esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study on the Movement Characteristics of Fuel in the Fuel Tank during the Maneuvering Process
## 3 Results and Analysis
### Analysis of Fuel Impact Force  
Figure 6 shows the changes in fuel impact pressure monitored by the sensors during the aircraft maneuvering process. It can be seen that the pressure values at the positions of sensors 1, 3, 4, 5, and 6 are relatively stable, indicating that, although there is fluctuation in the liquid level at these positions, the change in impact force is not significant. The pressure oscillation situation monitored by sensors 2, 7, and 8 is more complex. The three pressure curves monitored by sensors 2, 7, and 8 all have multiple peaks, which further indicates the existence of complex free surface phenomena at positions 2, 7, and 8. Although the times when these peaks occur are different, the overall trend is that they gradually decrease with damping.  
Figure 7 presents the fuel pressure field at a typical moment during aircraft maneuvering. At the onset of the maneuver, the aircraft experiences rapid yaw acceleration, causing the fuel to shift towards the rear of the aircraft. This movement creates a phenomenon known as \"water leap\" or \"traveling wave\", which generates impact pressure on the internal baffles and fuel tank wall panels. The presence of internal crossbeams and longitudinal walls increases the damping effect on fuel motion, impeding large-scale fuel displacement. Furthermore, the connecting holes on the baffles introduce flow resistance, dissipating the kinetic energy induced by acceleration and reducing the impact force exerted by the fuel on the fuel tank wall panels during motion. In localized areas of high pressure, such as the circular region indicating the intersection of the rear crossbeam and upper wall panel, the maximum fuel pressure reaches approximately 2.3 \(\times\) 10\({}^{5}\) Pa. At \(t\) = 1.5 s, the aircraft acceleration reaches its peak, resulting in the highest impact pressure observed, primarily concentrated on the rear wall panel and the right-side panel of the fuel tank. Subsequently, as the yaw acceleration of the aircraft gradually decreases, the fuel surges towards the flight direction due to inertia, leading to a more uniform stress distribution on the fuel tank wall panels.  
Figure 6: Shock pressure change curve monitored by sensors. (**a**) Sensor 1–4 data; (**b**) Sensor 5–8 data.
## 2 UAV in Wireless Communication Networks
### Use Cases of UAV Wireless Networks  
The major three use cases of UAV in wireless communication networks have been considered as follows: (1) UAV base station (BS) to provide a wireless service within a certain target area [21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41], (2) UAV relay station (RS) to set up wireless links to distant users with no direct connectivity from base station or command center [42; 43; 44; 45; 46; 47], and (3) UAV aggregator to disseminate an information to or collect a data from the distributed sensors or devices [48; 49; 50; 51; 52; 53; 54; 55; 56; 57], as shown in Figure 1. The first two use cases utilize a static UAV as aerial BS and RS, but a mobile UAV for a moving aggregator, in general.  
Figure 1: UAV Use Cases. (**a**) UAV base station: Multiple UAVs can cover the emergency or disaster area as aerial BSs where no terrestrial BS is available. A terrestrial MBS or a satellite can provide wireless backhaul links to UAV BSs, and UAV BSs can adjust their positions for performance maximization of networks utilizing UAV-to-UAV links. Note that the coverage area of aerial UAV BSs varies with the altitude of UAVs. (**b**) UAV relay station: UAV RS can extend a terrestrial BS coverage by utilizing a strong air-to-ground LoS link. Scenario 1 considers UE\({}_{2}\), who cannot be served by BS\({}_{2}\) due to natural disaster or destroyed infrastructure. The distant BS\({}_{1}\) cannot provide a reliable communication link to UE\({}_{2}\) due to severe pathloss attenuations. UE\({}_{3}\) in Scenario 2 cannot have a direct link from BS\({}_{1}\) due to severe blockage. For both scenarios, UAV RS can provide a reliable communication link to UE\({}_{2}\) or UE\({}_{3}\), where it receives a signal from BS\({}_{1}\) through a wireless backhaul link, and then forwards the received signal to UE\({}_{2}\) or UE\({}_{3}\) via a strong air-to-ground LoS access link. (**c**) UAV aggregator: UAV aggregator flies over sensors or IoT devices to collect data from or disseminate information to them. The mobility of UAV and delay-tolerant transmission (i.e., store-carry-and-forward transmission) can save the transmit power at sensor/IoT devices. A reliable data delivery from sensor/IoT devices to core network is guaranteed, and vice versa.
