esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Reserch Scope and Method  
### Research Scope  
The purpose of this experiment was to assess the performance of six supervised classifiers (REPTree, SMO, LogitBoost, BayesNet, RBF, and NBTree) in detecting intrusion at the lowest model building time possible with high detection accuracy in two phases. To do so, the authors used the Weka ML package and the NSL-KDD training dataset, which were downloaded to an LG PC with a configuration of 2.20 GHz Intel(R) Core (TM) i7-8750H and 16 GB of RAM running on Microsoft Windows 10. In the first phase, the authors evaluated the performance of each classifier on a full NSL-KDD training dataset using 10-fold cross-validation in the Weka tool. The aim of this phase was to identify additional options to outperform classifiers with the lowest model building time and high detection accuracy and to use this as a baseline for evaluating the efficiency of different feature selection/reduction methods used in the second phase. The authors utilized the Weka tool because it is well known and widely used among researchers for developing ML algorithms and their applications to real-world data mining problems. It has a collection of ML algorithms and supports various standard data mining tasks, particularly data pre-processing, clustering, classification, regression, visualization, and feature selection. It has a cross-validation mode and global minimum adjustment to lessen any possible overfitting.  
In the second phase, the authors evaluated the performance of each classifier on a full NSL-KDD training dataset using 10-fold cross-validation with different feature selection/reduction techniques, including the wrapper and filter methods in the Weka tool. The authors also used a discretize filter classifier individually and in combination with the wrapper and filter methods to evaluate the performance of the tested classifiers. The aim of this phase was to assess the effect of different feature selection/reduction methods and the discretize filter on the performance of the tested classifiers in terms of detection accuracy and model building time; this would allow the authors to determine the most appropriate classifiers for securing a network with high detection accuracy at the lowest possible time. During the two phases, the authors used model building time, detection accuracy, and correctly and incorrectly classified instances as performance measures to identify the classifiers with both the highest detection accuracy and lowest model building time simultaneously.
## 4 Discussion  
### Microorganisms as the Agents of Biodeterioration  
This work summarizes the literature results concerning microorganism biodiversity in a limestone substrate of cultural heritage significance and presents a crucial foundation for further explorations for further characterizing the diversity of the bacteria, cyanobacteria, fungi, algae, archaea, and lichens involved in the deterioration of limestone monuments or buildings. Several authors [26; 59; 60] consider lichens as one of the most severe stone biodeteriorators and saxicolous crustose lichens as the most difficult organisms to remove from limestone. However, some authors [15; 61; 62; 63] have also emphasized the bioprotective role that lichens possess when present on cultural heritage assets. Lichens can, for example, soothe the abrasive effect of airborne sand particles, reduce temperature fluctuations, especially where freeze-thaw cycles occur, and even limit the dissolution of stone material when in contact with water [15; 61; 62]. The different microbial communities coexisting in the biofilm have different metabolic functions (they can include phototrophs, chemolithotrophs, and chemoorganotrophs) and they cooperate to achieve substrate uti  
Figure 9: The number of molecular biology studies on CHL conducted using CD approaches, CI approaches, and both CD and CI approaches together through the years in ascending order.  
lization and the recycling of nutrients for their common survival [64]. On the surface of rocks, microorganisms always exist as a community and collaborate to perform various tasks. Therefore, to comprehend the impact of the microbial community on rock, studies should begin with a functional analysis of the entire population [6]. The alterations caused by microbial biodeterioration are, according to [PERSON] et al. [18], classified into the following three different categories, namely: (i) biophysical, (ii) biochemical, and (iii) aesthetic changes, all of which can occur simultaneously or separately. Biophysical changes such as exfoliation and biopitting happen due to the penetration of fungal hyphae through the limestone surface [46], while biochemical alterations occur due to the production of organic acids, metabolites, exoenzymes, and metal-chelating compounds [65].
