esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments  
### Testbed Description  
We evaluate our protocol in an indoor wireless sensor network testbed that is deployed on the twelfth floor of an office building. The testbed comprises a total of ten Arduino-based sensor nodes and multiple Linux-based gateways. Each sensor node is equipped with appropriate sensor hardware that enables it to collect data of various physical environmental modalities, such as temperature, noise and humidity. The sensor data from each node is periodically transmitted to the gateways through multiple hops, via an IEEE 802.15.4-compliant radio interface.  
Figure 1 illustrates the location of the nodes (101 to 110 for the sensor nodes and 500 and 501 for the gateways) in the testbed, which forms a connected multi-hop topology. Due to the lack of energy harvesting sources in the building, each node is connected to a constant power supply source.
## 3 Results and Discussion
### Seismic Fragility Assessment
#### 3.1.2 Development of Fragility Functions  
Based on the derived fragility parameters, the corresponding fragility functions can be developed using Equation (1). Figure 10a presents the \(PGA\)-based fragility functions for the examined tunnel in soft soil. Naturally, the exceedance probability of damage would increase with increasing \(PGA\). As the value of \(PGA\) is lower than 0.3 g, the probability of extensive damage is negligible, which means that the examined shallow tunnel may withstand this level of earthquake intensity well, practically with no damage. The minor and moderate damage probabilities are 15% and 2%, respectively. When \(PGA\) equals 0.4 g, the exceedance probabilities for minor, moderate, and extensive damage will increase to 60%, 22%, and 5%, respectively. In comparison, as the value of \(PGA\) increases to near 1.0 g, there is a 97% probability that the tunnel will be exposed to minor damage. The relevant possibilities for moderate or extensive damage are 83% and 52%. The latter observations highlight the significant importance of sufficient seismic designs for shield tunnels in similar sites. Similar conclusions can also be obtained for the fragility functions developed in terms of \(PGV\) at the ground surface.  
Figure 9: Evolution of the \(DI\) with (**a**) \(PGA\) and (**b**) \(PGV\) at the free-field ground surface.
# Effect of Microwave-Assisted Processing of Acorn Flour on Muffin Quality
## 3 Results and Discussion
### Effect of Microwave-Treated Acorn Flour Addition on Organoleptic Characteristics of Muffins  
Changes in the technological quality and physical properties of wheat-acorn muffins, resulting from the microwave processing of the acorn flour, influenced the organoleptic evaluation of the baked goods (Table 6). Statistically significant differences were predominantly observed at the longest processing times and/or the highest microwave power levels. For muffins made with dry microwave-treated acorn flour, increases in color and taste scores were recorded for samples processed at 925 W for 3.0 min (by 12.5% and 24%, respectively) and at 1850 W for 1.0 min (by 10.9% and 18.9%, respectively). However, these samples simultaneously received statistically significantly lower scores for texture. In muffins prepared with wet microwave-treated acorn flour, the effects of microwave treatment on scores followed a similar pattern to those observed for dry-treated flour. Longer microwave durations at 925 W and 1850 W significantly improved aroma and taste scores compared to the control sample. Nevertheless, extended microwave processing led to lower texture scores.  
The overall acceptance of the wheat-acorn muffins indicated that the microwave treatment of acorn flour prior to incorporation into the muffins enhanced product acceptability. Specifically, dry microwave treatment of acorn flour improved muffin acceptance, particularly for samples prepared with flour treated at 925 W and those subjected to the longest processing time. For muffins containing wet microwave-treated acorn flour, the highest acceptance was noted in samples with flour treated at the lowest power level.
# Impulse Signal Detection for Bearing Fault Diagnosis via Residual-Variational Mode Decomposition
## 2 Proposed Method
### Rvmd  
A novel method named RVMD is proposed in this study to extract the impulsive component accurately. RVMD is designed to have two stages. The first stage is to roughly find the impulses, and the second stage is to determine the impulses. The main steps of RVMD can be summarized as follows:  
Figure 1: The flowchart of IEFI-MVMD.  
Step 1: Initialize system parameters: \(\alpha=1000\) and \(K=1\).  