## 3 Results and Discussion
### Kinetics of the Acid Hydrolysis and Auto-Hydrolysis Step
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Compound (mg/L)** & \multicolumn{5}{c}{**Time of Hydrolysis (min)**} \\ \cline{2-7}  & **0** & **10** & **20** & **30** & **45** & **180** \\ \hline
5-HMF Acid hydrolysis & 67.73 \(\pm\) 11.68 & 90.14 \(\pm\) 2.49 & 97.51 \(\pm\) 2.98 & 83.28 \(\pm\) 3.43 & 58.91 \(\pm\) 4.22 & - \\ Auto-hydrolysis & nd & 18.77 \(\pm\) 3.37 & - & - & - & 80.07 \(\pm\) 5.86 \\ \hline Furutral Acid hydrolysis & 155.65 \(\pm\) 5.34 & 388.55 \(\pm\) 21.97 & 458.42 \(\pm\) 32.38 & 785.49 \(\pm\) 7.73 & 785.44 \(\pm\) 53.49 & - \\ Auto-hydrolysis & 6.37 \(\pm\) 0.02 & 275.40 \(\pm\) 15.09 & - & - & - & 828.48 \(\pm\) 140.30 \\ \hline
5-HMF Acid hydrolysis & 0.79 \(\pm\) 0.14 & 1.22 \(\pm\) 0.06 & 2.15 \(\pm\) 0.06 & 3.68 \(\pm\) 0.16 & 3.23 \(\pm\) 0.01 & - \\ Auto-hydrolysis & nd. & 9.67 \(\pm\) 0.14 & - & - & - & 17.36 \(\pm\) 2.04 \\ \hline Vanillin Acid hydrolysis & 2.98 \(\pm\) 0.38 & 4.26 \(\pm\) 0.26 & 4.72 \(\pm\) 0.27 & 5.66 \(\pm\) 0.26 & 5.51 \(\pm\) 0.04 & - \\ Auto-hydrolysis & 5.31 \(\pm\) 0.18 & 17.74 \(\pm\) 0.15 & - & - & - & 26.43 \(\pm\) 1.73 \\ \hline Springaldehyde Acid hydrolysis & 18.78 \(\pm\) 1.45 & 9.64 \(\pm\) 0.54 & 9.61 \(\pm\) 0.27 & 7.32 \(\pm\) 0.34 & 6.39 \(\pm\) 0.17 & - \\ Auto-hydrolysis & 3.42 \(\pm\) 0.42 & 15.33 \(\pm\) 1.27 & - & - & - & 20.44 \(\pm\) 2.59 \\ \hline p-cumamic acid acid hydrolysis & 8.83 \(\pm\) 0.92 & 2.97 \(\pm\) 0.30 & 3.22 \(\pm\) 0.48 & 1.67 \(\pm\) 0.20 & 1.19 \(\pm\) 0.15 & - \\ Auto-hydrolysis & nd. & nd. & - & - & - & 5.94 \(\pm\) 0.80 \\ \hline Ferulic acid Acid hydrolysis & 58.44 \(\pm\) 4.26 & 26.27 \(\pm\) 2.52 & 23.97 \(\pm\) 3.12 & 9.55 \(\pm\) 0.50 & 6.38 \(\pm\) 0.74 & - \\ Auto-hydrolysis & 604.40 \(\pm\) 5.04 & 1064.71 \(\pm\) 148.47 & - & - & - & 1264.32 \(\pm\) 244.39 \\ \hline Acetic acid Acid hydrolysis & 1827.51 \(\pm\) 225.99 & 2498.57 \(\pm\) 195.67 & 2561.61 \(\pm\) 322.03 & 2440.16 \(\pm\) 98.33 & 2333.79 \(\pm\) 267.20 & - \\ Auto-hydrolysis & 1495.80 \(\pm\) 12.95 & 3114.70 \(\pm\) 23.70 & - & - & - & 3081.95 \(\pm\) 670.20 \\ \hline Formic acid hydrolysis & n.d. & n.d. & n.d. & 2029.26 \(\pm\) 221.39 & 2969.06 \(\pm\) 156.66 & - \\ Auto-hydrolysis & n.d. & n.d. & - & - & - & 80.07 \(\pm\) 5.86 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Phenolic profile and sugar degradation compounds content of auto-hydrolysis and acid hydrolysis liquors of wheat straw at different hydrolysis times. HMF: hydroxyl-methyl-furral; MF: methylf fururl. Values are reported as mean values (\(n=3\)) \(\pm\) sd.
