esa-sceva/satcom-chunk-collection huggingface dataset:
# The Tractor-Cart System Controller with Fuzzy Logic Rules
## 3 Control Algorithm  
The adaptive control model's flowchart of the tractor-cart system is shown in Figure 4. The control strategy is similar to our previously proposed model [8]. The purpose of this controller was to control heading angle of the cart to adjust its position change with respect to coordinates of the reference path. First, the current position of tractor and cart are calculated with respect to the reference trajectory. Errors of tractor and cart position from the reference trajectory are calculated and entered as the inputs to the designed fuzzy logic controllers to estimate the desired steering angle for tractor and required  
Figure 3: Hydraulic hitch model and its parameters.  
pump flow rate for cart's hydraulic hitch, in order to generate the sufficient torque to correct the position of tractor and cart, respectively. Next, the new positions of tractor and cart are calculated again to compare the offset between reference trajectory and system. A threshold range of \(\pm\) 2 cm is used to repeat the whole procedure in case of error beyond the threshold; otherwise, the process continues to the next point. An active controller for the cart was implemented and a fuzzy logic rule-based controller was applied to a steer tractor and hitch of a cart simultaneously with respect to a given reference row patterns of plants. In a feedback control algorithm shown in Figure 5, input variables to fuzzy logic controller (FLC) were the position error \((e)\) and change-of-error rate \((\dot{e})\) signals. Fuzzy logic inputs to tractor's steering angle controller were the differences or errors \((e_{t,x},e_{t,y})\) of the current position of tractor's C.G. with respect to the reference model \((X_{ref},Y_{ref})\), which were lateral and longitudinal coordinate positions. The error differences or change rates \((\dot{e}_{t,x},\dot{e}_{t,y})\) also were fed to tractor's steering controller. The output of this controller was the steering angle \(u_{1}(\delta)\) also were fed to tractor's steering controller. The output of this controller was the steering angle to correct the position of tractor. The same approach was applied for the cart's controller. In the cart's controller, the input signals were errors \((e_{c,x},e_{c,y})\) used to compute cart's coordinates with respect to the reference pattern \((X_{ref},Y_{ref})\) as well as the error difference or change rates \((\dot{e}_{c,x},\dot{e}_{c,y})\). The output of this controller was the flow rate into hydraulic hitch to correct the cart position. These estimated values of fuzzy controllers were substituted in tractor and cart position Equations (13), (16), and (17), which are linearized forms of (5), (8), and (9), in order to calculate the current positions of the tractor and cart.  
Figure 4: Flow chart of position control procedure for the tractor-cart system for path tracking.
## 3 Results and Discussion
### Additional Test to Understand Aroma Transformation during Storage  
Additional tests were designed to further investigate the transformation of aroma during KBT storage. Considering all aroma volatiles with OAVs > 1, apart from \(\beta\)-damascenone, were more abundant in the FKBT than in the 1Y-KBT infusion, we deduced that the loss of key aroma volatiles during storage may contribute to the transformation of KBT aroma. Therefore, to evaluate the effects of individual compounds on the aroma, aroma compounds with an OAV of >1 in the 1Y-KBT infusion were added to match the levels of those in the FKBT infusion and \(\beta\)-damascenone was added to 150% of the level in the 1Y-KBT infusion for triangle testing to determine significant differences.  
As illustrated in Table 4, several aroma volatiles significantly inhibited the woody aroma, especially 3-methylbutanal, dimethyl sulfide, and benzeneacetaldehyde; (E,Z)-2,6-nonadienal, 3-methylbutanal, dimethyl sulfide, and (E,Z)-2,6-nonadienal also made the infusion feel fresher. In addition, the addition of benzeneacetaldehyde, linalool, and geraniol increased the strength of the floral aroma of the infusion and both \(\beta\)-damascenone and methional made the infusion aroma sweeter. The sensory evaluation results indicated a direct contribution of these compounds to the KBT aroma.
