esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Offline Handwritten Text Recognition
### Text Decoding with Linguistic Knowledge  
We can perform text recognition of images for digital medium only using the character predictions generated by the optical model, specifically by CTC decoding [44]. However, from a linguistic perspective, we have an initial understanding of the context and what results the HTR system should return, which means in a set of predefined linguistic context about the texts to be decoded [18].  
A conventional approach to modeling this contextual information is through the language model and its dictionary (vocabulary). This component is responsible for defining the set of words that the system can recognize and the words that have not been defined in this set are considered out-of-vocabulary [51]. Thus, depending on the context, the size of the dictionary can vary between small (dozens), medium (hundreds), and large (thousands) of defined words, in the latter case, it is considered an unrestricted text recognition [51].  
Statistical approaches, specifically \(N\)-gram models, remains conventional to HTR systems [52, 53, 54]. The \(N\)-gram language model consists of a probabilistic distribution of characters or words, in which the probability of a term depends on the previous terms. The \(N\)-gram model follows a Markovian assumption to make it possible approximate the probability of a term, given the previous \(n-1\) terms [55].  
Currently, a common decoder in HTR systems, optical model output, and language model, are integrated by means of the Hidden Markov Model (HMM), which disregards the CTC decoding step and takes into account only the RNN output [2,21,56,57]. The role of HMM in a decoding process is to recover the most likely sequence of states that produce a given observed sequence [58]. Regarding the HTR context, the HMM retrieves the most likely sequence of words, given the sequence of the observed characteristic vector [21,57].
## 2 Theoretical Background
### Waveguides of Arbitrary Cross Section Filled by M Metallic Conductors
Figure 3 shows the condition number \(\mathit{cond}\ A_{N}(kb)\) as a function of the non-dimensional wave number \(kb\), for these four values of eccentricity and truncation number \(N=256\). As can be seen, the spectral dependence of \(\mathit{cond}\ A_{N}(kb)\) is highly sensitive to the roots of the characteristic equation \(\mathit{det}A_{N}=0\), indicated by the sharp resonance peaks at those points where \(kb\) coincides with one of the roots of this equation. Ideally, in the absence of the heat losses in the walls of the waveguide, the resonance peaks of \(\mathit{cond}\ A_{\infty}(kb)\) occur exactly at those values \(kb\), which match precisely the zeros of the characteristic equation \(\mathit{det}A_{\infty}=0\) and the resonance maxima of \(\mathit{cond}\ A_{\infty}(kb)\) should be of infinite magnitude. In practice, when we employ the truncated equations, the magnitude of the resonance peaks is finite and increases with the growth of the truncation number \(N\); the accuracy of the calculated cut-off wavenumbers strictly depends on its value. The second kind nature of the Fredholm matrix system obtained by application of the MAR to the waveguide problem ensures that the algorithm for calculating the cut-off wavenumbers with any prescribed (i.e., pre-specified) accuracy is reliable and fast converging. The scheme for calculating the cut-off wavenumbers is as follows. First, we calculate the spectral dependence \(\mathit{cond}\ A_{N}(kb)\) in a fixed interval of \(kb\) values locating all (approximate) values of the cut-off wavenumbers: in Figure 3, two or three values are observed. Utilizing a suitable choice of increment \(\Delta kb\) of the wavenumber (usually \(\Delta kb\) lies in the range 0.0001 to 0.001) and increasing the truncation number \(N\) as necessary (as a rule \(N=128\)), the resolving power of the method is sufficient to ensure that no modes of the spectrum are missed.
