esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Results Analysis
### Simulated Results
#### 7.2.2 Discharging State  
The discharge mode is simulated for the case when the FESS is initially fully charged and a load torque (\(T=-6.7\) N\(\cdot\)m) is applied to extract energy from the system for a duration of 15 min (Figures 12 and 13). At the instant when the torque is applied, the three-phase current increases, while the stored energy and SOC start falling. The FESS delivers energy by regulating the flywheel speed and maintaining the DC-bus voltage. The system dynamic response is tested by instantly switching the flywheel to charging mode for another 5 min. Figure 12 shows the system responsiveness and compliance of the current and torque at \(t=900\) s and \(t=1200\) s. At the last stage, the system returns to standby mode for 15 min. Compliance of the flywheel speed and stored energy for each state is shown in Figure 13.  
Figure 11: FESS in charging mode: Three-phase current (top left); Flywheel stored energy (top right); Electromagnetic torque (bottom left); Flywheel SOC (bottom right).  
Figure 12: FESS in discharging mode: Three-phase current at \(t=900\) s (top left); Three-phase current at \(t=1200\) s (top right); Torque _Te_ at \(t=900\) s (bottom left); Torque _Te_ at \(t=1200\) s (bottom right).  
During the discharge mode, 25% of the total charge is reduced, and approximately 2.93 kWh is extracted from the system (Figure 13). The self-discharge or system standby loss is approximately 1.25% of the total stored energy during the first 20 min. This indicates that the flywheel will lose nearly 70 Wh if left at standby mode for 30 min; although, it can store or deliver 5 kWh of energy during the same period if an accelerating torque or load torque is applied.
## 3 Results and Discussions
### Optical Characterization  
The optical absorption of the ZnO thin films was measured at room temperature using a UV-visible spectrophotometer within the range of 300 to 1100 nm. In Figure 5, the absorption spectra for ZnO thin films deposited at substrate temperatures of 350 \({}^{\circ}\)C, 200 \({}^{\circ}\)C, 100 \({}^{\circ}\)C, and room temperature are presented. All the films exhibited a sharp absorption edge in the UV region at about 390 nm. Using the absorbance data, the direct transition band gap energies (E\({}_{\text{g}}\)) were calculated based on [PERSON]'s equation [37]:  
\[\alpha=\frac{A\left(h\
u-E_{g}\right)^{\frac{1}{2}}}{h\
u}\]  
where \(\alpha\) is the absorption coefficient, \(A\) is the band edge sharpness constant, \(E_{g}\) is the band gap, and \(h\
u\) is the incident photon energy. By plotting \((ah\
u)^{2}\) versus the incident photon energy, the [PERSON]'s plots are obtained, all the data being presented in Figure 5. By regressing to zero the linear portion of the \((ah\
u)^{2}\) from Tauc plot, the energy band gap of the thin films is found. The values obtained were as follows: 3.32 eV, 3.25 eV, 3.13 eV, and 3.12 eV for the films deposited at 350 \({}^{\circ}\)C, 200 \({}^{\circ}\)C, 100 \({}^{\circ}\)C, and room temperature, respectively. Compared to the value of 3.37 eV for bulk ZnO, the obtained values of the energy gap are slightly smaller for the films deposited at temperatures lower than 300 \({}^{\circ}\)C as a consequence of various defects and zinc and/or oxygen vacancies present in the lattice of ZnO. As for the higher deposition temperature, it is clearly seen that the energy band gap is approaching the ideal value for bulk ZnO (\(\sim\)3.7 eV) [1]. This conclusion supports the improvement of the crystallinity of the thin films as seen from the XRD measurements earlier discussed.  
Figure 4: EDS spectra of deposited ZnO thin films with corresponding relative elemental composition estimated from the spectra.
# The Effect of Speed Humps on Instantaneous Traffic Emissions
## 2 Related Work
### Cellular Automata and Traffic Flow  
Studies based on data collected from specific regions are limited to cases with large variations on vehicles types, driver behaviors, road conditions, vehicles sizes, time of the day, weather conditions and topography, just to mention a few. The use of simulators allows control of these variables and test their effect on specific configurations.  
A major approach on the study of traffic flow is that based on cellular automata. These models are widely used due to its flexibility to incorporate new rules, high generalizations and rich phenomena capturing features [28; 35; 36; 37; 38; 39]. The cellular automata introduced by [PERSON] and [PERSON] in [40], also known as the NaSch model, has been widely used to study different mechanism in traffic flow [37; 41; 42; 43; 44; 45; 46].  
