esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis
### Applications  
As mentioned earlier, it is possible to detect threats, even with encrypted traffic streams, where entropy deviates significantly from expected ranges, or changes during the lifespan of the session. Where content is being passed over a network, high entropy tends to indicate that data are either encrypted or compressed (in general, encryption tends to produce the highest entropy values compared with compression. Further, naive compression techniques may not achieve high entropy). Knowing this, we can analyse payload entropy dynamically and use this as an indicator for encrypted data streams, potentially identifying covert channels [38; 39] and encapsulated malware. For example:  
* Where a particular service is expected to encode content as plaintext (such as DNS), the detection of high entropy may indicate the presence of a covert channel, which could be used for data exfiltration.
* Unexpected plaintext on an encrypted channel may indicate a misconfiguration of the SSL/TLS encryption settings, or a security vulnerability in the system. For example, the Heartbleed vulnerability found in OpenSSL in 2014 is triggered when a malicious heartbeat message causes the SSL server to dump plaintext memory contents across the channel [40].
* On an encrypted channel (such as an SSH tunnel or an HTTPS session), after a connection is established (i.e., after key exchange), we would expect the entropy to sit close to eight bits per byte, once encrypted. Shifts in this value might indicate some form of compromise.
* Many legacy protocols still use ASCII encoded plain text encodings. If we detect a higher entropy than expected on a known plaintext channel, this may indicate an encrypted channel is being used to send covert messages or exfiltrate sensitive data (e.g., by using encrypted email, or DNS tunnels [21]).
## 5 Results and Discussion
### Hyperparameter Experiments
#### 5.4.4 Effect of State Expansion Factor of Mambas  
The SSM state expansion factor is a crucial parameter of Mamba. Figure 15 demonstrates the effectiveness of the state expansion factor, where the higher state expansion factor raises the hidden state dimension, which allows the model to encode features in a higher-dimensional space. Meanwhile, a higher state expansion factor leads to increased computational complexity and overfitting, while the model may learn unnecessary noise or irrelevant features. Therefore, the best prediction result is achieved when the state expansion factor is set to 32.  
Figure 14: Impacts of value and channel embedding dimensions on metrics: (**a**) and (**b**) are the MAE and RMSE of the value embedding dimension, respectively; (**c**) and (**d**) are the MAE and RMSE of the channel embedding dimension, respectively.
# Mechanical and Metallurgical Properties of CO\({}_{2}\) Laser Beam INCONEL 625 Welded Joints
## 2 Materials and Methods
### Melt Run Trials and Butt-Welding  
Melt run trials were carried out on 5 mm-thick Inconel plates using a CO\({}_{2}\) laser source in order to analyze the effects of welding parameters on geometrical characteristics and microstructure of the bead. Particular attention was paid to those parameters assuring full penetration in the subsequent butt-welding operations. The previous experiment using a laser power of 2.5 kW did not show good results (Figure 1a), and so it was decided to start with a minimum power of 3 kW, as shown in Figure 1b. L\({}_{4}\) orthogonal array approach was selected for the experimentation. Laser power, welding speed, and shielding gas flow rate were chosen as critical process parameters for the present work. Laser power and welding speed are the main parameters regulating the heat input during the welding operation. On the other hand, weldments responses are affected above all by the heat input. Four combinations of the three selected process parameters were chosen according to Table 2 (design of experimentation (DOE)). Argon was employed as shielding gas (purity equal to 99.9%) during welding. During welding operations, plates were clamped in jaws in order to assure the absence of gaps, as shown in Figure 2a. The CO\({}_{2}\) laser beam welding setup is shown in Figure 2b.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ni** & **C** & **Mn** & **S** & **Cu** & **Si** & **Cr** & **P** & **Others** \\ \hline Bal. & 0.1 & 0.5 & 0.015 & 0.5 & 0.5 & 20–23 & 0.015 &
\begin{tabular}{c} Fe 5, Al 0.40, \\ Mo 8-10, Ti 0.1 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Nominal composition of Inconel 625 (wt.%).  
Figure 1: Images illustrating Inconel 625 melt run trials: (a) 2.5 kW laser power, showing poor penetration, (b) 3 kW laser power, inducing good penetration.  
