esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Proposed Adaptive Beaconing System
### Characterization of Node Distribution  
We use the _quadrat statistic method_ to characterize the node spatial distribution [56]. The quadrat method measures how a set of nodes are evenly spaced. We obtain the quadrat value (\(Q\)) which is defined as the ratio of the variance to the mean. When \(Q>1\), it means that vehicles are clustered in 1D and oriented uniformly (scenario 1), while \(Q\) near 1 means vehicles are uniformly randomly distributed in 2D (scenario 2).  
From Table 1, in nondense conditions, if the quadrat value characterized the environment as scenario 1, Set 1 is used, and if the quadrat value characterized the environment as scenario 2, Set 2 is used. If the network is dense, Set 3 is used. Once the protocol selects the suitable set of parameters, it utilizes the fuzzy logic system to produce a final congestion rank.
## 2 Materials and Methods
### Selected Soil Properties and Heavy Metal Detection  
The air-dried soil samples were analyzed for EC (by 1:5/soil:water suspension), pH (by 1:2.5/soil: water suspension), OC (by Walkley-Black method) [21], and CEC (by EDTA extraction method) [22]. The total nitrogen of the soil was measured by Kjeldahl and available phosphorus was measured by extraction with sodium bicarbonate method [23]. Measurement of soluble potassium in soil saturation extract was measured following the method of [PERSON] and Sparks [24]. The concentration of potassium in the extracts was measured using the Jenway PFP7 Flame Photometer.  
Bioavailable cadmium was determined by the single extraction method [25]. To determine Cd concentrations in powdered plant material, 0.5 g of homogenized samples, 0.5 ml of 37% hydrochloric acid, 9.0 mL of 69% nitric acid, and 1 mL of 30% hydrogen peroxide was added into the digestion vessel in an open system. All chemical materials were purchased from Sigma-Aldrich Corporation. After the digestion program, the Cd concentration in samples was analyzed by an atomic absorption spectrophotometer (PinAAcle 900F).  
The tolerance index (TI%) [26] and removal efficiency (RE%) [27] were calculated by the following formulas:  
\[\mathrm{TI}(\%)=\left(1-\frac{\overline{X}_{\mathrm{f}}}{X_{\mathrm{c}}} \right)\times 100 \tag{1}\]  
where \(\overline{X}_{\mathrm{f}}\) is the mean biomass in polluted soil, and \(X_{c}\) is the mean biomass in the control.  
\[\mathrm{RE}(\%)=\left[1-\frac{C}{C_{0}}\right]\times 100 \tag{2}\]  
where \(C_{0}\) and \(C\) are the initial and final extractable Cd concentrations in the soil, respectively.
# New Vector-Space Embeddings for Recommender Systems
## Appendix A Complexity Analysis  
Let \(N\) be the number of vectors (number of users plus the number of items). Also, let \(M\) be the dimension of the vector and let \(R\) be the average number of ratings per user (or average number of ratings per item if we are dealing with items). The computations involve the following for the basic method:  
1. Computing the matrix \(A\) according to Equation (4): The complexity is \(O(M^{2}R)\). Note that the summation is over existing ratings for user \(i\), rather than over all items, so it is not a large summation.
2. Computation of \(b\) according to Equation (5) is \(O(MR)\).
3. Computation of Equation (3) is \(O(M^{3})\).
4. Computation of \(\lambda\) through the bisection search is \(O(M^{3}B)\), where \(B\) is the number of bisection search steps. Typically \(B\) is around 20.
5. Each of these computations is repeated \(N\) times (the total number of vectors or users and items).
6. Typically \(N\) is very large (several thousands), while \(M\), the dimension of each vector is usually small (we take it as 10, 20 or 50). Using this fact and retaining only the largest terms, we get the amount of computation per cycle as approximately equal to \(O(NM^{2}R+NM^{3}B)\).
7. The term \(N\times R\) (number of users times number of ratings per user plus number of items times number of ratings per item) is essentially twice the total number of ratings (twice because we count that from the users' side and also from the items' side).
