esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology  
The acquisition time and the precision of signal parameters lie in the signal search space and each search step size. That is, the guessed number of trial search point determines each time the signal search time. Although a smaller step size increases the precision of estimated signal parameter, it also increases search times, which causes longer acquisition time. Contrariwise, the larger the step size, the more loss of correlation. In the GC process, the search space (including frequency and code space) and the number of acquisition trial points are adjusted to shorten signal search time. The adaptive scaling scheme is to enhance the accuracy of signal parameters. In the following, the whole process of proposed method applied to signal acquisition is illustrated.
## 2 Methods
### Model
The architecture of the RAU cell is shown in Figure 2.  
The attention weights of the attention gate in the RAU are calculated as follows:  
\[a_{t}^{i}=\frac{\exp\left(c_{t}^{i}\right)}{\sum_{i}\exp\left(c_{t}^{i}\right)} \tag{2}\]  
where \(c_{t}=W_{c}[x_{t},\ h_{t-1}]+b_{c}\); \(W_{c}\) and \(b_{c}\) are learnable parameters; \(x_{t}\) is the external input vector at time step \(t\); and \(h_{t-1}\) is the hidden state of the previous time step. The attention  
Figure 2: (**a**) The architecture of the RAU cell. The part with red links is the attention gate. (**b**) The architecture of the BiRAU design. gate assigns a high weight to valuable information and a low weight to unimportant information. It enables the network to identify important feature dimensions for the current and subsequent hidden states by combining the input at the current time step and previous hidden state. Furthermore, the hidden state with attention can be computed as  
\[\hat{h}_{t}=a_{t}\odot ReLU\left(W_{\hat{h}}[x_{t},h_{t-1}]+b_{\hat{h}}\right) \tag{3}\]  
where \(W_{\hat{h}}\) and \(b_{\hat{h}}\) are learnable parameters; \(ReLU(\cdot)\) is the activation function, which is short for rectified linear unit; and \(\odot\) denotes the Hadamard product. In this work, we proposed a BiRAU structure, which is shown in Figure 2b, to extract temporal information from the high-dimensional feature vectors produced by the 1D CNN, which is essential for accurate classification of motor imagery tasks. By incorporating bidirectional processing, the BiRAU processes the input sequence in both forward and backward directions; it can effectively capture both past and future context information of the input sequence, and produces a set of hidden states that capture the contextual information of the sequence. The output of BiRAU can be computed as follows:  
\[ \begin{cases}\overset{\rightarrow}{h}_{t}=RAU_{\text{FW}}\bigg{(}x_{t}, \overset{\rightarrow}{h}_{t-1}\bigg{)}\\ \overset{\leftarrow}{h}_{t}=RAU_{\text{BW}}\bigg{(}x_{t},\overset{ \rightarrow}{h}_{t+1}\bigg{)}\\ \qquad\qquad h_{t}=\bigg{[}\overset{\rightarrow}{h}_{t},\overset{ \leftarrow}{h}_{t}\bigg{]}\end{cases}  \tag{4}\]  
where \(\bigg{[}\overset{\rightarrow}{h}_{t},\overset{\leftarrow}{h}_{t}\bigg{]}\) is the concatenation of the forward state \(\overset{\rightarrow}{h}_{t}\) and the backward state \(\overset{\leftarrow}{h}_{t}\) of the same size.
# Inter-UAV Routing Scheme Testbeds
## 2 Ad Hoc Routing
### Testbed-Friendly Ad Hoc Routing Protocols
#### 2.1.1 Olsr  
OLSR is one of the most popular proactive routing protocols, mostly due to the open-source nature of OLSRd enabling researchers to utilize it. Due to OLSR's popularity and configurability, it has become the foundation of a spectrum of routing protocols: Predictive OLSR (POLSR) [37], Directional OLSR (DOLSR) [35], Energy-aware Mobility Prediction OLSR (EMP-OLSR) [38], OLSR Fuzzy Cost (OLSR-FC) [39], Cross-layer OLSR (C-OLSR) [40], QoS Aware Link Defined OLSR (LD-OLSR) [41] and many more.  
OLSRd can function either as classical OLSR or additionally consider the Expected Transmission Count (ETX) metric, essentially implementing OLSR-ETX to obtain link quality (_LQ_, network-wide metric) as well as neighbor link quality (_NLQ_, node-specific metric) information [29]. The ETX metric is defined as follows:  
\[ETX=\frac{1}{1-e_{pt}} \tag{1}\]  
\[e_{pt}=1-LQ\times NLQ \tag{2}\]  
\[ETX=\frac{1}{LQ\times NLQ} \tag{3}\]  
where \(e_{pt}\) is the packet error probability.  