Step 2: Run the VMD and compute the correlation coefficient (\(C\)) between the raw signal and the decomposition result. If \(C>0.95\), proceed to Step 3. Otherwise, let \(K=K+1\) and repeat Step 2.  
Step 3: Compute \(NEK\) for each mode. The residual signal is determined as follows: if the largest \(NEK\) is the first or the last mode, the residual signal is obtained by adding the mode that has the largest \(NEK\) with its neighbor mode. Otherwise, the residual signal will be obtained by adding the mode with the largest \(NEK\) with its right and left neighbors.  
Step 4: Fix \(K=2\) and run VMD with \(\alpha=\alpha+500\). Compute \(NEK\) for each \(\alpha\). Repeat Step 4 until \(\alpha>3000\).  
Step 5: Run VMD with the optimal parameters, which correspond to the largest \(NEK\). The sub-mode with the larger \(NEK\) is analyzed by the envelope analysis technology to show the fault feature.  
The flowchart of the proposed method is presented in Figure 2.
# INS/Vision Integrated Navigation System Based on a Navigation Cell Model of the [PERSON]
## 4 Experiment and Verification

Table 1: Latitude and longitude errors (m). SD: standard deviation; INS: inertial navigation system; RMSE: root-mean-square error. Figure 8: Place nodes of image matching. (**a**) The first place cell node; (**b**) The second place cell node; (**c**) The third place cell node; (**d**) The fourth place cell node. Figure 9b,c represent the latitude and longitude errors, respectively. The blue line in Figure 9b is the pure INS position error, the green line is the position error based on the traditional image matching correction method, and the red line represents the error obtained by the brain-like navigation method. As we can see from the Figure 9b,c, the position error estimated by the brain-like navigation method is improved compared with the traditional visual correction method and the pure INS mode. From Table 1, we can see that the mean error of latitude calculated by the traditional image matching method is better than the traditional visual correction method. Figure 10: Place nodes of image matching. Figure 9: Place and error experimental results. (**a**) Trajectory; (**b**) Latitude error; (**c**) Longitude error. correction method is reduced by 61.6% compared with the pure INS mode, and the mean error estimated by the brain-like navigation method is reduced by 15.5% compared with the traditional image matching correction method. In terms of longitude error estimation, the error obtained by the traditional image matching method is reduced by 2.7% compared with the pure INS method, and the error obtained by the brain-like navigation method is reduced by 1.6% compared with that of the traditional image matching correction method. At the same time, the root-mean-square error (RMSE) and standard deviation (SD) of the position estimated by the brain-like navigation method are smaller than the traditional image matching correction method and pure INS mode. In a word, the above experimental results prove the validity and correctness of the proposed brain-like navigation method.
## 4 Hybrid Covering Location Problem Formulation
### MCLP-MCLP  
If we assume that the facilities are located using MCLP having a predefined number of them and the module assignment and demands allocation are modeled using MCLP, then the formulation of this problem is different from HCLP. MCLP-MCLP locates the facilities utilizing MCLP, and the decisions for module assignment and demand points allocation are determined by MCLP as well. In contrast to the HCLP that gives the solution to both numbers and location of the facilities, the number of the facilities in MCLP-MCLP is a parameter, and the problem only finds the optimal location of these predefined number of facilities. In addition, while the goal of HCLP is to cover all demand points by the facilities, this goal is not valid anymore in MCLP-MCLP, and it only seeks to maximize the total covered demand points. The main difference of HCLP and MCLP-MCLP is the constraint (7) that should be substituted by constraint (17) that implies the total number of located facilities in each strategic period cannot exceed the number defined beforehand. As mentioned before, the main motivation of using MCLP for locating facilities can be the budgetary limitations when the decision makers can afford only to open a specific number of facilities and look for the optimal location of these facilities with the aim of maximizing the demand points covered by them. In MCLP-MCLP, both the number of facilities and modules are a given problem parameter, and the model has to find the solution for their location and assignment. As a result, the objective of the MCLP-MCLP is just to maximize the profit gained from covering demands i.e., the cost of facilities is no longer included in the objective function. Thus, the difference between MCLP-MCLP and HCLP is in the objective function and constraints (7), and the mathematical formulation of the MCLP-MCLP can be modified as follows:  
\[\max\sum_{i\in I}\sum_{j\in I}\sum_{l\in L}\sum_{t\in T}\sum_{t\in T_{t}}a_{ jlt\tau}g_{ij}d_{jlt\tau}x_{ijlt\tau} \tag{16}\]  
\[\sum_{i\in I}z_{it}\leq p_{t} \tag{17}\]  
\[\forall t\in T\] (8)-(15).  
