esa-sceva/satcom-chunk-collection huggingface dataset:
# Plasma-Activated Water Affects the Antioxidant Contents in Water Spinach
## 3 Results and Discussion
### TF Content
\begin{table}
\begin{tabular}{l l l} \hline
**Treatment** & **TP Content (mg GAE/g DM)** & **TF Content (mg QE/g DM)** \\ \hline RO & 14.05 \(\pm\) 0.54 \({}^{\text{A}}\) & 9.99 \(\pm\) 2.71 \({}^{\text{B}}\) \\ RO + Fert & 14.61 \(\pm\) 0.75 \({}^{\text{A}}\) & 13.65 \(\pm\) 1.79 \({}^{\text{CD}}\) \\ PAW & 14.99 \(\pm\) 0.59 \({}^{\text{A}}\) & 11.54 \(\pm\) 0.27 \({}^{\text{BC}}\) \\ PAW + Fert & 13.99 \(\pm\) 0.14 \({}^{\text{A}}\) & 15.50 \(\pm\) 1.19 \({}^{\text{D}}\) \\ _p-_value & 0.162 & 0.021 * \\ \hline \end{tabular} Note: Different uppercase letters indicate the significant difference between treatment (\({}^{\text{*}}\)_p_ < 0.05). Samples used in the analysis are the mixtures of three pots of plants and the data shown are the average values of three repeats. RO: reverse osmosis water; Fert: fertilizer; PAW: plasma-activated water.  
\end{table} 
Table 5: TP content and TF content of water spinach grown under different treatments in Phase-2 pot experiment. Figure 6: TP content and TF content of water spinach grown under different treatments. PAW: plasma-activated water; RO: reverse osmosis water; Fert: fertilizer. Different uppercase letters indicate the significant difference between treatment. Additionally, the acidic environment offered by PAW during irrigation may influence the TP and TF contents of the water spinach under heavy metal stress. According to [PERSON] et al. [51], the concentration of heavy metals in soil tend to increase with decreasing pH due to the increased activity of hydrogen ions when there is a decrease in pH. This event displaces metals from exchangeable sites on solid surfaces which increases the availability of those metals for plant uptake. For instance, [PERSON] et al. [52] found that the degree of Cd uptake by plants increased with decreasing pH. Therefore, we hypothesize that the lower soil pH introduced by the acidic nature of PAW increased the uptake of heavy metals by water spinach in this study, which in turn increased the amount of TP and TF due to heavy metal-induced stress. However, to our knowledge, there are no other reports on the use of PAW on water spinach leaves, therefore, the observed effect of low pH introduced by PAW on the TP and TF concentrations should be further clarified. Nowadays, plants could grow in farms, greenhouses, and plant factories. Compared with traditional farming and greenhouses, a plant factory is a new way of farming. Plant factories are a closed growing environment where lighting sources such as fluorescent lamps and light-emitting diodes as well as air conditioning are used to replace sunlight and natural ventilation. Such indoor farming could ensure adequate and efficient production of fruits and vegetables [53; 54]. This study's results found that the use of PAW could increase the content of TP or TF in water spinach under different conditions. Polyphenols and flavonoids are powerful antioxidants; therefore, PAW can increase the antioxidant potential of water spinach.
## 3 Results and Discussion
### NIRS/NIRS-Based Hyperscanning Research
#### 3.2.2 Decision-Making Task  
NIRS and fNIRS hyperscanning has been used in a variety of decision-making task applications, particularly forms of cooperative and competitive games. Common experimental tasks include computer games [128; 129; 130; 9; 131], card games [33; 132], an economic exchange game [30], creative thinking [133; 134], and cooperative tasks [135; 136]. A unique experimental task incorporated the game of jenga [130].  
_Setting._ Out of the thirteen studies, two were conducted in a natural setting [130; 9], while the majority were conducted in a lab setting.  
_Orientation._ Five studies oriented participants side-by-side [134; 135; 136; 137; 9; 138; 139], while eight studies had participants sit face-to-face [135; 136; 130; 131; 132; 133; 135].  
_Group Size._ The majority (12) organized participants into pairs as in the cognition application e.g., [137], while two studies organized participants in groups greater than two [135; 136; 138].  
