esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A  
_Derivation of the Formula of Inverse SSST_  
The definition of the SSST is as follows:  
\[SSST_{x}(\widetilde{f_{l}},b)=\left(\Delta\widetilde{f_{l}}\right)^{-1}\times \sum_{f_{k}:|\widetilde{f_{l}}(f_{k},b)-\widetilde{f_{l}}|\leq\frac{1}{2} \Delta\widetilde{f_{l}}}|ST_{x}(f_{k},b)|f_{k}\Delta f_{k} \tag{1}\]  
According to Parseval theorem, the ST of the signal can be expressed as follows:  
\[\begin{array}{ll}ST_{x}(f,b)&=\frac{|f|}{\sqrt{2\pi}}\int_{-\infty}^{\infty} x(t)\exp(\frac{-(t-b)^{2}f^{2}}{2})\exp(-i2\pi ft)\mathrm{d}t\\ &=|ST_{x}(f,b)|\exp(i\varphi(f,b))\end{array} \tag{2}\]  
The following is true:  
\[C_{\varphi}=e^{-i|2\pi ft+\varphi(f,b)|}f^{2} \tag{3}\]  
We found the following:  
\[\frac{SSST_{x}\left(\widetilde{f_{l}},b\right)}{C_{\varphi}}=\left(\Delta \widetilde{f}\right)^{-1}\times\sum_{f_{k}:|\widetilde{f}(f_{k},b)-\widetilde {f_{l}}|\leq\frac{1}{2}\Delta\widetilde{f}}|ST_{x}(f_{k},b)|exp(-i2\pi f_{k} b)f_{k}^{-1}\Delta f_{k} \tag{4}\]  
According to Equation (5) and using the continuous form of the right side of Equation (4), we found the following:  
\[\begin{array}{ll}&\int_{0}^{\infty}ST_{x}(f,b)|exp(i2\pi ft)f^{-1}df\\ &=\frac{1}{2\pi}\int_{-\infty}^{\infty}\int_{0}^{\infty}X(\xi)\overline{ \widetilde{\psi}(f^{-1}\xi)}f^{-1}exp(ib\xi)dfd\xi\\ &=\int_{0}^{+\infty}-\overline{\widetilde{\psi}(\xi)}\xi^{-1}d\xi\cdot\frac{ 1}{2\pi}\int_{0}^{+\infty}X(\xi)exp(ib\xi)d\xi\end{array} \tag{5}\]  
Let \(C_{\psi}=0.5\int_{0}^{+\infty}-\overline{\widetilde{\psi}(\xi)}\xi^{-1}d\xi\) and assuming that the signal is real-valued, the signal can then be reconstructed by the following:  
\[\begin{array}{ll}x(b)&=\frac{1}{\pi}RE\left[\int_{0}^{\infty}\hat{x}(\xi) \exp(ib\xi)\mathrm{d}\xi\right]\\ &=RE\left[C_{\psi}^{-1}\int_{0}^{\infty}ST_{x}(f,b)\exp(i2\pi ft)f^{-1}\mathrm{ d}f\right]\end{array} \tag{6}\]  
In the piecewise constant approximation corresponding to the binning in \(f\), the following is true:  
\[x(b)=RE[(C_{\psi}C_{\varphi})^{-1}\sum_{l}SSST_{x}(\widetilde{f_{l}},b)\Delta \widetilde{f_{l}}] \tag{7}\]
# Experimental and Empirical Studies to Evaluate Liquefaction Resistance of Partially Saturated Sands
###### Abstract  
Partially saturated sands with occluded gas bubbles may occur naturally in the field or can be created artificially as a way of mitigating liquefaction effects. This study aimed to investigate the main parameters that influence the liquefaction resistance of partially saturated sands containing occluded gas bubbles. It also adopted a simplified approach and proposed empirical relationships that linked the liquefaction-resistance ratio to the bulk modulus, maximum volumetric strain, and excess pore-pressure ratio. Considering these objectives, a total of 60 stress-controlled dynamic triaxial tests were conducted. Nearly/fully saturated and partially saturated sand specimens prepared using sodium percarbonate were subjected to undrained cyclic loading with a loading frequency of 0.1 Hz. The loosely or densely packed sand specimens at different degrees of saturation (79-100%) were examined under different initial effective confining stresses (25-100 kPa) and back pressures (100-400 kPa). The analysis of the test results indicated that the liquefaction resistance of the partially saturated sands constantly increased as the degree of saturation decreased, and this effect was more prominent under higher initial effective confining stresses and lower back pressures. The adopted method satisfactorily captured this trend and showed reasonable matches between the empirical predictions and experimentally observed results from this study and previous research.  
