esa-sceva/satcom-chunk-collection huggingface dataset:
# Retro-Reflective-Marker-Aided Target Pose Estimation in a Safety-Critical Environment
## 3 Results
### Synthetic Data  
The main synthetic dataset is composed of 20 images corresponding to different relative poses of the camera and the target. The poses were generated randomly, while assuring that the target was fully visible and within the following limits:  
(1) Distance of 300 to 500 mm from the camera to the target, corresponding to the working range established by the application.  
(2) Horizontal and vertical deviations of \(-\)50 to 50 mm and \(-\)100 to 100 mm from the central position in the x and y axis. Rotations of \(-\)10 to 10 degrees in relation to the aligned position in each rotation axis. The operator is expected to be able to easily make a preliminary alignment within these limits based on the camera feeds.  
The images of the main synthetic dataset are used to evaluate the performance of single-camera approaches and correspond to the left-most camera in approaches using a stereo camera pair. In order to study the effect of different baseline values in estimations using stereo vision, five additional sub-datasets were generated, each composed of 20 images corresponding to the right-most camera for a certain baseline value. In addition to being translated by the baseline value, the right-most camera is rotated horizontally in the direction of the other camera to increase the shared field of view of the stereo pair. Sets of values for baseline and rotation are 100 mm and 10\({}^{\circ}\), 150 mm and 15\({}^{\circ}\), 200 mm and 20\({}^{\circ}\), 250 mm and 25\({}^{\circ}\), and 300 mm and 30\({}^{\circ}\).  
We generated synthetic images of both a checkerboard and our developed bead-array target (Figure 8). Synthetic images were generated using our own software, in which the retro-reflective beads are modeled as flat white diffuse circles on the black target plane. Ground truth checkerboard corners and bead-array center values are extracted from the rendering software and are used as ground truth for evaluating the accuracy of feature detection. These datasets also have associated ground truth intrinsic and extrinsic camera calibration parameters. The rigid body transformation between the left-most camera and the origin of the target is extracted as the ground truth value for evaluating the accuracy of pose estimation methods.
## 3 Proposed System Model for Optimal Energy Management
### Demand Side Management Module  
The DSM module have smart meter, a GWDO-based ECS, an in-home display, home appliances, and a computer control unit. The smart meter receives data from DSO and establishes and maintains communication with the supply side. Its role is to collect the users' energy consumption data and communicate it with DSO, acting as a gateway to the demand side. The smart meter also receives the data from the DSO like an energy pricing signal and then communicates it to the ECS and later broadcasts this data to the in-home display. The GWDO-based ECS collects the energy pricing signal, power ratings of the appliances, energy consumption pattern, and priority set by the user and schedules the power usage pattern of home appliances for optimal EM. Moreover, the priority and consumption pattern of the appliances can be adjusted or can be changed by the user via a user interface such as an in-home display, etc.  
The overall load of the consumer is divided into three categories: portable interruptible, portable uninterruptible, and consistent. The appliances' power ratings and operation time are shown in Table 2. The overall set of appliances is represented by \(A_{r}\), where the further classification of application into the portable interruptible, portable uninterruptible, and consistent sets of appliances can be represented by \(A^{r}_{p^{i}}\), \(A^{r}_{pmi}\), \(A^{r}_{c}\), respectively, whereas the operation time of the appliances is considered for the 24 h and represented by T = \(\tau_{1}\), \(\tau_{2}\), \(\tau_{3}\)... \(\tau_{24}\).  
Figure 1: Developed framework for electricity market energy management.
## 4 Computational and Mathematical Aspects
### Anisotropic Finite Element Macro-Model
#### 4.3.2 Strain (Total)  
The total strain at any point within the element can be defined by its three components which contribute to the internal work. Thus,  
\[\varepsilon=\left[\begin{array}{c}\varepsilon_{xx}\\ \varepsilon_{yy}\\ \gamma_{xy}\end{array}\right]=\left[\begin{array}{c}\frac{\partial u(x,y)} {\partial x}\\ \frac{\partial u(x,y)}{\partial y}\\ \frac{\partial u(x,y)}{\partial y}+\frac{\partial v(x,y)}{\partial x}\end{array}\right] \tag{13}\]  
With displacements known at all points within the element, the strains at any point can be determined. These will always result in a relationship that can be defined in matrix notation according to:  
\[\varepsilon=B\delta^{e} \tag{14}\]  
where B is a suitable linear operator.