# Addressing ZSM Security Issues with Blockchain Technology
## 3 The ZSM Framework Overview: Architecture and Open Security Issues
### Description of ZSM Architecture and Main Elements  
The main blocks in the ZSM architecture are illustrated in Figure 1 and are the following: the management services, the management functions, the management domains (MD), the end-to-end (E2E) service management domain, the cross-domain integration fabric and the data services. Management services are the core component as they can be offered and consumed by other services and ZSM participants to support network services and applications. The management services consumption and/or offering is conducted using management functions as presented in Figure 1. A management function can either be a \"management service producer\", a management \"service consumer\", or both at the same time. Moreover, management domains are used to define different areas of responsibility that belong to different ZSM participants. Each management domain can use its own management services or services offered for consumption by other management domains using the ZSM framework. The E2E service management domain depicted in the upper part of Figure 1, is a special management domain that provides end-to-end management of customer-facing services, composed of the customer-facing or resource-facing services pro vided by one or more management domains. The \"cross-domain integration fabric\" located at the center of Figure 1 is responsible for the interoperation and communication between the management functions within or across different domains. The registration, discovery and invocation of management services and the communication between management functions are implemented by the integration fabric. Finally, data services enable consistent means of shared management data access and persistence by authorized consumers across management services within or across management domains.  
Figure 1: The ZSM architecture [34].  
Considering the role of the aforementioned blocks, an example of ZSM operation is examined: supposing there is a multi-domain network where a network provider (say NP1) is responsible to support a demanding network service with characteristics that require a specific set of resources to provide the necessary QoS level. In the presented scenario, assume that NP1's resources are unavailable in the area where the service must be deployed. According to the standard's functionality, NP1 exploits the ZSM elements, such as management functions and cross-domain integration fabric, to find and consume a management service offered by another network provider (say NP2), which implements the necessary actions to cover the needs that NP1 has defined.
# A Proposal for a New Python Library Implementing Stepwise Procedure
## 2 Background
### Applications of the Stepwise Procedure in Multiple Regressions  
The academic literature presents several relevant applications of the stepwise procedure in multiple regression machine learning models, as shown in Table 1.  
Based on the examples of the application of the stepwise procedure in machine learning models of multiple regressions, a wide range of studies are observed, covering several areas of research.  
Its use aims to identify and select the most relevant and statistically significant variables, in the most diverse applications, for example, in the prediction of meteorological events, understanding of factors that affect prices and production in different sectors, such as real estate and agriculture, as well as providing valuable information in environmental fields, such as studies on soil, deforestation and air quality.  
The approach also has applications in medicine, helping to estimate ages, predict disease and analyze the impact of health conditions on drug responses. Its versatility allows employment in socioeconomic and urban issues, such as impact and balance analyses in various contexts.  
Notably, this literature review is not exhaustive since the stepwise procedure has an extensive and multifaceted scope. However, the various studies analyzed in this section allowed us to demonstrate its multidisciplinary applicability.  
From predicting meteorological phenomena to analyzing socioeconomic impacts and evaluating health issues, the stepwise procedure has proven to be a versatile and valuable tool for the judicious selection of statistically significant variables in various research fields. In the study of sample data within applied sciences, it is essential to select only those variables that demonstrate statistical significance. A model constructed from sample data that includes betas that are not statistically significant, particularly for predictive purposes, cannot be considered the final model [3; 6; 24; 26].
# Effects of Vibration on Adjacent Pipelines under Blasting Excavation
## 4 Numerical Simulation and Dynamic Response Analysis
### Numerical Calculation and Verification  
Ten monitoring points were arranged to verify the reasonableness of the simulation results on the numerical model according to the actual situation of monitoring deployment in Section 2.  
Table 7 shows the error rate of the simulation results compared to that of the field results. It can be observed the maximum value of the simulation results appeared at the T5 monitoring point, being a value of 1.40 cm/s, and the minimum value appeared at the T10 monitoring point, being a value of 0.78 cm/s, which was relatively consistent with the monitoring results. In addition, it was found the numerical simulation results were more significant than the monitoring results, which was caused by the inability to comprehensively consider the impact factors of the weakening vibration of the model. The differences between the results of the simulation and field monitoring at each monitoring point were minor and within a reasonable range [17]. The maximum error rate was 7.69% at monitoring point T10, and the minimum value was 2.86% at monitoring point T5. Although there were some errors, they were within acceptable limits [6]. Using a triangular equivalent load to simulate the effects of explosions can obtain effective results; thus, this method can be used as the basis for subsequent dynamic analysis.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Points**} & \multicolumn{3}{c}{_PPV of Numerical Simulation (cm/s)_} & \multicolumn{3}{c}{_PPV of Monitoring (cm/s)_} & \multicolumn{1}{c}{_Error Rate_} \\ \cline{2-10}  & **X** & **Y** & **Z** & **X** & **X** & **Y** & **Z** & **Resultant** & **\%** \\ \hline T1 & 0.65 & 0.76 & 0.57 & 1.
