esa-sceva/satcom-chunk-collection huggingface dataset:
# Morphological, Anatomical, and Phytochemical Studies of _[PERSON]_ [PERSON]. [PERSON]
## 2 Results and Discussion
### Pentacyclic Triterpenes
#### 2.2.4 Polyphenol Content  
The chromatographic analysis showed the presence of one dominant compound with a retention time of approx. 20 min, which was identified as chlorogenic acid. In addition, small amounts of neochlorogenic acid and trace amounts of more polar unidentified metabolites were visible in the chromatogram in the first 10 min of the analysis. An example of a chromatogram is presented in Figure 6. The content of neochlorogenic and chlorogenic acids was 0.22 \(\pm\) 0.02 g/kg and 21.47 \(\pm\) 1.36 g/kg, respectively. It should be emphasized that the content of chlorogenic acid in the _C. acaulis_ cypsela is very high in comparison with other plants from the Asteraceae family. For example, sunflower seeds contain about 0.73-0.97 g/kg of this acid [38]. It is interesting that chlorogenic acid in the green parts of _C. acaulis_ was at the level of 10 g/kg [12], which is more than two-fold lower than in the cypsela. In turn, 3,5-di-caffeoylquinic acid determined in the green parts [12] was not detected in the cypsela.  
Chlorogenic acid is a well-investigated compound. Its multidirectional biological activity has been described in numerous scientific reports. It has been shown to have antioxidant, anti-inflammatory,  
Figure 6: Example of an HPLC-DAD chromatogram of _Carlina acaulis_ L. cypsela extract. (**A**). neochlorogenic acid, (**B**). chlorogenic acid. The analysis was performed on an RP18e LiChrospher 100 column (25 cm \(\times\) 4.0 mm i.d., 5 \(\upmu\)m particle size). The column temperature was set to 25 \({}^{\circ}\)C. Acetonitrile-water-trifluoroacetic acid (8:92:0.025 \(\
u\)/\(\
u\)/\(\
u\)) at a flow rate of 1 mL/min was used as eluent.  
antilipidemic, antidiabetic, and antihypertensive effects [39; 40; 41] and, therefore, it is a desirable component of plant-derived products.  
Some reports described the presence of flavonoid compounds in _C. acaulis_ plants [10], whereas the HPTLC screening of the hydrolyzed extract did not reveal the presence of flavonoid aglycones (myricetin, quercetin, apigenin, and kaempfer). This may indicate the absence of glycoside derivatives of these flavonoids in _C. acaulis_ cypselae.
###### Abstract  
We consider multipath TCP (MPTCP) flows over the data networking dynamics of IEEE 802.11_ay_ for drone surveillance of areas using high-definition video streaming. Mobility-induced handoffs are critical in IEEE 802.11_ay_ (because of the smaller coverage of mmWaves), which adversely affects the performance of such data streaming flows. As a result of the enhanced 802.11_ay_ network events and features (triggered by beamforming, channel bonding, MIMO, mobility-induced handoffs, channel sharing, retransmissions, etc.), the time taken for packets to travel end-to-end in 802.11_ay_ are inherently time-varying. Several fundamental assumptions inherent in stochastic TCP models, including Poisson arrivals of packets, Gaussian process, and parameter certainty, are challenged by the improved data traffic dynamics over IEEE 802.11_ay_ networks. The MPTCP model's state estimation differs largely from the actual network values. We develop a new _data-driven stochastic framework_ to address current deficiencies of MPTCP models and design a foundational architecture for intelligent multipath scheduling (at the _transport layer_) considering lower layer (hybrid) beamforming. At the heart of our cross-layer architecture is an intelligent learning agent for actuating and interfacing, which learns from experience optimal packet cloning, scheduling, aggregation, and beamforming using successful features of multi-armed bandits and federated learning. We demonstrate that the proposed framework can estimate and optimize jointly (**explore-exploit**) and is more practicable for designing the next generation of low-delay and robust MPTCP models.  
