esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
With the large-scale application of electronic devices, electromagnetic interference and electromagnetic pollution have become a major problem [1]. Nowadays, some researchers have developed the mixture of microwave absorption materials and paraffin to absorb microwaves in specific frequencies, and the research in this field is developing towards a low filling rate, ultra-thin, broadband, and strong absorption [2; 3; 4].  
Metal-organic frameworks (MOFs) are skeletal crystalline materials composed of metal ions and organic ligands. Under the action of high-temperature pyrolysis, the carbon in the organic ligands is converted to elemental carbon, and the metal ions are reduced to metal elements or metal oxides [5]. Some studies found that MOFs derived composites with a double-loss mechanism have great application prospects in the field of microwave absorption, for example, Co/Ni-MOFs-derived porous and hollow CoNi@C microspheres [6], Co-MOF@Zn-MOF-derived Co-C@C hollow composites [7], acidified bimetallic MOFs constructed Co/N codoped low-dimensional hybrid carbon networks [8]. However, due to the limitation of the type of organic ligand, the electrical loss of some MOF-derived composites is relatively poor, which will affect their microwave absorption performances. It is worth noting that some researchers have found that the inherent defects of MOFs can be overcome by combining MOFs with carbon material and reasonably designing the morphology and spatial structure of the composite, for example, the multicavity-structured MOF derivative/porous graphene hybrid [9], the in situ-derived carbon nanotube-decorated nitrogen-doped carbon-coated nickel hybrids from MOF/melamine [10], and the Co/ZnO/C@MWCNTs based on carbon nanotube-coated MOFs [11].  
Inspired by the above research, and in order to improve the microwave absorption performance of Co-MOF-derived Co@N-C composites, 3D-graphene@C/Co@N-C composites were prepared by high-temperature pyrolysis. Compared with other graphene composites, the synthesis method of the 3D-graphene@C/Co@N-C composite is relatively convenient, which provides a reference for large-scale preparation [12; 13; 14; 15]. By constantly adjusting the ratio of raw materials, the composite obtained excellent microwave absorption performances in the Ku band. Importantly, the research results of this work can be applied to a wide range of scenarios. For example, the mixture of composite and paraffin can act not only as the sandwich material for a wave-permeable device to block the transmission of the Ku band microwave, but also as the coating material of the wall of the room where precision instruments are stored to absorb Ku-band clutter in the room.
## 3 Simulative Experiment and Discussion
### Errors of EPnP under Different Central Angle Thresholds  
When the maximum threshold of the Earth's central angle is 0.6 degrees, the average radius of the Earth, \(R=6400\) km. The diameter range of the largest surface circular area that the central angle can cover is \(R\times\sin(0.6)\), about 67.02 km. Figure 6 shows that  
Figure 4: The relationship between the number of calculation points and the average error.  
Figure 5: The relationship between the number of calculation points and the calculation time.  
when the number of calculation points is 30 and the flight height is 30 km, the error of the EPnP algorithm under the EarM_EPnP model can be obtained by changing the size of the central angle.  
As shown in Figure 6, the central angle also ranges from 0 to 0.6 degrees. The upper-left figure shows the results of X, Y, and Z orientation. From the figure, it can be understood that when the central angle is relatively small (about 0.5 degrees), the results obtained from both the planar solution and the spherical solution are nearly identical, with an error of approximately the GPS error (about 10 m). When the central angle is 0.18 degrees, the error is approximately 1 m. However, as the central angle becomes bigger, errors become bigger, too. If analyzing the X, Y, and Z orientation separately, the errors of direction X and Y become bigger as the central angle increase. Additionally, the fluctuation in direction Z is relatively dramatic.  
To further demonstrate the improved accuracy of EarM_EPnP compared to the EPnP method, this paper defines the formula for calculating the positioning accuracy, denoted as '\(\eta\)':  
\[\eta=\left|\frac{p_{s}-p_{f}}{p_{s}}\right| \tag{23}\]  
As the central angle continuously varies from 0 degrees to 0.6 degrees, according to Equation (23), the average positioning accuracy is determined to be 16.42%. When central angle \(\partial=0.5\), and the average radius of earth \(R=6400\) km, the maximum surface area covered by central angle can be calculated 6400 km this method by using \(R\times\sin(\partial)\). By the way, the maximum length of a side in this surface is about 55850 m. If the surface is seen as a square, its area is 55850\({}^{2}\) square meters.
