esa-sceva/satcom-chunk-collection huggingface dataset:
# An Environmental Perception Framework for Robotic Fish Formation Based on Machine Learning Methods
## 4 The Velocity Prediction of Two Robotic Fishes
### The Phalanx Formation
#### 4.3.2 The Construction and Validation of Predictive Models  
We used the data simulated by settings in Table 4 to train the predictive model and used the data simulated by settings in Table 6 to validate the model. The kinematic pressures used to train and validate the predictive model are the average values of \(p_{0}\) at different spacing at a certain \(U\). In Section 3, we construct the predictive model of the inlet flow velocity for single robotic fish. We compared the kinematic pressure of the No. 1 fish head (\(p1_{0}\)), the kinematic pressure of the No. 2 fish head (\(p2_{0}\)) and that of the single fish head (\(p_{0}\)) at the different inlet flow velocity \(U\). As shown in Figure 15, \(p1_{0}\), \(p2_{0}\) and \(p_{0}\) have few differences and the predictive model of \(p_{0}\) in Section 3 can be used to predict \(U\) for  
Figure 14: The relationship between \(U\) and \(p_{i}\) at different spacing. The dotted line is the average value of \(p_{i}\) at different spacing at a certain \(U\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multicolumn{7}{c}{**U (\(L\)/s)**} \\ \hline  & 0 & 0.2 & 0.4 & 0.6 & 0.8 & 1.0 \\ \(p1_{0}\) & 0.000005 & 0.000021 & 0.000012 & 0.000041 & 0.000091 & 0.000249 \\ \(p1_{1L}\) & 0.000113 & 0.000139 & 0.000277 & 0.000469 & 0.000862 & 0.001587 \\ \(p1_{7L}\) & 0.000031 & 0.000032 & 0.000015 & 0.000016 & 0.000105 & 0.000275 \\ \hline \(p2_{0}\) & 0.000006 & 0.000020 & 0.000033 & 0.000100 & 0.000089 & 0.000231 \\ \(p2_{1R}\) & 0.000021 & 0.000019 & 0.000040 & 0.000442 & 0.001458 & 0.003232 \\ \(p2_{7R}\) & 0.000099 & 0.000072 & 0.000035 & 0.000043 & 0.000312 & 0.000385 \\ \hline \end{tabular}
\end{table}
Table 8: The mean square deviation of \(p_{i}\) at different spacing at a certain U.  
Figure 15: The contrast between the kinematic pressures of fish in the phalanx formation and that of single fish.  
each fish in the phalanx formation. The polynomial as the predictive model for each fish in the phalanx formation is as follows:  
\[U=0.1693+1.9999\times pi_{0}+(-0.8366)\times pi_{0}^{2}+0.1400\times pi_{0}^{3} \qquad-0.1\leq pi_{0}\leq 0.6,\;\;i=1\;or\;2, \tag{10}\]  
For the case of two robotic fishes, No. 1 fish in the tandem formation and each fish in the phalanx formation have the same predictive models. No. 2 fish in the tandem formation has a specific predictive model.
# Effect of Hydrogen Ion Presence in Adsorbent and Solution to Enhance Phosphate Adsorption
## 2 Material and Methods
### Batch Adsorption Tests  
A series of batch tests were carried out to determine the removal efficiency (%) and uptake (mg/g) of phosphate at different initial solution pH values without and with pH adjustment, which was carried out by the addition of dilute HCl or NaOH at the desired time (0.17, 0.5, 1.3, 6, 12, and 24 h). Two sets of batch isotherm tests were initiated by adding 0.2 g of the GAC and the GACA into the 50 mL solution that contained from 1 to 50 mg/L of phosphate. The mixture was then gently rotated at 20 rpm for 48 h. The initial pH was first set to 4.0 \(\pm\) 0.2, 7.0 \(\pm\) 0.2, and 9.0 \(\pm\) 0.2, and the pH was adjusted for one set of batch isotherm tests. Other tests were also performed at various initial pH of 3, 4, 5, 6, 7, 8, and 9, to investigate the optimized pH and the effect of the solution pH on the phosphate removal. The adsorption rate of the phosphate was explored for 96 h by adding 2 g of the GAC and the GACA into 500 mL of a solution containing 15 mg/L of phosphate at pH values of 4.0 \(\pm\) 0.2, 7.0 \(\pm\) 0.2, and 9.0 \(\pm\) 0.2 with or without pH adjustment. At predetermined time intervals, an aliquot of the sample was removed and stored in a refrigerator at 5 \({}^{\circ}\)C until analysis.  