internet of drones (IoD); multi-armed bandits (MAB); multipath TCP (MPTCP); scheduling; beamforming; cross-layer approach; federated learning; packet replication; opportunistic scheduling; IEEE 802.11_ay_
## 4 Simulation Results and Convergence Analysis
### Optimal Power Allocation of Smart Home Appliances
As the simulation result shows in Figure 7, when the objective load power of the DLC increases from 0 to 55 kW, the total output power of the customers reaches the objective load power of the DLC after the iteration process. Figure 5: The incremental cost (IC) of each customer with neighbor interconnection. Figure 6: The power of each customer with neighbor interconnection. Figure 7: The total output power of customers when the objective load power is changed. As shown in Table 1, the power is allocated according to the priority order of appliances of each customer. When the objective load power is increased to 55 kW, the actual working status of the home appliances of each customer is shown in Table 3.  
In order to verify the economic efficiency of the proposed strategy, the following three scenarios are compared. Scenario 1: The distributed control strategy is used for power allocation of customers based on the ICC algorithm. Scenario 2: The objective power of the DLC is allocated proportionally according to the power capacity of each customer. Scenario 3: The objective power of the DLC is equally sent to each customer. Scenario 2 and Scenario 3 can be realized through non-IC-based consensus control. The consensus variable is the objective power of the DLC. The total costs of customers in different scenarios are shown in Table 4. It is able to be found that the total costs to customers is the minimum in Scenario 1. It also proves that the economic efficiency is improved based on the ICC algorithm through the distributed control strategy.
###### Abstract  
The ultra-short baseline positioning system (USBL) has the advantages of flexible application and easy installation, and it plays an extremely important role in the underwater positioning and communication. The error of the USBL in underwater positioning is mainly caused by a ranging error due to ray tracing, a phase difference error of the USBL, and acoustic noise in the underwater communication. Most of these errors are related to the changes in the sound speed during its propagation through the ocean. Therefore, when using the USBL for underwater detection, it is necessary to correct the sound speed profile in the detection area and optimize the ray tracing. Taking into account the actual conditions, this paper aims at correcting the model of underwater sound speed propagation and improving the tracking method of sound lines when the marine environment in the shallow sea area changes. This paper proposes a combined ray tracing method that can adaptively determine whether to use the constant sound speed ray tracing method or the equal gradient ray tracing method. The theoretical analysis and simulation results show that the proposed method can effectively reduce the error of slant distance in USBL compared with the traditional acoustic tracking method and the constant sound speed ray tracing method. The proposed sound ray correction algorithm solves the contradiction between the number of iterations and the reduction of positioning error and has engineering application value.  
acoustic positioning; constant sound speed ray tracing; equal gradient ray tracing; sound speed profile; ultra-short baseline (USBL) +
Footnote †: journal: Sensors
## 4 Results and Discussion
### Power Consumption Evaluation of the Three DKF Algorithms: Scenario 1
#### 4.1.2 Results from Laboratory Experiments
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**DKF Algorithm**} & \multirow{2}{*}{**Node’s Lifetime from Power Measurements**} & **Node’s Lifetime from Battery Energy Consumption** \\  & & **Measurements** \\  & & **(Method 2)** \\ \hline EDKF & 52 h & 59 h \\ ICF & 54 h & 53.9 h \\ SGG-ICF & 54 h & 58.1 h \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Sensor node’s lifetime computed from power profile versus battery energy consumption measurements. Figure 8: Current profile of sensor node implementing duty cycling and hierarchical sensing. For instance, higher communication interference leads to increased power consumption during such periods compared to periods with lower interference. Moreover, the results presented in Table 6 indicate that the real implementation, as determined by Method 2, yields a lifespan that is longer compared to the estimated lifespan derived from power measurements (Method 1). This holds true for all DKF implementations except ICF, signifying that the real implementation offers extended node lifespan. Lastly, from the results presented in Table 6, it can be seen that the lifespan of the sensor node is short (slightly greater than 2 days). This is because in these implementations, the ESP32 main core is operating in the modem sleep (active) mode throughout. As shown in [36], the ESP32 is a very power-hungry chip when operating in the modem sleep mode, given that it is a middle-end device. This huge power consumption is not appropriate for a WWPM system as it is required to operate for long periods without the need to recharge or replace the batteries. Thus, reducing the power consumption necessitates that the ESP32 be used as a low-end device by putting it most of the time in the deep sleep mode (relying only on the ULP coprocessor). However, to ensure real-time leak detection, it is necessary that the ESP32 awakes from sleep when there is a leak event to perform the processing tasks required to reliably detect the leak. Thus, the challenge is how to achieve low-power consumption without compromising the real-time performance of the WWPM system. We tackle this by proposing hierarchical sensing and duty cycling to achieve both low-power consumption and real-time detection. In the next sub-section, we present how we reduced the sensor node power consumption via duty cycling and hierarchical sensing, while maintaining real-time leak detection.
