esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Experimental Design and Data Collection
#### 2.1.5 Correlation Test  
In order to fully understand the degree of correlation between corn moisture content and different temperature and humidity variables and to identify the factors with higher correlation with the target variables, this study first analyzed the dataset for Pearson's correlation coefficient [33,34].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample** & **Maximum** & **Minimum** & **Mean** & **SD/\%** & **C.V.** \\
**Size/Sets** & **Value/\%** & **Value/\%** & **Value/\%** & **SD/\%** & **C.V.** \\ \hline
207 & 23.01 & 12.41 & 2.962 & 2.691 & 0.153 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of corn moisture content data.  
Figure 3: Drying section monitoring point. (1: Upper outlet; 2: Lower outlet; 3: Drying section A; 4: Drying section B; 5: Drying section C; 6: Drying section D; 7: Drying section E; 8: Hot air pipe).  
The Pearson correlation coefficient is calculated as follows:  
\[r=\frac{\sum((x_{i}-\overline{x})(y_{i}-\overline{y}))}{\sqrt{\sum(x_{i}-\overline {x})^{2}}{\sum(y_{i}-\overline{y})^{2}}} \tag{1}\]  
(\(x_{i}\) and \(y_{i}\) denote the observed values of the two variables, respectively; \(\overline{x}\) and \(\overline{y}\) denote the mean values of the two variables, respectively).  
From Tables 2 and 3, the vast majority of temperature and humidity characteristic variables show strong positive or negative correlations with outgoing corn moisture content. Except for ambient temperature and ambient humidity, there are 16 temperature and humidity variables significantly correlated with corn moisture content. Among these, the absolute value of the correlation between hot air duct temperature, hot air duct humidity and outgoing corn moisture content is lower than 0.5. Hence, this study excludes the four temperature and humidity characteristic variables, namely, ambient temperature, ambient humidity, hot air duct temperature, and hot air duct humidity, and gives priority to the remaining 14 variables as the full set of temperature and humidity variables to construct the prediction model.
# Recent Improvements in the Development of Soft Grippers
## 2 Soft Grippers
### Actuation and Sensing  
The common actuation techniques used are fluidic elastomeric actuators (FEAs), and the most used FEAs are soft pneumatic actuators (SPAs). Other actuators used are shape memory alloys and polymers (SMAs and SMPs); electroactive polymers (EAPs); tendon-driven actuators and external motors associated with the grippers' passive structures. On the other hand, semi-active actuation consists of manipulating actuators in an indirect way, i.e., the actuation is based on the mechanical properties of the materials; examples of semi-active actuation technologies are jamming, electro, and magneto-rheological materials (ERMs and MRMs), and low melting point alloys and polymers (LMPAs and LMPPs) [5; 7], whose stiffness varies when they are subjected to an electric or magnetic field.  
The main sensing technologies used are resistive ionic sensors (force, pressure, and strain); soft electronics (pressure); piezoelectric-resistive-capacitive sensors (strain); capacitive sensors (strain); thermoplastic resistive conductive sensors (strain); sensing fabrics and fibers (strain); and optical sensors (deformation) [5; 6; 27].  
Some soft dexterous grippers rely uniquely on fluidic actuation, [13; 15; 17; 22; 24; 28; 29], while others combine fluidic actuation with motor actuation [23], or some others combine FEAs with motor actuation and other actuation like a suction cup or particle jamming [21]. In [30], FEAs  
Figure 1: Figure showing the different architectures of soft grippers. (**a**) Anthropomorphic soft gripper [10]; (**b**) Non-anthropomorphic soft gripper [11].  
are embedded with low-melting-point alloys. Some of the studied dexterous soft grippers did not integrate FEAs but rather exploited tendon-driven actuation, ref. [12], combined with motor actuation, ref. [20] and motor actuation combined with particle jamming [16]. A combination of SMA actuators with electromagnets and DC motors is presented in [31].  
The introduction of sensors into the functioning loop of soft grippers confers them with desirable dexterous performances as discussed in Section 3.2.2. Pressure sensors are the most commonly used [13; 17; 21; 22; 23; 24; 29; 32]. Other sensors, such as tactile sensors, are used in [21; 33]. A six-axis force/moment sensor is introduced in [34].
