esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
A refined seismic design method improving the structural collapse resistant capacity without an increase of construction cost for an RC frame structure is proposed. A five-story RC frame structure designed according to the CSDBC [4] is optimized to illustrate the feasibility and availability of the proposed optimization method. Based on the analytical results, some conclusions can be drawn:  
* Since the longitudinal reinforcement of columns is adopted as the optimization variable, the optimization procedure just needs a few iterative steps to achieve the optimization target. In addition, the constrained provisions (e.g., the maximum and minimum reinforcement ratio of the columns, etc.) in code are also taken into account during the iterative loops. It is a practical and computationally efficient method and easily implemented in engineering practice.
* After optimization, the maximum inter-story response is reduced while the structure suffers inelastic deformation. The reduction level is increased with the nonlinear state developing.
* Compared to the code-based structure, with the same construction cost, the collapse resistant capacity of the optimized structure is remarkably increased (on average up to 60.9%); and the \(TR\) distributes more uniformly under the earthquake intensity of the initial structure collapse state.
* Though the RC frame is analyzed in this study, the other type of structure can be optimized by a similar methodology. Similarly, the proposed optimization methodology also can be extended to strengthen and retrofit frame structures.  
Conceptualization, M.G. and S.L.; methodology, M.G.; software, M.G.; validation, M.G.; formal analysis, M.G.; investigation, M.G.; resources, M.G.; data curation, M.G.; writing--original draft preparation, M.G.; writing--review and editing, S.L.; visualization, M.G.; supervision, M.G.; project administration, S.L.; funding acquisition, S.L. All authors have read and agreed to the published version of the manuscript.  
This research project was supported by the National Natural Science Foundation of China (Grant No. 51578202), Natural Science Foundation of Heilongjiang (Grant No. YQ2019E021).  
There is no conflict of interest.
## 6 Conclusions  
In this paper, a review was carried out on the improvement of the fault ride-through capability of the doubly-fed induction generator by use of fault current limiting devices. Almost all of the types of fault current limiters, employed in the fault ride-through of the doubly-fed induction generator, have been discussed. For all prototypes, their cost and the impact of their operation on rotor transient over-current limitation, stator current limitation, excess active power evacuation, and terminal voltage sag compensation have been analysed and provided in Table 1. With respect to the comparison, the best operation is granted to the resistive type fault current limiter located in the stator side, due to the voltage sag compensation capability in the doubly-fed induction generator terminal, the excess active power consumption, and the increase in controllability of the rotor side converter. As all fault current limiters have the same performance after fault detection, the operation of the fault current limiters was simulated in regards to their location in the doubly-fed induction generator and the impedance types. Three main locations, including the stator side, the rotor side, and the terminal side, were taken into account. Furthermore, for all three locations, the inductive and resistive type fault current limiters were placed, and their impacts on the rotor and stator current limitations were discussed. Regarding the simulation, it was demonstrated that the resistive type fault current limiter placed in the stator side was the most effective fault current limiter in enhancement of the doubly-fed induction generator. For future works, in order to achieve a good fault current limitation with the fault current limiter at the rotor side, the fault current limiter should utilise a high impedance value compared to the fault current limiter located in the stator side. In addition, for fault current limiters in the stator side, a resistive type FCL should be utilised in order to achieve a good fault ride-through capability enhancement in the doubly-fed induction generator.  
All authors equally contributed in conceptualization, methodology, software, formal analysis, investigation, resources, writing-original draft preparation, writing-review & editing. M.N. and F.B. supervised and administrated the project.  
This research has not received any external funding.  
The authors declare no conflicts of interest.  
The list of abbreviations used in the paper is provided as follows:  
Figure 13: With the resistive type fault current limiter in the stator side (L1), in the terminal side (L2), and in the rotor side (L4) during a three-phase fault: (**a**) The stator currents; and (**b**) the rotor currents.