[PERSON] and [PERSON] [47] presented a modified version of the NaSch model to study the effect of speed humps on traffic flow, their findings show that a single speed hump has a negative effect on traffic flow. [PERSON] et al. [48] introduced different driver behaviors when encountering speed humps: aggressive drivers, which do not reduce their speed at the minimum to pass over the speed hump; and cautious drivers, which reduce their speed at its minimum to pass over the speed hump. They found that as the number of cautious drivers increases, the average speed decreases from 50 to 85% from the case with no speed humps on the road. Conversely, as the number of aggressive drivers increases, the impact of speed humps on traffic flow and average velocity decreases.
## 4 Design of the Current Loop for the Multi-PMSM Speed Control System
Define the Lyapunov function \(V_{2}\) as:  
\[V_{2}=\frac{1}{2}S_{1}^{2}+\frac{1}{2}S_{2}^{2} \tag{43}\]  
The time derivative of \(V_{2}\) can be obtained as:  
\[ \begin{array}{ll}\dot{V}_{2}&=S_{1}\dot{S}_{1}+S_{2}\dot{S}_{2}\\ &=S_{1}\dot{S}_{1}+S_{2}\left[\dot{S}_{1}-\lambda S-2\lambda sign^{a}(S_{2}-S_ {1})\right]\\ &=\dot{S}_{1}S-\lambda S_{2}S-2\lambda S_{2}sign^{a}(S_{2}-S_{1})\\ &=S\left[-\lambda S_{1}-\rho sat(\frac{S}{\varphi})-\zeta+\lambda sign^{a}(S_{ 2}-S_{1})\right]-\lambda S_{2}-2\lambda S_{2}sign^{a}(S_{2}-S_{1})\\ &=S\left[-\lambda S-\rho sat(\frac{S}{\varphi})-\zeta\right]-\lambda(S_{2}-S_ {1})sign^{a}(S_{2}-S_{1})\\ &=-\lambda S^{2}+S\left(-Y_{n}u^{*}_{hit}\right)+S(-\zeta)-\lambda(S_{2}-S_{1} )sign^{a}(S_{2}-S_{1})\\ &\leq\left.-\lambda S^{2}+\left|S\right|(|\zeta|-\rho)-\lambda\left|S_{2}-S_{1 }\right|^{a+1}\\ &\leq-\lambda S^{2}+\left|S\right|(|\zeta|-\rho)\end{array}  \tag{44}\]  
Based on \(|\zeta|\leq\rho\); \(\dot{V}_{2}(S_{1},S_{2})\leq 0\), the system satisfies the Lyapunov stability conditions, ensuring that the state trajectory from any position can reach the boundary layer within a finite time and eventually reach the sliding surface, thereby guaranteeing the stability of the system [28]. The schematic diagram of the CSMC is illustrated in Figure 3.
## 2 Background
The major advantage of these systems is that hand movements are detected during interaction in VR using cameras [38] or the LMC [39], facilitating natural interaction with the hands and avoiding the need for wearable sensors to measure coordination. However, the lack of haptic feedback may affect motor performance. A recent study compared eye-hand coordination from a previously validated real-world object interaction task with the same task recreated in controller-mediated VR [40]. Their findings suggest that current VR technology does not replicate real-world experiences in terms of EHC. Since EHC may be enhanced by hand contact with objects, more sophisticated methods of EHC assessment involve the use of haptic feedback. [PERSON] et al. [41] presented a tapping-task system with force feedback. A head tracker is used to estimate eye position, and a Phantom force feedback device allows the user to touch the targets. Similarly, [PERSON] et al. [17] proposed metrics for eye-hand coordination, using a mapping of a robotic haptic device to a virtual environment, and correlating it with the eye gaze using an eye tracker. These studies found that simulating contact with the Phantom device improved performance. However, the reduced workspace of the Phantom constrains the interjoint demand for finger-wrist-elbow coordination (fine motor skills). Furthermore, [PERSON] et al. found similar performance when the arm is constrained to reduce shoulder movement, suggesting that fine motor skills are independent of whether the arm is constrained [17]. Finally, there are technical solutions that aim to improve the rehabilitation process of EHC. For example, the LMC was used as an input device for a self-adaptive game aiming to improve eye-hand coordination and provide short-term training [42]. The game requires a player to pop specific balloons appearing on a screen, aiming to prevent them from flying. In a study by [PERSON] et al. [43], a novel approach for the rehabilitation of EHC and finger dexterity was developed incorporating Augmented Reality (AR) technology. This application promotes interaction with virtual piano keys while the user wears a glove to detect finger motion. On this basis, eye-hand coordination was considered in this study because it is essential for performing activities of daily living (ADL) autonomously. Neurological diseases significantly affect hand-eye coordination, reducing the autonomy of performing ADL for a large number of people. The proper evaluation of how neurological deficits affect the relationship between eye control and limb control is a fundamental gap in the knowledge base and an essential component for defining adequate rehabilitation strategies. Similar to other cases of functional assessment automation, gaming technology can contribute to developing useful tools for both assessment and rehabilitation. Hence, the authors hypothesise that the use of multimodal devices and serious games can help to measure fine and gross motor coordination based on strategies that demand different levels of interjoint coordination.