The heat input (_HI_, J/mm), i.e., the ratio between the laser power and the welding speed, is calculated for each experimental trial by using Equation (1)  
\[HI=\frac{60W}{V} \tag{1}\]  
where \(W\) is laser power (kW), and \(V\) is the welding speed (m/min). In general, it is well known that the lower the heat input, the better the welded joint quality in terms of bead microstructure and mechanical properties. The heat input values resulting from the combinations of the chosen values of process parameters are collected in Table 2.
## 5 Results and Discussion
### Limits of Applicability
The maximum correction given in the table, in parts per million, corresponds to a error in the range vector of \(\sim 10\) m for a typical altitude above ground of 5 km. This limit is far outside the range of variation encountered in nature [30]. Finally, another limiting factor to be considered in practice is the signal to noise ratio (SNR). The impact of noise may be an issue, as the proposed approach does not involve azimuth compression and therefore does not benefit from azimuth compression gain. The results of Section 5.5 are relevant in this regard: while they demonstrate that azimuth under-sampling does not present any fundamental difficulty in the calibration approach, under-sampling does ultimately have a detrimental impact on the accuracy attainable. Increasing under-sampling leads to increased clutter levels, even after clutter filtering, as more clutter contributions become aliased with the reference target response. This increase in clutter noise is evident in the plots of \(\delta\text{RCS}^{s}_{pq}(az)\) in the last row of Figure 16. The SNR is clearly not an issue for the sensor and acquisition geometries considered in the preceding sections. Should low SNR be a practical concern, however, the proposed calibration technique may yet remain applicable if either the PRF or the strength of the reference target radar return can be increased.
# A SLR Pre-Processing Algorithm Based on Satellite Signature Effect
## 2 Simulation and Analysis of Normal Point Algorithm
To verify the effectiveness of the above simulation, we compare the numerical simulation result with the observation data provided by Changchun station (see in Figure 4). The Pearson correlation coefficient of these two long-term full rate residual profiles is calculated to be 0.98, which means the simulation based on the revised satellite signature effect can accurately reflect the long-term distribution of the returned signal. Figure 3: Simulated reflection distribution of LAGEOS-1 with the laser incident direction from (0\({}^{\circ}\),1\({}^{\circ}\)) to (0\({}^{\circ}\),6\({}^{\circ}\)).
## 2 Literature Review
### Data-Driven Model  
There are two primary categories of data-driven surface-roughness prediction methods that are commonly used in surface quality prediction: machine learning methods and deep learning methods. [PERSON] et al. [5] proposed an improved whale optimization algorithm-based support vector regression model for predicting the surface roughness of robotic arm grinding of irregular stones. [PERSON] et al. [30] developed a gradient-boosting regression model to accurately predict surface roughness in aluminum alloy milling. [PERSON] et al. [31] used machine learning and meta-heuristic optimization algorithms to optimize grinding process parameters. [PERSON] et al. [32] developed a robust and deterministic multi-criteria function based on four machine learning methods, achieving effective predictionsof surface roughness. However, the data-driven models they used suffer from difficulties in parameter selection and high computational complexity.  
Deep learning is dedicated to end-to-end learning and does not require user intervention, unlike machine learning techniques that necessitate intricate feature architecture. This helps reveal the deeper trends in the data [33]. [PERSON] & [PERSON] [34] used the force data features obtained from the force sensor at the end of the robotic arm and, after statistical dimensionality reduction, proposed a surface-roughness prediction model based on linear regression and artificial neural networks. However, it only uses force information as the model input and ignores other factors that may affect surface roughness.  
To further improve the prediction accuracy, [PERSON] et al. [6] proposed a method based on LSTM-MLP-NSGAII, using neural networks to fit the relationship between process parameters, surface roughness, and grinding time, but the selection of model structure and process parameter combinations still needs to be improved.
