esa-sceva/satcom-chunk-collection huggingface dataset:
_Applied_  
_sciences_  
_Identifying Groundwater Fluoride Source in_  
_a Weathered Basement Aquifer in Central_  
_Malawi: Human Health and Policy Implications_  
**[PERSON] 1,*\(\copyright\)**, [PERSON] 1, [PERSON] 2, [PERSON] 2, [PERSON] 2,**  
**[PERSON] 2, [PERSON] 3, [PERSON] 3 and [PERSON] 1\(\copyright\)**  
Footnote 1: [[http://www.mdpi.com/journal/applsci](http://www.mdpi.com/journal/applsci)]([http://www.mdpi.com/journal/applsci](http://www.mdpi.com/journal/applsci))  
Received: 22 June 2020; Accepted: 18 July 2020; Published: 21 July 2020  
**Abstract:** Consumption of groundwater containing fluoride exceeding World Health Organization (WHO) 1.5 mg/L standard leaves people vulnerable to fluorosis: a vulnerability not well characterised in Malawi. To evaluate geogenic fluoride source and concentration, groundwater fluoride and geology was documented in central Malawi where groundwater supplies are mainly sourced from the weathered basement aquifer. Lithological composition was shown as the main control on fluoride occurrence. Augen gneiss of granitic composition posed the greatest geological fluoride risk. The weathered basement aquifer profile was the main factor controlling fluoride distributions. These results and fluoride-lithology statistical analysis allowed the development of a graded map of geological fluoride risk. A direct link to human health risk (dental fluorosis) from geological fluoride was quantified to support science-led policy change for fluoride in rural drinking water in Malawi. Hazard quotient (HQ) values were calculated and assigned to specific water points, depending on user age group; in this case, 74% of children under six were shown to be vulnerable to dental fluorosis. Results are contrary to current standard for fluoride in Malawi groundwater of 6 mg/L, highlighting the need for policy change. Detailed policy recommendations are presented based on the results of this study.  
**Keywords:** fluoride; groundwater; Sustainable Development Goal 6; water quality; rural community water supply; weathered basement aquifer; hydrogeology; policy change
## 2 Antenna Design and Fabrication  
The design diagram of a single-element MIMO antenna is shown in Figure 1. This MIMO antenna is designed on RT/Duroid substrate with relative permittivity of 2.2 and dielectric loss tangent (tan\(\delta\)) of 0.0009 [26], having a cross-section of 25 mm x 25 mm x 1.6 mm. A microstrip feed line of 50 ohms is utilized to provide the input supply to the antenna.  
Figure 1: (**a**) Multiple-input multiple-output MIMO antenna with substrate, (**b**) dimensional geometry of one element in the four-element MIMO antenna, (**c**) top view of the fabricated antenna, and (**d**) back view of the ground plane. Substrate is absent in (**b**) to highlight the ground plane.  
The reduced ground plane was designed at the bottom of the substrate and straight stubs of 0.3 mm width are used to establish a connection with the reduced ground plane. Figure 0(a) shows the MIMO antenna with the substrate understudy in ANSYS HFSS V19.0, and Figure 0(b) shows the structural parameters of the T-shaped antenna. Initial values of design parameters were a = 1 mm, b = 2 mm, c = 0.5 mm, d = 2 mm, tw = 3 mm, tl = 8.5 mm, ga = 11 mm, gb = 3 mm, gc = 25 mm, and gd = 0.3 mm. Figure 0(c),d shows the top and back views of the fabricated MIMO antenna.  
Figure 2 shows the simulated and measured return loss of the initial antenna design as shown in Figure 1. The difference between simulated and measured results may be attributed to the fabrication discrepancies and associated tolerances because of the variation of dielectric constant (\(\epsilon_{t}\)) and loss tangent (tan\(\delta\)) of the RT/Duroid substrate with frequency and relative humidity [27; 28; 29].  
