esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results  
### Spatial and Temporal Distribution of \(\rm F_{v}\), \(\rm F_{b}\), and NPP  
The spatial distribution of long-term mean grass \(\rm F_{v}\), \(\rm F_{b}\), and NPP in the Mongolia Plateau is shown in Figure 1. The grass \(\rm F_{v}\) value is relatively higher in northern and northeastern MP, while lower in southwestern and western MP (Figure 1A). On the contrary, \(\rm F_{b}\) greater than 60% distributed over the southwestern and western MP, while \(\rm F_{b}\) less than 40% mainly distributed over the northeastern and northern MP (Figure 1B). The mean actual NPP showed obvious spatial heterogeneity, too (Figure 1C). Areas with mean actual NPP larger than 200 g C/(m\({}^{2}\).yr) were scattered in the northern and northeastern MP with good vegetation growth conditions. Areas with mean actual NPP lower than 100 g C/(m\({}^{2}\cdot\)yr) were mainly scattered in the regions with relatively scarce water resources and vegetation in the transition area of grassland and desert such as southwestern and western MP. We counted the different pixel values of grassland F\({}_{\rm v}\) (Figure 1a), F\({}_{\rm b}\) (Figure 1b), and NPP (Figure 1c) in the MP. The average F\({}_{\rm v}\), F\({}_{\rm b}\), and NPP values were 18.42%, 15.53%, and 61.41 g C/(m\({}^{2}\cdot\)yr), respectively, whereas the corresponding distribution rates of their peak value were 60-80%, 40-60%, and 150-200 g C/(m\({}^{2}\cdot\)yr). The F\({}_{\rm v}\), F\({}_{\rm b}\), and NPP of IM were 9.17%, 6.84%, and 24.82 g C/(m\({}^{2}\cdot\)yr), respectively. MG had higher values of the three indexes than IM. The corresponding distribution rates of IM and MG peak values were similar to the MP.  
In this study, grassland F\({}_{\rm v}\) in the MP exhibited an increasing trend from 2000 to 2013, with a 14-year cumulative increment of 0.18 (Figure 2). MG had a higher F\({}_{\rm v}\) value than IM (0.21 vs. 0.09). On the contrary, grassland F\({}_{\rm b}\) showed an overall decreasing trend in the MP, with the decreased rate of \(-\)0.08. The decrease rates of MG and IM were \(-\)0.09 and \(-\)0.05, respectively. NPP had the largest change rate compared with the two other indexes, with a 14-year cumulative increment of 0.43. MG had a higher increase value than IM (0.63 vs. 0.39).
# Efficient Metal Corrosion Area Detection Model Combining Convolution and Transformer
## 3 Analysis and Discussion of Experimental Results
### Experimental Results and Analysis
#### 3.3.2 Robustness Testing
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Operation** & **Parameters** & **mIOU/\%** & **Reduction/\%** & **F1/\%** & **Reduction/\%** \\ \hline None & None & 68.67 & None & 81.03 & None \\ Salt-and-pepper noise & \(P_{salt}\) = 0.001 & \(P_{pepper}\) = 0 & 67.02 & \(-1.65\) & 79.67 & \(-1.36\) \\ Salt-and-pepper noise & \(P_{salt}\) = 0.001 & \(P_{pepper}\) = 0.001 & 67.01 & \(-1.66\) & 79.67 & \(-1.36\) \\ Salt-and-pepper noise & \(P_{salt}\) = 0.001 & \(P_{pepper}\) = 0.001 & 63.77 & \(-4.90\) & 77.27 & \(-3.76\) \\ Gaussian noise & \(\mu\) = 0 & \(\theta\) = 0.001 & 65.93 & \(-2.74\) & 78.93 & \(-2.10\) \\ Brightening & \(f\) = 1.5 & 64.75 & \(-3.92\) & 77.82 & \(-3.21\) \\ Dimming & \(f\) = 0.5 & 68.46 & \(-0.21\) & 80.89 & \(-0.14\) \\ Dimming & \(f\) = 0.3 & 67.86 & \(-0.81\) & 80.24 & \(-0.79\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance variations of Model-2. For Model-2, under brightness enhancement conditions, the mIOU dropped from 68.67% to 64.75%, and the F1 score fell from 81.03% to 77.82%, indicating that segmentation performance was somewhat affected by increased brightness. When brightness was reduced (\(f=0.5\)), the mIOU was 68.46%, and the F1 score was 80.89%, showing a minimal decrease and indicating good adaptability to reduced brightness. MCD-Net, when the brightness was enhanced (\(f=1.5\)), saw its mIOU decrease from 73.55% to 69.05%, and its F1 score decreased from 84.53% to 81.38%. When brightness was reduced (\(f=0.5\)), the mIOU dropped by only 0.04%, demonstrating excellent adaptability to brightness changes. The experimental results