###### Abstract  
Thermal ice storage has gained a lot of interest due to its ability as cold energy storage. However, low thermal conductivity and high supercooling degree have become major issues during thermal cycling. For reducing the cost and making full use of the advantages of the graphene oxide-Al\({}_{2}\)O\({}_{3}\), this study proposes heat transfer enhancement of thermal ice storage using novel hybrid nanofluids of aqueous graphene oxide-Al\({}_{2}\)O\({}_{3}\). Thermal conductivity of aqueous graphene oxide-Al\({}_{2}\)O\({}_{3}\) nanofluid was measured experimentally over a range of temperatures (0-70 \({}^{\circ}\)C) and concentrations. Thermal conductivity of ice mixing with the hybrid nanoparticles was tested. The influences of pH, dispersant, ultrasonic power and ultrasonic time on the stability of the hybrid nanofluids were examined. A new model for the effective thermal conductivity of the hybrid nanofluids considering the structure and Brownian motion was proposed. The results showed that pH, dispersant, ultrasonic power level and ultrasonication duration are important factors affecting the stability of the hybrid nanofluids tested. The optimum conditions for stability are pH = 11, 1% SDS, 375 W ultrasonic power level and 120 min ultrasonic application time. The thermal conductivity of hybrid nanofluids increases with the increase of temperature and mass fraction of nanoparticles. A newly proposed thermal conductivity model considering the nanofluid structure and Brownian motion can predict the thermal conductivity of hybrid nanofluids reasonably well.  
cold energy storage; thermal conductivity; stability; graphene oxide; hybrid nanofluid +
Footnote †: journal: Applied Physics Letters
## 2 Materials and Methods
### Experimental Setup  
A camera-based optical motion capture system was used to record the dancers' motion (OptiTrack (NaturalPoint, Corvallis, OR, USA)) [19]. This measuring system has an accuracy of submillimeters (mean error: 0.565 mm) [19]. The motion was recorded using 18 OptiTrack Flex 13 cameras with a sampling frequency of 100 Hz. Spherical markers (\(\varnothing\) 12.7 mm) covered with reflective coating were placed on the distinct anatomical landmarks. This present study examined loads of the lower extremities; thus, 16 markers were placed on the dancers' lower body according to the pre-implemented conventional lower body biomechanical marker set in the Motive software (NaturalPoint, Corvallis, OR, USA, v.1.10.0.) [20]. The measurements were performed at the Motion Capture Laboratory of the Department of Mechatronics, Optics, and Mechanical Engineering Informatics at Budapest University of Technology and Economics, Budapest, Hungary.  
Simultaneous with the motion tracking, one pair of BTS P6000 (BTS Bioengineering, Milan, Italy) force plates were used to capture the ground reaction force (GRF) and center of pressure (COP) values. Data were recorded and exported using the SMART Capture (BTS Bioengineering, Milan, Italy, v.1.10.470.0) software. One force plate had a 40 cm-wide and 60 cm-long measurement area. The two platforms were placed side by side in parallel with a 600 \(\times\) 800 mm total detection area. The maximum load capacity of a single force plate was 8000 N. The measurements were captured with a sampling frequency of 1000 Hz [21]. The origin of the force plate and the motion tracking system was calibrated as one point.  
The two system's synchronization was fulfilled via a NI-DAQmx (National Instruments, Austin, TX, USA) trigger box. Recordings were started using the motion capture system, which triggered the recording of the force platforms. Measurements were performed under almost identical environmental conditions (21-23 \({}^{\circ}\)C, using artificial lighting).
# Spatio-Temporal Variability of Water Temperature of Arctic Rivers in Russia over the Past 60 Years
## 2 Materials and Methods
### Assessment of Water Temperature Change Patterns  
The role of individual factors in the formation of water temperature has been revealed through the use of linear regression methods. Variables used include direct average monthly water and air temperatures, as well as data on average annual water runoff, average monthly precipitation, and anomalies of these characteristics relative to the 1991-2020 average.  
Calculations were performed for each hydrological station. The degree of influence of each factor on water temperature was assessed using R\({}^{2}\) values, [PERSON]'s correlation coefficients, and regression equation parameters.  
The degree of influence of changes in air temperature on water temperature was studied in several ways. Firstly, we compared the significance of trends in water and air temperatures. Secondly, we analyzed the monotonic relationship between these two variables. Both approaches were used for different time periods within the 1961-2021 period. A comparison was made of the significance of these relationships across the entire study area. The significance was assessed using the \(p\)-values of the correlations.  
