esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Chemicals  
Carbon dioxide (CO\({}_{2}\), SFE grade), contained in a dip tube cylinder, was purchased from Air Products Sp, Poland. Methanol for HPLC-super gradient were purchased from POCh (Gliwice, Poland), Folin-Ciocalteu's reagent, and hydrochloric acid 35-38% purity was purchased from Chempur (Piekary Slaskie, Poland). Glacial acetic acid, 3,4,5-trihydroxybenzoic acid (gallic acid), and 2,2-diphenyl-1-picrylhydrazyl (DPPH), iron (III) chloride, sodium acetate, sodium carbonate anhydrous, potassium acetate, phosphate buffered saline tablet, potassium persulphate, 2,4,6-tris(2-pyridyl)-s-triazine (TPTZ), and 2,2\({}^{\prime}\)-azino-bis(3-ethylbenzothazoline)-6-sulphonic acid (ABTS) were purchased from Sigma-Aldrich (St. Louis, MO, USA). All phenolic standards including _trans_-ferulic acid, _trans_-cinnamic acid, _trans_-hydroxycinnamic acid, syringic acid, synapic acid, vanillic acid, _p_-coumaric acid, caffeic acid, caffeic acid phenethyl ester (CAPE), kaempherd, quercetin, (+)-catechin, (\(-\))-epicatechin, apigenin, rutin, myricetin, pinoconbrin, pinobanksin, pinostrobin, galangin, chrysin, and naringenin were purchased from Sigma-Aldrich (St. Louis, MO, USA). All chemicals were of analytical grade.
# An Overview of Hydrogen's Application for Energy Purposes in Lithuania
## 6 Current Situation and Prospects of Hydrogen Use in Lithuania
Judging from the tendencies and strategic documents it is most likely that the largest industrial companies and heavy transport will dominate the hydrogen production and consumption market in Lithuania absorbing the largest share of power from renewable sources including offshore wind farms in the Baltic Sea. The first commercial power-to-gas project is being implemented in Lithuania; it began in 2021 and is expected to start producing green hydrogen in 2024. The innovation agency predicts [58] that Lithuania's industry could produce around 20 KT of green hydrogen in the period of 2026-2030 and could gradually increase this amount to 125-320 KT H\({}_{2}\) between 2041-2050 (Figure 3). As mentioned in previous chapters, while green hydrogen's cost cannot compete with grey hydrogen yet, it could become competitive by 2030 under two main conditions: electricity price in the range of 10-40 EUR/MWh and 3000-6000 electrolyser's full-load hours per year (capacity factor 34-68%) [58]. Lithuania has high chances of meeting these conditions, due to the rapid increase in renewable energy (wind and solar PV) deployment which will lead to gradual reduction in electricity prices locally. Moreover, renewable energy generation has exceeded the total national power demand for one hour (>1 GW) recently; this will become more frequent with growing installed capacities periods with surplus electricity, which will pave the path to competitive power-to-gas project implementations. Hydrogen demand in Lithuania is going to exceed the limits of industrial sector as a well-developed natural gas network is planned to be adapted to hydrogen infrastructure. Synergies between the energy sectors will allow the development of joint projects (e.g., the creation of hydrogen valleys), where cooperation between industry, transport, energy and other sectors is possible. Moreover, clear signs of serious determination to be in front of technological development and readiness to sectoral changes are shown by state gas transmission operator Amber Grid. Similarly to other gas transmission system operators  
Figure 3: Green hydrogen production scenarios in Lithuania’s industry (2026–2050). Adapted from [58]. Figure 2: Electricity demand for green hydrogen production in 2030 and 2050 (data taken from [63]). across Europe, they are ready and are already taking actions to upgrade their infrastructure to allow hydrogen or a mixture of hydrogen and natural gas to flow through existing natural gas pipelines to the end users.
