esa-sceva/satcom-chunk-collection huggingface dataset:
# Proposed Fuzzy-NN Algorithm with LoRa Communication Protocol for Clustered Irrigation Systems
###### Abstract  
Modern irrigation systems utilize sensors and actuators, interconnected together as a single entity. In such entities, A.I. algorithms are implemented, which are responsible for the irrigation process. In this paper, the authors present an irrigation Open Watering System (OWS) architecture that spatially clusters the irrigation process into autonomous irrigation sections. Authors' OWS implementation includes a Neuro-Fuzzy decision algorithm called FITRA, which originates from the Greek word for seed. In this paper, the FITRA algorithm is described in detail, as are experimentation results that indicate significant water conservations from the use of the FITRA algorithm. Furthermore, the authors propose a new communication protocol over LoRa radio as an alternative low-energy and long-range OWS clusters communication mechanism. The experimental scenarios confirm that the FITRA algorithm provides more efficient irrigation on clustered areas than existing non-clustered, time scheduled or threshold adaptive algorithms. This is due to the FITRA algorithm's frequent monitoring of environmental conditions, fuzzy and neural network adaptation as well as adherence to past irrigation preferences.  
water planting systems; neural network algorithms; fuzzy algorithms; smart farming; agriculture; soil sensor; smart irrigation; IoT +
Footnote †: journal:  
_Future Internet_**2017**, 9, 78; doi:10.3390/f9040078
## 3 Results
### Occurrence of Virulence Genes  
Virulence genes were distributed as follows: _fintH_ (55.5%, 15/27), _papGI_ (11.1%, 3/27), _papGI_ (74.1%, 20/27), _fyuA_ (3.7%, 1/27), _iha_ (59.3%, 16/27), _iutA_ (37.1%, 10/27), _ompT_ (77.7%, 21/27), _traT_ (25.9%, 7/27), _kpsMTII_ (22.2%, 6/27), _malX_ (55.5%, 15/27), and _usp_ (62.9%, 17/27). The virulence scores varied from 0 to 9 (median: 5). \begin{table}
\begin{tabular}{c c c c c} \hline
**Strains** & _int_**Genes** & **PRT** & **PG** & **PFGE** \\ \hline EC 1 & - & F & B2 & P6 \\ EC 2 & _int1_ & F & D & P2 \\ EC 3 & _int2_ & FIA & D & P20 \\ EC 4 & - & - & B2 & P11 \\ EC 5 & - & - & A & P7 \\ EC 6 & - & - & B2 & P21 \\ EC 7 & - & - & D & P9 \\ EC 8 & _int2_ & FIA-F & B1 & P10 \\ EC 9 & _int1_ & - & B2 & P8 \\ EC 11 & - & F & B2 & P14 \\ EC 12 & _int1_ & FIA-F & B2 & P19 \\ EC 14 & - & - & B2 & P8 \\ EC 15 & _int1_ & FIA & B2 & P18 \\ EC 16 & _int1_ & - & B2 & P19 \\ EC 17 & - & FIA & B2 * & P1 \\ EC 18 & - & FIA & B2 * & P1 \\ EC 19 & - & FIA & B2 * & P1 \\ EC 20 & - & - & B2 & P16 \\ EC 21 & - & - & B2 & P15 \\ EC 22 & - & - & B2 & P16 \\ EC 23 & - & - & D & P12 \\ EC 24 & - & FIA-F & D & P5 \\ EC 25 & - & - & B2 & P4 \\ EC 26 & - & - & A & P13 \\ EC 27 & _int1_ & FIA-F & B2 & P3 \\ EC 28 & _int1_ & FIA-F & B2 & P3 \\ EC 29 & - & - & B2 & P1 \\ \hline \end{tabular} \({}_{:}\): Negative character; *: strain belongs to ST131 clone; P: pulsotype; PG: Phylogenetic Group; PRT: Plasmid Replicon Type; PFGE: Pulsed-Field Gel Electrophoresis.  
\end{table} 
Table 4: Identification of associated plasmid-mediated _int1_ and _int2_ genes, phylogenetic groups, ST131 clone, and virulence factors and pulsotypes.
