esa-sceva/satcom-chunk-collection huggingface dataset:
# Overview on the Evolution of Laser Welding of Vascular and Nervous Tissues
## 3 Conclusions  
An overview on the evolution of vascular and nervous surgeries by laser welding was presented in this research. The fundaments of the process and the technological evolution registered in this field were analyzed. The following conclusions can be drawn:  
* Although laser welding presents many advantages, such as increased accuracy, decreased operative time, reduction of foreign-body reactions, and reduction of aneurysms formation, an accurate control of several process variables is required. The laser type and parameters, the exposure time, the tissue exposition, the temperature, and the absorptive conditions present high relevance.
* Thermal damage may occur in vascular and nervous laser welding. Different strategies have been tested to overcome this concern. Temperature-controlled feedback systems present the advantage of reducing the thermal damage with a very precise control of the laser parameters.
* The bonding strength of vascular and nerve welds can be enhanced with the application of solder solutions, bonding materials, and laser-activated dyes. The use of dyes with albumin-based solution solders has presented an especially beneficial effect on the welding conditions.
* Alternative techniques to thermal laser welding, such as photochemical tissue bonding and electrosurgical high-frequency technologies, have been tested for vascular and nervous repairs.  
Conceptualization, D.F.G., I.G., M.A.R.L.; methodology, D.F.G., I.G., M.A.R.L.; formal analysis, D.F.G., I.G., M.A.R.L.; investigation, D.F.G., I.G., M.A.R.L.; writing--original draft preparation, D.F.G.; writing--review and editing, D.F.G., I.G., M.A.R.L; supervision, I.G., M.A.R.L.  
This research received no external funding.  
The authors acknowledge the support of ISEL-Polytechnic Institute of Lisbon, CIMOSM, CEMMPRE, IDMEC, and NOVA UNIDEMI. The third author also acknowledges the support of FCT through IDMEC, LAETA, project UID/EMS/50022/2019.  
The authors declare no conflict of interest.
### Motivation and Contribution  
The infrared small target and its background in the image correspond to the fluctuating background, noise, and target signal in different regions, bringing uncertainty to the spatial characteristics of the observed data. Due to the uncertainty of observation caused by the spatial variation of each component, the previous work did not analyze the characteristic uncertainty of dim target detection, which can significantly impact the detection of infrared small targets. Moreover, the parallel processing capability and reconfigurability of the FPGA enable it to exhibit a superior performance in infrared small target detection.  
In this paper, considering the relationships between signals of different components and between signals of the same component, we propose the Energy-weighted Local Uncertainty Measure (ELUM) based on signal component consistency. This method is especially suitable for the rapid detection of small single-frame infrared targets in complex scenes on the FPGA. Single-frame image processing is mainly based on the spatial-domain information of the image and utilizes single-image data for target recognition, while multi-frame image processing introduces time-domain information on this basis and utilizes multi-frame images with temporal continuity for target recognition. Multi-frame image processing can obtain the target's motion data with the help of spatio-temporal information to analyze and predict its motion trajectory, and this method is more effective when the background is more similar to the target [8]. However, when the target moves too fast or the background is not fixed, the effect of multi-frame image processing will be greatly affected [9]. In addition, the computational complexity of multi-frame image processing is large, and the computational efficiency is greatly limited. Therefore, single-frame image processing has a wider range of application scenarios. The method used in this paper adopts single-frame image processing, which has great advantages in processing efficiency and can better meet the efficiency and real-time needs of target recognition. The main contributions of this paper are as follows:  
1. This paper first analyzes the characteristic uncertainty of dim target detection and emphasizes its critical importance for enhancing the performance of infrared small target detection.
2. This paper introduces a method that constructs a local consistency structure window to analyze the consistency of signal components within a small target's detection space, which is particularly effective in complex backgrounds or under low signal-to-noise ratio conditions.
3. This paper employs a variable entropy operator to measure uncertainty in the local region, which is essential for improving the robustness of small target detection.  
The algorithm further incorporates an energy-weighting function designed with a Gaussian matching filter.
* A classical adaptive threshold segmentation algorithm is used to extract the target, demonstrating its capability to process complex backgrounds using real data.
