esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Experiments and Results
### Experimental Results  
#### 4.5.1 Coverage Cost  
For each subject program, the genetic algorithm finds a test cover to execute all du-pairs and another test cover to execute the du-based killable second-order mutants. The genetic algorithm has been applied many times to obtain more accurate results. The genetic algorithm has been applied three times on each subject program to find an adequate cover for the du-based mutants. In the same way, the genetic algorithm has been run on each subject program three times to find a sufficient covering for all du-pairs. The average number of the obtained test cases was computed for each subject program (given in Table 4). Table 5 gives the statistical results of the \(t\)-Test for the test-covers of the two criteria.  
The data given in Table 4 show that all du-pairs testing criterion needs 20.3 test cases (on average) to cover all the set of du-pairs for each tested program. Furthermore, the second-order mutation testing criterion requires 59.1 test cases (on average) to kill all second-order mutants for each tested program. The statistical results of the \(t\)-Test (given in Table 5) show that the size of the test cover of all du-pairs criteria is significantly smaller than the size of the test cover of the second-order mutation criterion.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Tested** & **All 1 st** & **Killable** & **All 2 nd** & **Killable** & **Du-Based Killable** & **Du-** \\
**Program** & **Order** & **1 st Order** & **Order** & **2 nd Order** & **2 nd Order** & **2 nd Order** & **Pairs** \\ \hline Triangle & 163 & 146 & 30,799 & 23,799 & 2459 & 102 \\ \hline Sort & 120 & 104 & 15,960 & 11,547 & 2333 & 95 \\ \hline Stack & 70 & 57 & 5146 & 3507 & 354 & 68 \\ \hline Reversed & 98 & 78 & 10,444 & 6620 & 956 & 77 \\ \hline Clock & 78 & 74 & 7638 & 7036 & 109 & 64 \\ \hline Quotient & 156 & 140 & 27,184 & 21,338 & 2597 & 89 \\ \hline Product & 117 & 101 & 17,018 & 12,321 & 759 & 71 \\ \hline Area & 112 & 88 & 15,555 & 8656 & 405 & 64 \\ \hline Dist & 83 & 73 & 9207 & 6774 & 274 & 64 \\ \hline Middle & 74 & 70 & 6876 & 5458 & 319 & 70 \\ \hline Total & 1071 & 931 & 145,827 & 107,056 & 10,565 & 764 \\ \hline \hline \end{tabular}
\end{table}
Table 3: No. of first order, no. of second order, and no. of du-pairs.  
The mean mutation coverage ratio of the all du-pairs adequate test cover is 80.9%, and the mean data flow coverage ratio of the second-order mutant adequate test cover is 98.7%. Therefore, one can accept H1 and reject H2. Consequently, second-order mutation testing \"ProbSubsumes\" the all du-pairs data flow testing.
# Conservative Interference Injection to Minimize Wi-Fi Sensing Privacy Risks and Bandwidth Loss
## 6 Application of Defence Model Against Keystroke Attacks
### Experimental Setup  
We utilise the same experimental apparatus as before, with the addition of an interference device as depicted in Figure 12.  
The interference device is a Macbook Pro laptop on the same communications channel (Channel 2 of the 2.4 GHz Wi-Fi spectrum). It is configured to propagate ping packets at varying rates between 1300 Hz and 0 Hz, whilst the keyboard sensing apparatus operates with Tx propagating ping packets at 1300 Hz. As demonstrated by the ellipses in Figure 12, the proximity of these devices causes the range of their communications to overlap, resulting in competition for control over the channel resources. As illustrated by the wavy red signal in Figure 12, movement of fingers on the keyboard causes time varying changes in the Wi-Fi signal which are classified by the keystroke sensing system. To investigate how the varying interference packet rate influences the accuracy of the keystroke sensing  
Figure 11: CSI snapshot rate with varying number of stations.  
system, we once more collected CSI data during 100 keystroke events for each of the letters Q, Z, H, P and M and used the model in Section 4 to train a sensing system. The system was then evaluated with a dataset of CSI collected during 50 keystrokes for each of the same keys. We collected 6 such sets of testing data, under varying rates of contention from the laptop.
