esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
The polarizer can be a general accessory of any optic system, located at any readily accessible place in the incident optic-path such as in front of the laser output, and even be replaced by a 360\({}^{\circ}\) continuously adjustable sample stage. In addition, the NA can also be used to identify crystalline orientation of other anisotropic two-dimensional materials with Raman activity [27]. For example, the Raman intensity of ReS\({}_{2}\) at 211 cm\({}^{-1}\) shows periodic variation with specimen rotation and reaches its maximum when the crystalline orientation of the ReS\({}_{2}\) specimen paralleling to the polarized direction of the incident laser at NA configuration [28], and so do the Raman intensities of WTe\({}_{2}\) at 164 cm\({}^{-1}\)[29]. Layered b-AsP and GeS [30; 31] also have the orthorhombic structure similar to black phosphorus. Existing Raman experiments using NA configuration showed that the Raman intensities of A\({}_{\text{g}}\) and B\({}_{\text{2g}}\) modes for b-AsP periodically variated with Armchair-polarization angle. Moreover, the A\({}_{\text{g}}\)\({}^{1}\) and A\({}_{\text{g}}\)\({}^{2}\) intensities of GeS showed periodic variation at PP configuration. Due to the crystal structure of GeS similar to that of black phosphorus, it is reasonable to determine the crystal orientation of GeS according to the Raman intensity to polarization angle relationship of A\({}_{\text{g}}\)\({}^{1}\) and A\({}_{\text{g}}\)\({}^{2}\) modes at NA configuration. In sum, the proposed method in this work is also applicable to identify crystalline orientations of ReS\({}_{2}\), WTe\({}_{2}\), b-AsP, GeS, etc.
# Effects of the Extraction Technology on Pomegranate Juice Quality
## 2 Materials and Methods
### Chemical Analytical Determinations
#### 2.5.7 SPME-GC-MS Analyses  
Gas Chromatography-Mass Spectrometry (GC-MS) analyses were performed using a 5890 GC system (Hewlett-Packard, Palo Alto, CA, USA) interfaced with an HP 5973 quadrupole mass spectrometer detector (Hewlett-Packard, Palo Alto, CA, USA). As a stationary phase an HP5-MS capillary column (5% diphenyl--95% dimethylpolysiloxane 30 m--0.2 mm, 0.25 \(\upmu\)m film thickness, J&W Scientific, USA) was used. Injector and detector temperatures were 250 \({}^{\circ}\)C and 270 \({}^{\circ}\)C respectively. Helium was used as the carrier gas. The GC oven temperature program was 40 \({}^{\circ}\)C for 5.00 min, than increased by 10 \({}^{\circ}\)C/min to 280 \({}^{\circ}\)C. Electron impact ionization spectra were obtained at 70 eV, recording mass spectra from 42 to 550 \(\upmu\)m.  
Compound identification was carried out using a commercial NIST 07 mass spectra library search and by comparison with standard analytical grade compounds purchased from Sigma-Aldrich (St. Louis, MO, USA). For this analysis the pomegranate juice elutes from different pressure were collected and the volatile organic fractions were extracted by solid-phase microextraction (SPME) with a polydimethylsiloxane/divinylbenzene (PDMS/DVB, 65 mm) coated fiber purchased from Supelco, Sigma-Aldrich (Bellefonte, PA, USA). The extraction was carried out after having mixed 3 g of pomegranate juice with 3 mL of saturated NaCl solution in a 15 mL glass headspace vial with PTFE/Silicone septa (Supelco, Sigma-Aldrich, Bellefonte, PA, USA) and a magnetic stirring bar. The vial was gently heated and magnetically stirred for 20 min at 60 \({}^{\circ}\)C to allow equilibrium. Then, the SPME fiber was inserted through the septum and exposed to the sample for 30 min. The fiber was then retracted and introduced on the GC injector and held for 3 min to allow the desorption of the analytes in the GC inlet port. Prior to use, the PDMS fibres were conditioned at 250 \({}^{\circ}\)C for 30 min. The linear retention indices were calculated with references to n alkanes (C6-C22), obtained from FLUKA (Sigma-Aldrich, Bellefonte, PA, USA), and run under the chromatographic condition described above. Standard mixtures of selected compounds for each different class were also injected in the GC inlet port and retention indices determined. 1 as superscript numbers near the compound name identifies the verified authentic standard molecules (as shown in the table reporting the GC-MS analyses results). Response factors of reference compounds from different classes of monoterenes, sequiterpenes, alcohols, esters, aldehydes, ketones and phenolic compounds were determined and found to range from 0.85 to 1.2 versus n hexanol, averaging 1.0. Response factors were therefore taken as 1.0 for all compounds and on the base of integrated area of each analytical chromatographic peak, data has been presented as percentage of the total area.
