esa-sceva/satcom-chunk-collection huggingface dataset:
# Collision Avoidance Resource Allocation for LoRaWAN
## 4 LoRaWAN MAC Proposal
If required, for example, due to changing radio conditions or network load, these parameters may be sent at any time for class B and class C devices or in ACK messages for class A devices (i.e., after transmitting a data frame). To avoid collisions between devices, we use a cyclical shift between each row. As a result, two devices \(a\) and \(b\) with \(RB_{j_{a}}\
eq RB_{j_{b}}\) will never collide (in the case of both devices using all SFs). These can be easily seen in Figure 4, in which two rows with different initial values (column 1) will never have the same value in the same column. This is thereason why our scheme highly reduces the number of collisions, being null if the number of devices is lower than the number of resource blocks (48). For the sake of readability, we also include in Figure 5 a flowchart which describes the procedure from the node's perspective. CARA requires synchronization among devices. For such purpose, the _DeviceTime_ MAC commands [14] may be used, which provides a worst case accuracy of \(\pm 100\) ms. GPS clock synchronization may also be used if available on devices. If required, for example, to avoid problems due to the accuracy of the time synchronization, a guard time at the end of each window may be added (e.g., 100 ms when using _DeviceTime_ MAC messages). Extension to Avoid Border EffectsEven if two devices transmit in different resource blocks in the same window, it may occur that one of the transmissions finishes during the next time window and produce a collision. For example, in Figure 4 device 2 may start transmitting in the first window (\(RB_{2,1}=2\)) but may finish this transmission in the second window, which may cause a collision if device 1 start transmitting in the second window (\(RB_{1,2}=2\)). An example is shown in Figure 6.  
Figure 4: Resource block matrix \(RB_{matrix}\). Figure 3: Join scheme in Collision Avoidance Resource Allocation (CARA) with the assignment for the assignment of resource blocks.
## 1 Introduction
Supported by time-route matrices dedicated to both the sender and receiver, the proposed scheme conceals not only the content of the transmitted information but also its transmission characteristics. The use of alternative transmission routes enhances the secrecy and safety of the information concealment process. This integration introduces flexibility and diversity in transmitting concealed information across networks, reducing the potential for the detection and blocking of confidential data. The results demonstrate the effectiveness of the proposed model in various structured steganographic systems. The article is structured as follows: In the second section of the paper, we focus on evaluating more than 30 research works from the past 20 years that are relevant to our research topic. The evaluation results demonstrate that while there are numerous quality studies in narrow fields, there has not been any research that truly delivers outstanding outcomes regarding the proposed model for selecting optimal routes to transmit messages in steganographic network to enhance security. The third section of the paper introduces the relevant theoretical foundations for our research. In the fourth section, we provide a concise overview of the method's concept that we propose. The fifth section concentrates on presenting the method for determining optimal routes for message transmission. We list all specific cases and propose calculation formulas to find out the most optimal model for message transmission. In the sixth section, we evaluate our method by employing hypothetical data with various different cases that we previously considered. Finally, in the conclusion section, we summarize the main research results.
# Roll Motion Prediction of Unmanned Surface Vehicle Based on Coupled CNN and LSTM
## 2 Methodology  
### Framework of the Proposed Prediction Model  
In this paper, a coupled CNN and LSTM model is proposed for USV roll motion prediction. The target roll motion time series data are denoted as \(R=\{r_{t-D},r_{t-D+1},\cdots,r_{t}\}\). The other degree-of-freedom movement status data and control status data are denoted as \(F^{i}=\{f^{i}_{t-D},f^{i}_{t-D+1},\cdots,f^{i}_{t}\}\), where \(t\) and \(D\) denote the time step and the length of setting time window, respectively. In our proposed prediction model, the input time series data is \(S=R\cup F^{1}\cup F^{2}\cup\cdots\cup F^{i}\), and the output is the target roll motion data at time step \(t+1\), denoted as \(r_{t+1}\). The framework of the proposed model is shown in Figure 1. The multi-channel one-dimensional convolution layer is utilized to eliminate data redundancy and extract spatially relevant features. The LSTM layer is exploited to extract the long-term time-series features and model the USV roll motion. The fully connected layer is used to decode the LSTM output and calculate the final predicted results at the next moment. The input data are mapped into higher-dimensional feature maps, and these feature maps are the input to the LSTM layer.  