# Strategies and Methods for the Fault-Tolerant Function Development of Multi-Domain Systems +
## 5 Countermeasures for Faults
* Applying the strategies and measures proposed in the scope of design for safety: The measures described in the design for safety guidelines, such as inherent safe design or danger warnings, seek to minimise the risk of injury or malfunctions and can consequently also be applied in fault-tolerant design. * Searching for solutions in non-technical domains: Frequently fault accommodation possibilities are not only present within the boundaries of the technical system, but in the context of the system operation, e.g., if certain operation conditions leading to faults are avoided. * Searching for solutions which isolate components or reduce the interdependences: Some faults only appear in the interplay of several components, e.g., when vibration resulting from one component leads to malfunction of another component. Possible countermeasures aim to isolate components or reduce the interdependences; all kinds of filters may be appropriate as well as insulation entities (mechanical, electronic, software), and system compartmentalisation. These aspects are summarised in Figure 8.  
Besides these rather concrete measures, which may help to increase certain aspects of the fault-tolerance of technical systems, conscious function development of fault accommodation entities is inevitable because the measures on the concrete levels described above cannot counteract a principal system weakness in terms of fault-tolerance. Conscious development of fault accommodation functions at the system design and function development stage (see Figure 3) is sensible. On a functional level, faults can be understood as harmful functions (see Figure 6) and useful functions need to be defined in order to counteract (\"eliminate\" in Figure 6) them. Their definition is essentially comparable to the definition of \"normal\" product functions (see Section 3).
## 2 Materials and Methods
### Ionospheric Observations and Satellite Measurements  
We used ionospheric observations obtained from SwA and F18 satellites. We analyzed the eclipse-induced local ionospheric reactions using numerous space-based measurements, such as VTEC, in situ \(N_{e}\), and electron temperature (\(T_{e}\)) data provided by SwA. This satellite has an inclination of 88\({}^{\circ}\) and a circular orbit at about 450 km height over the sea level. We also used in situ \(N_{e}\) measurements provided by F18 in the topside ionosphere at about 850 km height over the sea level with an inclination of 99\({}^{\circ}\) and a circular orbit. The measurements from these satellites help us to corroborate the observations on the EIA crests at 350 km, although with different amplitudes since the measurements above 850 km correspond to the plasmosphere.
# Numerical and Experimental Studies of the LK Type Shaped Charge
## 3 Experimental Method and Results
### The Structure of Shaped Charge Tests  
For the purposes of the test, six charges of each type were made. The purpose of this test was to assess the efficiency of charges with two types of detonation initiation. The differences in the detonations process could have influence to characteristics of the detonation wave acting on the liner and finally on the cumulative jet effectiveness. The ARMOX 370T (see Table 1) armored plate (80 mm class 2 and 100 mm class 1 thickness, and dimensions of \(1000\times 700\) mm) was loaded dynamically with an accumulation charge (70 mm in diameter) according to prepaid charges (see Figure 3).  
The cumulative charge used an insert made of the original LK, with the geometry shown in Figure 4 and a 2 mm thick liner.  