## 3 Results
### Nutrients Fluxes
#### 3.3.2 Mineral Nitrogen and Soluble Phosphorus Transferred per Sub-Basin  
The mineral nitrogen and soluble phosphorus spatial distribution on the basis of fertilizers used by crop type is presented in Figure 6A,B. In Figure 6A, the variations in mineral nitrogen vary from 0 to 0.049 kg ha\({}^{-1}\). The highest mineral nitrogen fluxes are observed overall in the northern part of the watershed. The sub-basins concerned are 1, 2, 3, 4 and 5. Sub-basin 3 presents the largest contribution (0.049 kg ha\({}^{-1}\)) of the entire Lobo catchment area taken in Nibehibe. The south and southwest parts also show high flows but a little less compared to the north part. These quantities of nitrogen per hectare vary from 0 to 0.037 kg ha\({}^{-1}\). The sub-basins with large quantities are sub-basins 28, 29 (containing the Lobo water reservoir), 30, and 31. It is generally observed that the mineral nitrogen inputs over the entire watershed are weak. Figure 6B shows the spatial variation in soluble phosphorus levels by sub-watershed. The soluble phosphorus flux varies from 0 to 0.31 kg ha\({}^{-1}\). The higher values are observed in the central, eastern, and western parts  
Figure 5: Flow calibration result at Nibéhibé hydrometric station.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Crop** & **Fertilizer (NPK)** & \begin{tabular}{c} **Quantity** \\ **(kg ha\({}^{-1}\))** \\ \end{tabular} & \begin{tabular}{c} **N Quantity** \\ **(kg ha\({}^{-1}\))** \\ \end{tabular} & \
\begin{tabular}{c} **P Quantity** \\ **(kg ha\({}^{-1}\))** \\ \end{tabular} \\ \hline Cotton & 15-15-15 & 200 & 30 & 13.2 \\ Cocoa tree & 0-23-19 & 500 & 00 & 50.6 \\ Coffee & 12-06-20 & 784 & 94.08 & 79.34 \\ Cashew & 11-22-16 & 81.6 & 8.2 & 9.69 \\ Rice & 12-24-18 & 200 & 24 & 21.12 \\ Banana & 25-04-23 & 200 & 50 & 4.224 \\ Corn & 15-15-15 & 250 & 37.5 & 16.5 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{Observed mean (CNRA)} & \multicolumn{2}{c}{41 kg ha\({}^{-1}\)} & \multicolumn{2}{c}{28 kg ha\({}^{-1}\)} \\ \hline \hline  & \multicolumn{2}{c}{SWAT} & \multicolumn{2}{c}{47.24 kg ha\({}^{-1}\)} & \multicolumn{2}{c}{21.25 kg ha\({}^{-1}\)} \\ \hline \hline \end{tabular}
\end{table}
Table 5: Nutrient requirements for crops.  
of the basin with rates of up to 0.31 kg ha\({}^{-1}\). It is also noted that the flows of mineral nitrogen are lower than those of phosphorus. This difference stems from the NPK fertilizer formula used.
# Time-Dependent Probabilistic Approach of Failure Mode and Effect Analysis
## 2 Time-Dependent FMEA Model (TD-FMEA Model)
### Risk Evaluation Modeling
#### 2.2.2 Detection Time of a Failure-Cause  
The probability density function of a corresponding failure-cause detection is generally assumed to be uniform before a failure occurrence, because a failure-cause is usually detected in accordance with the regularized detection schedule of a uniform time interval. Now, assume that we do not detect the failure-cause until the failure occurs. In the early stages of a failure, intensive detection will be attempted to detect failure-cause, so the probability density function of detection will be high at the initial state and will then gradually reduce. Thus, we considered an exponential function in this study, because it is the easiest to integrate and demonstrates excellent expansion. Therefore, probability density function of a failure-cause detection will be a case function conditioned by the time of occurrence.  
Now, set up the following variables for obtaining the exact probability density function of detection time. Let us consider a failure and its failure-cause. Here, \(s\) is the elapsed time between occurrence and detection of the failure-cause. Similarly, \(t\) is the elapsed time between occurrence of failure and that of its failure-cause. As mentioned before, the conditional probability density function \(g_{S|T=t}(s)\) is uniform in the domain where a detection time \(s\) is less than the given occurrence time \(t\). Otherwise, \(g_{S|T=t}(s)\) has an exponential form. Next, we have to determine the weight of the uniform and exponential section among the total probability 1. Let \(w(t)\) be the cumulative probability of the uniform distribution; then, \(1-w(t)\) is the cumulative probability of the exponential distribution as shown in Figure 3. Thus, the conditional probability density function \(g_{S|T=t}(s)\) is expressed as a function of the failure-cause detection rate \(\mu\), given the failure occurrence time \(T=t\).  
