esa-sceva/satcom-chunk-collection huggingface dataset:
# Autonomous and Earth-Independent Orbit Determination for a Lunar Navigation Satellite System
## 2 Lunar Scenario
### Satellite Design  
As introduced, five different types of guidance and navigation sensors are evaluated in this work: star tracker, horizon navigation, triangulation, crater navigation, and inter-satellite ranging. Each of the techniques are illustrated in Figure 2.  
For each optical-based sensor, which encapsulates all except ISLs, parameters of a standard space-rated, commercial off-the-shelf camera are treated. Cameras are very common to most lunar missions, and so impose no cost to the satellite architecture. However, they may constrain the satellite design. Each sensor is assumed to be positioned on the spacecraft at an attitude that is in constant view of the target. Reorientation of the satellite during flight is ignored.  
Potential design limitations of each proposed sensor are introduced in Table 2. It is immediately obvious that not all sensors could be used simultaneously, due to pointing requirements, as well as general size, weight, and power constraints for a small spacecraft. However, a carefully managed integrated approach might be possible, overcoming the specific challenges of each.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Sensor** & **Design Limitations** & **Challenges to Performance** \\ \hline \multirow{4}{*}{Star Tracker} & \(\bullet\) & Requires pointing to the star field with & \\  & minimal stray light interference. & \(\bullet\) & Misalignment correction would need to be \\  & & All horizon, triangulation, and crater & highly accurate to ensure required \\  & & navigation techniques are dependent & positioning performance. \\  & & on attitude. & \\ \hline Horizon Navigation & \(\bullet\) & Requires pointing of the camera to the & \(\bullet\) & Lunar eclipse and shadow make it difficult to \\  & & horizon limb. & differentiate limb points. \\ \hline \multirow{3}{*}{Triangulation} & \(\bullet\) & Requires multiple cameras to point at a few & \\  & & geometrically diverse celestial sources. & \\ \cline{1-1}  & \(\bullet\) & Requires pointing with minimal stray & \(\bullet\) & Accuracy is poor for navigation. \\ \cline{1-1}  & & light interference. & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Spacecraft sensor choice design implications and challenges to performance.  
Figure 2: Concept of potential navigation sensors for an autonomous lunar navigating spacecraft, including star, horizon, triangulation, crater, and inter-satellite ranging techniques.
###### Abstract  
Accurate perception is crucial for autonomous vehicles (AVs) to navigate safely, especially in adverse weather and lighting conditions where single-sensor networks (e.g., cameras or radar) struggle with reduced maneuverability and unrecognizable targets. Deep Camera-Radar fusion neural networks offer a promising solution for reliable AV perception under any weather and lighting conditions. Cameras provide rich semantic information, while radars act like an X-ray vision, piercing through fog and darkness. This work proposes a novel, efficient Camera-Radar fusion network called NeXtFusion for robust AV perception with an improvement in object detection accuracy and tracking. Our proposed approach of utilizing an attention module enhances crucial feature representation for object detection while minimizing information loss from multi-modal data. Extensive experiments on the challenging nuScenes dataset demonstrate NeXtFusion's superior performance in detecting small and distant objects compared to other methods. Notably, NeXtFusion achieves the highest mAP score (0.473) on the nuScenes validation set, outperforming competitors like OFT (35.1% improvement) and MonoDIS (9.5% improvement). Additionally, NeXtFusion demonstrates strong performance in other metrics like mATE (0.449) and mAOE (0.534), highlighting its overall effectiveness in 3D object detection. Furthermore, visualizations of nuScenes data processed by NeXtFusion further demonstrate its capability to handle diverse real-world scenarios. These results suggest that NeXtFusion is a promising deep fusion network for improving AV perception and safety for autonomous driving.  