When used in an actual scenario, OLSRd is responsible for detecting neighboring network nodes and performing a link quality evaluation using received signal strength indication (RSSI) information. It therefore does not handle packet transmissions, yet populates and updates nodes' routing tables as the network topology is updated by broadcasting HELLO messages and selecting MPRs. In addition to HELLO messages, another type of messages used by OLSRd is Topology Control (TC) messages, responsible for informing the network for each node's accessibility by an MPR.  
Route selection in classical OLSR is implemented by considering the path consisting of the smallest number of hops. Since OLSRd can be configured to consider the ETX metric, in most cases route selection also considers the possibility of packet loss and end-to-end throughput. The ETX metric can be utilized to enable QoS awareness using OLSR [41]. The only drawback stemming from the usage of the ETX metric is the protocol's inability to sense the difference between links' data rates (it assumes that all sensed links have an identical throughput). This is counteracted by introducing the Expected Transmission Time (ETT) metric (natively implemented by OLSR-ETT) [42].  
\[ETT=ETX\times\frac{S}{B} \tag{4}\]  
where \(S\) = packet size in bytes and \(B\) = link capacity (measured using packet-pair technique).
## 3 Experiments and Analysis
### Power Generation Performance Experiments  
The sensor utilizes the triboelectric and electrostatic induction principles. Its operation inherently generates power during the sensing process. Therefore, its power generation characteristics were evaluated, with the results presented in Figure 6. Figure 6a shows that the output voltage decreased from 20.64 V to 16.92 V with increasing rotational speed. This is attributed to the reduced recovery capability after friction between the rotor and stator at higher speeds, resulting in a smaller contact area and consequently a lower output voltage. Figure 6b illustrates that the output current increased with rotational speed, reaching 1.52 \(\upmu\)A at 1000 rpm. The sensor's power generation performance under varying external loads was also investigated, with the experimental rotational speed set to 1000 rpm. As depicted in Figure 6c, the output current decreased while the output voltage increased with increasing external load, consistent with [PERSON]'s law. Finally, Figure 6d reveals a non-linear relationship between the generated power and the external load. A maximum power output of 6.4 \(\upmu\)W was observed at a 10 M\(\Omega\) external load. This power level can currently only supply the sensor itself and cannot power the subsequent circuits. Additionally, when  
Figure 5: Relative rotational angle measurement experimental results. (**a**) Correspondence between sensor output waveform and angle; (**b**) schematic diagram of subsequent data processing for sensor angle measurement; and (**c**) screenshot of sensor data processing software (Version 2.0.0).  
the sensor is used as a power generation device, it needs to be connected to a load. However, when used as a sensor, no load is required.
# The Impact of Contact Force on Signal Quality Indices in Photoplethysmography Measurements
## 5 Discussion
This leads to differences in the number of samples between the strength regions and asymmetry between the increasing and decreasing phases of the CF. Although such variations could affect the individual results, the overall statistical analysis of all the records helps to balance these discrepancies. Future research should aim at a larger and more diverse population to validate the results, especially in clinical settings. On the other hand, the observed outliers are likely due to technical factors, measurement errors, or specific experimental conditions unrelated to the demographic or physical characteristics of the subjects. The nature of the manual method for adjusting the CF during the measurements resulted in sudden and very short amplitude changes or artifacts due to inconsistent pressure application between the CF steps. Despite the efforts undertaken during the data preprocessing phase to remove the segments of the signal that contained obvious artifacts, some of these artifacts persisted to some extent. Additionally, the nature of the analysis using time windows may have contributed to the presence of outliers. These combined elements could lead to irregularities that manifest as outliers in the analysis.
## 3 Results
### Spatial Resolution with the GEMPix
#### 3.3.2 Line Spread Function  
The study of the spatial resolution using the LSF method and the GEMPix was carried out using two copper plates of different thicknesses and hole patterns. Holes with a diameter smaller than 5 mm were imaged using a 1 mm thick plate placed in front of the GEMPix. Results with this mask and the LaGEMPix are not reported in this paper, as they do not provide additional information. This mask had 23 holes of several sizes and spacing. The results for three 1.3 mm holes spaced by 1 mm (edge to edge) are depicted in Figure 11. Following the FWHM criterion introduced in Section 3.2.2, we can conclude that two holes of 1.3 mm diameter at a distance of 1 mm (edge to edge) can be resolved. Measurements with the 3 mm thick copper plate and 5 mm diameter holes showed a spatial resolution of 5.23 \(\pm\) 0.05 mm (FWHM). The results from the LSF method are summarized in Table 1.