## 3 Results and Discussion
### Effects of pH on OTC Degradation  
In heterogeneous PS activation, the initial pH has a significant influence on the degradation of pollutants [40]. Therefore, the degradation efficiency at various solution pH conditions (3, 4, 6, and 8) was investigated (Figure 7). As shown in Figure 5, the removal ratio of OTC at 4 min was 70.8 \(\pm\) 7.0%, 70.7 \(\pm\) 0.8%, 31.0 \(\pm\) 1.9%, and 28.6 \(\pm\) 0.9% at a pH of 3, 4, 6, and 8, respectively. The degradation performance at a pH of 3 and 4 showed no significant change, but the efficiency significantly decreased when the solution pH increased from 4 to 8, therefore implying that acidic conditions were more favorable for the degradation of OTC by heterogeneous PS activation. This result was observed because the surface properties of the catalyst and the lifetimes of the generated radical species changed  
Figure 5: Oxytetracycline (OTC) degradation in the presence of scavengers ([OTC]\({}_{0}\) = 20 uM, [PS]\({}_{0}\) = 1 mM, [HWWC]\({}_{0}\) = 0.2 g/L, [scavenger]\({}_{0}\) = 10 mg/L, pH = 6). PS: persulfate; HWWC: hand-warmer waste catalyst.  
Figure 6: Oxytetracycline (OTC) degradation with (**a**) different catalyst dosages and (**b**) different persulfate concentrations ([OTC]\({}_{0}\) = 20 uM).  
as the solution pH changed. When the point of zero charge of the catalyst was higher than the solution pH, the surface of the catalyst displayed a positive charge; thus, it could adsorb more SO4*- [41]. In addition, the lifetimes of HO* and SO4*- decreased under alkaline conditions; thus, the radical species that diffused into the bulk phase might have been insufficient for further degradation [42].
## 5 Conclusions  
The aim of this study was to analyze SOC characteristics by setting the driving output and capacity of the lithium-ion battery as parameters of the TMED-type parallel HEV. We first conducted driving tests on a TMED-type parallel HEV using a chassis dynamometer and signal-measuring equipment. Subsequently, we developed a numerical simulation model using CRUISE vehicle simulation code and the acquired experimental data. The driving torque and capacity (energy density) of the lithium-ion battery, which are factors that influence fuel economy, were set as variables in this model. From our numerical analysis, we drew the following conclusions regarding the SOC characteristics and current balance of the lithium-ion battery.  
1. At low-speeds, there is negligible engine operation, and the motor operates primarily. The difference in SOC drop was not significantly caused by the energy recovered by the regenerative braking of the drive motor and the advantage of the motor, which is more efficient than the engine.
2. For efficient battery use, SOC is charged to near 50%. Therefore, if the battery capacity is small, it is charged to 50% quickly, and the extra electricity cannot be charged. For this reason, the difference in SOC drop occurs because the change in torque is more significant than the change in battery capacity.
3. When the battery capacity is reduced by 20%, the SOC between the IM240 and FTP-75 modes differs by 3% only. By using a variety of strategies (e.g., downsizing, engine operation strategy, SOC increase) to compensate for the SOC drop, it will be possible to operate HEVs at lower battery capacities.  
[PERSON] conducted the HEV vehicle experiment to acquire data, arranged and analyzed the data, and wrote the paper; [PERSON] supervised the study and provided advice on all aspects of the preparation of this paper. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the R&D project on Industrial Core Technology of MOTIE (Ministry of Trade, Industry and Energy) in the Republic of Korea.  
The authors declare no conflict of interest.
# Settlement Prediction of Footings Using \(V_{s}\)
## 2 Development of Settlement Prediction Method Based on \(V_{s}\)  
### [PERSON]'s Approach  
Here, we adopt the conceptual framework of [PERSON]'s method to establish a new settlement prediction method. The settlement of footing as suggested by [PERSON] et al. [PERSON] et al. (2010) is expressed as:  
\[s_{i}=C_{1}\cdot C_{2}(q_{b}-\sigma_{z,d}^{\prime})\sum\frac{I_{zi}\Delta z_{i}} {E_{i}} \tag{1}\]  
where \(C_{1}\) and \(C_{2}\) are depth and time factors, \(q_{b}\) is the footing load, \(\sigma^{{}^{\prime}}_{z,d}\) is the initial vertical effective stress at the footing base level, \(I_{zi}\) is the vertical strain influence factor, \(\Delta z_{i}\) is the thickness of each sublayer, and \(E_{i}\) is the elastic modulus of each layer.  