###### Abstract  
This in vitro study evaluated the surface roughness (Sa) of two high-strength silicate ceramics, lithium disilicate IPS e.max CAD, Ivocalr Vivadent (LDS group), and zirconia-reinforced lithium silicate Vita Suprinity, VITA Zahnfabrik (ZLS group). The surface roughness was investigated before and after milling using different polishing systems and timings relative to the final crystallization of the ceramics. Forty-eight samples per group were polished by a single calibrated operator using two polishing systems: Datilde LD (Brasseler) and Lithium Silicate Polishers (Meisinger) for the LDS group and Datilde LD (Brasseler) and Vita Suprinity Polishing Set Technical (VITA Zahnfabrik) for the ZLS group, both pre- and post-crystallization. Surface roughness was measured using a confocal laser microscope (OLS4000 LEXT/Olympus), with scanning electron microscopy (SEM) used to evaluate surface morphological changes. Significant differences in Sa values were found between baseline groups, with ZLS exhibiting lower values. All polishing methods significantly reduced surface roughness compared to baseline (\(p\leq 0.05\)). No significant differences were found in LDS samples when polishing pre- or post-crystallization (\(p=0.129\)), while for ZLS samples, post-crystallization polishing achieved significantly smoother surfaces (\(p<0.001\)). The study concluded that the choice of polishing system and timing did not significantly affect surface roughness for LDS. However, it is recommended that post-crystallization polishing be performed for the optimal smoothness of ZLS. This study aimed to evaluate the post-milling polishing procedures of CAD/CAM high-strength restorations, emphasizing the importance of an optimal surface roughness to prevent issues such as increased risk of abrasion on opposing teeth, enhanced plaque adhesion, and mechanical failures. Investigating these polishing techniques enables clinicians to optimize clinical performance, thereby improving the quality and longevity of high-strength silicate ceramics.  
CAD/CAM; dental ceramics; high-strength silicate ceramics; crystallization; polishing; surface roughness Article
# Concrete Gas Permeability: Implications for Hydrogen Storage Applications
## 1 Introduction  
In the pursuit of sustainable energy solutions, hydrogen has emerged as a promising vector for clean and efficient power generation. With its high energy density and ability to produce only water as a byproduct, hydrogen offers a tantalizing vision of a greener future. However, realizing this potential requires safe and reliable means of storing and transporting hydrogen gas [1, 2].  
Natural caves or geological formations have been considered potential hydrogen storage solutions. These formations offer large underground spaces with minimal modification requirements. Such caves provide a natural containment system, which can be beneficial for the large-scale storage of hydrogen [3, 4]. The primary advantage of using natural caves lies in their cavern capacity, enabling the storage of vast quantities of hydrogen, and their geological properties can often create an impermeable barrier, reducing the risk of hydrogen leakage. Additionally, natural caves can have stable and favorable temperature conditions, which can enhance the safety and stability of hydrogen stored within them [5].  
However, using natural caves for hydrogen storage also presents challenges. Retrofitting these caves for hydrogen containment may require substantial investment and engineering expertise. Furthermore, access and monitoring of the stored hydrogen can be more complicated compared to concrete structures, which can be more easily designed with built-in safety features [6].  
Facing that, concrete has been widely used in various industries and construction applications, and its properties can make it suitable for hydrogen containment [7, 8]. Themain advantage of using concrete is that it can be shaped into various forms, allowing for the construction of custom-designed storage facilities or vessels for hydrogen gas. Additionally, with advancements in concrete technology, it is possible to produce low-permeability concrete that significantly reduces the transport of hydrogen molecules, enhancing safety and containment efficiency [9,10].  
Concrete gas permeability, which determines the flow of gases through its pores and capillaries, is a critical factor to consider. To ensure effective hydrogen containment, engineers and researchers strive to optimize the concrete's composition and microstructure, aiming for lower porosity and increased impermeability. As a result, concrete becomes a viable and cost-effective option for storing hydrogen at various scales, from small stationary systems to larger industrial facilities [11].  
This article explores the intricate relationship between concrete permeability and its pivotal role in securing hydrogen gas, thereby addressing a significant research need in the field of sustainable energy solutions.
## 5 Results and Discussion  
The ex-PdBI receiver was upgraded for its installation on the SRT. The following modifications were made:  
* Its cooling system was redesigned, as described in Section 3.3;
* The ALMA Band 3 local oscillator was used as a replacement for a Gunn oscillator;
* A new electronic control system was developed for the remote control from the control room of SRT (Section 4).  
Following these changes, tests and measurements were necessary to evaluate the performance of the system. In Section 5.1, we describe the suitability of the ALMA Band 3 local oscillator, considering the power requirements and extended LO frequency range 85.75-114.25 GHz to drive the ex-PdBI receiver with its optimum parameters. The measurements of the noise temperature in the bandwidth of the receiver are reported in Section 5.2.
