esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Commercial Distillation Test Results
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Parameter** & **FCCPT Feed** & **FCCPT** & **FCCPT** & **H-Oil VGO** & **H-Oil** & **H-Oil** & **H-Oil** & **H-Oil** \\  & & **Diesel** & **HTVGO** & **Blend** & **HVGO** & **LVGO** & **HAGO** \\ \hline Density at 15 \({}^{\circ}\)C, g/cm\({}^{3}\) & 0.9254 & 0.8895 & 0.9120 & 0.9392 & 0.9539 & 0.9490 & 0.9344 \\ Sulphur, wt\% & 2.08 & 0.105 & 0.207 & 0.61 & 0.79 & 0.63 & 0.54 \\ \hline Distillation, wt.\%/\({}^{\circ}\)C & & & & ASTM D 7169 & & & \\  
543 & 496 & 477 \\ Boiling up to 360 \({}^{\circ}\)C, vol.\% & 0 & 66.9 & 0 & 1.7 & 0.0 & 4.5 & 1.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Characteristics of FCCPT feed, HTVGO, and diesel, and those of the H-Oil heavy distillates measured by ASTM D 1160, and the ASTM D 7169 methods for Case 2. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameter** & **Case 1** & **Case 2** \\  & **without H-Oil Diesel Separation** & **with H-Oil Diesel Separation** \\ \hline FCC feed rate, t/h & 174 & 173 \\ H-Oil VGO in FCC feed, wt.\% & 21.8 & 20.6 \\ H-Oil diesel in FCC feed, wt.\% & 2.0 & 0.0 \\ Riser outlet temperature, \({}^{\circ}\)C & 550 & 550 \\ Combined feed temperature, \({}^{\circ}\)C & 313 & 318 \\ Regenerator dense bed temperature, \({}^{\circ}\)C & 704 & 703 \\ Regenerator dilute bed temperature, \({}^{\circ}\)C & 692 & 691 \\ Air rate for coke burning, k\(\text{N}\text{N}\text{m}^{3}\)/h & 105.3 & 106.3 \\ CTO ratio & 9.59 & 9.68 \\ Heat of reaction, kJ/kg & 599 & 617 \\ Delta coke, wt.\% & 0.54 & 0.54 \\ Yields and conversion, wt.\% & & & \\ Dry gas & 5.69 & 5.82 \\ C3 fraction & 8.59 & 8.62 \\ C4 fraction & 14.00 & 14.16 \\ Gasoline & 43.90 & 44.14 \\ LCO & 7.71 & 7.01 \\ HCO & 8.81 & 9.36 \\ Slurry oil & 6.05 & 5.68 \\ Coke & 5.22 & 5.21 \\ Losses & 0.03 & 0.01 \\ Total & 100.00 & 100.00 \\ Conversion & 77.43 & 77.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: FCC section operating conditions and FCC yields registered for Case 1, and Case 2 studied in this work.
## 3 General Concept and Passive Detection
### Practical Issues in Passive Detection  
As mentioned earlier in Section 3.1, the expected transmission costs of incoming packets need to be calculated in the passive detection phase. In addition, this determination process needs location information of nodes on the route those packets have passed through. Later in this section, details on how to solve these practical issues are given.
# Machine Learning Ship Classifiers for Signals from Passive Sonars
###### Abstract  
The accurate automatic classification of underwater acoustic signals from passive SoNaR is vital for naval operational readiness, enabling timely vessel identification and real-time maritime surveillance. This study evaluated seven supervised machine learning algorithms for ship identification using passive SoNaR recordings collected by the Brazilian Navy. The dataset encompassed 12 distinct ship classes and was processed in two ways--full-resolution and downsampled inputs--to assess the impacts of preprocessing on the model accuracy and computational efficiency. The classifiers included standard Support Vector Machines, K-Nearest Neighbors, Random Forests, Neural Networks and two less conventional approaches in this context: Linear Discriminant Analysis (LDA) and the XGBoost ensemble method. Experimental results indicate that data decimation significantly affects classification accuracy. LDA and XGBoost delivered the strongest performance overall, with XGBoost offering particularly robust accuracy and computational efficiency suitable for real-time naval applications. These findings highlight the promise of advanced machine learning techniques for complex multiclass ship classification tasks, enhancing acoustic signal intelligence for military maritime surveillance and contributing to improved naval situational awareness.  
