 2 Technical Background
### Vertical Characteristic Slope  
The vertical characteristic slope is defined according to these two parameters [2],  
\[Slope_{m}=\sqrt{\frac{a_{3m}^{2}}{s_{mm}}} \tag{7}\]  
where \(a_{3m}\) and \(s_{mm}\) respectively characterizes \(VPE\) and \(Ts\) change caused by the measurement bias of the \(m\)-th visible satellite, signed as \(VS_{m}\), is the faulty satellite. The details of \(a_{3m}\) and \(s_{mm}\) can be seen in Appendices A and B, respectively. For a specific bias value, a faulty satellite with a large slope value will present a high \(P(\text{HMI}|1F)\), and a faulty satellite with a small slope value will present a low \(P(\text{HMI}|1F)\). The \"characteristic slope\" will be abbreviated as \"slope\" hereafter.
# Silica Mesoporous Structures: Effective Nanocarriers in Drug Delivery and Nanocatalysts
## 5 Application of Silica Mesopores in Drug Delivery
### ATP-Responsive Drug Delivery  
ATP is the most abundant ribonucleotide used in cells as a co-enzyme, and it can be used as a trigger for the controlled release of anticancer drugs. In 2011, [PERSON] et al. designed a novel and generally bioresponsive controlled-release MSN system based on aptamer-targeted interactions [204]. In their system, the pores of the MSNs were capped by Au nanoparticles that had been modified with the ATP aptamer. They showed that the cargo was released when the MSNs were uncapped in the presence of ATP molecules.
# Discriminating Pattern Mining for Diagnosing Reading Disorders +
## 3 Technique
### Phase 1: Discriminating Prototype Search  
In this phase, we consider only matrices \(\mathbf{DS}^{s}_{e}\) and \(\mathbf{DS}^{s}_{r}\) since the goal is the mining of peculiarities in the structure of the words able to discriminate between \(\mathbf{W}_{e}\) and \(\mathbf{W}_{r}\) and since the contextual features are already expected to be discriminative. Moreover, the contextual features could affect the results since they hide difficulties of the user in structural component of the words.  
The idea of the approach exploits a non-negative matrix factorization technique and an innovative optimization objective precisely designed for the situation at hand. The aim is to decompose matrix \(\mathbf{DS}^{s}_{e}\) having size \(n_{e}\times m\) into two less-ranked matrices \(\Theta_{e}\) (having size \(n_{e}\times n_{p}\)) and \(\mathcal{P}^{s}_{e}\) (having size \(n_{p}\times m\)). Here, \(\mathcal{P}^{s}_{e}\) represents the \(n_{p}\) prototype words, and \(\Theta_{e}\) represents the impact of each word in \(\mathbf{W}_{e}\) for the emerging of each prototype. Analogously, \(\mathbf{DS}^{s}_{r}\) should be decomposed as \(\Theta_{r}\cdot\mathcal{P}^{s}_{r}\). In order to find matrices \(\Theta_{e}\), \(\mathcal{P}^{s}_{e}\), \(\Theta_{r}\) and \(\mathcal{P}^{s}_{r}\), we have to keep in mind three desiderata:  
1. \(\mathbf{DS}^{s}_{e}\approx\Theta_{e}\cdot\mathcal{P}^{s}_{e}\);
2. \(\mathbf{DS}^{s}_{r}\approx\Theta_{r}\cdot\mathcal{P}^{s}_{r}\);
3. the prototype words should be able to discriminate between \(\mathbf{W}_{e}\) and \(\mathbf{W}_{r}\).  
To achieve the third goal, the sets of prototypes in \(\mathcal{P}^{s}_{e}\) and in \(\mathcal{P}^{s}_{r}\) should be as much different as possible. Thus, for handling the goals, given matrices \(\mathbf{DS}_{e}\) and \(\mathbf{DS}_{r}\), we aim at finding matrices \(\Theta_{e}\), \(\mathcal{P}^{s}_{e}\), \(\Theta_{r}\) and \(\mathcal{P}^{s}_{r}\) such that the function  
\[\phi_{s}(\Theta_{e},\mathcal{P}^{s}_{e},\Theta_{r},\mathcal{P}^{s}_{r}) = \frac{1}{2n_{e}}\|\mathbf{DS}^{s}_{e}-\Theta_{e}\cdot\mathcal{P}^ {s}_{e}\|^{2}_{F}\,+\,\frac{1}{2n_{r}}\|\mathbf{DS}^{s}_{r}-\Theta_{r}\cdot \mathcal{P}^{s}_{r}\|^{2}_{F}\,-\,\gamma(\mathcal{P}^{s}_{e},\mathcal{P}^{s}_{ r}) \tag{1}\]  
is minimized, where \(\|\cdot\|^{2}_{F}\) represents the Frobenius norm and \(\gamma(\cdot,\cdot)\) is the discriminating power between \(\mathcal{P}^{s}_{e}\) and \(\mathcal{P}^{s}_{r}\), as defined in the next section.
## 1 Introduction  
Among the innovations that have changed modern orthodontics, the introduction of new digital technologies in daily clinical practice has had a major impact, in particular the use of 3D models of dental arches. The possibility for direct 3D capture of arches using intraoral scanners or the digitisation of plaster models has brought many clinicians closer to the digital world. The advantages of this revolution are many: the 3D models can be used for dental analysis as precisely as stone cast [1, 2], and virtual models simplify storage procedures by eliminating stone cast.  
Digital 3D models are generally saved and displayed in the STL format (Standard Triangle Language), which has now become the standard format for model evaluation. The STL format does not have color maps, so it is monochrome. If you want to include the color texture as well, the reference formats are obj (Object File Wavefront 3D) and ply (Polygon File Format).  
The use of digital models has made it possible to modify the diagnostic approach, improving and simplifying classic orthodontic analyses [3, 4, 5, 6] and allowing virtual simulation of the patient and dental movements [7, 8]. In addition, the decreased invasiveness of the method and the possibility of overlapping the models allows greater control of therapeutic progress [6, 9, 10]. The possibility of using 3D printing machines and very high precision resins allows the creation of any orthodontic products [11]. There is also the possibility of managing a full digital workflow: there are many devices that can be made without going through the prototyping of a model, but rather by designing the entire device digitally with dedicated software [12; 13; 14]. Finally, the use of digital models also plays a fundamental role in scientific research [15; 16; 17], since stereolithography (STL) surfaces can be compared and analysed without loss of detail by different research groups in different places.  
This digital revolution has clearly affected the orthodontic world, but there are some negative aspects, including the need for the clinician to learn how to use new software. In this context, we can distinguish two main software types: dedicated software (i.e., developed by orthodontic companies) and open-source software. Dedicated software tends to have a much more user-friendly interface, and be easier to use and more intuitive, due to being designed and developed for a non-expert user, but very high rental or purchase costs are an issue. Therefore, younger clinicians with more extensive digital skills have begun to look with increasing interest at open-source software. These programs represent a very interesting resource: some software is totally free or marketed on a 'pay-as-you-go' basis. However, disadvantages include a fairly long learning curve.  
The aim of this study was to present and discuss some of the best-known open-source software for analysis of 3D models and the creation of orthodontic devices: Blue Sky Plan, MeshMixer, ViewBox, and Blender (Table 1).