esa-sceva/satcom-chunk-collection huggingface dataset:
# A Dermoscopic Inspired System for Localization and Malignancy Classification of Melanocytic Lesions
## 2 Methods
### Extraction and Classification of Features
#### 2.2.5 Classification and Diagnosis  
The features selected from the groups of \(f_{shape}\), \(f_{color}\), \(f_{texture}\), \(f_{PN}\) are concatenated to benefit from the complimentary information captured by the feature types. The classification of the observations into two classes classifier (benign and malignant) \(C\) yielding the largest probability \(P(G)\) which is performed using a probabilistic SVM, as given in (10).  
\[C=\max\Big{(}P_{shape}(G),\ P_{color}(G),P_{texture}(G),P_{PN}(G),\Big{)} \tag{10}\]  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Feature Type** & **Description (Number)** \\ \hline Shape & Shape Asymmetry Index (1), Compactness Index (1), and Fractal Dimensions (1) \\ \hline Color & Color Asymmetry Index (4), Color similarity score (6), Color variation (8), color entropy (4), color co-relation (12), and PCA (3), \\ \hline Texture & Coarseness (1), Contrast (1), and Directionality (1) \\ \hline Dermoscopic Structure & Pigment Network (5) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of the features extracted.  
Figure 8: Comparison of pigment network detection: (**A**) dermoscopic images with pigment network marked, (**B**) pigment network masks detected by [PERSON] et al. [15], (**C**) pigment network masks detected by the proposed method.  
Figure 7: Detection of pigment network: (**A**) dermoscopic image, (**B**) pigment network mask detected, (**C**) corresponding mask ((**A**) overlaid on (**B**)).  
\(P_{shape}(G)\), \(P_{color}(G)\), \(P_{texture}(G)\), \(P_{PN}(G)\) indicates the cumulative probabilities of shape, color, texture and pigment network features. The features are concatenated and used to train a single SVM classifier. [PERSON]'s method is used for the computation of posterior probabilities [26; 27]. Linear kernel is used to map the scores.
## 4 Discussion  
### Salinity Type Affected Biomass Growth Only in Salt-Sensitive Species  
The comparison between NaCl and seawater salinity stress did not lead to any significant difference in the growth of the salt tolerant _Tetragonia_. In fact, the two stresses at comparable EC did not lead to significant differences either in the FW or in the DW and the yield. In addition, for these growth parameters, both M-SW and M-SA led to a biomass production comparable to that of the control, whereas H-SW and H-SA to significantly lower values, thereby suggesting a salinity threshold among the two tested groups of EC: 14 and 24 dS m\({}^{-1}\). This is in agreement with results obtained by [PERSON] et al. [11],  
Figure 5: Phenolic concentration in the edible leaves of _Tetragonia_ (**a**) and lettuce (**b**). Values are means (\(n\) = 4) \(\pm\) SEM measured at the end of the crop cycles. Different letters indicate significant differences at \(p\) < 0.05 (Tukey’s test).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Treatment**} & _Tetragonia_ & **Lettuce** \\ \cline{2-3}  & & \(\mu\)**g g\({}^{-1}\) FW** \\ \hline C & 221.2 \(\pm\) 84.7 b & 122.4 \(\pm\) 35.6 b \\ M-SW & 1006.3 \(\pm\) 72.7 a & 123.3 \(\pm\) 4.6 b \\ M-SA & 1226.2 \(\pm\) 89 a & 427.2 \(\pm\) 165.1 b \\ H-SW & 1067.8 \(\pm\) 99.4 a & 816.2 \(\pm\) 31 b \\ H-SA & 1289.8 \(\pm\) 163.8 a & 3168.1 \(\pm\) 580 a \\ \hline \hline \end{tabular} Values are mean values (\(n\) = 4) \(\pm\) SEM measured at the end of the crop cycles and expressed in \(\mu\)g on gram of fresh weight. Different letters indicate significant differences assessed at \(p\) < 0.05 (Tukey’s test).  
\end{table}
Table 5: Proline concentration in the edible leaves.  
assessing for _Tetragonia_ a salinity tolerance threshold up to EC of 18 dS m\({}^{-1}\). Lettuce, on the other hand, showed a different response to the stress. Firstly, both M-SW and M-SA led to significant reductions in both FW and yield compared to control conditions (even if the two types of salt stress tested led to comparable results), as expected with lettuce being a salt-sensitive crop [9]. Even more important for the goal of the current trial, in the case of high-salinity treatments, the two stress types at comparable EC resulted in a significantly different FW, DW and yield, with H-SW showing higher biomass than its NaCl corresponding H-SA. These findings would support the findings of early research in which seawater was proved a less stressful growing medium than other media characterized by the same salinity but lacking most macro- and microelements dissolved in seawater [22]. In addition, similar results have been observed in _Lactuca sativa_ L. cv. Mother-red, where a significant higher salt resistance was found when grown with seawater (EC of the culture was 10.6 dS m\({}^{-1}\)) compared to NaCl (EC 12.6 dS m\({}^{-1}\)) [24], even if the reason is probably also connected to the difference in EC between the two treatments.