acoustic signal; machine learning; ship classification; SoNaR
# A Novel Approach for the Estimation of Doubly Spread Acoustic Channels Based on Wavelet Transform
## 2 Channel Model  
UWA channel features large multipath spread for the interaction with ocean surface, bottom medium, as well as inhomogeneous particles of water column. MSML channel model can well describe the UWA multipath channel, that is:  
\[h(\tau,t)=\sum_{l=1}^{L}A_{l}(t)\delta(\tau-(a_{l}\tau_{l}-(a_{l}-1)t)) \tag{1}\]  
where \(L\) is the number of channel taps. \(A_{l}(t)\) is the time-varying amplitude of the \(l\)th path and can be assumed to be constant during a short period of time, for example, the transmission duration of one data frame [7]. \(\tau_{l}\) and \(a_{l}\) are the time delay and Doppler scale factor of the \(l\)th path, respectively. In addition, \(\delta(t)\) is a delta function defined as follows:  
\[\delta(t)=\begin{cases}1&t=0\\ 0&\text{otherwise}\end{cases} \tag{2}\]  
Let \(s(t)\) be the transmitted signal and the corresponding received signal \(r(t)\) can be written as  
\[r(t)=\sum_{l=1}^{L}A_{l}(t)s(a_{l}(t-\tau_{l}))+n(t) \tag{3}\]  
where \(n(t)\) is the additive noise.  
Given the sparsity of UWA channel, only some channel taps are nonzero in (1), which means that \(r(t)\) is a superposition of only a few scale-delayed versions of \(s(t)\). Therefore, the calculation complexity for channel estimation is significantly reduced.
## 2 Materials and Methods  
### Site Conditions  
The IM station was installed near the Dzieckowice water reservoir and two mines: the Sobieski Mine and the Ziemowit Mine in the Upper Silesian Coal Basin (USCB), Poland (Figure 1).  
Figure 1: Study site information: (**a**) Investigation area on the map of Poland; (**b**) Sketch of USCB with study site location (modified after [28]); (**c**) Map of the study site with seismic event distribution  
and IMI station locations; (**d**) Generalized geological profile of the IMI station site; (**e**) Map of the study site with seismic event distribution and PLA station locations; (**f**) Generalized geological profile of the PLA station site.  
The PLA station was located in the City of Chorzow, in the central part of the USCB, and recorded events from mines located at greater distances, such as the Bielszowice Mine, the Murcki-Staszic Mine, and the Sosnica Mine. The study presented here deals with the site effects; hence, we investigated the local geology from a physical perspective for a three-layered model: loose material (subsoil), intermediate layer, and rigid basement (Figure 1d,f). The rigid basement for both station sites is a Carboniferous coal-bearing formation built mainly of sandstones, mudstones, and siltstones interbedded with hard coal seams [27; 29; 30]. The upper depth level of the rigid basement reached 62 and 10 m for IMI and PLA station sites, respectively. The intermediate layer, which exists in the IMI station, is represented by weathered Triassic sandstones (Buntsandstein), which reach a thickness of 52 m (Figure 1d). This layer is intermediate because its elastic properties are between rigid and loose material due to the strongly weathered rocks [28; 31]. A similar intermediate layer is present beneath the PLA station. However, this resonant layer is approximately 15 m thick and comprises Carboniferous rocks, which are strongly weathered and fractured, and near-surface sediments are 5 m thick clay (Figure 1f). The top layer near the stations comprises Quaternary sediments (Figure 1d,f), represented by a mix of sand, pebbles, mud, alluvia, and peats [28; 31]. The physical properties of the subsurface have already been investigated. The average S-wave velocity value for the IMI and PLA at a depth of 30 m (Vs30 parameter) below ground level was 408 m/s and 512 m/s, respectively, as obtained from The Multichannel Analysis of Surface Waves (MASW) [9].