## 4 Results and Discussion
### Technological Selection  
Bibliometric analyses are computed by the analysis ad hoc runner container. For implementation, Apache Spark (spark.apache.org (accessed on 6 May 2021)), bibliometrix, and custom implementation in Python were considered as candidates. Apache Spark _\"is a unified analytics engine for large-scale data processing\"_[44], while bibliometrix is a R package that implements many bibliometric analyses. Initially, Apache Spark was tested by implementing the term frequency algorithm as an example.  
The algorithm compares the similarity of records by the words given in their abstracts resulting in a similarity matrix. It took 10.0 minutes to compute the term frequency of 1000 records with Apache Spark on a 2.6 GHz 6-Core Intel Core i7 with 6.2 GB RAM. In bibliometrix, all analyses are not implemented in a parallelizable way, which means that, for example, only one processor core is used for calculations. This led to the decision not to use Apache Spark or bibliometrix and to instead implement bibliometric analysis in Python for ad hoc analysis, as speed is critical for smooth display of bibliometric networks in the web front end and numeric frameworks are well established in the Python ecosystem.  
The Spring framework was chosen for implementation as it comes with many required functionalities, such as providing an interface for the implementation of a REST API, an interface for accessing the Java persistence API, and an implementation of an OAuth2 authorization server.  
Docker is selected as the runtime container, since it is the de facto standard for application containerization. For cloud orchestration, Kubernetes was chosen, as it is an open source product with great popularity (65.5k stars on Github in April 2020) and stability (initial release: 7 June 2014). KrakenD was chosen for realizing the API gateway, as it is suitable for distributed runtime setting in Kubernetes.  
For work scheduling, Apache Airflow (airflow.apache.org (accessed on 6 May 2021)) was considered. After investigation and a proof-of-concept implementation, we determined that Apache Airflow did not meet the requirements of ad hoc work scheduling and execution. Instead, it is used for regularly running work tasks that are scheduled by time [45].  
Due to the lack of tools providing ad hoc work scheduling, the work scheduler has to be implemented from scratch based on lock-free Single-Producer/Single-Consumer (SPSC) queues [46]. For job scheduling, Kubernetes Job API was selected as it provides container-based batch processing and dynamic scaling of compute and memory resources based on per job defined resource requirements. Furthermore, it fits to the infrastructure concept, as Kubernetes is already used for orchestration.
## 2 Materials and Methods
### Channel Characterization  
The goal of the channel characterization process was to determine a suitable radio channel model for the wireless environment. To model the channel within the RHEA scenario, range tests were performed and numerous RSSI measurements were collected at different distances between the receiver (_i.e._, the UPD) and the transmitter (_i.e._, the wireless router mounted on the autonomous tractor). Because the UPD operates in a dynamic environment in which the formation of the autonomous vehicles often changes, the communication channel model accounts for the orientation of the UPD relative to the router. The measurements indicated that the RSSI performance was influenced by the UPD orientation relative to the wireless router (e.g., whether the UPD was facing the router or the user had turned his back to the router).
## 4 Results and Discussion
### Source Bearing Estimation Results in Each Division Frequency Band  
According to the bearing estimation method described in Section 3, the maximum power rotation method combined with the vertical filtering method was used. The estimated bearing of 15 different frequency bands of the first explosion acoustic source at site S1 is shown in Figure 4.  