A small change in design, relative permittivity, and thickness of substrate were incorporated into our antenna simulation in order to investigate the causes of the difference between the simulated and measured results. Figure 3 shows the modifications made to the antenna design, where the area under the black eclipse represents an area of change, while the red box represents a zoomed-in area. The changes were made in the angle of the patch in the antenna design (to mimic the actual antenna), dielectric constant value to 2.22 from 2.2, and substrate thickness to 1.575 mm from 1.6 mm. All these minor changes resulted in the matching of the losses between the simulated and measured results. However, negligible changes were observed in EMI, radiation pattern, peak gain, and surface current distribution, as shown in Figure 4. Hence, we proceeded with our ideal antenna design instead of the physical antenna design that contained non-ideality.  
Figure 2: Return loss of initial antenna (**a**) simulated and (**b**) measured.  
Figure 4: _Cont_.  
Figure 3: Antenna design for (**a**) modified and (**b**) initial antenna to match the measurement results. Area under the black eclipse represents an area of change, while the red box represents the zoomed-in area.  
Figure 4: \(Cont\).
# Fire Resistance of Ultra-High-Strength Steel Columns Using Different Heating Rates
## 3 Parametric Analysis of CHS Columns
### Effect of the Element Length on the Critical Temperature Using Different Heating Rates  
For the parametric analysis involving column length, it was necessary to modify the mesh and reintroduce imperfections. Therefore, for the longer columns, the number of divisions in the direction of their length was increased. The columns with a length of 3 m were divided into 150 elements along their length, resulting in 5440 elements, while the column with a length of 6 m was divided into 300 elements, resulting in 10,240 elements. As for imperfections for these longer lengths, only the global imperfection was used. Moreover, simulations at room temperature were developed, as shown in Figure 16. These simulations were developed to determine the load-bearing capacity; in Figure 16a, the results are presented for CHS columns with length of 3 m, and in Figure 16b, the results are presented for CHS columns with 6m length.  
Figure 16: CHS columns at ambient temperature with (**a**) length of 3 m (**b**) length of 6m.  
Figure 15: Effect of CHS thickness variation on room temperature resistance.  
With these simulations completed, the analyses with the three heating regimes were carried out to examine the effect of length on critical temperature, as shown in Figure 17. Figure 17 presents the results for heating rate R5, Figure 17 for heating rate R20 and Figure 17 for ISO 834. The results indicate that the variation in length has a complex impact on critical temperature, which aligns with observations made by [PERSON] et al. [11]. They also noted the complexity of critical temperature concerning slenderness variation.  
Additionally, [PERSON] et al. [11] mentioned that for load levels below 60%, higher slenderness columns generally led to higher critical temperatures. Conversely, for load levels exceeding 60%, in intermediate slenderness columns exhibited the lowest critical temperature values. These findings are supported by Figure 17.
## 3 Platform for Real-Life Testing of Communication Protocols
### Testbed Environment
To achieve this, distinct routing decisions should be used on input and output paths, and a routing rule should be created that matches only for ingress traffic, packets with the \"input interface\" attribute (i.e., if), and performs the route lookup in a custom \"local\" routing table (see Figure 4). 3. To pass traffic through the GNU radio application under test, two entries are created in the main routing table that routes the traffic destined to the far end of the \"tunnel\" (e.g., output interface) through the near-end interface. In this way, to send a packet with the destination of the far end of the tunnel (e.g., 3.3.3.3), it is routed through the near-end interface (e.g., tap0) from where the application reads (see Figure 5). 4. In the case of tap interfaces, which are layer 2 interfaces, static ARP (address resolution protocol) entries are set to eliminate ARP-specific traffic through the application under test (see Figure 6). This setup allows any traffic generator with the destination set to the far end of the tunnel to pass traffic through the application. Since the local routes have been removed, the traffic is passed according to the main routing table through the near-end interface. On the far-end interface, due to the incoming traffic local rules, the traffic is passed to the host. This means that any application listening for that traffic will receive it. For instance, in Figure 7, the ping utility is used to send probes with the record route (-R) option, with the far end of the tunnel as the destination. As anticipated, the packets are routed through the near-end interface (IP address 2.2.2.2). As previously discussed, when using SDR (software-defined radio) boards to test in real radio channel conditions, the Tx (Transmitter) and Rx (Receiver) applications run in separate environments. The channel is replaced by SDR's IO components, such as source/sink GNU radio blocks. Each environment has only one TUN/TAP interface (as shown in Figure 1), and the environments can be hosted on the same or different machines. If the environments are hosted on different machines, they must be synchronized to align the monitoring data acquired at both ends of the transmission system in time. PTP (precision time protocol) [41] can synchronize the physical machines running the GNU radio applications. The routes are set as presented in Figures 4-6. Python and libraries such as pyroute2 [42] and python-iptables [43] are used to perform environment setup and management programmatically. These libraries enable easy integration into automated testing frameworks. The implementation of the testing environment setup can be found in [44].