# Effect of the Transition Points Mismatch on Quanta Image Sensors
## 3 Imaging Models for the Effect of the Transition Points Mismatch
### Single-bit QIS
If different values of \(m\) and \(z\) are used while the value of \(m^{2}z\) is fixed, the imaging quality would not change because the shot noise and the read noise are random in both the spatial and temporal domains. Then, the mismatch of transition points is introduced into column parallel 1-bit ADCs and the standard deviation \(U_{thm}\) is 0.15e-, which is suggested in Section 2. The reconstructed image is shown in Figure 9c, and its GSI is 3.31 LSB. Compared to Figure 9b, Figure 9c shows obvious stripes. The white stripes are caused by column 1-bit ADCs with lower transition point values, and the dark stripes are caused by 1-bit ADCs with higher transition point values. Figure 9d illustrates a better image with few stripes, and its GSI is 1.74 LSB. It is reconstructed from the same conditions as Figure 9c, except that \(m=64\) and \(z=1\). Other GSI values for different spatial oversampling factors are summarized in Table 1. Different from shot noise and read noise, the mismatch of ADC transition points is actually a kind of noise in the spatial domain. In the same cubic, a false positive count and a false negative count could cancel each other. A larger \(m\) value means a more sufficient cancellation, and also means that a bit error in a cubic has a smaller impact on the final image. That could explain why a larger spatial oversampling factor is beneficial to the image quality, and why the BER is not suitable for comparing reconstructed images with different spatial oversampling factors. In Figure 9, it is observed that the transition point mismatch causes column stripes in the reconstructed image, which is actually a kind of fixed-pattern noise (FPN). To clearly understand the relationship between the transition point mismatch and the FPN, a 256 \(\times\) 256 image with a uniform gray level of 128 is used in place of the \"lena\" image in the imaging model. In the model, \(m=64\) and \(z=1\). We calculate the mean gray level of every column in the reconstructed image, then the standard deviation of this group of mean values is divided by their average. The result is used to represent the FPN. Figure 10 shows the FPN of reconstructed images with different standard deviations of the transition point, and it proves that the FPN increases with a stronger transition point mismatch. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \(m^{2}\) & \(\mathbf{1^{2}}\) & \(\mathbf{2^{2}}\) & \(\mathbf{4^{2}}\) & \(\mathbf{8^{2}}\) & \(\mathbf{16^{2}}\) & \(\mathbf{32^{2}}\) & \(\mathbf{64^{2}}\) \\ \hline GSI (LSB) & 9.71 & 8.44 & 5.28 & 4.30 & 3.31 & 2.11 & 1.74 \\ \hline \end{tabular}
\end{table} 
Table 1: The GSI values for different spatial oversampling factors with \(U_{thm}=0.15\). Figure 10: The fixed-pattern noise (FPN) of reconstructed images with \(m=64\) and \(z=1\).
## 1 Introduction and Background
Consequently, this study turns to game design to enhance user experience and engagement by integrating gaming elements into virtual museum navigation. According to a Wikipedia list of the world's most visited museums [28], among the top 30 museums that have virtual online exhibition hall, only four (the National Art Center, the Museum of Modern Art, the Moscow Kremlin Museums, and the Victoria and Albert Museum) lack virtual exhibitions. This highlights the rapid growth of OVM as primary channels for accessing cultural heritage. The need to optimize navigation design in OVM is becoming increasingly urgent. Mini-maps, as an effective navigational aid, have been demonstrated to be crucial in games for enhancing spatial cognition and navigational efficiency [22]. Thus, this study explores the potential application of mini-maps in virtual museums and their possible impact on user experience, further deducing which elements of navigation affect spatial cognition in virtual museums. Additionally, the present work addresses whether game mini-map navigational design elements can be directly translated to this context and how these elements' impact on spatial cognition can be evidenced, forming the core focus of this investigation. This study aims to address the following three issues. Firstly, validated gaming mini-map navigation design elements are integrated into virtual museum environments in this study, aiming to utilize game design elements to enhance non-gaming virtual environments. Secondly, this study aims to determine which gaming navigation design elements are applicable to virtual museums. Upon identifying essential design elements, their potential to enhance user immersion and presence is assessed. Immersion and presence, crucial to the user experience in virtual museums, significantly contribute to sustaining and increasing user engagement [29; 30]. Thirdly, considering spatial cognition's critical role in virtual environment navigation, impacting how users interpret, understand, and remember information [31; 32], this study seeks design elements that positively affect spatial cognition, addressing issues of user disorientation and enhancing the virtual museum experience. The remainder of the article is structured as follows: Section 2 provides a literature review on spatial cognition theory, immersion and presence in virtual environments, and research on gaming mini-maps, enabling us to formulate nine hypotheses to construct our research model. Section 3 elaborates on the research design and implementation methods, including data collection and analysis procedures. Section 4 presents the empirical findings of the study. Section 5 discusses the implications of the research findings. Finally, Section 6 summarizes the main conclusions of the study, highlights its limitations, and suggests directions for future research.
