esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Contents of Pollutants in Roof Runoff
That revealed that precipitation in Krakow was characterized by comparable values of most parameters, while EC, Cl\({}^{\prime}\), SO\({}_{4}\)\({}^{2}\), Pb, and Zn were usually a bit higher. Emissions from steelworks and waste-treatment plant facilities were indicated as main sources of metals in roof runoff in Poznan [100]. An important role in increasing concentrations of pollutants in the atmosphere was also assigned to mining and industrial activities, responsible for increasing concentration of metals in ambient air and precipitation [61]. The contents of elements in the roof runoff samples, collected during the winter of 2020, were investigated in parallel, (i) after filtration (raw) and (ii) after filtration and acid digestion. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Parameter** & **Unit** & **Kraków \({}^{*}\)** & **Gdansk \({}^{**}\)** & **Torrui \({}^{**}\)** & **Bialystok \({}^{**}\)** & **Zielona Gó \({}^{**}\)** & **Katowice \({}^{**}\)** \\ \hline pH & - & 6.8 & 6.13 & 7.05 & 5.42 & 5.35 & 4.73 \\ EC & \(\upmu\)S/cm & 26.1 & 15.9 & 23.7 & 12.8 & 13.0 & 31.4 \\ CT & mg/L & 1.72 & 1.54 & 1.17 & 0.45 & 1.12 & 2.83 \\ SO\({}_{4}\)\({}^{2-}\) & mg/L & 2.725 & 1.23 & 2.43 & 1.08 & 1.12 & 2.77 \\ Ca & mg/L & 0.6096 & 0.635 & 1.645 & 0.480 & 0.290 & 1.255 \\ Cu & mg/L & 0.0054 & 0.0064 & 0.0075 & 0.0077 & 0.0189 & 0.0107 \\ K & mg/L & 0.3649 & 0.745 & 0.215 & 0.100 & 0.310 & 0.265 \\ Mg & mg/L & 0.0602 & 0.090 & 0.290 & 0.075 & 0.070 & 0.140 \\ Na & mg/L & 0.7649 & 0.750 & 0.900 & 0.215 & 0.480 & 0.645 \\ Pb & mg/L & 0.0026 & 0.0005 & 0.0004 & 0.0007 & 0.0016 & 0.0074 \\ Zn & mg/L & 0.6826 & 0.0890 & 0.0555 & 0.1260 & 0.0370 & 0.2635 \\ \hline \multicolumn{8}{l}{\({}^{*}\)Mean results in the roof runoff samples, investigated in 2019/2020 season; ** Data based on the results of the Chief} \\ \multicolumn{8}{l}{Inspectorate of Environmental Protection in 2018 [78]; \(<\)LOQ—below the limit of quantification.} \\ \end{tabular}
\end{table} 
Table 2: Contents of the selected elements identified in the rainwater in Polish cities.
# Property Analysis of Slag Composite Concrete Using a Kinetic-Thermodynamic Hydration Model
## 2 Hydration-Based Property Evaluation Model of Binary Blends of Cement-Slag
### Reaction Model of Slag in Cement-Slag Composite  
Slag can be used as a mineral admixture to replace partial cement. Based on the experimental results for the isothermal hydration heat of binary blends of cement-slag, [PERSON] et al. (2018) found that the reaction of slag has a similar kinetic process to that of cement. On the other hand, slag is a cementitious material that has both hydraulicity and pozzolanicity (2018). The reaction of slag can be activated by calcium hydroxide (CH). In hydrating cement-slag blends, CH is produced by the hydration of cement and CH is consumed by the reaction of slag. Considering the kinetic process of the slag reaction and the cementitious material property of slag, the reaction equation for slag in binary blends can be written as \(\alpha_{SG}=\int_{0}^{t}\left(\frac{da_{SG}}{dt}\right)dt\), where \(\alpha_{SG}\) is the extent of the reaction of slag. The differential equation of the reaction rate of slag in the binary blends is shown as follows:  
\[\begin{array}{l}\frac{da_{SG}}{dt}=\frac{CH(t)}{P}*\lambda_{2}*\frac{\lambda _{2}*f_{SG}}{\frac{1}{\alpha_{SG}*C\alpha_{SG}*C\alpha_{SG}*C}}\frac{1}  
In Equation (3), the item \(\frac{CH(t)}{P}\) considers the dependence of the reaction of slag on the available CH content in hydrating cement-slag blends. The item \(\lambda_{2}\) (\(\lambda_{2}=\frac{W_{cap}}{W_{0}}\)) considers the dependence of the slag reaction on the available capillary water content in hydrating blends of cement-slag. In summary, Equation (3) considers the hydraulicity of slag using item \(\lambda_{2}\) and considers the pozzolanicity of slag using item \(\frac{CH(t)}{P}\). In addition, the item \(D_{eSG0}*\ln(\frac{1}{\alpha_{SG}})\) considers the densification of hydration products and the decrease in the accessibility of reactants during the diffusion control process [24].