# Downlink Training Design for FDD Massive MIMO Systems in the Presence of Colored Noise
## 1 Introduction  
The last decade has witnessed a rapidly increasing growth in the number of wireless devices, which introduces huge demands for data services such as multimedia applications and video streaming [1]. As such, the currently deployed mobile networks are unlikely to support the explosionof the data traffic [2], and thus, new technologies become crucial. The massive multiple-input multiple-output (MaMi) system, which uses a large number of antenna arrays at the base station (BS), is presented as a key technique for the fifth-generation (5G) networks and beyond. A MaMi system is essentially needed to overcome the increasing demands of data traffic [3; 4]. Specifically, the massive use of antenna arrays at BS increases the degrees of freedom in the propagation channel, which improves the data rate and link reliability [5]. MaMi systems offer many advantages, such as: (a) ability of providing a uniform quality of service; (b) ability of improving the energy efficiency; (c) capability of mitigating the impact of fast fading and interference; (d) capability of delivering high data rate; and (e) ability of using low complexity linear precoding schemes [6; 7]. These key features introduce MaMi transmission as the core technique for the future wireless communications [8]. The performance of MaMi transmission depends entirely on the estimation of channel state information (CSI). In particular, the availability of CSI knowledge depends on the duplexing operation scheme given by the spectrum allocation, i.e., either time division duplex (TDD) or frequency division duplex (FDD) operation mode. The MaMi system is typically assumed to operate in TDD mode, where the required CSI is obtained in the uplink (UL) by transmitting the orthogonal training sequence of length equal to the number of users \(K\)[3; 4; 6; 9; 10; 11; 12; 13; 14]. Although, TDD operation mode has attractive results with MaMi systems, most of the current wireless communications networks operate in FDD mode. In addition, the new 5G radio access technology is expected to support both FDD and TDD duplex operations [8]. However, downlink (DL) training sequence (DTS) and CSI estimation in FDD MaMi systems is a challenging issue with limited coherence time. This is because the number of DTS needs to be linearly scaled with the number of transmit antenna elements \(N\) (\(N\gg K\)) to design the precoder [15; 16; 17]. As such, the coherence time that is available would be fully consumed by DL channel training, leaving no time for sending useful information to the users.
# Dynamic Characteristics Analysis of a Rigid Body System with Spatial Multi-Point Supports
## 3 Structural Parameters Determination and Model Verification
### Modal Analysis and Model Verification  
The simplified oil tank is modeled using solid187 elements, which are divided into 57,610 elements and 116,638 nodes. The oil tank body has a large stiffness and small deformation under working conditions. Therefore, its deformation is ignored in the numerical simulation and is considered as a rigid body [26]. The mounting pedestal components are flexible. There is friction contact between damping ring surface and connecting pin surface, as well as between the mounting pedestal lifting lug surface and connecting pin surface; the friction coefficient is 0.3. Fixed constraints are imposed on the bolt holes of each mounting pedestal. The finite element model (FEM) is established as Figure 5. The modal calculation results are obtained in Table 5 via Block Lanczos modal extraction method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Stiffness (N/m)**} & \multicolumn{4}{c}{**Mount \#**} \\ \cline{2-5}  & **A** & **B** & **C** & **D** \\ \hline \(k_{x}\) & \(6.40\times 10^{7}\) & \(2.90\times 10^{8}\) & \(8.40\times 10^{7}\) & \(1.50\times 10^{8}\) \\ \(k_{y}\) & \(9.10\times 10^{8}\) & \(2.80\times 10^{9}\) & \(4.00\times 10^{8}\) & \(4.50\times 10^{8}\) \\ \(k_{z}\) & \(2.00\times 10^{8}\) & \(3.90\times 10^{9}\) & \(1.70\times 10^{8}\) & \(3.10\times 10^{8}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: The stiffness of each support in the global coordinate system.  
