esa-sceva/satcom-chunk-collection huggingface dataset:
# Reducing PAPR with Low Complexity Filtered NOMA Using Novel Algorithm
## 3 Simulation Results
### BER Analysis  
To examine the throughput of a Power Amplifier (PA) utilized in the NOMA framework, the BER analysis of the recommended SLM is given in Figure 5. It is seen that the BER of \(10^{-3}\) is achieved at the SNR of 5.3 dB for SLM (S = 4 \(p\) = 4), 6.5 dB for SLM (S = 4 \(p\) = 2), 7.7 dB for SLM (S = 2 \(p\) = 4), 8.6 dB for SLM (S = 2 \(p\) = 2), 10.6 dB for F-NOMA, and 12.1 dB for NOMA, respectively; hence, it is concluded that the BER is effectually enhanced for projected method.
## 2 Materials and Methods
### Data Splitting  
In this study, our dataset consists of 317,323 records and 9 variables after initial filtering and feature selection. During the time series analysis of the variables, we identified a problem with sensor reconfiguration in April 2017, which changed the measurement range in some cases. Therefore, we only considered data from after this date because including earlier data could have a negative impact on model training. After removing the affected data, we retained 176,707 records and 9 variables, which accounts for 55.69% of the original data.  
For data splitting, a period of approximately one year of normal operation, between April 2017 and April 2018, was selected for training, which allows capturing seasonal patterns and short and long term variations. Subsequently, the testing phase was performed using the data labeled with failures.  
It is important to note that no failure data were included in the training, as the model focuses on reconstructing the input depending on the relationships with other variables. The objective is that the model attempts to reconstruct the expected behavior, with a high discrepancy potentially indicating an anomaly.
# Accurate Parcel Extraction Combined with Multi-Resolution Remote Sensing Images Based on SAM
## 2 Materials and Methods
### Method
#### 2.3.3 Accuracy Assessment  
To evaluate the results of parcel extraction, we use four metrics commonly used in the field of image segmentation, precision (P), recall (R), F1-score (F1), and Intersection over Union (IoU), and they are defined as follows:  
\[precision=\frac{TP}{TP+FP} \tag{4}\]  
Figure 5: Different Point parameters lead to different segmentation results.  
\[recall=\frac{TP}{TP+FN} \tag{5}\]  
\[F1-score=\frac{2\cdot precision\cdot recall}{precision+recall} \tag{6}\]  
\[IoU=\frac{TP}{TP+FP+FN} \tag{7}\]  
where _TP_ is true positive, _FP_ is false positive, and _FN_ is false negative. P demonstrates the ability of the classifier to not label negative samples as positive, while R reflects the ability of the classifier to find all positive samples. F1 is the reconciled mean of precision and recall, and is a combination of the two. The higher F1, the more robust the model is. The IoU measures the degree of overlap between the predicted segmentation region and the true segmentation region. We used manual outlining to label ground truth samples for metrics calculations.
## 2 Design Method
### Arbitrary Termination Impedance 3 dB Branch-Line Hybrid
Moreover, the proposed arbitrary termination impedance 3 dB branch-line hybrid provides high isolation between the through and coupled ports, as shown in Figure 4c. Figure 4d depicts the phase difference between the output ports of the arbitrary termination impedance 3 dB branch-line hybrid. A phase difference of 90\({}^{\circ}\) is achieved at the center frequency \(f_{0}\). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & \(\mathbf{Z_{1}}\) [\(\Omega\)] & \(\mathbf{Z_{2}}\) [\(\Omega\)] & \(\mathbf{Z_{3}}\) [\(\Omega\)] & \(\mathbf{Z_{4}}\) [\(\Omega\)] & \(\mathbf{C_{i}}\) [\(p\)F] **or \(\mathbf{L_{i}}\) [nH] (6 = 1–4)** & \(\mathbf{Z_{A}}\) [\(\Omega\)] & \(\mathbf{Z_{B}}\) [\(\Omega\)] & \(\mathbf{Z_{C}}\) [\(\Omega\)] & \(\mathbf{Z_{D}}\) [\(\Omega\)] \\ \hline Case 1 & 20 & 50 & 30 & 80 & 0/0/0/0 & 22.36 & 38.73 & 34.64 & 40 \\ \hline Case 2 & 25 & 50 – j60 & 40 + j20 & 120 & 0/3.9 nH/3.25 pF/0 & 25 & 44.72 & 48.99 & 54.73 \\ \hline Case 3 & \(35-j40\) & 30 + j125 & 75 – j60 & 45 + j10 & 2.6 nH/0.52 pF/3.9 nH/6.5 pF & 22.91 & 47.43 & 41.08 & 39.69 \\ \hline \end{tabular}
\end{table} 
Table 1: Calculated values of \(C_{m}\), \(L_{m}\), \(Z_{A}\), \(Z_{B}\), \(Z_{C}\), and \(Z_{D}\) according to different \(Z_{m}\) at \(f_{0}\) = 2.45 GHz.