where \(p_{t}\) is the number of predefined facilities for each strategic period that is defined by the decision-makers as a parameter. Note that having the number of located facilities depending on the time periods is to keep the expansion capabilities of the model. Otherwise, it can be a fixed number for all periods.
# Morphology-Related Foot Function Analysis: Implications for Jumping and Running
## 2 Materials and Methods
### Data Acquisition  
Previous studies have outlined data collected from insole pressure sensors and pressure plates and show high reliability [32]. All the anatomical region division analysis was performed in the Novel Database in the data post-procession based on an auto-masking algorithm [33]. For trials of barefoot and shod vertical jumping, only the data in the forefoot and toes were included. The collected plantar pressure data while performing vertical jumping were separated into the push-off and landing phases for analysis. Thus, the plantar surface was divided into five anatomical regions: medial forefoot (MF), central forefoot (CF), lateral forefoot (LF), hallux (H), and other toes (OT), as this study mainly focused on the instant push-off and landing phase of the vertical jump. For trials using barefoot and shod running, the plantar surface was divided into eight anatomical regions, including medial rearfoot (MR), lateral rearfoot (LR), medial midfoot (MM), lateral midfoot (LM), medial forefoot (MF), lateral forefoot (LF), hallux (H), and other toes (OT). The variables for jumping and running included peak pressure, contact area, and the pressure-time integral of each anatomical region.  
The kinematic test used the eight-camera Vicon motion analysis system (Oxford Metric Ltd., Oxford, UK) to collect the lower extremity kinematic data, with a frequency of 200 Hz. Sixteen reflective points (diameter: 14 mm) were attached with adhesive tape on the lower limbs of subjects, following a previously published protocol [34]. The anatomical landmarks included the anterior-superioriliac spine, posterior-superior iliac spine, lateral mid-high, lateral knee, lateral mid-shank, lateral malleolus, second metatarsal head, and calcaneus. A Kistler Force Platform (Model 9281B, Winterthur, Switzerland) was used to record ground reaction forces (GRFs), with a frequency set at 1000 Hz, to define the running foot striking patterns and contact time. The force platform was zero-levelled prior to testing each participant. The on and off force platform was defined from the value of vertical GRF as 20 N. Participants were required to strike the force platform with the right foot while performing the running and jumping tests on the force platform. The variables of running included spatiotemporal parameters, such as stride length, stride time and contact time, peak angles during stance, and joints range of motion (ROM) in a gait cycle. The spatiotemporal parameters were generated from the Workstation in the Vicon Nexus software (v1.8.5), including hip, knee, and ankle angles in the sagittal plane, coronal plane, and horizonal plane, computed from the Vicon Plug-in-Gait Model using established protocols [20; 30]. Vertical jump height was calculated by Equation (1) [35]:  
\[\mathrm{Jumpheight(m)}=\frac{9.80m\cdot s^{-2}\times flighttime(s)^{2}}{8}. \tag{1}\]
## 3 Results
### [ATP]/[ADP] Ratio, ATPase Activity, and cAMP Concentration Determinations
Metabolite abbreviations: Glc, glucose; G6P, glucose-6-phosphate; F6P, fructose-6-phosphate; P1,6 dP, fructose-1,6-biphosphate; DHAP, dihydroxyacetone phosphate; G3P, glyceraldehyde 3-phosphate; 1,3-dGP, 1,3-biphosphoglycerate; 3 PG, 3-phosphoglycerate; 2 PG, 2-phophoglycerate; PEP, phosphoenolpyruvate; PYR, pyruvate; 6 PGLN, 6-phosphoglucono-8-lactone; 6 PGNT, 6-phophogluconate; KDPGNT, 2-keto-3-deoxy-6-phosphogluconate; RUSP, ribulose-5-phosphate; RSP, ribose-5-phosphate; X5P, xylulose-5-phosphate; S7P, sedoheptulose-7-phosphate; E4P, erythrose-4-phosphate; F6P, fructose-6-phosphate; AcCoA, acetyl coenzyme A; CoA, coenzyme A; Ac-P, acetyl phosphate; ACE, acetate; LAC, lactate; FOR, formate; CIT, citrate; ICTT, isocitrate; GOx, glyoxylate; a-KG, \(\alpha\) -ketoglutarate; SUC-CoA, succininyl-coenzyme A, SUC, succinate; FUM, fumarate; MAL, malate; OAA, oxaloacetate; SA, shikimic acid; DAHP, 3-deoxy-D-arabino-heptulosonate 7-phosphate; cAMP, cyclicAMP. The corresponding names of the genes and the complete RT-qPCR values for all analyzed genes are shown in Table 3.