\[g_{S|T=t}(s)=\left\{\begin{array}{ll}w(t)\frac{1}{t}&,s<t\\ [1-w(t)]\mu e^{-\mu(s-t)}&,s\geq t\end{array}\right.. \tag{4}\]  
The function \(g_{S|T=t}(s)\) may jump up at \(s=t\). This is because more efforts are expected to be made to repair the failure and normalize the system at a time \(t\). Thus, for \(s=t\) with jump up coefficient \(c\),  
\[cw(t)\frac{1}{t}=[1-w(t)]\mu e^{-\mu(t-t)}=[1-w(t)]\mu. \tag{5}\]  
Rearrangement of Equation (5) yields  
\[w(t)=\frac{\mu t}{c+\mu t}. \tag{6}\]  
The weight function \(w(t)\) equals zero when \(t\) is zero and becomes unity as \(t\) approaches infinity. This result is intuitively appropriate, since the probability of detection before failure would nearlyequal zero when \(t\) approaches zero, and as \(t\) becomes infinitely large, the detection probability must approach unity. Thus,  
\[g_{S|T=t}(s)=\left\{\begin{array}{ll}\frac{\mu}{c+\mu t}&,s<t\\ \frac{c\mu}{c+\mu t}e^{-\mu(s-t)}&,s\geq t\end{array}\right.. \tag{7}\]
# Wake-Up Receiver-Based Routing for Clustered Multihop Wireless Sensor Networks
## 4 Proposed Approach
### Routing Process Description  
The key idea of the proposed method is that the sink performs transmission scheduling while the Fog Node uses a multicast strategy to wake up nodes for asymmetric data communication. The process is depicted in the Algorithms 1 and 2 and summarised as a flow diagram shown in Figure 6.  
Figure 4: Manchester-coded wake-up signal consisting of carrier burst, preamble, and address pattern [34].  
Figure 5: Example of forming the multicast wake-up pattern.  
1. The SINK decides the routing and transmission timing of each sensor node. When the SINK tries to collect sensor data from the Destination Node DST, it checks its routing table to find out which Fog Node FN is in charge for the DST. Then the SINK transmits an REQ to the responsible FN.
2. The FN is always on and main powered. It receives the request REQ from the SINK and checks its routing table to specify the nodes that will act as Relays REL. The wake-up messsage WuPt is sent via multicast to wake up the DST and its corresponding RELs. The FN remains in receiving mode.
3. The DST receives the WuPt and starts data transmission to the REL. Each node that finalises its transmission returns to the sleeping mode.
4. The FN receives the data and forwards the packet to the SINK. The FN goes to receive mode.
5. The SINK receives the data.  
```
1:procedureWaxing UP NODES USING multicast WuPt (SINK, FN, REL, DST)
2:ifSink requests data from DST then
3: SINK checks cluster table for addressing responsible FN sending REQ to FN
4:ifFN receives REQthen
5: check cluster member table
6:ifDST is in direct rangethen
7: send unicast WuPt
8:elsesend multicast WuPt for DST and REL
```  
**Algorithm 1** WuPt transmission  
```
1:procedureData forwarding after multicast WuPt (SINK, FN, REL, DST)
2:ifWuPt is receivedthen
3: bit-wise check of WuPt address
4:ifcluster-bit and ID-bit = = own cluster-bit and ID-bitthen
5: wake-up sensor node and switch to receive mode
6:ifreceiving request from FNthen
7: check source route table
8:ifnext hop!= FNthen
9: check source route table and send DataPt to REL
10:else send Data to FN and go to sleep mode
11:endprocedure.
12:elsereceive DataPt and check source route table
13:ifnext hop!= FNthen
14: check source route table and send DataPt to REL
15:else send Data to FN and go to sleep mode
16:else go to sleep mode
```  
**Algorithm 2** Data transmission
# The Influence of Motion Data Low-Pass Filtering Methods in Machine-Learning Models
## 2 Materials and Methods
### Experimental Setup  
Participants were asked to walk along a 7 m walkway for at least 10 trials at their preferred (self-selected) speed and manner, and then experienced at least one unexpected slip and one unexpected trip in a pseudorandom order. For each participant, the following trials were selected for analysis: (1) a regular walking trial before perturbation exposure, (2) the novel slip trial, and (3) the novel trip trial.  
Slip perturbations were induced by low-friction, movable platforms embedded near the middle of the walkway. During the slip trial, the platform could slide up to 60 centimeters forward or 10 centimeters backward in the anteroposterior direction. Force plates were installed under the walkway (AMTI, Newton, MA, USA), and once the force plates detected the subject's right foot in contact with the right platform, a computer-controlled mechanism would immediately release the platform. Trip perturbations were induced by an obstacle device consisting of a hinged metal plate (8 cm height, 27 cm width, and 0.5 cm thickness) locked flat by electromagnets during regular walking. In trip trials, the electromagnets powered off when the vertical ground reaction force under the unperturbed limb exceeded 80% of the participant's body weight, causing a trip to occur in the mid-late swing phase.  
