esa-sceva/satcom-chunk-collection huggingface dataset:
# Nutritional Analysis of Red-Purple and White-Fleshed Pitaya (_Hylocereus_) Species
## 2 Materials and Methods
### Total Antioxidant Capacity (TAC)  
The free radical scavenging activity (RSA) of pitaya flesh was measured according to the DPPH (2,2-diphenyl-1-picrylhydrazyl) assay by [PERSON] et al. (2016) with slight modifications. A methanolic extract (methanol/ water; 70:30; \(v/v\)) was used for both total antioxidant capacity and total phenol analysis. Briefly, 0.06 \(\mu\)M of ethanolic DPPH was freshly prepared. Then, 1950 \(\mu\)L of DPPH was added to 50 \(\mu\)L of pitaya fruit sample. The mixture was shaken for 1 min and kept in the dark for 30 min at room temperature. The radical scavenging activity was measured at 515 nm for 5 min intervals using a Multiscan GO microplate spectrophotometer. The solvent was used as blank and the % radical scavenging activity of the pitaya flesh extract was calculated using the following formula:  
DPPH radical scavenging activity (%) = [(Control absorbances \(-\) Sample absorbances) / Control absorbances] \(\times\) 100
# Effects of 450 MeV Kr Swift Heavy Ion Irradiation on GaN-Based Terahertz Schottky Barrier Diodes
## 2 Experimental Procedure  
The irradiation damage in GaN is determined by a complex interplay between irradiation parameters (type, energy, and dose) and material properties (carrier density, impurity content, and dislocation density), such as the carrier removal effect due to trapping of carriers into radiation-induced defects varies depending on the growth method of GaN layers [18]. Metal-organic chemical vapor deposition (MOCVD) technology is a widely used technique for epitaxial GaN growth, which is attributable to low inherent defect density, appropriate epitaxial growth rate, and cost-effectiveness. The low intrinsic defect density of GaN materials grown using this method can effectively mitigate the carrier removal effect caused by irradiation. The designed GaN-based THz SBD is shown in Figure 1. The GaN materials used in this work were grown on a 2-inch silicon carbide (SiC) substrate by MOCVD. The GaN epitaxial layers consist of a 450 nm high-resistance (HR) buffer layer, a 2.1 \(\upmu\)m n\({}^{+}\)-GaN layer (doping concentration of 10\({}^{19}\) cm\({}^{-3}\)), and a 150 nm n\({}^{-}\)-GaN layer (with a doping concentration of 5 \(\times\) 10\({}^{17}\) cm\({}^{-3}\)). Fabrication of GaN-based THz SBD mainly involves four steps: (1) GaN materials were dry etched onto SiC substrates to achieve mesa isolation using inductively coupled plasma (ICP); (2) the cathode metals of Ti/Al/Ni/Au (20/120/50/80 nm) were deposited on n\({}^{+}\)-GaN layer by electron-beam evaporation (EBE), followed by rapid thermal annealing in the N\({}_{2}\) environment to form the ohmic contact; (3) the anode metals of Ni/Au (50/100 nm) were deposited on the n\({}^{-}\)-GaN layer by EBE to form the Schottky contact, with an anode diameter of 6 \(\upmu\)m; (4) the gold air bridge (thickness of 1 \(\upmu\)m) was achieved by the electroplating technique, as shown in Figure 1b,c.  
The SHI irradiation experiment was conducted at the Space Environment Simulation Research Infrastructure (SESRI), Harbin Institute of Technology. The GaN-based THz SBDs were irradiated by 450 MeV \({}^{84}\)Kr\({}^{18+}\) beam with irradiation fluences of 5 \(\times\) 10\({}^{6}\) ions/cm\({}^{2}\), 10\({}^{7}\) ions/cm\({}^{2}\), and 2.5 \(\times\) 10\({}^{7}\) ions/cm\({}^{2}\), respectively. The Hall samples of n-GaN were also irradiated simultaneously.  
The current-voltage (_I-V_) and capacitance-voltage (_C-V_) characteristics of the GaN-based THz SBD were measured by an Agilent B1500A semiconductor parameter analyzer and Keysight E4980A LCR meter, respectively. PL spectra were detected by a microregion spectrometer with a 325 nm He-Cd laser. LFN spectroscopy was performed using a Primarius FS-Pro system.