show that MCD-Net exhibits strong robustness against various types of noise (such as salt-and-pepper noise and Gaussian noise) and changes in brightness (both increases and decreases). Although segmentation performance declines under extreme lighting or high noise conditions, the overall segmentation accuracy remains high. This robustness is due to the combined advantages of convolutional neural networks and Transformer components in MCD-Net, allowing the model to capture both local details and global information, thereby maintaining high performance in complex environments. Additionally, we provided visual results under different test conditions, as shown in Figure 12, which show that the
## 2 Test Matrix and Material Properties
### Material Properties  
According to relevant regulations in ASTM C469 [53], the compressive strength, the ultimate strain, and the modulus of elasticity of unconfined concrete were measured. Single-layer flat coupons of AFRP/BFRP/CFRP were fabricated to test their tensile mechanical properties. The nominal thicknesses of the AFRP, BFRP, and CFRP samples were 0.167 mm, 0.169 mm, and 0.167 mm, respectively. According to the ASTM standard [54], a sample with a broken FRP end was not used for the material properties. The details of the properties of the FRPs are shown in Table 2. Their tensile stress versus the strain curves are shown in Figure 1, demonstrating the linear elastic tensile behavior of all the FRPs.  
Each sample is given its own name, with the initial letter \"S\" or \"D\" representing \"static\" or \"dynamic\", respectively. The second part of the name includes a letter and a number to denote the type of FRP and the number of FRP layers, respectively (e.g., 'B2' denotes a two-layer BFRP jacket). The last part indicates the number of the repeated specimens (there are 3 duplicated specimens for the quasi-static compression tests and 5 repeated specimens for the dynamic compression tests). For example, \"S-C3-2\" means the second specimen of the three-layer CFRP-confined high-strength concrete specimens under quasi-static compression.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Cement/kg\(\cdot\)m\({}^{-3}\)**} & **Water/kg\(\cdot\)m\({}^{-3}\)** & **Fine** & **Course** & **Water** & \multirow{2}{*}{\(f_{co}\)} & \multirow{2}{*}{\(\epsilon_{oo}\)} & \multirow{2}{*}{\(E_{c}\)} \\  & & **Aggregate/kg\(\cdot\)m\({}^{-3}\)** & **Aggregate/kg\(\cdot\)m\({}^{-3}\)** & **Reducer/kg\(\cdot\)m\({}^{-3}\)** & & \\ \hline
477.8 & 172.0 & 560.07 & 1190.1 & 5.0 & 71.2 & 0.0023 & 44.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mix proportions of concrete.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Specimen**} & \multicolumn{2}{c}{**Tensile Strength (MPa)**} & \multicolumn{2}{c}{**Rupture Strain (\%)**} & \multicolumn{2}{c}{**Modulus of Elasticity (GPa)**} \\ \cline{2-7}  & **Test** & **Average** & **Test** & **Average** & **Test** & **Average** \\ \hline S-A-1 & 2288.2 & & 1.33 & & 172 & \\ S-A-2 & 2533.5 & 2350.7 & 1.42 & 1.32 & 178.4 & 177.7 \\ S-A-3 & 2230.5 & & 1.22 & & 182.8 & \\ S-B-1 & 949.2 & & 1.66 & & 57.2 & \\ S-B-2 & 910.5 & 890.2 & 1.62 & 1.6 & 56.2 & 56.6 \\ S-B-3 & 850.1 & &
# Computer Vision as a Tool to Support Quality Control and Robotic Handling of Fruit: A Case Study
## 2 Materials and Methods
### 2F-85 Actuator and Soft Gripper Development  
The Robotiq 2F-85 Adaptive Gripper [23] is a tool specifically designed to be integrated with collaborative robots, such as the UR3e. This adaptive gripper is known for its ability to automatically adjust the force and position of the grippers to manipulate a wide variety of objects of different sizes and shapes, which makes it ideal for applications such as assembly, pick and place, and handling sensitive products including fruit. In terms of actuation, the Robotiq 2F-85 operates in two modes, linear and rotary. The linear mode allows the grippers to move forward and backward, adapting to the size of the object to be manipulated. The rotary mode allows the gripper to rotate the parts around its axis, facilitating operations that require specific orientation of the parts.  