Figure 5: FEM of a simplified rigid body system.  
The theoretical calculation and FE modal calculation results of the simplified rigid body model are shown in Table 6. The first six natural frequencies obtained by the two methods are consistent, indicating that the established dynamic model of a rigid body system with multi-point elastic supports is effective.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Order** & **Analytical Method (Hz)** & **FEM-ANSYS (Hz)** & **Error (\%)** \\ \hline
1 & 576 & 565 & 1.9\% \\
2 & 896 & 916 & 2.2\% \\
3 & 1586 & 1582 & 0.3\% \\
4 & 1947 & 1937 & 0.5\% \\
5 & 2295 & 2177 & 5.4\% \\
6 & 5655 & 5376 & 5.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of theoretical calculation and FEM.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Order** & **Analytical Method (Hz)** & **FEM-ANSYS (Hz)** & **Error (\%)** \\ \hline
1 & 576 & 565 & 1.9\% \\
2 & 896 & 916 & 2.2\% \\
3 & 1586 & 1582 & 0.3\% \\
4 & 1947 & 1937 & 0.5\% \\
5 & 2295 & 2177 & 5.4\% \\
6 & 5655 & 5376 & 5.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: The calculation results of the first six modes of the simplified rigid body model.
# Imaginary Speech Recognition Using a Convolutional Network with Long-Short Memory
## 5 Discussion
### Brain Area Analysis  
The final goal of an automatic ISR system is to obtain the best possible accuracy using a portable device with limited resources. Considering this, we further studied the system behavior by using a smaller number of electrodes, located in specific areas of the brain, for classifications. This approach enhanced the portability of the device and decreased the needed resources used for development, but with the cost of accuracy, as can be seen in Table 9.  
When the number of channels was reduced, the accuracy dropped, as well. However, when using the electrodes positioned on the anatomical regions of the brain responsible for speech intention and production, the accuracy of the system reached 0.40, a value with a drop of only 3%, in comparison to all channel accuracy. This means that 93% of speech information is concentrated in these channels, and only 7% of information is distributed to the parietal and occipital regions. The main advantage of using only the channels corresponding to the anatomical structures of the brain regarding speech is that the number of electrodes is reduced, in this case by more than half, from 62 to 29, which is a big computational and cost gain.  
Important information obtained after the study of the brain areas involved in imaginary speech recognition is that the data acquisition using the visual stimulus appearing on a prompt does not affect the study because the occipital area is less involved in the decision-making of the system. This is due to the two-second period introduced in the protocol design, the time between the prompt display and the actual imagination of the phoneme.
## 6 Offline Optimization
### Offline Optimized Proportional Gain-Scheduled PID Controller  
First, let the scheduled gain be \(P(e)=f(e)\) with fixed \(I\) and \(D\) gains and obtain the Pareto front of the two objectives (\(I_{1}\) and \(I_{2}\)) using the NSGA-II algorithm using the parameters summarized in Table 2, where \(f(e)\) is defined in (9).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(N_{p}\) & 100 & \(a_{range}\) & [0, 1500] \\ \(b_{range}\) & [0, 20] & \(c_{range}\) & [0, 1000] \\ \(I_{range}\) & [0, 100] & \(D_{range}\) & [0, 100] \\ \(P_{C}\) & 90\% & \(D_{C}\) & 10 \\ \(P_{M}\) & 30\% & \(D_{M}\) & 20 \\ \(Generation\) & 100 & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Genetic Algorithm parameters used in Step 1.  
Figure 8: Simulation structure used for iterative search.  
Figure 7: Iterative NSGA-II procedure; the search space starts with offline simulation and evolves within the global search space to arrive at the optimal solution.  
