esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Material and Methods
### EMS Treatment, Determination of Cell Survival, and Screening for Mutants  
For EMS mutagenesis, _Euglena_ cells in the early-exponential growth phase (3.17 \(\times\) 10\({}^{5}\) cells mL\({}^{-1}\)) in a 50 mL T-flask were exposed to 0.008, 0.012, 0.016, 0.024, 0.032, and 0.040 M EMS (Sigma-Aldrich), and then observed under a microscope at 24 h intervals for 5 days. The percentage of motile of cells was scored from \"\(-\)\" (0%) to \"++++\" (100%), based on these daily observations. The survival rate of cells was determined by spreading a 10 \(\mu\)L sample onto an YM agar plate, using the same light and temperature conditions as above. After 2 weeks, the number of colonies was calculated, and cell viability was expressed relative to a control group (no EMS). A concentration of EMS that yielded a survival rate below 5% was considered suitable for mutagenesis and screening [18].  
Then, colonies that were lighter green than the WT were selected, transferred into a 50 mL T-flask, and cultured in a modified YM liquid medium. During the screening process, the standard YM agar and a modified YM agar (which contained half the amount of agar powder) were prepared to characterize cell movement. Colonies that were colorless were screened on the modified YM agar, transferred into a 50 mL T-flask, and cultured in the modified YM liquid medium for determination of the growth rate.  
Under the selected conditions, a living colony from the first EMS treatment that had no phenotypic differences from the WT was chosen and re-cultivated for a second EMS treatment. This serial EMS treatment was repeated until a colony exhibited the desired trait: decreased pigmentation (subsequently named _Euglena_ Mutant 335) and increased motility (subsequently named _Euglena_ Mutant 333).
## 1 Introduction
In addition, the elevations of the control stations located in the two regions were also determined by sea-crossing leveling and compared to elevations from GNSS-derived and spirit leveling to analyze the possibility of replacing sea-crossing leveling with GNSS-derived height determination.
## 1 Introduction  
In the Global Positioning System (GPS) receiver, the lock detector is usually used to indicate the signal tracking status by comparing with a threshold. Code, frequency and phase are tracked by delay, frequency, and phase locked loops, respectively. These loops have their own indicators [1; 2]. The GPS receiver mainly relies on phase locked loop (PLL) and delay locked loop (DLL) to track signal and frequency locked loop (FLL) is a transition part to bridge acquisition and PLL + DLL. However, in high performance GPS receivers, such as high dynamic and high sensitivity receivers, the FLL has been widely used when the carrier phase could not be tracked by PLL or aids PLL to track signal [3; 4]. It indicates the significance of analyzing frequency-locked detector. For example, a detailed discussion and comparison of PLL and FLL can be found in [5]. [PERSON] and [PERSON] [6] proposed a non-linear carrier NCO unit to track carrier precisely by selecting interpolating filter orders, which is derived from the frequency discriminator in high dynamic situation. [PERSON] et al. [7] discussed the design and steady-state performance of one first-order, two second-order FLL loop filters, and four carrier frequency discriminators, then a new FLL design under weak signal conditions was proposed. Meanwhile, FLLs have some distinct advantages over PLL counterparts [8]. By neglecting absolute phase error and permitting relative phase rotation of the received signal and the local carrier replica, an FLL can, typically, acquire and track signals that are at higher frequency offsets than a PLL. It is easy to understand that FLL plays a key role in facilitating reliable signal tracking. The frequency-locked detector (FLD) is used to indicate FLL signal tracking state. [PERSON] [9] presented several AFC loops and performance for different signal. [PERSON] [10] has described two simply implemented frequency detectors to aid PLL timing and carrier acquisition, even with very small loop bandwidths and large initial frequency offsets. [PERSON] and [PERSON] [11] proposed an FLD based on square law, which experiences less degradation due to phase jitter than the absolute value detector. [PERSON] and [PERSON] [12] suggested a family of PLDs for M-PSK receivers operating in additive white Gaussian noise channels and derived the statistical properties of PLD. [PERSON] et al. [13] discussed Frequency detector for fast frequency lock of digital PLDs, which provides frequency difference information at each reference cycle to guarantee fast frequency acquisition. Previous researches mainly focus on the structure of FLL and its detector, but how to set the FLL detector threshold is seldom discussed, which make it hard to indicate the tracking frequency is stabilized or not. Our main purpose is to analyze the statistical performance of FLD by mean-time-to-lose-lock approaches, and make comparison with PLD, which has been studied by [PERSON] et al. [14].