# Preference for Number of Friends in Online Social Networks
## 5 Conclusions  
In this work, we studied 6.8 million Sina Weibo users systematically and found that a significant fraction of users have a special preference for number 200, i.e., they prefer to follow 200 friends. We comparatively portrayed users with the preference for the number 200 from several different perspectives, such as user activity, popularity, attention tendency, regional distribution, economic, and education level. The results show that users with a 200 preference have lower social activity and fewer followers compared with users without the preference for the number 200. We found that they are more inclined to pay attention to users with higher popularity, and their social portraits change relatively slowly. Moreover, we also found the preference intensity of number 200 is stronger in regions with relatively undeveloped economic and educational levels. Further analysis revealed that there exists a significant negative correlation between the preference intensity and the development of regions. That indicates users with the preference for the number 200 are likely to be vulnerable groups in society, and influenced by opinion leaders in the network.  
We argue that people have a special preference for 200 friends for a few reasons. On the one hand, in traditional Chinese culture, people have a special preference for some numbers, such as 6, 8, 10, 100, 200, etc. On the other hand, due to the limits of brain capacity, people can only maintain effective social relationships with a certain number of friends. However, online social networks make information exchange among people more convenient than ever, which is the most likely reason why the number of friends is a little higher than the Dunbar number (150) [47] in online social networks.
## 1 Introduction  
Environmental issues are becoming increasingly important. Increasing fossil fuel usage has increased the amount of carbon dioxide (CO\({}_{2}\)) in the atmosphere, causing global warming. Consequently, fuel cell technologies are being developed because of their efficiency, convenience, and environmental friendliness [1, 2, 3, 4, 5].  
Many numerical and experimental studies have been conducted in the area of solid oxide fuel cells to help understand the complex interactions between their electrochemical characteristics and material structure [6, 7, 8, 9, 10, 11, 12, 13, 14]. Many researchers have focused on the simulation of transport characteristics to study the behavior of SOFCs [15, 16, 17, 18, 19]. Simulation studies have investigated the effects of metal foam distribution on reducing electrical resistance, improving heat management, and water removal [20]. The cell performance, electrochemical reactions, and heat transfer effects in SOFCs have been investigated in previous models [21, 22, 23].  
A 2D model with an equivalent electric circuit and the dusty-gas model was simulated. The results showed that the structure effectively improved performance [24]. A model for the SOFC stack was simulated to determine the heat transport characteristics by considering the electrochemical reactions with the model validation based on the experimental data [25]. Dimensional effects such as changes in channel width and electrode thickness were investigated through simulations [26; 27; 28]. Temperature and current density curves were simulated [29]. The temperature distribution on a planar SOFC was investigated. The results show that the temperature of the cell is strongly affected by the thermal boundary conditions [30; 31].  
Several 3D model simulations were implemented in many previous studies. The software COMSOL Multiphysics was used to simulate the SOFC model. The results show that the current density on the anode side is exchanged through the highly exothermic process of the formation of water [32]. A simulation study for a planar SOFC was implemented. The results show that thermal gradients are caused by electrochemical reaction processes [33]. The effect of operating pressure on cell characteristics was also studied. The results show that cell performance can be improved by increasing the voltage and by decreasing the activation overpotential [34]. A model was used to investigate the current density and temperature distribution in the SOFC unit, showing interesting results [35].  
In this work, the characteristics of the cell electrodes in an SOFC were investigated using a 3D numerical simulation method. The simulation model was implemented to determine the SOFC's characteristics, including the distribution of hydrogen in hydrogen electrodes with different porosities and the distribution of voltage and current on the electrolyte layer surface. The effects of operating temperatures of 650, 700, and 750 \({}^{\circ}\)C on current densities and potentials were also investigated.
## 4 Methodology
### Vehicle Trajectory Prediction Module (VTP)  
To strive for consistency and reduce complexity without losing precision, we further employ the GRU network to perform trajectory anticipation conclusively. It is well known that the prediction task of trajectory is the sequence-generating issue, intending to approximate the real distribution as accurately as possible. Taking the \(Enc_{out}\) built by the attention-based spatial-temporal fusion graph module and previous hidden state as input, the VTP module predicts vehicle position in the next several time steps. The GRU forms a chain structure, where the former output is fed into latter object as input. Among them, for the first GRU, its input also embeds previous time coordinates of the target vehicle, as shown in Figure 5. Moreover, with the purpose of adjusting the model's prediction rate as the vehicle's moving speed changes, the residual connection is imposed between each input and output of the GRU decoder. In such a way, the outputs of the GRU decoder are combined to form a tensor \(Dec_{out}\in\mathbb{R}^{B\times S\times N}\), where \(B\) is the batch size, \(S\) is the hidden size and \(N\) is the prediction horizon. Finally, by applying the full connection, the future trajectories \(Y_{t+n}\) are generated.