## 2 Materials and Methods
### Remote Sensing Data
#### 2.4.2 Sentinel-2  
Remote sensing images were obtained from the Sentinel-2 MultiSpectral Instrument for the tile 30 TVK from January 2018 to July 2020 (186 images). Level-2A images (atmospherically corrected) were downloaded from ([[https://scihub.copernicus.eu/dhus/#/home](https://scihub.copernicus.eu/dhus/#/home)]([https://scihub.copernicus.eu/dhus/#/home](https://scihub.copernicus.eu/dhus/#/home)) accessed on 10 September 2023). Sentinel-2 images with the highest spatial resolution (10 m) and a temporal resolution of 5 days were used. The bands Red (R, b4: 646-685 nm) and Near InfraRed (NIR, b8: 767-908 nm) were used for the calculation of the NDVI following Equation (5).  
Then, a Savitzky-Golay filter [53] was applied to reduce noise and smooth the time series using a second-order polynomial with a smoothing window of 7. [PERSON] et al. [58] evidenced that this type of filter was adequate for smoothing Sentinel-2 NDVI in a variety of croplands and natural ecosystems. Thus, this filter was also applied to the MODIS and Sentinel-2 NDVI of the grassland ecosystem.
## 4 Discussion
The soil sample from the train station dump site (ID3) was dominated by _Amaricoccus_, which had previously been detected in the activated sludge system [71] and was found to contribute to sulfamethoxazole biodegradation. Recent research has also shown that they are the primary degrader of Triclosan (5-Chloro-2-(2,4-dichlorophenoxyoxy) phenol, TCS), an active component found in many common personal care products such as soaps, shampoos, and toothpastes [72]. Other dominant members of the respective samples, ID4, ID5, and ID6 were also reported in various landfill soils [50; 54; 56; 73; 74], demonstrating its important ecological function. Intriguingly, _Nocardioides_ (Figure 2), a significant genus in _Actinomycetes_, was present in all samples used in this study. This genus was previously reported to be the dominant member of Dominican amber and Israeli amber preserved at about 120 million years ago [75]. This genus has the potential to withstand the exposure to toxic heavy metals and is involved in the breakdown of hexachlorobenzene (HCB) and pentachlorophenol (PCP) [76]. Findings from this study suggest the idea that this potentially contributes to biodegradation and thus could be an important bioinoculant for landfill sequestration. Functional prediction analysis revealed the presence of putative degradative pathways in all dump site locations (Figure 4). To our knowledge, this is the first study to investigate functional genes from informal dump sites, whereas many studies have been reported in active or abandoned landfalls that have matured for many years. However, our findings are quite consistent with those reported by others [50; 74; 77]. A possible explanation is due to the fact that long-term landfalls host higher bacterial diversity than short-term informal dump sites, hence an enrichment of several functional genes cannot be expected as in the other landfill soils. In addition to function prediction, 31 different carbon substrates were used in this study to assess the active utilization and metabolic potential of the bacterial members residing in the dump site soils (Figure 5B). Sample ID5 showed no significant utilization on the given substrates because, as previously discussed, this sample had very low moisture content, which significantly affected the active microbial populations compared to other samples. Both the functional prediction analysis and the active utilization study found evidence supporting the idea that the dump site soils host active bacterial communities with significant degradative roles. A similar observation has previously been reported in heavy metal-contaminated soil, demonstrating that bacterial consortia have the ability to metabolize more complex substrates such as polymers [78]. Although the substrate utilization tends to vary between collected samples, the bacterial communities present in dump site soils clearly demonstrate that they have potential in the bioremediation of toxic contaminants and other biotechnological applications.