Keywords:CondenseNeXt; sensor fusion; object detection; autonomous vehicle; PyTorch +
Footnote †: journal:
# Geometric Similarity Measurement Method for Micro Scene Generalization
## 2 Methodology
### Geometric Similarity Measurements of Micro Scenes  
At present, the overall similarity is mainly determined through the weight calculation of each similarity index, and the similarity calculation formula is obtained by combining the similarity evaluation factors. However, due to the complex structure of the micro scenes, the analytic hierarchy process (AHP) and entropy methods have certain limitations. Thesemethods may be suitable for specific areas, but they are not capable of describing a wider range of situations. For example, the weight value of river + islands obtained through AHP is not applicable to roads + buildings.  
Referring to recent research [31; 49], we took the geometric mean of the direction similarity, position similarity, and shape similarity as the value of the global similarity (Equation (19)); we found the calculation results were more stable and more in line with the actual situations by tests, indicating the geometric mean is suitable for the micro scene similarity calculation.  
\[\text{SIM}=\sqrt[3]{\text{Sim}_{\text{shape}}\times\text{Sim}_{\text{ direction}}\times\text{Sim}_{\text{position}}} \tag{19}\]  
Here, \(\text{Sim}_{\text{shape}}\) is the shape similarity calculated by Equation (10), \(\text{Sim}_{\text{direction}}\) is the direction similarity calculated by Equation (12), \(\text{Sim}_{\text{position}}\) is the position similarity calculated by Equation (18), and SIM is the overall similarity.
# Escaping Printable Encoded Streams to Embed Out-of-Band Data
## 4 Printable Encodings and Case Studies of Payload Data Embedding
### Parameter Separation  
A printable encoding may be also employed to encode parameters passed to a function in a context where binary data cannot be directly transmitted, for example, in the query string of a Web address. To separate the various encoded parameters, it is possible to use a single sequence \(S_{d}\) belonging to the previously defined set \(\mathcal{U}\) and another sequence \(S_{t}\) from the same set to indicate the end of the parameters (Figure 4). The framework is instantiated with \(\mathcal{Z}=\left\{S_{d},S_{t}\right\}\).  
Another possibility is to identify the data types of the various parameters employing sequences from the set \(\mathcal{U}\) (Figure 5): for example, it is possible to use a use sequence \(S_{i}\in\mathcal{U}\) to identify an integer, another sequence \(S_{f}\in\mathcal{U}\) to specify a float, then \(S_{o}\in\mathcal{U}\) to specify an octet string, \(S_{p}\in\mathcal{U}\) to express a binary pointer, and two sequences \(S_{rs},S_{rs}\in\mathcal{U}\) to indicate the beginning and the end of a record made of fields in turn identified with these delimiters (with a possible recursive structure). The parameter's list can be terminated with the sequence \(S_{t}\) from the same set \(\mathcal{U}\). In this case, the framework is instantiated with \(\mathcal{Z}=\left\{S_{i},S_{f},S_{o},S_{p},S_{rs},S_{re},S_{t}\right\}\).  
Nonetheless, the encodings proposed in Sections 4.1 and 4.2 may be used as an additional data protection feature for the parameters, taking care to choose \(S_{i}\), \(S_{f}\), \(S_{o}\)  
Figure 3: Secondary channel information interleaved in printable encoded data.  
Figure 2: Simple scheme to show possible encodings of security information for data protection (gray arrows show the sequences covered by the hash, MAC, or digital signature).  
\(\mathrm{S}_{p}\), \(\mathrm{S}_{rs}\), \(\mathrm{S}_{nr}\), \(\mathrm{S}_{d}\), and \(\mathrm{S}_{t}\) among the sequences in \(\mathcal{U}\)**not** encoding a CRC (Figure 1) **nor** a type of hash, MAC, or digital signature (Figure 2). The proposed framework has \(\mathcal{Z}=\left\{\mathrm{S}_{i},\mathrm{S}_{f},\mathrm{S}_{o},\mathrm{S}_{p },\mathrm{S}_{rs},\mathrm{S}_{re},\mathrm{S}_{t},\mathrm{S}_{c},\mathrm{S}_{lr },\mathrm{S}_{mt},\mathrm{S}_{ds}\right\}\).  