Figure 9 shows the non-dominated solutions of both the gain-scheduled and fixed-gain PID controllers after 100 generations. Note that the Pareto front of the gain-scheduled PID controller is much closer to the origin, indicating improved closed-loop performance over the fixed-gain controller. The optimal averaged step response overshoot is restricted to 0.01% and 0.04% for scheduled and fixed-gain PID controllers, respectively, and the optimal ISEs are 4.68% and 4.79%. In addition, the widely distributed results from the gain-scheduled PID controller indicate that it provides more multifarious controller options for decision-makers based on different system performance requirements. For example, if a fast settling time with a minimal steady-state error is much more crucial than minimizing the overshoot, the solution at the lower right corner of the Pareto front can be selected to satisfy these requirements.  
Optimization solutions for both the scheduled and fixed \(P\) gain PID controllers, with \(I\) and \(D\) gains fixed in both cases, are summarized in Table 3, with both controllers selected based on the multiple-criteria decision-making method. In this case, considering equivalent significance of both the system overshoot \(J_{1}\) and ISE \(J_{2}\), the point \((J_{1},\ J_{2})\) with minimal distance to the origin is selected as the optimal result. With the scheduled \(P\) gain PID controller, the objective functions \(J_{1}\) (overshoot) and \(J_{2}\) (ISE) are minimized to 0.0110 and 0.0473, compared with 0.0428 and 0.0479 for the fixed-gain case, representing an improvement of 74% and 1.3%, respectively.
## 2 Theoretical Model
### Equation of Motion
Equation (1) can be expressed as:  
\[\overline{u}^{\prime\prime}=-(\frac{1}{2}\overline{W}^{\prime 2})^{\prime}+( \overline{u}^{\prime}\overline{W}^{\prime 2})^{\prime}+\frac{I_{A}}{A}\left[ \overline{W}^{\prime}\left(\overline{W}^{\prime\prime\prime}-\overline{u}^{ \prime\prime}\overline{W}^{\prime}-2\overline{u}^{\prime\prime}\overline{W}^{ \prime\prime}-3\overline{u}^{\prime}\overline{W}^{\prime\prime\prime}\right) \right]^{\prime} \tag{7}\]  
Equation (7) is integrated to obtain the expressions of \(\overline{u}\prime\) and \(\overline{u}\):  
\[\overline{u}^{\prime}=-\frac{1}{2}\overline{W}^{\prime 2}+c_{1}(\overline{t}), \;\overline{u}=-\frac{1}{2}\int_{0}^{\overline{l}}\overline{W}^{\prime 2}d \overline{x}+c_{1}(\overline{t})\overline{x}+c_{2}(\overline{t}) \tag{8}\]  
Assuming a steady deformation of \(\overline{u}\), and substituting Equations (7) and (8) into Equation (2), the equation of this nonlinear beam can be simplified to the transversal direction (\(\overline{W}\)) as follows:  
\[\overline{m\ddot{W}}-EI_{A}\overline{W}^{\prime\prime}=EA(\overline{u}^{\prime }\overline{W}^{\prime}-\overline{u}^{\prime 2}\overline{W}^{\prime}+\frac{1}{2}\overline{W}^{ \prime\prime})^{\prime}+EI_{A}[\overline{u}^{\prime}\overline{W}^{\prime \prime}+(\overline{u}^{\prime}\overline{W}^{\prime})^{\prime\prime}-( \overline{u}^{\prime 2}-\overline{W}^{\prime 2})\overline{W}^{\prime\prime} \tag{2}\]0  
where \(\overline{\mu}\dot{\overline{W}}\) is the structural damper, \(\overline{W}^{\prime}\overline{W}^{\prime 2}+\overline{W}^{\prime\prime\prime}\overline{W}^{ \prime 2}\) represents the geometric nonlinearity, \(-\frac{1}{2}\overline{m}[\overline{W}^{\prime}\int_{\overline{l}}^{\overline{ x}}(\int_{0}^{\overline{x}}\overline{W}^{\prime 2}d\overline{x})d\overline{x}]^{\prime}\) denotes the shortening effect. The dimensionless form of Equation (9) is then expressed as:  