As shown in Figure 4, the estimated bearing of the 15 frequency bands to the same acoustic source at the same site is not completely consistent. The true bearing value of the site is \(-\)5\({}^{\circ}\), as shown in Table 1. By considering prograde particle motion in the vertical-radial plane, the 180\({}^{\circ}\) bearing ambiguity can be solved [23], but it can still be found that some bearing estimation errors (difference from the true value, without considering 180\({}^{\circ}\) ambiguity.) are large and some slightly changed with the frequency band of the acoustic signal. Combined with the previous three-direction displacements, which are shown in Figure 3b-d, it is found that the bearing estimation error of the frequency band corresponding to small displacement is large (\(f\)\(\geq\) 630 Hz), and the error of the frequency band corresponding to large displacement is small (\(f\) < 630 Hz). It seems that the bearing estimation error of different frequency bands is related to the peak displacement in the same frequency band. More details will be provided in the next section.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Frequency/Hz** & **1000** & **800** & **630** & **500** & **400** \\ \hline x peak/10\({}^{-7}\) m & 1.1376 & 1.2432 & 5.295 & 13.085 & 38.113 \\ y peak/10\({}^{-7}\) m & 0.6745 & 1.0727 & 1.6269 & 1.712 & 4.252 \\ z peak/10\({}^{-7}\) m & 1.5068 & 2.6327 & 3.9844 & 8.7499 & 44.337 \\ \hline
**Frequency/Hz** & **315** & **250** & **200** & **160** & **125** \\ \hline x peak/10\({}^{-7}\) m & 29.355 & 28.626 & 43.977 & 49.736 & 42.540 \\ y peak/10\({}^{-7}\) m & 11.638 & 8.931 & 24.205 & 32.477 & 28.067 \\ z peak/10\({}^{-7}\) m & 16.462 & 17.636 & 24
# Securing IoT Data Using Steganography: A Practical Implementation Approach
## 4 Performance Evaluation
### Scenarios  
Four scenarios were designed to analyze the adequacy of the proposed algorithm for the IoT environment and to evaluate its performance by conducting a comparative study between the stego channel and the cover signal.  
1. Perceptual undetectability against payload capacity: In this scenario, the perceptual similarity between the original cover and the stego channel is evaluated against the payload capacity using the PESQ test and \(SegSNR\). We aimed to maximize the payload capacity (Kbps) while maintaining a good stego signal quality to determine the scheme's capability to send a high payload capacity. The PESQ is used to measure the quality of the stego signal. The output of the test is a number \(\in\) [1 4.5]. An output value of 4.5 indicates that the stego signal is the same as the cover signal. A value of 1 indicates the severest degradation. Achieving a high payload capacity while maintaining perceptual undetectability confirms that the algorithm is capable of accommodating a high data traffic rate summed up from the mass of IoT device transmissions.
2. Resilience to noise channel: In this scenario, we measured the bit error rate (\(BER\)) in the stego channel due to noise-induced by the injected payload and the channel. A lower \(BER\) corresponds to low retransmission probability and therefore the scheme capability to send high traffic load.
3. Scheme application dependencies: We propose a new performance measure to evaluate the average embedding ratio (\(AER\)) against different types of applications (e.g., speech, music, and video). An \(AER\) value analysis will allow us to prove that our algorithm is not application-specific and can accommodate heterogeneous and high traffic.
4. Statistical undetectability and time complexity: In this scenario, we measure the performance of our algorithm against steganalysis attacks to determine how well the system can distinguish between stego and cover signals. The accuracy of our predictions is measured by F-measure and the receiver operating characteristic (ROC). In this scenario, the time complexity of our scheme is also measured.
# A Simplified CityGML-Based 3D Indoor Space Model for Indoor Applications
## 3 Simplified 3D Indoor Space Model (3 DSISM) Based on CityGML  
Indoor spatial information in 3D indoor models is determined by different indoor applications. Indoor navigation is concerned with indoor spaces (e.g., rooms and corridors) and constraints (e.g., walls and doors) represented by various indoor spatial elements [27] of appropriate semantic type and geometric representation to define various physical and virtual spaces [26]. In addition, indoor spatial information should not only reflect the indoor space environment of a building but also consider navigation constraints, such as adjacencies of indoor spaces and indoor paths [5; 13]. Therefore, 3D indoor spatial information should contain detailed geometric and rich semantic information about various indoor functional spaces and building components, such as rooms, stairs, walls, ceilings, doors, and windows, which organize the indoor spaces into a number of spatial units and spatial  
Figure 4: Simplification of walls (2D footprints). (**a**) Exterior and interior boundary surfaces of a building. (**b**) A building with irregular outer contours.  
relationships and provide appropriate information for indoor applications, such as indoor navigation. It is worth mentioning that there are a large number of facilities in indoor spaces, such as furniture. These furnishings occupy a certain amount of interior spaces and are obstacles to indoor navigation and emergency rescue, which should be avoided when developing indoor paths. Therefore, indoor spatial information should include the description of furniture. However, in this study, we put emphasis on the representation of building components and indoor functional spaces, while the description of furniture is beyond the scope of this research.