# The Exploitation of Lysosomes in Cancer Therapy with Graphene-Based Nanomaterials
## 2 Lysosomes in Cancer  
Lysosomes are membrane-enclosed acidic organelles that contain over 60 hydrolytic enzymes, including proteases, nucleases, lipases, glycosidases, phospholipases, and phosphatases. These degradative enzymes are mainly active at the low pH (4.8) of the lysosomal lumen, which is maintained by the activity of the lysosomal proton pump V-ATPase. The main function of lysosomes is the degradation of extracellular components consumed by endocytosis, phagocytosis, or intracellular contents obtained by autophagy [18]. Lysosomes are essential for tumor growth, invasion, and metastasis. As part of the autophagy machinery, lysosomes support tumor growth by removing damaged organelles and aggregated proteins accumulated during uncontrolled cell proliferation. Autophagy also provides substrates for energy metabolism and building blocks for the de novo synthesis of macromolecules. Lysosomes promote tumor cell invasion and metastasis by secreting cathepsin and metalloproteinases that degrade the extracellular matrix. Additionally, the fusion of lysosomes with the plasma membrane allows V-ATPase to pump protons from lysosomes into the extracellular space, further promoting cancer invasion. Lastly, lysosomes also capture and exocytose hydrophobic and weakly basic chemotherapeutics, leading to drug resistance [3; 18].  
It is not surprising that cancer cells exhibit an increase in the number, volume, and enzyme content of lysosomes. Additionally, cancer cell lysosomes are more fragile compared to normal cells, due to their larger size and unique characteristics of the lysosomal membrane structure [18]. The membrane of cancer cell lysosomes has decreased concentrations of the stabilizing molecule lysosomal-associated membrane protein (LAMP) and increased concentrations of the lysosome-destabilizing lipid sphingosine. Additionally, cancer cell lysosomes are highly sensitive to oxidative stress and enriched in iron, which is involved in the generation of highly toxic hydroxyl radicals (OH\(\bullet\)). Consequently, cancer cell lysosomes are highly susceptible to LMP, which can lead to LCD. The enlarged and unstable lysosomes of cancer cells provide a selective advantage for tumor growth but can also represent a vulnerability that can be targeted for cancer therapy [3; 18].
## 5 Simulation Results
### Simulation Results of the Proposed Converter  
Figure 10 gives waveforms of the simulated input voltage \(V_{in}\), output voltage \(V_{O}\) and the duty cycle \(D\) signal at a full load of 300 W. An input voltage of 80 V was raised to an output voltage of 400 V when \(D\) was 0.567.
# Remote Sensing for Irrigation of Horticultural Crops
## 3 Sensors and Platforms
### Remote Sensing Platforms
#### 3.2.1 Satellite
[68] produced composited maps of the difference between weekly mean soil moisture over one week, one month, and three months. The NASA report \"_Microwave Technologies Review and Strategy_\" [69] contained an overview of technologies for Earth science measurements involving passive microwave radiometry for soil monitoring. The Soil Moisture Active Passive mission goal was to produce global maps of surface soil moisture at low (36 km) and high resolution (9 km). The mission was based on L-band radiometer and radar/radiometer measurements for low and high-resolution maps, respectively. The preliminary results of the Multi-Temporal Dual Channel Algorithm (MT-DCA) for retrieving (i) soil moisture; (ii) Vegetation Optical Depth; and (iii) scattering albedo estimates, from passive microwave measurements alone and without reliance of a priori information were reported. At L-band, the Vegetation Optical Depth was related to total vegetation water content and albedo to structural changes. The analysis of these two parameters, at different temporal and spatial scales, disclosed the potential of L-band microwave sensors for ecological studies on a global scale [70]. [PERSON] et al. [71] report an estimate of Soil Moisture Distributions across Small Farm Fields with ALOS/PALSAR. The ALOS (Advanced Land Observing Satellite) has an active microwave sensor; PALSAR (Phased Array L-band Synthetic Aperture Radar) has a fine resolution of 6.5 m. Because of the fine resolution, PALSAR provides the possibility of estimating soil moisture distributions in small farmlands. Making such small-scale estimates has not been possible with traditional satellite remote sensing techniques.
# Copper- and Iron-Based Nanoflowers in Cancer Theranostics
## 3 Experimental Parameters' Effect on Morphology of Nanofibwers
### Effect of Temperature  
Moderate temperatures are usually required both in the synthesis of NFs and HNFs. [PERSON] et al., investigated the effect of temperature on the final morphology of CuMoO\({}_{4}\) NFs [64]. The formation of CuMoO\({}_{4}\) NFs took place at a narrow temperature window around 35 \({}^{\circ}\)C, and at higher temperatures, underdeveloped and deformed NFs were produced, while at lower temperatures no NFs were formed. On the other hand, self-assembled COD-Cu HNFs can be obtained at a wider temperature range (4-25 \({}^{\circ}\)C), with their size and \"petal\" density increasing with temperature. However, at 37 \({}^{\circ}\)C, stacked \"petals\" rather than spherical flower-like structures were observed [104]. It should also be noted that the HNF system's functionality is not always proportional to the morphology of the petals. Because of their nature, each enzyme shows optimal activity at its own temperature.