# Evaluating Prediction Models for Airport Passenger Throughput Using a Hybrid Method
## 2 The Proposed Hybrid Evaluating Method
### Models
#### 2.1.1 Time Series Models
Similarly, the cubic exponential smoothing model is based on the quadratic exponential smoothing model, which is defined as:  
\[S_{t+1}^{\rm es3}=\alpha_{3}S_{t}^{\rm es2}+(1-\alpha_{3})S_{t}^{\rm es3} \tag{7}\]  
where \(\alpha_{3}\) is the smoothing coefficient of the model, \(S_{t}^{\rm es3}\) is the quadratic smoothing result at time \(t\) and \(S_{t+1}^{\rm es3}\) is quadratic the smoothing result at time \(t+1\). The predicted result of APT \(\hat{x}_{t+T}^{\rm es3}\) at time \(t+T\) can be calculated by:  
\[\hat{x}_{t+T}^{\rm es3}=a_{t}^{\rm es3}+b_{t}^{\rm es3}*T+c_{t}^{\rm es3}T^{2} \tag{8}\]  
\[a_{t}^{\rm es3}=3S_{t}^{\rm es1}-3S_{t}^{\rm es2}+S_{t}^{\rm es3} \tag{9}\]  
\[b_{t}^{\rm es3}=\frac{\alpha_{3}}{2(1-\alpha_{3})^{2}}[(6-5\alpha_{3})S_{t}^{ \rm es1}-2(5-4\alpha_{3})S_{t}^{\rm es2}+(4-3\alpha_{3})S_{t}^{\rm es3}] \tag{10}\]  
\[c_{t}^{\rm es3}=\frac{\alpha_{3}^{2}}{2(1-\alpha_{3})^{2}}[S_{t}^{\rm es1}-2 S_{t}^{\rm es2}+S_{t}^{\rm es3}] \tag{11}\]  
where \(a_{t}^{\rm es3}\), \(b_{t}^{\rm es3}\) and \(c_{t}^{\rm es3}\) are the parameters which can be calculated by Equations (9)-(11). For the grey model GM (1,1), the raw sequence \(X^{(0)}=\{x_{0},x_{1},\ldots,x_{k}\}\) can be accumulated by \(X^{(1)}=\sum_{i=0}^{k}x_{i},k=0,1,2,\ldots\) to obtain the accumulated sequence. Then, the accumulated sequence can be used to fit \(\frac{dx_{1}^{(1)}}{dt}+a^{\rm gr}x_{k}^{(1)}=b^{\rm gr}\) by:  
\[ \begin{bmatrix}a^{\rm gr}\\ b^{\rm gr}\end{bmatrix} =\left(\mathbf{B}^{T}\cdot\mathbf{B}\right)^{-1}\cdot \mathbf{B}^{T}\cdot\mathbf{Y} \tag{12}\]  
where  
\[\mathbf{B}= \begin{bmatrix}-0.5\times(x_{1}^{(1)}+x_{0}^{(1)})&1\\ -0.5\times(x_{2}^{(1)}+x_{1}^{(1)})&1\\ \vdots&\vdots\\ -0.5\times(x_{k}^{(1)}+x_{k-1}^{(1)})&1\end{bmatrix} , \tag{13}\]  
And  
\[\mathbf{Y}= \begin{bmatrix}x_{1}^{(1)}&x_{2}^{(1)}&\cdots&x_{k}^{(1)}\end{bmatrix}  ^{T}. \tag{14}\]  
The predicted value of the accumulated sequence can be primarily obtained by:  
\[\hat{x}^{(1)}(t)=(x^{(0)}(1)-\frac{b^{\rm gr}}{a^{\rm gr}})e^{-a^{\rm gr}t}+ \frac{b^{\rm gr}}{a^{\rm gr}},t=1,2,\cdots k \tag{15}\]  
and the predicted result of APT \(\hat{x}^{(0)}(t+1)\) at time \(t+1\) can be iteratively calculated by:  
\[\hat{x}^{(0)}(t+1)=\hat{x}^{(1)}(t+1)-\hat{x}^{(1)}(t)=(1-e^{-a^{\rm gr}t})(x^ {(0)}(1)-\frac{b^{\rm gr}}{a^{\rm gr}})e^{-a^{\rm gr}t},t=1,2,\cdots k \tag{16}\]
## 5 Conclusions  
In this work, a new concrete crack damage detecting model, using multilayer ELM-based feature learning and classification, was proposed. First of all, through cropping concrete images using a sliding window operation and image rotation, we obtained plenty of crack and non-crack image patches. Then, an efficient sparse ELM auto-encoder was advocated for building the hierarchical feature learning pipeline, which was utilized for extracting the meaningful high-level features of image patches. Secondly, the online incremental ELM classified network was applied for separating the crack defect features from the background features. For better adaptation to changeable environments, the online incremental retrained equation of the crack region detector was designed. Finally, both qualitative and quantitative experimental evaluations demonstrated the robustness and effectiveness of the developed crack damage detection model. To be specific, with an efficient training speed and testing speed (i.e., 0.76 s for one concrete image with a resolution of 4608 \(\times\) 3456 pixels), the presented crack detector can obtain satisfactory detecting performances (i.e., with a \(FNR\) of 1.99% and \(FPR\) of 2.11%).  