# Smart Agriculture Applications Using Deep Learning Technologies: A Survey
## 3 Deep Learning  
Deep learning is a subset of machine learning and AI, and is essentially a neural network with three or more layers. These neural networks aim to mimic the activity of the human brain; however, they fall far short of the human brain's ability to learn from large amounts of data. While a single-layer neural network can provide approximate predictions, additional hidden layers can aid in optimizing and refining the accuracy. Machine learning is a subfield of AI that allows the system to learn from concepts and knowledge without being explicitly programmed. It begins with observations, such as face-to-face interactions, to prepare for data features and trends and improve future results and judgments. Deep learning is built on a combination of machine learning algorithms that use multiple nonlinear transformations to model high-level abstractions in data [7]. Feature learning, or the automatic extraction of features from raw data, is a significant advantage of deep learning. Features from higher hierarchy levels are produced by the composition of lower-level components [8]. Recurrent neural network (RNN) and convolutional neural network (CNN) are two standard deep learning networks used in agriculture.
###### Abstract  
Intervertebral disc (IVD) degeneration is characterized by a loss of cellularity, and changes in cell-mediated activity that drives anatomic changes to IVD structure. In this study, we used single-cell RNA-sequencing analysis of degenerating tissues of the rat IVD following lumbar disc puncture. Two control, uninjured IVDs (L2-3, L3-4) and two degenerated, injured IVDs (L4-5, L5-6) from each animal were examined either at the two- or eight-week post-operative time points. The cells from these IVDs were extracted and transcriptionally profiled at the single-cell resolution. Unsupervised cluster analysis revealed the presence of four known cell types in both non-degenerative and degenerated IVDs based on previously established gene markers: IVD cells, endothelial cells, myeloid cells, and lymphoid cells. As a majority of cells were associated with the IVD cell cluster, sub-clustering was used to further identify the cell populations of the nucleus pulposus, inner and outer annulus fibrosis. The most notable difference between control and degenerated IVDs was the increase of myeloid and lymphoid cells in degenerated samples at two- and eight-weeks post-surgery. Differential gene expression analysis revealed multiple distinct cell types from the myeloid and lymphoid lineages, most notably macrophages and B lymphocytes, and demonstrated a high degree of immune specificity during degeneration. In addition to the heterogeneous infiltrating immune cell populations in the degenerating IVD, the increased number of cells in the AF sub-cluster expressing _Ngf_ and _Ngfr_, encoding for p75 NTR, suggest that NGF signaling may be one of the key mediators of the IVD crosstalk between immune and neuronal cell populations. These findings provide the basis for future work to understand the involvement of select subsets of non-resident cells in IVD degeneration.  
intervertebral disc degeneration; single-cell RNA sequencing; cell type +
Footnote †: journal: Applied Sci.
## 3 Methods
### Noise Removal and Signal Range Selection
As given by Equation (4), \(t_{S}\), making the start of the water surface return with a distinct intensity increase, is selected as the point where the amplitude increases by more than \(3\sigma_{N}\). To avoid the failure of detecting the bottom peak, which may have a relatively low intensity, \(t_{E}\) was selected as the point where the amplitude decreased to \(1.5\sigma_{N}\) or less, as shown in Equation (5).
# Data Preprocessing Method and API for Mining Processes from Cloud-Based Application Event Logs
## 4 Cloud Pattern API-Process Mining Method
### Clean-Up Step  
In this step, initial cleaning of the dataset is done. Seven sub-steps are included here, and the last two sub-steps (1.6 and 1.7) can be interleaved.  
1. Read the CSV log file, which must include the three main columns that are needed to apply process mining (case ID, event, and timestamp). It may also include other columns, for example resources and other properties.  
