esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Skin melanoma, one of the deadliest forms of cancer worldwide, demands precise diagnosis to mitigate cancer-related mortality. While histopathological examination, characterized by its cost-effectiveness and efficiency, remains the primary diagnostic approach, the development of an accurate detection system is pressing due to melanoma's varying sizes, shapes, and indistinct boundaries shared with normal tissues. To address the efficient segmentation of skin melanoma, we propose an innovative hybrid neural network approach in this study. Initially, a fuzzy neural network is constructed using fuzzy logic to preprocess medical images, supplemented by wavelet transformation for image enhancement. Subsequently, the Swin Transformer V2 and ResNet50 networks are introduced to parallelly extract features and apply them to the task of skin melanoma segmentation. Extensive experimental comparisons are conducted with other classic and advanced medical segmentation algorithms on publicly available skin datasets, namely ISIC 2017 and ISIC 2018. Experimental results reveal that our method outperforms the optimal algorithms by 1.3% in the Dice coefficient and 1.3% in accuracy on the ISIC 2018 dataset. The evaluation metrics indicate the effectiveness of the constructed fuzzy block in identifying uncertain lesion boundaries, while the Transformer-CNN branch adeptly extracts global features while accurately capturing underlying details. Additionally, we successfully apply our method to colon polyp segmentation tasks with similar indistinct boundaries, achieving remarkable segmentation outcomes.  
segmentation; medical image; fuzzy logic; CNN; Swin Transformer Article  
A Fuzzy Transformer Fusion Network (FuzzyTransNet) for Medical Image Segmentation: The Case of Rectal Polyps and Skin Lesions  
[PERSON], et al. (2020), [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]  
School of Artificial Intelligence, Chongqing University of Technology, Chongqing 401135, China; [EMAIL_ADDRESS] (S.D.); [EMAIL_ADDRESS] (L.X.); [EMAIL_ADDRESS] (L.L.); [EMAIL_ADDRESS] (J.L.); [EMAIL_ADDRESS] (Y.Z.)
# The Pre-Heating Effect for Porosity Control during the Laser Welding of GAlvanized Steel Sheets
## 4 Conclusions  
In this study, the reliability of weld quality was secured by comparing and analyzing weld bead, mechanical, and metallic characteristics to verify the zinc removal effect during the three-step laser stitch welding on the overlap joint of galvanized steel sheets.  
1. The maximum tensile-shear strength value of 10.2 kN was obtained under the conditions of a laser power of 2.8 kW and a focal position of 20 mm in the three-step laser welding process. As a result, the tensile-shear strength value increased by about 37% compared to conventional laser welding. These results are due to the mitigation of pores in the welds by the introduction of the pre-heating step.
2. As a result of radiographic testing and SEM-EDS analysis, the three-step laser welding process effectively avoids the generation rate of internal pores due to the first and second steps of removing the galvanized layer in advance. These results indicate that zinc was not detected in welds without pores.
3. The result of the hardness profile analysis showed slightly higher at about 11 HV in the upper sheet and 27 HV in the lower sheet during three-step laser welding compared to conventional laser welding. However, it does not affect the weld quality.  
[PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] contributed to the study conception and design. The data analysis and manuscript were written by [PERSON]. The mechanical characteristics were investigated by [PERSON]. The metallic characteristics were investigated by [PERSON], [PERSON] and [PERSON] supervised the entire work. All authors have read and agreed to the published version of the manuscript.  
This paper has been written with the support of Jeolannam-do (\"2022 R&D supporting program\" operated by Jeonnam Technopark), development project: J012029-03.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Welding Condition** & **Radiographic Testing** \\ \hline LP (kW): 2.4 FP (mm): 20 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: X-ray radiograph of conventional laser welds.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Welding Condition** & **Radiographic Testing** \\ \hline LP (kW): 2.4 FP (mm): 20 & \\ \hline LP (kW): 2.4 FP (mm): 25 & \\ \hline LP (kW): 2.4 FP (mm): 25 & \\ \hline \hline \end{tabular}
\end{table}
Table 5: X-ray radiographs of the three-step laser welds.