# Bi-LS-AttM: A Bidirectional LSTM and Attention Mechanism Model for Improving Image Captioning
## 3 Methodology
### Bi-Lstm  
Both RNN and LSTM units leverage past temporal information to predict forthcoming outputs. However, in some instances, the desired output is associated with not just the previous state but also the future state. For instance, predicting a missing word within a textual context requires comprehension of both the preceding and succeeding context. This dual  
Figure 2: LSTM cell structure.  
directional context analysis provides a more comprehensive and accurate interpretation, thereby achieving a genuine contextual understanding and decision-making process.  
In the traditional LSTM, the forecast of the word \(x_{t}\) using the optical context \(V\) and historical context \(x_{1:t-1}\) is performed by estimating \(\log P(x_{t}|V,x_{1:t-1})\). However, in the Bi-LSTM with attention, the prediction of the word \(x_{t}\) depends on the forward and backward results of separately maximizing \(\log P(x_{t}|V,x_{1:t-1})\) and \(\log P(x_{t}|V,x_{t+1:t})\) at time \(t\).  
In the Bi-LSTM cell structure, the input sequence is processed in both forward and backward directions by two distinct LSTM cells to extract features. As illustrated in Figure 3, the output vectors generated are amalgamated to form the final word representation. The core concept behind the Bi-LSTM cell is to facilitate the capture of features at any given time point, encompassing information from both preceding and succeeding time steps. It is worth noting that the two LSTM units within the Bi-LSTM cell operate with independent parameters while sharing a common word-embedding vector space.
###### Abstract  
Evapotranspirationpiration (ET) is a major component of the water budget in Bottomland Hardwood Forests (BBFs) and is driven by a complex intertwined suite of meteorological variables. The understanding of these interdependencies leading to seasonal variations in ET is crucial in better informing water resource management in the region. We used structural equation modeling and AIC modeling to analyze drivers of ET using Eddy covariance water flux data collected from a BHF located in the Russel Sage Wildlife Management Area (RSWMA). It consists of mature closed-canopy deciduous hardwood trees with an average canopy height of 27 m. A factor analysis was used to characterize the shared variance among drivers, and a path analysis was used to quantify the independent contributions of individual drivers. In our results, ET and net radiation (\(\mathrm{R_{n}}\)) showed similar variability patterns with Vapor Pressure Deficit (VPD) and temperature in the spring, summer, and autumn seasons, while they differed in the winter season. The path analysis showed that \(\mathrm{R_{n}}\) has the strongest influence on ET variations via direct and indirect pathways. In deciduous forests like BHFs, our results suggest that ET is more energy dependent during the growing season (spring and summer) and early non-growing season (autumn) and more temperature dependent during the winter season.  
Keywords:bottomland hardwood forests; evapotranspiration; latent heat flux; AIC modeling; Eddy covariance; Lower Mississippi Alluvial Valley; structural equation modeling +
Footnote †: journal: _Atmosphere_
# A General Calibration Method for Dual-PTZ Cameras Based on Feedback Parameters
## 3 The Dual-PTZ Camera Model  
The schematic diagram of the general, but not idealized, model for dual-PTZ cameras in this paper is shown in Figure 1a. The positions of the left camera and right cameras are relatively fixed, and they can be rotated in the horizontal and pitch directions, respectively. \(\mathbf{v}_{l}\), \(\mathbf{h}_{l}\) and \(\mathbf{v}_{r}\), \(\mathbf{h}_{r}\) are the direction vectors of the left and right cameras' horizontal rotation axis and pitch rotation axis, respectively. They can also be collectively referred to as the rotation vector. \(O_{l}\) and \(O_{r}\) are the optical centers of left and right cameras, and \(O_{l}-xyz\) and \(O_{r}-xyz\) are the coordinate systems of the left and right camera, respectively. The camera coordinate system is a right-handed coordinate system, and the origin of the coordinate system is the optical center. In our model, the optical center does not coincide with the horizontal and pitch rotation vectors, and the horizontal and pitch rotation vectors are not absolutely perpendicular. In the process of zooming, the optical center moves slightly back and forth along the optical axes (\(z_{l}\) and \(z_{r}\)) in order to obtain images at different depths.  