## 3 Discussion  
Ascorbic acid (AsA) plays an important role in many plant processes, including cell wall biosynthesis, photosynthesis, hormone synthesis, and defense response [36]. When genes in the AsA biosynthesis pathway are modified, it affects the previously mentioned physiological pathways and mechanisms. In this study, an inclusive investigation of the _OsVTC1-1_ gene in the AsA synthesis pathway and how it influenced defense responses after rice blast inoculation was conducted. In comparison with the WT, _OsVTC1-1_ RII-2 and RII-3 lines demonstrated enhanced resistance to rice blast fungus by exhibiting fewer and smaller lesions and the induction of several defense mechanisms. Silencing _OsVTC1-1_ leads to reducing the amount of AsA, an ROS-scavenging antioxidant. AsA decreases lead to an increase in ROS production, which results in HR and cell death. The silencing of _OsVTC1-1_ also impacts the abundance of non-cellulosic cell wall polysaccharides. After rice blast infection, non-cellulosic cell wall polysaccharides increased in the _OsVTC1-1_ RII-2 line. These changes could influence the ability of the fungus to penetrate the cell wall. Photosynthesis parameters alter as a result of pathogen infection [37]. This study revealed that the maximum efficiency of photosystem II (PSII), measured as \(\mathrm{F_{v}/F_{m}}\), was reduced in the infected leaves of the WT but not in the _OsVTC1-1_ RII-2 line. The reduction in _OsVTC1-1_ gene expression protected photosynthetic efficiency in _OsVTC1-1_ RNAi lines during _M. oryzae_ infection. Moreover, the amount of AsA decreased 24 h after rice blast inoculation in both WT and _OsVTC1-1_ RII-2 lines. These results are consistent with a previous report, in which AsA contents decreased in infected leaves and roots at 24 h after infection with _Phytophthora cinnamomi_[38]. Additionally, it was found that the _OsVTC1-1_ RII-2 line accumulates more \(\mathrm{O_{2}}^{-}\) and MDA than the WT. \(\mathrm{O_{2}}^{-}\) is an ROS that is essential in initiating signaling pathways and defense-related gene activation, as well as interacting with proteins, DNA, and lipids to cause cell damage [39]. MDA is a byproduct of lipid
# A Measurement Software for Professional Training in Early Detection of Melanoma
## 3 Results
In detail, after three months, the NED group was asked to re-examine the database which also included both the detection results (images highlighting the dermoscopic structures) corresponding to the single criterion and the overall lesion classification provided by the MS. Tables 4-6 report, respectively, the Seven-Point Checklist scores and the diagnostic/clinical accuracy achieved by the dermatologist groups and the automatic measurement system. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**EXAMINER Performance Index**} & \multicolumn{2}{c}{**ED**} & \multicolumn{2}{c}{**NED**} & \multicolumn{2}{c}{**MS**} & \multirow{2}{*}{**Biopsy/ Reference**} \\ \cline{2-2} \cline{6-7}  & **S.C.** & **B.B.** & & & & & & \\ \hline
**Atypical pigment network** & 86 & 71 & 85 & 93 & 89 & 106 & 92 & 72 \\ \hline
**Blue-whitish veil** & 54 & 32 & 47 & 105 & 74 & 35 & 58 & 53 \\ \hline
**Atypical vascular pattern** & 15 & 28 & 12 & 19 & 37 & 18 & 28 & 18 \\ \hline
**Irregular streaks** & 24 & 11 & 16 & 18 & 19 & 23 & 32 & 23 \\ \hline
**Regression structures** & 74 & 15 & 76 & 66 & 35 & 91 & 87 & 68 \\ \hline
**Irregular pigmentation** & 74 & 53 & 56 & 122 & 114 & 103 & 46 & 65 \\ \hline
**Irregular dots/globules** & 60 & 51 & 55 & 70 & 71 & 63 & 80 & 53 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of detection results by expert (ED)/non-expert (NED) dermatologist groups and automatic measurement system (MS). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**EXAMINER Performance Index**} & \multicolumn{2}{c}{**ED**} & \multicolumn{2}{c}{**NED**} & \multicolumn{2}{c}{**MS**} & \multirow{2}{*}{**Biopsy/ Reference**} \\ \cline{2-2} \cline{6-7}  & **S.C.** & & & & & & & & \\ \hline
**Correct (classified)** & 59/69 & 55/68 & 54/67 & 46/67 & 42/61 & 45/66 & 55/109 & 64 \\
**melanoma (MN)** & & & & & & & & \\ \hline
**Correct (classified)** & 64/67 & 65/70 & 63/68 & 53/63 & 55/64 & 53/61 & 46/52 & 73 \\
**atypical nevus (AN)** & & & & & & & & \\ \hline
**Correct (classified)** & 25/26 & 21/25 & 23/27 & 25/30 & 26/35 & 25/33 & 22/24 & 26 \\ \hline
**Correct (classified)** & 107/108 & 107/107 & 107/108 & 107/110 & 106/110 & 107/110 & 84/85 & 107 \\
**benign nevus (BN)** & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of lesion classification results by expert (ED)/non-expert (NED) dermatologist groups and automatic measurement system (MS).
## 6 Outcome Analysis
S-LEACH's energy efficiency does not improve because the energy expenditures in managing cluster nodes are more than the energy consumed during the data transmission period. A further cause for node energy loss is the extended transmission lengths that start from mn to CH and then to BS.
# A Survey on Massive MIMO Systems in Presence of Channel and Hardware Impairments
## 4 Waveform Selection
### Candidate Waveforms
#### 4.1.2. Ofdma  
OFDMA is the multiple access form of OFDM. In this waveform instead of allocating all the subcarriers to a user, the subcarriers are allocated with a specific pattern to the users. Figure 4 shows the block diagram of OFDMA massive MIMO system in the uplink and the downlink. If we assume \(N\) is the number of subcarriers allocated to a user and we have total of \(K\) users, then considering \(\tilde{K}\) to be the number of users that use the same subcarriers, the total number of subcarriers will be \(\tilde{N}=\frac{NK}{K}\). We should note that this is true if there are no guard subcarriers.
## 4 Methodology
### Self-Design CNN (RoadNet)  
This section describes the architecture of the proposed CNN used to detect road damage. The model consists of six layers: two convolution layers followed by a pooling layer, two fully connected layers, and a ReLU layer. Several filters were used for each convolution layer for various sorts of feature extraction. To reduce the over-fitting problem, dropout was presented as a regularization in our model in both max-pooling and fully connected layers. The input images were resized to 256 \(\times\) 256 \(\times\) 3. The training process was speeded up by selecting a batch size of 32 and using a learning rate of 0.0001. Two deep layers were defined; the first layer has an input channel of one, with a kernel of 5 \(\times\) 5, a stride of one, and padding of two. After convolution, the image measure was decreased, and the value of padding was set to zero to maintain the same size. The ReLU function was used as the activation layer in this model because it is difficult to saturate and has an acceptable gradient in comparison to other activation functions. The max-pooling layer used a kernel and stride value of two. In the second convolution layer, the size of the input is 32, the size of the output is 64, the kernel size is 5 by 5, the size of stride is one, and the size of padding is two. In addition, ReLU and max pooling are the same in both layers. A dropout layer was added to solve and reduce the fit problem. Two fully connected layers were added in the end for the connection of all neurons. An SGD optimizer with momentum = 0.9 was utilized to update weights. It updates learning rates for various parameters in an automatic way. Figure 6 displays the structure of our CNN model.