###### Abstract  
Most soils in natural environments undergo wetting and drying cycles, without reaching full saturation. Therefore, it is essential to analyze their properties under unsaturated conditions. However, these analyses often require expensive equipment. This study proposes an empirical-experimental methodology to evaluate the elastic modulus and shear strength of unsaturated soils under total stress conditions using undisturbed and reconstituted samples of silty soil from Quito, Ecuador. Techniques for suction measurement, soil water characteristic curve (SWCC), and predictive models for shear strength and stiffness in partially saturated soils were reviewed. Unconfined compression tests were performed, and the SWCC was determined using the filter paper method. A three-dimensional (3D) plot was generated to correlate the matric suction, shear strength, and normal stress across varying suction levels. In the reconstituted samples, the shear strength and elastic modulus exhibited nonlinear increases in the low suction range (\(\leq\)500 kPa). In the high-suction range, the strength declined beyond 2228 kPa (40.23% saturation), whereas the elastic modulus stabilized. Undisturbed samples displayed greater variability owing to their heterogeneity, macrostructure, and hysteresis. The results suggest that matric suction enhances the shear strength and stiffness of the surface layers, whereas a higher saturation at depth reduces these properties. This paper further discusses the limitations and practical applicability of the proposed methodology.  
matrix action; soil water characteristic curve; extended Mohr-Coulomb; unsaturated shear strength; unsaturated elastic modulus +
Footnote †: journal: Applied Sciences
## 2 Material and Methods
### Soil and Treatments  
The experiment was conducted on loamy sand soil, which, according to Polish Soil Classification, was of the anthropogenic order, culture earth type, and hortisole subtype [14]. With pH of 6.7 and its C:N ratio of 10.6:1, the soil contained 14.50 g kg\({}^{-1}\)DM of organic carbon, 1.36 g kg\({}^{-1}\)DM of total nitrogen, 170.00 mg kg\({}^{-1}\)DM of available phosphorus, 84.00 mg kg\({}^{-1}\)DM of magnesium, and 114.00 mg kg\({}^{-1}\)DM of potassium.  
Soil conditioners, the compositions of which are presented in Table 1, were used every year in early spring at the following doses, according the producer's recommendations: UGmax--0.6 L ha\({}^{-1}\); Humus Active--50 L ha\({}^{-1}\). Slurry, produced by dairy cows, was applied three times during each growing season at 30 m\({}^{3}\) ha\({}^{-1}\). Its C:N ratio of 8:1 was narrow, and it contained 10% of dry matter, N-33.0 g kg \({}^{-1}\)DM, P\({}_{2}\)O\({}_{5}\)-16.0 g kg\({}^{-1}\)DM, K\({}_{2}\)O-16 g kg\({}^{-1}\)DM, MgO-10.0 g kg\({}^{-1}\)DM, and Ca-21.0 g kg\({}^{-1}\)DM.  
The following amounts of mineral fertilizers were applied each growing season: 100 kg ha\({}^{-1}\) of ammonium nitrate (NH\({}_{4}\)NO\({}_{3}\)): 80 kg ha\({}^{-1}\) of triple granular superphosphate (Ca(H\({}_{2}\)PO\({}_{4}\))\({}_{2}\)); 120 kg ha\({}^{-1}\) of potassium salt (KCI). The doses of nitrogen and potassium were divided into three parts, applied before each growth cycle, while phosphorus was applied once, before the start of the growing period.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Soil Conditioner**} & \multicolumn{6}{c}{**Macronutrients**} & \multicolumn{6}{c}{**Micronutrients**} & \multirow{2}{*}{**Microorganism and Others**} \\ \cline{2-2} \cline{4-11}  & & & & (g kg\({}^{-1}\)) & & & & (mg kg\({}^{-1}\)) & & **Microorganism and Others** \\ \cline{2-11}  & **N** & **P** & **K** & **Ca** & **Mg** & **Na** & **Mn** & **Fe** & **Zn** & **Cu** \\ \hline \multirow{2}{*}{UGmax} & \multirow{2}{*}{1.2} & \multirow{2}{*}{0.2} & \multirow{2}{*}{2.9} & \multirow{2}{*}{-} & \multirow{2}{*}{0.1} & \multirow{2}{*}{0.2} & \multirow{2}{*}{0.3} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & _Azobacter, Pseudomonas_, \\  & & & & & & & & & & & & & \\ \cline{1-1}  & & & & & & & & & & & & & \\ \cline{1-1}  & & & & & & & & & & & & & \\ \hline \multirow{2}{*}{Humus Active} & \multirow{2}{*}{0.2} & \multirow{2}{*}{1.3} & \multirow{2}{*}{4.6} & \multirow{2}{*}{3.0} & \multirow{2}{*}{0.5} & \multirow{2}{*}{-} & \multirow{2}{*}{15} & \multirow{2}{*}{500} & \multirow{2}{*}{3} & \multirow{2}{*}{1} & active humus with beneficial microorganisms} \\ \cline{1-1
## 3 Implementation of The Proposed System  
Based on the needs of plant electrophysiological data sharing and analysis platform, we implemented the system prototype. For computing framework, Spark can replace MapReduce in Hadoop to improve efficiency.  