The Fin Ray Effect (FRE) fingers were specifically designed and customized to optimize the ability to pick delicate products such as, in this case, fruit. The design takes into consideration the need to apply gentle yet secure gripping force to avoid damaging the product while ensuring a reliable grip. The grippers were customized to where the adaptive gripper is located, as shown in Figure 3, which, according to [PERSON] et al. [11], is a simpler and more economically viable approach to grasping objects of various shapes, in this case, tomatoes. This gripper is biologically inspired by fish fins. These fins comprise two soft longitudinal bundles symmetrically connected in a V-shaped configuration, with several indents of increasing length spaced between the tip and the base. The biomimetic structure  
Figure 2: Tomatoes being held by soft grippers.  
eliminates the need for inbuilt actuation in the fingers, thus facilitating passive adaptation to the surface of an object on contact [24; 25].  
The grippers were drawn in Computer-Aided Design--CAD (SolidWorks)--and printed using TPU Filament Filaflex with a shore hardness of 60A [26] in an Artillery Sidewinder X1. The design involved four grippers spaced 37.04\({}^{\circ}\) apart between each two fingers. The connecting parts between the gripper and the robotic arm were printed in PETG and bonded with M3 screws and epoxy. The development process of the grippers was based on the research work developed by [PERSON] et al. [27; 28]. The parameters used for printing the soft grippers are listed in Table 1.  
No supports were used throughout the printing process. After printing, the FRE fingers were carefully cleaned and smoothed, and the burrs and stringing were removed as much as possible.
## Materials and Methods
### Data Processing  
ChromaTOF version 4.61.1.0 software (LECO Corp., St. Joseph, MI, USA) was used for peak finding, mass spectral deconvolution, peak area integration, and library searching. Automated peak finding and spectral deconvolution with a baseline offset of 0.5 and S/N of 100 after evaluating serval options (i.e., 25, 50, 100, 150, and 200). All analyses were performed in triplicate for each extraction method. The existence of the compound is considered reliable only when the number of detections is greater than 2 at the same retention time. Tentative identification was based on the comparison of mass spectra with the NIST 2014 and Weliy 9 databases using a minimum similarity value of 700 as the criterion, as well as experimentally determined linear retention indices compared to NIST library values. A series of n-alkanes were analyzed under the same conditions to determine first dimension linear retention indices (LRIs) for each compound. A maximum deviation of 30 between the experimental and literature RI values was used as the criterion. Some identification results may be consistent with MS and RI identification, but the 2 nd dimensional retention time may not meet the linear distributions of homologous series. The ordered chromatograms of homologous series canalso be used for identification. In addition, positive verification of 247 compounds (~41% of the total number) was based on comparison of retention time with authentic standards.
# Machine Learning for Evaluating Hospital Mobility: An Italian Case Study
## 2 Background
### Application Context  
Incorporating data from 2021, our study provides a sharp and scientifically rigorous overview of healthcare facilities in the regions of Apulia and Emilia-Romagna, following the guidelines of the Ministry of Health. In Apulia, the healthcare system is organized around six Local Health Authorities (ASL), each responsible for delivering a comprehensive range of health services to the local population. Similarly, Emilia-Romagna's healthcare services are structured around eight Local Health Units (USL), which play a pivotal role in ensuring access to health services across the region. These entities, ASLs in Apulia and USLs in Emilia-Romagna, are crucial for implementing national health policies at the local level, adapting them to meet the specific needs of their communities. Through detailed analysis, significant differences in the distribution and efficiency of healthcare facilities emerge, reflecting the diversity and complexity of the Italian healthcare system. First-level hospitals, providing essential services such as emergency care, diagnostics, regular hospitalization, and outpatient services, represent the community's first contact with healthcare. Their widespread presence across the national territory ensures access to primary care for the local community. Second-level hospitals, characterized by greater specialization compared to first-level hospitals, offer more complex services such as specialized surgery, intensive care, and hemodynamics services. These centers, distributed in numerous regions of Italy, serve as reference points for the provision of advanced care. Basic hospitals, primarily focused on primary care functions, offer basic care, outpatient services, and primary level diagnostics. Their role is crucial in connecting primary care with more specialized hospital facilities, ensuring continuity in healthcare. Institutes of Scientific Research and Care (IRCCS), dedicated to scientific research and highly specialized healthcare, play a fundamental role in the development of new therapies and medical research, offering highly specialized care. Accredited Private Healthcare Facilities, which have obtained accreditation from the National Health Service (SSN), collaborate with the public healthcare system by providing care and rehabilitation services, operating in compliance with the quality and safety standards set by the SSN.