The charges were elaborated with a plastic explosive (PMW-14) containing 84% hexogen (RDX) and 16% binder. This plastic explosive has the following parameters: a density of 1.43 g/cm\({}^{3}\), and a detonation speed of 7500 m/s.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Specimens Thickness** & **Yield Strength** & **Tensile Strength** & **Hardness** & **Chary-V** & **Elongation A\({}_{5}\)** & **Elongation A\({}_{50}\)** \\
**(mm)** & **R\({}_{\text{poz}}\) (MPa)** & **R\({}_{\text{m}}\) (MPa)** & **(HBW)** & **(J)** & **(min \%)** & **(min \%)** \\ \hline plate 80 & 800 & 900–1100 & 280–330 & 60 \(\mathrm{J}\)/\(\mathrm{-}\)40 \(\mathrm{{}^{\circ}C}\) & 13 & 15 \\ plate 100 & - & - & 300–350 & 40 \(\mathrm{J}\)/\(\mathrm{-}\)40 \(\mathrm{{}^{\circ}C}\) & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical properties [18, 43].  
Figure 3: Cumulative charges prepared for testing: (a) axial of cumulative charge; (b) peripheral of cumulative charge.
# Linomycin-Induced Transcriptional Alterations in the Green Alga _Raphidocelis subcapitata_
## 4 Discussion
### Genes Related to Xenobiotic Metabolism
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Pathway** & **Category** & **Up-gene** & **Down-gene** & **\(p\) Value** & **FDR** \\ \hline
**Control vs. Low** & & & & & \\ \hline Photosynthesis—antenna proteins & Energy metabolism & _llca2, lhca4, lhcb1_ & - & \(1.43\times 10^{-7}\) & \(4.30\times 10^{-7}\) \\ \hline Porphyrin and chlorophyll metabolism & Metabolism of cofactors and vitamins & _hemE, hpe_ & - & \(0.0044\) & \(0.0065\) \\ \hline photosynthesis & Energy metabolism & _psPY_ & - & \(0.096\) & \(0.096\) \\ \hline
**Control vs. Medium** & & & & & \\ \hline Photosynthesis - antenna proteins & Energy metabolism & _lhca4, lhcb1, lhcb2_ & - & \(2.60\times 10^{-16}\) & \(2.34\times 10^{-15}\) \\ \hline Porphyrin and chlorophyll metabolism & Metabolism of cofactors and vitamins & _chlD, clhkl, hornA, hemD, por_ & - & \(2.51\times 10^{-8}\) & \(1.13\times 10^{-7}\) \\ \hline
**Control vs. High** & & & & & \\ \hline Pentose and glucuronate interconnections & Carbohydrate metabolism & _rpe, xk1_ & _ugdh, tsp2, tsp4_ & \(0.0022\) & \(0.15\) \\ \hline Starch and sucrose metabolism & _cgd, isa, spp, tps, uavay_ & _E2.4.4.14, E3.2.1.2, E3.2.1.4,_ & \(0.0034\) & \(0.15\) \\ \hline Amino sugar and nucleotide sugar metabolism & Carbohydrate metabolism & _E3.2.1.14, uap1_ & _E1.6.2.2, gmpy, rrhm, ugdh,_ & \(0.056\) & \(0.74\) \\ \hline Taurine and hypotaurine metabolism & Metabolism of other amino acids & - & \(\mathit{ado}\), ccl1 & \(0.014\) & \(0.41\) \\ \hline Arginine and proline metabolism & Amino acid metabolism & _agua, E3.5.1.4_ & _pluu, pip_ & \(0.065\) & \(0.74\) \\ \hline Purine metabolism & Nucleotide metabolism & _apnl_2, nnk, pk_ & _akk, aac2, adrpm, E2.7.1.20,_ & \(0.038\) & \(0.74\) \\ \hline Pyrimidine metabolism & Nucleotide metabolism & _nnk, nuk, up1_ & _cmpkl, ppvB, rrm1, rrm2_ & \(0.098\) & \(0.90\) \\ \hline Carbon fixation in photosynthetic organisms & Energy metabolism & _aldo, gspa, ttl1_ & _Gapdh, ppvK_ & \(0.063\) & \(0.74\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of main KEGG pathways changed by limomycin treatment (\(p<0.1\)).