## 5 Implementation of \(\mu\)ONOS SDN Controller  
Microgrids are changing into more complex and extensive networks in which applications, service virtualization, and edge computing are highly related to control strategies [29; 52]. Micro ONOS (\(\mu\)ONOS) is a new version of the SDN controller, developed by the Open Network Foundation (ONF [53]), which uses microservices to deploy a scalable infrastructure with excellent throughput and low latency. It is based on Docker containers deployed in a cloud-based infrastructure or local data centers. Unlike monolithic controllers that integrate multiple APIs, it comprises a few interfaces such as Google Remote Procedure Call (gRPC), gRPC Network Management Interface (gNMI) and P4 Runtime [25].  
The \(\mu\)_ONOS_ infrastructure, network functions and monitoring services only support Kubernetes applications through Helmcharts implementation [54]. To install, manage and delete the device configurations and their performance, \(\mu\)_ONOS_ configures the gNMI as an open-source data management protocol for network devices. The functionalities provided by gNMI can be modeled using YANG (Yet Another Next Generation data modeling language) [25]. The network manager interacts with the SDN controller through the gRPC interface, accessing both the onos-cli and onos-gui services for network management, network modifications and reverting changes. According to ONF [53], the onos-config service offers a gNMI endpoint for various functions, including reading states, configuring settings, and subscribing to specific features. This interface can also prevent invalid values and enable the operational state.  
The \(\mu\)_ONOS_ identification component facilitates the controller's management from external applications, as depicted in the top layer of Figure 7. A proposed middleware enables information exchange between external applications (power-sharing information from networked MG) and the SDN control plane. As shown in Figure 7, the core of the SDN controller is separated from the event handler within the middleware, allowing communication between the REST API and the controller microservices. This service, named onos-config, is responsible for linking the module's functionality to the specific microservices that are executed in a distributed manner.  
The essential microservice of Figure 7 is the block onos-config, as it manages the configuration of devices through gNMI interfaces and registers all events to send them to the Atomix driver. The Atomix driver implements an API to scale the \(\mu ONOS\) Kubernetes resources. This property of scaling resources adds greater redundancy to the controller. It provides a system with distributed additional resources and is responsible for maintaining and managing the \(ONOS\) service. Atomix controller details can be found in the GitHub repo of the Open Networking Foundation [55].
## 2 Materials and Methods
### Data
#### 2.2.3 Cereal Yield Data  
We obtained annual cereal yield data for durum wheat, soft wheat, and barley from 2003 to 2018 in eight provinces (Al Haouz, Chichaoua, El Kela des Sraghna, Essaouira, Marrakech, Rehama, Safi, and Youssoufia) from the Ministry of Agriculture's economic services. These data are aggregated at the provincial level, representing the total yield across the area of each province.  
The provinces of Rehamna and Essaouira are notable for dedicating 21.5% and 17.5% of their total area to cereal yields, respectively. In contrast, the provinces of Al Haouz and Marrakech allocated smaller percentages, each dedicating 9% of their land to cereal cultivation. The province of Safi is the leading contributor, accounting for 26.5% of total production, followed by El Kelaa des Sraghna with 24.5%, highlighting its significant role in the regional cereal supply. The provinces of Chichaoua and Youssoufia had the lowest contributions, with 5% and 6% of total production, respectively (Figure 2).
## 2 Numerical Modelling Using Plaxis 2D
### Material Input Parameters
#### 2.3.5 Phase Construction of the Numerical Models  
At the stage construction mode in the Plaxis 2D input program, the numerical models are simulated with 35 to 36 construction phases prior to the load application phases. In each construction phase, the pressures and deformations are carried over to the next phase until the end of the construction. By default, an initial phase had been assigned. At this phase, all structures are deactivated and only the original soil and foundation are present. The construction begins with an excavation, thus the first phase is then created, followed by the second to fourth phases that correspond to the construction of the RSF, which includes the placement of the reinforcement, soil backfill, and compaction. Starting from phase five, the following construction phases will differ for each numerical model.  