# Reliability Analysis of Multi-Process Machining Based on Information Entropy
## 4 The Reliability Analysis of the Machining Process
### Establishment of Multi-Chain Model and Simulation Verification of Double-Chain Model  
#### 4.4.1 Establishment of Multi-Chain Model  
In the multi-chain subgraph shown in Figure 3, the vertices \(V_{X_{1}},\ldots,V_{X_{2}}\) may be correlated with each other. Therefore, the single-chain model cannot simply be generalized to the multi-chain subgraph. Based on Equation (8) and combined with the characteristics of the machining process, the multi-chain model is established as Equation (10).  
\[\begin{array}{ll}H\big{(}Y^{1},\ldots,Y^{n}\big{)}&=\sum\limits_{i=1}^{n}H \big{(}Y^{i}\mid Y^{i-1},\ldots,Y^{1}\big{)}\\ &=\sum\limits_{i=1}^{n}Y^{i}-\sum\limits_{i=1}^{n-1}I\big{(}Y^{i+1};Y^{i},Y^{i -1},\ldots,Y^{1}\big{)}\end{array} \tag{8}\]  
Taking Equations (1) and (7) into Equation (8), then:  
\[\begin{array}{ll}H\big{(}Y^{1},\ldots,Y^{n}\big{)}&=\sum\limits_{i=1}^{n}H \big{(}Y^{i}\big{)}-\sum\limits_{i=1}^{n-1}I\big{(}Y^{i+1};Y^{i},Y^{i-1}, \ldots,Y^{1}\big{)}\\ &=\sum\limits_{i=1}^{n}\big{[}H\big{(}Y^{i}_{int}\big{)}+I\big{(}X^{i};Y^{i} \big{)}\big{]}-\sum\limits_{i=1}^{n-1}I\big{(}Y^{i+1};Y^{i},Y^{i-1},\ldots,Y^{ 1}\big{)}\\ &=\sum\limits_{i=1}^{n}H\big{(}Y^{i}_{int}\big{)}+\sum\limits_{i=1}^{n}I \big{(}X^{i};Y^{i}\big{)}-\sum\limits_{i=1}^{n-1}I\big{(}Y^{i+1};Y^{i},Y^{i-1 },\ldots,Y^{1}\big{)}\end{array} \tag{9}\]  
According to the characteristics of the machining process \(Y^{1}_{iht},\ldots,Y^{n}_{iht}\) are independent of each other [31], so Equation (9) becomes:  
\[H\big{(}Y^{1},\ldots,Y^{n}\big{)}=H\big{(}Y^{1}_{int},\ldots,Y^{n}_{int}\big{)} +\sum\limits_{i=1}^{n}I\big{(}X^{i};Y^{i}\big{)}-\sum\limits_{i=1}^{n-1}I \big{(}Y^{i+1};Y^{i},Y^{i-1},\ldots,Y^{1}\big{)} \tag{10}\]  
The first term on the right side of Equation (10) is the vertex intrinsic entropy of the multi-chain vertex. To ensure consistency with the form of the single-chain model, the sum of the second and third terms on the right side of Equation (10) is taken as the sum of multiple transfer coefficients, i.e.,:  
\[\sum\limits_{i=1}^{n}T\Big{(}X^{i};Y^{1},\ldots,Y^{n}\Big{)}=\sum\limits_{i= 1}^{n}I\Big{(}X^{i};Y^{i}\Big{)}-\sum\limits_{i=1}^{n-1}I\Big{(}Y^{i+1};Y^{i},Y^{i-1},\ldots,Y^{1}\Big{)} \tag{11}\]  
The transfer coefficients in the multi-chain model are defined as:  
\[T\big{(}X^{i};Y\big{)}=I\big{(}X^{i};Y^{i}\big{)}-\frac{1}{n}\sum\limits_{i=1 }^{n-1}I\big{(}Y^{i+1};Y^{i},Y^{i-1},\ldots,Y^{1}\big{)} \tag{12}\]  
Then, the multi-chain model is obtained, i.e.:  
\[H(Y^{1},\ldots,Y^{n})=H(Y^{1}_{int},\ldots,Y^{n}_{int})+\sum\limits_{i=1}^{n} T(X^{i};Y\,) \tag{13}\]
# Path Planning and Collision Avoidance in Unknown Environments for USVs Based on an Improved D* Lite
## 4 Simulation Results and Analysis
### Optimization of Priority Queue  
In this section, we use the minimum binary heap to improve the priority queue of D* Lite. To evaluate the efficiency of the minimum binary heap, we simulate D* Lite with 8-neighbor node search, 8-neighbor node search with the minimum binary heap, 48-neighbor node search and 48-neighbor node search with the minimum binary heap. We compare their path length, the number of searched nodes and path planning time on three maps of 500 \(\times\) 500, 800 \(\times\) 800 and 1000 \(\times\) 1000, respectively. Experimental results are shown in Figure 14  