## 2 Materials and Methods  
### Elements of the Recirculating Aquaculture System (RAS)  
Two sets of RASs to be used for _Litopenaeus vannanei_ culture were constructed in Dalian, Liaoning Province, China. The structure and elements involved in the process are shown in Figure 1 and Figure S1. Each RAS consisted of culture tanks, a reservoir, circulation pumps, a micro filter, biofilters, a foam fractionator, an ozone generator, ultraviolet disinfection equipment, and a central control system. Each system contained 13 rectangular cement culture tanks, and each tank was 6.3 m long, 3.2 m wide, and 1.6 m high. The water depth was maintained at 1.3 m. The total water volume for each RAS was 338 m\({}^{3}\). The effective volume of the reservoir was 40 m\({}^{3}\). The flow of the recirculation pump was 200 m\({}^{3}\)/h, which could be adjusted using a frequency converter. The microfilter consisted of 260 mesh nylon screens that were able to filter out large particles such as uneaten feed and feces in the water body and was equipped with an automatic backwash device controlled by the water level. The biofilter consisted of six cylindrical moving bed biofilm reactors (MBBR), which measured 1.5 m in diameter and 2.6 m in height and which were combined in parallel. These were mainly used to remove ammonia nitrogen, nitrite, and nitrate from the water. The total volume of the biofilter was 27.6 m\({}^{3}\), and the available volume was 24 m\({}^{3}\). The biofilter was filled with polyethylene (PE) biomedia rings that measured 25 \(\times\) 4 mm; their specific surface area was 1300 m\({}^{2}\)/m\({}^{3}\), and their specific gravity was 0.97-0.99 g/cm. The flow rate of the foam fractionator, which was connected to the ozone generator, was 200 m\({}^{3}\)/h. Dissolved and suspended materials in the water were removed through the foam fractionator and ozone oxidation reactions, and simultaneously sterilization, disinfection, and aeration were carried out. Eight UV disinfection lamps with a total power of 2.6 kW were set up. The liquid oxygen Dewar tank was connected to the water outlet pipe of the foam fractionator through an oxygen pipe, and a gas flow meter was used to control the flow of pure oxygen. The central control system was used to maintain the parameters for the optimal functioning of the equipment, including the micro filter, circulating pumps, and foam fractionator. Variations of the recirculating pump frequency, liquid oxygen flow rate and ozone flow rate over time during the RAS culture period are shown in Table 1.
# Data Driven Leakage Detection and Classification of a Boiler Tube
## 2 Methods and Procedures for Leakage Classification of the Boiler Tube
### Deep Neural Network (DNN)  
Recently, researchers have introduced deep learning or DNN techniques, which have been used in numerous fields, especially image processing. DNN can reduce the data dimensionality and effectively map the input to the outputs. Moreover, one interesting property of a deep network is that it can explore the salient information from the input data efficiently without expert knowledge in a specific field. Such networks are popular in fields that require nonlinear mapping. In the current work, a sophisticated signal processing method was adopted to analyze AE signals while not relying on traditional handmade features where in-depth domain expertise is needed.  
In a DNN, multiple layers of units are stacked on each other to create a deep or hierarchical structure, as presented in Figure 3. There is connectivity between the layers of the DNN, but no connectivity among the units of the same layer. A typical DNN consists of one input layer, one output layer, and one or multiple hidden layers. The input and output layer dimensions of the DNN are selected according to the input data and output classes, respectively. There is no specific rule for hidden layer dimension selection. Normally, the number of units in the hidden layers is set to less than the preceding layer.  
Figure 2: A 4-level wavelet packet tree having \(2^{4}\) nodes.  
Backpropagation (BP) was applied to fine tune the DNN and reduce the cost function. The cost function was updated negatively to the gradients of the cost function. Once the training and fine tuning of the DNN were completed, the trained model was used to identify the samples that belong to the leakage of the boiler tube.