Only one study selected all female participants [135], while the remaining incorporated both male and female subjects in the studies. A study with a large sample size (n = 222, 112 males) sought to understand how cooperation is influenced by male-male, female-female, and male-female pairings [131]. For example, the most recent fNIRS hyperscanning study which focused on decision-making tasks investigated how different feedback affects group creative performance to reveal the underlying interpersonal neural correlates [134]. The experiment consisted of a group brainstorming and feedback session with a large sample size (n = 118, 16 males).
# Cybersecurity in Hospitals: An Evaluation Model
## 8 Discussion
### Model Validation  
In this section, we will compare our model to two models from the literature in terms of the structures, types of results, and numbers of factors and trials. A previous paper [15] discussed the development of a security evaluation model that focused on a specific type of threat, which is not similar to our model focusing on five types of threats. However, they used 26 indicators to create their model, which gave promising results [15]. Our model included ten indicators, meaning it is not similar to what their model used. However, the number of indicators in our model fully covers all types of cyber-threats. Increasing the number of indicators could result in different results, but not necessarily better results. Their model did not result in a clear number that defines a specific score or percentage of the tested methods, instead it resulted in factors that organizations can use to prevent information leakage. On the other hand, our model does give a score that defines the vulnerability of the hospital to cyber-attacks.  
A previous paper [16] developed a model that evaluates the security of virtual learning websites using six indicators only. The paper gave promising results as well. The number of indicators we included in our model was more than the number they used in their model, which is six indicators. They tested their model on two applications only while we tested our model on three different hospitals [16]. This model resulted in percentages that determine the security of the applications. The two models along with our model are compared in Table 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Model 1** & **Model 2** & **Our Model** \\ \hline Number of factors & 26 indicators & 6 indicators & 10 indicators \\ \hline Number of trials & 3 Validation methods & 2 Systems & 3 Hospitals \\ \hline Type of results & The results are the factors that they use & Percentage & Percentage(score) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison between the two models from the literature and our model.
# Advancing Nighttime Object Detection through Image Enhancement and Domain Adaptation
## 4 Discussion
### Experimental Setup or Environment
#### 4.1.2 SHIFT [50] Datasets  
The SHIFT [50] (Synthetic and High-Resolution Image for Target Detection) dataset is a large-scale, high-resolution, synthetic image object detection dataset. It covers a variety of scenes, including urban roads, rural roads, residential areas, and commercial areas, simulating different weather conditions and lighting variations such as sunny, rainy, foggy, daytime, and nighttime. The SHIFT [50] dataset contains 50,000 high-resolution synthetic images with various object types, such as cars, pedestrians, and traffic signs, totaling 150,000 annotated instances. There are similarities between the categories in SHIFT [50] and ExDark [49]. Due to the difficulty of acquiring high-resolution images in real-world scenarios, the SHIFT [50] dataset provides extensive multi-scene, multi-lighting condition image data through synthetic techniques, greatly aiding the research and development of object detection algorithms. By offering a substantial number of high-resolution synthetic images, the SHIFT [50] dataset helps address detail recognition and detection accuracy issues in adverse environments. For our evaluation, we used images labeled \"night\" as our data. We further ensured that the weather label was \"clear\" to isolate other weather conditions from the evaluation. In this experiment, the SHIFT dataset was manually filtered to isolate the \"nighttime\" data. The dataset was then split into 80% for the training set and 20% for the validation set. No additional preprocessing was performed.
# Review on the Electrical Resistance/Conductivity of Carbon Fiber Reinforced Polymer
## 2 Structure of CFRP and Its Electrical Conductivity  
### Structure of CFRP  
Typical structures of carbon fibers contain \(\sim\)90-100% carbon. The plies are arranged to obtain a multilayer anisotropic composite through tensioning and heating [13; 23].  