\[\overline{m\ddot{W}}-EI_{A}\overline{W}^{\prime\prime}=EA(\overline{u}^{\prime }\overline{W}^{\prime}-\overline{u}^{\prime 2}\overline{W}^{\prime}+\frac{1}{2}\overline{W}^{ \prime\prime})^{\prime}+EI_{A}[\overline{u}^{\prime}\overline{W}^{\prime \prime}+(\overline{u}^{\prime}\overline{W}^{\prime})^{\prime\prime}-( \overline{u}^{\prime 2}-\overline{W}^{\prime 2})\overline{W}^{\prime\prime} \tag{2}\]1  
where \(\mu=\frac{\overline{l}^{2}}{\sqrt{m}EI}\;\overline{\mu}\), \(\tau=\overline{\omega}\overline{t}\), \(q=\frac{\overline{q}}{\overline{m}\dot{\overline{u}}\dot{\overline{u}}^{2}}\), \(\overline{\omega}=\sqrt{\frac{EI}{\overline{m}\dot{\overline{l}}}}\), \(W=\frac{\overline{W}}{\overline{l}}\), \(l=\frac{\overline{l}}{l}=1\), \((\;)^{\prime}\) represents \((\frac{d}{d\overline{x}})\), \((\;)*\) represents \((\frac{d}{d\tau})\).
# IoT Firmware Emulation and Its Security Application in Fuzzing: A Critical Revisit
## 3 Challenges Faced by Firmware Emulation and Its Application in Fuzzing
### Peripheral Interaction Emulation Support  
As mentioned in Section 2, while CPU emulation can be manually achieved, the diverse configurations of individual devices make manual support for hundreds or thousands of peripherals impractical. For instance, _QEMU_ supports only a few peripherals for fewer than ten MCU versions. Although _QEMU_ lists the STM32F405 MCU, it actually supports only six on-chip peripherals, like UART and SPI, out of more than 20 available. Moreover, _QEMU_ does not fully support peripheral configurations and interactions; for example, it supports SPI only via peripheral register read/write, lacking interrupt and DMA support. Initially, research like _Avatar_[14] proposed a hardware-in-the-loop solution that emulates only the CPU and forwards peripheral access to real hardware. However, this method suffers from performance issues, as peripheral operations on MCUs are much slower than virtual memory operations on servers (MCU CPU frequency is in the hundreds of MHz, compared to x64 CPUs operating over GHz) and cannot be used for parallel testing. Peripheral emulation should ideally be automatic, have high applicability (capable of emulating any MCU peripheral functions), and offer high fidelity (replicating the control and data flow of a real device without actual hardware).
# Device-To-Device Communication in 5G Environment: Issues, Solutions, and Challenges
## 7 Challenges in D2D Communication
### Challenges in Security
#### 7.3.5 Privacy and Security  
Elliptic Curve Cryptography, AES-128, and access points are some of the cryptography techniques that are traditionally used for protecting privacy and security [89]. These cryptography techniques are not suitable for a distributed network and heterogenous network environment. Since D2 Dcommunication allows mobile users to directly connect with other devices, it will create privacy and security concerns due to malicious users that pose threats to the privacy of users. This issue raises the demand for trust management schemes that are efficient and effective, which assess the merit of trust in every device before establishing a connection.