# Development of Power-Assist Device for a Manual Wheelchair Using Cycloidal Reducer
## 2 Materials and Methods
### Power-Assist Device  
In this section, using the designed cycloidal reducer, we configure the PAD. A DC servomotor with a rated output of 200 W, rated speed of 3000 rpm, and rated torque 0.64 Nm was used for the drive motor. The electromagnetic brake used was the BXW-03-12S ([PERSON], Kanagawa, Japan) with a rated capacity of 9 W, maximum rotational speed of 5000 rpm, and static friction torque of 1.5 Nm, that maintains the wheelchair at a standstill when on a ramp. The motor speed control can obtain an enhanced quadrature encoder pulse (eQEP) using a TMS320F28035 (Texas Instruments, Dallas, TX, USA) chip installed on the rear of the motor. The power unit consists of the motor, brake and cycloidal reducer. The weight of the power unit was 2.5 kg. The size of the cycloidal reducer was 70 mm in diameter and 34.5 mm in thickness, and its weight was 0.8 kg. The battery was packed with 63 cylindrical Li-ion batteries (2.6 Ah, 3.7 V) and hung under the wheelchair seat, and its specifications are 25.9 V, 23.4 Ah, and 4.5 kg. A button was installed at the end of the output shaft on the power unit, and when pressed, it releases the wheel when loading the wheelchair into a vehicle.  
Figure 8 shows the configuration of the power unit. A block between the motor and reducer connects the power unit to the wheelchair frame. There is a motor driver board located on the back of the motor. The motor and reducer are connected in series coaxially, and the electromagnetic brake is connected to the motor via a timing belt. Figure 9 shows the PAD consisting of the power unit, battery, and joystick. Unlike the position where the manual wheel was installed, the PAD is mounted behind the back through a block for combining with the frame. In addition, the PAD allows the reclining of the back and helps with the easy installation of the manual wheel.
Meanwhile, the theoretical methods of passive microwave remote sensing for river runoff simulation are relatively mature, among which the \(M/C\) signal proposed by [PERSON] et al. [32; 33; 34] is the most typical method. It has been applied in many basins around the world and works effectively. For example, [PERSON] et al. [35] used the \(M/C\) signal to simulate the monthly average runoff at 442 rivers worldwide and the results show that its performance is better in small rivers or tropical areas than that in arid areas. [PERSON] et al. [36] also applied the \(M/C\) signal method in typical river basins in Europe, Asia, Africa and the South America. Climate type, LULC type and basin area were found out to be the dominant influencing factors. In this paper, we explored how microwave remote sensing \(T_{b}\) data can promote the current hydrological drought assessment by combining the \(M/C\) signal method with the SRI algorithm in typical basins of Mainland China. The main objectives of this study are to discuss the utility of microwave remote sensing data for drought assessment and to reveal the patterns of hydrological drought over the last few decades in Mainland China, as well as to gain a better understanding of drought disasters in areas lacking in long-term station observed data but frequently hit by drought hazards, such as the northern Tibet, the Tarim Basin, the Yunnan-Guizhou Plateau and remote areas in northeastern China. First, 79 stations in 9 major basins of Mainland China were selected for runoff modelling using the latest global high-resolution passive microwave \(T_{b}\) dataset based on the \(M/C\) signal method. Then, hydrological drought assessment based on simulated runoff data was conducted using the SRI algorithm. Further investigation was made to explore how topographic and hydro-meteorological factors influenced the runoff simulated results. In the end, the spatial and temporal patterns of hydrological drought in the major basins of Mainland China were discussed and summary and conclusions were drawn.