# Strategies and Challenges in Detecting XSS Vulnerabilities Using an Innovative Cookie Collector
## 5 Discussion
### Behavioral Segmentation Using Clustering Algorithms  
The use of clustering algorithms for behavioral analysis allowed users to be segmented based on their cookie patterns, revealing the following:* Clusters of users differentiated according to the prevalence and type of third-party cookies present in their browsing.
* Significant correlation between these clusters and exposure to XSS vulnerabilities.
* A trade-off between model accuracy and performance in high data volume environments.  
These findings expand the use of clustering in cybersecurity, as documented in previous work by [PERSON] [57], by demonstrating its applicability in educational contexts to accomplish the following:  
* Detection of anomalies in browsing patterns, which could alert to risky behavior of students.
* Optimization of web recommendations, adapting them to the risk profiles of users.
* Identification of groups with a greater propensity to visit vulnerable domains, allowing proactive interventions.
# Things2 People Interaction toward Energy Savings in Shared Spaces Using BIM
## 4 Personalized Context Data Prediction
### Modelling
#### 4.2.1 SARIMA Model  
The SARIMA model is a version of the ARMA model. The ARMA model is comprised of 2 sections--autoregression (AR), which is represented by '\(p\)', and moving average (MA), represented by '\(q\)'. In case the data is not stationary, this model does not provide excellent results. The model is explained in Equation (1)--ARMA Model Equation.  
\[Y_{t}=\ \mu+\sum_{j=1}^{p}\phi_{j}\Big{(}Y_{t-j}-\ \mu\Big{)}+\ \sum_{j=1}^{q} \psi_{j}\epsilon_{t-j}+\epsilon_{t} \tag{1}\]  
To be able to deal with the trend in data, the integrated term '\(i\)' is applied, converting the not stationary data into stationary data and naming the model ARIMA. Data that has a pattern that repeats in a set of periods implies that there is seasonality. To take that into account, the ARIMA model developed to a seasonal ARIMA (SARIMA). The SARIMA model can be explained by Equation (2)--Sarima Model Equation, where \(D\) represents the seasonal differencing and \(s\) the seasonal period.  
\[\Phi(B)\phi(B)\
abla_{s}^{D}\
abla^{d}Y_{t}=\ \Theta(B)\theta(B)Z_{t} \tag{2}\]
## 4 The Model
### The Dataset  
The experiments utilize a dataset comprising historical stock price data for Google, retrieved from the Yahoo Finance platform from August 2004 to August 2019. This dataset spans approximately 15 years of daily financial data and consists of 3779 observations. Due to its widespread use in academic research on synthetic time series generation, the dataset provides an excellent benchmark for comparative analysis with existing models.  
The dataset encompasses six key variables, which are fundamental indicators in financial time series analysis:  
* Open: Opening price;
* High: Maximum price;
* Low: Minimum price;
* Close: Closing price;
* Volume: Number of shares traded;
* Adjusted Close: Closing price adjusted for dividends and stock splits.  
Furthermore, these variables offer a complete and detailed picture of a company's share price fluctuations, facilitating the examination of both historical trends and volatility in the financial markets.  
The dataset exhibits no evidence of missing values or outliers in its original form. To guarantee the quality of the experiment and the optimal functioning of the model, the data underwent a multi-stage preprocessing procedure. The initial step involved the division of the dataset into a training and a test set. The former comprised 90% of the data, which was then used for model training. This split threshold was chosen to maximise the availability of training data, ensuring effective learning despite the small dataset. Subsequently, the data were normalised using a min-max scale on all variables, moving them into a range between 0 and 1. This step was crucial to ensure stable model training, especially in circumstances where some of the variables have a significantly different order of magnitude (e.g., volume). Finally, the dataset was converted into a sequential format suitable for the model. The time sequences were created by segmenting the data into 14-length windows. Each sequence contains consecutive values of all six variables, thus allowing the model to capture the main temporal patterns in the financial series.  
