esa-sceva/satcom-chunk-collection huggingface dataset:
# Membrane Engineering for Sustainable Development: A Perspective
## 2 Membrane Operations in Sustainable Development: Case Scenario
### Energy Consumption and Production
Internal concentration polarization (due to the increase of concentration inside the membrane) should be minimized by properly designing the support layer of the membrane. At the overall system level, better pretreatment strategies and energy conversion systems are needed. For what concerns RED, currently membrane price counts for 50%-80% of the capital cost. Therefore, the process will become economically viable when membrane price will be reduced. More specifically, the membrane price should decrease from the existing price of 10-30 euros/m\({}^{2}\) down to 2-5 euros/m\({}^{2}\). The composition and concentration of high concentrated solution should be optimized. Better and cheap pretreatment of low concentrated solutions need to be sought. Better pumping systems capable to reduce the energy associated with circulation of the two streams are also needed. In addition to PRO and RED, MD is also emerging as a candidate to produce energy [18; 19]. Operational principle of MD involves the use of a microporous hydrophobic membranes that allows the passage of vapors only. The vapors are condensed on the other side by using different approaches, depending upon the configuration. In direct contact membrane distillation (DCMD), cold water is used as the condensing media. Hydrophobic membrane material and small pore size resists the penetration of liquid into the pores from either sides of the membrane. Under the constant volume, the condensing vapor create the pressure on condensing (permeate) side of the membrane which, similar to PRO, can be used to run a hydro-turbine as shown in Figure 3. Thus MD can be used for the simultaneous production of freshwater (and also raw materials when operated as membrane crystallization) and energy. However, for commercial viability of this concept, better membranes with improved hydrophobic properties (liquid entry pressure > 10 bar) are required. Figure 2: Operational principle of the pressure retarded osmosis (PRO) process. Reproduced with permission from [17]. Copyright Elsevier, 2013.
# The Simulation of Dielectric Barrier Discharge for Breakdown Voltage in Starch Modification
## 3 Results and Discussion
### Effect of Starch Granule Diameter  
The dielectric layer was initially a sphere-shaped starch with a 10-micron diameter, 3.1 relative permittivity values, and a thickness of 0.6 mm. Following that, the diameter was adjusted to 50 and 150 microns, respectively. The same procedures as in the previous investigation to determine the ionization voltage were used. The breakdown voltage from this study is presented in Table 6.  
Figures 17 and 18 refer to the breakdown voltage of the product of the pressure and gap distance (\(pd\)) between any two electrodes for the dielectric layer as sphere shapes with 10-, 50-, and 150-micron diameters, based on the data shown in Table 6.  
The findings from the simulation indicate that the mesh-plate electrode may initiate discharge by utilizing a voltage lower than the parallel plate electrode when considering the effect of the type of electrode. The difference in breakdown voltage is about 5.7% for the small gap, while the large gap is about 2.1% at the same pressure for three diameters. Additionally, the results demonstrate that the breakdown voltages of the mesh electrode and standard electrode for three starch dimensions are very similar, as illustrated in Figure 18. This means that the voltage sufficient to initiate the gas to break down does not impact the diameter factor. This may be due to the electric field quantities in this work, associated with macroscopic averages all over the entire number of starch molecules, and it is not a result that occurs with a single starch grain.  
Figure 16: Paschen curves for three different starch thickness layers (0.6, 1.0, and 1.5 mm) using (**a**) parallel plate electrode and (**b**) mesh-plate electrode.
## 2 Materials and Methodology
### Methodology  
#### 2.2.1 Experimental Setup  
The HS under study is designed as an aluminum rectangular enclosure (51 mm \(\times\) 21 mm \(\times\) 25 mm) to avoid the leakage problem during the melting stage of the PCMs. HS is accommodating equally spaced (1.6 mm) vertically aligned rectangular fins. The four lateral walls and top cover of HS are 1-mm thick aluminum sheets. Figure 1A depicts the schematic diagram of the thermal management experimental setup, while Figure 1B shows a zoomed section of the HS enclosure.  