## 4 Module of Trajectory Prediction  
The module of trajectory prediction generates the future trajectory based on state and control parameters using the six-degrees-of-freedom simulation. Three planned trajectories corresponding to three maneuvers are generated and are shared among participants. According to the principle of noninterference, activation of the maneuver often occurs three to four seconds before the collision, and most maneuvers last only two to three seconds. Therefore, the forecast time is required to exceed 4 s, but long forecast time will prominently increase the amount of computation. Comprehensively considering the collision avoidance effect and the operation cost, the forecast duration in this paper is set to 5 s, and the module generates the future planned trajectories from the present to 5 s later. As shown in Figure 2, the planned trajectory is a conical region whose size depends on the uncertainty over time. Considering the factors such as accuracy, efficiency and the performance of UAVs provided by our team comprehensively, the frequency of this module is set to 10 Hz. The predicted trajectory consists of 50 discrete positions. As is shown in the figure, the planned trajectory is a conical region \(\text{CR}(t,t+\Delta t),\Delta t\in(0,T)\), the midline of it is the predicted trajectory constructed by 50 predictive positions \(\text{P}(t+\Delta t)\), and the cross-section size represents the error radius \(\text{UD}(\Delta t)\), which depends on the uncertainty of the trajectory prediction and increases over time \(\Delta t\). The set of error radius is to ensure that most of the actual predicted trajectories fall within the conical region. The error radius is relevant with uncertainties including navigation uncertainty, trajectory prediction uncertainty, trajectory reconstruction uncertainty, data link transmission uncertainty, and uncertainty in track data computation. The error radius \(\text{UD}(\Delta t)\) is the quadratic function of time: \(\text{UD}(\Delta t)=a.(\Delta t)^{2}+b.\Delta t\). The index of a and b is relevant with types of aircraft, equipment and environment. They can be obtained by actual experiment through comparing the predicted trajectories with the real trajectories. Considering UAVs provided by our team and the performance of experiments, the value of a is 0.425, the value of b is 1.19. The model of conical region is as follows:  
\[\forall\text{CR}(t+\Delta t)\subseteq(\text{P}(t+\Delta t)-\text{UD}(\Delta t )\ \text{P}(t+\Delta t)+\text{UD}(\Delta t)),\Delta t\in(0,T) \tag{1}\]  
The module of trajectory prediction is based on the six-degrees-of-freedom aircraft model. According to standard build-up method using dimensionless coefficients, we establish and approximate force and moments [29].  
Figure 2: Planned trajectories.
###### Contents  
* 1 Introduction  
higher bandwidths over longer distances and for extended periods of time. Nevertheless, their energy efficiency is lower in comparison to FSOs, and this aspect justifies our research.  
Nowadays we may cite the ThoR consortium, active in Europe and Japan, working with state-of-the-art chip sets and modems operating in the standardized 60 and 70-80 GHz bands. Very recent applications (see [2]) allow one to build RF wireless P2P links featuring >100 Gbps over 1 km at 300 GHz. The quest for high bandwidth links has increased the center frequency, so currently radar bands [3] and millimeter waves (THz) [4] are studied as possible extensions of 5G and beyond technologies, further strengthening the importance of establishing directive links. It has to be noted that, as the energy increases with center frequency, so does the concern for impacts on human health--controversial results have been found [5]. Another aspect of relevance is wireless power transmission (WPT), fueled by the escalation of Internet-of-Things (IoT) (see [6; 7]). Here, a crowd of sensor nodes dispersed in a physical environment requires either a distributed energy harvesting capability, or the wireless transmission of energy from a central harvesting unit. In WPT applications, three approaches have been developed: (i) near-field coupling of inductive or capacitive loads in resonant or non-resonant mode; (ii) far-field directive power beaming; (iii) far-field non-directive power transfer. Item (i) is used for extremely small scale environments; (ii), which is our aim, is used for intentional power transfer from nodes in a broader network and implements radio frequencies or microwave means; (iii) is used for electromagnetic energy harvesting of sources nonintentionally conceived for the scope (see, e.g., [8]). The benefits of using directional antennas instead of omni-directional ones in wireless sensor networks (WSNs) have been pointed out, e.g., in [9], in order to significantly reduce interference between network components. Several models of directional antenna in the framework of WSNs are compared in [10]. There are currently two categories of commercial directive radio antennas: On one hand are the traditional ones, including helix, log periodic array antennas, aperture horn antennas, reflectors and patch antennas. On the other hand are smart antennas, each consisting of an array of elements that can offer adaptive transmission. Depending on the specific application, shapes, sizes and designs of directional antennas can be quite different. The signal pattern from a directional antenna consists of a cigar-shaped great lobe, usually surrounded by smaller side lobes. For a survey on radiation patterns of these devices and an application on wireless networks, we mention, for instance, [11; 12].