8. So the total complexity per cycle is \(O(KM^{2}+NM^{3}B)\) where \(K\) is the total number of ratings.
9. We have also implemented an accelerated version of the part that searches and computes \(\lambda\) using eigenvalue decomposition. This relieves us from solving the linear equations \(B\) times. The complexity of the \(\lambda\) search using bisection becomes \(O(NM^{3})\) instead of \(O(NM^{3}B)\) per cycle, i.e., better than the previous basic algorithm. So the total complexity per cycle becomes \(O(KM^{2}+NM^{3})\). The details are not given here to avoid distraction to algorithmic issues.
10. For the off-line algorithm, i.e., applying the algorithm from scratch to train all vectors the complexity equals the preceding times the number of cycles \(C\), i.e., \(O(CKM^{2}+CNM^{3})\). Typically \(C\) is around 10 to 20. For the update phase, i.e receiving an extra rating or user and hence implementing only one cycle, the complexity becomes \(O(M^{2}R+M^{3})\) (for the eigenvalue \(\lambda\) search method).
11. The terms \(C\), \(M\), and \(B\) in the complexity formula are usually small, e.g., 10 to 50. The only impactful terms are \(K\) (the total number of ratings) and \(N\) (the total number of users plus items). These can for example be 100,000 or a million. Whatis important in the complexity analysis is that these terms (i.e., the \(K\) and the \(N\)) turned out to appear as linear terms, not more.
# Review of CMOS Integrated Circuit Technologies for High-Speed Photo-Detection
## 4 CMOS Transimpedance Amplifier (TIA)
### Feedback-Based TIA  
Along with the CG-based TIAs, a shunt-shunt feedback TIA has been widely employed in optical receivers. Figure 25 shows a basic feedback-based TIA topology. With an ideal amplifier, the gain and the bandwidth can be approximated as follows:  
\[R_{T}=R_{F} \tag{12}\]  
\[f_{-3 dB}=\frac{1+A}{2\pi R_{F}C_{PD}} \tag{13}\]  
As (13) indicates, the bandwidth can be enhanced by the higher gain of the amplifier. Therefore, designing an amplifier that exhibits a high gain and a low output impedance is crucial for achieving good performance. In general, this type of TIAs is more advantageous than the CG-based topology in terms of voltage headroom and features a relatively simple architecture favorable for optimization.  
As shown in Figure 26, a variety of versions of the feedback-based TIA have been attempted with different implementations of the amplifier. Figure 26a illustrates a traditional TIA implementation which was first demonstrated with CMOS technology in [76] and achieved 1-Gb/s operation. This topology has been widely used and has succeeded in achieving high-speed operations of 10 and 25 Gb/s [44; 77]. Alternatively, as shown in Figure 26b, the combination of a common-source amplifier, a source follower, and a feedback resistor has been commonly employed to provide a low output impedance [78; 79; 80]. Especially, the work in [80] achieves a 10-Gb/s operation by combining this topology with bandwidth-enhancement techniques. In order to achieve a higher gain, the feedback can be applied to a multi-stage amplifier as illustrated in Figure 26c. In [81], the feedback resistor is connected between the input and the output of the three-stage inverter amplifier. The feedback can also be applied to the differential topology as implemented in [82; 83] with two-stage and four-stage  
Figure 24: CG-feedforward TIA.  
Figure 25: Shunt-shunt feedback TIA.  
amplifiers, respectively. In spite of several advantages of the feedback-based TIAs, they suffer from an inherent stability issue which is particularly evident in the multi-stage topologies.
## 3 HELIOS Concept in the Real Estate Market--Methodology and Implementation
### HELIOS Concept in Use--Empirical Example  
Utilization of a unified and standardized database that aligns with human perception entails the consequences of employing a concept based on NLP modeling. Therefore, it is necessary to employ a suitable method for analyzing transactions encoded in plain text. The simplest and fastest approach is to characterize all entities for word frequency counting in the form of a Bag of Words (BoW). However, this method does not meet the necessary requirements and fails to capture contextual expression.  