[PERSON]'s method utilizes the CPT tip resistance, \(q_{c}\), to determine [PERSON]'s modulus using soil type-based empirical coefficients. The stress transmitted into the soil media, which is caused by the footing load, can be calculated by using a strain influence factor, \(I_{z}\), which describes the distribution of the applied load in the subsoil layer with respect to depth. Then, the settlement of each sublayer can be evaluated using the converted modulus and transmitted stress based on elasticity theory, and finally the total settlement of footing over the soil profile is obtained.  
In this study, the use of \(q_{c}\) in the determination of [PERSON]'s modulus was replaced by the \(V_{S}\) incorporating the nonlinearity of soil. Based on the \(V_{S}\), the modulus was determined considering the confinement stress and strain level caused by pressure distribution under the footing. In addition, the shape of \(I_{z}\) was discussed to provide reliable results using the developed method.
# Extending Wireless Reschargeable Sensor Network Life without Full Knowledge
## 2 Related Work
### WRSN Power Replenishment  
There are a variety of methods to replenish the power level of sensor nodes in WRSN. For instance, energy harvesting is adopted to recharge the sensors in WRSN using solar cells [24]. Other sources like wind [25], vibration [26], difference in temperature [27], and even human blood pressure are also used for energy harvesting [28]. However energy harvesting is unpredictable and hard to control [29] because the ambient source of power is unknown or unstable. On the other hand, a more predictable replenishing technique involves wireless power transfer from a battery based power source. Wireless charging can be implemented in several methods. One way to charge nodes is to use radio frequency [30]. It has been shown that, using a radio frequency (RF) harvester [31], both ambient RF power from different sources or from dedicated transmitters can be utilized [32]. Another method is using strong-coupled magnetic resonance [33]. In our work, we adopt the strong-coupled magnetic resonance technique due to the power hazards and limitations [34], and the rapid decay of power transfer efficiency with increased distance [35], in radio frequency charging.  
Mobile sensor based networks can also benefit from wireless power transfer. Using stationary charging stations with partial coverage, the WRSN life can be extended [36]. One or more charging stations can be used to extend the life of a mobile WRSN [37]. In a stationary sensor based WRSN, a single MCR [38] may be deployed to periodically travel inside the network and charge sensor nodes or several MCR units can be used [39]. In our work, we focus on a single MCR to charge a stationary sensor based WRSN. We also do not require exhaustively visiting the whole network.
## 3 Experiment Setup and ML Tuning Parameters
### Random Forest (RF)  
An ensemble learning approach known as RFs or random decision forests can also be utilised to classify and predict data. RF provides strong ensemble classifiers with a capacity to generate a large number of trees using random bootstrapped samples of the training dataset, as demonstrated in Figure 4. Two parameters are modified in RF, namely,  
Figure 3: The effect of different complexity parameters on the decision tree model.  
Figure 2: KNN model calculation and technique visualisation.  
the number of trees (ntree) and the number of variables or features (mtry) [28]. The ntree parameter allows experimentation with the number of trees based on the data size and type. However, using more than the required number of trees may be unnecessary because it may not negatively impact the model [29]. The mtry parameter refers to the number of variables randomly chosen as potential candidates at each split. The default value of mtry is usually the number of predictor variables, as shown in [30]. The RF model is more time consuming and difficult to understand and analyse than the decision tree because it creates many trees [31]. In this study, we evaluate a wide range of values for ntree and mtry to determine the optimum values for the best RF model performance.
# BigGRU-Based Adaptive Receiver for Indoor DCO-OFDM Visible Light Communication
## 3 Principles of the BiGRU-Based Receiver
### Complexity Analysis  
The complexity of the algorithm was analyzed and compared with a bidirectional LSTM (BiLSTM) NN-based receiver. The number of parameters and multiplications required for each symbol was noted during the analysis.  
The parameters of the proposed BiGRU network include the parameters of the BiGRU and linear layers. Moreover, two fully connected layers were used in this study (Figure 3). The parameters of the GRU unit (Figure 2) contained three weight matrices, three deviation vectors for the input, and three deviation vectors for the previous state \(h_{t-1}\). The size of the weight matrix of input \(x_{t}\) was associated with the size of its input and output features and that of the hidden state \(x_{t}\). If the size of the input features of input \(x_{t}\) was assumed to be \(1\times F\) and that of the hidden state \(h_{t}\) was \(1\times H\), then the weight matrix of input \(x_{t}\) was \(F\times H\). Similarly, the size of the input and output of the previous state \(h_{t-1}\) was the size of the hidden state; thus, the size of its weight matrix was \(H\times H\), and that of the bias vector  
Figure 3: Structure of the (**a**) BiGRU-based receiver and (**b**) a bi-GRU model.  