Carbon fibers are characterized by high strength, high modulus, light weight, excellent fatigue resistance, and low density. Carbon fibers realize the high modulus (900 GPa), thermal conductivity (1,000 W/mK), and electrical conductivity (106 S/m) [24]. These fibers have become the main material in many industries. A carbon fiber reinforced composite is composed of matrix, fiber, and an interphase region [16]. The matrix can endow the composite with key properties. The coefficient of thermal expansion of carbon fibers is typically much lower than that of most matrices, so carbon fibers significantly increase the composite stability. Carbon fiber composites are classified based on the fiber's length. Compared to short fibers, continuous carbon fibers significantly affect the mechanical, electrical, and thermal properties of the composites and endow the composites with higher anisotropy [24].  
In CFRP, carbon fibers are embedded in a resin matrix. Parallel carbon fibers (conductor) act as the reinforcement material, whereas the polymer resin acts as the matrix [25] (Figure 1). The matrix with low strength and stiffness determines the component shape [26]. The plastic matrix can transfer loads. In a CFRP composite, the thickness of a single layer is 0.05-0.2 mm and a lamina with fibers in only one direction is called a unidirectional lamina. In order to obtain engineering components, some layers are stacked to form a laminate composed of several plies with different orientations according to the required thickness [27] (Figure 2).
# Dynamic Frequency Support from a DFIG-Based Wind Turbine Generator via Virtual Inertia Control
## 6 Dynamic Simulation Results
### Case 1: Wind Speed = 10.0 m/s  
Figure 8 displays the simulation results, where the wind speed condition of the DFIG-based WTG is 10.0 m/s (partial load case). Thus, the kinetic energy available from the DFIG-based WTG is 3.96 s, which is 74% that of the total kinetic energy available. In this case, the pitch angle controller is not activated due to the lower wind speed.  
The maximum _dfdf_ in the proposed strategy, the conventional strategy, and the MPPT operation are -0.390 Hz/s, -0.475 Hz/s, and -0.503 Hz/s, respectively (see Figure 8a). The frequency nadirs in the MPPT operation, the conventional strategy, and the proposed strategy are 59.174 Hz, 59.194 Hz, and 59.265 Hz, respectively. In the MPPT operation, no power is injected to the grid, an as a result, there is no change in the rotor speed. For the conventional strategy, a small amount of power is injected to the power gird so that the rotor speed reduces, and the performances in terms of the system frequency nadir and the maximum _dfdf_ are better than those of the MPPT operation. In the proposed strategy, a large amount of active power is injected to the power grid due to the proposed control gain in (13); as a result, the reduction in the rotor speed is large, and the performances of the frequency nadir and the maximum _dfdf_ are better than the conventional strategy (see in Figure 8b,c).  
Figure 8: \(Cont.\)  
Furthermore, the nadir-based frequency response of the proposed strategy is 122.45 MW/Hz, which is more than in the conventional strategy by 10.82 MW/Hz and more than that of the MPPT operation by 13.49 MW/Hz. This is because of the high frequency nadir. As displayed in Figure 8c and Table 4, the released kinetic energy from the DFIG-based WTG in the proposed method is 0.563 s, which is more than that of the conventional method by 0.422 s due to the large control gain in the proposed strategy.
# Scalable Name Lookup for NDN Using Hierarchical Hashing and Patricia Trie
## 2 Background  
### Forwarding in NDN  
In NDN, a packet is forwarded according to the name in the packet. The name is represented as a sequence of components each of which is separated by a slash [8]. For example, a name, /com/google/www/drive contains four components: /com,/google, /www, and /drive.  
There are two types of NDN packets: an interest packet and a data packet [3]. Figure 1 shows how these packets are forwarded. Each NDN router contains three tables: a content store (CS), a pending interest table (PIT), and a forwarding information base (FIB). The interest packet is generated by consumers who want to obtain some content from a producer. When more than one consumer is interested in the same content and sends the same interest packet, the router delivers only one interest packet. The data packet is replied by the producer that has the content associated with the interest packet. It is forwarded along the reverse direction to which the corresponding interest packet is sent.  
Figure 2 shows the forwarding processes for interest and data packets in an NDN router by using a CS, a PIT, and an FIB. A CS is a caching storage that stores the content of data packets for reuse when the same interest arrives. A PIT maintains the received interest names along with their incoming interfaces. If a received interest name is the same as one of names already stored in a PIT, then its incoming interface is appended to that entry in the PIT and the received packet is not forwarded further. An FIB is a lookup structure to find name prefixes.  