# Experimental Characterization of Thermal Conductivity with a New Compact Hot-Box Prototype
## 2 Materials and Methods  
The experimental cell was developed in the Sustainable Construction Laboratory (Santo Domingo, Dominican Republic) with the primary objective of characterizing the thermal conductivity of insulating panels made from both traditional materials and new materials derived from agricultural or industrial waste. A schematic of the experimental cell is presented in Figure 1, designed following several recommendations from the ISO-8990:1994 standard. It is important to note that the constructed prototype falls outside the full scope of the standard, which specifies a minimum specimen size of 1 m \(\times\) 1 m. The proposed flexible design addresses laboratory space constraints, maintenance, and operational costs and improves the speed of test preparation.
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 4 Smart Metering Applications--Smart Grid Projects
### ICT for in-Home and Building Energy Management
#### 4.3.5 Smart Build  
Energy management in buildings through an ICT system has been the topic of investigation in the \"Smart Build\" project, which contains participants from five countries. The project's scope is to initially collect data from the buildings under test using sensors and meters and then forward this data to the control center through a hub. Further on, analysis of data for energy efficiency issues is performed, thus accomplishing energy monitoring and control in smart buildings. For achieving communication among sensors, several communication protocols have been used and wireless connections have been employed [85].
# Fault Tolerant DHT-Based Routing in MANET
## 4 Fault Tolerant DHT Network (FTDN)
### Challenges  
Synchronizing Replicas and Anchor Node: FTDN replicates and maintains mapping information across critical regions besides actual AN. This creates multiple replicas (i.e., ANs) alongside the actual AN. In this scenario, synchronizing the replicas and the actual AN is crucial. For this purpose, FTDN updates the mapping information maintained at the replicas as well as at the actual AN after a certain time interval, i.e., the Partition_Timer (the Partition_Timer is set to three times the hello interval). In this manner, a fresh copy of mapping information is available after every refresh period. However, the synchronization is achieved at the cost of extra transmission overhead.  
Power constraints: FTDN identifies critical nodes/links and replicates the mapping information accordingly besides the actual AN. If the designated actual AN or the replicated node is a low-powered node then this would cause extra power consumption in the energy-constrained nodes. Therefore, measures are needed to cope with this issue.  
Transmission Overhead: DHT routing protocols rely on periodically broadcasting hello messages and building logical structure network (i.e., nodes compute LIDs) over the ad hoc physical topology. Similarly, FTDN exploits the existing hello messages and exchanges control information such as 1-hop neighbors, root, and rank, among the neighboring nodes. This additional information in hello packets introduces extra transmission overhead. However, there is a trade-off between fault tolerance and overhead.
## 5 Parameter Analysis  
A mathematical analysis is conducted for key performance parameters, including end-to-end latency, contention probability, and network lifetime, as detailed below. These metrics are crucial for evaluating network performance in IIoT applications operating under stringent environmental conditions.
# Numerical Fit Modeling for Temperature Mitigation in
## 1 Introduction
### Modeling Options
#### 1.1.2 Single-Column Models  
Another option is to use a single-column model (SCM). An SCM is a one-dimensional analysis, but it can allow for horizontal air flow at pre-determined values. SCMs have poor matching to actual conditions because they do not account for interaction between air columns [14]. Advection, or horizontal flow of heat, cannot be accurately modeled with one-dimensional methods, but can be estimated. However, the purpose of this research is not to perform exact weather prediction, but to establish whether mitigation of heat with calcite has a beneficial effect. Net changes in temperatures are needed, but not the absolute values.  