# Intelligent Transaction Scheduling to Enhance Concurrency in High-Contention Workloads
## 7 Experimental Results
### Experimental Setup  
We implement a prototype of DCoS within the DBx1000 [33] transaction-processing engine and integrate a partitioning algorithm following [15]. Our experiments are conducted on two machines equipped with two 16-core AMD 7302 processors and an NVIDIA 24 GB Ampere A30 GPU. One machine initializes the OLTP application, while the other serves as the transaction-processing engine.  
**Hyperparameters.** The embedding network of the executor consists of two layers, each containing two hidden layers with a hidden dimension of 128 neurons. Following common practice in DRL tasks [31], we set the coefficients for clipping, policy loss, value function, and entropy to 0.2, 2, 1, and 0.01, respectively. During training, we use the widely adopted Adam optimizer [34] with a learning rate of \(lr=1\times 10^{-3}\), which is a standard choice in policy gradient algorithms.  
**Metrics.** We measure the performance of the transaction-processing system using the following metrics:  
* _Makespan_: The total execution time of a transaction batch, highlighting the effectiveness of scheduling under contention.
* _Throughput_: The number of transactions committed per second, reflecting overall system efficiency.
## 2 The Numerical Model of Shield Tunnel with Stagger-Jointed Assembly
### Load and Boundary Conditions  
Taking the strata of China Shaoxing urban rail transit as the research background, the soil layer types are shown in Figure 10. In this paper, the burial depth of the shield tunnel is selected as 10 m, 15 m, and 20 m, and the external load is calculated by the water and soil balance method [26; 27]. The external load mode is shown in Figure 11. Table 3 shows the external loads in the model, where P1 is the overlying pressure of the tunnel, P2 is the resistance at the bottom of the tunnel, and P3 and P4 are both the lateral pressure on the tunnel. As the tunnel is located in the muddy soil layer, the strata resistance when deformation of the tunnel occurs is ignored.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\mathbf{E_{c}}\)/\(\mathbf{GPa}\) & \(\mathbf{\
u}\) & \(\mathbf{\psi}\) & \(\mathbf{\kappa}\) & \(\mathbf{f_{b0}}\)/\(\mathbf{f_{c0}}\) & \(\mathbf{K_{c}}\) & \(\mathbf{\mu}\) \\ \hline
20 & 0.2 & 38\({}^{\circ}\) & 0.1 & 1.16 & 0.667 & 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Concrete parameters used in the FEM model.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{5}{c}{**Compressive Behavior**} & \multicolumn{5}{c}{**Tensile Behavior**} \\ \hline
**Yield Stress/MPa** & **Inelastic Strain** & **Damage Parameter** & **Yield Stress/MPa** & **Cracking Strain** & **Damage Parameter** \\ \hline
22.663797 & 0 & 0 & 3.212006 & 0 & 0 \\
32.407299 & 0.00062 & 0.207 & 2.647291 & 0.000024 & 0.115139 \\
30.263324 & 0.00110 & 0.313 & 2.271104 & 0.000064 & 0.2
## 4 Experimental Validation of V2 SOM
### Preliminary HR Collision Tests  
Currently, a safety-oriented control strategy is in ongoing development, wherein a V2 SOM-based robot functions under preemptive safety conditions in terms of joints' angular velocities. For this purpose, an optimization problem that maximizes the safely reachable angular velocities was formulated by taking into account the actual robot configuration. These constraints result from the simulation and are stored as lookup tables for real-time access.  
In this paper, a simple example of tackling a collision with a human subject using V2 SOM is demonstrated. The experimental setup is shown in Figure 16.  
Figure 14: V2 SOM first prototype.  
Figure 15: V2 SOM characteristics: theoretical (in solid lines) and experimental (in dotted lines) with the 11 different SAB settings.  
In Figure 17, the results of two collision tests are presented. In the first test (Figure 17a,b), the link's inertia is \(I_{link}=0.050\) Kgm\({}^{2}\) with an impact velocity of 90\({}^{\circ}\)/s. In the second test, (Figure 17c,d), the link's inertia is \(I_{link}=0.179\) Kgm\({}^{2}\) with an impact velocity of 32\({}^{\circ}\)/s. In both cases, V2 SOM presents small deflections corresponding to the normal working conditions range (range I; see Figure 8). Upon collision, the safety threshold is exceeded and V2 SOM warns of the collision by sending a collision detection message via a CAN (Controller Area Network) bus to the robot's main controller. Hence the robot, through its controller, stops the joint rotation. In the in-between phase (range I and range II in Figure 8), V2 SOM passively absorbs the shock's kinetic energy. The elastic end stroke of the V2 SOM should not be reached either by a V2 SOM maximum storable elastic energy or by a possible slow control reaction. For this reason, both previous features are considered in developing the safety-oriented control strategy.  