# Fast Pre-Diagnosis of Neoplastic Changes in Cytology Images Using Machine Learning
## 4 Discussion
### Photo Background  
In the analyzed cytological preparation, apart from neoplastic cells, there may also be inflammatory cells and erythrocytes in the background. Experienced vet doctors hardly ever confuse them with the cells of interest. On the other hand, the ML-based model sometimes confuses unimportant cells with ones subjected to pathological cancerous changes. A solution for this problem could be training a model using a dataset with all objects (including 'background' ones) annotated, either for detection or segmentation.
# An Authentication Method for AMBTC Compressed Images Using Dual Embedding Strategies
## 1 Introduction
The generated codes were divided into two equal parts of 3 bits, and embedded into the bitmaps using matrix encoding. To avoid damages to the bitmaps caused by embedding, the positions of to-be-flipped bits in the bitmap were recorded and embedded into the quantized values. This method resulted in higher image quality as well as higher detection accuracy. Methods [25; 26; 27] could detect most tampering and also provided satisfactory image quality. However, some special tampering might escape detection using these methods. For example, the generation of authentication codes in [25] was independent of block position information. Therefore, tampering could not be detected when two blocks were interchanged. In addition, the embedding techniques of these methods did not take the smoothness of blocks into account, which resulted in low image quality. To improve image quality and security, this paper proposes an authentication method for AMBTC images using dual embedding strategies. Blocks are classified into smooth and complex ones according to a predefined threshold, and appropriate embedding strategies are employed based on their smoothness. Blocks are classified as smooth blocks for quantized values less than the threshold, or otherwise, as complex ones. Both quantized values and bitmaps are used to carry authentication codes. However, the difference of quantized values of complex blocks can be large and flipping bits of bitmaps can result in significant distortion. Therefore, the authentication codes of complex blocks will be embedded into the quantized values. In contrast, smooth blocks have lower distortion from flipping bits of bitmaps, and thus authentication codes will be embedded into bitmaps. Since the dual embedding strategies are based on block smoothness, the aim is to obtain higher image qualities. Moreover, the generation of authentication codes in the proposed method is related to the to-be-protected contents, which will increase the probability of detecting some special tampering. The rest of this paper is organized as follows. Section 2 describes related works and Section 3 presents the proposed method. Sections 4 and 5 show the experimental results and conclusions, respectively.
## 3 Real-time Capable Robust Maximum Power Point Tracking
### Algorithm for Combined Parameter Identification and Maximum Power Point Tracking
: _To guarantee feasible, i.e., non-negative optimized current values \(I_{k}^{*}\), the update rule in Equation (34) needs to be parameterized by a suitable combination of \(\eta\) and \(\sigma_{\mathrm{FEL,min}}\) such that both inequalities_  
\[-\mu_{x,2,k}^{\mathrm{e}}+\eta\cdot\sqrt{\mathbf{e}_{2}^{T}\cdot\mathbf{C}_{x,k}^{\mathrm{e}}\cdot\mathbf{e}_{2}}<0 \tag{37}\]  
_and_  
\[\mu_{x,3,k}^{\mathrm{e}}-\eta\cdot\sqrt{\mathbf{e}_{3}^{T}\cdot\mathbf{C}_{x,k}^{\mathrm{e}}\cdot\mathbf{e}_{3}}<0 \tag{38}\]  