* For Case 1: The first layer of backfill lift is activated, which includes the equivalent height of the gravel bags, soil backfill, reinforcement, and interfaces. The same procedure is repeated until the full height of the wall abutment is reached. The GRS wall has to be completed before the FHR facing. Thus, at this point, the dead load representing the approach backfill shall be activated. Then, the integral bridge is activated, including the dead load representing the girder.
* For Case 2: The first layer of backfill lift is activated, which includes the CMU block facing, soil backfill, reinforcement, and interfaces. The same procedure is repeated until the full height of the wall abutment is reached. At the end of the bridge construction, the dead load representing the girder and approach backfill is activated.
* For Case 3: This case is different from the other two cases because it has no GRS abutment. Here, the integral bridge is activated first, including the equivalent dead load representing the girder. Then, the first layer of the soil backfill is activated. The same procedure is repeated until the full height of the wall abutment is reached. At the end of the bridge construction, the dead load representing the approach backfill is activated.  
Finally, the external loading will be applied and activated in the model explorer window. For static loading, the uniform line loads, dead load and live load representing the general traffic and railway loads, are activated. For the seismic loading, the prescribed line displacement and dynamic load multiplier are activated.
###### Abstract
However, the computational cost of the numerical solver remains a crucial issue for 3D simulations. Different numerical codes [38; 39; 40] have been devised for simulating flaw scattering but have limited performances for 3D problems. In this paper, we use a 3D hybrid model [41; 42], coupling FEM (Finite Element Method) for flaw scattering and a ray model for beam propagation. In this model, the numerical parameters are set automatically, and the use of high-order spectral elements [43; 44] and techniques of domain decomposition ensures high numerical performance. A comparison of analytical, semi-analytical and numerical techniques has been performed for 2D/3D ultrasonic field modeling [45]. For flaw scattering simulation, different analytical approximations (such as GTD, UAT, UTD, KA and PTD) have been compared for the wedge scattering problem in acoustics [46] or for the rigid half-plane problem in elastodynamics [25]. When modelling a complete inspection with both beam propagation and flaw scattering, a comparison between an analytical model (integral equation [47]) and two numerical (COMSOL FEM and finite differences) models has been recently carried out in some 2D ultrasonic NDT configurations [48]. A comparison between the semi-analytical PTD model and the hybrid 2D numerical model CIVA ATHENA model has also been studied in detail for 2D configurations [26]. Preliminary numerical comparisons between PTD and the 3D hybrid FEM model have been reported in two simple 2D configurations [42]. To our knowledge, no study has yet compared the most advanced semi-analytical (as PTD) and numerical 3D crack ultrasonic NDT models in 3D configurations and their results with experience. The models' comparison proposed here relate to embedded planar defects inspected in 3D configurations with a special interest in defect characteristic lengths of the order of the wavelength for which asymptotic models are no longer valid. The modelsinvolved are the semi-analytical (GTD, PTD) and the hybrid numerical FEM models. We also present comparisons between simulations and experiments for the models' validation. The analytical and numerical simulation methods are briefly recalled in Section 2 of this paper; then, numerical (Section 3) and experimental (Section 4) validations are described for 3D-embedded flaws.
# A Temperature-Robust Envelope Detector Receiving OOK-Modulated Signals for Low-Power Applications
###### Abstract  
This paper presents a passive Envelope Detector (ED) to be used for reception of OOK-modulated signals, such as in Wake-Up Receivers employed within Wireless Sensor Networks, widely used in the IoT. The main goal is implementing a temperature compensation mechanism in order to keep the passive ED input resistance roughly constant over temperature, making it a constant load for the preceding matching network and ultimately keeping the overall receiving chain sensitivity constant over temperature. The proposed ED was designed using STMicroelectronics 90 nm CMOS technology to receive 1 kbps OOK-modulated packets with a 433 MHz carrier frequency and a 0.6 V supply. The use of a block featuring a Proportional-to-Absolute Temperature (PTAT) current yields a 5 dB reduction in sensitivity temperature variation across the \(-40\)\({}^{\circ}\)C to \(120\)\({}^{\circ}\)C range. Moreover, two different implementations were compared, one targeting minimal mismatch and the other one targeting minimal area. The minimal area version appears to be better in terms of estimated overall chain sensitivity at all temperatures despite a higher sensitivity spread.  
envelope detector; temperature compensation; ultra-low-power; wake-up receivers (WuRXs) +
Footnote †: journal: Physics Letters  
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Footnote †: journal: Physics Letters  
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Footnote 1: [[https://www.mdpi.com/journal/sensors](https://www.mdpi.com/journal/sensors)]([https://www.mdpi.com/journal/sensors](https://www.mdpi.com/journal/sensors))