The time elapsed between the collision detection (exiting deflection range I) and returning is 180 ms in the first test and 140 ms in the second test.  
Figure 16: Experimental setup.  
Figure 17: Results of experimental collision tests: (**a**) angular velocity and position of the link in the first test; (**b**) V2 SOM deflection angle in the first test; (**c**) angular velocity and position of the link in the second test; (**d**) V2 SOM deflection angle in the second test.  
These illustrative tests are meant first to show the inertia decoupling and torque decoupling capability of the V2 SOM and then to shed light on the problem of a collision control strategy that is specifically designed to benefit from the V2 SOM properties (e.g., inertia decoupling capacity). This problem is the subject of our upcoming work (for a general review of HR collision control see [4]).
## 3 Proposed Work
### Data Pre-Processing  
Parameters such as kinds of vehicles, vehicle parking space requirements, and minimal traffic lane widths must all be set up before the suggested system can be operated. The Hong Kong Special Administrative Region Planning Department has developed rules that relate to these. As a result, the system may contrast the collected photos to templates in the database, allowing for more precise image and data processing in the future. Variations in the size of everything produced by non-equivalent distance from the vision equipment are neglected in the computing machine to make it easier to calculate parking gaps and accessible parking areas. In another sense, irrespective of its orientation in association to the vision equipment, each of the things shown in a case is presumed to have the same measurements in millimeters or pixels. Initial variables in the calculating procedure involve:  
* The vision device's total coverage is 640 \(\times\) 480 pixels.
* Each truck must have 11 m of the controlled parking area.
* Each cargo van requires 7 m of controlled parking areas.  
Figure 1: The schematic representation of the proposed method.  
* Each private car has a 5 m-controlled parking place.
* The minimum width of traffic lanes is 6.75 m.
* All conceivable vehicles and things are represented as templates.
* A trust value for every pixel for every template, indicating how confident the discrepancy is.  
The phase begins the first level of the timed loop, vision collection after the variables are supplied into the developed framework. The platform's wireless vision sensors may then continuously and automatically capture photos from the roadway. Following that, the information is used for (i) noise minimization, (ii) vehicles and objects detection and matching, (iii) data filtering and refining, and (iv) data imputation and validation.
## 4 Discussion
### Soil Respiration from Tropical Dry Forests in a Global Context  
Dryland ecosystems cover about 40% of the terrestrial surface ([PERSON] et al., 2018). These ecosystems have received a significant role in the global carbon cycle (C) due to dynamic biogeochemical processes that define their productivity and control the global CO\({}_{2}\) concentration in the atmosphere ([PERSON] et al., 2018). At the global scale, they represent 51% and 39% of the global land C sink and the net C flux interannual variability ([PERSON] et al., 2018), respectively, and nearly three-quarters of C stocks are stored in soil ([PERSON] et al., 2018).  
Tropical dry forests (TDF) in northwestern Mexico can contribute an important amount of CO\({}_{2}\) emissions from soils. We found that it is possible to compare our seasonal R\({}_{\rm S}\) rates across several drylands and seasonally dry ecosystems. For instance, [PERSON] et al. (2018) reported R\({}_{\rm S}\) variations for Mediterranean shrubland from 0.4 to 0.8 \(\mu\)mol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\) during the dry season, and during the wet season these rates ranged from 0.9 to 2.1 \(\mu\)mol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\); [PERSON] et al. (2018) for TDFs in southeast Mexico found a mean rate of 1.5 \(\mu\)mol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\) and 5.3 \(\mu\)mol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\) during the dry and wet season, respectively. Meanwhile, for a TDF in Thailand, [PERSON] et al. (2018) reported mean R\({}_{\rm S}\) rates from 1.98 to 3.40 \(\mu\)mol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\) in the dry season and from 3.20 to 4.49 \(\mu\)mol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\) in the wet season; [PERSON] et al. (2018) observed seasonal ranges from 1.93 to 2.20 \(\mu\)mol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\) in dry periods, whereas in the wet periods it varied from 2.70 to 3.93 \(\mu\)mol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\). Regarding R\({}_{\rm S}\) in TDF sites with succession, during the dry season, [PERSON] et al. (2018) found mean rates of 0.75 \(\pm\) 0.85 \(\mu\)mol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\) for an abandoned pasture (AP), 0.72 \(\pm\) 0.28 \(\mu\)mol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\) in the early-secondary forest (ES) and 1.26 \(\pm\) 0.79 \(\mu\)mol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\) for a mid-secondary forest (MS), then mean rates reported at the wet season were 4.15 \(\pm\) 2.25 \(\mu\)mol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\), 6.85 \(\pm\) 2.82 \(\mu\)mol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\) and 6.53 \(\pm\) 2.56 \(\mu\)mol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\) for AP, ES and MS, respectively.  