Figure 2a shows the process to forward an interest packet in an NDN router. When receiving an interest packet, the router first checks whether the name in the packet exists in a CS or not. If the matched name is in a CS, the corresponding data packet is sent back as a reply. Otherwise, it is checked whether the name in the packet is already located in the PIT or not. If there is the matched name in the PIT, the incoming interface of the packet is added to the PIT. Otherwise, the packet is finally forwarded according to the result in the FIB. The matching method in the FIB is the longest prefix matching, while that in the CS or the PIT is the exact matching.  
Figure 1: Overview of packet forwarding in named data networking (NDN). CS: content store. PIT: pending interest table. FIB: forwarding information base.  
When an interest packet arrives in the producer, the corresponding data packet is constructed and sent back. Figure 2b shows the forwarding process for the data packet. For an incoming data packet, a router searches for its name in the PIT and sends the packet to each incoming interface that is pending for the name in the PIT. The packet is cached in the CS for the future use as well.
## 4 Results from Case Studies
### Validation of Synchrophasor Applications Using Dynamic Grid Simulations
#### 4.3.2 Results from Synchrophasor Disturbance Classification
For this, a GRU-based classifier with a local parametric attention based embedding function is investigated using different hidden dimensions. The average accuracy deviations to the _base scenario_ are shown in Figure 23. Larger changes in the test accuracies can be observed when decreasing (\(-\)3.1%) or increasing (\(+\)2.4%) the number of available PMUs, decreasing (\(-\)4.5%) or increasing (\(+\)4.1%) the sample overlapping of the training instances and increasing the post-disturbance time from 10 s to 40 s (\(-\)2.3%). A higher number of PMUs or sample overlapping increases the input data size and improves the classification accuracies. In contrast to that, a high post-disturbance time complicates the recognition of disturbances especially with short disturbance durations like short circuits or line outages. The sole use of voltage magnitudes and angles as input measurements reduces the training accuracy (\(-\)1.6%) but increases the test accuracy (\(+\)1.1%). Combining voltage phasors and  
Figure 21: Receiver operating characteristics for training (**left**) and validation (**right**) predictions. frequencies has no significant impact on the test accuracy (\(-0.2\%\)). The necessary calculations are performed in Python(r) with additional packages for statistical analysis [68] as well as for creating and training of neural networks [70].
## 3 Results and Discussions
### Error Types  
During the experiments, the upper computer software generated a large amount of error data, and the hexadecimal digits of the write data and those of the read data were binary converted. In this way, it would be possible to compare the number of erroneous bits in each piece of data. A single byte error on an address bit is regarded as an SBU. When multiple byte errors occur at the same time for an address bit, we refer to this event as an MBU. Figure 8 summarizes the statistics of SBU and MBU.  
Figure 8: Percentage of error types (SBU/MBU) of SEE and SESD. (**a**) Percentage of SBU and MBU with the variation of pulsed laser energy. (**b**) Percentage of SBU and MBU with the variation of discharge voltage.  
Figure 8a illustrates that in the SEE experiment, the vast majority of errors were SBU, and a small number of errors were MBU. Using transistor T1 illustrated in Figure 9 as an example, when a single particle is incident on the drain, the ionizing charge generated is collected by the reverse biased PN junctions formed in the drain and trap areas due to the electric field in the space charge region, resulting in transient currents. Transient currents can alter the potential of the storage node, thereby upsetting its logic state. Multi-bit upsets can occur when laser traces ionize between multiple storage cells and generate a large number of charges. Due to the EDAC redundant design of this SRAM, MBU are restricted to generate.Therefore, the SEE appears to produce errors primarily due to SBU.  
In addition, from Figure 8b, it is evident that most of the errors that occur during the SESD experiment are caused by multi-bit upsets. Discharge pulses couple the power supply pin of the device to form transient voltage pulses at the power and ground terminals. The transient pulses are then transmitted through the internal power supply circuit of the device to the power input of the internal memory circuit. If the core supply voltage changes instantly from the normal operating voltage to the lowest negative value, it is very easy for the power supply track of the device to collapse. In other words, when the power supply voltage value is lower than the minimum bias voltage required to maintain the data of the memory cell, the stored information of the memory cell will be erroneous. In SRAMs, the internal power supply circuit supplies power to multiple storage arrays, which causes multiple storage bit errors when track collapse [11; 12] occurs in the internal power supply. It is indicated that the radiation-hardening design for SEE was no longer applicable to SESD.