## 6 Security Analysis
### Computational and Communication Costs  
#### 7.2.1 Complexity Analysis  
We analyze the computational and communication complexity of the proposed scheme. It is easy to see that each user's computational complexity can be broken up as (1) calculating a secret value in **KeyBroadcasting**, which takes \(\mathcal{O}(1)\) time; (2) calculating three parameters \(Au_{i,t}^{1}\), \(Au_{i,t}^{2}\), \(Au_{i,t}^{2}\) and encrypting the local model \(w_{i,t}\) in **LocalTraningandMasking**, which takes \(\mathcal{O}(n)\) time in total; (3) when some users drop out accidentally, computing a parameter \(Y^{\prime}\) in **MessagesCollecting**, which takes \(\mathcal{O}(1)\) time. During each training round, across its four distinct phases, the communication cost incurred by each participating user amounts to \(\mathcal{O}(1)\) in **SystemInitialization**, \(\mathcal{O}(1)\) in **KeyBroadcasting**, \(\mathcal{O}(n)\) in **LocalTraningandMasking** and \(\mathcal{O}(n)\) in **AggregatingandUnmasking**. To summarize, the computational and communication complexity of each user is \(\mathcal{O}(n)\) and \(\mathcal{O}(n)\).
applied sciences Article  
A Method of Reducing Errors Due to Sampling in the Measurement of Electric Power [PERSON]  
Faculty of Electronics, Telecommunication and Information Technology, National University of Science and Technology Politechnica of Bucharest, 060042 Bucharest, Romania, [EMAIL_ADDRESS]  
**Featured Application: Improving the measurement of electricity consumption by reducing errors is the main objective of this article. As a concrete application, the presented algorithm is used for the determination of electricity meters by the end user. With minimal costs, a correct assessment of energy consumption can be made, and therefore the calculated electricity costs should be accurate.**  
Although data acquisition is a very usual technique, several aspects are not always considered, such as the synchronization of the acquired measures and the evaluation of the resulting errors. This paper aims to highlight this fact by the mathematical determination of the necessary correction and the implementation of software meant to evaluate the performances of acquisition systems. As an example, a three-phased acquisition system was developed in order to monitor the currents and voltages on the three phases. Also, other measures were performed, such as of power and phase. The components on each phase did not have to be fully identified because a whole system calibration could be performed in the first stage. The calibration consisted in finding the weighting coefficients for each measured quantity. The implemented solution for three-phased measure acquisition started from the hypothesis of a sampling frequency that respected the Shannon theorem. The distance between two samples was small enough to consider a linear evolution between two moments for the same measure. Errors that affected the above-mentioned measures, due to the fact that the samples were examined in different moments, were analyzed and brought to the minimum value. Finding a solution to reduce the sampling errors is closely related to reducing the costs.  