_are satisfied, while concavity of the approximated current-power characteristic is ensured for each estimate with \(\mu_{x,3,k}^{\mathrm{e}}<0\), making Equation (38) satisfied. If possible violations of the condition in Equation (37) are detected during the application of the maximum power point tracking, the computed current \(I_{k}^{*}\) is rejected and the a priori chosen value of \(\eta\) is reduced by a line search procedure to make the inequality in Equation (37) feasible._  
**Remark 4**. : _If the extended system model in Equation (26) is used as a substitute for the quadratic approximation in Equation (12), analytic solutions such as Equation (32) usually no longer exist. The maximization of Equation (26) can then be performed by numerical techniques such as gradient-based or gradient-free extremum seeking approaches [27]. In such cases, real-time capability can be achieved by means of implementations that are based on an online sensitivity analysis (cf. [14; 15])._
## 2 Materials and Methods
### Radiomic Feature Extraction  
One hundred and five classical radiomic features were computed within the tumor on the CT images using PyRadiomics open-source software (version 3.7.7) [36], implemented in Python and compliant with the Image Biomarker Standardization Initiative (IBSI) indications [7]. Specifically, the following features were extracted from the original non-filtered images: 14 shape features, 18 first-order statistics, 22 from the gray-level co-occurrence matrix (GLCM), 16 from the gray-level run-length matrix (GLRLM), 16 from the gray-level size-zone matrix (GLSZM), 14 from the gray-level dependence matrix (GLDM), and five from the neighboring gray-tone difference matrix (NGTDM).  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **LUNG1** & **LUNG2** \\ \hline Image parameters & & \\ Pixel size & \(0.977\times 0.977\) mm\({}^{2}\) & \(0.604\times 0.604\)–\(0.865\times 0.865\) mm\({}^{2}\) \\ Slice thickness & 3.0 mm & 0.8–3.0 mm \\ \hline Patient information & & \\ Number of patients & 403 & 130 \\ Age (years) & 68.2 \(\pm\) 10.1 & 69.7 \(\pm\) 8.6 \\ Gender & & \\ Male & 279 (69.2\%) & 98 (75.4\%) \\ Female & 124 (30.8\%) & 32 (24.6\%) \\ Histology & & \\ Adenocarcinoma & 47 (11.7\%) & 102 (78.5\%) \\ Squamous Cell & 149 (37.0\%) & 25 (19.2\%) \\ Large Cell & 108 (26.8\%) & 0 (0\%) \\ Other & 99 (24.6\%) & 3 (2.3\%) \\ T stage & & \\ T1 & 88 (21.8\%) & 68 (52.3\%) \\ T2 & 150 (37.2\%) & 44 (33.8\%) \\ T3 & 49 (12.2\%) & 13 (10.0\%) \\ T4 & 116 (28.8\%) & 5 (3.8\%) \\ Median overall survival time (years) & 1.5 \(\pm\) 2.2 & 3.9 \(\pm\) 1.8 \\ Survival \textgreater{} 2 years & 161 (40.0\%) & 102 (78.5\%) \\ Survival \textless{} 2 years & 242 (60.0\%) & 28 (21.5\%) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of CT images and patients from LUNG1 and LUNG2 datasets.  
Figure 1: Examples of CT images and tumor volumes (marked with a green area) from LUNG1 (**upper row**) and LUNG2 (**lower row**) datasets: axial (**A**,**D**), sagittal (**B**,**E**), and coronal (**C**,**F**) views.  
Image intensities were discretized before computing texture matrices using a fixed bin width of 25, as suggested by the IBSI guidelines for CT intensity quantization [7]. Then, features were computed in the 3D space after resampling the images to isotropic voxels of \(1\times 1\times 1\) mm.  
Radiomic features are highly sensitive to differences in scan protocols; therefore, since LUNG1 and LUNG2 images came from different institutions, radiomic features may present different distributions due to the batch effect. Hence, a harmonization method was applied to the extracted radiomic features, a standard procedure in radiomics to increase model robustness [37]. Specifically, the BM-ComBat method, a modified version of the original ComBat method [38], allows transforming features to a reference batch (in this case, the LUNG1 dataset), using the Bayes theorem to estimate the scanner effect with a bootstrap strategy by matching the statistical distributions of the feature values without altering the biological information [37].