## 2 Experimental Measurements
Third, in some cases, the time-delay profiles of the SH signal qualitatively trace the square of the terahertz field waveform (dashed line in Figure 3a) in both gas atmospheres, but in some cases, this rule is violated (see Figure 3c). Figure 3: Normalized SH signal as a function of time delay between optical and terahertz pulses for (**a,b**) Si, (**c**) TWLA, and (**d**) As\({}_{2}\)S\({}_{3}\). Detections are (**a**) from the exit facet of SiO\({}_{2}\), and (**b,c**) from the entrance facet of the sample. Red diamonds—measurements in air; blue filled circles—measurements in helium. Normalized coefficients are given for the corresponding curves. Dashed curve in (**b**) is normalized terahertz electric field squared; dotted and dash-dotted curves in (**a,c,d**) correspond to theoretical modeling (see text).
## 3 Results and Discussion  
### Physical Properties of WPC  
Figure 2a shows that the density of all WPCs (>1.0 g\(\cdot\)cm\({}^{-3}\)) was higher than that of poplar, MDF and HDF, which was also higher than the same type of WPC (tensile strength of 12.3 MPa and flexural strength of 21.3 MPa) [19]. This finding could be attributed to the application of high pressure to compress and flatten the wood cells which resulted in the porosity decreasing significantly (Figure 2b,c). The decrease of porosity increased the mass per unit volume of fibers, which improved the density of WPC. With the increase of contact area between fibers, the number of hydrogen bonds between fibers was increased due to the high density of WPC, which improved the mechanical properties of WPC. It was revealed that WPC with mechanical properties better than those of MDF and HDF could be prepared using the proposed method as shown in Figures 3b,c and 4b,c. The WPC made of ABS powder and poplar flour showed the best mechanical properties in terms of tensile strength (36.9 MPa) and flexural strength (44.7 MPa) (Figures 3b and 4b) compared with those of MDF (tensile strength of 16.9 MPa and flexural strength of 18.6 MPa) (Figures 3a and 4a) and HDF (tensile strength of 38.8 MPa and flexural strength of 41.7 MPa) (Figures 3a and 4a) [20,21]. As such, both tensile strength and flexural strength of WPC that were made of five different plastics (i.e., PE, PP, PVC, ABS, HDPE) were compared. In general, the higher the mass ratio of wood powder, the higher the tensile strength and flexural strength (except ABS), which was also basically confirmed by ANOVA (\(\alpha\) = 0.05,  
Tables 1 and 2). When a higher plastic mass proportion was adopted, there could be less hydrogen bonding and other chemical bonding presence within the cellulose, hence the mechanical properties of the resulted WPC would be weakened. Since ABS possesses better mechanical properties (tensile strength of 50 MPa and flexural strength of 65 MPa) as an engineering plastic [22] than HDF, it is expected that the WPC prepared with ABS powder (WF/ABS-8) (wood flour/ABS powder = 80:20 by weight) would demonstrate excellent mechanical properties.  
Figure 2: (**a**) Density of poplar, MDF, HDF and WPC. (**b**) Microscopic image of the cross-section of poplar. (**c**) Microscopic image of the cross-section of WF/ABS-8.  
Figure 3: (**a**) Tensile stress–strain curves of WPC, MDF and HDF. (**b**) Tensile strength of WPC. (**c**) Tensile modulus of WPC.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Different Kinds of WPCs** & **F-Value** & \(p\)**-Value** & **F Crit** \\ \hline WF/PE & 33.05 & 0.001 & 5.14 \\ WF/PP & 4.27 & 0.071 & 5.14 \\ WF/PVC & 1.38 & 0.321 & 5.14 \\ WF/ABS & 1.84 & 0.238 & 5.14 \\ WF/HDPE & 8.41 & 0.018 & 5.14 \\ \hline \hline \end{tabular}
\end{table}
Table 1: ANOVA of tensile strength of WPCs.  