Specific data manipulation techniques were implemented to simulate realistic scenarios and evaluate the model's performance under non-ideal conditions, including the introduction of missing values. In particular, data were randomly removed according to a uniform distribution on both the temporal and feature axes. This step, designed to reproduce real-world conditions, is intended to evaluate the model's performance in the context of imputation tasks. This scenario reflects the characteristics of markets that display extreme behaviour, as observed during periods of high volatility. Therefore, the selection of the dataset is based on its ability to represent the intricate financial dynamics, its widespread use in the literature enabling direct comparisons with other models, and its public accessibility, which supports the reproducibility of experiments.
# Improved Adaptive Successive Cancellation List Decoding of Polar Codes
## 2 Theory of Polar Codes  
A method for constructing polar codes based on channel polarization is given in [1]. The construction of the polar codes can be defined by \((N,K,\Lambda,u_{\Lambda^{C}})\), where \(N\) denotes the code length, \(K\) denotes the number of information bits, the code rate is \(K/N\), \(\Lambda\) denotes the set of the index of the information bits, \(u_{\Lambda}\) denotes the set of the information bits, and \(u_{\Lambda^{C}}\) denotes the frozen bits. the frozen bits are usually set to zero. \(u_{1}^{N}\) denotes the information sequence before encoding, and then the encoding process can be expressed as  
\[x_{1}^{N}=u_{1}^{N}G_{N} \tag{1}\]  
where \(x_{1}^{N}\) is the encoding sequence, \(G_{N}\) is the generator matrix of polar codes. \(G_{N}\) is represented by  
\[G_{N}=B_{N}F^{\otimes n} \tag{2}\]  
where \(n=log_{2}N\), \(F^{\otimes n}\) denotes \(n\)-order Kronecker power, and \(F=\left[\begin{array}{cc}1&0\\ 1&1\end{array}\right]\).  
The decoding of polar codes can be regarded as the process of channel splitting, and the transition probability of each sub-channel is related to each other. For the information bit \(i\), the hard decision value \(\hat{u}_{i}\) is calculated by the received sequence \(y_{1}^{N}\) and the sequence \(\hat{u}_{1}^{i-1}\) that has been decoded previously. The specific calculation formula is [1]  
\[\delta\Big{(}y_{1}^{N},\hat{u}_{1}^{i-1}\Big{)}=\left\{\begin{array}{l}0,L_{ N}^{(i)}\Big{(}y_{1}^{N},\hat{u}_{1}^{i-1}\Big{)}\geq 0\\ 1,L_{N}^{(i)}\Big{(}y_{1}^{N},\hat{u}_{1}^{i-1}\Big{)}<0\end{array}\right. \tag{3}\]  
where \(L_{N}^{(i)}\Big{(}y_{1}^{N},\hat{u}_{1}^{i-1}\Big{)}\) represents the log likelihood ratio of \(i\)-th bit, which is  
\[L_{N}^{(i)}\Big{(}y_{1}^{N},\hat{u}_{1}^{i-1}\Big{)}=ln\Bigg{(}\frac{W_{N}^{i }\Big{(}y_{1}^{N},\hat{u}_{1}^{i-1}\big{|}0\Big{)}}{W_{N}^{i}\Big{(}y_{1}^{N},\hat{u}_{1}^{i-1}\big{|}1\Big{)}}\Bigg{)} \tag{4}\]  
It can be seen that the decoding result of the current codeword is related to all previous decoding results in SC decoding, that is, the previous error bit will affect the decoding result of the current bit. In order to solve this problem, the SCL algorithm proposed in [3] expresses all possibilities of SC decoding in the form of a binary tree, as shown in Figure 1. The red dotted line represents a path of list4, and the two branches of each node represent the two possibilities of the node with \"1\" or \"0\". For example, the information bit \(u_{1}\) corresponds to the two paths of the first layer in Figure 1. PM\({}_{1}^{1}\) and PM\({}_{1}^{2}\) represent the reliability of the two paths, respectively.