Additionally, the magnitude of the water temperature trend was compared to the strength of the connection between water and air at each hydrological station.  
Since we assumed the possibility of a non-linear relationship between air and water temperatures, especially in spring and autumn, due to a shift in the beginning and end of winter, we used the non-parametric Mann-Kendall test to assess the significance of the trends in both air and water temperatures and the presence of any relationship between them.
## 3 Machinability of AM Ti6 Al4V Alloy
### Energy Consumption
Several studies [3; 93] have used this mapping approach to developenergy maps during machining. Energy mapping involves plotting feed rate (rev/mm) against cutting speed (m/min) to divide the map into three energy zones: low, medium, and high. One report shows that SCE of <1.00 is classified as a low energy zone, 1.00 to 1.10 as a medium energy zone, and >1.10 as a high energy zone [3]. This study primarily focused on varying cutting speed and feed rates without considering tool geometry or depth of cut, which can significantly influence tool wear and energy consumption. While the research successfully identifies zones of wear and energy consumption, it lacks a comprehensive analysis of the underlying mechanisms driving these phenomena, thereby limiting the depth of understanding of tool wear processes. Additionally, the energy consumption maps developed may not account for variations in machine tool dynamics and environmental factors, which could potentially impact their applicability in diverse manufacturing settings. The report shows that in the milling process of Ti6 Al4V alloy, SCE, the depth of cut, and cutting speed increase with a decrease in the feed rate [94]. Another researcher showed that energy consumption was heavily dependent on the cutting speed and feed rate. However, an increase in both leads to lower energy consumption in turning of Ti6 Al4V alloy [73]. The report shows that an increase in feed rate leads to an increase in the shear plane angle and results in lowering the SCE by 27% per 1 kg of the MRR, as indicated in the energy map [93].
# Automatic Detection of Airway Invasion from Videofluoroscopy via Deep Learning Technology
## 3 Results
### Evaluation Metrics  
Both the global localization of the cervical spinal column to define the dynamic ROI for each individual image frame and the classification of airway invasion occurrence in each ROI image are one-class classification problems. We evaluated the performance of the U-Net model for the global localization of the cervical spinal column and the Xception-based CNN for airway invasion detection on the basis of the receiver operating characteristic (ROC) curve, which is widely used in detection problems. The main metrics used for performance evaluation are as follows: (1) accuracy: the ratio of the number of correctly predicted observations to the total number of observations; (2) recall: the ratio of the number of correctly predicted positive observations to the total number of observations actually belonging to the positive class; (3) precision: the ratio of the number of correctly predicted positive observations to the total number of predicted positive observations; (4) F1-score: the weighted average of precision and recall, i.e., F1-score = 2 * (recall * precision)/(recall + precision); (5) negative predictive value (NPV): the ratio of the number of correctly predicted negative observations to the total number of predicted negative observations.
## 2 Experimental Test and Machine Learning Technique
### Degree of Rock Damage and AE Parameters  
From the result of the compression test, the volumetric strain, stiffness, and in-elastic volumetric strain were calculated to evaluate the degree of damage for rocks according to the vertical stress level (loading rate: 0.2 MPa/s) (Figure 5). The in-elastic volumetric strain can be calculated by the Equations (1) and (2) based on the stress-strain relationship;  
\[\epsilon_{v}^{ie}=\epsilon_{v}-\epsilon_{v}^{e}=\epsilon_{v}-(\epsilon_{Axial}^ {e}+\epsilon_{lateral}^{e}) \tag{1}\]  
\[\epsilon_{v}^{ie}=\epsilon_{v}-\frac{1-2v}{E}\;\sigma_{Axial} \tag{2}\]  
where, \(\epsilon_{v}\) is a volumetric strain, \(\epsilon_{v}^{e}\) is an elastic volumetric strain, and \(\epsilon_{v}^{ie}\) indicates the in-elastic volumetric strain. \(\epsilon_{Axial}^{e}\) and \(\epsilon_{lateral}^{e}\) mean the axial and lateral strain. \(E\), \(v\) and \(\sigma_{Axial}\) indicate the elastic modulus, Poisson's ratio, and axial stress, respectively. [PERSON] [32] defined this in-elastic volumetric strain as the crack volumetric strain that is attributed to axial cracking and proposed using the crack volumetric strain to characterize a crack initiation. Based on this principle, in this study, the crack volumetric strain was associated with rock damage to quantify the degree of damage. The crack volumetric strain can be calculated by subtracting an in-elastic volumetric strain by their maximum value. By normalizing the crack volumetric strain, the degree of damage is finally calculated.  