## 5 Results Discussion
### Effect of Nanofluid Loading  
Figures 13-15 characterize the influence of the solid volume fraction. A growth of \(\phi\) characterizes a weak modification of streamlines and isotherms, while isoconcentrations are modified significantly. A solid volume fraction increment characterizes an increase in the entropy plume adjacent to the top of the cavity. Other changes are insignificant. Figures relating to the local Nusselt number and total entropy versus \(\phi\) are shown in Figures 14 and 15 at Ra = \(10^{6}\) and Da = 0.01, and different \(\varepsilon_{p}\). A solid volume fraction reflects the  
Figure 11: Total entropy as a function of Rayleigh number (Ra) at various values of solid volume fraction (\(\phi\)). Ha = 0, Da = 0.01 and \(\varepsilon_{p}\) = 0.4.  
Figure 12: Total entropy as a function of Rayleigh number (Ra) at various values of Darcy number (Da). Ha = 0, \(\phi\) = 0.02 and \(\varepsilon_{p}\) = 0.4.  
temperature gradient increase along the heater zone. It should be noted that the negative values about Nu correspond to the upper part of the cylinder where the cylinder is heated from the hot temperature wave, while the positive magnitudes correspond to the bottom part of the cylinder where the cylinder heats the surrounding liquid.  
Figure 14: Average Nusselt number (Nu\({}_{\text{ave}}\)) as a function of solid volume fraction (\(\phi\)) at various values of porosity \(\varepsilon_{p}\). Ra = 10\({}^{6}\), Ha = 0, and Da = 0.01.  
Figure 13: Variations of the streamlines (**left**), isotherms (**middle**), and entropy generation (**right**) with various solid volume fraction (\(\phi\)) at Ra = 10\({}^{6}\), Ha = 50, \(\varepsilon\) = 0.4, and Da = 0.01.  
Figure 14 reveals the role of increasing \(\phi\) on heat transfer, and thus the increase in entropy, shown in Figure 15. On the contrary, the impact of varying \(\varepsilon_{p}\) is insignificant (themaximum is less than 3% for Nu and 11% for entropy, both are occurring at the highest \(\phi\)).
## 5 Buck-Boost Converter
### Determination of Generator-Supplied Current  
In Figure 10, the mean current supplied by the generator within a given time period \(T\) (see Figure 2 and (11)) is:  
\[I_{g}=\frac{1}{T}\int\limits_{0}^{T_{ON}}i_{g}\ dt=\frac{1}{T}\int\limits_{0}^{T_{ON }}i_{L}\ dt. \tag{95}\]  
Next we will proceed like in the previous sections.  
\(\mathbf{0}\leq\mathbf{t}\leq\mathbf{T_{ON}}\)  
In this time interval the diode branch is disconnected. Figure 11 shows the equivalent circuit.  
Figure 11: Equivalent buck-boost converter circuit for \(0\leq t\leq T_{ON}\).  
Figure 10: Buck-boost converter circuit with main non-idealities (losses).  
Reasoning in Figure 11 for \(V_{g}\) as was done from (13) to (14):  
\[I_{g}=\frac{1}{T}\int\limits_{0}^{T_{ON}}i_{L}\ dt=\frac{V_{g}}{r+R_{S}}\delta- \frac{f\cdot L}{r+R_{S}}\Delta I. \tag{96}\]  
Equation (96) is analogous to (14), wherewith and as might be expected, (97) is also (19):  
\[I_{1}=\frac{V_{g}}{r+R_{S}}(1-e^{\gamma_{1}})+I_{2}\,e^{\gamma_{1}}. \tag{97}\]  
\(\mathbf{T_{ON}}\leq\mathbf{t}\leq\mathbf{T_{X}}\)  
Now the generator branch is disconnected. Figure 12 shows the equivalent circuit.  