# Mapping National Mangrove Cover for Belize Using Google Earth Engine and Sentinel-2 Imagery
## 1 Introduction
Historically, high-resolution mangrove mapping has largely been limited to local-scale studies because high-resolution data can be prohibitively expensive (both financially and computationally) at broader scales. However, the relatively recent introductions of free-to-use Sentinel-2 satellite imagery and Google Earth Engine have significantly reduced the costs of acquiring and processing high-resolution data [23; 24]. In recent years, Google Earth Engine and Sentinel-2 data have been widely used to map mangrove cover accurately and efficiently at a variety of scales [25; 26; 27]. As we discuss in more detail below, we specifically used a random forest classification algorithm. Random forest classification has also been widely used to map mangrove cover accurately and efficiently at a variety of scales [22; 25; 28; 29; 30; 31].
## 4 Materials and Methods
### Data Processing  
#### 4.8.1 Data Processing  
The default settings of the multiplier script wiff_2_mgf.mz were adopted. The TripleTOF 5600 data files were directly accessed and converted into mgf format for each experiment. The files were then searched with Mascot version 2.3 against a forward-reversed Swiss-Prot database (22,318 forward entries) along with a 643-entry cRAP database (the common repository of adventitious proteins (cRAP) database. Ion tolerances were set at 0.6 and 0.1 Da for the precursor and product ions, respectively. The search parameters, in addition to trypsin specificity, included carbamidomethylation (C, 57 Da), variable deamidation (NQ, 1 Da), oxidation (M, 16 Da), phosphorylation (STY, 80 Da), and fixed iTRAQ modification (N-term, K 145 Da). A spreadsheet encapsulating Mascot search results was crafted using the search results. Reverse database hits were removed using FDR_filter.mz and forward hits with a false discovery rate (FDR) higher than 1.0% were marked. A list of unique peptides (FDR 1%) from each analysis was generated using Multiplier's multifile detection feature, and phosphorylated peptides were tallied using a script. Consequently, unique phosphorylated peptides can be distinguished by merging their sequences and phosphorylation states. Sequences phosphorylated at diverse residues were counted once; however, sequences with different counts of phosphorylation sites were enumerated separately.
# Optimization Strategy for Electric Vehicle Routing under Traffic Impedance Guidance
## 5 Case Analysis
### Results of Electric Vehicles Route Planning Optimization
#### 5.2.2 Electric Vehicle Midway Charging Paths  
Taking vehicle 558, which triggered the charging demand, as an example, its O-D nodes are 30-2. As shown in Figure 12, in the absence of guidance, based on its initial path (30-29-25-19-13-9-4-3-2), when reaching node 13, the charging demand is triggered at 9 am. The vehicle owner then goes to the nearest charging station, which is station 3. The EV charging path is 13-14. As for vehicle 558, following the shortest time guidance, the initial path is (30-29-28-23-22-12-7-1-2). At 9 am, when reaching node 7, the charging demand is triggered. According to real-time road network data, the EV is guided to charging station 1, and the shortest time charging path is 7-8.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Driving Path** & **Distance (km)** & **Time Cost (h)** & **Energy Consumption (kWh)** \\ \hline Unguided & 29–30–26–20–16–17–11 & 35 & 1.10 & 9.74 \\ Time-guided & 29–30–31–18–17–11 & 36 & 0.90 & 9.53 \\ Difference & \(\backslash\) & +1 & \(-\)0.2 & \(-\)0.21 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Driving path comparison in two scenarios.  
Figure 11: Comparison of impedance levels along the route segments.  
Figure 12: Comparison of EV midway charging paths.  
In the two scenarios, due to the different initial planned paths, the location where the EV triggers the charging demand is also different. Combining the distribution of charging stations within the road network, in the absence of guidance, the EV charging path chooses the nearest charging station, which is station 3 (node 14). However, it is located in the central business district, where the road sections are relatively congested, resulting in a longer charging travel time for the vehicle owner. Under the guidance of the shortest time, the EV selects charging station 1 (node 8), which is located in the working area. Although the charging travel distance is slightly longer, it saves travel time. At the same time, smoother road sections reduce the power consumption of the EV during the charging journey. The comparison between the two is shown in Table 3.
# Spacing Allocation Method for Vehicular Platoon: A Cooperative Game Theory Approach
## 5 Simulation Experiments and Analysis
### Performance of the Spacing Policy  
#### 5.2.1 Initial Velocity-Error Situation  
In this section, a velocity jump will used to test the performance of the proposed method. The initial state of the vehicles is shown in Equation (35), where the velocity of the leading vehicle is different with the following ones. The following vehicles should cooperate to adapt the leading vehicle. The results of the simulation are shown in Figure 8.  
\[\left\{\begin{array}{l c c c}X=\left[\begin{array}{cccc}24&16&8&0\end{array} \right]^{T}\\ V=\left[\begin{array}{cccc}15&10&10&10\end{array}\right]^{T}\end{array}\right. \tag{35}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Control Parameters** & **Default Value** & **Other Parameters** & **Default Value** \\ \hline \(\eta\) & 5 & \(L\) & 3 \\ \(\omega_{v}\) & 10 & \(d\) & 5 \\ \(\omega_{c}\) & 5 & \(n\) & 4 \\ \(u_{upper}\) & 6 & \(u_{lower}\) & \(-6\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The key parameters of the model.  