Keywords: liquefaction mitigation; partially saturated sand; dynamic triaxial testing; empirical model; confining stress; degree of saturation +
Footnote †: journal: Applied
## 5 Active Control Algorithm  
In the active control systems, the FxLMS algorithm is frequently implemented [20; 21]. Its advantages are, e.g., simplicity of implementation, robustness and low complexity of computations [22]. A scheme of basic FxLMS algorithm is presented in Figure 13, where \(P(z)\) is the primary path, \(S(z)\) is the secondary path, \(W(z)\) is a control filter adapted by the LMS algorithm, and \(\hat{S}(z)\) is a model of secondary path \(S(z)\). The algorithm name FxLMS (Filtered-x Least Mean Squares) follows from usage of \(\hat{S}(z)\) model--the reference signal usually noted as \(x(n)\) is passed through the secondary path estimate \(\hat{S}(z)\) to obtain a filtered reference signal \(r(n)\)[2]. It is also passed through the primary path \(P(z)\), which outputs signal \(d(n)\)--the primary disturbance. Adaptive filter output \(u(n)\) is passed through secondary path \(S(z)\) to obtain a signal that is summed with the primary disturbance \(d(n)\). The summation results in signal \(e(n)\), which is a residual error signal. The control system attempts to minimize the sum of squares of the error signal \(e(n)\) over the length of filter \(W(z)\).  
Figure 11: The amplitude functions of the frequency functions of the impulse response estimates for the paths between back casing panel’s selected actuator and one of the room microphones, at all distances, in both setups.  
Figure 12: The amplitude functions of the frequency functions of the impulse response estimates for the paths between right casing panel’s selected actuator and one of the room microphones, at all distances, in both setups.  
Figure 13: The scheme of the FxLMS algorithm [2].  
The scheme presented in Figure 13 depicts a single-channel control system, however, if the plant is controlled by multiple inputs and returns multiple outputs, the control system can be generalized to a Multiple-Input Multiple-Output (MIMO) FxLMS, as described in [23], and such is used for this research. However, this paper is focused more on the investigation of the control system performance for specific configurations, rather than on introducing the control algorithm itself. Hence, the reader is referred to previous publications, where a more detailed description of the control system with a switched-error modification employed for the active casing is given [12; 24].  
It is noteworthy that varying the selected control algorithm may impact active control performance, e.g., the convergence rate or the final noise reduction levels. However, the preliminary experiments have shown that such an impact is similar for all evaluated experimental setups (it is independent of the particular configuration). Hence, it has been assumed that by employing the unchanged control algorithm through the whole research, the drawn conclusions also remain valid for altered or different control algorithms.
# Software-Defined NB-IoT Uplink Framework--The Design, Implementation and Use Cases
## 5 Uplink Implementation
### Demodulation Reference Signals  
The given module is responsible for the DMRS signal generation. Generally speaking, DMRS symbols are multiplexed with the NPUSCH symbols during the resource grid arrangement. The exact DMRS symbols allocation and the number of its occurrence depends on the parameters of the given transmission, as depicted in Figure 6.  
\begin{table}
\begin{tabular}{c c c} \hline
**b(i), b(i+1)** & **I** & **Q** \\ \hline
00 & \(1/\sqrt{2}\) & \(1/\sqrt{2}\) \\ \hline
01 & \(1/\sqrt{2}\) & \(-1/\sqrt{2}\) \\ \hline
10 & \(-1/\sqrt{2}\) & \(1/\sqrt{2}\) \\ \hline
11 & \(-1/\sqrt{2}\) & \(-1/\sqrt{2}\) \\ \hline \end{tabular}
\end{table}
Table 6: QPSK modulation [2].  
\begin{table}
\begin{tabular}{c c} \hline
**NPUSCH Format** & \(\mathbf{N_{sc}^{RU}}\) & **Modulation Scheme** \\ \hline
1 & BPSK, QPSK \\ \(>\)1 & QPSK \\ \hline
2 & 1 & BPSK \\ \hline \end{tabular}
\end{table}
Table 7: NPUSCH modulation schemes [2].  