Furthermore, owing to the many challenges in the practical application (e.g., illumination changes, blurring, pockmark, and so on), effective crack detection using a single view of images remains to be a difficult issue. For future development, crack detection based on multi-view image processing should be taken into account, to address the troublesome background false-alarms. Specifically, as for concrete surface image acquisition, many angles are needed to comprehensively detect crack damages.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Canny** & **Otsu** & **SVM** & **DL** & **MECD** \\ \hline Average timing (second) & 1.62 & **0.25** & 5.34 & 28.7 & 0.76 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average timing of processing the test concrete images.  
Multi-view image processing will contribute to improving the performance of crack detection in physical visual inspection. In addition, minimizing false positives is also a significant item for robust crack damage detection. Training a reinforcement learning model for separating cracks from the similar-looking background noises may be a possible strategy.  
[PERSON], [PERSON], and [PERSON] designed the global structure and experiments. [PERSON] performed the experiments. [PERSON], [PERSON], and [PERSON] analyzed the data. [PERSON] wrote the paper.  
This work was supported by National key research and development program (2016 YFB1200401-107, 2016 YFC0802207), National Natural Science Foundation of China (51808358), Natural Science Foundation of Hebei Province (E2017Z10113), China Postdoctoral Science Foundation (2017M621100), and Collaborative Innovation Center of Large Infrastructure Disaster Prevention and Reduction of Hebei Province.  
The authors declare no conflict of interest.
## 3 Taxonomy and Essential Oil Production of Thyme
### Essential oil Biosynthesis in Thyme  
Thyme plants have been found to produce volatile oil in thyme glandular hairs, primarily carried out by peltate glandular trichomes (PGTs), which are responsible for the biosynthesis and accumulation of monoterenes [35].  
In _Lamiaceae_, glandular trichomes contain specialized secretory cells where volatile compounds are synthesized and stored within a subcuticular space, facilitating their retention in a liquid state until release [36]. The principal constituents of essential oils (EOs) are synthesized through three biosynthetic pathways: terpen are generated via the plastidic methylerythitol phosphate pathway (MEP) and the cytosolic mexulonic acid pathway (MVA), while phenylropenes are synthesized through the shikimic acid pathway [37; 38].  
The isopenoid units (C5), dimethylallyl pyrophosphate (DMAPP), and isopentenyl pyrophosphate (IPP) are provided, respectively, via the MEP and MVA pathways. In plastids, they combine to form geranyl pyrophosphate (GPP, C10), the primary precursor for monoterenes and diterpenes. Conversely, in the cytosol, they produce farnesyl pyrophosphate (FPP, C15), the precursor for sesquiterpenes [39; 40]. Teprene synthases (TPSs) catalyze the formation of these terpen' basic skeletons, constituting a diverse class of enzymes responsible for the vast structural variety of mono- and sesquiterpenes [40; 41]. Examples of compounds generated by TPSs include thymol, carvacrol, geraniol,linal, camphor, and \(\beta\)-caryophyllene. [42]. Figure 2 illustrates the molecular structure of the most significant terpen found within the _Thymus_ genus.  
Figure 2: Chemical structures of the most important terpenes found among the genus of _Thymus_ (After Stahl-Biskup [42]).