## 2 Materials and Methods
### Model Validation and Cross-Validation Approach  
We employed 10-fold cross-validation to statistically validate the machine learning models used in this study. This method is widely recognized for its reliability in assessing model performance. The dataset was divided into ten equal parts, where nine parts were used for training the model and one part for testing. This process was repeated ten times, each time with a different part of the dataset used as the test set. The cross-validation results provided an average accuracy score, offering a more robust evaluation of the model's predictive capabilities.  
Additionally, the use of 10-fold cross-validation helped mitigate the risk of overfitting, as it ensured that each subset of the data was used for both training and validation. This approach provided a comprehensive assessment of model performance, highlighting its generalizability to new data. Accuracy was computed for each fold, and the final model performance was evaluated based on the mean values of this metric across all folds.
# Recurrent Neural Network-Based Hybrid Localization for Worker Tracking in an Offshore Environment
## 3 Materials and Methods
### Positioning Module of RHLS
#### 3.4.2 Middle Layer  
The middle layer of the RNN positioning module is mainly composed of an LSTM cell. Firstly, the normalized Wi-Fi fingerprint \(x_{0}\) at the input layer is passed through a single sigmoid layer to obtain processed feature vector \(h_{0}\).  
\[h_{0}=\sigma(W_{0}x_{0}+b_{0}), \tag{7}\]  
\[\sigma(x)=\frac{1}{1+e^{-x}}, \tag{8}\]  
where \(W_{0}\) is the weight matrix of the sigmoid layer, and \(b_{0}\) is the bias vector of the sigmoid layer. The composition of input vector \(x_{t}\) at a specific time \(t\) is set in the order of the length of the step \(l_{t}\), direction of movement \(o_{t}\), magnetic field intensity \(h_{t}\), and magnetic inclination \(i_{t}\).  
\[x_{t}=\Big{[}\begin{array}{ccc}l_{t}&o_{t}&h_{t}&i_{t}\end{array}\Big{]}. \tag{9}\]  
When the input value enters the LSTM cell, the result value is output through the following process:  
\[f_{t}=\sigma(W_{f}\cdot[h_{t-1},\;x_{t}]+b_{f}), \tag{10}\]  
\[i_{t}=\sigma(W_{i}\cdot[h_{t-1},\;x_{t}]+b_{i}), \tag{11}\]  
\[\widetilde{x}_{t}=\tanh(W_{c}\cdot[h_{t-1},\;x_{t}]+b_{c}), \tag{12}\]  
\[c_{t}=f_{t}*c_{t-1}+i_{t}*\widetilde{x}_{t}, \tag{13}\]  
\[o_{t}=\sigma(W_{0}\cdot[h_{t-1},\;x_{t}]+b_{0}), \tag{14}\]  
\[h_{t}=o_{t}*\tanh(c_{t}), \tag{15}\]  
where result value \(h_{t}\) is a feature vector after \(t\) seconds from feature vector \(h_{0}\) of the basic Wi-Fi fingerprint.  
Looking at the middle layer for a continuous time span, it appears similar to the structure on the right in Figure 7. In the case of a general RNN, the length of the sequence is determined, and, when the input is received for this sequence, the RNN is initialized. However, in the proposed system, the RNN cell is initialized in accordance with the Wi-Fi signal scan. When the Wi-Fi signal scan is finished and the RSS vector is constructed, the vector is normalized through the input layer. This vector is processed once through the sigmoid layer and initializes the LSTM. Then, the LSTM cell continues to store and use the values until a new scan is completed. This structure creates a tracking effect between Wi-Fi scans.