Figure 6: DMRS allocation [2].  
The DMRS may be obtained based on the reference sequence \(r_{u}(n)\) that varies in relation to \(N_{sc}^{RU}\), as follows:  
\[r_{u}(n)=\frac{1}{\sqrt{2}}(1+j)(1-2c(n))w(n\bmod 16),\quad 0\leq n<M_{rep}^{ NPUSH}N_{slots}^{UL}N_{RU} \tag{9}\]  
NPUSCH format 1: \(r_{u}(n)=\bar{r}_{u}(n)\)  
NPUSCH format 2: \(r_{u}(3n+m)=\bar{w}(m)\bar{r}_{u}(n),\quad m=0,1,2\)  
for \(N_{sc}^{RU}=1\) and:  
\[r_{u}(n)=e^{jan}e^{j\phi(n)\pi/4},\quad 0\leq n<N_{sc}^{RU} \tag{10}\]  
for \(N_{sc}^{RU}>1\).  
It should be noted that \(c(n)\) represents the pseudorandom Gold sequence described in Section 5.3, reinitialized based on \(c_{init}=35\). The \(w(n)\), \(\alpha\) and \(\phi\) parameters are specified and tabularized in [2].  
In order to divide the pilot sequences between the separate slots, the additional group-hopping parameter is introduced. Enabled group hopping provides the DMRS variety, due to changing \(u\) index that indicates the current value of both \(w(n)\) and \(\phi\) for each NPUSCH slot.  
In general, such a module with complicated calculations involving complex numbers that is used in real systems and thus is restricted by, e.g., the real-time operation limitations, may be a good didactic example demonstrating the importance of the computational effectiveness and reinforcing student awareness regarding connection between the implementation itself and its further practical use. Finally, it may be an effective exercise to develop good programming habits.
# A Multi-Branch DQN-Based Transponder Resource Allocation Approach for Satellite Communications
## 1 Introduction
Through well-planned and rapid allocation of leasing tasks, as shown in Figure 2, the satellite resource allocation method can improve the utilization rate of satellite transponder resources (i.e., frequency and its occupation time) under the condition of multiple constraints. The MBDQN model possesses several excellent features. The model is highly efficient in terms of inference and decision-making. Despite its time-consuming training process,  
Figure 1: The process of transparent satellite transponder resource allocation. MBDQN can have the training done offline, and the actual deployment and inference can be performed using the trained model, thus achieving near real-time decision-making. The main contributions of this paper are summarized as follows:  
(1) A reinforcement learning solution to the satellite resource allocation problem is proposed and established, where the satellite operation control system and the task requirements are defined as the agent and environment of DRL, respectively. (2) Multi-Branch Deep Q-Network (MBDQN) is designed for resource allocation, which employs TL-Branch and RP-Branch to simultaneously extract features of the satellite resource pool state and task list state. Our proposed MBDQN achieves state-of-the-art performance on the generated task datasets. (3) Two actions, namely frequency priority and time priority, are introduced into the action space, which further improves the average resource utilization rate of the system compared with when only time priority is involved. In contrast to traditional heuristic algorithms, our method is more suitable for online deployment and significantly reduces the time consumption during inference.
# An Improved Approach to DNS Covert Channel Detection Based on DBM-ENSec
## 4 Experiments and Analysis of Results
### Comparative Analysis
#### 4.6.3 Comparison with Domain-Based Detection Method  
This section will conduct a comparative analysis of the DBM-ENSec model alongside four methods for detecting covert channels in DNS communication that are based on domain names. The methods under consideration include (1) an analysis of four categories of attribute features, namely the length of DNS request domain names, character proportions, randomness characteristics, and semantic features, coupled with the application of four machine learning algorithms to train classifiers for detection; (2) a detection approach utilizing a Long Short-Term Memory (LSTM) model that directly examines the Fully Qualified Domain Name (FQDN) present in DNS packets; (3) the DSR-GAT method, which constructs a domain name graph to illustrate the relationships between domain names and employs a one-dimensional convolutional neural network to automatically extract semantic features of the domain names, further enhancing these feature representations through a graph attention network; and (4) a detection method that identifies anomalous behavior by analyzing various statistical features of DNS traffic to train a LightGBM model. The results of this comparative analysis are presented in Table 6.  