# Energy Efficient Routing in Wireless Sensor Networks: A Comprehensive Survey
## 3 Network Structure
### Hierarchical
#### 3.2.2 Zone Based
The regions have a predefined number of nodes distributed randomly, while the first one has twice the number of nodes. Every region acts as a cluster including the cluster head election process, which is based on maximum residual energy. Furthermore, packets are transmitted from each region's cluster head to the base station excluding region one, in which nodes use direct transmission to transmit their data. In order to reduce packet loss, REECH-ME utilizes the Uniform Random Distribution Model. REECH-ME is considered to be suitable for monitoring applications. It provides improved stability period, prolonged network lifetime, and balanced energy consumption. On the other hand, its architecture is based on the use of strict settlements which limit the scalability provided. Zone-Stable Election Protocol (Z-SEP) [105] is a clustering routing protocol for heterogeneous WSNs that combines nodes with two energy levels and a double communication scheme, as some nodes can communicate directly to the base station while others form clusters and transmit their data to the base station using the SEP algorithm. Z-SEP divides the network into three zones:  
1. Head Zone 0, which includes the base station and normal energy nodes. 2. Head Zone 1, which includes randomly deployed advanced nodes. 3. Head Zone 2 that includes randomly deployed advanced nodes as Head Zone 1. During data transmission, nodes in Head zone 0 transmit packets directly to the base station while nodes in Head Zones 1 and 2, which are more dense than Head zone 0, transmit their data through an elected cluster head using an SEP algorithm. With ZSEP, energy consumption is decreased, high throughput is achieved, and stability period is increased. Yet, the suggested network infrastructure may not be realistic. Also, overhead is added. Zone based Stable cluster head Election Protocol -Enhanced (ZSEP-E) [106] is a clustering protocol created to face the problems of Z-SEP and SEP-E. It divides the network in three zones and utilizes three energy leveled nodes: normal, intermediate and advanced. ZSEP-E forms hierarchical clusters within the zones. Zones 1 and 3 include homogeneous advanced nodes distributed evenly, while zone 2 includes the base station, which is located in the center with the other two types of nodes normal and intermediate. During cluster head election process, in zone 2, intermediate nodes are only elected as cluster heads, while, in the other two zones, advanced nodes are elected. ZSEP-E extends network lifetime and achieves even distribution of advanced nodes. On the other hand, non-random deployment of nodes is used and the structural considerations made may be unfeasible in real case scenarios. A synopsis of the characteristics of the zone based subcategory hierarchical protocols is presented in Table 8.
## 3 Results and Discussion  
### Synthesis of Chalcone and Flavone Analogs (2a-2k, 3a-3k)  
Chalcones are usually synthesized by condensation reactions with acid or basic catalysis, even though recently a great number of new procedures for the synthesis of these molecules due to the great interest on their biological properties have appeared. Claisen-Schmidt reaction with basic conditions is the most widely synthesis reported in the literature, because of the easily process and efficiency in the product formation [35]. The substituted 2-hydroxychalcones 2a-2k were prepared by the Claisen-Schmidt condensation of the respective substituted benzaldehyde 1a-1k (1 eq) and 2-hydroxyacetophenone (1 eq) in the presence of NaOH in ethanol/water at room temperature by the known literature method (Scheme 1) [36]. This methodology allowed for the obtention ofeleven compounds with moderate to high yields, between 40-97%, being the highest one the yield for compound **2h**. The oxidative cyclization of 2-hydroxychalcones **2a-2k** to flavones **3a-3k** was performed using the classical iodine (1 mmol) in DMSO (Scheme 1) system. This method is particularly useful in flavone synthesis starting from 2-hydroxychalcones as it has been observed the iodine does not give secondary reactions despite the high temperature employed [37]. The corresponding flavones were successfully synthesized with yields between 40-85%.  