Figure 4: (**a**) Flexural stress–strain curves of WPC, MDF and HDF. (**b**) Flexural strength of WPC. (**c**) Flexural modulus of WPC.
## 4 Methodology
### Applying chemical reactions
#### 4.1.3 Load Balance  
While forwarding data packets, each node picks the next-hop node in the inner layer with the least network loading to achieve load balancing. Each node publishes its network loading value to let its outer-layer nodes obtain its network loading in its beacon message. When a node in the inner layer sends a data packet, it picks the next hop with the least network loading from among its neighbors. Load balancing is based on the design layer service because each data packet is sent to the node with the least network loading.  
Nodes often modify their next-hops if network loading is assessed based on the current network loading over a short period. It can result in a significant increase in network traffic and associated bottlenecks. As a result, load balancing assesses network loading using an exponential weighted moving average algorithm. Recent data have a stronger weighting and a greater effect on the subsequent estimation value. In contrast, the impact of data declines exponentially with time. Hence, the projected loading can represent long-term network loading, allowing nodes to swap the next hops seamlessly.  
Given a time slot \(i\), the current sample network loading (SNL) is denoted as SNL\({}_{i}\), and the estimated network loading (ENL) is denoted as ENL\({}_{i}\). If SNL\({}_{i}\) is not 0, ENL\({}_{i}\) is calculated as \((1-w)*ENL_{i-1}+w*SNL_{i}\) and determines the effect of the current traffic load to estimate long-term traffic by considering the weight w for the SNL.  
The SNL weight is calculated to determine the effect of the current traffic load for estimating long-term traffic if it is not 0. In order to prevent ENL\({}_{i}\) from being 0 when SLN\({}_{i}\) is 0 even though w is 1, \(ENLi\) is set to 1/2 \(ENL_{i-1}\). \(ENL_{0}\) is first set to \(SNL_{0}\). If w is small, the ENL will change slowly; if w is large, the ENL will change rapidly due to the large effect of the SNL on the ENL. Traffic is dynamically routed through nodes with the lowest long-term traffic loading utilizing load balance on the design layer. Hence, it achieves load balancing through dependable multipath layered routing.
# Resizer Swin Transformer-Based Classification Using sMRI for Alzheimer's Disease
## 2 Background and Related Work  
### CNN-Based Classification for AD  
The main types of existing AD classification techniques based on the CNN model include the region of interest (ROI), voxel, patch, and attention mechanism. Notably, this categorization does not imply that the four approaches mentioned above are entirely distinct.  
ROI-based methods require the pre-segmentation of brain regions based on prior knowledge, such as brain atlases. For instance, [PERSON] et al. segmented the hippocampal area, one of the most AD-sensitive regions, and employed a dense convolutional neural network (Dense CNN) model to classify normal control (NC) and AD samples [14]. On the other hand, one study tried each brain region when training a 3D-CNN ensemble model [15].  
The voxel-based technique, which does not require any prior knowledge or laborious preparation, obtains features directly from sMRI images and fully exploits the global characteristics [16]. For instance, [PERSON] et al. employed LeNet, AlexNet, VGG, DenseNet, and other models to classify AD while evaluating their effectiveness [17]. In addition, the classification accuracy may be increased by combining the CNN model with transfer learning [18] and data augmentation [19].  
Others noticed that only localized brain areas in early AD patients exhibit minor structural abnormalities, which leads to a possibility that features obtained at the voxel or region level cannot contribute to AD identification completely. Patch, the intermediate level between voxel and region, has gained more attention. With flexibility in size and location, patch-based models improve classification accuracy and avoid laborious preprocessing procedures. However, the choice of patches significantly impacts the categorization outcomes. By using anatomical marker detectors, [PERSON] et al. first identified the patches discriminative of AD and then trained a CNN model to learn from those patches [20]. The landmark-baseddeep multi-instance learning (LDMIL) system was introduced the following year to learn local patch information as well as global information from all patches [21].  