was \(1\times H\). Therefore, the number of parameters for a single-layer GRU can be calculated as  
\[N_{GRUL\_p}=3\times(FM+H^{2}+2H), \tag{12}\]  
The number of parameters for a linear layer can be calculated as  
\[N_{Linear\_p}=2\times(2 HM+M), \tag{13}\]  
The number of parameters for the BiGRU network can be calculated as follows:  
\[N_{BiGRUNET\_p}=6\times(FH+H^{2}+2H)+4 HM+2M, \tag{14}\]  
As the number of multiplications per symbol is dependent on the BiGRU and linear layers and lets the length of the input sequence be \(L\), then the number of multiplications for the BiGRU layer can be calculated as [13]  
\[N_{BiGRUL\_m}=6\times(FH+H^{2}3H)\times L, \tag{15}\]  
The linear layer was \(2 HM\), so the number of multiplications for the BiGRU network can be calculated as  
\[N_{BiGRUNET\_m}=6\times(FH+H^{2}+H)\times L+4 HM, \tag{16}\]  
In contrast, the parameters of the LSTM unit contain four weight matrices and four deviation vectors for the input \(x_{t}\) and four weight matrices and four deviation vectors for the previous state \(h_{t-1}\)[23]. The number of parameters for the BiLSTM network can be calculated as  
\[N_{BiLSTMNET\_p}=8\times(FH+H^{2}+2H)+4 HM+2M, \tag{17}\]  
The number of multiplications for the BiLSTM network can be calculated as  
\[N_{BiLSTMNET\_m}=2\times[4\times(FH+H^{2})+3H]\times L+4 HM, \tag{18}\]
## 1 Introduction
### Motivation  
With the rapid development in electronics and wireless technology, WSN will certainly find more and more application when the need for environment sensing arises. On the other hand, the developments of WSN theory and systems have received a lot of attention in both the industry sector and research community. Among many alternative approaches, GT has been increasing applied in the design of WSNs, thus, the scope of this paper is restricted to the use of GT for WSNs.  
From 2003 to 2011, about 330 research papers with topics on or closely related to GT for WSN were published. Figure 1 shows the yearly distribution of these published papers. The number of records for 2011 is not complete because some publications have not been included in the indexing databases. A relatively smaller portion of the contributions in this area has been summarized in [5, 6]. [PERSON] and [PERSON] [5] reviewed 29 publications which mainly focusing on the use of game-theoretic approaches to formulate problems related to security and energy efficiency. [PERSON] _et al_. [6] summarized 30 publications of the existing game theoretical approaches that are designed to strengthen WSN security.  
Among the variety of developed methods using GT, the main differences and remarkable features can be briefly summarized below. Cooperative game theory provides analytical tools to study the behavior of rational players when they cooperate and consider the utility of all the players [7, 8]. Non-cooperative game theory also covers a broad range of applications in WSN [9, 10]. In non-cooperative game theory, the nodes buy, sell, and consumer goods in response to the prices that  
Figure 1: Yearly publications on GT for WSN.  
are exhibited in a virtual market. A node attempts to maximize its profit for taking a series of actions. Whether or not a node receives a profit is decided by the success of the action. Note that non-cooperative game theory is mainly focused on each user's individual utility rather than the utility of the whole network. On the contrary, cooperative game theory can achieve general pareto-optimal performance and maximize the entire network's payoff while maintaining fairness. In addition to cooperative and non-cooperative game theories, repeated game theory is concerned with a class of dynamic games, in which a game is played for numerous times and the players can observe the outcome of the previous game before attending the next repetition [11]. The commonly used GT methods for solving WSN problems are listed in Table 1.
# A Bird Vocalization Classification Method Based on Bidirectional FBank with Enhanced Robustness
## 4 Research Methods
### Bidirectional FBank Feature Extraction
#### 4.2.2 Inverse FBank Feature Extraction  
As illustrated in Figure 6, the traditional FBank is dense at low frequencies and sparse at high frequencies, emphasizing low-frequency information while neglecting high-frequency information. However, high-frequency information is equally crucial in practical applications and should be addressed.  