All of the compounds were characterized by IR, \({}^{1}\)H, and \({}^{13}\)C-NMR and mass spectroscopy; in the \({}^{1}\)H NMR spectra of the compounds **2a-k** can be noticed the hydroxyl proton in ring A between \(\delta\) 12.63-13.18 ppm, due to the intramolecular hydrogen bond made with the carbonyl oxygen atom. The vinylic protons of the \(\alpha,\beta\)-unsaturated system are present as doublets for H\({}_{\alpha}\) = 7.48-8.10 ppm and H\({}_{\beta}\) =7.69-8.89 ppm, being the coupling constants JH\({}_{\alpha}\)-JH\({}_{\beta}\) = 14.9-15.8 Hz, which indicates a _trans_ configuration for these protons. All of the aromatic protons were observed at their expected shifts, so as their coupling constants. In \({}^{13}\)C-NMR spectra, the carbonyl carbon is observed at \(\delta\) 193.3-193.8 ppm, being the \(\alpha\)- and \(\beta\)- carbons at 117.7-121.7 ppm and 129.7-145.8 ppm, respectively.  
For the flavone type compounds **3a-k**, carbonyl \(\alpha\)-proton H-3 is observed as a singlet with shifts of 6.69-7.12 ppm, the aromatic protons of the A and B rings were at their expected shifts and coupling constants. Regarding \({}^{13}\)C-NMR, the carbonyl carbon shifts to a lower frequency with \(\delta\) 177.0-193.7 ppm, being the carbons C-2 and C-3 identified at 155.9-163.9 ppm and 104.5-116.3 ppm, respectively. The assignments were made by HSQC and HMBC experiments.  
NMR spectra, including bidimensional ones, of selected chalcones and flavones can be observed in Figures S1-S24 in the supplementary materials.
# Reflection Propagation Law of Electromagnetic Waves in U-Shaped Roadway
## 4 Reflected Field Strength Calculation
### Reflected Field Strength
#### 5.1.2 Sending and Receiving Points Location  
Figure 12 shows how the number of valid reflection lines varies with increasing horizontal position of the launch point, where \(w_{1}\) gradually increases from 0 to 5 m, \(w_{2}\) = 2.5 m, \(v_{1}\) = \(v_{2}\) = 2 m, and \(g_{1}\) = \(g_{2}\) = 7 m.  
As shown in Figure 12, the number of high valid reflection lines was higher than the number of low valid reflection lines as the horizontal distance from the launch point increased. In particular, the maximum number of valid reflection lines was found 5 m from the launch point to the reflecting surface \(m_{1}\), indicating that the reflection lines were less obstructed by the obstacle plane at that location. However, when that distance is 2.5 m, the number of valid reflection lines increases, indicating that when the horizontal position of the transmitting point \(w_{1}\) is 2.5 m and that of the receiving point \(w_{2}\) is 2.5 m, the conditions for electromagnetic wave propagation are favorable.  
Figure 13 demonstrates how the number of valid reflection lines varies with increasing vertical position of the launch point, in which \(w_{1}\) = \(w_{2}\) = 2.5 m, \(v_{1}\) gradually increases from 0 to 4 m, \(v_{2}\) = 2 m, and \(g_{1}\) = \(g_{2}\) = 7 m.  
Figure 12: Number of valid reflection lines as a function of the horizontal distance from the launch point.  
Figure 13: Number of valid reflection lines as a function of the vertical distance of the launch point.  
From Figure 13, it can be concluded that the number of valid reflection lines does not change significantly as the vertical distance from the launch point increases. The shading by the obstacle plane makes the number of high valid reflection lines higher than that of low valid reflection lines. The number of valid reflection lines is more sensitive to changes in the horizontal than the vertical position of the launch point. This is due to changes in the horizontal position changing the initial angle of incidence.
# A Conceptual Design Methodology for e-VTOL Aircraft for Urban Air Mobility
## 5 Preliminary Design of a Tilt-Wing Air Taxi with Box-Wing Architecture
### Potential TiltOne Configurations
#### 5.2.1 TiltOne Single Mission Configuration  
The top view of the _TiltOne_ configuration selected for the single mission case is shown in Figure 20a. Details of the related mass breakdown are given in Figure 20b, whereas Table 14 reports the main features of the configuration.  