## 3 Proposed Method
### Multi-Receptive Field Fusion-Based Network (MRFFN)  
As it is difficult to collect sufficient defect samples for the deep network, the main challenge in this study is to extract more significant features from limited data. Concretely, two pre-trained GoogLeNets are adopted as the backbone architecture of the MRFFN. Here, the input size of the level 0 model is modified to 200 \(\times\) 200, which contains the exact same resolution as the input images. Thereafter, the Gaussian pyramid downsamples the original images into a spatial resolution of 100 \(\times\) 100 pixels, providing less information for the training model. This, however, appears to be inadequate for the deep structure like GoogLeNet to tackle the lower resolution images. Thus, the input size of the level 1 model is set to 100 \(\times\) 100 pixels to prevent the risk of an overfitting phenomenon. Besides, the last two inception modules (i.e., inception module 5a and inception module 5b) of the  
Figure 2: The architecture of the pre-trained GoogLeNet. Conv represents the convolutional layer, M. Pool represents the max pooling layer, Norm represents the local response normalization, and Avg Pool represents the average pooling. pre-trained GoogLeNet have been discarded to scale down the computational load of the level 1 model. The overall framework of the proposed method is shown in Figure 3.  
Succinctly, the GoogLeNet was pre-trained with 1.2 million samples from 1000 categories (e.g., animal, flower, tool, building, and fruit) and thus equipped with optimal weights for the classification task. However, the target object herein is steel surface defects, which have a large discrepancy to that of pre-trained samples. Hence, to better characterize the pattern of the steel surface defects, the shallower layers of both the level 0 and level 1 models are adopted with higher learning rate factors. Here, the learning rate factors of the Conv 1, Conv 2-reduce, Conv 2, inception module 3a, inception module 3b, and inception module 4a are applied as 9, while the other layers remain the same. By increasing the learning rate factors of the shallower layers, the convergence speed of the training models can be improved while reducing the gradient vanishing problem. Furthermore, the average pooling of both models is replaced with the global average pooling (GAP) to extract the global information of each feature map. Meanwhile, the fully connected layer of the
## 2 Concept of Operations
### Robustness and Integrity
#### 2.3.2 Integrity  
For integrated communication and pLEOT services, non-dedicated clock data signals can interfere with existing communication systems. This interference is likely to increase the bit-error-rate (BER) performance, which means that the power of these clock data signals should be limited to meet the BER performance requirements. To address this issue, it is beneficial to subtly integrate clock data signals within a range of noise distributions that have unknown parameters, which communication receivers need to estimate.  
This work presents the non-dedicated clock data service as a type of Gaussian distributed signal. Consequently, clock data signals are observed within additive white Gaussian noise. According to the results presented in Equation (4), communication receivers need to test whether the observed data follows a Gaussian distribution. If the CIR of \(\gamma_{clk}^{i}\), as shown in (4), is maintained at a specific threshold, denoted as \(\overline{\gamma}_{clk}^{i}\), then the cumulative distribution function of the observations will appear indistinguishable from a normal distribution.  
Advancements in achieving programmability and flexibility for integrated communication and pLEOT services largely depend on the effectiveness of the feedback loop in achieving the minimum unobtrusive guarantee. Additionally, this progress is contingenton the feasibility of the steady-state errors that define the explicit relationship between the current CNR, denoted as \(\gamma_{c}^{i}\), and the desired unobtrusive threshold, represented as \(\gamma_{c}^{si}\)  
\[e_{c}^{i}(k)=\gamma_{c}^{i}(k)-\gamma_{c}^{si}\,. \tag{15}\]  
In the light of (8), it follows that the increment of \(CN0^{i}_{total}\), i.e., \(u_{c}^{i}\) for communications can effectively affect the transient behavior of (15)  
\[e_{c}^{i}(k+1)=e_{c}^{i}(k)+CN0^{i}_{total}u_{c}^{i}(k)\,. \tag{16}\]  
To eliminate potential steady-state errors, an integrator of the errors, as governed by (15), is added to the integral control-based CN0 allocation by the local agent \(i\) at the \(i\)th host orbital platform  
\[q_{c}^{i}(k+1)=q_{c}^{i}(k)+e_{c}^{i}(k)\,. \tag{17}\]  
Please note that the selection of \(\overline{\gamma}_{n}^{i}\) underscores the need for pLEOT information that is distributed as noise. However, the focus remains on non-dedicated clock data with low-power CN0, which can have minimal effects on primary communication receivers that are uninformed or unaware.