Therefore, the authors have decided to employ a solution based on a broader understanding of phrases rather than individual words. In the presented example of utilizing the technology, a simplified methodology for classifying objects into homogeneous groups was developed. To achieve this, machine learning technology based on sentence bidirectional encoder representations from transformers (S-BERT) was utilized. S-BERT transforms the input plain text into a numerical vector representation of the context of the text. This means that each word in the analyzed text is processed within its immediate context and placed within the semantic domain of the S-BERT model. The resulting transformation of the property description database into the S-BERT space constituted a matrix of vectors shaped 4372 \(\times\) 768, where the number of rows corresponds to the number of transactions and the number of columns represents a number of features of the S-BERT model.  
In the next stage, vector clustering was performed on this matrix. To achieve this, the self-organizing map (SOM) method was employed as one of the more efficient methods for classifying vectors of immense dimensions. The cosine similarity model was applied when comparing vectors, which is recommended for vectors in the S-BERT domain.
## 4 Conclusions  
An adaptive AdaBoost algorithm is proposed to evaluate the reliability of multi-failure mode structures. This method shows excellent effectiveness in complex mechanical structures where explicit functions are difficult to establish. The optimal training sample set is obtained by the adaptive method, and then the AdaBoost algorithm is trained on this refined sample set. The generated proxy model not only meets the accuracy requirements, but also can better distinguish whether the target structure is invalid under the given parameters.  
Compared with the traditional Monte Carlo method, this model significantly improves the computational efficiency and can accurately calculate the failure probability in a shorter time.  
The method has good universality. Compared with the general alternative model, the adaptive AdaBoost algorithm proposed in this paper has the advantages of strong applicability, low dependence on the operator's engineering experience, and high precision.  
However, we must be aware that the studies conducted to date have some limitations. The adaptive AdaBoost algorithm proposed by us has not been applied to a multi-failure mode reliability analysis of more complex structures. More and more complex research scenarios put forward higher requirements and challenges for our proposed algorithms.  
However, the method proposed in this paper is of great significance to the exploration of complex mechanical structures, which is helpful to improve the application level of mechanical structures and promote their wide application.  
Conceptualization, F.Z. and Z.Q.; Methodology, F.Z., Y.T., M.W. and X.X.; Software, Z.Q.; Formal analysis, F.Z., Z.Q., Y.T., M.W. and X.X.; Writing--original draft, Z.Q., Y.T. and M.W.; Funding acquisition, F.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Fundamental Research Funds for the Central Universities (NWPU-310202006 zy007).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors upon request.  
The authors declare no conflict of interest.
# Static Response of Double-Layered Pipes via a Perturbation Approach
## 4 Numerical Results
### Distributed Load  
The outcomes for the first load case are shown as functions of \(s/l\) in Figure 4, where (1) the perturbation solution is marked by the dark blue lines; (2) the numerical one obtained via the finite difference method is marked by light blue lines; and (3) that provided by the FEM model is marked by gray lines (note that variables \(a_{g}\) and \(a_{w}\) as given are not directly observable by the FEM model).  
As expected, the effect of the distributed vertical load is evident on the variables \(v\) and \(\vartheta\) (Figure 4b,c), which are directly activated at the linear order. However, due to the nonlinear coupling, the longitudinal displacement \(u\) is activated as well (Figure 4a), together with the local distortion variables (Figure 4d-f). In particular, the cross-section ovalization amplitude \(a_{p}\) attains its maximum approximately at \(s/l=0.35\), where the  
Figure 4: Response in the case of the uniformly distributed vertical load: (a) longitudinal displacement \(u/l\) (\(\times 10^{-3}\)); (b) \(v/l\); (c) transversal displacement \(\vartheta\) (rad); (d) amplitude of ovalization \(a_{p}/R\); (e) amplitude of relative sliding \(a_{g}/R\); (f) amplitude of warping \(a_{w}/R\). Blue lines: perturbation solution; light blue lines: finite difference method; gray lines: FEM.  