The heat generator (RCHB-10 ND, Digi-Key Electronics, Thief River Falls, MN, USA) is connected to a DC Power Supply (PPT-3615, Stantronic Instruments, Northampton, UK) to apply the heat load. The heat is conducted from heat generator surface to HS surface contacted through a thin sandwich layer of conductive thermal interface material (\(K\) = 3.8 W/(m\(\cdot\)K) [39] intended to reduce the contact resistance. Calibrated K-type (Chromel-Alumel) thermocouples with an error within \(\pm\)0.5 \({}^{\circ}\)C are installed at different HS locations, as shown in Figure 1B, to measure the transient temperature rise. A 1.6 W DC fan (259-1547-ND, Digi-Key Electronics, Thief River Falls, MN, USA) is applied to  
Figure 1: Schematic diagram of the whole thermal management experimental setup (**A**); and zoomed in section of heat sink (HS) enclosure (**B**).  
venilate the HS at an air exchange rate of 9.9 Cubic feet per minute (CFM) at 25 \({}^{\circ}\)C. A Mini-Vane CFM Anemometer (AN340, Extech Instruments by Flir, Townsend West Nashua, NH, USA) measures air velocity past the HS walls. The test setup is thermally guarded in an insulated compartment employing 10 cm-thick EPS (Expanded Polystyrene) insulator sheets with a thermal conductivity of 0.042 W/(m\(\cdot\)K) to assure stable ambient conditions. The data are logged using an NI Compact-RIO Data Acquisition System (NI DAQ Pro-cDAC-9174, National Instruments, Austin, TX, USA) interfaced with Lab-VIEW GUI on a dedicated computer.
# A Novel Fused Multi-Class Deep Learning Approach for Chronic Wounds Classification
## 4 Experimental Setup and Results
### Datasets
#### 4.1.1 Medetec Dataset  
The Medetec wound dataset contains the free stock images of different types of wounds, including venous ulcers, pressure ulcers, burn injuries, and other miscellaneous wound types. Overall, this dataset has 14 wound types and 592 wound images. All of the images are in the jpeg format and have different sizes, with the width ranging from 358 to 560 pixels, and the height ranging from 371 to 560 pixels. For our experimentation, we have considered the three types of wounds, namely Diabetic, Pressure, and Venous.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Training**} & \multicolumn{2}{c}{**Testing**} & \multicolumn{2}{c}{**Total**} \\ \cline{2-7}  & \multirow{2}{*}{**Original**} & \multicolumn{2}{c}{**After**} & \multirow{2}{*}{**Original**} & \multicolumn{2}{c}{**After**} & \multirow{2}{*}{**Original**} & \multicolumn{2}{c}{**After**} \\  & & **Augmentation** & & & **Augmentation** & & **Augmentation** \\ \hline \multicolumn{7}{c}{Medetec Dataset} \\ Diabetic & 36 & 576 & 12 & 192 & 48 & 768 \\ Pressure & 130 & 2080 & 44 & 704 & 174 & 2784 \\ Venous & 100 & 1600 & 34 & 544 & 134 & 2144 \\ \hline \multicolumn{7}{c}{Extended Medetec Dataset} \\ Diabetic & 130 & 2080 & 44 & 704 & 174 & 2784 \\ Pressure & 130 & 2080 & 44 & 704 & 174 & 2784 \\ Venous & 100 & 1600 & 34 & 544 & 134 & 2144 \\ \hline \multicolumn{7}{c}{AZH Dataset} \\ Background & 75 & 1200 & 25 & 400 & 100 & 1300 \\ Normal & 75 & 1200 & 25 & 400 & 100 & 1300 \\ Diabetic & 139 & 2224 & 46 & 736 & 185 & 2960 \\ Pressure & 100 & 1600 & 34 & 544 & 134 & 2144 \\ Venous & 185 & 2960 & 62 & 992 & 247 & 3952 \\ Surgical & 122 & 1952 & 46 & 736 & 168 & 2688 \\ \hline \multicolumn{7}{c}{Extended AZH Dataset} \\ Diabetic & 185 & 2960 & 62 & 992 & 247 & 3952 \\ Pressure & 185 & 2960 & 62 & 992 & 247 & 3952 \\ Venous & 185 & 2960 & 62 & 992 & 247 & 3952 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Datasets Details.