From Figure 14, we find that when we apply the minimum binary heap to the 8-neighbor and 48-neighbor searches, respectively, on the three maps, their path lengths are the same as in the original neighbor search approaches separately. This is because the minimum binary heap only improves the storage structure of the priority queue, but the search strategy is the same, resulting in the same path length and number of searched nodes. We also find that the path length of 48-neighbor node search is much smaller than that of the eight-neighbor node search. We believe that this is because that the 48-neighbor node search has more choices to select a steering angle for turning, which can lead to a shorter planned path. We also find that when we increase the map size from 500 \(\times\) 500 to 800 \(\times\) 800 and 1000 \(\times\) 1000, respectively, the path length is also increased.  
Figure 14 shows that the number of searched nodes is also not changed when we use the minimum binary heap or not in D* Lite. However, when we increase the number  
Figure 12: Path optimization on 400 \(\times\) 400 map.  
Figure 13: Comparison of path length.  
of neighbor nodes for the search, more neighbor nodes will be expanded, resulting in an increase in the number of searched nodes. Similar to the path length, the number of searched nodes is also increased accordingly when the map size is increased.  
Figure 14c shows that on the same map, the path planning time of the 48-neighbor node search is a little bit longer than that of the eight-neighbor node search. The path planning time is increased from 8.91 s to 15.2 s, from 22.1 s to 37.2 s and from 120.1 s to 218.3 s, respectively, on the three maps with different map sizes. We believe that this is because the 48-neighbor node search needs to search more neighbor nodes, which increases the path planning time. When we apply the minimum binary heap to the eight-neighbor node search on the 500 \(\times\) 500 map, the path planning time is decreased from 8.91 s to 6.26 s (29.7% reduction). We find similar results on the 800 \(\times\) 800 and the 1000 \(\times\) 1000 maps.  
Based on the analysis above, we conclude that the minimum binary heap can significantly decrease the path planning time for D* Lite while still obtaining the same optimized path.
# Oceania's 5G Multi-Tier Fixed Wireless Access Link's Long-Term Resilience and Feasibility Analysis
## 3 5G Base Coverage Network Layout
### Antenna Array Design for 5G Transmission  
Many new 5G-capable items operating in sub-GHz and mmWave bands are entering the market as the 5G rollout advances and researchers continue to propose 6G. Small stations/repeaters and handheld devices with 5G front ends use phased arrays as high-gain antenna systems to deliver great data throughput without reducing range at higher frequencies [31]. The ITU-R report's definition of base station antenna parameters for 30 GHz operating frequency is used in this paper [48]. Path diversity, also known as MIMO, is provided by the antenna array, and it grows as the size of the array does [49; 50; 51]. Due to their large losses, mmWave frequencies have never been thought to be viable for mobile device communication. GHz wireless antennas are not viable for long-distance communication in mobile devices because air attenuates signals more than at lower frequencies, necessitating the operation of transmitters at substantially higher power to compensate. The radiation emitted from a collection of antennas can be focused using beamforming techniques despite the attenuation of mmWave frequencies [49; 52].  
The phased array also allows for electronically adjusting each element's phase, which can be utilized to redirect the radio beam away from directional interferences. This study demonstrates how using a larger size array for each transmitter at a cell site can also enhance the 5G network SINR. Figure 7 displays the 3D, azimuth, and elevation patterns for a 16-by-16 uniform rectangular array with a 30 GHz operating frequency. Antenna design and simulations are carried out using several MATLAB toolboxes, such as the Antenna Toolbox.  
Figure 7: A 16-by-16 uniform rectangular array with 30 GHz operating frequency, array properties, and 3D, azimuth, and elevation patterns.