We built different rotation models for PTZ cameras with different slave relationships between the horizontal and the pitch axes (as seen in Figure 1b,c) and derived different calculation formulas for different models.  
Separately, we will take the intrinsic and extrinsic parameter variation models of dual-PTZ cameras into consideration.
## 4 Experimental Setup  
An attractive solution for implementing an imaging system described in Section 3 is the _dual-output_ or _gated_ CMOS sensor [32, 33]. Each pixel of such sensor has a single photodiode with duplicated charge storage and readout circuitry. Storages can be switched at the stage of integration by the external modulation signal of considerably high frequency, which can be simultaneously used as a laser strobe. Using this technique, we could have two separate images captured during the same exposure time: one with enabled laser and another with disabled laser. At the readout phase, one image is subtracted from another, resulting in image containing only the difference induced by the laser illumination. Currently, the only disadvantage of this approach is poor availability of gated sensors.  
To prove the concept of modulated acquisition, we have built our experimental setup (Figure 2) around a fast Velociraptor (Optomotive mechatronics Ltd, Ljubljana, Slovenia) industrial camera [34] based on high-speed conventional commercially available non-gated CMOS sensor CMOSIS CMV2000 and FPGA processing core. Instead of performing \"in-pixel\" demodulation, the demodulation logic has been programmed in FPGA: conditional adder and modulation register were introduced at the data path from the sensor to RAM.  
The camera makes series of snapshots at high frame rate (up to 600 fps) with short exposure time. Each time a snapshot is taken the laser is turned ON or OFF according to the control (highest) bit of pseudo-random binary sequence \(c\in\{0;1\}\) of length \(N\) being cyclically shifted in modulation register. Through the full cycle of the sequence (_frame time_), each pixel of the currently captured image (_sub-frame_) is conditionally added to the cumulative sum of corresponding pixels of the previously captured sub-frames. If the control bit is 1 (laser is enabled) the pixel value is added to the sum, otherwise (if laser is disabled) it is subtracted respectively.  
Figure 2: Experimental setup: (**a**) hardware implementation and (**b**) block scheme.
###### Abstract  
The distributed denial of service (DDoS) attack is one of the most pernicious threats in cyberspace. Catastrophic failures over the past two decades have resulted in catastrophic and costly disruption of services across all sectors and critical infrastructure. Machine-learning-based approaches have shown promise in developing intrusion detection systems (IDSs) for detecting cyber-attacks, such as DDoS. Herein, we present a solution to detect DDoS attacks through an ensemble-based machine learning approach that combines supervised and unsupervised machine learning ensemble frameworks. This combination produces higher performance in detecting known DDoS attacks using supervised ensemble and for zero-day DDoS attacks using an unsupervised ensemble. The unsupervised ensemble, which employs novelty and outlier detection, is effective in identifying prior unseen attacks. The ensemble framework is tested using three well-known benchmark datasets, NSL-KDD, UNSW-NB15, and CICIDS2017. The results show that ensemble classifiers significantly outperform single-classifier-based approaches. Our model with combined supervised and unsupervised ensemble models correctly detects up to 99.1% of the DDoS attacks, with a negligible rate of false alarms.  
network security; DDoS attack detection; machine learning; ensemble
# SAR Target Incremental Recognition Based on Hybrid Loss Function and Class-Bias Correction
## 3 Methodology
### Old Sample Preservation by Herding Selection  
The old sample preservation approach refers to retaining a small portion of the old classes data during incremental learning. According to existing studies, keeping a tiny subset of old classes is able to significantly improve the recognition rate of old classes by the model. Algorithm 1 describes the selection process of representative samples. When one or a group of new target classes are added in the training phase, the most representative subset of samples is selected and stored in memory. The number of representative samples for each class, denoted by \(k\), is set the same (\(k=20\) in this paper).  
The old sample preservation strategy in this paper is based on herding selection [34]. According to the distance from each sample to the mean value of the sample, a ranked list of samples within the class is generated. For each class, select the samples closest to the center of the current class, namely the top \(k\) samples among them are selected based on the list of samples after sorting. The advantage of herding selection is that the final sample mean is closest to the actual class mean. In terms of the mean, these samples we selected are most representative and able to represent the class better. Therefore, this paper chooses this method to reserve representative samples for each target class.  
