esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Figures of Merit
#### 2.3.2 Accuracy and Precision  
Accuracy was evaluated by recovery spikes of the three thiols into a commercial Pinot grigio wine at high (500 ng/L) and low (60 ng/L) concentrations of each thiol. The native concentrations of thiols in the wine were: 3-MH (228 ng/L), 3-MHA (\(<\)LOD), and 4-MMP (2.1 ng/L). Recovery was acceptable, and ranged from 90%-109% of the expected values (Table 2). The calibration curves were generated in model wine, indicating that the isotopically labeled standards effectively compensated for any matrix  
\begin{table}
\begin{tabular}{c c c c} \hline
**Spike Level (ng/L)** & **ng/L of 4-MMP** & **ng/L of 3-MH** & **ng/L of 3-MHA** \\ \hline
6 & 8.0 (\(\pm 0.3\)) & 4.1 (\(\pm 0.1\)) & n.d. \\
20 & 19 (\(\pm 0.7\)) & 19 (\(\pm 2.1\)) & n.d. \\
63 & 50 (\(\pm 3.2\)) & 66 (\(\pm 3.3\)) & 72 (\(\pm 4.9\)) \\
200 & 179 (\(\pm 12.2\)) & 202 (\(\pm 11.3\)) & 178 (\(\pm 10.0\)) \\
632 & 525 (\(\pm 50.1\)) & 722 (\(\pm 5.7\)) & 589 (\(\pm 26.7\)) \\
2000 & 1792 (\(\pm 133.2\)) & 2245 (\(\pm 16.4\)) & 2080 (\(\pm 42.5\)) \\
6325 & 5848 (\(\pm 219.4\)) & 6335 (\(\pm 88.8\)) & 6301 (\(\pm 218.5\)) \\
20,000 & 20826 (\(\pm 2599.2\)) & 19652 (\(\pm 191.0\)) & n.d. \\ \(\sigma_{\rm i}\), ng/L & 0.31 & 0.33 & 5.8 \\ LOD, ng/L & 0.9 & 1.0 & 17.3 \\ \(r^{2}\) & 0.996 & 0.998 & 0.999 \\ Lack of Fit Test (\(p\) value) & 0.9946 & 0.0741 & 0.8969 \\ \hline \end{tabular} Standard deviations (\(n=2\)) were stated in parenthesis. Abbreviations: \(\sigma_{\rm i}=\) signal independent background noise, LOD = Limit of detection, n.d. = not detected.  
\end{table}
Table 1: The observed concentrations at varying levels of 4-mercapto-4-methyl-2-pentanone (4-MMP), 3-mercaptoethanol (3-MH), and 3-mercaptoethanol-MHA) resulting from standard addition.  
differences between the real and model wine systems. Precision was evaluated by spikes at low (10 ng/L 4-MMP; 25 ng/L 3-MH; 25 ng/L 3-MHA) and high concentrations (100 ng/L 4-MMP; 200 ng/L 3-MH; 100 ng/L 3-MHA) of each thiol into the same Pinot grigio. Relative standard deviations were 5%-7% for the high concentration spikes and 7%-11% for the low concentration spikes, with the worst performance for the low concentration 3-MHA spike.
## 5 Discussion
### Comparison with Current Research on the 2019 Ridgecrest Earthquake Sequences  
The study employs the Ridgecrest earthquake as a case study to demonstrate the large-scale surface deformation monitoring capabilities of RapidEye. The displacement field calculated by SPC accurately captured the 2D offset field of the earthquake. The obtained offset field shows good consistency with GNSS observations in terms of deformation magnitude and direction. In addition, the maximum deformation magnitude of the earthquake obtained in this study matches the magnitude reported in [40; 41]. The accuracy of SPC deformation monitoring is closely related to spatial resolution, typically ranging from 1/20 to 1/10 for the spatial resolution of optical images. The maximum horizontal deformation magnitude of the earthquake obtained in [40; 41] ranges from 4 to 5 m, exhibiting a deviation of 1 to 2 m from our findings. This difference may be attributed to the higher spatial resolution of the images used by them, such as PlanetScope and WorldView, as well as the fusion of rich and multi-source satellite images. This fusion enhances the accuracy of the deformation calculation by recognizing the mutual supplementation and constraints among these images.  