# Exploring Boost Efficiency in Text Analysis by Using AI Techniques in Port Companies
## 5 Discussion
### Strategic and Technological Analysis in Ports  
The integration of hybrid models using machine learning (ML) and natural language processing (NLP) improves port management and strategic decision-making. These models facilitate better planning and resource utilization, optimize security and compliance through automated document analysis, and enable accurate, data-driven decision-making. In addition, they foster improved communication within the global port ecosystem and drive service innovation, thereby increasing port competitiveness.  
As detailed in Section 4, the strategic text analysis of ports and terminals in Chile and Spain highlights regional differences in the integration of sustainability and innovation--key elements for evolving into smart and sustainable ports. For example, the Port of San Antonio and the Port of Valparaiso in Chile are noted for their sustainability initiatives and effective management, demonstrating a proactive stance in adopting advanced technologies to improve logistics chain management. These ports are committed to sustainability and innovation, although actual implementation remains limited. The implementation of intelligent traffic management systems and real-time analytics has significantly improved logistics dynamics and reduced waiting times, thereby increasing port operational efficiency.  
In Spain, companies such as APM Terminals Algeciras and CSP Iberian Bilbao Terminal S.L. demonstrate a strong commitment to social responsibility and environmental sustainability. APM Terminals Algeciras has implemented technologies to reduce energy consumption and prevent pollution, while CSP Iberian Bilbao has become a SMART logistics center, adopting practices that promote sustainable development and improve operational efficiency. These cases illustrate different regional approaches to sustainability and innovation: Chilean ports prioritize operational efficiency and logistics integration, while Spanish ports emphasize social responsibility and environmental impact.  
Despite the progress made, there remains an essential need to integrate advanced technologies such as machine learning (ML) and natural language processing (NLP) into the operational strategies of all ports under study. The ability of these models to analyze and classify strategic information could greatly enhance sustainability management and innovation, offering significant opportunities to improve strategic decision-making and strengthen global competitiveness.  
Future research should focus on optimizing the use of these hybrid models to address critical challenges such as safety, operational efficiency and environmental sustainability. In addition, it is imperative to further explore the challenges and opportunities associated with using ML and NLP for business classification and strategic information management in the port context. Identifying how these technologies can be tailored to the specific needs of each port will be critical to maximizing their impact and ensuring successful implementation.
# Strain Measurement Distributed on a Ground Anchor Bearing Plate by Fiber Optic OFDR Sensor
## 3 Preparation of the Bearing Plate of Anchor System  
The bearing plate of the anchor system is shown in Figure 3 with a sensing fiber and four electrical strain gauges (ESG). The sensing fiber was installed on the bearing plate in a concentric circle with approximately five rotations in a radial direction at intervals of 0.5 cm. In addition, there is also an overlap of the optical fiber, which is installed with an additional rotation on the bearing plate. As a result, there were six rounds of data measured in the longitudinal direction of the sensing optical fiber; however, the data obtained from the overlapping fiber were removed prior to analyzing the results. The strain gauges were also installed, as shown in Figure 3a, at different distances from the anchor head, so that the effect on the spatial position on the bearing plate could be confirmed. Figure 3b is a photograph of the produced bearing plate. Figure 4 shows the distance domain signal of the bearing plate, which is obtained from the sensing optical fiber by the OFDR. This signal is obtained by fast Fourier transform (FFT) processing of the frequency domain signal obtained from the sensor. The light source was swept at a speed of 5 nm/s for a frequency range of 500 GHz to have a distance resolution of 0.2 mm (z = c/(2 ng\(\Delta\)v)). In Figure 4, the intensity of this signal is approximately 5 dB, and the  
Figure 2: Fiber optic optical frequency domain reflectometry (OFDR) sensor. (**a**) Schematic diagram (**b**) Fabricated OFDR. (TLS: tunable laser source, PD: Photo-detector, DAQ: data acquisition, CLK; Clock)  
length of the sensing fiber installed on the bearing plate is between 4 m and 9.5 m depending on the connector reflection.  