The comprehensive results of the comparative analysis demonstrate that the DBM-ENSec model surpasses current domain-based detection methods across all performance metrics. This further substantiates its detection capabilities and generalization performance in addressing complex attack scenarios.
## 3 Methodology
### Feature Selection (FS)
#### 3.2.3 Relief (RFF)  
Relief (RFF) is a frequently used feature ranking method that determines the significance of features based on how effectively they evaluate and distinguish the sampled item from its nearest hit (an instance of the same category) and nearest miss (an instance of a different category) (different categories). The Relief feature selection method picks feature instances at random from the training data. For every sampled occurrence, the nearest hit and miss are identified. Suppose a characteristic differs across various classes and has a comparative assessment for examples of the same classes. In that case, it is given a valuable weight. It tries to obtain the most accurate assessment from the probability below to use as the weight for each word feature \(f\)[57].
## 4 Results and Discussion
### Frequency Response  
Next, three of the proposed IPC schemes and CPC were compared in the frequency domain. For blade 1, Figure 9 shows the Fourier spectrum of the OoP blade bending moment \(\rm M_{1}\) on the left and the pitch signal \(\rm\beta_{1}\) on the right. The IPC schemes significantly decreased the large peak of the 1P component of the OoP moment \(\rm M_{1}\) around the frequency of 0.126 Hz when compared with the CPC scheme. The \(\rm IPC\_I1\psi\) virtually removed this peak. Regarding pitch signals, CPC without the IPC did not display 1P peaks; however, all IPC schemes showed a significant peak associated with the 1P pitch component, which was primarily responsible for the greater pitch effort.  
Based on the analysis performed in this section, when the IPC schemes are optimally tuned to decrease the DEL of OoP blade moments, we can state the following: (1) The IPC schemes with integral controllers without the azimuth offset had roughly the same performance as the IPC schemes with proportional controllers (with or without the azimuth offset), (2) using identical or different gains in the decentralized controllers of IPC did not have a significant impact, (3) including the azimuth offset in the IPC schemes with integral actions helped to reduce the DEL value by a further 10%, and (4) including the azimuth offset in the IPC schemes with proportional controllers did not show significant improvements.
# Spatial Deformation Calculation and Parameter Analysis of Pile-Anchor Retaining Structure
## 4 Engineering Background and Numerical Simulation Model
### Numerical Simulation Model
#### 4.2.3 Calculation Steps  
The model's calculations do not consider the incremental impact of excavation; instead, they focus on the final deformation of the retaining structure once the excavation reaches the design elevation. To achieve this, the calculations are divided into three steps:  
Step 1: Material parameters corresponding to the soil along the depth are assigned, and a geostatic step is performed to obtain the initial stress field.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & **Elastic Modulus (MPa)** & **Poisson’s Ratio** & **Bending Stiffness (N-m\({}^{2}\))** \\ \hline Crown beam & 30,000 & 0.25 & 6.86 \(\times\) 10\({}^{6}\) \\ Waist beam & 30,000 & 0.25 & 25,000 \\ Pile & 30,000 & 0.25 & 4.25 \(\times\) 10\({}^{6}\) \\ Anchor cable & 200,000 & 0.20 & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material Parameters.  
Figure 6: Numerical simulation model.  
Step 2: The displacement field from step 1 is cleared while retaining the initial stress field. Material parameters are assigned to the crown beam, pile, waist beam, and anchor cable. The contact surface between the pile and soil is activated, and calculations are performed to simulate the excavation and construction of the retaining structure.  
Step 3: In step 3, the displacement field is reset to zero. The soil within the design elevation is completely removed, and calculations are conducted to simulate the excavation to the bottom of the pit.  
By following these three steps, the model captures the final deformation of the retaining structure accurately as the excavation progresses, while considering the specific stages and parameters involved in the process.  
Figure 7 illustrates the calculation process employed for the theoretical predictions. It provides a visual representation of the steps and procedures involved in generating the theoretical predictions. By referring to this figure, one can easily grasp the sequential flow and methodology utilized in the calculation process for the theoretical predictions.