In this study, the degree of damage only higher than the crack initiation stress (\(\sigma_{ci}\)) was considered as valid values to collect the reliable dataset. Here, the crack initiation stress refers to the stress point at which the crack volumetric strain becomes zero [3]. If the stress level is lower than the crack initiation stress, the crack closure is dominant in this range, not crack initiation [1].  
From the AE data acquisition system, we also collected the AE dataset for 10 granite specimens. As AE input parameters were chosen, an amplitude, hit, count, rise time, absolute energy, and initiation frequency were selected through the literature review in Section 1. A visual explanation of these AE parameters is depicted in Figure 6.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**List** & **Content** \\ \hline Uniaxial compression testing machine & KDU-200 (Kyung Do Precision) \\ \hline Data logger & UCAM 20 PC (KYOWA) \\ \hline Strain gauge & AP-11-TS50N-120-EC (CAS) \\ \hline AE data acquisition system & RECTUSON 10 channel analyzer (RECTUSON) \\ \hline AE sensor & AE603 SW-GA \\ \hline \hline \end{tabular}
\end{table}
Table 2: Equipment specification for uniaxial compression and acoustic emission test.  
Figure 6: Example of six acoustic emission (AE) parameters in an AE signal. In this study, these AE parameters were transformed as cumulative values and used as inputs for predicting the degree of rock damage.
## 3 Our Approach
### Algorithm
#### 3.3.1 Exploration Strategies  
In an episode under the autonomous action space exploration strategy, the learner tries to optimize the action \(\pi\) to produce \(\omega_{g}\) by stochastically choosing between random exploration of actions and local regression on the k-nearest action sequence neighbours. The  
Figure 3: SGIM-PB architecture: the arrows show the data transfer between the different blocks, the numbers between brackets refer to specific line number in Algorithm 1.  
probability of choosing local regression over random actions is proportional to the distance of \(\omega_{g}\) to its nearest action neighbours. This action optimization is called Goal-Directed Action Optimization and based on the SAGG-RIAC algorithm [15].  
In an episode under the autonomous procedural space exploration strategy, the learner tries to optimize the procedure \((\omega_{i},\omega_{j})\in\Omega^{2}\) to produce \(\omega_{g}\), by stochastically choosing between random exploration of the procedural space and local regression on the k-nearest procedure neighbours. The probability of choosing local regression over random actions is proportional to the distance of \(\omega_{g}\) to its nearest procedure neighbours. This process is called Goal-Directed Procedure Optimization.  
In an episode under the mimicry of an action teacher strategy, the learner requests a demonstration of an action to reach \(\omega_{g}\) from the chosen teacher. The teacher selects the demonstration \(\pi_{d}\) as the action in its demonstration repertoire reaching the closest outcome from \(\omega_{g}\). It has direct access to the parameters of \(\pi_{d}=(\pi_{d1},\pi_{d2},\ldots,\pi_{dl})\), and explores locally the action parameters space (we do not consider the correspondence problem from teachers' demonstrations).  
In an episode under the mimicry of a procedural teacher strategy, the learner requests a task decomposition of \(\omega_{g}\) from the chosen teacher. The demonstrated procedure \((\omega_{di},\omega_{dj})\) will define a locality in the procedure space for it to explore.  
When performing nearest neighbour searches during the execution of autonomous action and procedure exploration strategies (for local optimization of procedures or actions or when executing a procedure), the algorithm uses a performance metric which takes into account the complexity of the underlying action selected:  
\[perf(\omega_{g})=d(\omega,\omega_{g})\gamma^{n} \tag{1}\]  
where \(d(\omega,\omega_{g})\) is the normalized Euclidean distance between the target and the reached outcomes \(\omega_{g}\) and \(\omega\), \(n\) is the size of the action chosen (the length of the sequence of primitives), \(\gamma>1\) is a constant used to balance accuracy and complexity of the action. In our case, we want the learner to mainly focus on accuracy while only avoiding overcomplex actions whenever possible, so we manually set a small \(\gamma=1.2\) very close to the lower limit.