The phosphate uptake was calculated based on the following mass balance equation:  
\[q_{e}=\frac{(C_{0}-C_{e})V}{M}, \tag{1}\]  
where \(C_{0}\) and \(C_{e}\) are the initial and final concentrations of the phosphate (mg/L), respectively. \(V\) and \(M\) are the volumes of the solution (L) and the mass of the GAC or GACA (g), respectively.
## 3 Sliding Mode Control Theory and Terminal Sliding Mode Controller Design  
### Sliding Mode Control  
Sliding mode control is a non-linear control technique extensively used in dynamic systems with non-linearity, un-certainties, and disturbances. The fundamental idea of sliding mode control is to design an appropriate sliding surface so that the system's state trajectory can reach this surface within a finite time and then slide along the surface, ultimately achieving stable control of the system. Sliding mode control has been widely applied in industrial automation, robotic control, automotive electronics, aerospace, and other fields. Its robustness and superior dynamic performance have garnered broad recognition in practical applications [10]. Sliding mode control has significant advantages over other control methods. First, it exhibits strong robustness and can effectively cope with the un-certainty of system parameters and external disturbances, so that the system can still maintain stability and good performance in the presence of modeling errors or disturbances. The strong anti-interference ability of sliding mode control enables it to perform well in complex and disturbed environments, while its fast dynamic response capability ensures that the system can quickly adjust to the target state. In terms of design, sliding mode control is relatively simple, and only requires the definition of an appropriate sliding surface, which greatly simplifies the controller design process. In addition, sliding mode control has low requirements for the precise model of the system, making it highly adaptable in practical applications, especially when dealing with non-linear systems. By converting complex non-linear problems into linear problems for control, sliding mode control effectively simplifies the control challenges of non-linear systems, while its adaptability ensures stability when the system state and environmental conditions change. These advantages make sliding mode control a very effective and reliable control strategy when facing various un-certainties, and disturbances.
## 3 Application Results
### Comparison of Standalone and Hybrid Models  
In order to compare standalone and hybrid models in streamflow prediction, Figure 7 was used. The first graph shows the Taylor diagram of Dizaj station, while the second demonstrates the Tapik station diagram. These two diagrams represent the optimal predictions of four standalone and hybrid models of monthly streamflow prediction, namely OPELM, ARCH-OPELM, CHAID, and ARCH-CHAID models. These diagrams could represent graphically that which of the models has the closest prediction of streamflow with the historical calibration monthly streamflow data. All models compared graphically with three important evaluation metrics, namely correlation coefficient, standard deviation, and root mean square error (RMSD). Centered RMSD showed the reference point (observed value), and the distance of a particular model with this point shows the ideal model. As shown in the diagrams, the ARCH-CHAID model outperformed all other standalone and combined methods of DDM at two candidate study stations.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Station**} & \multirow{2}{*}{**Model**} & \multicolumn{5}{c}{**Statistical Error Indices**} \\ \cline{3-6}  & & **R** & **RMSE (m\({}^{3}\)/s)** & **NSE** & **MAE (m\({}^{3}\)/s)** & **RSD** \\ \hline \multicolumn{6}{l}{_Total available data in calibration stage_} \\ \hline Dizaj & Dizaj-ARCH-OPELM & 0.94 & 2.168 & 0.81 & 1.268 & 0.319 \\ \cline{2-6}  & Dizaj-ARCH-CHAID & 0.92 & 2.395 & 0.83 & 2.039 & 0.348 \\ \hline Tapik & Tapik-ARCH-OPELM & 0.92 & 6.896 & 0.82 & 3.215 & 0.422 \\ \cline{2-6}  & Tapik-ARCH-CHAID & 0.93 & 5.678 & 0.85 & 3.167 & 0.418 \\ \hline \multicolumn{6}{l}{_Total available data in validation stage_} \\ \hline Dizaj & Dizaj-ARCH-OPELM & 0.91 & 1.496 & 0.38 & 3.903 & 0.426 \\ \cline{2-6}  & Dizaj-ARCH-CHAID & 0.96 & 1.289 & 0.92 & 0.719 & 0.301 \\ \hline Tapik & Tapik-ARCH-OPELM & 0.89 & 3.671 & 0.78 & 2.018 & 0.487 \\ \cline{2-6}  & Tapik-ARCH-CHAID & 0.94 & 2.662 & 0.86 & 1.467 & 0.419 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance results of hybrid ARCH-OPELM and ARCH-CHAID models for Dizaj and Tapik stations.