This research performs a deformation characteristic analysis of the 2019 Ridgecrest earthquake utilizing just the 2D displacement field derived from RapidEye images without considering deformation in the vertical direction. SAR satellites, due to their unique imaging principles, have a higher sensitivity to vertical surface deformation and are important  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Satellite** &
\begin{tabular}{c} **The Number of** \\ **Satellites** \\ \end{tabular} & **Spatial Resolution** & **Revisit Period** & **Swath Width** \\ RapidEye & 5 & 5 m & 1 d & 77 km \\ PlanetScope & 170 & 3/4 m & 2 d & 20 km/24.6 km \\ SkySat & 13 & 0.8/1 m & \textgreater{}1 d & 8 km \\ WorldView & 4 & 0.31/0.45/1.8/1.24 m & 1 d & 16 km/13 km \\ Sentinel-2 & 2 & 10/20/60 m & 5 d & 290 km \\ \hline \hline \end{tabular}
\end{table}
Table 8: Parameters of constellation optical satellites.  
sources of three-dimensional surface deformation information. By contrast, SPC, which is based on single optical imagery, can only retrieve 2D surface deformation information. To avoid losing crucial vertical deformation information of the earthquake, the 3D offset field was created by combining data from RapidEye and Sentinel-1 before and after the event with the least square method [43] (Figure 8). As shown in Figure 8, the displacement component of fault rupture primarily emerges in the horizontal dimension, with a smaller displacement in the vertical direction. The northern portion of the main fault experiences a larger vertical deformation component than its southern counterpart, with a maximum vertical deformation magnitude of 0.5 m. This result is consistent with the vertical deformation monitoring result obtained from [44].
## 5 Numerical Studies
### Sensitivity Analysis
#### 5.4.3 Impact of Time Window Length  
We now continue to analyze the impact of time window length on the previously discussed metrics by changing the time window length from 4 to 5.5 h in increments of 0.5 h. The experiments are conducted on the same instances used in Section 5.4.2. Figure 9 depicts how the metrics change with the time window length. When the time window length is extended, a decline is witnessed in both the total cost and the number of dispatched trucks, which is similar to the impact of the depot capacity. The reason lies in that when the time window grows large, trucks spend less time waiting at customer locations and thus they can serve more orders. Therefore, fewer trucks are required, resulting in substantial savings in fixed costs. Contrary to the effect of the depot capacity on the travel cost, an increase in the time window length will lead to a decrease in the travel cost. This is due to the fact that the widened time window implies a larger feasible domain and trucks are granted more flexibility in routing. As a result, with wide time windows, trucks can find more efficient and cost-effective routes. This finding suggests that broader time windows may be advantageous to inland container transport from the standpoint of the trucking firm.
# Feasibility, Design, and Deployment Requirements of TCR for Bistatic SAR radiometric Calibration
## 3 Bistatic RCS Characteristics
### Monostatic Bistatic Equivalence Theorem  
The monostatic bistatic equivalence theorem indicates that the bistatic RCS can be approximated by the monostatic RCS measured on the bistatic angle bisector and measured at \(\cos\frac{\beta}{2}\) times lower than the true frequency for small bistatic angles [24], which can be written by:  
\[\sigma\bigg{(}f,\alpha-\frac{\beta}{2},\alpha+\frac{\beta}{2}\bigg{)}\approx \sigma\bigg{(}f\cos\frac{\beta}{2},\alpha,\alpha\bigg{)} \tag{5}\]  
where \(\alpha\) is the incident angle and \(\beta\) is the bistatic angle.  
Figure 3: The monostatic and bistatic radar configurations.  
Consider a TCR with sides comprised of isosceles right triangles. With the geometry defined as per Figure 4, by using geometrical optics approximation theory, the RCS of a TCR can be formulated as [25]:  
\[\sigma(\theta,\varphi)=\frac{4\pi}{\lambda^{2}}A^{2}(\theta,\varphi) \tag{6}\]  
\[A^{2}(\theta,\varphi)=\left\{\begin{array}{cc}l^{4}\Big{(}\frac{4c_{1}c_{2}} {c_{1}+c_{2}+c_{3}}\Big{)}^{2}&if\,c_{1}+c_{2}\leq c_{3}\\ l^{4}\Big{(}c_{1}+c_{2}+c_{3}-\frac{2}{c_{1}+c_{2}+c_{3}}\Big{)}^{2}&if\,c_{1} +c_{2}\geq c_{3}\end{array}\right. \tag{7}\]  
where \(c_{1},c_{2},c_{3}\) are each assigned one of:  
\[\left.\begin{array}{c}c_{1}\\ c_{2}\\ c_{3}\end{array}\right\}=\left\{\begin{array}{cc}\cos\theta\\ \sin\theta\sin\varphi\\ \sin\theta\cos\varphi\end{array}\right. \tag{8}\]  
where \(\lambda\) is the radar wave length, \(A\) is the effective aperture of a trihedral, \(\theta\) is the elevation angle, and \(\varphi\) is the azimuth angle.  