# Secure and Lightweight Cloud-Assisted Video Reporting Protocol over 5G-Enabled Vehicular Networks
## 1 Introduction
Even under the huge computation capabilities of 5G-enabled vehicular network, the cloud entities should not waste much time and computation capabilities before they discard bogus, unauthenticated and unauthorized videos. In addition, for the proposed protocol to attain the _non-repudiation_ property, the conditional traceability of all the vehicles should be achieved. To fulfill the above-mentioned goals of 5G-enabled cloud-assisted video reporting protocol and to overcome the drawbacks of the Eiza-Ni-Shi protocol, we propose a new secure and lightweight video reporting protocol for 5G-enabled vehicular networks. We summarize the contributions of this work as follows:  
* We define an application model for a secure and lightweight cloud-assisted video reporting protocol over 5G-Enabled vehicular networks. The model highlights the security objectives that the protocol should satisfy within the 5G-Enabled vehicular networks architecture. * We develop a secure and lightweight cloud-assisted video reporting protocol for 5G-enabled vehicular networks. Without using the conventional public key certificates, the proposed protocol supports entities' _authorization_ through anonymous credential. Since the reported videos are broadcasted by the fixed entities, the designated vehicles can recover the reported videos without making any time-consuming communication. Also, by using lightweight security primitives, the proposed protocol minimizes the computation overhead and meets the performance requirement for the real-time ITS-based services in 5G-Enabled vehicular networks. * We evaluate the performance of the proposed protocol in terms of security objectives, computation cost and communication overhead. The rest of this paper consists of as follows. After we describe the related work in Section 2, cryptographic primitives for constructing the proposed protocol are overviewed in Section 3. After describing the overall operation of the proposed protocol in Section 4, we show the detailed operations in Section 5. In Section 6, we show the performance analysis results of the proposed protocol. Finally, we conclude this paper in Section 7.
## 3 \(l\)-\(k\)-Nearest-Neighbors Query Processing
### Problem Definition  
The \(l\)- \(k\)NN problem is formally defined as follows:  
* **Definition 1 (\(l\)-distant \(k\)-nearest neighbor problem).** Given a set \(N\) of sensor nodes, a query point \(q\) and valid time \(V\), find a subset \(N\)'of \(N\) with \(k\) nodes _(i.e._, \(N^{\prime}\subseteq N\), \(|N^{\prime}|=k\))_ such that at time \(V\), \(\forall n_{1}\in N^{\prime}\), \(\forall n_{2}\in(N-N^{\prime})\), \(DIST(n_{1},q)\leq DIST(n_{2},q)\) and \(\forall n_{3}\in N^{\prime}\), \(\forall n_{4}\in N^{\prime}\), \(DIST(n_{3},n_{4})\geq l\), where \(DIST(\ )\) denotes the Euclidean distance function.
* We assume that the sensor nodes are randomly distributed and moving in wireless sensor networks. Each sensor node's movement direction and velocity are randomly determined. All sensor nodes have their own storage and computing power to process queries. Each sensor is aware of its own location via GPS or other localization technique, such as beaconing.
* We define new concepts to traverse the search space for \(l\)-\(k\)NN.
* **Definition 2 (sector).** Given a query point \(q\), a sector is the fan-shaped area \(S\) between two radii centered at \(q\). Given that the number of sectors is \(n\), each sector \(S_{i}\) has the same central angle \(\frac{2\pi}{n}\). Sector numbers are reassigned in each track.
* **Definition 3 (track).** Given a query point \(q\), a track is the ring-shaped area \(T\) centered at \(q\). The \(i\)-th track centered at \(q\) is defined as presented in Equation (1), where \(C_{q}(r)\) denotes a circle centered at \(q\) with a radius \(r_{i}\), where \(r_{i}>r_{i-1}\). \[T_{i}=\left\{\begin{matrix}C_{q}(r_{i}),if\ i=1\\ C_{q}(r_{i})-C_{q}(r_{i-1}),if\ i>1\end{matrix}\right.\] (1)
* **Definition 4 (track-sector).** A track-sector \(TS\) is the intersecting area of a track and a sector.