**Example 6**.: _Assume having a program running on a Web server that needs a (variable) set of parameters in binary form. In this case, the various data can be encoded with Base41 [1] and sent as a query string to the program, separating the various parameters with a single sequence from \(\mathcal{U}\) and terminating the parameter list with another sequence in \(\mathcal{U}\). At the receiving side, the program can split the data using the separator and recover the original binary values decoding the Base41 strings._
## 4 Results and Discussion
### Deterministic Analysis Results  
A series of deterministic analyses are carried out in OptumG2 before the probability analysis during the process of the slow drawdown of the WL. The finite element limit analysis with the mesh adaptivity technique is used for this numerical model. Figure 7 shows that the number of the adaptive finite elements in 2000 is suitable for obtaining stable FOSs.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Mean** & **Coefficient of Variation** & **Horizontal Correlation Length** \\ \hline \(c\) (kPa) & 15 & 0.1, 0.2, 0.3, 0.4, 0.5 & 1 m, 30 m, 40 m, 50 m, 500 m \\ \hline \(\varphi\) (\({}^{\circ}\)) & 20 & 0.1, 0.2, 0.3, 0.4, 0.5 & 1 m, 30 m, 40 m, 50 m, 500 m \\ \hline \(E\) (MPa) & 40 & - & - \\ \hline \(v\) & 0.3 & - & - \\ \hline \(\gamma_{\text{d}}\) (kN/m\({}^{3}\)) & 18 & - & - \\ \hline \(\gamma_{\text{sat}}\) (kN/m\({}^{3}\)) & 20 & - & - \\ \hline \(k\)s (m/s) & \(2\times 10^{-6}\) & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical properties of soil parameters.  
Figure 6: Plane computational model of a test embankment.  
Several different WLs are set to simulate the slow water drawdown, because the pore water pressure can completely dissipate during the process of water falling slowly. The FOSs of the embankment slope at six different WLs of 5 m, 4 m, 3 m, 2 m, 1 m, and 0.5 m are calculated to describe the slow water drawdown. As shown in Figure 8, the FOSs increase with the slow water drawdown. The reason is that it is assumed to be a steady-state seepage during the slow drawdown. The effective stress increases with the dissipation of pore water pressure, and the matric suction also increases during the slow drawdown. These two aspects will strengthen the stability of the embankment slope.  
As mentioned in Section 2.4.3 of this paper, the upper and lower bound values of FOSs are calculated by strength reduction technology. The true solution then falls somewhere between the upper bound and lower bound values, and thus, the mean value of the upper and lower values is regarded as the true solution, as shown in Figure 8. The probabilistic results described in the following sections are also calculated using this method.
# Research on Fractional-Order Control of Anchor Drilling Machine Optimized by Intelligent Algorithms
## 4 Mathematical Modeling and Simulation Test
### The Stability Analysis of the Model Under Fractional-Order Controllers
However, tuning the weighting functions and the Ho controller introduced phase lag, potentially degrading performance. To counteract this, a triple-delay compensator was incorporated. The performance of the final system, after optimizing both the Ho controller and the compensator, is evaluated and presented in \[E_{\alpha}(Z)=\sum_{K=0}^{\infty}\frac{Z^{K}}{\Gamma(\alpha K+1)},\alpha>0 \tag{7}\]1  
The Bode plot in Figure 14a indicates that the improved system maintains a stable, low gain in the low-frequency range, which significantly increases in the mid-frequency range (around 10\({}^{2}\) rad/s), demonstrating strong tracking performance. At high frequencies, the gain rapidly rolls off, providing substantial attenuation, which aids in mitigating the effects of external noise and rapid disturbances. Regarding the phase characteristics, the system exhibits approximately 720\({}^{\circ}\) of phase at low frequencies, gradually decreasing towards 180\({}^{\circ}\) as frequency increases, contributing to an enhanced phase margin. In the high-frequency region, the phase drops below \(-\)360\({}^{\circ}\), reflecting the system's high-frequency dynamic phase lag. \[E_{\alpha}(Z)=\sum_{K=0}^{\infty}\frac{Z^{K}}{\Gamma(\alpha K+1)},\alpha>0 \tag{7}\]2 Fitting a closed-loop system response Bode plot. The Nyquist locus presented in Figure 14c does not encircle the critical point \((-1,j0)\), confirming closed-loop stability, with a minimum gain margin of approximately 4 dB. Furthermore, the pole-zero plot in Figure 14d shows that all closed-loop poles reside in the left-half plane, and the dominant poles possess a damping ratio greater than 0.5, indicating good system stability. The calculated \(\mathsf{Ho}\) norm of the system is 1.3. This signifies that the maximum singular value of the closed-loop transfer function is constrained below 1.3 across the entire frequency spectrum, ensuring that the maximum system output response to disturbances remains bounded. The disturbance rejection ratio, denoted as \(\gamma\), is 2.3 dB, indicating the system's capability to attenuate external disturbances to levels below 2.3 dB. Although the improved system exhibits a significant phase lag, this resulting slower response beneficially prevents overshoot and oscillation, thereby enhancing mechanical stability. As observed in Figure 14b, the settling time remains around 0.02 s, indicating that the phase lag has a limited impact on overall system performance. Therefore, in the engineering application for the anchor drilling machine, accepting a moderate phase delay is a suitable trade-off for achieving enhanced disturbance rejection and stability.