Our proposed prediction model for USV roll motion can be expressed by the following:  
\[C_{t}=f(W^{c}X_{t}), \tag{1}\]  
\[L_{t}=g(W^{l}C_{t}+U^{l}L_{t-1}), \tag{2}\]  
\[Y_{t}=h(W^{y}L_{t}), \tag{3}\]  
where \(X_{t}\) denotes a set of input data and \(Y_{t}\) represents the output of the prediction model. \(C_{t}\), \(L_{t}\), and \(Y_{t}\) are the outputs of the convolution layer, LSTM layer, and fully connected layer, respectively. \(W^{c}\), \(W^{l}\), \(U^{l}\), and \(W^{y}\) are the weight matrices of the model: \(W^{c}\) denotes the weight matrices of the input layer to the convolutional layer; \(W^{l}\) denotes the weight matrices of the convolutional layer to the LSTM layer; \(U^{l}\) is the weight matrices of the LSTM hidden layer at last moment to the output of the current moment; \(W^{y}\) means the weight matrices of the LSTM layer to the fully connected layer.
## 2 Materials and Methods
### Model Training and Implementation  
In implementing the hybrid models and the ANN model, the selection of the number of neurons in different layers of the model was strategically determined to ensure optimal convergence and fitting of the models. The regression models were built using the Keras library (which is a library in Python open-source programming package) while mean square error (MSE) is adopted as the loss function. Adam optimizer is used to minimize the cost function, and the rectified linear unit (ReLU) is applied as the nonlinear activation function due to its ability to make the network sparse and efficient. ReLU is one of the non-linear activation functions available and it is specifically used after each layer in a neural network to ensure that the computed output is activated (such that not all the neurons are activated at the same time). This is also adopted to apply non-linearity and overcome the vanishing gradient problem. The supervised learning feature of deep learning models creates room for further improvement of the model (especially when applied in other locations), however, the model overfitting problem is avoided by increasing the number of epochs gradually while checking the model performance. The optimal number of epochs is determined when the reduction in training losses stops. All the models developed were implemented in a Python environment running under core i7, 2.20 GHz system with 16 GB RAM, and GTX1060 6 GB Graphics card. The specifications of the computer used for the simulation are chosen considering the implementation of the models developed and these specs are common in the market nowadays. The optimal number of epochs required for the training of each model for different locations is summarized in Table 3.  
The neural network models try to learn the relationship between the data features by computing and updating the weight and bias functions of a logistic regression operation. As the model is trained, the weights and biases are finetuned and updated to yield better prediction at every cycle by comparing the prediction to the data label. The differences between the prediction and label are computed as the loss function and averagely as the cost function. The cost function is then minimized using the backpropagation algorithm such that a global minimum is reached, ensuring that the predicted value is as close to the label as possible.  
Specifically, for CNN integrated models, kernels are designed to run convolution on the data to update the weights and bias for refining the model prediction. The model predicted values are compared to the labels and the cost functions are modified by updating the model parameters using backpropagation. In the case of the LSTM model, the input, forget and output gates help the model to determine which values to retain for keeping long-term computational dependencies.
# Improving Real-Time Position Estimation Using Correlated Noise Models +
## 3 Position and Uncertainty Estimation Methods
### Earlier Work  
Since the late 1990s, few researchers outlined alternative ways to estimate distributional parameters of time-series of GPS signals and thereby analysed the noise inherent in these signals [19; 20; 21; 22]. [PERSON] and [PERSON] even demonstrated, although for crustal velocities, that temporal correlation in observation can decisively influence quantities estimated from the measurement [23]. In general, the temporal correlation reduces the uncertainties of the geophysical parameters inferred from the signal; thus, one can be more certain of a potentially incorrect estimate if the temporal correlation is not considered. Most approaches relied on Maximum Likelihood Estimators (MLE) either in fitting ad-hoc models to the data or in estimating the covariance matrix of the signal components [19; 24; 25; 26]. This MLE approach was considered to provide robust results [27] compared to linear regression. Similar to [PERSON]'s earlier study, [PERSON] et al. [28] and [PERSON] et al. [29] concluded that incorrectly modelled noise in synthetic time-series introduces bias in the velocity data, and even worse, increasing the length of the time-series does not guarantee diminishing bias.  