Hadoop mainly includes two important parts: HDFS (Hadoop distributed file system) and MapReduce computing framework. Raw data is stored in HDFS. Hadoop computing tasks are assigned by the task scheduler to DataNodes, and then MapReduce completes the data segmentation, mapping, shuffle, reduce steps, and results collection.  
HBase, a distributed column-oriented database, runs on the top of HDFS. Compared with other NoSQL Databases, such as Cassandra and MongoDB, HBase is easier to integrate with Hadoop and Spark. In our application, we used HBase to manage the small files. It supports random, real-time read/write operation on large datasets.  
Spark is a memory-based distributed computing framework, which is more efficient than MapReduce and ensures high reliability and scalability at the same time. Both Spark and Flink are efficient big data computing frameworks. They both can integrate with Hadoop platform. The main advantages of Flink are streaming computing, iterative computing and memory management. Considering that our computing is still in the batch mode and Spark is more mature, Spark is a suitable candidate for parallel processing image and text data in our system. Spark uses the Resilient Distributed Datasets (RDD) as the core data units for transformation operation and computation in memory when memory is sufficient. However, MapReduce reads the hard disk data by the split and partition, and the intermediate results of the calculation are written into a disk. Spark organizes tasks with directed acyclic graphs. The RDD can generate new RDDs, and each RDD can perform the corresponding computational tasks. It is suitable for efficient iterative computation, interactive query and stream processing. Therefore, Spark is more suitable for iterative computation compared to MapReduce. After Spark 2.0, Spark also provides the Dataframe and Dataset API, both of which are the high-level API for processing structured data. In our system, we used RDD to process the unstructured data, such as image data and other plant electrical signals data. It can operate data in the low-level API to achieve more flexible data processing.
## 3 Case Studies
### Old Crafts Virtual Museum  
#### 3.2.1 Motivation  
Intangible heritage seems even more difficult to preserve than the tangible. Customs, legends and crafts are slowly disappearing from public memory. After the successful Mostar Bridge Diving VR project [30] where we digitized the 300 year old tradition of jumping from the Old Bridge in Mostar, Bosnia and Herzegovina, we decided to use the VR technology for the presentation and preservation of crafts present in Old Town Sarajevo--Baskarsija since the 15 th century. Throughout history their number has been growing and in 1898 57 different crafts and 31 guilds were recorded in the census of Bosnian Sanjak [31]. Unfortunately, today many of them are not performed any more. There is only a small number of families who still preserve the craftsmship in their family tradition. The Old Crafts Virtual Museum has the goal of drawing the attention of city authorities and public to this issue and at least in Virtual Reality preserve the selected crafts.
# Gear Wear Process Monitoring Using a Sideband Estimator Based on Modulation Signal Bispectrum
## 2 Theoretical Basis for Gear Wear Monitoring
### Modulation Signal Bispectrum-based Sideband Estimation  
In general, these sidebands have small amplitudes and can be easily contaminated by random noise. In addition, there is also significant interference from different components in a multiple stage gear system, which can have high density clusters of sidebands associated with many different meshing components. To suppress noise and extract the fault-related sidebands accurately, MSB method is used in this study. According to the definition of MSB in the frequency domain, for gear vibration signal \(x(t)\) with corresponding Fourier transform (FT) \(X(f)\), the MSB-SE can be defined in the frequency domain as [17; 18; 19].  
\[B_{\text{MS}}^{\text{SE}}(f_{r},f_{m})=E\Big{\langle}X(f_{m}+f_{r})X(f_{m}-f_ {r})X^{*}(f_{m})X^{*}(f_{m})/|X(f_{m})|^{2}\Big{\rangle} \tag{5}\]  
where \(X^{*}(f_{m})\) is the complex conjugate of \(X(f_{m})\) and \(E(\,)\) denotes the statistical expectation operator, showing that a statistical average operation is necessary for the MSB-SE estimation process and therefore for achieving effective noise reduction, which is important to accurately estimate the sideband effects for measuring the fault severity.  