## 2 Method and Materials
### Process Modeling and Optimization
#### 2.3.2 Methodology of DE Optimization
\[Fit=\frac{1}{\left(\varepsilon_{rate}\right)_{n}}+\left(\sigma_{eff}\right)_{n}+ \left(\varepsilon_{eff}\right)_{n}+\left(F\right)_{n} \tag{5}\]  
The normalized values for AISI 1010, 1018 and 1045 are expressed in Equations (6)-(8), respectively,  
\[\text{AISI 1010 }\left(F\right)_{n} =\frac{F}{131};\left(\sigma_{eff}\right)_{n}=\frac{\sigma_{eff}} {1050};\left(\varepsilon_{eff}\right)_{n}=\frac{\varepsilon_{eff}}{5.5}; \left(\varepsilon_{rate}\right)_{n}=\frac{\varepsilon_{rate}}{76}; \tag{6}\] \[\text{AISI 1018 }\left(F\right)_{n} =\frac{F}{133};\left(\sigma_{eff}\right)_{n}=\frac{\sigma_{eff}} {1060};\left(\varepsilon_{eff}\right)_{n}=\frac{\varepsilon_{eff}}{5.7}; \left(\varepsilon_{rate}\right)_{n}=\frac{\varepsilon_{rate}}{90};\] (7) \[\text{AISI 1045 }\left(F\right)_{n} =\frac{F}{177};\left(\sigma_{eff}\right)_{n}=\frac{\sigma_{eff}} {1230};\left(\varepsilon_{eff}\right)_{n}=\frac{\varepsilon_{eff}}{5.7}; \left(\varepsilon_{rate}\right)_{n}=\frac{\varepsilon_{rate}}{73}; \tag{8}\]  
The DE algorithm for all three AISI steel grade materials with 50 chromosomes (population size), 50 maximum generations, crossover coefficient (\(K\)) of 0.5, and mutation coefficient (\(F\)) of 0.5 has been set and run through the developed MATLAB DE program. The plots for optimal combinations of control factors and responses have been explained in the subsequent Section 3.3.
## 4 Results and Discussion  
### Back-Arc Spreading Vents and Deposits  
Many similarities exist between the contemporary marine geologic formation of back-arc areas and deposits that are currently mined on land [56; 57]. Sulfide deposits are reported in back-arc spreading, like Central Manus Basin [58] and the Mariana Trough [59; 60]. These findings prompted extensive exploration of marginal basins, arc systems, and back-arc systems in the Western and Southwestern Pacific during the late 1980s. Since then, a wide range of mineral deposits have been found in back-arc rafts at various stages of development (immature versus mature), on volcanoes along the active volcanic fronts of arcs, and in rifted fore-arc environments. Notable examples of polymetallic massive sulfide deposits have been reported from mature back-arc spreading centers such as  
Figure 5: Results of the analytic signal on the residual magnetic anomaly map of the Tyrrhenian Sea.  
the North Fiji Basin [61], along propagating back-arc rifts like the Valu Fa Ridge in the southern Lau Basin [62], and in embryonic back-arc rifts such as the Okinawa Trough [63].  
In 1991, extensive sulfide deposits were discovered in association with felsic volcanism in the Eastern Manus Basin [64]. Hydrothermal deposits have also been located in the Western Woodark Basin, where seafloor spreading extends into the continental crust of Papua New Guinea [65]. Later, more than 100 sites of hydrothermal mineralization have been identified on the seafloor [66], including at least 25 sites with high-temperature (350-400 \({}^{\circ}\)C) black smoker venting  
In the Santorini Archipelago (Eastern Mediterranean Sea), the deposition of iron oxide occurred at an exceptionally high rate. It was also found that deposition in the central part of the Santorini Bay proceeded within specific vertical zones in the water column, and deposition was mediated by bacteriological processes that varied in intensity depending on their specific location in the inner bay of the archipelago [67; 68; 69]. Many other episodes are reported for the Aegean Arc but remain poorly explored in terms of active fluid flows or associated deposits. However, the rocky substrate varies in terms of the thickness and chemistry of marine sediments and mineral deposits, and the variability is extreme, often even for two very close vent-sites [70; 71].
# Research on Task-Oriented Computation Offloading Decision in Space-Air-Ground Integrated Network
## 2 Materials and Methods
### Related Research
In Reference [23], the goal of minimizing delay and energy consumption was achieved by combining OREO online algorithm and the Lyapunov algorithm to dynamically optimize the server cache and the task computation offloading process in the unknown system state scenario. In Reference [24], a task offloading algorithm for distributed optimization of mobile edge computing was proposed to optimize the selection of offloading terminals and the transmission power of CPU frequency, to reduce system delay and energy consumption. Among the latest computation offloading techniques, [PERSON] et al. [25] proposed a three-layer task offloading framework named DCC applied to mobile terminals that distinguishes between types of application tasks and reduces delay by shunting tasks with higher computational requirements to the cloudlet and cloud layers, and placing low computational and high communication cost tasks to be executed on the device layer. [PERSON] et al. [26] investigated task offloading in virtual vehicle servers and proposed a fully distributed and infrastructure-less congestion avoidance and traffic optimization system for vehicular ad-hoc networks in urban environments, called DIFTOS, which divides the city map into a server with a hierarchical structure and has been demonstrated in simulations under different traffic conditions its effectiveness and scalability. Most of the existing computation offloading decision schemes have been applied in mobile edge computing scenarios for mobile terminals or in-vehicle networks, but without considering the time-varying factors of the network, the communication quality of the channel, and the tasks' attributes. For tasks of excessive magnitude, they are still unable to effectively reduce delay and energy consumption in the task offloading process. The Space-Air-Ground Integrated Network has a complicated and changeable environment, and the amount of offloading tasks is huge so that the existing computation offloading decision schemes are not applicable. Considering the Space-Air-Ground Integrated Network structure, network channel states, task characteristics, and terminal devices performance, combining the Markov State Transition Principle [27; 28] and neural networks, we propose a computation offloading decision method based on Markov and DQN. This method performs data segmentation processing on the task, and abstracts the computation offloading decision process of the task into the state transition process of Markov model, and then uses the improved DQN algorithm to update the model, and finally selects the most optimal offloading decision according to the attributes of the tasks and the performance of the offloading sites.