# An ECC-Based Anonymous and Fast Handover Authentication Protocol for Internet of Vehicles
## 3 Preliminaries
### Security Requirements  
1. Mutual Authentication: During the mutual authentication process, both parties mutually authenticate each other's legitimacy and verify their authorization to conduct secure encrypted communications.
2. Anonymity: During handover authentication procedures, ensuring the confidentiality of vehicle privacy information assumes paramount importance. Vehicle privacy data must not be transmitted in plaintext format, with access restricted exclusively to authorized entities possessing legitimate entitlements to such sensitive information.
3. Unlinkability: During the handover authentication process, even if an attacker intercepts certain messages, it remains fundamentally infeasible to ascertain whether these messages originate from the same user terminal.
4. Perfect Forward Secrecy: To protect the confidentiality of messages exchanged during prior sessions, the protocol must ensure the provision of perfect forward secrecy. This guarantees that even if an adversary obtains the long-term private keys of the participants, the session keys established in previous communications remain secure against compromise.
5. Defend Against Various Common Attacks: To enhance the security of the authentication process, the proposed protocol should be capable of resisting common attacks such as vehicle impersonation attacks, RSU impersonation attacks, replay attacks, man-in-the-middle (MITM) attacks and other known attacks.
6. Penalty and Exposure: Malicious vehicles compromising system security must be subject to sanctions by the Trusted Authority (TA) through cryptographic revocation mechanisms.
# Review on Rail Damage Detection Technologies for High-Speed Trains
###### Abstract  
From the point of view of the intelligent operation and maintenance of high-speed train tracks, this paper examines the research status of high-speed train rail damage detection technology in the field of high-speed train track operation and maintenance detection in recent years, summarizes the damage detection methods for high-speed trains, and compares and analyzes different detection technologies and application research results. The analysis results show that the detection methods for high-speed train rail damage mainly focus on the research and application of non-destructive testing technology and methods, as well as testing platform equipment. Detection platforms and equipment include a new type of vortex meter, integrated track recording vehicles, laser rangefinders, thermal sensors, laser vision systems, LiDAR, new ultrasonic detectors, rail detection vehicles, rail detection robots, laser on-board rail detection systems, track recorders, self-moving torelleys, etc. The main research and application methods include electromagnetic detection, optical detection, ultrasonic guided wave detection, acoustic emission detection, ray detection, vortex detection, and vibration detection. In recent years, the most widely studied and applied methods have been rail detection based on LiDAR detection, ultrasonic detection, eddy current detection, and optical detection. The most important optical detection method is machine vision detection. Ultrasonic detection can detect internal damage of the rail. LiDAR detection can detect dirt around the rail and the surface, but the cost of this kind of equipment is very high. And the application cost is also very high. In the future, for high-speed railway rail damage detection, the damage standards must be followed first. In terms of rail geometric parameters, the domestic standard (TB 10754-2018) requires a gauge deviation of \(\pm\)1 mm, a track direction deviation of 0.3 mm/10 m, and a height deviation of 0.5 mm/10 m, and some indicators are stricter than European standard EN-13848. In terms of damage detection, domestic flaw detection vehicles have achieved millimeter-level accuracy in crack detection in rail heads, rail waists, and other parts, with a damage detection rate of over 85%. The accuracy of identifying track components by the drone detection system is 93.6%, and the identification rate of potential safety hazards is 81.8%. There is a certain gap with international standards, and standards such as EN 13848 have stricter requirements for testing cycles and data storage, especially in quantifying damage detection requirements, real-time damage data, and safety, which will be the key research and development contents and directions in the future.  
today2020 IEEE/RSJ International Conference on Intelligent Systems (ISIS), 2015 IEEE International Conference on Intelligent Systems (ISIS), 2015 IEEE International Conference on Intelligent Systems (ISIS), 2
## 3 Results and Discussion
### Method Validation
#### 3.2.7 Robustness  
For the robustness of the system, the effect of small changes (\(\pm 1\)) of some chromatographic parameters (\(\Delta\)max, flow rate of the mobile phase, and column temperature) on the tailing factor (Tf) and the AUC value (area under the curve) for each substance was investigated separately. According to the results (% RSD), none of these small changes affected the efficiency of the method (Table 5).