Reasoning on Figure 12 like from (20) to (21),  
\[\frac{1}{T}\int\limits_{T_{ON}}^{T_{X}}i_{L}\ dt=\frac{V_{o}-V_{\gamma}}{r+R_{ D}}\delta_{f}+\frac{f\cdot L}{r+R_{D}}\Delta I. \tag{98}\]  
Now, operating like from (22) to (26),  
\[I_{2}=\frac{V_{0}-V_{\gamma}}{r+R_{D}}(1-e^{\gamma_{2}})+I_{1}\,e^{\gamma_{2}}. \tag{99}\]  
As we did in (27), It is now suggests the equation system comprising (97), (99) and (6):  
\[I_{1}=\frac{V_{g}}{r+R_{S}}(1-e^{\gamma_{1}})+I_{2}\,e^{\gamma_{1}}. \tag{100a}\] \[I_{2}=\frac{V_{0}-V_{\gamma}}{r+R_{D}}(1-e^{\gamma_{2}})+I_{1}\,e^{\gamma_{2}}.\] (100b) \[\Delta I=I_{2}-I_{1}. \tag{100c}\]  
By solving (100) in the same way that from (27) to (31):  
\[\Delta I=\beta\bigg{(}\frac{V_{0}-V_{\gamma}}{r+R_{D}}-\frac{V_{g}}{r+R_{S}} \bigg{)}, \tag{101}\]  
with \(\beta\) given in (32).  
Figure 12: Equivalent buck-boost converter circuit for \(T_{ON}\leq t\leq T_{X}\).  
Now we can calculate the current supplied by the generator. So substituting (101) into (96),  
\[I_{g}=\frac{V_{g}}{r+R_{S}}\delta-\frac{f\cdot L}{r+R_{S}}\beta\bigg{(}\frac{V_{0 }-V_{\gamma}}{r+R_{D}}-\frac{V_{g}}{r+R_{S}}\bigg{)}. \tag{102}\]  
Taking into account \(k_{1}\) and \(k_{2}\) values from (18) and (25) respectively,  
\[I_{g}=\frac{V_{g}}{r+R_{S}}(\delta+k_{1}\ \beta)-\frac{V_{0}}{r+R_{S}}k_{2}\ \beta+\frac{V_{\gamma}}{r+R_{S}}k_{2}\ \beta \tag{103}\]
## 4 Evaluation  
### Dataset  
For evaluation of the proposed method, real traffic data were used for the analysis [30]. The traffic flow on a highway in Germany was recorded by a drone. The data were gathered at six locations, and the time series data for the sets of 110 and 500 vehicles were included. The position, speed, acceleration, size, and other parameters of each vehicle were described. A 4K camera was implemented within the drone, and the measurement accuracy was approximately 10 cm. The measurement rate was 25 Hz. The highway at location 1 has two lanes per direction, and the other locations have three lanes per direction. To validate the robustness of the proposed method with respect to driving conditions, the performance was evaluated using the data from all locations. For the evaluation, 5917 lane-keeping vehicles and 1010 lane-changing vehicles were considered.
# How 5G Wireless (and Concomitant Technologies) Will Revolutionize Healthcare?
## 3 How Technologies Can Fix the Current Healthcare System
### Disruptive Health Innovations
#### 3.5.2 Wearable Sensors to Wireless Charging Implants  
Recent advancements in sensing technologies have made great progress in monitoring patients' health in an unobtrusive manner. The wearable devices are widely being used to measure various physiological signs and physical activities. Recent advances in microelectronics and nanofabrication have shifted the trend from wearable sensors to implants, which transmit health-related information from inside the body. These implantable sensors have enhanced capabilities to capture vital health-related signs along with critically accelerated detection of failing implants, thereby minimizing healthcare hazard [34]. SWIPT has the potential to ease sterilization and reduce procedure and maintenance time by increasing reliability for these implants. Now, it is not too distant in the future when a patient having severe memory loss due to injury or Alzheimer's stroke will be able to create long-term memories with the help from an electronic implant [35].