As shown in Figure 5, the bistatic RCS azimuth patterns versus the azimuth angles of the TCR under different bistatic angles were simulated by using the monostatic bistatic equivalence theorem. It can be seen that the main lobe of the RCS patterns were very wide and flat, and the bistatic RCS values for different bistatic angles obtained by the monostatic bistatic equivalence theorem were similar to the monostatic RCS value near the peak. However, the azimuth angles corresponding to the peak RCS were different due to the monostatic RCS measured on the bistatic angle bisector.  
Figure 4: The monostatic RCS observation geometry of the trihedral corner reflector (TCR).
## 4 Simulation Analysis of CRPDC
### Current Signal Transformation and Amplification  
The current-sampling performance of CRPDC is investigated by simulating the current sampling circuit (based on the mathematic model and the Multisim model in Figure 4b). The input voltages of the current sampling circuit (voltage between point 1 and point 2) are set to be 0 mV, 50 mV, 100 mV, 150 mV, and 200 mV (corresponding to the coil current of 0.0 A, 0.5 A, 1.0 A, 1.5 A, and 2.0 A, considering the current sampling resistance is of 0.1 Ohm), respectively. As shown in Figure 13, the simulation results from the Multisim model (in Figure 4b) agree well with the calculation results of the mathematical model. Both curves (the relationship between the voltage of the sampling resistance and the output voltage of the differential amplifier) display linear characteristics. Therefore, the current sampling circuit is able to accurately transform and amplify the coil current.  
Figure 11: The resulting coil currents of CRPDC under different driving voltages: (**a**) 16 V; (**b**) 20 V; (**c**) 24 V; (**d**) 28 V.  
Figure 12: The current characteristics of the CRPDC under different driving voltages (results from Multisim model): (**a**) Average current; (**b**) current ripple (peak-to-peak value).  
Figure 13: Relationship between the voltage of sampling resistance and the output voltage of current sampling circuit.
# Early Detection of Residual/Recurrent Lung Malignancies on Post-Radiation FDG PET/CT
## 2 Materials and Methods
### Radiomics Model
During the training phase, the model's hyperparameters were tuned and selected based on the highest validation accuracy. Particularly, the box constraint was set to a value of 100 to prevent overfitting by controlling the maximum penalty imposed on margin-violating observations. For testing, the trained SVM model was evaluated on the samples from a folder, distinct from data from the training and validation folders (four folders in total). This procedure provides an unbiased assessment of the model's predictive accuracy. Performance metrics such as accuracy, sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve were computed to quantify the model's effectiveness. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Intensity**} & \multicolumn{2}{c}{**Texture**} & \multicolumn{2}{c}{**Geometry**} \\ \hline \multicolumn{2}{c}{} & \multicolumn{1}{c}{**GLCM**} & \multicolumn{1}{c}{**GLSZM**} & \multicolumn{1}{c}{**GLRLM**} & \multicolumn{1}{c}{**NGTDM**} & \\ \hline \multirow{8}{*}{Features} & minimum & energy & SZE & SRE & coarseness & volume \\  & maximum & contrast & LZE & LRE & busyness & major diameter \\  & mean & entropy & GLN & GLN & contrast & minor diameter \\  & standard-variation & homogeneity & ZSN & RLN & complexity & eccentricity \\  & Sum & correlation & ZP & RP & strength & elongation \\  & median & variance & LGZE & LGRE & & bounding box volume \\  & skewness & dissimilarity & HGZE & HGRE & & perimeter \\  & kurtosis & autocorrelation & SZLGE & SRLGE & & orientation \\  & variance & Sum Average & SZHGE & LRLGE & & \\  & & & LZLGE & LRRIGE & & \\  & & & LZHGE & GLV & \\  & & & ZSV & RLV & \\  & & & GLV & SRHGE & & \\ \hline \hline \end{tabular} Abbreviations: SZE = small zone emphasis; LZE = large zone emphasis; GLN = gray-level nonuniformity; ZSN = zone-size nonuniformity; ZP = zone percentage; LGZE = low gray-level emphasis; HGZE = high gray-level emphasis; SZLGE = small zone high gray-level emphasis; SZLGE = small zone high gray-level emphasis; LZLGE = large zone low gray-level emphasis; LZHGE = large zone high gray-level emphasis; GLV = gray-level variance; ZSV = zone-size variance; SRE = short run emphasis; LRE = long run emphasis; HGE = high gray-level run emphasis; SRLGE = short run high gray-level emphasis; LRLGE = long run low gray-level emphasis; LRLGE = short run high gray-level emphasis; RLGE = short pay-level emphasis; RLGE = long run high gray-level emphasis; RLHGE = long run high gray-level emphasis; RLHGE = long run high gray-level emphasis; RLV = run-length variance.  