# A Reinforcement Learning Based Intercell Interference Coordination in LTE Networks
## 5 The Proposed ICIC Scheme
### Dynamic Power Allocation Step
The whole process of dynamic power allocation is summarized in Algorithm 5. The algorithm has some input parameters: users coordinates in the network, maximum and minimum transmit power, population size, generations number, crossover rate, and mutation rate. Details of how initial population is generated, and evaluation and selection of policies, and crossover and mutation operations are done can be viewed in Algorithm 6. Figure 8: Mutation operation for power allocation. Figure 7: Crossover operation for power allocation. ```
1 Set position of users in each cell
2 Set maximum transmit power value
3 Set Minimum transmit power value
4 Set size of policies population
5 Set generations number
6 Set crossover rate
7 Set mutation rate
8 foreachTTIdo
9 Generate initial policies population
10 foreachgenerationdo
11 Select chromosomes to be crossed
12 Cross selected chromosomes
13 Select chromosomes to be mutated
14 Mutate selected chromosomes
15 Select new policies for the next generation population
16 Evaluate policies population
17
18 end for
19 Choose the best chromosome according to fitness function
20 foreachnodeBdo
21 foreachRBdo
22 Set transmit power value with corresponding value in best policy
23 end for
24
25 end for
```
**Algorithm 5**Power allocation summary algorithm
## 4 Proposed SARP Routing Protocol for BASNs
### Working Principle of Sensors in SARP
#### 4.4.1 Updating Neighbors with Recent Information
The biosensor node which received the \(dis\_route\) messages from the neighboring sender updates its routing table with the recent information only if the entry for a particular node exists in the routing table. Otherwise, a new entry is created for each neighboring biosensor in the routing table. After updating the table, each receiver biosensor replies via \(rep\_msg\) message to the sender node and waits for the acknowledgment message (\(ackno\_msg\)). Upon receiving the first reply message, each receiver biosensor waits for a distinct time to collect all other \(rep\_msg\) messages from the neighboring nodes. This waiting period is kept constant so that the maximum number of reply message can be collected before it expires. Consequently, the receiver biosensor node updates its routing table and sends the neighboring node information to the sink, which updates its information in its routing table and marks the new neighboring node with increasing hop number since it is away from the sink. Later, it broadcasts an \(ackno\_msg\) message to the neighboring nodes with their hop count information in the network. Both the \(rep\_msg\) and \(ackno\_msg\) messages contain similar information of the sender and receiver, including the node identity, energy level, distance, and current location on the body. However, some extra bits are included in the \(ackno\_msg\) message in order to ensure the successful transmission over a link between the biosensors in the body area sensor network. This process repeats until each biosensor node and the sink have the information of neighboring nodes in the wireless body area sensor networks. Throughout the process, each biosensor node sets the neighboring hop count level values in both upward and downward directions in its routing table. This not only helps to provide the distance and location information of the biosensor nodes from the sink but also controls the packet loops in the network. In addition, during the message transmission process, an acknowledgment message is forwarded by the receiver to the sender node in order to ensure successful packet exchange over a link in the network. Moreover, packet collision is avoided throughout the message exchange process by considering the TDMA mechanism.
# Blind Spot Detection Radar System Design for Safe Driving of Smart Vehicles
## 5 BSD Radar System Performance Test
### Results
In Case 5, as a test of pedestrian approaching test vehicle, the test vehicle drove at a constant speed of 10 kph and the distance was measured when a pedestrian approached the BSD area of the test vehicle. To conduct the test, models of an adult with a height of 180 cm and a child with a height of 120 cm were used. As shown in Figure 21, the warning was activated when the pedestrian entered the BSD area of the test vehicle. The warning was deactivated when the pedestrian exited the BSD area. The test results confirmed that the warning system of the designed BSD radar operated properly in each of the scenarios. To produce the BSD radar, this paper presented target tracking procedures and tracking algorithms, and fabricated antennas and modems. After installing these in a vehicle, performance tests were conducted, and ideal results were achieved. The BSD radar system developed in this paper is a promising technology  
Figure 20: Test Operation Scenarios and Results of Cyclist Approaching Test Vehicle: (**a**) Before the cyclist enters the BSD area of the test vehicle; (**b**) When the cyclist enters the BSD area of the test vehicle (Warning ON); (**c**) Before the cyclist exits the BSD area of the test vehicle; (**d**) When the cyclist exits the BSD area of the test vehicle (Warning OFF). that can enhance the active safety system of vehicles and support the emergence of fully autonomous driving systems in the future. However, in complex driving environments, determining the priority of multiple target vehicles remains a challenge that requires additional technological advances. Currently, radar systems allocate the same amount of time to detect all target vehicles, which can lead to tracking failures under certain circumstances. To overcome this challenge, incorporating AI technology to determine the priority of target vehicles and allocate different tracking times could improve the accuracy of target tracking. Based on the BSD radar system presented in this paper, we are conducting research on assigning priorities to target vehicles using AI technology.