While some steps have been made to depart from the use of the white noise model, e.g., replacing it with time-correlated (i.e., coloured) noise [19; 30], it is still often assumed that the noise can be modelled  
Figure 4: Fitted Ornstein–Uhlenbeck (OU) parameters to GPS time-series from Dunedin, New Zealand (grey), North Stradbroke Island, Australia (blue), Port Douglas, Australia (green), and Thursday Island, Australia (red). (**a**) Estimates of \(\theta\) show no significant variation over geographic locations; however, the difference between the types of coordinates is apparent, \(\theta\) being much smaller for altitude (alt) than for either latitude (lat) or longitude (lon). (**b**) Estimates of parameter \(\sigma^{2}\) are depicted and show a similar pattern to \(\theta\) in (**a**), although, here, the altitude is much larger than the other two coordinates. Qualitatively, this can be understood by the larger variability of the altitude residuals (see Figure 3).  
by independent, identically distributed Gaussian random variables. [PERSON] et al. [21] developed a technique that substantially increases the efficiency of the MLE methods, which has since been improved [25]. Other, more advanced and computationally costly methods, e.g., Markov chain Monte Carlo methods [22; 31] or wavelet analysis [32], have also been proposed to obtain non-biased probability distributions for the noise within the time-series.
## 5 Computational Cost Reduction Methods
### AP Selection Methods
#### 5.1.3 Random Combination  
The above two methods select informative APs according to different criteria, but in this method, the selection of informative AP does not consider the behavior of APs in time or special domains. The random scheme selects K APs randomly of L APs, and RM is changed to indices of the RSS measured vector accordingly [43]. As a result, this method needs less computation complexity during the online phase, and it does not need a large number of RSS time samples.
## 2 Proposed PLL Architecture
### Four-Stage Differential Ring Oscillator and Delay Cell  
The programmable VCO has a four-stage differential ring oscillator for low noise and low power supply applications, as shown in Figure 9a. The circuit of the proposed  
Figure 8: (**a**) Frequency vs. VCO tuning voltage; (**b**) frequency vs. VCO sub-band optimization.  
Figure 7: VCO circuit programmable iDAC.  
delay cell of the differential ring oscillator is shown in Figure 9b. The differential pair of PMOS transistors M1 and M2 acts as the input stage of the delay cell, and the tail current is provided by the different options of the bias voltage generation. The active loads are composed of a pair of NMOS transistors, M3 and M4, and a pair of cross-coupled latches, NMOS transistors M5 and M6.
## 2 Materials and Methods
### Total Carotenoid Content  
A modified method of carotenoid content measurement in red bell pepper tissues was used [28; 29]. Each sample was analyzed in triplicate using spectrophotometric measurement. The analysis involved weighing 0.2 g of ground material, and the absorbance of the colored solutions was measured at 450 nm using a Spectronic 200 spectrophotometer from Thermo Fisher Scientific Inc., Waltham, MA, USA. The total carotenoid content (TCC) was calculated using the following Equation (1):  
\[TCC\left(\frac{mg\ \beta\ caroten}{100\ g\ d.m.}\right)=\frac{A_{450}\cdot A_{1 cm} ^{1\%}\cdot 10^{5}\cdot m_{2}}{m_{1}\cdot d.m.} \tag{1}\]  
where:  
\(A_{450}\)--absorbance;  
\(m_{2}\)--mass of total extract (g);  
\(A_{1 cm}^{1\%}\)--extinction coefficient of \(\beta\)-carotene in petroleum ether (value 4);  
\(m_{1}\)--sample mass (g);  
\(d.m.\)--dry substance specific for each sample (-).  
Texture Determination  
The textural properties of both the raw and fermented samples were analyzed using texture profile analysis (TPA) with a TA.XT2i texture analyzer (Stable Micro Systems, Surrey, UK) [30]. Cylindrical samples (5 mm height, 5 mm diameter) underwent 2 compression cycles using a 75 mm piston, with a 5 s interval between cycles. A 5 kg force sensor and a speed of 0.5 mm/s were used for ten repetitions, calculating hardness, elasticity, cohesion, and chewability.