# Efficient Node Deployment of Large-Scale Heterogeneous Wireless Sensor Networks
## 5 Ant Colony Optimization Based Solution
### Pheromone calculation  
In this paper, the pheromone concentration is affected by the distance between each mobile node's location and its new location on the target zone since energy conversation is an essential issue in WSN. The mobile node selection with minimum distance is required to minimize energy consumption in the movement and, hence, conserve much more energy as possible. Thus, the distance between the location of node \(s\) on zone \(i\) and the new location on the target zone \(j\) is used to calculate the increasing density of pheromone value as follows:  
\[\Delta\tau_{ij}=d_{sij}^{t} \tag{32}\]  
After each ant, \(k\), has reached its new deployment point, the pheromone trail value is updated according to the following rule:  
\[\tau_{ij}(t)=(1-\rho)\tau_{ij}(t-1)ij+\rho\Delta\tau_{ij} \tag{33}\]  
where \(\rho\in(0,1)\) is the evaporation factor [25].
# Health Care Accessibility Analysis Considering Behavioral Preferences for Hospital Choice
## 3 Case Study and Analysis
### Data Collecting and Pre-Processing
#### 3.2.1 Vector Map Data  
Vector map data are the basic spatial data for this study, and they are obtained in many ways. Among them, water bodies, road networks and community neighborhood data were downloaded from the Geospatial Data Cloud [32]. The hospital locations were obtained by calling the API of Amap service. Vector map data were used as input parameters to obtain the time cost and distance cost required for the accessibility analysis through the Amap path planning API, as well as for visualization and analysis of the experimental results.  
Firstly, water body, road network and community block data were downloaded from geospatial data cloud, and hospital's location was obtained by calling the API of Amap service. Secondly, coordinate systems of these data were uniformly converted to geographic coordinates. Finally, center points of communities were extracted and used together with the hospital points as two input parameters of navigation prediction.  
Water body and road network were used to carry out the auxiliary analysis of later results, and other data were directly used for analysis and calculation.  
Figure 2 shows the spatial distribution of the district, community, population density and 3A-grade hospitals (3 AH) of the study area. The 3 AHs are the highest level of hospital in China, and they excel in healthcare services and management, quality and safety of care, and technical proficiency and efficiency. The density of population and 3 AHs near the intersection of the Yangtze River and Han River are the highest, especially in Qiaokou, Jianghan and Jiang'an district.
## 2 Methodology
### Hybrid Time-Frequency Loss
The model achieves this goal through many innovations: first, the sEMG signal, joint angle and their combination are used as three input branches, integrated into the adaptive weight module, and the deep separable convolution combined with the MLP adaptive module is used to dynamically assign weights to specific branches, effectively improving the efficiency of feature utilization and model learning effect; second, SFT is proposed based on the Transformer architecture, MLP replaces the traditional attention mechanism and changes the traditional encoder-decoder format, while reducing the computational burden, it can also retain the ability to recognize complex data relationships, thereby improving efficiency and prediction accuracy; third, the new Hybrid Time-Frequency Loss (HTFLoss) function is introduced to better integrate time domain and frequency domain information, enhance the joint torque feature structure, and make the transition state prediction smoother, thereby improving the prediction quality and the model's ability to respond to dynamic changes.
# Multi-Authority Revocable Access Control Method Based on CP-ABE in NDN
## 3 Design and Implementation of Access Control Method  
Based on the Kai Fan scheme [17], we propose a CP-ABE-based multi-authority revocable access control method that is different from the traditional single-authorization center scheme to enable fine-grained access control of cached data. In the multi-authorization center attribute-based encryption mechanism, the user's attributes come from multiple attribute domains that are jointly managed by different authorization centers. This approach can greatly reduce the administrative burden of a single authorization center and can improve the system's ability to resist collusion attacks.
# SDN-Based Routing Framework for Elephant and Mice Flows Using Unsupervised Machine Learning
###### Abstract  
Software-defined networks (SDNs) have the capabilities of controlling the efficient movement of data flows through a network to fulfill sufficient flow management and effective usage of network resources. Currently, most data center networks (DCNs) suffer from the exploitation of network resources by large packets (elephant flow) that enter the network at any time, which affects a particular flow (mice flow). Therefore, it is crucial to find a solution for identifying and finding an appropriate routing path in order to improve the network management system. This work proposes a SDN application to find the best path based on the type of flow using network performance metrics. These metrics are used to characterize and identify flows as elephant and mice by utilizing unsupervised machine learning (ML) and the thresholding method. A developed routing algorithm was proposed to select the path based on the type of flow. A validation test was performed by testing the proposed framework using different topologies of the DCN and comparing the performance of a SDN-Ryu controller with that of the proposed framework based on three factors: throughput, bandwidth, and data transfer rate. The results show that 70% of the time, the proposed framework has higher performance for different types of flows.  