# Novel Internet of Things Platform for In-Building Power Quality Submetering
## 3 Power Quality Functionality Implementation
### Voltage Sag  
The flowchart followed by the software implemented in the MCU is depicted in Figure 9. The interrupt request is continuously being checked. If an interrupt request is detected, the MCU reads the RSTATUS register in the ADE7758 IC through the SPI port, and the process continues if the sag interrupts flag is activated. If that happens, the first step is to read the V\({}_{\text{RMS}}\) of the sag, further updating the minimum saved value if the last reading contains a smaller RMS value. Whenever a sag occurs for the first time, the start time is saved (with a 1 s resolution obtained from the RTC  
Figure 7: Communication between ADE7758 IC and ATMega328P when the V\({}_{\text{RMS}}\) register is read (Ch1. SS, Ch2. SCLK, Ch3. MOSI, Ch4. MISO).  
peripheral). As the minimum resolution in the IC is 20 ms (1 cycle in the 50 Hz system), the next time that the interrupt request is detected, the sag duration is increased in 20 ms, and the same holds for the following detections. A more detailed explanation for the consecutive detections is shown in Figure 9. Figure 10 shows the timeline sequence in a sag detection.  
In this case, the communication starts when the sag interrupt flag is set in the RSTATUS register. Figure 11 contains a reading of the V\({}_{\text{RMS}}\) (0x0D) register, request seen in MOSI line. The MISO line corresponds to the V\({}_{\text{RMS}}\) reading, in this case, 192.52 V\({}_{\text{RMS}}\).  
Figure 8: Energy measurement process implemented in the MCU.  
Figure 9: Sag detection process implemented in the MCU.  
Figure 11: Communication between ADE7758 IC and ATMega328P when the V\({}_{\rm RMS}\) register is read (Ch1. SS, Ch2. SCLK, Ch3. MOSI, Ch4. MISO).  
Figure 10: Timeline sequence in a sag detection process.
# Finite-Time Tracking Control of a Flexible Link Manipulator Based on an Extended State Observer
## 3 Control Design
### Controller Design Based on an Extended State Observer  
In this section, using the above estimates, an ANTSM controller based on ESO is developed to realize the finite-time trajectory tracking of the FLM in the joint space in finite-time and vibration suppression.
# Bond Fatigue of TRC with Epoxy Impregnated Carbon Textiles
## 3 Results
### Residual Strength  
If specimens withstood the cyclic loading, their residual strength was tested subsequently. In Figure 15, the residual strengths are compared to the reference strengths from Section 3.1. The results above the grey line mean a higher residual strength compared to the reference strength, and the results below the line mean a lower residual strength. Figure 15a displays the results of the specimens with a minimum stress of 30%, and Figure 14b displays the ones with 50% minimum stress. Different symbols stand for the different anchorage lengths, and the darker the color of the symbols, the higher the related maximum stress. The single values of the results can also be found in Table A3.  
As one can see in Figure 15, the residual strengths are higher or at least at the same level than the reference strengths, and even high maximum stresses seem not to cause damage, leading to lower residual strengths. That means that there is no negative impact on the load-bearing capacity of the material by fatigue loading. A load increase after cyclic loading is not unusual, and is often noticed in  
Figure 14: S-N diagram with absolute maximum stresses for DPO tests with different anchorage lengths.  