Figure 19: Selection of the configuration for different design missions: (**a**) single mission; (**b**) multi mission. For both, the cruise speed is equal to 45 m/s.  
Table 14 shows some relevant features of the configuration from several points of view (operability, noise etc.). It is possible to highlight the potential benefits and disadvantages as follows:  
* low MTOW and battery mass, which can allow a reduction of the manufacturing costs of both airframe and electrical components;
* lighter and smaller battery packages allow also to reduce the ground operation time needed for charging or swapping the batteries;
* low required thrust and disk loading, which both can lead to a significant reduction of ground noise annoyance;  
* significant reduction of the maximum range that can be covered with a single complete battery recharge;  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Single Mission** \\ \hline N. of passengers & 4 \\ \hline W/S [kg/m\({}^{2}\)] & 70 \\ \hline T/A [kg/m\({}^{2}\)] & 40 \\ \hline Wingspan [m] & 11.5 \\ \hline MTOW [kg] & 1427 \\ \hline Battery mass [kg] & 142 \\ \hline Cruise speed [m/s] & 45 \\ \hline Harmonic range [nm] & 50 \\ \hline Maximum hovering time [min] & 13.5 \\ \hline Missions @ 7 nm & 2 \\ \hline Missions @ 11 nm & 2 \\ \hline Missions @ 15 nm & 2 \\ \hline Missions @ 25 nm & 1 \\ \hline Missions @ 35 nm & 1 \\ \hline Missions @ 50 nm & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Main performance data of the _TiltOne_ configuration for single mission case.  
Figure 20: Details of the _TiltOne_ configuration for single mission case: (**a**) top view; (**b**) mass breakdown.  
significant reduction of the number of repeated typical missions possible with a single complete battery recharge;  
general reduction in the flexibility and operability of the aircraft.
# Optimized Score Level Fusion for Multi-Instance Finger Vein Recognition
## 4 Proposed Method  
The proposed solutions adopt multi-instance finger vein features as the feature descriptors for user recognition. At first, preprocessing of the finger vein images is performed to improve the image quality. A histogram of oriented gradients (HOG) is then used to extract the feature from each finger vein instance, and this is followed by matching for score level fusion. Score level fusion can be approached in two ways: classification or combination approach [12]. In the proposed method, BSSF uses the combination approach and BSBF uses the classification approach.  
In BSSF, the fusion can be viewed as a SVM combination problem and termed SVM score fusion, where the score is generated from the scoring function of trained SVM on the HOG feature of each finger vein instance. The resulting SVM scores of each finger vein instance are then multiplied with the score weight-optimized using a Bayesian optimizer. The scores will be fused with a weighted sum for final decision (accept or reject). In this context, the hyperparameters of HOG feature extractor, SVM matcher, and the weights infusion are optimized using Bayesian optimization approach. The overall framework of the proposed BSSF is presented in Figure 1.  
In the second classification approach which is termed as SVM based fusion (BSBF), the HOG feature of different instances is matched using Euclidean distance to construct the normalized scores. For example, the resulting scores of the index and middle finger are concatenated as a score vector. The score vector is then fed into SVM for classification purposes. The overall framework of the proposed BSBF is presented in Figure 2.
## 3 Result and Discussion
### EK Remediation Experiments
#### 3.2.2 Current Density and Outflow  
The pore water within the sample was found to move toward the cathode via electroosmosis. The outflow and current density of the improver are presented in Figure 5. To check the appropriate use of the improver per use, the result of the outflow was used to indicate whether the improver was smoothly injected into the sample.  
Figure 5(a) shows a comparison between the current density and outflow in the copper- and phenanthrene-contaminated ground using DDW. The current density first increased in the copper-contaminated Exp. 1, and the outflow also increased following the increase in the current  
Figure 4: Water content within the sample of the test (Exp. 1–Exp. 5).  
Figure 5: Result of the current density and total flow during the test (Exp. 1–Exp. 5). (**a**) Exp. 1–Exp. 2; (**b**) Exp. 3–Exp.5.  
density before decreasing. The heavy metal copper ion responded with an EK phenomenon faster than the organic phenanthrene.  