Software-Defined Networks (SDN); Data Center Networks (DCN); Machine Learning (ML); K-means; Principal Components Analysis (PCA); elephant flows; mice flows; flow identification; SDN application +
Footnote †: journal: Network  
1
# Cellular-V2X Communications for Platooning: Design and Evaluation
## 4 Platooning in Cellular V2X
### Mapping to C-V2X
However, given the high-mobility characteristics of V2V scenarios, it is difficult for the eNodeB to predict how much interference two VUEs will suffer for the entire duration of the SPS grant. For the above reasons, we only consider dynamic scheduling as allocation scheme in the following and leave the investigation of SPS (e.g., studying the impact of the SPS grant duration) as a future work. As mentioned in the previous subsection, CAM exchange foresees two steps, i.e., PL-to-PMs and PM-to-PM. These two logical connections have to be mapped to C-V2X communication modes. According to the semantics of the two steps, we employ point-to-multipoint (P2 MP) V2V communications for the PL-to-PM step, whereas we select point-to-point (P2P) V2V communications for the PM-to-PM phase. We now discuss the issues related to the two modes separately.
## 3 System Implementation Based on LPA Synchronization Algorithm
### Multi-_h_ CPM Transmitter Implementation  
In [33], the authors propose a single-_h_ and multi-_h_ CPM transmitter, which can be reconfigurable with an ignorable increase in memory. It provides a better tradeoff between memory and DSP operations. However, the quantization noise from computing the cumulated phase increases when the modulation indices do not have an exact representation in a given fixed-point format, e.g., \(h=\frac{1}{3}\). To deal with this, modular arithmetic units are used to obtain the accurate signal computation for the CPM transmitter in [34]. Here, the modulation indices can be represented accurately in a given fixed-point format. Thus, we use the method based on the read-only memories (ROMs) to calculate the modulated phase (See [33]), which is composed of the correlative phase calculation and cumulative phase calculation. Compared with the integration-based method, the ROM-based method brings lower quantization error and higher complexity. The increased implementation complexity of the modulator can be ignored, considering nowadays software-defined radio platforms. AWGN is generated based on the Box-Muller transform, which provides highly accurate  
Figure 3: System implementation diagram.  
noise samples [35]. The implementation details of the AWGN generator are presented in [36].
###### Abstract  
Having an accurate and easily accessible geochemical database is crucial for a correct groundwater management. Here, for the first time in Italy, chemico-physical data of groundwater collected by different Environmental Protection Agencies during the 2018 were integrated into a single database to assess the geochemical status of a wide and complex aquifer system. Data were assembled, reformatted, corrected, homogenized, and then grouped according to the aquifer type (phreatic, semi-confined, and confined) and the sampling seasons. A total of 3671 validated samples were classified into hydrothermal facies; inorganic N compounds and trace elements were also evaluated. The water were classified mainly as Ca-HCO\({}_{3}\) and Ca-Mg-HCO\({}_{3}\) (90%); locally, Na-HCO\({}_{3}\), Mg-HCO\({}_{3}\), Ca-SO\({}_{4}\), Na-Cl, and Ca-Cl types were detected. In the phreatic aquifers, NO\({}_{3}\) contamination and high concentrations of Na\({}^{+}\), K\({}^{+}\), and NH\({}_{4}\)\({}^{+}\) were found and linked to anthropogenic sources, such as agricultural and livestock activities. Along the Adriatic coast, Na-Cl water confirmed saltwater intrusion phenomena. Landward, evaporitic rocks dissolution, and the upcoming of relict marine water explained high EC, Na\({}^{+}\), K\({}^{+}\), Cl\({}^{-}\), and SO\({}_{4}\)\({}^{2-}\) concentrations. The dissolution of Fe-Mn oxide-hydroxides coupled with organic carbon oxidation under reducing environment justified high NH\({}_{4}\)\({}^{+}\), Fe, Mn, and As recorded in the semi-confined and confined aquifers.  
geochemistry; aquifer; hydrochemical facies; database design; Northern Italy