sampling error; improving accuracy; data acquisition
# Research on a Convolution Kernel Initialization Method for Speeding Up the Convergence of CNN
###### Abstract  
This paper presents a convolution kernel initialization method based on the local binary patterns (LBP) algorithm and sparse autoencoder. This method can be applied to the initialization of the convolution kernel in the convolutional neural network (CNN). The main function of the convolution kernel is to extract the local pattern of the image by template matching as the target feature of subsequent image recognition. In general, the Xavier initialization method and the He initialization method are used to initialize the convolution kernel. In this paper, firstly, some typical sample images were selected from the training set, and the LBP algorithm was applied to extract the texture information of the typical sample images. Then, the texture information was divided into several small blocks, and these blocks were input into the sparse autoencoder (SAE) for pre-training. After finishing the training, the weight values of the sparse autoencoder that met the statistical features of the data set were used as the initial value of the convolution kernel in the CNN. The experimental result indicates that the method proposed in this paper can speed up the convergence of the network in the network training process and improve the recognition rate of the network to an extent.  
convolution neural network; convolution kernel; local binary patterns; sparse autoencoder 2022  
126333
###### Abstract  
This paper presents a comparative analysis of the biomechanical characteristics of an external fixator with a frame made of two different materials (stainless steel and composite material) during anterior-posterior bending. Before the test itself, two representative configurations of the Sarafix fixator were selected for application on the lower leg and upper extremities under the designations B50 and C50, which are most widely used in orthopedic practice. The examination of the biomechanical characteristics of the external fixator was carried out using the structural analysis of the construction performance of the Sarafix fixator using the finite element method, the results of which were verified through experimental tests. The developed experimental and FEM models study the movement of the fracture crack and enable the determination of the stiffness of structural designs as well as the control of the generated stresses at the characteristic locations of the fixator. The results show that the fixator with a carbon frame has lower stresses at critical points in the construction compared to the fixator with a steel frame, in the amount of up to 49% (at the measuring point MT+) or up to 46% (at the measuring point MT-) for both fixture test configurations. The fixator with a carbon frame has greater displacements at the fracture site compared to the fixator with a steel frame, in the amount of up to 45% (for configuration B50) or up to 31% (for configuration C50). The stiffness of the structure for both test configurations of the fixator is lower in the fixator with a carbon frame compared to the fixator with a steel frame by up to 27%. Based on the findings of this study, we can conclude that a fixator with a steel frame has better biomechanical characteristics compared to a carbon frame.  
external fixation; stainless steel; composite materials; structural rigidity; fracture rigidity; principal stresses; von Mises stress +
Footnote †: journal: Applied Sciences
## 4 Results and Discussion
### Uncertainties, Errors, and Accuracies  
To validate the selection of the six bio-indicators, MCARI/OSAVI[750; 750], Vogelmann Index, Chlorophyll Index RedEdge 710, EVI, SPRI*EVI, and SPRI*NDVI, and to assess the accuracy of the regional inversion GPP from our model, we compared the output of three independent models, the VPM [70], the BEPS model [71], and MOD17A2 GPP products [72]. The GPP spatial pattern agreement between our model and the three independent models is illustrated in Figure 10. Statistical histogram comparisons showed good agreement between VPM and MOD17A2. The regional inversed GPP outputs of the four models were distributed within the range of 0 to 15 gC\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\) for VPM, 2 to 17 gC\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\) for BEPS, 2 to 8 gC\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\) for MOD17A2, and 2 to 10 gC\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\) for our model. Although all four models are within a similar regional inverted GPP range, improvements in model agreement were constrained by spatial resolution and the magnitude of pixel number among the four models.  
Additional uncertainty and errors of model output can be attributed to the influence of snow cover on the reflectance spectrum of remote sensing pixels and the delimitation of C fluxes footprint. The ratio of the snow cover to canopy cover influences the spectrums of pixels in the VIS and NIR wavelengths. The footprint can be influenced by the atmospheric stability of wind speed and direction. Due to the variation of daily atmospheric conditions, the footprints vary based on satellite imaging times, and the fixed spatial region of extracting image spectrums vary for each model footprint. Also, the impacts of land-cover/land-use (LCLU) change on vegetation growth affected by various human activities, including residential andcommercial development, reforestation, and agriculture, may introduce error in the correlation of hyperspectral remotely sensed surface characteristics indicated by bio-indicators to measure regional forest C fluxes data.