# Business Intelligence through Machine Learning from Satellite Remote Sensing Data
###### Abstract
They have led to applications in various fields, such as those mentioned in our literature review. Traditional land use and land cover studies mostly focus on city expansion and suburban sprawl, while they pay almost no attention to the state of the buildings themselves. Recently, there have been studies focusing on building data extraction based on traditional image detection methods [1], using Google Street View images [2], or even images taken from mobile phones [3]. Nevertheless, they do not focus on the time series extracted from the derived spectral indices. Finally, studies that indeed use spectral indices derived from satellite images rely on difficult-to-access, very high-resolution images, resulting in expensive operational systems [4]. The main innovation of our study is that it proposes a system that offers an easily accessible platform that does not require either high-resolution images or high-computational resources for anyone interested in performing experiments related to abandoned buildings, enabling further research on the subject. So, our study contributes to the effectiveness of the application of remote sensing in understanding the implications of socioeconomic phenomena in general. The rest of this paper is organized as follows. The next Section offers a brief introduction to remote sensing satellite data and an overview of important satellite characteristics and derived data products. In Section 3, we present a comprehensive literature review on the use of remote sensing machine learning for selected business intelligence sectors. Our model creation efforts, as well as the design and implementation of a prototype softwaresystem that they utilize, are given in Section 4. An extensive presentation of the experimentation and its evaluation is given in Section 5, together with the outcome reasoning. Section 6 contains our synopsis and plans for future work.
# Hybrid Machine Learning-Statistical Method for Anomaly Detection in Flight Data
## 3 Local Outlier Factor
### Implementation
#### 3.2.2 Pre-Processing  
One hundred and thirty-two relevant flight parameters out of a total of one hundred and eighty-six parameters recorded by the aircraft sensors were selected for the analysis of flight data. The selection of these flight parameters was based on discussions with industrial experts. Dynamic parameters relating to weather were removed so all flights may be treated on the same grounds and independent of any environmental influence. Any event related to weather influences the pilot's behavior, which is captured by other flight parameters. Three airports located in the USA were randomly selected for the analysis. These airports are Detroit (DTW), Minneapolis (MSP) and Memphis (MEM). Flights with the same tail number were grouped together. Therefore, each airport has flights from the same group or tail number.  
As presented in Figure 2, accidents during the approach and landing phase account for more than 50% of all the accidents, even though this phase is only 16% of the flight time [20]. Moreover, as per a report by Airbus [21], most of the accidents (fatal and nonfatal hull-loss accidents) over the last 20 years occurred during the approach and landing phases. In 2020, all three fatal accidents occurred in the approach and landing phases [21]. Therefore, for this study, the approach and landing phase was selected. In this phase, only three minutes before touchdown are studied to detect anomalous flights. The touchdown point for each aircraft was identified using phase (PH), weight on wheels (WOW) and latitude (LAT) and longitude (LONG) flight parameters. In order to avoid negative values of flight parameter altitude (ALT) for a given airport, the minimum altitude value for that airport was subtracted from all readings of the ALT flight parameter. Therefore, all the flightlandings at a given airport were synchronized in time with each other to touch down at the same time.  
Figure 1: Step-by-step implementation of FDM using hybrid machine learning–statistical method (Image created using BioRender.com).  
The dataset was cleaned of any noise or missing data by the onboard sensor. All flight parameters were normalized to accommodate different units and ranges of values of flight parameters.  
The data files were converted from MAT files to SQL tables for better access and analysis. Flight parameters were recorded at sampling rates ranging from 1 Hertz to 16 Hertz. LOF algorithm requires flight data to have the same number of data points for each flight. Therefore, flight parameters recorded at lower sampling rates were all converted to 16 Hertz by interpolating the values. This helped to synchronize data from all the flights. Figure 3 shows a plot of flight parameter altitude (ALT) plotted against time for 600 flights from one tail (652) arriving at Detroit airport after all data pre-processing was completed.  
Figure 3: Altitude vs. time plot for synchronized flights after data pre-processing.  
Figure 2: Phase wise fatal accidents (2011–2020) [20] (Image created using BioRender.com).