# Overview of Distributed Machine Learning Techniques for 6G Networks
## 4 Distributed Machine Learning Algorithms
### Main Applications  
The main applications of distributed learning methods related to 6G scenarios can be individuated in the following main categories (Figure 5):  
* **Non-terrestrial Networks:** Recently, various new non-terrestrial networking platforms have been integrated into the terrestrial networking systems to increase the available resource pool, limit the security-related challenges, and add flexibility and more sustainability to plans for natural disasters. Various distributed learning techniques have found their applications in the newly added non-terrestrial networks. The main applications include the cooperative spectrum sharing [30], trajectory design [31], traffic optimization [32], security [33], and task and resource allocation [34].
* **Vehicular Networks:** Distributed learning methods are widely used to solve the challenging problems in vehicular networks. The main applications include intelligent object detection [35], network resource allocation, vehicular data sharing [36], computation offloading to edge-computing-enabled systems [37], traffic light control [38], spectrum sharing [39], and intrusion detection [40].
* **Power System:** Recently, various distributed learning methods have been used to solve power system-related problems [41]. Voltage control [42], energy management [43], demand predictions [44], transient stability enhancement, and resilience enhancement [45] are some of the main areas of power systems where distributed learning is succinctly used.
* **E-health:** E-health systems are getting packed with various new applications with high computational complexities and resource requirements [46]. Various distributed learning techniques have found applications in e-health systems. Given the sensitive nature of medical data, privacy-preserving FL approaches have gained a lot of interest. The recent advances in FL technology, the motivations, and the requirements of using FL in smart healthcare, especially for the Internet of Medical Things, are presented in [47]. In [48], main security challenges and the mitigation techniques for using an FL for healthcare systems are discussed. Additionally, a multilayered privacy-protective FL platform is proposed for healthcare-related applications.  
Figure 5: Main applications of distributed learning.
###### Abstract  
Accurate labeling of the origin of food ingredients is essential to ensure quality and safety; however, establishing a reliable identification method remains an urgent task. The origin and variety of dried kelp are generally identified based on their morphological characteristics; however, they are difficult to distinguish unless experts are involved. In addition, genetically close varieties have almost no differences in their base sequences; therefore, the accuracy of conventional identification methods using genetic analysis is limited. This study aimed to develop a system for identifying the origin and variety of dried kelp using fluorescence fingerprint data obtained by fluorescence spectroscopy and a convolutional neural network (CNN). The fluorescence characteristics of dried kelp were measured in the range between 250 and 550 nm. The obtained fluorescence fingerprint data were converted into image data and analyzed using a CNN model implemented in Python, TensorFlow, and Keras. Unlike conventional methods that rely on morphological characteristics and genetic analyses, by combining fluorescence spectroscopy and CNN, a high identification accuracy of 98.86% was achieved even for genetically close varieties. These results highlight the excellent potential of fluorescent fingerprints in identifying the origin and variety of food and are believed to contribute to preventing food fraud and quality control.  
machine learning; convolutional neural network (CNN); fluorescence spectroscopy; fluorescence fingerprinting; food origin identification; food variety identification; kelp; kombu Article
## 2 Experimental Section
### Materials Characterization  
The X-ray diffraction (XRD) measurements were carried out using Rigaku SmartLab (Austin, TX, USA) [Cu K] radiation (1.54 A). The Raman spectra were produced using a WITEC/alpha 300 Raman system (Ulm, Germany) (with a wavelength of 532 nm and a He-Ne laser source). Field emission scanning electron microscopy (FESEM) apparatus JEOL/JSM-7800F (Tokyo, Japan) was used to study the morphology of as-synthesized electrode material and EDS (energy-dispersive X-ray spectroscopy). On a Compactstat.h IVIUM Technologies electrochemical workstation (Eindhoven, The Netherlands), electrochemical experiments were conducted on as-deposited (0.5 \(\times\) 1 cm\({}^{2}\)) electrode materials in a 1 M KOH electrolyte solution for three-electrode measurement (counter electrode-Pt wire and reference electrode-Ag/AgCl). The FTIR spectrum for electrode material deposited on NF substrate was obtained via FT-IR Microscope: HYPERION 2000 (Billerica, MA, USA).  