# Free Monoterpene Isomer Profiles of _Vitis Vinifera_ L. cv. White Wines
## 1 Introduction  
White wines are frequently characterized by floral and fruity aromas, and these characteristics are major factors in determining the white wine character and quality. Monoterpenes have been widely investigated, as they are known to contribute to the floral and fruity aromas that are typical to aromatic white wines [1]. Monoterpenes are secondary metabolites that are primarily derived from grapes and affected by fermentation. Therefore, monoterpene profiles, including linalool oxide, linalool, hotrienol, nerol oxide, and citronello have been used for varietal and place of origin classification [2; 3; 4; 5]. For instance, linalool, nerol, geraniol, terpineol, linalool oxide, rose oxide, and nerol oxide have been used to differentiate Muscat wines from different regions [6; 7; 8; 9]. Tepene profile patterns have been shown to differentiate Germany Muscat wines from neutral white wines (e.g., Sylvaner) as well [3; 10]. However, there is limited information on enantiomeric composition of monoterpenes in white wines, and the differences in isomers have the potential to greatly influence the aroma of these wines. Despite their importance, monoterpenes are generally minor compounds with trace concentrations in grapes and wines and quantitation for these isomers are very limited to date.  
There is an increasing interest in the determination of the enantiomeric ratios of chiral compounds in food flavors, fragrances, and additives [11; 12]. Monoterpene enantiomers possess different sensory characteristics, such as \(R\)-\((-)\)-linalool, with a described aroma of woody or lavender, while \(S\)-\((+)\)-linalool possessing sweet, petitgrain-like aroma [13; 14]. These different enantiomers also have very different perception thresholds, \(R\)-\((-)\)-linalool (0.8 ug/L) has the odor perception 10 timeslower than _S_-(+)-linal0 (7.4 \(\mu\)g/L) in air [15]. Therefore, wines with slight variations in monoterpene compositions, especially enantiomeric ratios, may result in very different aroma perception.  
The present study was designed to investigate the monoterpene isomers in white wines that are produced from eight grape varieties collected from different regions and two vintages. The main objective was to determine the important monoterpenes isomers and enantiomer fractions in varietal wines, including any effect due to wine style. The different profiles of isomers may be important to the sensory differences in wines and can be used by the industry as markers of varietal quality.
# Programmable Data Plane Applications in 5G and Beyond Architectures: A Systematic Review
## 3 Background in Programmable Data Plane and 5G and Beyond
### Software Defined Networking, Programmable Data Plane, and Devices
#### 3.1.2 Data Plane Programmability  
Data plane programmability is a concept that refers to the ability to customize and manipulate the forwarding behavior of network devices, by programming the data plane hardware or software directly. The data plane is responsible for the actual transfer of data packets across the network and is typically implemented in high-speed packet processing devices.  
Traditionally, the data plane has been fixed and determined by the firmware of the network devices, with limited ability for customization or dynamic adaptation to changing network conditions. However, with the rise of SDN, data plane programmability has become increasingly important as a means of enabling flexible and dynamic network architectures that can respond to changing traffic patterns, application requirements, and security threats. A few examples of new use cases are in-band network telemetry (INT), active queue management (AQM), time-sensitive networking (TSN), and traffic offloading.
## 3 Requirements Analysis
### Software Non-Functional Analysis
#### 3.3.2 Real-Time Processing of High-Load Concurrent Data  
The control of nodes during testing is concurrent, asynchronous, and non-blocking. Given that C-V2X requires the periodic broadcasting of BSM, the network throughput in a C-V2X environment increases exponentially with the number of active nodes. In high-density scenarios, the TS must be able to manage concurrent data under high-load conditions. Therefore, network throughput testing is imperative to ascertain that the system's throughput remains unsaturated during the concurrent operations of multiple nodes, ultimately preventing performance bottlenecks.  
Moreover, V2X applications are inherently time-critical, with rigorous latency requirements in testing, particularly during application testing. Real-time data management is pivotal throughout the execution of each task, entailing meticulous consideration of the accuracy of the data, security, monitoring, and prudent technical selection and system architecture design. Consequently, the TS must guarantee consistent and minimal program response latency across varying load conditions.
# Education Mobility as a Service: A Study of the Features of a Novel Mobility Platform
## 2 Review of New Technologies in Education and Lifelong Learning
### Mobile Applications  
Mobile applications are the general name for software that runs on mobile devices. They are written using a variety of platforms and programming languages. Increased mobility in access to information, data, and knowledge (independence from place and time) causes e-learning services to be more often adapted in terms of sending and publishing content on mobile devices. This form of teaching is called m-learning and is mainly used to support learning (e.g., applications supporting learning a given subject) or popularizing knowledge. More advanced applications use augmented reality that can serve as an additional asset for knowledge and skills acquisition or reinforcement [35].  