cross-section warping \(a_{w}\) assumes zero value. Finally, the inter-layer sliding \(a_{g}\), whose behavior is shown in Figure 4e, shows an almost linear behavior along the most part of the domain and a boundary layer close to the clamp (at \(s=0\)), where it suddenly goes to zero. It is remarked that the perturbation solution, with higher order corrections, well captures the response of the structure: marginal differences appear with the finite difference solution, since the curves are overlapped in the entire domain. Satisfying agreement is found in the comparison with the FEM solution, as well.  
In Figure 5 the evolution of the displacement at the tip \(v(l)/l\) (in abscissa) is shown when increasing the intensity of the load \(\tilde{p}:=\frac{P_{v}l}{\pi R(G_{w}h_{c}+G_{h}h_{i})}\) (in ordinate): the softening behavior due to the Brazier effect is well caught by the finite difference solution, and the perturbation solution correctly catches most of the curve except nearby the limit point. There, the agreement is less satisfying, perhaps due to the large distance of the limit point from the origin, and hence to the necessity of higher order approximation. The FE solution shows satisfying agreement as well, even if it is not very precise nearby the limit point, due to the approaching numerical instability. The dashed line in Figure 5 indicates the load condition corresponding to the previous Figure 4.
## 4 Materials and Methods
### Image Preprocessing  
In this phase, the preprocessing was carried out, whereby we resized the patches to 224 \(\times\) 224 pixels for CNN training, and to adjust the contrast level of the image, power law (gamma) transformation [49; 50] was applied to the resized images. The concept of gamma was used to encode and decode luminance values in image systems. Figure 4 illustrates the clarity of images before and after the application of this operation.  
The dataset splitting was performed for training, validating, and testing the CNN models. The data samples were labeled with 0 (non-cancerous) and 1 (cancerous) for accurate classification and randomly assigned to one of three groups for training, validation, and testing, as shown in Table 1. The dataset used for DL and ML classification holds a total of 6000 samples. Out of these, 3600 were used for training, 1200 for validation, and 1200 for testing. Before the samples were fed to the network for classification, data augmentation was performed on the training set, which enabled analysis of model performance,  
Figure 3: Proposed pipeline for image and feature classification based on a lightweight convolutional neural network (LWCNN) and ensemble machine learning (EML). LR: logistic regression, RF: random forest.  
reduction of overfitting problems, and improvement of generalization [51]. Therefore, to create some changes in the images, some transformations were applied using augmentation techniques, and these included rotation by 90 deg, transposition, random_brightening, and random_contrast, random_hue, and random_saturation, shown in Figure 5c,d. Keras and Tensorflow functions were used to execute data augmentation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Benign (0)** & **Malignant (1)** & **Total** \\ \hline Training & 1800 & 1800 & 3600 \\ Validation & 600 & 600 & 1200 \\ Testing & 600 & 600 & 1200 \\ Total & 3000 & 3000 & 6000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Assignment of benign and malignant samples into datasets for training, validation, and testing.  
Figure 4: Image preprocessing using smoothing and gamma correction. (**a,c**) Original images of benign and malignant tissues, respectively. Here, the images are blurry and exhibit low contrast. (**b,d**) Images after removal of random noise, smoothing, and gamma correction. (**e**) Transformation curve for images with low and high contrast. Because the images in (**a,c**) have low contrast, \(\gamma=2\) was applied to adjust their intensities, obtaining images in (**b,d**) that appear clear and “fresh.” Therefore, the tissue components were more visible after transformation, which was important for CNN classification.  
Figure 5: Randomly selected samples from the training dataset demonstrating data augmentation. (**a,b**) Images of benign and malignant tissues, respectively, before the transformation. (**c,d**) Transformed images from (**a,b**), respectively, after data augmentation.