## 2 Tower Capacity
### Limit Capacity
#### 2.2.1 Capacity Surface  
According to Equations (4)-(6), the wind load on a transmission tower can be decomposed into three components, the transverse wind load acting on the tower (i.e., \(H_{t}\)), the longitudinal wind load acting on the tower (i.e., \(H_{l}\)) and the wind load transferred from transmission wires (i.e., \(H_{w}\)). Taking the three components as the principal axes, the limit capacity of the tower under winds can be described by the surface shown in Figure 2a. Correspondingly, the limit capacity of the tower can be expressed by a function in the form of Equation (7). Connecting the origin to the capacity point on the surface, we get the vector \(\mathbf{H}_{\mathbf{C}}\), whose direction and magnitude represent one load combination/ratio of three components and the load-carrying capacity of the tower under the combination, respectively. It is noteworthy that the three wind load components are not fully independent and they are influenced by some mutual parameters. In the real case, the capacity surface is usually not a complete surface, as Figure 2a shows, but a partial region. If the information of the tower structure and the line is given, there are three independent parameters determining the wind loading to the tower, i.e., the wind speed \(U\), the wind angle of attack \(\varphi\) (here, we ignore the correlation between the wind speed and wind direction), and the horizontal span of the line \(L_{h}\). Therefore, the limit capacity of the tower can also be described by the capacity surface with the three independent parameters as the principal axes (Figure 2b) and the corresponding limit capacity function is written as Equation (8). One point on the surface means the limit wind speed that the tower can sustain under the certain wind angle of attack and horizontal span. Here, the two capacity surfaces are named surface \(\theta_{H}\), which is expressed by three wind load components, and the surface \(\theta_{L}\), which is expressed by three independent external parameters. It is noteworthy that as the principal axes of the surface \(\theta_{L}\) are independent of each other, the region for the surface is explicit and determined by the ranges of \(U\), \(\varphi\), and \(L_{h}\).  
\[f_{1}(H_{t},H_{l},H_{w})=0 \tag{7}\] \[f_{2}(U,\varphi,L_{h})=0 \tag{8}\]
## 2 Deployment Sites and Existing Infrastructure  
### Deployment Site  
The area of the experiment is the Ligurian Sea, a semi-enclosed basin in the North-West Mediterranean Sea (Figure 1). The basin is characterized by a complex orography, since it is surrounded on the North by Liguria region with steep mountains close to the coasts and on the South by the Corsica Island. These constraints render the basin an ideal site for studying air-sea interactions [15] and dense water formation, that occur especially in winter [16]. The area is also characterized by cyclogenesis and self-regenerating thunderstorms which mainly cause the frequently occurred floods on the coasts of Liguria and Tuscany Regions in the recent years [17; 18].  
Figure 1: The surface buoy of the W1-M3A observing system and the map of the Ligurian basin. The square at the center of the basin marks the position of the W1-M3A observing system, the circle shows the operational range of the weather radars in the Liguria region used in the analysis.
## 2 Experiment and Device Fabrication  
The fabricated device comprises a bottom gate, dielectric layer, active layer, and Source/Drian (S/D) electrode. Three samples with various In\({}_{2}\)O\({}_{3}\) target power values, namely, samples A, B, and C with 40, 50, and 60 W, respectively, were used in this study. First, heavily doped p-type silicon was cleaned with acetone, methanol, and water using an ultrasonic cleaner for 10 min in order to clean grease and particles from the glass. The p\({}^{++}\) silicon wafers served as a bottom gate and used for growing a 300 nm thickness of SiO\({}_{2}\) dielectric layer. After that, the active layer deposition 30 nm thick MgInO thin film used MgO and In\({}_{2}\)O\({}_{3}\) targets by the cosputtering method with an interdigitated shadow mask. MgO target power was fixed at 100 W; the In\({}_{2}\)O\({}_{3}\) target power was 40, 50, and 60 W for samples A, B, and C, respectively. Before opening the shutters, the targets were presputtered for 5 min to remove the impurities in the target surface. The sputtering time was carefully controlled to ensure the thin film thicknesses were equal, and that precision of sputtering power was maintained \(\pm\)1 W. The working pressure was 0.8 Pa at an argon flow of 3 sccm at room temperature. Samples were then annealed at 300 \({}^{\circ}\)C for 1 h in argon ambient. Finally, thermal evaporation with an interdigitated shadow mask was performed using a 100 nm source and drain electrode. The channel width and length were 1000 and 100 \(\upmu\)m, respectively. The photo and electric properties were measured in the dark region using Agilent B1500A and 150 W Xenon lamps (Agilent Technologies, Santa Clara, CA, USA), respectively. Glancing incidence angle X-ray diffractometry (GIAXRD) (XRD, D8 Discover, Bruker, Billerica, MA, USA) and X-ray photoelectron spectroscopy (XPS) (PHI 5000 VersaProbe, ULVAC, Chigasaki-shi, Japan) were applied to assess the crystal structure and determine the concentration of oxygen vacancy, respectively.