# Node-Based QoS-Aware Security Framework for Sinkhole Attacks in Mobile Ad-Hoc Networks
## 4 QoS-Aware Security Framework: Proposed Scheme
### Admission Control  
The admission control mechanism deals with QoS violations because of false admissions, changes in node neighborhood, node mobility, or signal propagation conditions. This component is responsible for admitting data sessions with QoS requirements that can be satisfied without violating previous sessions. The collection of information about available network resources is done to make admission decisions. The admission control protocol establishes if there are any nodes or node pairs with the necessary resources available. This action is completed during route discovery or even after the routes have been discovered. If no route completes the application's QoS requirements, then that data session is rejected. This is the reason why it is important that during this phase, information about the resources available with neighboring nodes is collected. The key responsibility of this component is the management of data sessions with QoS assurance violations and having those sessions re-admitted in an event when there was an interruption during its packet sending phase and maintenance of state information [18, 20].
## 3 Results and Discussion
### Enzymatic Activity Variation during Desalting Process
#### 3.3.2 Acid Phosphatase Activity Variation during Desalting Process  
In many studies of marine food, phosphatase activities in fish muscles are reported to cause the breakdown of adenosine triphosphate (ATP) and the subsequent formation of its by-products (adenosine diphosphate, adenosine monophosphate, and inosine monophosphate), which are related to the K-value, an index of fish freshness [33]. However, the behaviour of these enzymes in desalted fish and during the desalting process is not well known. As depicted in Figure 4A, salted cod showed an activity of 161.4 \(\pm\) 7.1 nmol p-NP/min/g IS, with a decrease (\(p<0.05\)) in this activity during desalting at both temperatures (4 and 20 \({}^{\circ}\)C). However, a different behaviour was observed between the two temperatures. When the desalting process was carried out at 4 \({}^{\circ}\)C, the initial activity decreased (\(p<0.05\)) to an average value of 85 nmol p-NP/min/g IS after 4 and 8 h, which corresponds to an initial activity reduction of 50%. At 20 \({}^{\circ}\)C, a significant and more pronounced decrease (\(p<0.05\)) in activity was observed when compared to 4 \({}^{\circ}\)C. Specifically, after 1 h of desalting at 20 \({}^{\circ}\)C, the acid phosphatase activity decreased (\(p<0.05\)) to a value of 82.2 \(\pm\) 0.7 nmol p-NP/min/g IS, corresponding to approximately 50% of the initial activity. After that, a slight decrease (\(p<0.05\)) was observed at a value of 24.0 \(\pm\) 0.6 nmol p-NP/min/g IS at 8 h of desalting, corresponding to an 85% reduction in the initial activity.  
On the other hand, at both temperatures, a slight increase in activity was observed between 8 and 16 h of desalting, which can possibly be related to microbial growth leading to an increase in microbial enzymes' content in the cod muscle. At the end of desalting (16 h), desalted cod muscle showed an acid phosphatase activity of 112.4 \(\pm\) 8.6 p-NP/min/g IS (4 \({}^{\circ}\)C) and 43.4 \(\pm\) 1.2 nmol p-NP/min/g IS (20 \({}^{\circ}\)C), which corresponds to 30 and 70% of the initial activity.  
Figure 3: Cathepsin B (**(A)**, FU/min/g IS) and cathepsin D (**(B)**, \(\mu\)g tyrosine/min/g IS) activities in cod fish during desalting. Different letters (a–d) denote significant differences (\(p<0.05\)) between desalting times. The symbol \({}^{\ast}\) denotes significant differences between desalting temperatures.
# Advances and Future Perspective on Detection Technology of Human Norovirus
## 2 Detection Techniques
### Molecular Detection Methods  
The HuNoV molecular detection technology was primarily used to detect the conserved fragments of the NoV genome. The junction region of ORF1-ORF2 was mostly conserved in the NoV genome, which was widely used as a target for molecular detection [50; 51]. To date, detection technologies based on HuNoV nucleic acid were primarily divided into two types according to different nucleic acid amplification methods. One is based on isothermal amplification, including reverse transcription-loop-mediated isothermal amplification (RT-LAMP), nuclear acid sequence-based amplification (NABSA) and recombinase polymerase amplification (RPA); the other was based on thermal cycling and amplification, including reverse transcription polymerase chain reaction(RT-PCR), quantitative real-time polymerase chain reaction (RT-qPCR), in situ capture-RT-qPCR (ISC-RT-qPCR) and reverse transcription digital polymerase chain reaction (RT-qPCR).