In this study, we found that the frequency of birdsong is initially concentrated between 1000 and 4000 Hz. After the framing and windowing, the signal frequency also concentrated in the latter half between 1000 and 4000 Hz (Nyquist sampling theorem). For example, when the sampling frequency is 48,000 Hz, the bird audio data concentrate from 1000 to 4000 Hz and 20,000 to 23,000 Hz. If we only focus on the low-frequency part of the audio, we lose the equally significant high-frequency information. Therefore, in this study, a new filter bank was developed, referred to as inverse FBank (iFBank). First, the iMel-scale transform is defined as follows:  
\[iMel=700\cdot\Big{(}10^{\frac{f}{1720}}-1\Big{)}. \tag{14}\]  
Second, the method of generating the filter bank is consistent with that of FBank. Following the generation of the isometric triangular filter on the iMel scale, it is converted back to a linear frequency:  
\[f=17270\cdot\log_{10}\Big{(}\frac{iMel}{700}+1\Big{)}. \tag{15}\]  
Subsequently, the new filter bank based on the iMel scale is shown in Figure 7.  
The characteristic of this filter is sparse at low frequencies and dense at high frequencies, indicating that iFBank emphasizes high-frequency information. The BiFBank feature extraction method is based on horizontal linear feature fusion, which focuses on both low-frequency and high-frequency information simultaneously. The complete process of BiFBank is illustrated in Figure 8.  
Figure 6: Mel filter bank.
## 7 Performance Analysis
### Energy Consumption  
During the authentication phase, entities consume energy during computation and transmission processes. Since IoT sensors have power limitations, it is essential to minimize the energy consumption for implementing a lightweight authentication scheme. According to [51], the total energy consumption \(E\) can be expressed as \(E=E_{comp}+E_{comm}\), where \(E_{comp}\) denotes the energy consumption required to execute the computation, and \(E_{comm}\) represents the energy cost consumed by the communication, including the message transmission and reception. The computation energy cost \(E_{comp}\) can be calculated using \(E_{comp}=3.5\) V \(\times\) 0.4 A \(\times T_{comp}\), where \(T_{comp}\) denotes the average computational time required for each function presented in Table 4. Based on the execution time in Table 4, the energy consumption for each operation was calculated as follows: hash function, fuzzy extractor, ECC point multiplication, ECC point addition, symmetric key encryption, and PUF were given by \(E_{h}=3.5\) V \(\times\) 0.4 A \(\times\) 0.309 ms \(=\) 0.433 mJ, \(E_{fe}=3.5\) V \(\times\) 0.4 A \(\times\) 2.288 ms \(=\) 3.203 mJ, \(E_{ecm}=3.5\) V \(\times\) 0.4 A \(\times\) 2.288 ms \(=\) 3.203 mJ, \(E_{ecm}=3.5\) V \(\times\) 0.4 A \(\times\) 0.018 ms \(=\) 0.025 mJ, and \(E_{buf}=3.5\) V \(\times\) 0.4 A \(\times\) 0.216 ms \(=\) 0.324 mJ, respectively. In addition, the communication energy cost can be calculated as \(E_{comm}=n_{s}E_{s}+n_{r}E_{r}\), where \(n_{s}\) and \(n_{r}\) denote the number of bytes sent and received, respectively. We assumed that the energy cost of sending and receiving messages was \(E_{s}\approx 5.9\) J and \(E_{r}\approx 4.7\) J [51]. Therefore, the energy consumptions of the IoT sensor in the proposed scheme could be calculated as \(E_{comp}=7E_{h}+1E_{buf}=3.355\) mJ and \(E_{comm}=64E_{s}+44E_{r}=0.584\) mJ, with a total energy consumption of 3.939 mJ. [PERSON] et al.'s scheme shows a lower energy consumption than the proposed scheme.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scheme** & **Number of Messages** & **Communication Cost (Bits)** \\ \hline [PERSON] et al. [32] & 4 & 2144 \\ [PERSON] et al. [34] & 4 & 2720 \\ [PERSON] et al. [35] & 4 & 2528 \\ [PERSON] et al. [36] & 4 & 3712 \\ [PERSON] et al. [37] & 3 & 2400 \\ [PERSON] et al. [39] & 4 & 2752 \\ [PERSON] et al. [15] & 4 & 2016 \\ Proposed & 4 & 2048 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Communication costs.  
However, it is vulnerable to various attacks, including impersonation and insider attacks. Thus, the proposed scheme offers improved efficiency in terms of both security and energy consumption. Table 7 presents the results of the energy consumption comparison.
# Sentinel-1 GRD Preprocessing Workflow +
## 2 Workflow
### Border Noise Removal  
While generating level-1 products, it is necessary to correct the sampling start time in order to compensate for the change of the Earth's curvature. At the same time, azimuth and range compression leads to radiometric artefacts at the image borders. The border noise removal algorithm [4], available as an operator in SNAP, was designed in order to remove low intensity noise and invalid data on scene edges.