The magnitude of MSB-SE is a combination of sidebands and normalized carrier components. Therefore, the sideband effect will be quantified without the effect of the meshing frequency amplitude. This means that the magnitude of MSB-SE peaks is determined only by the product of the upper sideband \(X(f_{m}+f_{r})\) and the lower sideband \(X(f_{m}-f_{r})\). According to the properties of MSB-SE, if \((f_{m}+f_{r})\) and \((f_{m}-f_{r})\) are both due to nonlinear effect between \(f_{m}\) and \(f_{r}\), a bispectral peak will appear at bifrequency \(B_{MS}^{SE}(f_{r},f_{m})\).  
In order to measure the coupling effects between sidebands and carrier components, MSB-SE coherence (MSB-SEc) can be defined as [20]:  
\[b_{MSB-SE}^{2}(f_{r},f_{m})=\frac{\left|B_{MS}^{SE}(f_{r},f_{m})\right|^{2}}{E \Big{<}\left|X(f_{m}+f_{r})X(f_{m}-f_{r})\right|^{2}\Big{>}} \tag{6}\]  
MSB-SEc will be close to 1 if the MSB-SE peak is produced from true modulation effects. On the other hand, a value of near 0 means an absence of interactions between the components. Thus, other values of MSB-SEc will indicate the interaction degree between the coupling components. Therefore, the amplitude of MSB-SEc can be used as an indicator to detect the nonlinear interactions and to measure the reliability of the MSB-SE peak. In this way, the modulation effects in vibration signals can be represented more accurately and reliably.  
Furthermore, MSB-SE takes the sidebands distributed in the both sides of the mesh frequency \(f_{m}\) into consideration simultaneously; therefore, it is a suitable indicator for monitoring the gear wear condition.  
As such, this study will use MSB-SE to accurately extract the sideband characteristics in vibration signals for the purpose of gear wear monitoring and severity detection.
# Malware Detection Using Memory Analysis Data in Big Data Environment
###### Abstract
Memory analysis is the phase of examining and analyzing the movements of malware, usually using a forensic memory tool [8]. In this way, it becomes possible to detect hidden malware with memory analysis. Analysis of malware data can often be considered in the context of big data. [3]. In malware analysis, the data to be examined is heterogeneous and large in volume. At the same time, it is necessary to work with streaming data to determine whether a suspicious activity is caused by malware. This requires the use of big data technologies in malware detection. In addition, machine learning methods are one of the most widely used techniques to automatically learn the patterns and behavior patterns they leave behind while detecting malware [9]. The deep learning method is another classification method used recently. In this study, a classification has been made on the CIC-MalMem-2022 dataset, which includes hidden malware families obtained by memory analysis. The study was carried out using Apache Spark big data platform on Google Colaboratory. Random Forest (RF), Decision Tree (DT), Gradient Augmented Tree (GBT), Logistic Regression (LR), Naive Bayes (NB), Linear Support Vector Machine (Linear SVC), Multilayer Perceptron (MLP) data for binary classification set), Deep Neural Network (DNN), and Long Short-Term Memory (LSTM) were the nine different machine learning and deep learning methods used. Results were evaluated with Accuracy, F1-score, Precision, Recall, and AUC performance metrics. The main contributions of this study can be summarized as follows:  
1. It has been shown through the study that malware detection can be performed using memory data. 2. This study provides a basis for future studies on the analysis and classification of memory data with the big data approach. 3. Another contribution of the study is that various deep learning and machine learning approaches, which are frequently used in many intrusion detection systems in the literature and are very popular, have been confirmed to achieve successful results in memory data malware detection. 4. The memory data and malware detection performance of nine different machine learning and deep learning algorithms were compared. The results will guide researchers about the techniques to be preferred in future studies. The rest of the study is organized as follows. Related studies are given in Section 2. Information on the malware families included in the dataset is in Section 3. In Section 4, the materials and methods used in the study are mentioned. In Section 5, model parameters and experimental results are given. Finally, Section 6 presents the results obtained.