\end{table} 
Table 3: Handcrafted features of the regions of interest extracted from PET and CT images.
###### Abstract  
The laser nitriding was performed in nitrogen gas at room temperature (20 \({}^{\circ}\)C) and low temperature (\(-\)190 \({}^{\circ}\)C) by a low power fiber laser to modify the wear and abrasion resistance of NiTi alloy. The surface roughness and element composition were analyzed by roughness device and energy-dispersive X-ray spectroscopy. The results of roughness show that laser treatment can change the surface roughness due to the laser reming. The effect of laser nitriding on the microhardness, friction coefficient, and worn scars of NiTi alloy was also studied, which shows that the microhardness of the NiTi alloy increases after laser nitriding. The optical microscope and scanning electron microscope were used to characterize the surface of NiTi alloy after wear testing to observe the microstructure of worn scars. The results show that the laser nitriding with different parameters can induce a nitride layer with different thicknesses and the higher energy deposition is the key factor for the formation of the nitride layer, which can decrease the friction coefficient and reduce wear loss during the application of NiTi alloy. The improvement of wear resistance can be attributed to the hard nitriding layer.  
laser nitriding; wear resistance; NiTi alloy; roughness; microhardness +
Footnote †: journal: Applied  
laser shock peening can increase the surface microhardness and enhance the mechanical property (such as wear-resistance) of NiTi alloy [13]. In our previous research, we also got similar results when the metals, such as aluminum, high strength, and NiTi alloy, were processed with laser shock peening [14; 15; 16]. In this research, the laser nitriding process with fiber laser was used to modify the surface mechanical property of NiTi alloy to enhance its wear and abrasion resistance. The laser beam irradiates on the surface of the substrate in a chamber filled with nitrogen gas, which would heat the metal surface to the temperature of the melting point. The above interaction can induce nitrogen ions and atoms to be bound on the surface and help the formation of the TiN layer [17]. [PERSON] produced a TiN layer on the surface of the Ti6 Al4V alloy with a high power direct diode laser [18] and found that the microhardness is up to 2400 HV\({}_{0.2}\) on top of the Ti6 Al4V alloy. [PERSON] et al. used laser nitriding to process a pure titanium sample that can improve its biocompatibility and corrosion resistance [19]. In this work, the NiTi alloy would be processed with low power fiber laser in nitrogen gas at different temperatures (room temperature (RT) about 20 \({}^{\circ}\)C and low temperature (LT) about \(-\)190 \({}^{\circ}\)C) to produce a TiN layer. The effect of laser nitriding on the surface roughness, microhardness and wear resistance of NiTi alloy is also investigated before and after the treatment.
## 1 Background
Laser creates biological modification not only through direct radiation effects but also through thermal effects [22]. A laser can be used in medicine as a mode of therapy. The laser permits surgeons to carry out their work with high precision by targeting a small area, minimizing damage to the surrounding tissue [17]. Patients who undergo laser therapy experience less pain, scarring, and swelling than in traditional surgery. Laser therapy is used to shrink and destroy tumors or precancerous growths, eliminate kidney stones, relieve cancer symptoms, repair detached retinas, treat hair loss due to aging or alopecia, and improve vision [23]. The used laser is a semiconductor-diode laser, which emits light near or in the red visible region after the electric current passes through. The emission happens at the p-n junction [17]. Red laser light was found to have a typical penetration depth of nearly 1 mm (a depth that reduces the incident energy to 37%), while infrared laser light has a penetration depth of 3 mm [24]. Since irradiation can result in an increase in the antimicrobial bioactivity of cyanobacterial extracts, as found by [PERSON] [15] using UV and gamma radiation, the laser irradiation effect on cyanobacterial extracts should be tested as well. The literature on the topic is scare; the only available report is the use of laser on cyanobacterial biomass to isolate lipids [25]. Therefore, the objectives of the present study were the selection of conditions and parameters that result in the enhancement of antimicrobial activity of the cyanobacterial extracts by laser treatment. In order to reach our goals, we first had to identify the most appropriate time for laser treatment. Thus, aqueous extracts both directly exposed to laser and those derived from pre-exposed biomass were tested for their antimicrobial activity to select the most appropriate laser treatment condition. Moreover, laser treatment is affected by the exposure time and distance from the source. Therefore, those parameters were tested to select the ones that resulted in the highest antimicrobial bioactivity of cyanobacterial extracts.