# Performance Assessment of Waste Cooking Oil-Modified Asphal Mixtures
## 5 Conclusions and Recommendations  
The main goal of this research is to assess the performance of Waste Cooking Oil (WCO)-modified asphalt mixtures using the Balanced Mix Design (BMD) approach. The following conclusions can be made:  
* Mixture resistance to cracking was improved with the increment in WCO dosages. CTIndex has an excellent direct linear relationship with the variation WCO dosage (i.e., R\({}^{2}\) of 0.87). A statistically significant discrepancy was found between the control and modified mixtures with high WCO dosages (e.g., 5% and 7%).
* HI-IDTstrength has a good indirect linear relationship with the variation WCO dosage (i.e., R\({}^{2}\) of 0.79). A statistically significant discrepancy was found between the control mixture and all modified mixtures.
* WCO was able to improve the cracking resistance of the control balanced mixture, but it declined its resistance to rutting. However, a significant effect can be observed at high WCO dosages. For instance, the modified mixture with 7% WCO increases the cracking resistance of the control mixture by 85% (i.e., from 29.7 to 54.8) but decreases its rutting resistance by 66% (from 114 KPa to 39 KPa).
* The interaction plot indicates that the overall performance of WCO-modified asphalt becomes softer and more flexible with the increase in WCO dosage. Thus, the designed balanced control mixture becomes unbalanced when using WCO oil at any dosage.
* Future research is recommended to investigate the ability to use a compound modifier consisting of WCO with other waste modifiers (e.g., ground tire rubber, plastic) to reduce its negative impact on mixture rutting resistance. Such an approach would lead to a more economically balanced WCO-modified balance asphalt mixture.  
* WCO enhanced mixture resistance to cracking; thus, the authors recommend further investigating the potential of using WCO as a rejuvenator for asphalt mixtures modified with Recycled Asphalt Pavement (RAP) or Recycled Asphalt Shingles (RAS).  
Conceptualization, H.A. and E.K.; Formal Analysis, H.A., M.B., A.A. and F.A.; Methodology, H.A., E.K., K.A.A. and M.B.; Supervision, H.A. and E.K.; Writing--Original Draft, H.A., E.K., K.A.A., M.B., A.A. and F.A.; Writing--Review and Editing, H.A., E.K., K.A.A., M.B., A.A. and F.A. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Deanship of Scientific Research at Hashemite University (grant number 63/2020).  
All data, models, or codes supporting this study's findings are available from the corresponding author upon reasonable request.  
The authors are thankful to the Deanship of Scientific Research at Hashemite University for funding this work (Grant No. 63/2020). Also, the authors appreciate the technical help provided by Eng [PERSON] at the Hashemite University and Eng. [PERSON].  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## 4 Proposed mmWave Assisted CR-IoV
### Comparative Analysis
#### 5.3.3 Analysis of Delay  
Delay is unfavorable in real-time applications since most of the vehicular applications are delay sensitive. Analysis of delay shows the applicability of the work in real time.  