Finally, our annual cumulative R\({}_{\rm S}\) estimates included ranges from 712.87 to 1005.89 g C m\({}^{-2}\) in the OG site, while those in the MS site ranged from 618.16 to 837.38 g C m\({}^{-2}\) and from 681.19 to 786.33 g C m\({}^{-2}\) in the ES site, which are consistent and within the ranges reported in other tropical, subtropical and seasonally dry forests around the world (Table 7).
## 1 Introduction
[36] have estimated the entropy from AE counts during fatigue tests of aluminum and titanium alloys. It was shown that information entropy derived from the AE counts mirrored the evolution of counts throughout the tests [35], and the cumulative information entropy from counts may be constant at failure [36]. For all of these previous studies that estimated information entropy from AE signals, the information entropy was calculated based on AE feature distributions formed from numerous AE signals. Instead of analyzing features from several AE hits at a time, one can estimate the information entropy of each individual AE waveform from the signals' voltage distributions. This technique theoretically utilizes more information carried in an AE signal compared to summary statistics like counts, amplitude, or average frequency and is expected to be a more representative measure of fatigue damage. This idea is the fundamental basis of the work presented in this paper: to extract information entropy from every individual AE signal during fatigue tests as a measure of the AE signal disorder and damage within a fatigued structure. In this paper, we propose three methods to quantify the information entropy from individual AE signals. The entropy values from these three methods, referred to as _instantaneous entropy, average entropy,_ and _weighted average entropy,_ are derived from AE signals received during fatigue experiments performed on a commonly used aerospace alloy, Al7075-T6. This work studies information entropy of AE signals both prior to crack initiation and after a crack began to propagate. Unlike previous research in which fatigue damage is easily quantified based on visible crack length, this work assumes that instantaneous elastic modulus can be an estimate of the unobservable microstructural damage before appearance and initiation of a visible crack. AE counts and AE energy, two traditional AE signal features, in addition to the three methods of quantifying information entropy from AE signals, are correlated to modulus degradation throughout crack initiation and growth[PERSON] Results suggest that employing information entropy from AE waveforms would be more advantageous than utilizing the traditional AE features as fatigue damage measures. The figures provided in this paper can be found in the Supplementary Materials.
# Topological Navigation and Localization in
## 3 Topological Navigation and Localization for LHD
### Navigation
#### 3.4.2 Deliberative Path Planning  
This module receives the next target position, which needs to be reached with a certain speed, as a relative pose from _Navigation Control_. Then, it calculates the path to be followed between the current pose and the desired destination, as a spline \(S(t)=\left[S_{x}(t),\ S_{y}(t)\right]\). The desired steering speed (\(\overline{\alpha}\)) and speed limit (\(v_{MAX}\)) are then computed, and sent to _Guidance_ (See Figure 9).  
In order to calculate the spline's coefficients, the following border conditions are used:  
\[S\left(t=0\right)=X_{0};\ S\left(t=t^{*}=\frac{\widehat{d}}{v}\right)=X_{1} \tag{7}\]  
\[\dot{X}_{0}=\left(v\cos\gamma,v\sin\gamma\right);\ \dot{X}_{1}=\left(v\cos\theta,v \sin\theta\right) \tag{8}\]  
where:  
\(X_{0},\ \dot{X}_{0}=\) Position and speed of the front bumper of the vehicle (See Figure 10).  
\(X_{1},\ \dot{X}_{1}=\) Position and speed at the desired target destination (See Figure 10).  
\(\widehat{d}=\) Estimated distance between \(X_{0}\) and \(X_{1}\).  