```
Input: Image set \(\boldsymbol{x}=\{x_{1},x_{2},...,x_{n}\}\) of class label \(y\); Samples size \(k\); Input: Trained model \(M\);  Use model \(M\) to obtain the feature map \(F_{\boldsymbol{x}}\) and feature function \(\phi\);  Let \(\mathbf{d}\) be an empty set;  Compute the class mean \(\mu\) through \(F_{\boldsymbol{x}}\); for\(1\), \(2\), \(3\), \(...\), \(k\)do \(d_{k}\leftarrow\underset{x\in\boldsymbol{x}}{\arg\min}\left\|\mu-\frac{1}{k}[ \phi(x)+\sum\limits_{j=1}^{k-1}\phi(d_{j})]\right\|\) endfor \(\mathbf{d}\leftarrow(d_{1},...,d_{k})\); Output: Representative samples set \(\mathbf{d}\);
```  
**Algorithm 1**Old Sample Preservation Process by Herding Selection.
## 2 Search Strategy  
The scope of the manuscript covers the mechanism of reaction of both titanium degradation and reversal processes by photofunctionalization, and the clinical efficacy of UV pre-treatment of titanium implants. The inclusion criteria involve original research or review articles published in any language and no restriction with regard to the publication year was applied including 'Online-early', 'Ahead of print', and 'In-press' articles. Given that the knowledge gap and the literature may be diffuse due to the robustness of available evidence, we considered adopting a scoping review approach. The following questions: \"how does titanium aged?\" and \"what are the mechanisms of reversing the aging of titanium?\" and \"how effective the UV light treatment of implant surface improved the clinical performance of the implants?\" were considered in the search strategy. The search was made on the MEDLINE (via PubMed), Embase (via Ovid), and Scopus databases for relevant articles. The strategy used a combination of MeSh terms and free text words of the following keywords: 'photofunctionalization', 'titanium degradation', 'titanium surface', 'ultraviolet' and 'UVA', 'UVC' and 'ultraviolet light' (different key words were connected with OR and AND). Example of the search was: 'photofunctionalization' (All Fields) AND 'dental implants (MeSH terms), (\"ultraviolet rays\"[MeSH Terms] OR (\"ultraviolet\"[All Fields] AND (\"dental implants\"[MeSH Terms] OR (\"titanium\"[All Fields] AND \"implants\"[All Fields]) OR \"dental implants\"[All Fields]) OR (\"titanium\"[All Fields] AND \"photofunctionalization\"[All Fields]) AND \"animals\"[All Fields]). Other relevant references were obtained from the citation in the selected articles. Since this is a narrative review, the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement was not strictly followed.
# A Study of the Thermodynamic Properties of Nd-C and Ce-C TRISO Fission Product Binary Systems
## 1 Introduction
### Objectives
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Item** & **Mol\%C** & **Mol\%Nd** & **Compound** & \(\Delta\)**H\({}_{\text{7}}\)**(25 \({}^{\circ}\)C) [k/mol]** & **Method** & **Ref.** \\ \hline
1 & 60 & 40 & Nd\({}_{2}\)C\({}_{3}\) & \(-34.1\pm 3\) & Electromotive Force; & [22] \\
2 & 67 & 33 & NdC\({}_{2}\) & \(-29.2\pm 1.4\) & Calorimetry; & [23] \\
3 & 67 & 33 & NdC\({}_{2}\) & \(-90.8\pm 6\) & Quadruple Mass Spectrometer; & [29] \\
4 & 67 & 33 & NdC\({}_{2}\) & \(-17.4\pm 3\) & Knudsen Cell Effusion; & [25] \\
5 & 67 & 33 & NdC\({}_{2}\) & \(-29.33\pm 3\) & Electromotive Force; & [22] \\ \hline \end{tabular}
\end{table} 
Table 2: Neodymium carbide enthalpy of formation values in the open literature. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Item** & **Mol\%C** & **Mol\%Ce** & **Equilibrium** & **Temperature [\({}^{\circ}\)C]** & **Method** & **Ref.** \\ \hline
1 & 60 & 40 & Ce\({}_{2}\)C\({}_{3}\)\(\rightleftharpoons\beta\)CeC\({}_{2}\) + (L) & 1529.9 \(\pm\) 50 & Thermal Analysis; & [19, 39] \\
2 & 60 & 40 & Ce\({}_{2}\)C\({}_{3}\) (L) & 1700 & Assessed; & [32] \\ \hline
3 & 67 & 33 & \(\beta\)CeC\({}_{2}\)\(\rightleftharpoons\) (L) & 2250 \(\pm\) 20 & Knudsen Cell Effusion; & [25] \\
4 & 67 & 33 & \(\beta\)CeC\({}_{2}\)\(\rightleftharpoons\) (L) & 2270 \(\pm\) 20 & Thermal Analysis; & [33] \\
5 & 67 & 33 & \(\beta\)CeC\({}_{2}\)\(\rightleftharpoons\) (L) & 2290.3 \(\pm\) 60 & Thermal Analysis; & [19, 39] \\
6 & 67 & 33 & \(\beta\)CeC\({}_{2}\)\(\rightleftharpoons\) (L) & 2245 \(\pm\) 20 & Assessed; & [32] \\ \hline
7 & 67 & 33 & \(\alpha\)CeC\({}_{2}\)\(\rightleftharpoons\beta\)CeC\({}_{2}\) & 1100 \(\pm\) 10 & Thermal Analysis; & [33] \\
8 & 70 & 30 & Ce\({}_{2,34}\) (L) & 2270 \(\pm\) 15 & Knudsen Cell Effusion; & [25, 32] \\ \hline \end{tabular}
\end{table} 
Table 3: Cerium carbide phase transition temperatures in the open literature.