# Positioning by Multicell Fingerprinting in Urban NB-IoT Networks +
## 4 Dataset Description and Analysis
### Datasets Analysis and Comparison
Since it could be expected that the loss of spatial coherence would reflect on positioning accuracy (as confirmed by the analysis presented in Section 5), an interpolation-based strategy to compensate such loss was introduced, as discussed in Section 4.4.
# Efficient Classification of ECG Images Using a Lightweight CNN with Attention Module and IoT
## 3 Proposed Method
### IoT-Based ECG Framework  
The system described primarily consists of three components: an electrocardiogram (ECG) sensing system, an internet of things (IoT) cloud, and a graphical user interface (GUI), as presented in Figure 2. The ECG sensing system often utilizes wearable sensors for continuous monitoring, which have minimal disruption to a user's daily life. These sensors allow for the long-term recording of ECG data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Research Work** & **Method** & **Dataset** & **Results** \\ \hline [PERSON] et al., 2022 [14] & Wearable ECG detection system & \begin{tabular}{c} ECG data from wearable \\ patient monitoring device and \\ MIT-BIH arrhythmia database \\ \end{tabular} & \begin{tabular}{c} Claimed good classification \\ performance \\ \end{tabular} \\ [PERSON] et al., 2017 [21] & Faster R-CNN & - & 99.21\% accuracy in detecting \\ [PERSON] et al., 2017 [22] & \begin{tabular}{c} Multiscale fusion of deep CNN \\ Deep learning techniques \\ \end{tabular} & \begin{tabular}{c} Single lead short ECG \\ \end{tabular} & \
\begin{tabular}{c} 96.99\% accuracy \\ \end{tabular} \\ [PERSON] et al., 2021 [34] &
\begin{tabular}{c} (AlexNet, Inception-v3, VGG-16) \\ + transfer learning \\ \end{tabular} & - & Claimed good results \\ \hline \hline \end{tabular}
\end{table}
Table 1: Literature review conclusions.  
Figure 2: IoT-based ECG framework.  
As presented in Figure 2, the IoT cloud system is composed of three main components: collection of data and transmission, ECG investigation, and disease alert. The data collection module involves the use of portable devices to collect ECG data, which are then transmitted to the cloud using a routing protocol. REL and DSR routing were employed for data transformation. The ECG analysis module is liable for extracting essential features for the identification of potential heart diseases. However, the ECG signals may be affected by noise in the data collection and communication process, which can impact the accuracy of the diagnosis. Consequently, the IoT cloud also presents data analysis architecture to extract relevant features using the ECG signals. Finally, the disease alert module is critical for protecting patients and providing an alert system to ease medical care when needed.
## 4 Experiments
### Object Detection on COCO and VOC
#### 4.2.1 Object Detection Datasets  
In the object detection experiments, we used two classic datasets, the Common Objects in Context (COCO) [60] and the Visual Object Classes (VOC) [61] datasets. Among these, the COCO dataset is a popular large-scale benchmark dataset which was designed for object detection and instance segmentation tasks. There are 80 object categories labeled in this dataset, and the annotations are provided in JSON (JavaScript Object Notation) format. The COCO dataset contains 118,287 training, 5000 validation, and 20,288 test-dev  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **Model** & **Epoch** & **Batch Size** & **Image Size** & **Params** & **MACs** & **Top-1** & **Top-5** \\ \hline MaxPool (baseline) & ResNet-18 & 60 & 256 & 224 & 11.69 M & 1.82 G & 59.73 & 82.87 \\ SoftPool & ResNet-18 & 60 & 256 & 224 & 11.69 M & 1.82 G & 59.77 & 82.86 \\ AdaPool & ResNet-18 & 60 & 256 & 224 & 11.69 M & 1.82G & 59.38 & 82.56 \\ Patch Merging & ResNet-18 & 60 & 256 & 224 & 11.71 M & 1.87 G & 41.36 & 66.95 \\
**SliceSamp (Ours)** & ResNet-18 & 60 & 256 & 224 & 11.71 M & 1.88 G & **61.03** & **83.74** \\ \hline \hline \end{tabular}
\end{table}
Table 1: The accuracy and computational cost of the models on ImageNet-1K.  