## 3 Method and Materials
### Reference Signals  
We apply the modified 0-1 test to different signals described below that represent the typical behavior of time series concerning regularity in the repeating patterns of the data. The classification of the signals is according to the order of less to greater complexity, in accordance with the aforementioned approach, from the most regular or periodic evolution to the most random one. Figure 1a-e show the amplitude variation, normalized to the interval \([0,1]\), of all reference signals in the time domain, and Figure 1f-j represent the Power Spectrum Density (PSD) of all of them.
# Small-Scale Foreign Object Debris Detection Using Deep Learning and Dual Light Modes
## 4 Conclusions  
The existing deep learning algorithms still have some problems when applied to FOD detection tasks. In visible light images, the targets are minuscule, with texture and color features that are not prominent, and the background features are noisy and diverse. This paper addresses the above problems and draws the following conclusions:  
1. In order to make up for the problem of missing target information in visible images, the method of complementing the information of visible images and infrared images is adopted to enrich the feature information of small targets;
2. A multi-attention mechanism is proposed to suppress the interference of background features for effective recognition of small targets. This method not only improves the accuracy of small target detection during the day but also takes into account the detection performance of small targets at night. The model detection speed can also meet the real-time detection demand of FOD;
3. A comprehensive experimental validation shows that the method in this paper is superior to other methods for detecting small-scale FOD. Although the model parameters are not the lightest, they are still at a high level and meet expectations in terms of quantitative results and visualization effects. The proposed model achieves a detection speed of 36.3 frame/s. The model also attains an average accuracy of 91.1%, which is 7.4% higher than the baseline network.  
The method in this paper has a promising application in practical FOD detection. In the future, we plan to augment the datasets across more weather variability (rain, snow, hail, etc.), broader foreign object scales and categories, as well as expanded lighting parameters (intensities, source types, spectra). Considering global airport diversity, targeted studies will also be initiated with high-altitude sites like Kunming Changshui Airport. These efforts seek to continuously boost model generalization and adaptability to novel environments.  
Conceptualization, H.X.; methodology, Y.M.; software, Y.M.; validation, C.C. and P.L.; investigation, C.C. and W.H.; resources, W.H.; writing--original draft preparation, Y.M.; writing--review and editing, H.X. and L.W.; visualization, Y.M.; supervision, L.W.; project administration, H.X.; funding acquisition, H.X. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (No. 12262015).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.
# Integrated DVB-X2 Receiver Architecture with Common Acceleration Engine
## 3 DVB-X2 Receiver Architecture
### LDPC Decoder  
The belief-propagation or sum-product algorithm (SPA) provides a powerful method for decoding LDPC codes [7]. However, a large amount of computation is required for check node operations [8]. The min-sum algorithm is introduced to reduce the complexity of the check node operations of the SPA [9]. The layered decoding algorithm is a form of partially parallel decoding by dividing a parity check matrix into multiple layers and it has a fast convergence rate [10]. LDPC codes in DVB-T2, DVB-C2, and DVB-S2 are quasi-cyclic (QC) LDPC codes, which suit the H/W implementation well due to the regularity in parity check matrices. By performing specific permutation on the original parity check matrix of the LDPC code, its parity check matrix can be transformed into a pattern similar to QC-LDPC codes with 360 by 360 sub-blocks.  
The LDPC decoder consists of a log likelihood ratio (LLR) memory update unit, two permutation network units, and P processing element (PE) units as shown in Figure 7. The LLR memory update unit is used for storing the initial and updated LLR values for each bit of a code word. Two permutation network units are used for shuffling and reshuffling messages, and they are implemented using two barrel shifters. The PE unit computes messages of variable and check nodes using the layered decoding scheme. It consists of two MIN_GEN units, a check node update (CNU) unit and random access memory (RAM) to store the check node messages. Two MIN_GEN units generate check node messages for previous and current iterations using the check node messages stored in RAM. The CNU unit finds the minimum value among variable node messages. The LDPC decoder is implemented with a partially parallel architecture by employing 90 PE units and it decodes the LDPC code using 6-bit LLR messages.  
Figure 6: Block diagram of the of DVB-S2 demodulator.  
Figure 7: Block diagram of Low Density Parity Check (LDPC) decoder.