The strain can be obtained from the correlation frequency shift multiplied by the strain sensitivity, which is 0.7 \(\mu\epsilon/\)GHz, and the property of the sensing optical fiber. The correlation frequency shift is determined by cross-correlation processing between the reference and sensing distance domain signals of each segment position. The segment size was selected to be 5 cm. In this experimental study, strain data were obtained by moving at 1 mm intervals using distance data corresponding to each segment. With this segment size, the theoretical frequency resolution is 2 GHz, which equates to a strain resolution of 14 \(\mu\epsilon\)[12]. Figure 5 shows the signals measured nine times in the 8500-8600 mm distance range of the sensing fiber. Outside of the 8560-8600 mm range, variation is less than approximately \(\pm\)1 \(\mu\epsilon\). In this figure, it was considered that strain variation in the range of 8560-8600 mm would be affected by small movements of the sensing fiber. Even with this effect in mind, the impact on strain measurement accuracy is less than \(\pm\)2 \(\mu\epsilon\).  
Figure 4: Distance domain signal of OFDR sensor.  
Figure 3: The bearing plate with a sensing optical fiber and four electrical strain gauges. (**a**) Conceptual drawing (**b**) Photograph.
## 3 Experimental Setup
### Hardware Implementation
#### 3.1.4 Advanced Metering Infrastructure (AMI) Monitoring Device  
The AMI monitoring device enables the real-time monitoring of power consumption, serving as a critical tool for continuous optimization. It measures the building's total power consumption and transmits these data to the AI-based prediction model, which uses the feedback to refine its forecasting accuracy through iterative learning. The AMI monitoring device also supports energy management decisions by providing precise consumption data, allowing comparisons between predicted and actual power usage. Smart meters within the AMI communicate via Ethernet, ensuring fast and reliable data transmission for real-time power monitoring and model enhancement.
## 1 Introduction  
ST is a giant gas field located in the north of the West Siberia Basin (Figure 1) [1, 2]. Previous studies in this area often focus on post-Jurassic shallow marine to continental sandstone reservoirs which host most of the dry gas reserves. A lot has been done in terms of its tectonoststratigraphic evolution [3, 4], structural framework [5, 6], stratigraphic sequence division and correlations [2, 7], interpretation of the sedimentary facies and facies associations [8, 9], as well as hydrocarbon accumulations [1, 10, 11, 12]. However, limited research has been conducted on the underlying Jurassic strata which are expected to host potential oil and wet gas, and may represent future exploration and exploitation targets [1, 2]. Therefore, we focused on the Jurassic strata in the ST gas field in this study by conductinga comprehensive stratigraphic sequence analysis to build a sequence evolution model of Jurassic strata in order to facilitate future exploration.  
Sequence stratigraphy deals with the characterization of genetically related strata, bounded by an unconformity surface or corresponding conformable surface [13]. The deposition of stratigraphic sequences is determined by factors like tectonic evolution, sea level change, and sediment supply [14; 15]. By using well logging data, the vertical stacking patterns of Jurassic strata are analyzed to determine the lithologic successions which should be consistent with the sea level change [14; 15]. Seismic profiles are also used to study the seismic characteristics of the sequence boundaries [16]. Guided by the theory of sequence stratigraphy, the sequence division and correlation can be carried out and a sequence framework can be built, which is the basis of further studies such as sedimentary facies and facies associations, as well as sedimentary models and the main types of reservoir sand bodies [17; 18].  
In this paper, the stratigraphic sequence framework of Jurassic strata is established using high-resolution sequence stratigraphy and correlation analysis of drill cores, well logs, and seismic sections from selected wells in the ST gas field. Furthermore, the sequence evolution model of the third-order sequence framework is developed on the basis of sequence division and correlation.
## 3 Methodology
### Long Short-Term Memory (LSTM)  
Figure 4 illustrates the LSTM structure [19; 49; 50]. A unit cell of LSTM receives, computes, and transfers a cell state and a hidden state. The cell state refers to a status of filtered data flow through the cell, considered long-term memory, whereas the hidden state is considered short-term memory [19; 49; 50].  
Figure 2: Distance matrix between 13 wellbores in the UBGH2 field.  