Figure 15: Comparison of residual and reference stresses: (**a**) 30% minimum stress; (**b**) 50% minimum stress.  
fatigue experiments with several materials. A final explanation for this phenomenon has not been found yet. In the present investigation, the reason for the load increase could be the activation of more filaments in a fiber strand due to the cyclic loading. However, this theory still has to be proven.  
In Figure 16, the dependence of the textile stress on the anchorage length, which is known from Section 3.1, is supplemented by the determined residual stresses. The test results of the reference stresses, all of them failed by splitting, are marked by grey rhombs, whilst orange rhombs mark the splitted specimens in residual strength tests. If specimens in residual strength testing failed by textile rupture, the results are displayed by red crosses.  
As one can see, textile rupture only occurred at the long anchorage lengths of 3\(a\) or higher, where the residual strengths reach the value of the mean textile stress. However, there was no dependence of the failure mode on the applied load level. Furthermore, as splitting and rupture occur at the same stresses, the failure mode is also not dependent on the value of the residual strength.
# Evolution of Hybrid LiFi-WiFi Networks: A Survey
## 2 Framework for HWLNet
### Modulation Schemes
It does not require any additional hardware or complex signal processing algorithms, which makes it a cost-effective solution for VLC systems. Researchers [12] have discovered the optimized scheme for the distribution of power and constellation sizes on different subcarriers, which is achieved by maximizing the minimum Euclidean distance of the constellations. These modulation schemes are used as a part of multiple access standards for LiFi. Carrier sense multiple access/collision avoidance (CSMA/CA) and time-division multiple access (TDMA) are now for only LiFi standards. LiFi, unlike WiFi, uses one channel for downlink (visible light) and another for uplink (infrared), which in turn causes collisions if  
Figure 5: DCO-OFDM modulation. CSMA/CA is used. Authors in [13] found an option to broadcast a channel busy tone to reduce collision. The better option is to use a different multiple access standard all together. TDMA achieves better power consumption and bandwidth utilization. The orthogonal frequency-division multiple access (OFDMA) and non-orthogonal multiple access (NOMA) are other two multiple access schemes researched for use in a LiFi environment. These schemes require a very complex system because of the coordination of resource assignment. OFDMA uses time-frequency resource blocks to support concurrent transmission, and NOMA groups users by power levels and channel condition rather than time-frequency in OFDMA. NOMA has better performance only if there is a large difference in the channel conditions. Authors in [12] found that the angle of LiFi transmitters also has high impact on NOMA. Data rates for LiFi modulation are as follows [14]:  
* from 10 kbps to 100 Mbps for OOK and VPPM;
* from 12 Mbps to 96 Mbps for CSK.
###### Abstract  
In practical engineering, due to the residual magnetism in the iron core of the converter transformer, the inrush current is generated in the case of no-load closing, and the inrush current leads to an oil flow surge inside the converter transformer. Under the influence of oil flow, the gas relay plate will be deflected, and when the deflection angle is too large, the gas relay will malfunction. Because of the lacking research on the influence of the misoperation of the gas relay under the excitation inrush of the converter transformer, it is difficult to effectively suppress the misoperation of gas relay under inrush current. Therefore, the finite element model of the electromagnetic hydraulic coupling of the converter transformer is established in this paper to obtain the oil flow velocity through the gas relay under the inrush current. A fluid-solid coupling model was established inside the gas relay to study the deflection characteristics of the baffle of the gas relay under the inrush current, and the relationship between the inrush current amplitude and the deflection angle of the baffle was mathematically fitted to effectively predict whether the gas relay has the risk of misoperation. From the experimental results, it can be seen that when the amplitude of the inrush current exceeds 6.37 kA, the gas relay will have the risk of misoperation. Finally, the influence of oil flow impact on the gas relay baffle under multiple cycles is considered. The results can provide an effective reference for analyzing the diagnosis of gas relay misoperation under the effect of inrush current.  
inrush current; gas relay; oil flow characteristics; fluid-solid coupling +
Footnote †: journal: Applied sciences