Figure 7: The relationships of the stable states and control parameters in \(AL\) value policies: (**a**) Spacing following error; and, (**b**) The velocity following error.  
As shown in Figure 8a,b, the velocities of the vehicles in the normal spacing policy and AL value both overshoot after reaching the reference velocity to adjust to the preset spaces. Unlike them, the vehicles in Shapley value much more focus on the consistency of the velocity which is reflected in the quick convergence of the velocity. This performance has two reasons: (a) the vehicles have ensured their desired spacing with their previous vehicle; (b) the desired spacing calculated from the Shapley value has strong sensitivity with the change of velocity. Additionally, the velocities controlled with normal spacing policy, Sharpley value and AL value met the requirements of speed error referring to Euro NCAP while vehicles reached the stable state. In Figure 8c, we may find that the actual spaces between the vehicles tend to be stable except the CD policy. The disturbance is increasing with the upstream of the platoon which demonstrates that proposed methods can ensure the string stability while CD policy can't.  
Figure 8: The performance of multiple policies in initial-velocity-error condition (**a**) Position; (**b**) Velocity; (**c**) Longitudinal space.
# Suitability Evaluation of the Lining Form Based on Combination Weighting-Set Pair Analysis
## 3 The Proposed Methodology
### CRITIC-EWM Method  
#### 3.2.1 CRITIC Method  
The CRITIC method was proposed by [PERSON] et al. in 1995, and the basic idea is to use two parameters (i.e., correlation coefficient and standard deviation) to determine the objective weight of the index [24]. The standard deviation indicates the contrast intensity of the index, and the larger the standard deviation is, the greater the value difference of each scheme [25]. Meanwhile, the correlation coefficient is a quantitative index of conflict, and the conflict decreases with the increase in the correlation coefficient [26]. It is mainly divided into five steps:  
1. The initial indicator data matrix \(X\) is defined as follows: \[X=\left(x_{ij}\right)_{m\times n}\] (8) where \(x_{ij}\) are the raw data of the \(j\)th index corresponding to the \(i\)th object.
2. Initial data normalization In order to eliminate the impact of different dimensions on the evaluation results, it is necessary to standardize each index. If the index value used is larger, the forward treatment is generally adopted by Equation (9). \[y_{ij}=\frac{x_{ij}-\min(x_{ij})}{\max(x_{ij})-\min(x_{ij})}\] (9) If the index value is smaller, the reverse treatment is generally used by Equation (10). \[y_{ij}=\frac{\max(x_{ij})-x_{ij}}{\max(x_{ij})-\min(x_{ij})}\] (10)
3. The standard deviation of each index is calculated by Equation (11). \[\sigma_{j}=\sqrt{\frac{1}{m-1}{\sum\limits_{i=1}^{m}(y_{ij}-\overline{y}_{j} )^{2}}}\] (11) where \(\sigma_{j}\) is the standard deviation of the index \(x_{j}\); \(\overline{y}_{j}\) is the average value of the \(j\)th index.
4. Correlation coefficient of the indices is calculated by Equation (12). \[r_{ij}=\frac{\sum\limits_{i=1}^{n}(y_{i}-\overline{y}_{i})(y_{j}-\overline{y} _{j})}{\sqrt{\sum\limits_{i=1}^{n}(y_{i}-\overline{y}_{i})^{2}\sum\limits_{j =1}^{n}(y_{j}-\overline{y}_{j})^{2}}}\] (12) where \(r_{ij}\) is the correlation coefficient between index \(x_{i}\) and \(x_{j}\).
5. The objective weight of the indices is calculated by Equations (13) and (14). \[B_{j}=\sigma_{j}{\sum\limits_{i=1}^{n}(1-r_{ij})}\] (13)\[\varphi_{j}=\frac{B_{j}}{\sum\limits_{j=1}^{n}B_{j}} \tag{14}\]  
where \(B_{j}\) is the composite information of the \(j\)th index.