# Deep Neural Networks for Spatial-Temporal Cyber-Physical Systems: A Survey
## 3 DNNs in CPS-Based Spatial-Temporal Data
### DNN Techniques
#### 3.3.2 RNN
GRU is a powerful and flexible neural network architecture that can capture long-term dependencies in sequential data and deal with the _\"vanishing gradient problem\"_ that bedevils standard RNNs. It has been used in various applications, including NLP, speech/voice recognition, time series prediction, etc.
## 2 Materials and Methods
### Flood Foresight Model Output  
Near real-time operations also take place on the Flood Foresight model flood extent valid at the current time, in order to produce an analogous model waterline height threshold map. A similar procedure to that used in the determination of effective heights (Section 2.3.1) is followed. The domain is subdivided into nonoverlapping rectangular \(m\) x \(n\)-pixel subdomains, to take into account the fall-off of water level down the reach and the fact that different parts of a domain may be flooded to different depths. In each subdomain, edges are detected in the binary model flood extent image using the Sobel edge detector. The edges are overlain on the DSM to obtain WLOs at the edges. The mean WLO and its standard deviation for the subdomain is calculated for those edges that are not close to high slopes in the DSM, as the WLO of an edge is likely to be determined more accurately on a low slope. It seems reasonable with model data to measure the mean WLO in each subdomain rather than searching for the highest maximum in the histogram of waterline heights as with the SAR WLOs, which may contain water levels that are too low (see previous Section). The mean WLOs in the subdomains are then interpolated over the whole domain using bilinear interpolation.
# High Density RF-DC Plasma Nitriding under Optimized Conditions by Plasma-Diagnosis
## 3 Experimental Results and Discussion
### Effect of Plasma-Processing Parameters on the Active Species  
RF-voltage, DC-bias and pressure work as the main plasma processing parameters to determine the population of generated species. In addition, when using the hollow cathode setup, the N\({}_{2}\) + H\({}_{2}\) plasma is intensively generated and confined into the hollow. This hollow cathode setup influences the activation process in the plasmas. The measured intensity of N\({}_{2}^{+}\) is employed as a parameter to describe this effect of processing parameters on the activation.  
Figure 3 depicts the variation in N\({}_{2}^{+}\) peak intensity with the increasing pressure in the plasma diagnosis, with and without the use of the hollow cathode device. As predicted from [14; 15], the ionization reaction presented in Equations (1)-(3) reduces with the increasing pressure of the N\({}_{2}\) + H\({}_{2}\) gas mixture. The yield of N\({}_{2}^{+}\) decreases monotonously with the increasing pressure. In the high-pressure range, the reaction cross-section for collision between the neutral nitrogen molecules and electrons reduces and results in less ionization. In addition, the mean free path becomes small enough to restrict  
Figure 2: A typical spectrum of N\({}_{2}\) + H\({}_{2}\) plasmas by OES.  
electron movement. When using the hollow cathode setup, this monotonous decrease in the yield of N\({}_{2}^{+}\) retards by itself. As had been discussed in [16; 17], this is because the generated electrons by Equations (1)-(3) are confined in the hollow.  
DC bias voltage was varied from \(-300\) V to \(-600\) V to investigate the enhancement of ionization with increasing the DC-bias. As depicted in Figure 4, the ionization process is enhanced by increasing V\({}_{\text{DC}}\). The RF-voltage was also varied from +100 V to +250 V to describe the enhancement of ionization with increasing the RF-voltage. As shown in Figure 5, the N\({}_{2}^{+}\) peak intensity linearly increases with the applied RF-voltage with and without the hollow cathode. This is because the ionization reaction cross-section in Equations (1)-(3) is proportional to the RF-voltage. Hence, higher DC-bias and RF-voltage is a necessary condition in this RF-DC plasma, nitriding at a low holding temperature.  
Figure 4: Variation in N\({}_{2}^{+}\) peak intensity with the increase in DC-bias voltage in the plasma diagnosis, with and without the use of the hollow cathode device in cases where \(p=50\) Pa and V\({}_{\text{RF}}=250\) V.  
Figure 3: Variation in N\({}_{2}^{+}\) peak intensity with the increase in pressure in the plasma diagnosis, with and without the use of the hollow cathode device in case where V\({}_{\text{RF}}=250\) V and V\({}_{\text{DC}}=-500\) V.