Another frequently employed module recently is the attention mechanism, which is quite useful for pinpointing AD-sensitive areas. For example, [PERSON] et al. added the attention mechanism to the ResNet framework, which effectively enhances the gray matter feature information and increases the accuracy of AD diagnosis [22].
# RF Energy Harvesting
## 7 Summary  
This paper presents fundamental concepts standing behind RF energy harvesting. The state of the research in the area was covered. Existing commercial products were briefly introduced. The design of a simple RF energy harvester model was presented. It was further used to conduct simulations in conditions similar to those of an LTE mobile network. Three different simulation environments were compared: KiCad EDA, LT Spice and Simscape Electrical. The first two are well known in research and design; the latter one is less popular. The outcomes of the simulations showed that Simscape Electrical provides the same accuracy as the other environments. However, it provides a large variety of additional tools that help to analyse the results and to design the modules. Furthermore, MATLAB provides a wide choice of libraries for researching radio frequencies. Therefore, it will be used in further research on this topic.  
During the research for this paper, we noticed that for the proposed frequencies, the diode's capacitance plays a crucial role in harvester performance.  
Research on RF energy harvesting will continue. The next steps are to propose a simulation design that is capable of harvesting energy not only from a single frequency but for a given bandwidth. This will allow for harvesting of all the energy that is used during communication in a mobile network. The next change to the design that will be tested will be to introduce a varying internal resistance in the voltage source that changes with the load. This will much better represent a real device and should increase the amount of energy available in the circuit. Also, a study on other radio frequency sources will be considered in order to find the one that provides the biggest amount of energy in a narrower band. Further, the concept of RF energy harvesting in a lossless environment such as outer space will be considered. In the end, a comparison of simulation and real-life hardware results should be done.  
One of the real applications that was brought up during research was to use an RF energy harvester to power an ultra-low-power ARM Cortex-M0+ MCU [29]. It is an ultra-low-power microprocessor for which the power consumption requirements meet the power available from RF energy harvesting modules. Using such a device can be the first step in creating RF-energy-powered sensors.  
Conceptualisation: M.P., P.P. and J.S.; methodology: M.P. and P.P. formal analysis: M.P. and P.P.; funding acquisition: P.P.; investigation: J.S. and M.P.; resources: M.P. and J.S.; software: M.P.; data curation: J.S.; supervision: P.P.; validation: M.P. and P.P.; writing--original draft: [PERSON]; writing--review & editing M.P., P.P. and J.S. All authors have read and agreed to the published version of the manuscript.  
This work was funded from AGH University of Krakow subvention for scientific activity no. 16.16.120.773.  
The data presented in this study are available on request from the corresponding authors.  
The authors declare no conflicts of interest.
## 3 The PFAC Measurements and the FRM Uncertainty Analysis
### Uncertainty Budget for Sea-Surface Calibration
#### 3.2.2 Reference Surface Constituent  
In the PFAC, the sea-surface height has to be transferred from the Cal/Val location in the harbor to open sea where satellite measurements are made uncontaminated by land mass (about 20 km away). To accomplish this transfer, the reference geoid and the mean dynamic topography (MDT) have to be accurately known. In the past, terrestrial, marine and airborne gravity surveys have been carried out in the vicinity of the PFAC Cal/Val sites. Regional geoid and MDT models have been developed and validated by dedicated field campaigns using boats and buoys. Details on those reference surfaces are presented in [29].  
In 2018, additional gravity field measurements have been carried out at the PFAC Cal/Val sites and around the Gavdos island (Figure 9). A CG-5 microgravity sensor with a resolution of 1 \(\upmu\)Gal and standard deviation less than 5 \(\upmu\)Gals has been used. Three control points with known absolute gravity values have been chosen as reference points to conduct this gravity survey. Absolute gravity measurements have been conducted in the past by the Hellenic Military Geographical Service and international research institutes. These absolute gravity points are located at Chania Airport (AIR), and Sfakia (SFK) and Paleochora (PAL) villages (Figure 9). Absolute gravity values for various locations at the PFAC sites have been determined by relying upon those absolute reference points (AIR, PAL, SFK), and measuring relative gravity between them and the PFAC Cal/Val sites. These new gravity observations have been incorporated in the regional gravity database to improve the geoid accuracy.