# From Brown-Field to Future Industrial Networks, a Case Study
## 2 Network Analysis and Modeling
### Methods and Tools  
The methods applied for analyzing network traffic and developing monitoring systems are highly dependent on the scope of the study and available data. More traditional approaches analyze the data using statistical and mathematical methods. In [15], a survey of methods found in the literature for network traffic analysis is presented. Recent years also witnessed applying various machine learning methods on network data to learn the behavior of the system from the data. Machine learning methods are used for flow-based characterization, or modeling [6; 16]. Classification methods for network flow analysis are surveyed in [17]. The objectives are commonly directed to anomaly and intrusion detection by flow characterization and traffic profiling. Supervised learning and classification methods have been more favorable since the results show higher accuracy in comparison to unsupervised learning. Nonetheless, due to the unavailability of data, the methods are usually carried out on simulated and emulated data.
# DFA-UNet: Efficient Railroad Image Segmentation
## 4 Experimental Data and Results
### Model Evaluation  
The evaluation metrics commonly used for image segmentation tasks are mIoU, Accuracy, Precision, Recall, and F1-score. One of the more dominant indicators is mIoU. To evaluate the performance of the models, we compared other more popular models and tested the metrics of each model for this dataset. Compared with other models, our model shows significant improvements in terms of mIoU, Precision, Recall, and F1-score. The specific experimental results are shown in Table 1. For the image segmentation task, the performance of the network on mIoU and F1-score metrics usually receives more attention. From Table 1, DFA-UNet achieves an F1-score of 0.8997 and a mIoU of 0.8662, which outperforms ResUNet++ by 1.84% in terms of F1-score and 2.12% in mIoU. Particularly, our proposed model provides a significant improvement over the two recent transformer-based architectures, where the mIoU of DFA-UNet is 11.28% and 4.27% higher than SwinUNet and LeViTUNet, and the F1-score of DFA-UNet is 7.83% and 3.13% higher than these two models, respectively.  
We visualized the mIoU during the training of different models, as shown in Figure 4. We use a violin drawing, which shows the overall distribution of the data in addition to the above statistics.  
Figure 4: MIoU performance on different models.
## 4 Results and Discussion
### Analysis of Coherence Coefficient Results
These coherence properties provide robust data support for subsequent phase unwrapping, the density of monitor points in time-series processing, and the reliability and precision of the final results.
# Advances in the Monitoring of Algal Blooms by Remote Sensing: A Bibliometric Analysis
## 3 Results  
### Number of Publications (Country)  
A total of 1292 peer-reviewed articles on remote sensing of phytoplankton blooms published between January 1999 and December 2019 were collected. Information on the article country was only selected based on the first author.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Citation Index Value** \\ \hline null relevance & 0–0.49 \\ \hline low relevance & 0.50–0.99 \\ \hline medium relevance & 1.00–2.00 \\ \hline high relevance & 2.00–4.00 \\ \hline very high relevance & \textgreater{}4.00 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Citation index values.  
The number of publications increased since 2004 and reached the maximum value of 128 in 2014 after two years of lower production (Figure 1). The publications originated from 47 countries; the United States (US) had the largest number of publications (total of 373 papers), followed by China (209), the United Kingdom (95), India (73), Canada (57), Japan (57), France (45), South Korea (45), Australia (37), Italy (37), and Spain (37) (Figure 2). The number of papers published from the top 10 countries accounted for 77% of the total publications. We must notice that there are many publications developed during a doctoral or post-doctoral stay of the first author in another country, this is especially important for many Chinese studies that were developed in USA research teams.