images with more than 2.5 million object instances. Moreover, the VOC dataset was first introduced in 2005 as part of the PASCAL (Pattern Analysis, Statistical Modelling, and Computational Learning) Visual Object Classes Challenge for the object detection task. It contains 20 object classes, and the annotations are provided in XML (eXtensible Markup Language) format. The VOC dataset has been updated and extended over the years, and the most recent version is VOC2012. The VOC dataset was obtained using the default code of the YOLOv5 algorithm, which includes 16,551 training and 4952 validation images.
# A Dynamic Surface Gateway Placement Scheme for Mobile Underwater Networks +
## 4 PADP Algorithm Description
### Assumptions  
The following assumptions are made:  
* The position of each gateway is known after deployment. And an underwater node can always know where it is [36; 37; 38].
* The studied underwater sensor nodes moving patterns are relative moving patterns to surface gateway.
* Underwater sensor nodes are organized in a layered structure and only nodes in Layer 1 can communicate with surface gateways.
* Localization and synchronization services are available so that an underwater sensor node is always able to estimate its current location.
* Gateways and sensor nodes share a common transmission range \(d\).
# Slicing on the Road: Enabling the Automotive Vertical through 5G Network Softwarization
## 6 Proof-of-Concept Experimental Evaluation  
We now turn our attention to the practical realization of a reference V2X network slice based on the Mininet-WiFi network emulator [18]. Mininet-WiFi is a fork of the popular Mininet SDN emulator and allows fast prototyping and experimental evaluation of wireless networks, including OpenFlow-enabled wired switches and wireless APs. The emulation of the wireless medium is supported by well-known propagation models. More details on the implementation and capabilities can be found in the documentation (e.g., user manual) available in the project repository [43].  
Despite experimenting with 802.11 and not an actual 5G protocol suite, the wireless emulation and SDN control features of Mininet-WiFi allow valuable PoC experiments on a subset of the functionalities of the envisioned framework on top of programmable, multi-access, and edge-dominated network infrastructures, as previously presented in [14; 42; 44]. In particular, the functionalities required for V2N connectivity of a vehicle with a V2X AS were emulated, due to the end-to-end scope and relevance in the case of cross-operator mobility. Further implementation details are discussed next.  
The main objective of the PoC experiments was to preliminarily showcase the viability and potential benefits of particular aspects of our V2X slicing framework proposal, considering off-the-shelf tools and technologies. More specifically, we explored the use cases of slicing federation in two different multi-operator reference scenarios to demonstrate, respectively: (_i_) slice capacity aggregation; and (_ii_) load-aware slice handover decision. The results from the experimental evaluation shed light on the potential V2X slice isolation and performance gains when operators agree to share resources through a proper slicing framework.
# Breath Analysis: A Promising Tool for Disease Diagnosis--The Role of Sensors
## 3 Types of Nanomaterial-Based Sensors in Breath Analysis
### Cross-Reactive Applications
Fe\({}_{3}\)O\({}_{4}\) NPs of varying diameter (7, 13, or 50 nm for 0.4 mg NPs/mL polymer solution) and concentration (0.2, 0.4, or 0.8 mg NPs/mL polymer solution for NPs 50 nm), have been embedded in PEI [52]; by employing a PEI functionalized sensor, a 6-sensor array was developed for the effective detection of methanol, ethanol, and toluene while sensitivity increased for smaller NP-diameters and greater concentrations. Remarkably, NPs of 7 nm permitted ethanol detection with a LOD five times better (65 ppm) than solely PEI-based sensors [52]. In comparison to QCM sensors, SAW based sensors are generally characterized by higher sensitivity, while the potential for surface modification is expanded. On the other hand, it is worth noting that in both cases apart from the sensing film composition that determines sensor selectivity, the high surface area of the nanostructures comprises the fundamental factor that enhances sensitivity due to the creation of more adsorption sites (defects) [52; 169; 171; 187]. In general, piezoelectric gas sensors, investigated primarily in synthetic samples, are characterized by increased sensitivity, small response time and low-powered operation. However, the low signal-to-noise ratio and the requirement for  
Figure 12: Sensitivity values (1/ppm) of (**a**) sensors S1–S3, (**b**) sensor S1–S4–S5 in radar plots, and (**c**) sensors S1–S5 in bar chart representation, for six different analytes under 0% RH provided with responses to 100% humidity. Inserted graph: magnified view of data below 2 \(\times\) 10\({}^{9}\) 1/ppm. (S1: SH-Calix[4]arene, S2: AuNRs, S3: Calix[4]arene modified AuNRs, S4: AgNPs, and S5: Calix[4]arene modified AgNPs, ISP: isoprene, ACE: acetone, n-HEX: n-hexane, EtOH: ethanol, CHL: chloroform, TOL: toluene). Calix[4]arene modification (S3, S5) increased the sensitivity, under 0% of humidity, especially for TOL and CHL, while thiol terminated calix[4]arene (S1) exhibited increased response towards CHL. S5 exhibited the highest responsiveness towards TOL among other VOCs and the highest among all sensors (\(\pi\)-\(\pi\) interactions leading to host–guest complexes). Reprinted with permission from Ref. [49]. Copyright © 2021 Elsevier B.V. All rights reserved. complex electronic circuits, may render this sensor type less enticing for the development of efficient e-Noses [40].