The unit cell consists of three gates to control data flow: the forget gate, input gate, and output gate. Let \(f_{t}\), \(i_{t}\), \(o_{t}\), and \(h_{t}\) be the outputs of the forget gate, input gate, output gate, and \(\tanh\) layer at the current \(t\)-th step, respectively [19; 49; 50]. Furthermore, the subscripts \(f\), \(i\), \(o\), and \(h\) are the forget gate, input gate, output gate, and \(\tanh\) layer, respectively.  
Figure 4: LSTM structure with a unit cell.  
Figure 3: ANN structure.  
The forget gate \(f_{t}\) conducts selective preservation of data transferred from the previous hidden state \(h_{t-1}\) and current input data \(x_{t}\) using Equation (2):  
\[f_{t}=\sigma\big{(}\omega_{f}\cdot[h_{t-1},\ x_{t}]+b_{f}\big{)}, \tag{2}\]  
where \(\omega\) is the weight, \(b\) is the bias, and \(\sigma\) is the sigmoid function. As \(\sigma\) converges to zero, data are forgotten, while \(\sigma\) = 1 indicates complete data preservation [19; 49; 50].  
The input gate \(i_{t}\) determines whether to store new data through two stages. First, new data in \(i_{t}\) are filtered from \(h_{t-1}\) and \(x_{t}\) (Equation (3)). Second, the hyperbolic tangent layer (i.e., tanh layer) generates candidate values \(\overline{C_{t}}\) that can be updated in the cell state (Equation (4)):  
\[i_{t}=\sigma(\omega_{i}\cdot[h_{t-1},\ x_{t}]+b_{i}), \tag{3}\]  
\[\overline{C_{t}}=\tanh(\omega_{c}\cdot[h_{t-1},\ x_{t}]+b_{c}). \tag{4}\]  
Here, \(\overline{C_{t}}\) is the candidate value and \(\tanh\) is the hyperbolic tangent function.  
The cell state receives data selectively from \(i_{t}\) and \(\overline{C_{t}}\). The current cell state \(C_{t}\) is updated using Equation (5), where \(\bullet\) is the Hadamard product:  
\[C_{t}=f_{t}\bullet C_{t-1}\ +i_{t}\bullet\overline{C_{t}}. \tag{5}\]  
Finally, the unit cell exports the current hidden state \(h_{t}\) using Equations (6) and (7). The amount of data from \(h_{t-1}\) and \(x_{t}\) to be released is determined in the output gate \(o_{t}\) (Equation (6)). Then, \(h_{t}\) is derived through Equation (7).  
\[o_{t}=\sigma(\omega_{o}\cdot[h_{t-1},\ x_{t}]+b_{o}), \tag{6}\]  
\[h_{t}=o_{t}\bullet\tanh(C_{t}). \tag{7}\]
## 3 Results and Discussion
### Qualitative Properties  
#### 3.5.1 Color  
As can be seen in Figure 7, by raising the air temperature from 50 to 70 \({}^{\circ}\)C, the overall color change significantly increased (\(p<0.05\)). One of the reasons is that, during the process of food drying, the pigments are decomposed due to slow heating and will cause discoloration in the products [18]. [PERSON] et al. showed that increasing the temperature causes papaya color changes in a hot air dryer [35]. [PERSON] and [PERSON] achieved similar results for drying pomeranganate [50]. For drying kiwifruit in a hot air dryer at different temperatures, [PERSON] et al. showed that the color changes of kiwifruit increased by increasing the temperature [20]. Additionally, by increasing the thickness of the sample, the overall color changes increased, which was due to the fact that at high thicknesses, as the drying time increases, the kiwifruit's slices were exposed to hot air for a longer period of time, and the high heat exposure to kiwifruit slices intensified the decomposition of pigments [26]. Therefore, at a thickness of 4 mm, the least amount of overall color changes was seen. The use of ultrasonic pretreatment also reduced color changes. Increasing the time of ultrasonic application reduced the rate of color changes in the ultrasonic pretreatment. Additionally, applying ultrasonic pretreatment time had less color changes than the control sample. The color changes during kiwifruit drying are due to various factors such as thermal degradation of chlorophyll, oxidation, enzymatic reactions and non-enzymatic browning reactions [51]. [PERSON] et al. studied the drying of blackberries and concluded that the least amount of overall color change was observed at 30 min ultrasonic radiation time [37]. In another study, [PERSON] et al. examined the color changes of _Mirabelle plum_ in the hot air dryer with ultrasonic pretreatment [52]. They showed that the amount of color changes decreases with increasing ultrasonic radiation time. Further, the results obtained by [PERSON] et al. study, which was gathered on kiwifruit slices in the hot air dryer with ultrasonic pretreatment, showed that the highest amount of overall color change was 32.45 in the control sample and the lowest color change amount of 9.59 was observed in the ultrasonic radiation time of 30 min [27].