A full-body safety harness connected by shock-absorbing ropes was used to protect participants from falling in situations of balance losses post-slipping. The harness enabled participants to walk freely while providing protection against body impact with the floor. Kinematics from a full body marker set (31 retro-reflective markers) were recorded by an eight-camera motion capture system (Motion Analysis Corporation, Santa Rosa, CA, USA). Specifically, 26 markers were placed on the body and another 5 markers were placed on the walkway. Kinematic data were sampled at 120 Hz.
# Analysis and Prospects of an Antarctic Krill Detection Experiment Using Drifting Sonar [PERSON]
## 4 Discussion  
### Experimental Findings and Improvements  
While the experiments successfully demonstrated the buoy's basic functionalities, they also revealed several issues. First, the low sunlight intensity in the Antarctic leads to suboptimal solar panel efficiency, potentially resulting in power deficits during extended operations. Second, the placement of buoys should be slightly far from the island to avoid stranding on islands. Finally, environmentally friendly, non-polluting materials should be used before deploying buoys on a large scale to prevent them from becoming marine debris and causing pollution.  
Our team has proposed buoy design improvements to address these issues. Firstly, these include replacing current batteries with lithium batteries to achieve higher energy density and longer operational times [39] and adding a smaller backup battery that activates to send only positional data when the primary battery fails, facilitating buoy recovery. At the same time, we will attempt to utilize wave energy as a power source for buoys [40; 41]. Secondly, a double-layer hull design is recommended: an inner layer of acrylonitrile butadiene styrene (ABS) for strength and an outer layer of acrylic (PMMA) for corrosion resistance [42; 43], enhancing the buoy's durability and ability to withstand harsh marine conditions. Thirdly, the placement location for subsequent attachments should be selected slightly further from the island to prevent the buoy from running aground.
# Prediction of Intraday Electricity Supply Curves
## 3 Methodology
### Models and Predictions
#### 3.4.9 BiLSTM Model  
Bidirectional long short-term memory (BiLSTM) [32] networks consist of two LSTM networks, processing sequential data both forwards and backward. This allows the model to capture information from both past and future contexts in a sequence. For an application of BiLSTM in power consumption prediction, see [34]. Once again, this structure is followed by fully connected layers in our model design. The considered hyperparameters were the following:n_LSTM. The number of LSTM layers. We tested one and two layers;
* n_dense. The number of fully connected layers besides the output layer. The considered values are one, two, three, and four;
* unit. The number of cells per LSTM layer. We tried 10, 20, 50 and 100 units;
* neuron. The number of neurons in each fully connected layer. The assessed values are 10, 40, 50, and 100;
* dropout. The random deactivation of some input units to reduce overfitting. The studied values are 0, 0.1, and 0.2.  
The selected hyperparameters for the BiLSTM model are available in Table A4 in Appendix A.
# Smart Building Surveillance System as Shared Sensory System for Localization of AGVs
## 4 Conclusions
For example, in the case of the possibility of using the image data, car-sharing or autonomous taxi companies could place unique markers on the roofs of their vehicles fleet, and the position of a vehicle could be inquired in the governmental system, which might be an additional benefit for a public city. Conceptualization, P.O. and D.H.; methodology, Z.B. and P.O. ; software, P.O. ; validation, J.B. and Z.B. ; formal analysis,V.K., D.H. and P.O. ; investigation, Z.B. and J.B.; resources, D.H., V.K. and J.B.; data curation, P.O. and J.B.; writing--original draft preparation, D.H. and P.O. ; writing--review and editing, D.H.,V.K. and Z.B. ; visualization, P.O., D.H.,V.K. and J.B.; supervision, Z.B. ; project administration,V.K. and Z.B. ; funding acquisition, V.K. and Z.B. All authors have read and agreed to the published version of the manuscript. This work was supported by the Research Platform focused on Industry 4.0 and Robotics in Ostrava Agglomeration project, project number CZ.02.1.01/0.0/0.0/17.049/0008425 within the Operational Programme Research, Development and Education. This article has also been supported by a specific research project SP2020/141 and financed by the state budget of the Czech Republic. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. The following abbreviations are used in this manuscript:  
AGV Automated Guided Vehicle  
FPS Frames per Second  
GNSS Global Navigation Satellite System  
Open Source Computer Vision Library  
RFID Radio Frequency Identification  
RGB Red Green Blue  
RTLS Real Time Locating System  
SLAM Simultaneous Localization and Mapping
## 3 Results and Discussion
The in-loop polarizer significantly reduces the relative fluctuations from 57.2 % (red line) to 1.18% (blue line). After the wavelength locking and stabilization of the laser source, the power stability of the transmission laser is as shown in Figure 7a. The standard deviation of the equivalent temperature fluctuation is 2.9 mK, with an actual environment temperature fluctuation of less than 1 mK (measured by the Pt100 temperature sensor), representing the measurement resolution of this thermometer. Figure 7b shows the behavior of the wavelength locking method. The black line is measured by the Pt100 temperature sensor for reference. The light red line is the origin data of PD, and the dark red line is the result of smoothing. We implement three processes in the bath, two heating and one cooling, and the variation trend of the device is consistent with the results of the Pt100 temperature sensor, showing a good temperature response characteristic. The combination of the wavelength scanning and the locking method can enable temperature measurement with a wide temperature range and high resolution.