# _remote sensing_
## 4 Calibration Results  
### Hy-2b  
The HY-2B satellite launched on 25 October 2018, which was the second altimeter satellite of China and operated by NSOAS. The orbit of the HY-2B satellite maintains a 14-day period for oceanic applications with an inclination of 99.34\({}^{\circ}\) and an altitude of about 970 km. The ascending Pass No. 375 of HY-2B flies over the WSCS on Zhi'wan island.  
The SSHs of the 64, 65, 94, and 110 cycles of Pass No. 375 were shown as an example of the SSH of HY-2B when it flies over WSCS (Figure 9a). In all these cycles, the SSHs of 20 Hz data were distorted about 10 km from the island (about 21.9\({}^{\circ}\)N) when the satellite flies over the WSCS on Zhi'wan island (the green x dots in Figure 9a), especially the 65 and 94 cycles. Therefore, the Cal/Val activities were shown using the data about 10-25 km south of the island. However, the SSH returns to normal after crossing the Zhi'wan island, as there is the presence of a large sea area which is about 30 km, until it flies over the islands of Hong Kong. There are 2\(\sim\)4 footprints of HY-2B and about 25 km between Zhi'wan and Wai'ling'ding islands (Figure 2), which can be used for the Cal/Val of the altimeter.  
Figure 8: MSS of the WSCS. Image (**a**) shows the orbits of HY-2A (red lines). Images (**b**,**c**) are the comparisons between the HY-2A SSH of Pass No. 190 and 203 and MSS of DTU2021, CLS2015, and EGM (MDT minus by CLS_MDT_2018), respectively.  
The measured sea surface and HAMTIDE12 tide model were used to maintain the tidal difference between the footprints of the HY-2B altimeter and the ATGs of Zhi'wan island. The Cal/Val result was \(-30.7\)\(\pm\) 39.6 mm. The Cal/Val results were \(-21.7\)\(\pm\) 44.5 mm and \(1.8\)\(\pm\) 56.9 mm for ATGs of Wai'ling'ding and Dan'gan islands, respectively, using the CLS 2015 MSS and HAMTIDE12 tide models. The total bias was \(-16.7\)\(\pm\) 45.2 mm, with a drift of 0.5 mm/year (Figure 9b). The bias of HY-2B SSH is in good agreement between the WSCS and the dedicated sites in Gavdos, Qianliyan, and Zhimaowan [3,15].
## 4 The Application of Edge Computing System
### State Estimation  
In modern energy systems, SE plays a crucial role in monitoring power grids by analyzing erroneous measurements and determining the correct values of unknown variables. With the increasing demand for real-time control, SE requires a high bandwidth and powerful computing resources. To address this challenge, solutions such as performing SE at distributed edge nodes and transmitting the results to the control center have been proposed. In this context, ref. [200] presents a Cloud-IoT-based architecture solution that combines the benefits of CC and EC for condition estimation in smart grids. This solution enhances operational flexibility and adaptability by separating measurement data from physical devices using virtualization technologies. Similarly, ref. [201] proposes a multi-layered hierarchical framework that integrates cybersecurity measures, performing condition estimation on edge devices while utilizing security mechanisms to detect malicious devices using Phasor Measurement Unit (PMU) data.  
To mitigate false data injection attacks, ref. [202] developed an edge-based FL framework. This approach addresses challenges such as distributed datasets and unknown system parameters while improving detection accuracy through a mechanism that incentivizes data owners. Ref. [203] examines how false data injection (FDI) attacks can affect the SE system. Furthermore, ref. [204] introduces a data-driven switching state identification algorithm for renewable energy integration and frequent line switch states. The EC framework distributes computational tasks to smart terminals, thereby reducing communication and computational load and enhancing algorithm performance. Additionally, ref. [205] presents an NSGA-III-based optimization, leveraging EC to address network partitioning and edge server placement challenges. This approach optimizes network performance by minimizing edge server costs.