###### Abstract  
We review recent results on exploiting microwave photonics to enable simultaneous interrogation of multiple fiber Bragg grating (FBG)-based sensors. In particular, we describe the use of (1) microwave photonic filtering and (2) chirped microwave pulse generation and compression as a means to map the wavelength (spectral) changes in the response of FBG-based sensors (specifically, an in-fiber Fabry-Perot cavity sensor based on FBGs, FBG sensors directly, and a linearly chirped FBG sensor) to applied temperature (or strain) to the power of a radio-frequency signal (i.e., a wavelength-to-power mapping) or to the correlation peak of the compressed microwave signal. The approaches support high-resolution and high-speed interrogation and can be suitable for large scale sensing networks.  
fiber Bragg grating (FBG) sensors; fiber optic sensors; microwave photonics +
Footnote †: journal: Applied Sciences
## 3 Results
### Land Surface Temperature Distribution during El Nino and La Nina in Kuching City from Landsat
ENSO events have a significant influence on local temperatures, and RS technology can effectively \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Date**} & \multirow{2}{*}{**ONI Value**} & \multicolumn{2}{c}{**Statistic Value**} & \multirow{2}{*}{**Date**} & \multirow{2}{*}{**ONI Value**} & \multicolumn{2}{c}{**Statistic Value LST**} & \multicolumn{1}{c}{**LST Difference**} \\  & & & & & (\({}^{\circ}\)C) & (\({}^{\circ}\)C) \\ \hline \multirow{3}{*}{24 October 2018} & \(-1\) & Maximum (31.39) & & & Maximum (35.90) & Maximum (4.51) \\ \cline{2-6}  & \(-1\) & Minimum (19.10) & \multirow{3}{*}{6 July 2016} & \(1\) & Minimum (24.29) & Minimum (5.29) \\ \cline{3-6}  & & Mean (24.8) & & & Mean (26.90) & Mean (2.10) \\ \hline \multirow{3}{*}{21 August 2018} & \(-1\) & Maximum (31.71) & & Maximum (34.33) & Maximum (2.62) \\ \cline{3-6}  & \(-1\) & Minimum (23.45) & \multirow{3}{*}{28 July 2015} & \(1.1\) & Minimum (25.19) & Minimum (2.74) \\ \cline{3-6}  & & Mean (24.2) & & & Mean (26.72) & Mean (2.52) \\ \hline \multirow{3}{*}{9 November 2011} & \(-1.1\) & Maximum (30.55) & \multirow{3}{*}{3 April 1998} & \multirow{3}{*}{(El-Nino)} & \multirow{3}{*}{3 April 1998} & \multirow{3}{*}{(El-Nino)} \\  & (La Nina) & & & & Maximum (33.31) & Maximum (2.76) \\ \cline{3-6}  & & Minimum (19.42) & & & Minimum (26.19) & Minimum (6.77) \\ \cline{3-6}  & & Mean (24.91) & & & Mean (27.81) & Mean (2.9) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Information on the statistical values of maximum, minimum, and mean LST Landsat during ENSO events in Kuching City. Figure 8: LST distribution during El Niño and La Nina from MODIS. record and provide information on hot and cold focal spaces during La Nina and El Nino events, as depicted in Figure 7.  
Before the advent of RS technology, predicting phenomena such as floods, earthquakes, hurricanes, and droughts was challenging. Researchers worldwide are actively developing specific models to alert the public and enhance preparedness for unexpected events, such as ENSO incidents. By utilizing RS data, the occurrence of natural disasters can be predicted, enabling responsible parties and residents in Kuching, Sarawak, to be better prepared to face events such as droughts and floods, thereby mitigating property damage and the loss of lives.