* We define the following notations to simplify the explanation of \(l\)-\(k\)NN. A _home node_ (\(H\)-_node_) is the sensor node nearest to the query point \(q\). We call a sensor node selected for query results a _query node_ (\(Q\)-_node_). If a track contains a query node, it is called a _query track_; otherwise, a _margin track_. If a sector contains a query node, it is called a _query sector_; otherwise, a _margin sector_.  
Figure 2 shows examples of sectors, tracks and track-sectors. \(T_{1}\), \(T_{2}\), \(T_{3}\) and \(T_{4}\) are tracks having radii \(l\), \(2l\), \(3l\) and \(4l\) centered at \(q\), repectively. The number of sectors is eight, and each sector has a central angle \(\frac{2\pi}{8}\). The track-sector \(TS_{2,8}\) represents the intersecting area of \(T_{2}\) and \(S_{8}\). \(T_{2}\) and \(T_{4}\) are query tracks, and the others are margin tracks. We regard \(T_{1}\) is a margin track, although the H-node is selected for the query results. \(S_{1}\), \(S_{3}\), \(S_{5}\) and \(S_{7}\) are query sectors, and the other sectors are margin sectors.
## 3 Results and Discussion
### Effect of MUAE Parameters on the Recovery Yield of Total Flavonoids from DOS  
The ANOVA of response 2 (Y\({}_{2}\)) is summed up in Table 4. The model \(p\)-value of \(<\)0.0001 implies the high significance of the regression model. An insignificant result in the lack of fit with a \(p\)-value of 0.1276 indicated the model fitted the data very well. In this case, the model terms of X\({}_{1}\), X\({}_{2}\), X\({}_{5}\), X\({}_{1}\)X\({}_{2}\), X\({}_{2}\)\({}^{2}\), X\({}_{3}\)\({}^{2}\) and X\({}_{5}\)\({}^{2}\) were highly significant (\(p<\) 0.0001). The terms of X\({}_{1}\)X\({}_{4}\), X\({}_{2}\)X\({}_{4}\), X\({}_{2}\)X\({}_{5}\), X\({}_{1}\)\({}^{2}\) were significant (\(p<\) 0.05). The other terms were insignificant (\(p>\) 0.10). For recovery of total flavonoids from DOS, the variance analysis of the model based on Table 2 suggested ultrasound temperature with three important interaction terms (X\({}_{1}\)X\({}_{2}\), X\({}_{2}\)X\({}_{4}\), and X\({}_{2}\)X\({}_{5}\)) and the ultrasound time with two terms of interactions (X\({}_{1}\)X\({}_{2}\), X\({}_{1}\)X\({}_{4}\)) followed by ethanol concentration with one interaction term (X\({}_{2}\)X\({}_{5}\)) were the most highly significant terms. The quadratic equation relating time and temperature of ultrasound, solvent-to-solid ratio, microwave time, and ethanol concentration is shown in Equation (4):  
\[Y_{2}=10.42-1.02X_{1}-0.68X_{2}+0.36X_{3}+0.64X_{1}X_{2}+0.19X_{1}X_{4}-0.23X _{2}X_{3}X_{4}-0.26X_{2}X_{5}+0.17X_{1}\)\({}^{2}-0.58X_{2}+0.27X_{3}\)\({}^{2}+0.30X_{5}\)\({}^{2} \tag{4}\]  
where Y\({}_{2}\) is the yield of total flavonoids and X\({}_{1}\), X\({}_{2}\), X\({}_{3}\), X\({}_{4}\), and X\({}_{5}\) are the coded variables for ultrasound time, ultrasound temperature, solvent-to-solid ratio, microwave time, and ethanol concentration, respectively. For the second response (Y\({}_{2}\)), the correlation coefficient R\({}^{2}\) and adjusted R\({}^{2}\) of the model were evaluated. R\({}^{2}\) with a value of 0.9812 indicates a good prediction for the