# Experimental Evaluation of Rigidity Center
## 2 Materials and Methods
### Test Setup  
To prevent accidental torsions at the floor level, which have direct effect on the displacements, it was decided to load the buildings by their own _weight_, i.e., dead load. In order to achieve this, buildings must be rotated 90\({}^{\circ}\). Figure 8 shows the test setup. A strong test frame was built to hold the building in place. Each building's base plate was fixed to the test frame by means of four M16 bolts. Six linear variable differential transducers (LVDTs) were attached to the slabs close to the slab corner by means of hot silicone to measure the vertical displacement from the _self-weight_. Data were collected by means of a data acquisition system, which was connected to a computer to save and see the recorded data. A camera and a video camera were placed in front of the test specimen to record the vertical displacements of the slabs, and one video camera was located at the side to record the bending behavior of the columns (or shear walls).  
It was known that, while rotating the buildings, floors would displace, and it might not be possible to measure additional displacement during the tests. In order to overcome the early displacements, a special trigger device was produced, as seen in Figure 9. This device was mounted on the base plate of the building and held the building in place. While rotating the building, this trigger device prevented early displacements and allowed the building to be attached to the test frame. After attaching the LVDTs to the slabs, the device was triggered by a rope and rotated 90\({}^{\circ}\), which allowed the building to displace vertically by its _self-weight_ (Figure 10). The vertical displacements of the slabs and the bending behavior of the columns can be clearly seen in Figure 10. There was no connection between the column caps and the slabs; slabs freely rotate due to the vertical displacements.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Bldg.** & \(A_{c\_EW}\)**(cm2)** & \(A_{c\_NS}\)**(cm2)** & \(A_{w\_EW}\)**(cm2)** & \(A_{w\_NS}\)**(cm2)** & \(A_{f}\)**(cm2)** \\ \hline D1 & 3.24 & 3.24 & 1.95 & 1.8 & 900 \\ D2 & 5.04 & 5.04 & 9.0 & 7.2 & 900 \\ D3 & 4.68 & 4.68 & 6.0 & 3.8 & 760 \\ D4 & 4.32 & 4.32 & 3.6 & 4.2 & 690 \\ D5 & 2.88 & 2.88 & 7.5 & 3.6 & 509 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Properties of Group D buildings.  
Figure 8: Test setup.  
No displacement or rotation was seen at the column base meaning that fixed support conditions were available during the tests. As no plastic deformations were recorded during the tests, buildings were tested in all directions (N-S, S-N, E-W and W-E) to measure a reliable rigidity center.
## 3 Satellite-to-Ground QKD Simulator
### Atmospheric Link Modeling
#### 3.2.2 Pointing Loss  
The necessity for a narrow beam width makes accurate pointing acquisition critical. The inaccurate point acquisition of the laser beam renders the receiver to be located off-axis from the far-field irradiance profile, resulting in a pointing loss. Pointing errors cause time-varying fading in the received signal power. They can be estimated for a specific probability from the Probability Density Function (PDF) of a normalized received intensity by taking into account the pointing error in the same manner as conducted in [54].  
\[p\big{(}I_{pp}\big{)}=\beta_{p}\overline{I_{pp}}^{\beta_{p}-1}\;,\;0\leq I_{ pp}\leq 1, \tag{3}\]  
where \(I_{pp}=\frac{\beta_{p}}{\beta_{p}+1}\) and \(\beta_{p}=\frac{\theta_{B}^{2}}{\lambda}\sigma_{p}^{2}\) is the divergence pointing ratio, with \(\theta_{B}\) defined as previously, and \(\sigma_{p}\) is the pointing error variance in radians. The total pointing error loss for a specific outage probability \(\text{p}_{0}\) was calculated using the following expression [54]:  
\[L_{pt}={p_{0}}^{1/\beta_{p}}. \tag{4}\]