# An IoT-Based Glucose Monitoring Algorithm to Prevent Diabetes Complications
## 2 Related Work
The integration of an IoT architecture and noninvasive sensors allows for portability and data accessibility. An example of this interaction is an infrared LED that bases its operation on measuring the concentration of glucose in the blood depending on the intensity of the received light. Due to the LED operation and configuration, its connection to the architecture is very simple [22]. Implantable glucose biosensors for long-term use have been studied with success [23]. These kinds of sensors represent a key element for reliable readings in monitoring systems. They have shown good results as compared with the finger-picking readings. The impact of a real-time prognosis algorithm has also been analyzed in recent proposals [24]. In the case of wireless technologies, limitations persist for both personal use and short-range technologies, especially when they are handled by older persons. The importance of IoT technologies has been proven in recent studies, for example, in [25], authors developed an architecture based on a Raspberry Pi, and they analyzed the sensor's accuracy and the effectiveness of the transmission. Another example, in [26] proposed a glucose data classification to validate the architecture using data classification techniques for a correct diagnosis bythe prediction system. The authors focused on appropriate transmission through SMS messages and the evaluation of the techniques used. In most cases, the drawbacks of traditional monitoring can be solved with this technology [27]. It is possible to use complex algorithms to perform the prognosis and to store data in the cloud. They are also a good option for closer tracking of many patients, reducing the charge for doctors and caregivers. However, policies and regulations are necessary for privacy and security issues [28]. A study using health information technologies on glycemic control with type 2 diabetes was considered in [29] reporting better control both clinically and statistically. A reduction of HbA1c was observed in all the interventions presented. According to the study, every 1% decrease in HbA1c over a ten-year period was associated with a risk reduction of 21% for diabetes-related death and 37% of microvascular complications. However, long-term studies should be carried out. These findings are a great incentive for future developments. With respect to systems that monitor older adults, an IoT system for heart rate, body temperature, and blood oxygenation was presented in [30]. Although glucose was not considered in the monitoring, the importance of the IoT system and the mobile application were emphasized. Commercial products such as Medtronic Guardian Connect Smart (CGM) also exist that claim to use a sensor based on interstitial fluid. The system transmits the glucose values wirelessly to a smart device to prevent hypoglycemia and hyperglycemia [31].
# Planning of High-Power Charging Stations for Electric Vehicles: A Review
## 2 Target Identification
### Charging Technologies
#### 2.3.2 Battery Swapping  
The Battery Swapping Station (BSS) is designed to provide the customer with a charged battery. It was initially adopted to supply public transport [85] and is now only implemented in Asian countries [76]. Large-scale implementation of battery swapping presents several challenges, such as the absence of a standard for battery interfaces across EV manufacturers and user acceptance [86]. In the current mobility system dominated by passenger vehicles, these obstacles can hinder the use of this technology for most EVs. Moreover, the cost of setting up a BSS is up to 10 times higher compared to a FCS. Therefore, the range of spatial distribution is decreased for the same budget [87]. The use of battery swapping can be better suited for vehicle fleets: in [88], long-term planning for electric taxis shows that BSSs are economically better than plug-in charging. Nevertheless, the fluid operation of an EV fleet requires a large stock of batteries, which means investors face large, upfront costs in the first phase of implementation. A useful cost-benefit comparison is provided in [52] on the allocation of both fast plug-in and BSSs in Shenzhen, China. A Pareto analysis shows a higher presence of plug-in stations if short-term economic benefits are prioritized, whereas implementation of BSSs results in a higher quality of the service provided to the final user. In addition, the load generated by plug-in technology can result in more severe peaks than battery swapping, as shown in [89]. Furthermore, BSSs can decrease the battery degradation through a slow charging process. Allocation and sizing of BSSs are also modeled in [25], where each station can be assigned no more than eight chargers that can operate simultaneously, each one with 5 kW of rated power. Study [90] compares different technologies in a long-term plan in Daegu, South Korea. The authors analyze plug-in, inductive, and battery swapping technology, and conclude that battery swapping is economically more beneficial for single and composite routes.