Using the calculated derivative of the spline, and the vehicle's kinematic model, given by (10) and (11), the desired steering rate \(\gamma\) can be calculated as:  
\[\dot{\gamma}=\frac{\left(L_{f}\cos\gamma+L_{r}\right)\dot{\alpha}(t=0)-v_{i} \sin\gamma}{L_{r}} \tag{9}\]  
with \(\dot{\alpha}(t=0)\) the calculated angle derivative of the spline, evaluated in \(t=0\); \(L_{r}\) the length from the LHD's pivot to the rear wheel axis; \(L_{f}\) the length from the LHD's pivot to the front wheel axis; \(v_{i}\) the linear speed of the LHD; \(\gamma\) the steering angle in the LHD's pivot; \(\omega=\dot{\gamma}\) the steering speed in the LHD's pivot.
# La-CTP: Loop-Aware Routing for Energy-Harvesting Wireless Sensor Networks
## 6 Evaluation
### Analyses of Experimental Results
#### 6.2.2 Comparison of CTP and La-CTP
Figure 9: Comparisons in parent updates and loop detections under two protocols running in EH2. However, CTP only turns to the beacons locally broadcasted, to evaluate the local network status, no matter how long the parent node has not received any passing-by packets due to loops or poor link quality. A speculation comes up naturally: La-CTP employs a more proactive beaconing policy, and does it need more beacon messages than CTP? Figure 10 plots the beacons broadcasted and received in experiments. Different from the speculation made above, La-CTP generates 6756 beacons and receives 92,754 beacons, while CTP generates more than 10,000 beacons and receives more than 138,000 beacons. The reduction of the amount of beacons in La-CTP is mainly attributed to the loop controlling and relieving schemes. Unable to suppress and relieve loops effectively, however, CTP is prone to be trapped in loops and consequently, the nodes detecting loops will always broadcast beacons even without any helpful replies. In some extent, the amount of beacons in network operation can profile the overhead of protocols in terms of energy and bandwidth resources. Therefore, Figure 10 also demonstrates that La-CTP overwhelms CTP in resource efficiency. Table 1 comprehensively compares the network performances of two protocols under four different scenarios. In the EH2 experiments, when the two protocols terminate, CTP still leaves 54% of loops that were detected during experiments, while La-CTP has already relieved all its loops. In the AllEH experiments, CTP and La-CTP do not solve around 60% and 6% of their loops, respectively. Loops may grab the packets, without forwarding them towards the sink; and finally the packets kept in loops will be dropped finally due to buffer overflow. Hence, CTP cannot achieve a good throughput in EH-WSNs unless it can solve loops properly. We can see from Table 1 that the throughput of La-CTP is almost four times and three times that of CTP in scenarios EH2 and AllEH, respectively. In the AllEH experiments, noticeably, we synthesized the time-stamped neighbor lists of all nodes and found that the network topology was partitioned dozen times at runtime and the largest network segment involved 27 nodes. So, both the parent updates and the loop detections of the two protocols are more frequent in the 10-min AllEH experiment than their counterparts in the 30-min EH2 experiment. In partitioned network, neither CTP nor La-CTP can effectively remove the loops in a segment until one node of this segment could be re-unioned with some nodes that can reach the sink. On average, La-CTP needs 8.8 s to unlock a loop in the AllEH experiment, while CTP needs 20 s to do this.
# Body Area Networks in Healthcare: A Brief State of the Art
## Data Link Layer Properties
### Sustainability
MBE focuses on the safety and the design methodology in the efficiency of the solution. In the profiling phase, trials are executed to obtain workload data from the processor, radio power, and thermal characteristics. Power consumption of devices is evaluated using different power-management strategies. In this phase, the sources are evaluated to know the amount of energy that can gather as well. The information obtained during the profiling phase is used in the modeling phase. The goal of this phase is to model the behavior of the BAN system and the human body. The last phase consists of two subphases: (1) analysis phase and (2) design phase. The analysis phase selects a model from the modeling phase and a set of requirements. These requirements are used as inputs and outputs of the model. The design phase uses the analysis phase to determine the best strategy for a sustainable BAN design. This phase finds the best solution faster than a brute-force search. Sustainability of healthcare BAN-based applications can benefit from other groundbreaking domains like Industry 4.0. Indeed, BANs are being introduced to assist and monitor workers. A recent article seeks to walk towards the social sustainability of the industry [49]. An intelligent band has been tested in an emulated industrial scenario to correct a worker's tasks to avoid possible injuries [50].