While educational platforms have many benefits, there are also some challenges that students may face when using these platforms. Some of the main problems include:  
* Technical issues: Technical problems such as slow Internet connection, software compatibility, and system crashes can disrupt the students' learning experience and make it difficult to complete coursework.
* Digital divide: Not all students have access to the technology and the Internet required to participate in EaaS, leading to a digital divide between those who have it and those who do not.
* Isolation: Online learning can be isolating, and students may miss the social interaction and support they would receive in a traditional classroom setting.
* Distractions: Students may be easily distracted by other online activities and find it difficult to stay focused and motivated during online courses.
* Quality of education: The quality of education delivered through EaaS platforms can vary greatly, and students may have difficulty determining the credibility of courses and instructors.
* Assessment and certification: The assessment and certification of EaaS students may not be recognized by all employers or educational institutions, making it difficult for the students to transfer their credits or demonstrate their skills and knowledge.  
To address these challenges, it is important to ensure that students have access to the technology and support they need and that the quality of education delivered through EaaS platforms is consistent and credible.
# POI Recommendation Scheme Based on User Activity Patterns and Category Similarity
## 3 The Proposed POI Recommendation Scheme  
The proposed POI recommendation scheme takes into account user activity patterns and the similarity of categories. It is used to organize users based on the activity level and consider the characteristics of both the user and location. Furthermore, it provides personalized recommendations by taking into account the time, location, and other data that are collected from users.
# CBM: An IoT Enabled LiDAR Sensor for In-Field Crop Height and Biomass Measurements
## 3 Results  
### Laboratory Sensor Calibration Results  
For sensor calibration, a total number of 72 observations were collected at a variable step distance of approximately 30 cm between 40 cm and 265 cm. The regression line that was plotted between the reference and measured distances in the laboratory is shown in Figure 8. The correlation was very high between each of the sensor's detector output ranges and the target distance. The fitted models for all detectors explained a 98.5% of the variability of the response. The \(R^{2}\) for all models averaged 0.98 with an _RMSE_ of 3.97 cm, which is very practical for the end-use case of the sensor in phenotyping plant canopies with fragile structures. The average error in absolute value was 3.54 cm.
## 3 Artificial Intelligence Techniques for 6G Networks
### Internet of Space Things with Cubesats and UAVs  
The Internet of Space Things (IoT) idea in 6G uses linked Cubesats (miniature satellites) and Unmanned Aerial Vehicles (UAVs) to build a flexible and adaptable communication infrastructure. Global coverage, on-demand network resource deployment, and real-time monitoring are all made possible by IoT. In order to optimize their location for smooth communication and effective data transmission, Cubesats and UAVs must move in concert and cooperate with one another. This is where AI comes into play. It may be possible to extend network coverage to remote locations and disaster-affected areas thanks to this cooperation between space-based and airborne assets [16].
## 6 Magnetic Properties
### Power Loss
#### 6.2.1 Hysteresis Loss  
The larger the loop area, the higher the loss occurring in each cycle. Hysteresis loss is a type of heat loss caused by the magnetic properties of iron. When the core of a motor is exposed to a magnetic field, the magnetic particles within the core tend to align with the field. As the core of an electrical machine core rotates, its magnetic field continuously changes direction. This constant movement of magnetic particles trying to align with the changing field direction leads to molecular friction, resulting in hysteresis loss.  
Hysteresis loss in transformer steels is characterized by the area under the B-H curve when measured at 0 Hz. During cyclic magnetization, a material's domain walls continually move back and forth, but they can be hindered by defects like dislocations, grain boundaries, impurities, or imperfections. Hysteresis loss occurs when these domain walls are created, moved, or annihilated. Although hysteresis loss is theoretically considered to be independent of frequency, its practical behaviour is influenced by the employed operating frequency, which affects the number of domain walls in motion [20].  
In Figure 9, as the magnetic field increases initially, the magnetization is reversible until reaching a specific threshold. Beyond this point, further increases in the magnetic field lead to irreversible magnetization. Point \"a\" on the loop represents saturation induction, indicating that additional increases in the magnetic field will have minimal impact on the magnetic flux density. When the magnetization is reduced to zero from point \"a\", some magnetism remains in the material, which is known as remanence.  
Figure 9: Hysteresis curve for a magnetic material [57].  
Subsequently, if the magnetic field is applied in the opposite direction, it reaches point \"Hc\", which is known as coercivity. Coercivity represents the level of magnetic field intensity required to reset the magnetization back to zero. Point \"d\" represents the saturation flux density in the negative direction. This cyclic loop formed by these points is commonly referred to as a hysteresis loop.