Figure 5b describes the current density and outflow of the heavy metal and the organic compound with respect to the purification time determined by experiments using DDW, acetic acid and Brij30 as the improver in the multiple contaminated ground. The current density increased and then decreased after 48 h, as shown in Exp. 1, and it reached a maximum with Brij30 after 24 h. The outflow alongside the increase in the current density responded faster in Exp. 5 than in the other samples. The amount of outflow was presented in descending order as Brij30 > acetic acid > DDW, and the current density in the soil with multiple contaminations reached a peak in descending order, Brij30 > acetic acid > DDW. The amount of outflow hardly changed after 200 h.  
The initial current density and outflow appeared fairly quickly with Brij30 as the improver, but slowly decreased after 24 h with acetic acid in the ground with multiple contaminations. Selective contaminant removal and effective purification will be achieved in the ground with multiple contaminations if the more responsive Brij30 is applied at the early stage to remove the organic substance first and the improver is changed into acetic acid.
###### Abstract  
In this article, a novel method of achieving a single-layer, dual-band, dual-circularly polarized (CP) shared-aperture phased array antenna with wide beam scanning coverage is presented. The space antenna was designed to provide direct-to-cellular communications services at S-/C-bands with a frequency ratio of 1:1.8. Using novel ceramic substrates with high dielectric constants for antenna miniaturization, the optimum interelement spacing can be ensured in one single layer to meet the large-angle scanning demand. The CP characteristic of the phased array is improved by the sequential rotation technique. A prototype of phased array, which is composed of an 8 \(\times\) 8 S-band Rx array and a 16 \(\times\) 16 C-band Tx array, is fabricated to verify this design. The measured results show that the shared-aperture phased array can provide \(\pm\)50\({}^{\circ}\) beam scanning coverage at both the S- and C-bands simultaneously to meet the direct-to-cellular communication demand in low earth orbit (LEO) satellites.  
satellite communication; shared-aperture array; dual-band; dual-circular polarized; single layer Article
## 2 Materials and Methods
### Study Variables  
The subjects filled out two questionnaires: the ASWS and PSS-C [18; 19; 20; 21]. The ASWS is a 28-item self-evaluation questionnaire used to assess sleep quality in subjects aged 12-18 years. The items describe the occurrence of different sleep behaviors and are grouped into 5 subscales: going to bed (5 items), falling asleep (6 items), maintaining sleep (6 items), reinitiating sleep (6 items), and returning to wakefulness (5 items). The respondents indicated how often sleep behaviors had occurred during the past month using the following six-point Likert scale: 1 = always, 2 = very often, 3 = quite often, 4 = sometimes, 5 = once in a while, and 6 = never. Eight items are reversed (Items 3, 4, 7, 11, 17, 23, 24, and 25). Mean subscale scores were calculated for each subscale and an ASWS total score was calculated as the mean of the 5 subscale scores. Higher scores indicate better sleep quality.  
The PSS-C is a 13-item measure of a child's perceived stress in several different areas including stress related to time, school, parent relationships, peer relationships, conflict, and feelings like fear, happiness, and anger. The form consists of 14 questions; the first one, which is not scored, checks that the child understands the response scale. Respondents answer on a four-option scale (never, a little, sometimes, or a lot) with scores ranging from 0 to 3. Seven questions (Numbers 3, 6, 7, 10, 11, 13, and 14) are scored in reverse. The total score ranges from 0 to 39, with higher scores indicating higher levels of perceived stress.  
Besides these questionnaires, saliva samples were taken by the subjects in the morning that they attended the visits to assess cortisol levels. Salivary cortisol was performed by electrochemiluminescence (ECLIA) (Elecsys(r) Cortisol II) (Roche Diagnostics GmbH, Sandhofer Strasse 116, D-68305 Mannheim, Germany). The results are expressed in \(\mu\)g/dL.  
Also, a Parental Evaluation Questionnaire consisting of 10 items was completed by the parents or legal guardian. The responders provided answers using an 11-point Numerical Rating Scale (NRS), ranging from 0 to 10 (0 being the lowest value and 10 the highest).  
At the end of this study, a psychologist evaluated the results for each subject, providing a final opinion if the treatment was considered efficient. The question asked to the psychologist was \"The treatment is efficacious\". The four possible answers are as follows: completely agree/agree/disagree/completely disagree.