# Quantifying Football Shooting Precision: The Expected Shot Impact Timing (xSIT) Approach
## 5 Validation of Relevance of the Proposed Metric  
To evaluate the robustness and practical relevance of the proposed metric within a performance analysis framework, a comparative assessment was conducted against the two principal post-shot expected goals models. Both the xGOT and xSIT metrics were developed to quantify critical post-shot attributes that influence shot outcomes. Based on their respective theoretical and mathematical formulations, xGOT primarily reflects GK performance, whereas xSIT was intended to assess the shooter's execution quality. To empirically support this distinction and validate the relevance of the proposed metric, a comparative analysis of both metrics was conducted using the dataset described in Section 2.  
The required sample size was estimated using G*Power 3.1, based on an a priori power analysis with an assumed effect size of \(d=0.8\), an alpha level of 0.05, and a desired statistical power of 80%. This calculation yielded a minimum sample of 50 shots. Nevertheless, in order to maximize the robustness of the analysis, all shots on target recorded across both tournaments were included.  
Figure 9 presents the outcomes of a comparative analysis between the xGOT values generated by the STATSBOMB model and the xSIT metric produced by the proposed model. Figure 10 presents a gender-based comparative analysis of the xSIT metric to examine potential differences between men's and women's tournaments. The variables studied included the following: total shots on target, shots resulting in goals, and shots resulting in saves. Descriptive statistics (mean and standard deviation) were computed, and inferential analysis was performed using an independent Student's t-test. Statistical significance was set at \(p<0.05\). To assess the practical significance of the results, effect sizes (Cohen's d) were calculated and interpreted using standard thresholds: small (<0.2), medium (0.5), and large (>0.8). Data normality was confirmed via the Shapiro-Wilk test. Based on the results obtained, statistically and clinically significant differences were observed between the xSIT and xGOT metrics across all analyzed variables, including total shots on target, goal shots, and saved shots. These findings supported the notion that, while both metrics assess post-shot-related performance, they capture distinct aspects of the action. Consequently, the authors propose that xSIT and xGOT should be considered as complementary tools within football performance analysis. Also, the analysis revealed significant differences between men's and women's competitions.  
Figure 9: A comparative analysis of the xSIT metric derived from our custom model and the xGOT metric based on STATSBOMB model outputs focused on shots on target. Abbreviations: xSIT, expected shot impact timing; xGOT, expected goals on target.
# An Effective Directional Residual Interpolation Algorithm for Color Image Demosaicking
## 2 Related Work
### Guided Filter  
As an example, we describe how to get the \(\overset{\sim}{R}\) image by the guided filter in detail. The guided filter can accurately transfer the structures of the guided image to the filtered output. It has a good performance in edge-preserving smoothing and does not suffer from reversal artifacts. Thus, it can be used in demosaicking area. Furthermore, the guided filter also can be applied in denoising, edge-preserving, dehazing, and guided feathering.  