# Output Force Density Saturation in COMSOL Simulations of Biomimetic Artificial Muscles
## 1 Introduction
Simultaneously, the total force of a muscle fiber bundle is the number of fibers times the generated force in each fiber, as the forces are additive when fibers are arranged in parallel [25,26]. As the force in each fiber is relatively small, a larger number of fibers need to be arrayed. This means that a practical macro-scale muscle would have at least tens of thousands of micro capacitors. Finally, COMSOL is very computationally intensive, particularly when multiple nodes are used simultaneously, and when the number of objects is large. Hence, directly simulating a macro-scale muscle made of micro capacitors is technically unfeasible due to insufficient computational power. To solve this problem, we came up with the hypothesis that a saturation of output force density can be reached as the size of the array is increased, but before we run out of computational power. If this hypothesis is correct, then that saturation value can be used as a good estimate for the output force of much larger arrays, avoiding the need to simulate them directly. That would circumvent the above problem of insufficient computational power for macro-scale muscles. To test this idea, we set up and ran simulations of artificial muscle structures at a 100 \(\upmu\)m scale, built as N \(\times\) N \(\times\) 10 arrays of micro-capacitors, where N was varied from 1 to 13, while tendon thickness within each array was swept. The maximal force density of each sweep was plotted vs. N to produce an overall curve that indicated saturation around and beyond N = 10, thereby proving our starting hypothesis. Hence, a 10 \(\times\) 10 \(\times\) 10 array is sufficiently representative and predictive of far larger arrays of such artificial muscle devices. This result is of great practical significance to further research in this field and to building practical artificial muscles of this type. Furthermore, the saturation level was determined to be \(\sim\)9 kPa at the 100 \(\upmu\)m scale, which suggests a force density of \(\sim\)1 MPa at the 10 \(\upmu\)m scale, indicating the feasibility of strong, energy-efficient, low-density muscles for a wide range of practical applications.
## 2 System Modeling
### The Dynamics of the Electrohydraulic Actuator  
Taking the oil compressibility into consideration, the pressure dynamics of the electrohydraulic actuator is written as [30; 31]  
\[\begin{array}{lcl}\frac{V_{1}}{P_{e}}\dot{P}_{1}&=&Q_{1}-A_{1}\frac{\partial x _{s}}{\partial\theta}\dot{\theta}-C(P_{1}-P_{2})+q_{1}(t)\\ \frac{V_{2}}{P_{e}}\dot{P}_{2}&=&-Q_{2}+A_{2}\frac{\partial x_{s}}{\partial \theta}\dot{\theta}+C(P_{1}-P_{2})-q_{2}(t)\end{array} \tag{6}\]  
where \(V_{1}=V_{01}+A_{1}x_{s}\) and \(V_{2}=V_{02}-A_{2}x_{s}\) stand for the volumes of the two chambers severally; \(V_{01}\) and \(V_{02}\) stand for the original volumes of the two chambers severally; \(\beta_{e}\) stands for the effective oil bulk modulus; \(C\) stands for the internal leakage coefficient; \(q_{1}(t)\) and \(q_{2}(t)\) stand for the unmodelled disturbances.  