An SCM was used in previous research about local SRM [6]. Other methods can be compared to those results.  
A problem with any complex model is that it is a black box. In preliminary research, it is harder to see the processes work and to explain what is occurring. Another difficulty is that complex models require more data.
## 3 Results and Discussions
### Confocal Laser Scanning Microscopy  
The 3D image representation of a kefir grain is presented in Figure 6, in which a kefir grain can be seen in control medium (Figure 6a) and granules in media enriched with FUC (Figure 6b) and GAL (Figure 6c). The kefir grains obtained from the treatments with the best biomass increase results were stained with Nile red (red color in the images), rhodamine B (green color) and calcofluor white (white color in the merged image) to observe the distribution of the microorganisms present in the granule.  
The presence of yeasts was observed in the periphery of the granule, the quantity of microorganisms in GAL being more abundant. The FUC, however, promoted a greater number of exopolysaccharides, the same factor that was reflected in the amount of final biomass in the treatments (Figure 6b).  
Higher viability levels have been reported for yeasts, highlighting the importance of the initial kefir culture [26,41], as well as the variation of environmental factors such as temperature. [PERSON] et al. [68] reported a stimulation in the growth of yeasts with inulin, which are linear fructans and offer a high availability of their carbohydrates. Agave fructans, on the other hand, are branched fructans [22,24]. It has also been reported that, in kefir grains, yeasts have the greatest diversity of species, in which they predominate mainly _Saccharomyceses_, _Hansenispora_, _Pichia_ and _Lachancea_[69,70], indicating that their metabolism comes mainly from carbon sources produced during the fermentation process. As for the bacteria species identified, there was a more significant predominance of lactic acid bacteria (LAB).
# TinyML for Ultra-Low Power AI and Large Scale IoT Deployments: A Systematic Review
## 2 TinyML
### TinyML Benefits
Additionally, on safety-critical systems like healthcare (such as microsurgery) and driverless cars, waiting for the cloud to decide might have disastrous consequences. As there is less reliance on external connectivity in such cases, TinyML will act as an infrastructure to enable decreased (near-zero) latency for ML service delivery. Local real-time data processing on the devices enables quicker reaction and analysis in emergency situations. Furthermore, the strain on the cloud is minimized [96; 97; 98]. 5. _Energy efficiency_ Another major and popular TinyML indicator in MCUs is this one. The majority of IoT devices operate on batteries and are constantly on in an IoT ecosystem. Coin batteries, like the CR2032, are frequently used to power IoT devices, and they should allow them to operate for several months or perhaps several years. To enable the MCU to review the data and power on when necessary, it is usually crucial that the device remain predominantly in a suspended state. Data transport may occasionally use more energy than local ML service provision, too. TinyML would be a very useful tool for resolving these problems [99]. 6. _Reliability_ The capacity to do data-driven calculations within the sensor network is the main feature that is much wanted for IoT. TinyML has been recognized as a resolution for executing work in situations where mobile connectivity/internet is extremely restricted, such as offshore and rural areas. IoT services become more dependable as a result. 7. _Low cost_ First off, by limiting data flow, bandwidth requirements are lowered, which results in cost savings. To achieve this, we believe that TinyML solutions paired with cloud technology can enhance the aforementioned performance metrics by adding new data management channels, expanding the capability of current cloud services, or delegating tasks to MCUs. TinyML is therefore the upcoming big thing, and many are touting it as the technology that will fuel the digitization of industries or the Industry 4.0 revolution [100]. * _Data Filtration_ The intelligence of the IoT device enables the designer to examine the data and remove residuals. When deployed in use cases with high traffic volumes, the intelligent support system at the edge can significantly outperform traditional systems. A surveillance system that focuses on anomaly detection, for instance, consists of a number of cameras, and the vast majority of the data that the cameras collect is redundant. In these cases, it makes more sense to filter the extra photographs locally rather than uploading them all to the cloud.