# A Systematic Review of Computer Vision and AI in Parking Space Allocation in a Seaport
## 6 Discussions
### Start-Ups in the Field of Smart Parking Solutions at the Seaport  
Several start-ups have developed in the sector of smart parking solutions in recent years, offering novel products and services to increase the efficiency and efficacy of seaport parking [54]. 'CUROPark', an Indian start-up, provides a variety of smart parking options. Their CUROPark Harbour system is particularly developed for seaports, and it assigns parking places based on the kind of container. The system employs several sensors, and the data is analyzed using AI to give real-time parking spot assessments. Parking heavy-duty trucks in seaports is becoming easier and more efficient thanks to 'CUROPark'.  
In a similar vein, a start-up has launched a test project at the Port of Barcelona, where AI is used to identify wagons and containers. This concept requires more development and assessment, but it has the potential to improve the efficiency of parking at seaports.  
Several start-ups have developed in recent years to provide smart parking solutions for seaports. These techniques aim to enhance overall harbor operations, reduce congestion, and boost parking allocation efficiency. Seaports can use \"Parknav\", for instance, which offers an AI-powered parking navigation system. Their system uses machine learning to forecast parking demand, identify free spaces, and direct vehicles to them. Additionally, the system provides real-time information on occupancy rates and parking availability, which may help seaport management make better choices and optimize parking allocation. Another firm, \"ParkPow\", uses artificial intelligence and computer vision to enable automated parking and reduce the amount of time and effort required for drivers to find parking spaces. Their system uses cameras and sensors to find vacant parking spaces and direct vehicles to them.  
Furthermore, smart parking solutions are not restricted to startups. Smart parking systems for seaports have also been developed by established firms such as Siemens and Cisco. Siemens has created a \"Smart Parking System\" that detects unoccupied parking places and guides cars to available spaces using sensor data. In addition, the technology gives real-time reports on parking availability and occupancy rates, which can assist harbor management in making educated decisions. Cisco has created a \"Smart Parking Management\" solution that monitors parking behavior and detects open parking spaces using cameras and sensors. The technology also offers real-time parking occupancy data, which can assist seaport administrators in optimizing parking allocation and reducing congestion.  
Overall, the rise of both start-ups and established firms in the market for smart parking solutions for seaports demonstrates the potential for additional innovation and growth in this industry. Further studies can investigate the efficacy of these methods in other seaport situations and scenarios.
# Remote Sensing Road Extraction by Road Segmentation Network
###### Abstract  
Road extraction from remote sensing images has attracted much attention in geospatial applications. However, the existing methods do not accurately identify the connectivity of the road. The identification of the road pixels may be interfered with by the abundant ground such as buildings, trees, and shadows. The objective of this paper is to enhance context and strip features of the road by designing UNet-like architecture. The overall method first enhances the context characteristics in the segmentation step and then maintains the stripe characteristics in a refinement step. The segmentation step exploits an attention mechanism to enhance the context information between the adjacent layers. To obtain the strip features of the road, the refinement step introduces the strip pooling in a refinement network to restore the long distance dependent information of the road. Extensive comparative experiments demonstrate that the proposed method outperforms other methods, achieving an overall accuracy of 98.25% on the DeepGlobe dataset, and 97.68% on the Massachusetts dataset.  
convolutional neural networks; semantic segmentation; self-attention mechanism +
Footnote †: journal: Applied Sciences
# Towards a Low-Cost Precision Viticulture System Using Internet of Things Devices
## 6 Experimental Results
### Location Accuracy  
In this experiment, the accuracy of the drone to locate a node in the network is examined. Experiments were conducted for all the 10 wireless monitoring nodes. The results are shown in Figure 14. The maximum value of the error was 1.3 m. This error was due to the behavior of the drone during different environmental conditions. According to the experimental results, factors such as the wind or the residual energy in the drone will affect the accuracy of the GPS, the flying stability of the drone and consequently the picture quality. In order to minimize these problems, better drones are available in the market. However, that might increase the cost of the system significantly.  
Figure 12: Components of the proposed system.