A PHI 5000 VersaProbe II XPS was used to perform the XPS analysis (monochromatic Al X-ray source and Ar ion gun). XPSPEAK (4.1) software was used to fit the XPS spectra curves (80 percent Gaussian/20 percent Lorentzian ratio).
One key issue when incorporating different additives into cement-bound mixtures is their possible effect on the durability of the resulting material. Particularly, due to the alkaline nature of cement-bound materials, alkaline-resistant glass fiber should be used. The use of such fibers in pavement Portland cement concrete was investigated by [PERSON] et al. [24], who found their high efficacy in increasing the mechanical properties of the composite. Despite the corrosion of the glass fibers not being completely halted, it was found that the strength of the reinforced samples was significantly improved. It was also confirmed that, by adding 40% of fly ash (by the weight of the cement), the performance of the glass-fiber-reinforced material was improved, as shown by other researchers [25]. Other works investigating fly-ash-stabilized soils and granular mixtures were also conducted. [PERSON] et al. [26] have investigated the use of 0.5% to 5% steel, polypropylene, and polyvinyl alcohol (PVA) by mass in fly-ash-bound geopolymer mixtures. This study showed that the improvement of certain mechanical properties (compressive strength, shrinkage, and creep) was possible with different fibers and their dosages--the increase in compressive strength was up to 11.4%. [PERSON] and [PERSON] [27] in their work have observed significant changes in the stress-strain performance of fly ash mixtures with 0.5 to 1.0% of polyester fiber, obtaining an up to 150% increase in unconfined compressive strength. The inclusion of the fibers also provided residual strength to the material, which otherwise exhibited brittle behavior. Based on the conducted literature review, it was concluded that different types of fibrous reinforcement materials can be used to significantly improve the performance of both low- and high-quality granular materials stabilized with different cementitious materials. Therefore, we have investigated the possibility of improving the performance of aggregates stabilized with a mixture of a small amount of Portland cement (1%) and increasing amounts of fly ash (3.5-14%) by using a mixture of polypropylene and alkali-resistant glass fibers added in amounts of 0.05-0.10%.
# Chinese Document-Level Emergency Event Extraction Dataset and Corresponding Methods
## 2 Related Work  
### Document-Level Event Extraction Dataset  
Event extraction datasets serve as the cornerstone of event extraction research, and in our investigation, we explore the existing DEE datasets.  
MUC-4 [6]: MUC-4 was presented at the Fourth Conference on Message Comprehension. The dataset consists of 1700 documents, where five types of events are annotated using an associated role-populated template. The dataset is mainly dedicated to the challenge of argument scattering in task-oriented scenarios.  
WIKIFEVENTS: The WIKIFENTS was published by [PERSON] et al. [7] as a benchmark dataset at the document level. The dataset is derived from English Wikipedia articles that describe real-world events.  
Google: The Google dataset is a subset of the GDELT Event Database1, consisting of event-related word searches and containing documents related to 30 event types sourced from 11,909 news articles.  
Twitter: The Twitter dataset was collected from tweets released in December 2010 applying the Twitter Stream API. It includes 20 event types and 1000 tweets.  
NO.ANN, NO.POS, NO.NEG [8]: The researchers studied four types of financial events: equity freeze events, equity pledge events, equity repurchase events, and equity increase events. They labeled 2976 announcements. NO.ANN represents the number of announcements that can be automatically labeled for each event type. NO.POS represents the total number of positive case mentions, while NO.NEG conversely represents the number of negative mentions.  
ChFinAnn [9]: Using financial reports as the data source, we use the event knowledge base for event annotation in a remote-supervisory-based manner. Based on NO.ANN, NO.POS, and NO.NEG, the Chinese DEE dataset in finance was further enriched and extended to include 32,040 documents and 5 event types, including equity freeze, equity repurchase, equity reduction, equity increase, and equity pledge.  
Roles Across Multiple Sentences (RAMS): RAMS was annotated by [PERSON] et al. [12], who used news as the data source. The dataset contains 3194 documents, encompassing 139 event types and a total of 9124 events.  
We performed preliminary statistics on the available DEE datasets, as shown in Table 1.  
The existing DEE datasets are mostly annotated in English, which is very helpful for DEE in English. However, the available datasets for Chinese DEE tasks are relatively sparse and do not cater to DEE tasks specifically related to emergency events.