The interpolated G image can be regarded as a guided image, and the observed R image can be regarded as an input image. To minimize the Laplacian energies of the residuals, the guided image and the input image are first passed through a sparse Laplacian filter. After that, the guided image uses a mask to obtain the value at the location of input image.  
The guided filter assumption is a local linear model between the guided image \(I\) and the filtering output \(q\) in a window as shown in Equation (1):  
\[q_{i}=a_{k}I_{i}+b_{k},\ \forall i\in w_{k}, \tag{1}\]  
where \(i\) is the pixel in \(w_{k}\), and \(w_{k}\) is a window centered at the pixel \(k_{i}\) (\(a_{k},b_{k}\)), which are assumed to be constant, are linear coefficients in \(w_{k}\). To determine the linear coefficients (\(a_{k},b_{k}\)), the cost function is given as:  
\[E(a_{k},b_{k})=\sum\limits_{i\in w_{k}}{(a_{k}I_{i}+b_{k}-p_{i})}^{2}, \tag{2}\]  
where \(p_{i}\) is a pixel at \(w_{k}\) in the input image. To minimize Equation (2), the solution is given by:  
\[a_{k}=\frac{\frac{1}{|w|}\sum\limits_{i\in w_{k}}{I_{i}-\mu_{k}\overline{p}_{k }}}{\sigma_{k}^{2}}, \tag{3}\]  
\[b_{k}=\overline{p}_{k}-a_{k}\mu_{k}, \tag{4}\]  
where \(\mu_{k}\) and \(\sigma_{k}^{2}\) are the mean and variance of \(I\) in \(w_{k}\), \(|w|\) is the number of pixels in \(w_{k}\), and \(\overline{p}_{k}=\frac{1}{|w|}\sum\limits_{i\in w_{k}}{p_{i}}\) is the mean of \(p\) in \(w_{k}\). However, a pixel \(i\) is involved in all the overlapping windows \(w_{k}\) that covers \(i\). The value of \(q_{i}\) in Equation (1) is not identical when it is computed in different windows, so we rewrite Equation (1) by:  
\[q_{i}=\overline{a}_{i}I_{i}+\overline{b}_{i}, \tag{5}\]  
where \(\overline{a}_{i}=\frac{\sum\limits_{k\in w_{i}}{a_{k}}}{|w|}\) and \(\overline{b}_{i}=\frac{\sum\limits_{k\in w_{i}}{b_{k}}}{|w|}\) are the average coefficients of all windows overlapping \(i\).
# A Hardware-Efficient Vector Quantizer Based on Self-Organizing Map for High-Speed Image Compression
## 3 Proposed Vector Quantizer Based on SOM  
### The Overall Architecture  
The overall architecture of the proposed vector quantizer is illustrated in Figure 1. It consists of an input buffer, \(k\) sub-blocks for codebook learning and image encoding, a block distance comparator, and a winner-block selector. To achieve a high image compression speed, we distribute the codebook into \(k\) sub-blocks. The operation principle of our proposed vector quantizer can be described as follows: firstly, the winner-neurons in each sub-block are found out and their corresponding minimum SEDs are output to the block distance comparator. In the block distance comparator, the \(k\) minimum SEDs are further compared. In this way, the sub-block where the winner-neuron exists is determined and then selected by the winner-block selector. Finally, the address of winner-neuron, as well as the results of block distance comparator, are output as the index.  
Figure 1: The overall architecture of the proposed vector quantizer.  
As the most critical element, each sub-block is composed of the address generator and memory for the codebook, the squared difference unit (SDU), the arithmetic block, and the minimum distance search unit. In contrast with the previous works, where dedicated circuits were required for codebook learning, we have thoroughly utilized the design concept of reconfigurable architecture to improve the hardware efficiency. Specifically, the SDU and arithmetic block can be reconfigured either into codebook learning mode or into image encoding mode. As a consequence, both the operations of Euclidean distance computation and new weight vector generation, which are the main sources of high computational complexity, would be simultaneously executed by the SDU and arithmetic block without any additional circuits.