Figure 2: Overview of the CPA-PM method and its three main iterative steps.  
* [leftmargin=*] Step 1.2: Clean column identifiers, e.g., by removing white spaces and dots from the column's names and ensuring their unicity. This returns a dataframe dataset with clean names that consist only of lowercase letters, numbers, and underscores.
* [leftmargin=*] Step 1.3: Ensure the time format of the timestamps reflects the granularity needed. For example, if the timestamp must include detailed information about the minutes, seconds, and milliseconds for when the event occurred, this may require merging separate date and time columns, or changing the encoding of the date/time information.
* [leftmargin=*] Step 1.4: Ensure that the cells in a given column comply with their expected column type and transform or cast the column, if necessary, to the required data type.
* [leftmargin=*] Step 1.5: Select only the columns needed for the analysis questions. For clickstream data, the analyst should select (a) the case identifier, event, and timestamp columns minimally required by PM techniques; (b) the resource columns relevant to the analysis questions; and (c) the attribute columns that refine events. Consider also excluding columns that are empty or that threaten re-identification.
* [leftmargin=*] Step 1.6: Filter the event log. Filtering is a popular preprocessing technique the goal of which is to reduce the complexity or to focus on a specific part of the process during the analysis. This technique is usually performed multiple times, iteratively.
* [leftmargin=*] Step 1.7: Remove incomplete cases. An incomplete case is a truncated case/trace that is missing the start and/or the end event(s) in a process instance.
## 5 Simulation Results and Discussions
### Performance Analysis  
BER and Q-factor are two metrics that indicate the quality of a communication link. BER is defined as the number of erroneous bits per total number of bits transmitted. Similarly, the Q-factor represents the difference between the average energy of correctly received bits and the average energy of incorrectly received bits, normalized by the standard deviation of the received signal. The relationship between the Q-factor and BER is inversely proportional: as the Q-factor increases, the BER decreases. Therefore, the Q-factor must be high, and the BER must be low.
## 4 Simulation Results and Discussion
### Presentation and Discussion of Results
Also, participation of both responsive loads and EVs in DR programs (case 3) can reduce the total operating cost more than the other cases where there are no DR action or EV support. In fact, in case 3, EVs discharging in peak hours (as shown in Figure 9) causes the decrement of expected cost of MG in comparison with cases 1 and 2. In other words, during peak hours, EVs discharge and supply peak loads; thus, the more expensive units may not be dispatched and, consequently, the energy cost of DGs is reduced. Moreover, in CPP programs, since customers are highly encouraged to reduce their consumption as much as possible at peak hours (at 19:00 and 20:00 as shown in Figure 13), the energy cost of DG units and as the result the total operating cost of MG has its lowest value. Moreover, in case 3, due to the uncertainty of EVs, the total cost of scheduling reserve of DGs and DR in each DR program is more than the one in case 2. However, it can be observed that the total deployed reserve cost of DGs and DR in cases 2 and 3 are almost the same, because deployed reserve is provided by DR and DG units and EVs do not participate, as they only affect the energy scheduling process. Figure 13: Daily load profile in three cases considering (**a**) RTP, (**b**) TOU and (**c**) CPP programs.
## 4 Experimentation and Testing
The Type-1 PV cells were able to charge the additional battery via BQ25504-EVM under 2500 lx lighting intensity in an indoor office environment over 8 h. The Type-2 cells also charged the same battery in 8 h.  
To demonstrate that light energy harvesting can support the power required for some critical operations of the smart devices, as shown in the system proposed in Figure 1, an experimental platform as shown in Figure 9 was developed. Four Type-1 PV cells were fitted on a wristwatch and smartphone surfaces, and using the BQ25504 power management module, electrical power was harvested under different light conditions. The harvested energy was stored in the capacitor then transferred using a switch to a ZigBee green power module (MeshBee). ZigBee modules are frequently used in smart and wearable devices. Whenever the storage capacitor inside BQ25504-EVM had sufficient energy, the changeover switch was used to utilize the harvested energy for ZigBee data transmission. Figure 3.3 The experiential platform for charging the small Li-Ion 3.7 V battery for emergency backup, using PVs on a smartphone cover.