# Fractional Order Processing of Satellite Images
## 3 Results  
In this section, the overall relevant results of the iterations with different parameters will be presented, and illustrated with the four images shown in Section 1.3. Again, the results for all the forty-three images in the database are available in repository [20].
## 3 Materials and Methods
### Calculation of Dependent Variable Values  
End-to-end delay (D\({}_{\text{EtoE}}\)) consists of the sum of the delay at the Medium Access Control (MAC)/Radio Link Control (RLC) layer, which makes up the largest part of D\({}_{\text{EtoE}}\), then of the delay due to signal propagation at the physical level and the transmission delay between the eNodeB and UE [22]. Therefore, the delay in this case implies \"the time duration that starts when a flow is generated by a traffic source, transmitted through the communication system, until it reaches the application layer of the user's equipment--UE\" [22].  
The values of the variable D\({}_{\text{EtoE}}\) were collected by estimation and calculation based on the results presented in paper [22]. In that paper, [PERSON] et al. (2018) used a simulation method to measure the end-to-end delay for VoIP traffic depending on the number of active UEs in the cell. It involved mobile users' movement speeds of 3 km/h and 120 km/h considered for each of the four observed scheduling algorithms: Exponential Rule (EXP-RULE), EXP-PF, PPM and Delay-based and QoS-Aware Scheduling (DQAS). Based on graphically presented simulation results in [22] (Figure 7 Average D\({}_{\text{E2E}}\) on RT VoIP flows in [22]), for an interval from 10 to 100 active UEs in a cell with a step of 10 and for a speed of 120 km/h, average values of the estimated delays for the four observedscheduling algorithms are calculated in this paper. The values calculated in this way are shown by points in Figure 3, where the regression curve that best describes the functional dependence of the average delay on the number of UEs is given.  
The curve shown in Figure 3 has a polynomial form of the second degree and can be represented by a quadratic equation as follows:  
\[Delay=0.0003\cdot UE^{2}+0.1197\cdot UE+1.8071 \tag{1}\]  
As an indicator of the quality of this model, a very high coefficient of determination (R\({}^{2}\)), R\({}^{2}\) = 0.9941, appears. Among other data, the database received from the M:tel operator provided the values of the average number of active UEs in the cells of the observed geographical area. As such, the D\({}_{\text{EtoE}}\) values were calculated indirectly, using the model given by Equation (1). The method of calculating the values of the dependent variable D\({}_{\text{EtoE}}\) is shown graphically in Figure 4.  
Descriptive statistics for the dependent variable are given in Table 2, and the histogram of the D\({}_{\text{EtoE}}\) variable is shown in Figure 5.  
Figure 4: Method of calculating the values of the dependent variable D\({}_{\text{EtoE}}\)[22].  
Figure 3: Average values of end-to-end delay of VoIP services calculated for EXP-RULE, EXP-PF, PPM and DQAS scheduling algorithms and for mobile user movement speed of 120 km/h.  
From a more detailed analysis of the histogram shown in Figure 5 (in Minitab 14 software), it is concluded that D\({}_{\text{EtoE}}\) values in the range between 2.375 ms and 2.625 ms have the highest frequency (3477 repetitions). The arithmetic mean of the delay values is 4.1503 ms, and of the median is 3.2516 ms.