# Advanced Technologies for Geosite Visualization and Valorization: A Review
## 2 Innovative Technologies for Geoheritage Visualization
### GIS Tools  
Since the 2000s, GIS tools have been instrumental in improving data access and dissemination, enabling spatial data exploration, and providing new options for processing, analyzing, and modeling data [84]. Several initiatives in the recent past have employed GIS features to enable promoting areas of interest and enhancing their tourism attractiveness. Just to mention two of the numerous, relevant applications, it is worth highlighting the use of interactive and dynamic web-based maps to promote the value of touristic areas [85] and the use of WebGIS platforms to foster valorization of city centers with historical value [86]. Moreover, [PERSON] et al. [87] have evaluated the strategies employed by countries at the global level and underscored the usefulness of visualizing topics such as the above by means of open-access WebGIS tools. Another study [88] has been centered on showing how spatial environmental databases can be visualized by way of open-source WebGIS systems and Google application programming interfaces (APIs).
# Optimization of Machining Parameters Using Fuzzy Taguchi Method for Reducing Tool Wear
## 4 Experimental Verification  
Whether parameters are optimal cannot be verified using semantic rules alone; thus, the orthogonal array of the Taguchi Method (Table 6) was employed to design a procedure for reducing the number of experiments required to identify the desired parameter combination. A PC-Base CNC lathe (ECOCA PC-3807, ECOCA, Taipei, R.O.C., Taiwan) was used to cut S45C mild carbon steel (specifications = 45 mm \(\times\) 250 mm; clamping length = 100 mm). The Mitsubishi NX2525 tool (Mitsubishi, Ibaraki, Japan) insert and Toshiba tool holder (Mitsubishi, Tokyo, Japan) were selected for diameter turning of the steel specimen. The reference width of the flank wear V\({}_{\text{B2}}\) (mm) (Figure 7) was automatically measured using a digital microscope, as shown in Figure 8 (SONY color video camera mold, SONY, Tokyo, Japan) under grayscale. The tool wear was defined as flank wear divided by the total volume removed by the cutting edge, tool wear ratio (um\({}^{-2}\)). The flank wear is usually measured at different points. V\({}_{\text{B1}}\) is the tool wear point for rough turning and V\({}_{\text{B2}}\) is the tool wear point for finish turning. Because the precision machining in this study is finish turning, the V\({}_{\text{B2}}\) is then measured for tool wear in verifying the accuracy of the semantic quantification. The machining parameters are listed in Table 7.  
Figure 6: Main effects plot of the SNR (smaller-the-better).  
Figure 7: Measurement of tool frank wear.  
The main effects plot of the SNR was drawn using Minitab 15 (Figure 9). The slope in the plots represents the effects of the specified condition on the control factor. The combination of maximum values in each plot indicates the optimal cutting parameters. In this case, the optimal combination of cutting parameters is A3 B3 C2.  
The optimal combination of the cutting parameters (A3 B3 C2) derived by the proposed Fuzzy Taguchi method was the same as that determined from experimental verification. This indicates that designing experiments using the fuzzy Taguchi method not only saves cutting cost but also provides  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline EXP & Cutting Speed (m/min) & Depth of Cut (mm) & Feed Rate (mm/rev) & **Tool Wear (mm\({}^{-2}\))** \\ \hline
1  
reliable and accurate optimal production parameters. Thus, the method proposed in this study is surely meaningful and applicable for the industry.