# Exploring Vitamin D Synthesis and Function in Cardiovascular Health: A Narrative Review
## 6 The Impact of Processing on Vitamin D Levels in Foods and Mitigation Strategies  
### Impact of Processing on Vitamin D Levels  
The effectiveness of dietary Vitamin D fortification in improving blood Vitamin D levels and cardiovascular outcomes is intricately linked to how Vitamin D is processed and maintained in fortified foods. Fortification strategies are crucial for enhancing Vitamin D levels in the population, especially in regions with limited sunlight exposure. However, the actual benefits of such fortification on cardiovascular health are still under debate, as studies show variable impacts on cardiovascular outcomes. This is compounded by the fact that the processing of Vitamin D-fortified foods can significantly affect the nutrient's stability and bioavailability. Without proper handling during processing, the potential cardiovascular benefits of Vitamin D fortification might not be fully realized, underscoring the need for optimized processing methods to maintain the efficacy of fortified foods in boosting Vitamin D status and potentially improving cardiovascular health. Both Vitamin D3 and D2 are susceptible to degradation from heat, light, oxidation, and other processing factors, which may compromise its stability and bioavailability in food products. Food scientists must account for these considerations during food processing, storage, and distribution to minimize nutrient loss and ensure product quality and safety. Moreover, innovative processing technologies and packaging materials are continuously explored to enhance the stability and retention of Vitamin D in processed foods. Figure 2 illustrates the impact of various processing methods on Vitamin D3 levels in foods and highlights the key mitigation strategies employed to counteract these effects.
## 7 Conclusions  
This paper presents an investigation of two spectrum management schemes for IoRT devices communications in 5G satellite networks. In addition, a newly proposed scheduler called CAQDB scheduler is presented and compared with existing MLWDF scheduler in order to study the impact of scheduling algorithms on the performance of spectrum management schemes especially in shared spectrum management schemes, where different traffic or use cases are expected. From the presented results, the shared spectrum management scheme produces a better performance in almost all the performance metrics for the two scenarios considered. Furthermore, it can be concluded that the packet scheduler has an impact on the performance of spectrum management scheme especially the shared spectrum management scheme, as the proposed CAQDB scheduler provides better performance in the future.  
Figure 10: Spectral efficiency in scenario B.  
Figure 9: Spectral efficiency in scenario A.  
performance compared to the MLWDF scheduler, mainly when the shared spectrum management is used. In conclusion, the shared spectrum management scheme is recommended provided an appropriate scheduling algorithm like the proposed CAQDB, that will consider not only the delay as QoS factor but also the state of queue, is used. For future work, a more dynamic spectrum management scheme can be considered in the investigation and more diverse use cases will be considered.  
Conceptualization, G.A. and P.O.; methodology, G.A. and P.O.; software, G.A.; validation, G.A. and P.O.; formal analysis, G.A. and P.O.; writing--original draft preparation, G.A.; writing--review and editing, G.A. and P.O.; supervision, P.O.  
This research received no external funding.  
The authors declare no conflict of interest.
# Toward an SDN-Based Web Application Firewall: Defending against SQL Injection Attacks
## 3 Related Work
* Behavioral Analysis: SDN controllers can use behavioral analysis techniques to establish baseline behavior for applications or users and detect deviations from the baseline that may indicate attacks. For example, by monitoring application-level traffic patterns in vehicular ad hoc networks (VANETs) over time, an SDN controller can learn normal behavior and raise alerts when it detects anomalies, such as sudden spikes in traffic or unusual communication patterns [35]. * Machine Learning (ML): ML algorithms can be used in SDN environments to detect application-level attacks by learning from historical data and identifying patterns that may indicate attacks [10]. ML algorithms can analyze large volumes of network data, including packet payloads, flow attributes, and communication patterns, to detect unknown or zero-day attacks that may not have known signatures [36]. * Hybrid Approaches: SDN controllers can use a combination of the above methods in a hybrid approach to detect application-level attacks. By leveraging multiple techniques, such as DPI, flow monitoring, behavioral analysis, ML, and signature-based detection, SDN controllers can increase the accuracy and effectiveness of attack detection. It is important to note that no single method is foolproof, and a combination of techniques may be needed to achieve robust application-level attack detection in SDN environments. The choice of methods to use will depend on the specific requirements, constraints, and capabilities of the SDN environment and the types of application-level attacks being targeted. The proper configuration, tuning, and monitoring of the chosen detection methods are also essential to minimize false positives and false negatives and ensure effective application-level attack detection in SDN environments.