## 4 Results
### Analysis of the Coaxiality in the Cross-Sections and Quality Ratio of the Rotary Kiln Shell  
To determine the RK's ovality, graphs corresponding to the cross-sections of the kiln shell were created. The interval between sections was set to 1 m. Fifty cross-sections in stationing were generated, where it was possible to make a complete cross-section from the point cloud. The selected graphs are shown in Figure 31. The red circle represents the nominal cylinder; the green curve represents the RK shell's real surface in this cross-section. The results of the analytical segmentation are shown. Differences are triply amplified. The nominal and real centers of the RK shell are also specified and showed. By combining multiple cuts, the whole RK's real longitudinal axis can be investigated in longitudinal section form (Figure 32). The results of determining the minimum and maximum radius of the shell and the calculated values of ovality \(\omega_{0}\) on the selected six cross-sections are given in Table 5. It also shows the deviations of the kiln shell's actual center from the theoretical axis in the transverse direction and in height.  
The eccentricity of the shell from the theoretical axis of the kiln determined from the cross-sections takes values from \(-\)36 mm to \(+\)40 mm in the whole range of the kiln and with regard to the eccentricity of the actual center of the section from \(-\)36 mm to \(+\)38 mm. The ovality parameter \(\omega_{0}\) calculated from all 50 cross-sections, reached the value of 1.396%. Its maximum value in one cross-section is 2.28%. The maximal misalignment of the real center of the whole RK shell in cross-sections is 11 mm. The average eccentricity of the centers in cross-sections is 9 mm. Considering the length of the RK, approximately 80 m, these values are acceptable for the real operation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Cross-Section** & \begin{tabular}{c} **rmin** \\ **[mm]** \\ \end{tabular} & \begin{tabular}{c} **rmax** \\ **[mm]** \\ \end{tabular} & \begin{tabular}{c} **\(\omega_{0}\)** \\ **[\%]** \\ \end{tabular} & \begin{tabular}{c} **\(\Delta\)X** \\ **[mm]** \\ \end{tabular} & \
\begin{tabular}{c} **\(\Delta\)Z** \\ **[mm]** \\ \end{tabular} &
\begin{tabular}{c} **R** \\ **[mm]** \\ \end{tabular} \\ \hline
1 & 2527 & 2558 & 1.22 & 1 & \(-\)9 & 9 \\ \hline
2 & 2526 & 2559 & 1.30 & 2 & \(-\)11 & 11 \\ \hline
3 & 2515 & 2570 & 2.17 & 2 & \(-\)4 & 4 \\ \hline
4 & 2510 & 2560 & 1.97 & \(-\)3 & \(-\)2 & 4 \\ \hline
5 & 2512 & 2547 & 1.38 & \(-\)3 & \(-\)4 & 5 \\ \hline
6 & 2515 & 2547 & 1.23 & \(-\)3 & \(-\)8 & 9 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Radiuses and ovality calculation in selected cross-sections.  
Figure 31: The selected cross-sections of the RK shell. Linear units of the ovality [mm].
## 3 Materials, Methods, and Findings
### RAG with the LSTM Hybrid Model
#### CNN Model  
The Convolutional Neural Network (CNN) model is effective in capturing local features in text classification tasks. In this study, the CNN model is structured with 1D convolutional layers and trained for 30 epochs. The dropout rate was set to 0.4. Figure 18 shows the complexity matrix of the CNN model.  
The model correctly classified 95 instances in the \"no verbal violence\" class and incorrectly labelled only 7 instances as \"verbal violence present\" (false positive). In the \"verbal violence present\" class, the model made 96 correct predictions and misclassified 12 instances as \"no verbal violence\" (false negative). These results show that the overall accuracy rate of the model is 90.95%. The false positive rate is low at 6.86%, which indicates that the model performs very reliably in the \"no verbal violence\" class. The false negative rate is 11.11%, indicating that the model keeps the rate of missing the \"verbal violence present\" class at a very low level.  
Figure 17: TF-IDF Nave Bayes confusion matrix.