## 3 Results
### Relationships between SIF and GPP and between VIs and GPP across Different Observation Ranges  
As the buffer zone diameter of flux sites varied, the relationship between the mean SIF (eSIF, TROPOSIF) and GPP, as well as the relationship between the mean VIs and GPP, changed, as depicted in Figure 10. At both sites, the R\({}^{2}\) values for eSIF and TROPOSIF with respect to GPP decreased as the buffer zone diameter increased, stabilizing at around 2000 m. At the SHB2 site, there was a process of increase followed by a decrease in the R\({}^{2}\) values for the SIF and TROPOSIF with respect to the GPP, with the inflection point  
Figure 9: Seasonal variations in VI\({}_{\rm ECW}\) and GPP at two sites.  
occurring approximately at 500 m. The monthly-scale flux footprint diagrams (Figure 3) illustrate that at both forest sites, the footprint diameter within the area where the footprint contributed 80% was approximately 500 m, while within the region where the contribution reached 90%, the footprint diameter ranged from about 1000 m to 2000 m. This range closely aligns with the optimal range for the relationship between SIF and GPP. Therefore, to ensure the accuracy of the relationship between SIF and GPP, the observation range of SIF should ideally coincide with the flux footprint range as much as possible.  
For VIs, at the SHB1 site, the pattern of R\({}^{2}\) values changing with the buffer zone diameter was similar to that for SIF and GPP: the R\({}^{2}\) decreased as the buffer zone diameter increased, stabilizing at around 2000 m. However, the distribution pattern at the SHB2 site was markedly different. For buffer zone diameters less than 500 m, the R\({}^{2}\) values for the VIs and GPP rapidly increased, stabilizing at around 500 m. This indicates that the VIs near the flux sites (<500 m) in SHB2 do not adequately represent the vegetation growth patterns in the flux source area, and in areas with significant landscape heterogeneity, the correlation between the VIs and GPP is poor. Furthermore, although the relationship between SIF and GPP weakened near the flux sites (<500 m) in SHB2, there was still a strong R\({}^{2}\) between SIF and GPP, indicating that SIF is more robustly associated with GPP compared to VIs.
# Effect of the Light Environment on Image-Based SPAD Value Prediction of Radish Leaves
## 5 Conclusions  
This study was conducted to clarify the influence of the light environment on SPAD value prediction using Random Forests, XGBoost, and LightGBM under different light source environments. Leaf patches with a diameter of 1.5 cm were prepared using Japanese radishes (_Raphanus sativus_ L. var. sativus) photographed under an established stable light environment and used for extracting variables for image-based SPAD value prediction. The variables obtained by the image analysis were the RGB values, which were pixel values, and the HSL and HSV values obtained by color space conversion from the RGB values. In addition, color temperature and illuminance, which were measured separately, were used as input variables. The results showed that, under a stable LED light source, the SPAD values could be predicted using Random Forests with high accuracy (COR 0.929 for RGB, color temperature, and illuminance as input variables; COR 0.922 for HSL, color temperature, and illuminance as input variables). It was also found that image-based SPAD value prediction was possible with relatively high accuracy, even under a halogen light source with a color temperature close to that of dusk (COR 0.895 for RGB and illuminance as the model input, and COR 0.876 for HSL and illuminance as the model input). The results suggest that SPAD values can be predicted under a wide range of lighting conditions, such as dusk, by training a data-driven model using data collected under different illuminance conditions in various light sources. Under LED light sources, the use of HSL values resulted in accurate SPAD value prediction by separating the illumination information into L (COR = 0.972, NSE = 0.944, and RMSE = 2.07). In addition, focusing on the H value, the H value tended to change from blue to green as the SPAD value increased. In this study, the SPAD value prediction was performed in a stable light environment in a closed indoor environment; therefore, the development of a method for outdoor conditions, where the light environment is spatiotemporally dynamic, is a future challenge.  