# Internet of Things for Modern Energy Systems: State-of-the-Art, Challenges, and Open Issues
## 4 Illustrative Examples
### Power IoT  
The issue of implementing IoT in smart grids is dissimilar from smart building in different aspects of security, data mining, etc. One of the main reasons that can be addressed is the scale of the system. As the size of the system increases in smart grids, wireless communication is mostly chosen for communication infrastructure. This leads to a great attention to ICT in IoT-based smart grids [18].  
Another matter in IoT-based smart grids that is worth a great amount of attention is a variety of applications and services, provided by either the grid or third parties. In this manner, a higher level of standards, protocols, architecture, etc. should be followed, which is called industrial IoT (IIoT) [128]. At the same time, as the systems' scale become larger, the need and importance of real-time applications increases. This is introduced as a Knowledge Discovery in Database (KDD) in the literature, as online monitoring is a desirable capability in smart grids [129]. To overcome this challenge, new technologies, protocols, and architectures should be developed. In [130], a new technology called Software Defined Networking (SDN) is developed to make data system more robust. By using SDN technology, real-time monitoring and control of the energy systems are available. To do so, an architecture including infrastructure, control, and application layers is presented based on the IIoT paradigm for energy systems. Talking about IoT-based smart grid architecture, a four-layer architecture for residential smart grids including device, network, cloud management, and application layer is presented in [131]. In this effort, the network layer connects device and application layer using IP-based protocols like Representational State Transfer (RESTful) based on Hypertext Transfer Protocol (HTTP). On the other hand, in the device layer, some other IP-based protocols like Bluetooth may be used for connecting things to gateways, as most devices have limitations from a communication point of view.  
To apply the IoT system into smart grid systems, some technologies should be considered: (1) Sensor technologies; (2) Communication technologies, like Bluetooth, ZigBee, etc.; (3) Data Mining technologies: as IoT nodes are limited in many aspects such as bandwidth, it is inappropriate to exchange all unstructured data. Hence, data fusion technologies may be hired in order to exchange useful data. In [132], necessary technologies to implement IoT into power systems are discussed in detail.
## 2 Methods
### Estimation of the State Sequences  
In order to eliminate dependence of the algorithm on future output data (\(Y_{sf}\)), the concept of geometrical projections of the future output onto the past output subspaces is deployed. The observability matrix of the system is obtained from:  
\[\Gamma_{i}=W_{1}^{-1}U_{1}S_{1}{}^{1/2}, \tag{13}\]  
Figure 2: The flowchart to calculate principle component (PC), unweighted principle component (UPC), and canonical variate analysis (CVA) weight matrices.  
The oblique projection \((O_{i})\) of the past and future output data are used to determine the state of a system \(\left(\hat{X}_{i}\right)\) and to estimate the extended observability matrix \((\Gamma_{i})\).  
\[\hat{X}_{i}=\Gamma_{i}{}^{\intercal}O_{i}, \tag{14}\]  
By shifting data sequences one step ahead, the oblique projection of \(O_{i-1}\) could be calculated. Multiplication of the pseudo-inverse of the extended observability matrix without the last block row \((\Gamma_{i-1})\) into the oblique projection results in the state sequence of the future data \(\left(\hat{X}_{i-1}\right)\).  
\[\hat{X}_{i+1}=\Gamma_{i-1}{}^{\intercal}O_{i-1}, \tag{15}\]  
As it can be seen in Equations (14) and (15), state sequence of Kalman filter is obtained directly from output data sequence without dependence on geometrical interpretation or priori knowledge of system matrices. In the next section the least square application to drive \(A\) and \(C\) matrices is discussed. The corresponding state sequences of \(\hat{X}_{i}\) and \(\hat{X}_{i-1}\) can be used to obtain the state-space representation of the system as shown in Equations (14) and (15).