Given that a high-performance proportional valve is utilized in this paper, the servo valve dynamics can be omitted. Consequently, the control input \(u\) is assumed to be proportional to the valve spool displacement. The flows \(Q_{1}\) and \(Q_{2}\) thus are modeled as [32]  
\[\begin{array}{lcl}Q_{1}&=&k_{t}u\Big{[}sig(u)\sqrt{|P_{s}-P_{1}|}+sig(-u) \sqrt{|P_{1}-P_{r}|}\Big{]}\\ Q_{2}&=&k_{t}u\Big{[}sig(u)\sqrt{|P_{2}-P_{r}|}+sig(-u)\sqrt{|P_{s}-P_{2}|} \Big{]}\end{array} \tag{7}\]  
where \(k_{t}\) stands for the flow gain; \(sig(u)\) is defined as  
\[sig(u)\ =\ \left\{\begin{array}{lcl}1,if\ u\geq 0\\ 0,if\ u<0\end{array}\right. \tag{8}\]  
From (6), it has  
\[\begin{array}{lcl}A_{1}\dot{P}_{1}-A_{2}\dot{P}_{2}&=&\frac{A_{1}\beta_{e}} {V_{1}}\bigg{[}Q_{1}-A_{1}\frac{\partial x_{s}}{\partial\theta}\dot{\theta}- C(P_{1}-P_{2})+q_{1}(t)\bigg{]}\\ &-\frac{A_{2}\beta_{e}}{V_{2}}\bigg{[}-Q_{2}+A_{2}\frac{\partial x_{s}}{ \partial\theta}\dot{\theta}+C(P_{1}-P_{2})-q_{2}(t)\bigg{]}\\ &=&\beta_{e}\bigg{(}\frac{A_{1}}{V_{1}}Q_{1}+\frac{A_{2}}{V_{2}}Q_{2}\bigg{)} -\beta_{e}\bigg{(}\frac{A_{1}^{2}}{V_{1}}+\frac{A_{2}^{2}}{V_{2}}\bigg{)} \frac{\partial x_{s}}{\partial\theta}\dot{\theta}\\ &-\beta_{e}\bigg{(}\frac{A_{1}}{V_{1}}+\frac{A_{2}}{V_{2}}\bigg{)}C(P_{1}-P_{ 2})+\beta_{e}\bigg{[}\frac{A_{1}}{V_{1}}q_{1}(t)+\frac{A_{2}}{V_{2}}q_{2}(t) \bigg{]}\end{array} \tag{9}\]  
Putting (7) into (9), it leads to  
\[\begin{array}{lcl}A_{1}\dot{P}_{1}-A_{2}\dot{P}_{2}&=&\beta_{e}k_{t}(\frac{ A_{1}}{V_{1}}s_{1}+\frac{A_{2}}{V_{2}}s_{2})u-\beta_{e}(\frac{A_{1}^{2}}{V_{1}}+ \frac{A_{2}^{2}}{V_{2}})\frac{\partial x_{s}}{\partial\theta}\dot{\theta}\\ &-\beta_{e}(\frac{A_{1}}{V_{1}}+\frac{A_{2}}{V_{2}})C(P_{1}-P_{2})+\beta_{e} \Big{[}\frac{A_{1}}{V_{1}}q_{1}(t)+\frac{A_{2}}{V_{2}}q_{2}(t)\Big{]}\end{array} \tag{10}\]  
in which  
\[\begin{array}{lcl}s_{1}&=&sig(u)\sqrt{|P_{s}-P_{1}|}+sig(-u)\sqrt{|P_{1}-P_{ r}|}\\ s_{2}&=&sig(u)\sqrt{|P_{2}-P_{r}|}+sig(-u)\sqrt{|P_{s}-P_{2}|}\end{array} \tag{11}\]
## 1 Introduction
In this paper, we address the energy and transmission efficiency enhancement problem of fronthaul network to support smart home networks. Moreover, the load balancing of the fronthaul is discussed and analyzed. Specifically, we first present a quantitative study of the problem using integer non-linear programming (INLP) model. Afterwards, an adaptive genetic algorithm (GA) based approach is proposed to reduce the complexity of the INLP model. Finally, we present numerical simulation analysis. This paper gives an in-depth detailed study of a joint optimization, including energy saving and transmission efficiency enhancement problem of optical fronthaul. Load balancing in the fronthaul links is also taken into consideration. The rest of the paper is organized as follows. Section 3 formulates the INLP model, the adaptive GA is described in Section 4, and the simulation results are discussed in Section 5. Finally, Section 6 summarizes the results.