# Bi-LSTM-Augmented Deep Neural Network for Multi-Gbps VCSEL-Based Visible Light Communication Link
## 4 Proof of Concept Demonstration
### Communication Test Result
An AC of 860 mV with a DC of 1.4 V is applied to the VCSEL. Figure 10a shows the BER performance according to the number of units in a single LSTM layer. 'Raw BER' means the case without any machine learning method. There is a BER improvement from 100 to 300 units, but the performance is slightly decreased compared to the improvement from 300 to 500 units. The Bi-LSTM layers are also tested, as shown in Figure 10b. It can be seen that the single Bi-LSTM layer outperforms the single LSTM layer, and the triple Bi-LSTM layers show excellent performance, with a data rate of 13.5 Gbps, satisfying the forward error correction (FEC) with a BER threshold of 3.8 \(\times\) 10\({}^{-3}\). Figure 8: Diagram of the proposed neural network. Figure 9: BER performance according to different AC values with a single LSTM network at DC bias of (**a**) 1.3 V, (**b**) 1.4 V, and (**c**) 1.5 V.
# Adaptive Filtering for Multi-Track Audio Based on Time-Frequency Masking Detection
## 2 Evaluation of Masking and Filtering
### Filter Design  
The masking vectors \(M_{T1}(f)\), \(M_{T2}(f)\ \ldots M_{TN}(f)\) obtained from Equation (6) represent the masking contributions of each track to others (see Figure 2d) and so provide then the basis for the reduction of masking by filtering. To design the filters, the most prominent peaks of each masking vector are found by comparing every sample to the two immediate neighbours to detect local maxima (see blue starts on Figure 3a). Then, an adaptive threshold [30] is implemented with a fixed lower limit (see Figure 3b). Applying the threshold reduced the number of peaks originally found by comparing neighbours. This is partic  
Figure 2: Calculation of masking in the frequency domain. **(a)** The masking matrix \(M_{12}^{\prime}(t,f)\) is compressed to a single dimension (\(M_{12}^{\prime}(f)\)) using Equation (3). **(b,c)** Masking of track 1 over track 2 (\(M_{1,2}(f)\),red) and track 2 over track 1 (\(M_{2,1}(f)\),blue) are separated using Equations (4) and (5), respectively. **(d)** The final masking of \(\mathrm{T_{r1}}\), \(\mathrm{T_{r2}}\ldots\mathrm{T_{rN}}\) over others (\(M_{T1}(f)\), \(M_{T2}(f)\...\ M_{TN}(f)\)), which is used for filter design (green), was calculated using Equation (6). In this equation, the contributions to masking from each track over the others (i.e., \(\mathrm{M_{1,2}}\mathrm{M_{1,3}}\ldots\mathrm{M_{1,N}}\)) are averaged and normalised. The same process can be expanded to any number of tracks (see the Results section).  
ularly more noticeable for higher frequencies (see Figure 3c). The central frequency and width of each of the remaining peaks are then used to generate a parametric equalisation filter [31] with the gain of each band proportional to each peak so the relative ratios of masking at different frequencies are preserved (see Figure 3d). The filter is normalised to have a maximum gain of 0 dB and a user-defined minimum gain (for instance, \(-3\) dB in Figure 3d). This process is performed for all masking vectors \(M_{T}(f)\) and the generated filters are applied to their correspondent tracks in the Fourier space. Finally, the tracks are added up and then this raw mix is normalised to unity (rough master) for evaluation.