# On Flexural Performance of Girder-To-Girder Wet Joint for Lightweight Steel-UHPC Composite Bridge
## 3 Comparative Model Test: The First Generation T-Shaped Joint and the Traditional I-shaped Joint
### Test Results  
#### 3.2.1 Load-Deflection Curve and Crack Development  
As shown in Figure 6a, the loading process can be divided into three stages:  
1. Elastic stage. During this stage, the UHPC panel does not show any cracks, and the specimen exhibits no stiffness reduction. When the applied load reaches 605 kN, the curve deflects slightly, indicating that the stiffness of the specimen decreases slightly, and a crack with a width of 0.02 mm is found at the joint interface.
2. Crack propagation stage. After 605 kN, the short cracks develop into long cracks, and the width gradually increases in the joint interface. Therefore, this stage is defined as the crack propagation stage.
3. Yield stage. When the applied load reaches 2190 kN, the load-deflection curve deflects obviously, indicating that the stiffness of the specimen decreases significantly. Subsequently, the structural  
Figure 5: Loading and measuring device: (**a**) Loading method; (**b**) measuring device layout.  
deformation increases rapidly, but the load cannot increase anymore. Thus, this stage is defined as the yield stage.  
During the loading process, the cracks can only be found at the interface of the two joints, and no visible cracks develop in the rest of the parts. The crack development curve is displayed in Figure 6b, where the \"T\" and \"I\" denote T-shaped joint and I-shaped joint, respectively, and the \"L\" and \"R\" indicate the left and right side, respectively. It is seen that the cracks develop at four interfaces with similar trend, and the crack on the right side of the I-shaped joint grows slightly faster than that of the left side.
## 3 Resource Management in LTE V2X Mode 4
Candidate Single-Subframe Resources (CSRs) that, after a filtering process, will be used in the selection window to transmit V2V packets, i.e., n SCIs + TBs. The following four steps are necessary in the resource selection process using SPS:  
* Step 1: CSRs are sensed in the sensing window which has a duration of 1000 ms, i.e., 1000 subframes or 1 s (see Figure 4). In this window, a vehicle selects all available CSRs, except those that, through the CSI, indicate that they have been reserved by other vehicles. * Step 2: CSRs whose average Reference Signal Received Power (RSRP) is above a certain threshold are filtered out. This threshold is defined from higher layers and depends on the priority of the packets, which may be related to the type of V2V service being used. Note that this step excludes CSRs that are possibly being reserved or used by other surrounding vehicles. * Step 3: It should be verified that the number of CSRs filtered in the previous step is at least 20% of the initially sensed CRSs. If this is not the case, then the threshold defined to measure the average RSRP is increased by 3 dB. Thus, step 3 is iteratively repeated until the condition of having at least 20% of CSRs available is met. * Step 4: finally, 20% of the CSRs (filtered in step 3) that experience the lowest average Received Signal Strength Indicator (RSSI) of all RBs are filtered out. Thus, a CSR is randomly selected to be used for the first transmission. Additionally, the following issues should be taken into account in the resource selection process using SPS in mode 4 V2V:  
* The number of subchannels of a CSR that a vehicle must reserve depends on the length of the message to be transmitted. That is, it depends on the V2V use case to be used. * The duration of the selection window depends on the latency requirements of V2V communications and has a maximum duration of 100 ms, i.e., 10 pps. In addition, sensing windows with a duration of 50 ms and 20 ms are possible for 20 pps and 50 pps, respectively [15]. * The vehicle can continue transmitting persistently after a certain number of packets. This number depends on a Reselection Counter (RC) that decrements depending on the transmission time of consecutive packets and is known as Resource Reselection Interval (RRI). The RC is randomly selected and can vary from [5; 15] for 10 pps, [10; 30] for 50 pps and [25; 75] for 50 pps. Note that there is persistent transmission for at least 1 s.  
Figure 4: Semi-Persistent Scheduling for LTE mode 4. * Each time the RC decreases to zero, the resource selection and reservation process must be performed again, where the probability of keeping the previous resources varies between 0 and 0.8. Alternatively, through the V2V mode 4, the standard allows the option of sending duplicate packets, in order to improve the reliability of communications.