## 5 Hardware Detection Circuit Debugging and Result Analysis
### Overall Hardware Circuit Testing  
In this subsection, the above modules are connected to form an overall dual-phase sensitive phase-locked amplification hardware circuit as shown in Figure 3, and Figure 14 shows its test diagram, where the amplitude of the DC component in the two-channel output signals is detected using multimeter 7 and multimeter 9, respectively, and the amplitude of the AC component in the output signals is detected using multimeter 8 and multimeter 10, respectively. To verify the performance and accuracy of the circuit in processing signals under different conditions, the reference signal is set to a 1 V, 1 MHz sinusoidal signal, and 1 MHz sinusoidal signals with different amplitudes and phases are used as valid signals and 0-1 MHz white noise signals as detection signals. The outputs of the two sets of signal amplitude, according to Equations (11) and (12) for data processing, are shown in Table 1.  
Figure 13: Modeling of low pass filter module testing. (**a**) Circuit of low pass filter, (**b**) Input and output waveforms of low-pass filters.  
The designed dual-phase sensitive phase-locked amplifier circuit, as demonstrated in Table 1, effectively extracts the 1 MHz AC signal while maintaining an amplitude error within 0.2%, resulting in a significant amplification of approximately 2000 times. By employing compensation algorithms, the measured phase angles are reduced to within 0.5% of the original fixed value of about 2.715\({}^{\circ}\). Thus, our objective of extracting and amplifying a valid signal is successfully accomplished.
# A Novel Overcurrent Suppression Strategy during Reclosing Process of MMC-HVDC
## 2 Fault Current Calculation of a Loop MMC-HVDC Grid  
### Characteristics of a Pole-to-Pole Fault  
A common three-phase MMC is shown in Figure 1. It is composed of six arms and each arm consists of N sub-modules (SMs) connected in series and an arm inductor L\({}_{0}\). Each SM includes two insulated gate bipolar transistors (IGBTs), two diodes and a DC capacitor C\({}_{0}\). If the SM is 'ON' state, the output of SM is the dc capacitor voltage. When the SM is 'OFF' state, the output of SM is zero.  
Figure 1: Fault current path of a pole-to-pole fault before converter blocking.  
The pole-to-pole fault is the most serious fault in the DC side of MMC-HVDC. The fault process can be divided into two stages according to whether the converter is blocked [18].  
Before the converter is blocked, the arm current is shown in Figure 1. During this stage, all the capacitors of SMs in 'ON' state will discharge through T\({}_{1}\) immediately (the red line), and thus the currents in the arms and DC lines increase extremely fast. For the AC system, the fault is equivalent to a three-phase short-circuit. So, the AC system also injects short-circuit currents (the blue line) during this stage, whereas they are not capable of flowing into the DC lines, because their contributions sum to zero in the converter. The arm current is a superposition of the discharging current and three-phase short-circuit current. Due to the sorting algorithm within the converter, there are always N SMs being switched in thus all the SMs will discharge during the fault. Given to the high control frequency, the SMs in the upper and the lower arm can be regarded as discharging at the same time. Therefore, the equivalent circuit of the converter during this stage is displayed as Figure 2.  
If the arm current in any arm exceeds the threshold of overcurrent protection of SM, the converter will be blocked immediately. The fault process after blocking can be further divided into two sub-stages. The first one is inductor free-wheeling circuit as is shown in Figure 2(a). The fault currents are maintained by the arm inductor. At this stage, each arm still carries a third of the DC line current, and no AC currents are injected in DC line.  
The stage of inductor free-wheeling will end when the inductor free-wheeling current decrease to zero. Then the converter starts to act as an uncontrolled rectifier as Figure 2(b). At this stage, the fault currents in the DC lines are fed by the AC system. Generally, the protection time in MMC-HVDC grid is within 5 ms after the fault occurrence [19], so this stage of the uncontrolled rectifier seldom occurs in practical system.  
Figure 3: The fault response after converter blocking: (**a**) free-wheeling; (**b**) uncontrolled rectifier.  
Figure 2: Equivalent circuit of the converter under a pole-to-pole fault before converter blocking.