## 3 Materials and Methods
### Simulated Attacks
#### 3.2.1 Adversarial Fast Gradient Sign Method (FGSM) Attack  
Adversarial examples were generated using the FGSM [18]. Perturbations were crafted to exploit the gradient of the loss function:  
\[x_{\text{adv}}=x+\epsilon\cdot\text{sign}(\
abla_{x}J(x,y)) \tag{5}\]  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Symbol** & **Meaning** \\ \hline \multirow{2}{*}{\(f\)} & Activation function (e.g., ReLU, Logistic, Tanh) applied to compute the layer output. \\ \hline \multirow{2}{*}{\(y\)} & True label of the input sample. \\ \hline \multirow{2}{*}{\(\hat{y}\)} & Predicted probability of the input sample. \\ \hline \multirow{2}{*}{\(I(\theta)\)} & Cross-entropy loss function for optimizing the model parameters. \\ \hline \multirow{2}{*}{\(x_{\text{adv}}\)} & Adversarial example generated using the FGSM method. \\ \hline \multirow{2}{*}{\(x\)} & Original input data. \\ \hline \multirow{2}{*}{\(\epsilon\)} & Magnitude of the perturbation applied in FGSM attack. \\ \hline \(\
abla_{x}J\) & Gradient of the loss function with respect to the input \(x\). \\ \hline \multirow{2}{*}{\(N(0,\sigma^{2})\)} & Gaussian noise with mean 0 and variance \(\sigma^{2}\), used in evasion attacks. \\ \hline \multirow{2}{*}{\(PAV1\)} & Post-Attack Vulnerability Index; quantifies the relative degradation in model performance. \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._where \(x\) is the input data, \(\epsilon\) is the perturbation magnitude, \(J(x,y)\) is the cross-entropy loss function and \(\
abla_{x}J\) represents its gradient with respect to \(x\). This method highlighted the model's sensitivity to minor but carefully crafted input changes that could lead to significant misclassifications.
## 1 Introduction  
Foamed bitumen stabilisation is a construction technique whereby foamed bitumen is used to bind the existing or imported granular material to produce a bitumen stabilised flexible pavement material. It is commonly used in base and subbase pavement layers for both pavement rehabilitation and new pavement construction [1]. The foam is produced by injecting cold water, or a water-bearing product, into a stream of hot bitumen [2] causing the bitumen to expand rapidly. In its foamed state, the bitumen has a significantly reduced viscosity and is able to coat the fine aggregate particles in the granular material [3]. The large foam collapses during mixing but fine foam bubbles remain until construction and compaction collapses them, resulting in a coarse aggregate skeleton that is spot-welded together by a drastic-like mixture of fine aggregate and bitumen [4]. Experience and research have shown that foamed bitumen stabilised base course (FBB) has high moisture resistance [5],is well-suited to expedient construction [6] and has an elastic modulus that significantly exceeds that of granular materials [7] and even many asphalt mixtures [8].  
As discussed below, FBB can be constructed in situ or ex situ and can use existing pavement materials or new granular materials [6]. The granular material can be either crushed rock or a naturally occurring gravel. Although crushed rock is usually tightly specified and produced to a controlled aggregate gradation, existing pavement materials and natural gravels are generally less controlled. In fact, some of these materials fall outside of the specification or guideline requirements applicable to their intended use. Such materials are generally referred to as'marginal' materials. Although the engineering properties of marginal materials present a challenge, their cost-effectiveness and local availability make them an attractive option, particularly in regional areas that are long distances away from established supply chains [9]. Furthermore, even though marginal materials are not necessarily as sound as standard materials, they may still be fit for purpose. In such circumstances, the use of marginal materials to achieve an acceptable outcome contributes to the sustainable development of the infrastructure network because higher quality standard materials remain available for uses where marginal materials would be less appropriate [10].  
This research compared the engineering properties of FBB produced with three marginal materials to the properties of FBB produced with a standard crushed rock. The particle gradation of the marginal materials was then improved by blending with other granular material and the engineering properties of the subsequently produced FBB were also compared, allowing the level of improvement to be assessed. Although the materials and test methods are necessarily Australian, the relative performance of FBB produced with standard, marginal and improved marginal materials is of interest to other countries, particularly those jurisdictions that use foamed bitumen stabilisation of marginal materials for road and other pavement construction.