Y.K. conceptualized this study and conducted the experiment and analysis. Y.K. drafted this paper, and [PERSON] finalized it. [PERSON] supervised this research. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the FLOuRISH Fellowship Program for Next-Generation Researchers at Tokyo University of Agriculture and Technology.  
Data Availability StatementData are contained within the article. We are planning to prepare the dataset available after publishing the entire achievement of the project.  
The authors declare no conflicts of interest.
## 3 Results and Discussion
### Power Consumption and Adiabatic Efficiency  
Figure 8 shows the effect of rotating speed, discharge pressure, and cooling water flow rate on power consumption. The two curves at the same discharge pressure were very close, indicating that the impact of the cooling water flow rate on power consumption was not significant.  
Power consumption showed an approximately linear increase with the increase in rotating speed, just like the volumetric flow rate. According to Formula (3), the adiabatic power is proportional to the volumetric flow rate, so the increase in the volumetric flow rate caused by the increase in rotating speed will lead to an increase in power consumption. At a certain cooling water flow rate and given rotating speed, power consumption increased with the increasing in discharge pressure.  
Figure 9 shows the effect of the rotating speed, discharge pressure, and cooling water flow rate on adiabatic efficiency. It can be noted that the adiabatic efficiency under various operating conditions was generally low, ranging from 0.50 to 0.58. This is because the dry twin-screw compressor has a large gap but does not receive a liquid seal, which leads to significant leakage and reduces adiabatic efficiency. The dry twin-screw compressor studied in Reference [10] also exhibits similar adiabatic efficiency.  
Figure 8: Effect of the rotating speed, discharge pressure, and cooling water flow rate on power consumption.  
Figure 9: Effect of the rotating speed, discharge pressure, and cooling water flow rate on adiabatic efficiency.  
At a given rotating speed and discharge pressure, adiabatic efficiency showed a significant improvement with the increase in the cooling water flow rate. This is because a higher cooling water flow rate makes the compressor work closer to isothermal compression, and the shaft power is closer to the adiabatic compression power.  
The adiabatic efficiency showed different trends with increasing rotating speed at different discharge pressures. When the discharge pressure was 2 bar, the adiabatic efficiency continued to increase with the increase in the rotating speed. When the discharge pressure was 1.5 bar, the maximum adiabatic efficiency occurred around 7000 rpm and 8000 rpm, and the adiabatic efficiency increased first and then decreased with the increase in the rotating speed. On the one hand, the increase in rotating speed will cause larger flow losses and larger friction losses, resulting in a decrease in adiabatic efficiency. On the other hand, the increase in rotating speed brings greater volumetric efficiency, resulting in an increase in adiabatic efficiency. The different growth rates of the former and the latter with increasing rotating speed led to different trends in adiabatic efficiency. Moreover, the adiabatic efficiency increased with the increase in the discharge pressure.
## 3 Results and Discussion
### Comparison of Zr-EPRC Preparation Methods
#### 3.1.3 Selection of Optimum Loading pH  
Figures 5 and 6 illustrate that the phosphorus removal efficiency of both modified steel slag and modified fly ash experiences an initial enhancement with an increase in the loading solution's pH. The optimal adsorption effect is observed when the loading pH  
Figure 4: Effects of different loadings on phosphorus removal effect of steel slag.  
Figure 3: Effects of different loadings on phosphorus removal effect of fly ash.  
reaches approximately 10. Beyond a pH of 10, the adsorption capacity of the modified materials gradually decreases.  
This behavior can be attributed to the predominance of zirconium in the form of zirconium oxides or hydroxides on the adsorbent material, making it more susceptible to ligand exchange with phosphate ions. The increasing alkalinity of the modified solution leads to the transformation of zirconium oxide in the solution into zirconium oxychloride octahydrate hydroxide or hydrated zirconium oxide, facilitating phosphate adsorption in the subsequent static experiments, resulting in an improved phosphorus removal effect.  
However, as the solution's alkalinity continues to rise, the adsorbent material's pore size also increases, hindering effective zirconium attachment and reducing the specific surface area. Consequently, this weakening of the adsorption capacity occurs. Therefore, it is advisable to prepare the adsorbent material at a pH value of approximately 10.