In Figure 12, the delay is compared with respect to the number of vehicles. The graphical analysis shows small variations in delay with an increase in the number of vehicles. However, these variations do not affect the proposed mmWave-assisted CR-IoV since the proposed work has only 17 ms of delay, which is 50% lower than SVM based approach. The main cause for delay is excess time introduced during data transmission. In the proposed work, FZ-MOWO considers delay as one of the criteria which selects the optimal forwarder intending to minimize the delay. This objective function reflects the reduction in delay analysis. In existing works, the delay is attained up to 34 ms, which is twice the time higher than the proposed work. Mainly, all existing works rely on either security provisioning or routing. However, both processes are important for transmitting data between SUs without an increase in delay. Thus, the lack of significant security provisioning and routing increases delays in existing works. Similarly, presenting an optimal security provisioning scheme and routing in the proposed work decreases delay.  
An increase in the number of attackers is also a major reason for an increase in delay. It can be seen from Figure 13 in which delay is compared with respect to the number of attackers. In all three works, an increase in the number of attackers increases the delay. When a single attacker is presented in the network, then the delay is 10 ms (mmWave assisted CR-IoV), 12 ms in the ML-NN method and 11 ms in the SVM method. When the number of the attacker is increased to 5, the proposed work has 12.1 ms of delay while the ML method has 32 ms of delay and the SVM method has 35 ms of delay which is nearly thrice the time higher than the proposed work. The reason behind this variation is the inefficiency of the SVM and ML-NN method in detecting the attackers accurately. Due to this fact, the attacker involvement is increased in the network and delay is also increased. On the other hand, we have presented HK-SVM and FA-TDoA for SSDF and PUE attack detection, respectively. In addition, optimal 3D beamforming and FZ-MOWO assist in minimizing delay in the network. Overall, we have attained minimum delay through multiple significant procedures.  
Figure 12: Delay with respect to the number of vehicles
## 3 Design of Mobile Unit (MU)  
The requirements placed on the portable, personal device mobile unit (MU), mainly the 20 ms round trip response time, force us to use a communication protocol based on _polling_. The base station (BS) is a master device and initiates the communication (query), while the MU is a slave device and only responds to the query from the BS (MU does not initiate the communication). Such a solution simplifies both MUs and BSs: only one unit can transmit at a time, so there is no need to implement collision detection/collision avoidance (CDCA) techniques. Another important factor is the determinism of the wireless communication time. Mobile units must be small and lightweight: belt or shoulder mounted for personal wear. Other assumptions are listed in Table 2.
# A Helping Hand: A Survey About AI-Driven Experimental Design for Accelerating Scientific Research
## 3 Results
### AI Methodologies for Experimental Design
#### 3.2.2 Hybrid Approaches  
Optimization and Supervised Learning:  
In their work, [PERSON] et al. [226] presented a framework for catalyst design and optimization. The authors employed an LLM equipped with a multi-objective Bayesian optimization (MOBO) approach. The LLM serves as an experimental design preparation stage and is used to identify processes and their relevant parameters in the provided raw texts. The texts are automatically filtered from the scientific literature by a keyword-based search. The LLM applied is a ChatGPT model. BO is then utilized to optimize the surrogate model and the experimental parameters and select new experiments to perform, based on the acquisition function. A GP regression model is used as the surrogate model, while EHI is applied as the acquisition function. To enhance the learning process at the beginning of the optimization, experiments are initially selected by LHS.  
Supervised Learning and Supervised Learning:  
[PERSON] et al. [224] proposed an approach for electrical conductivity optimization and discovery of doped conjugated polymers based on two supervised learning methods. It consists of an RF classifier to filter out materials with low conductivity and a least absolute shrinkage and selection operator (LASSO) regression model, which determines the conductivity and the experimental results, respectively, using different descriptors as input, such as spectral, process, material, and measurement descriptors.  
Reinforcement Learning and Supervised Learning:  
[PERSON] et al. [220] proposed a framework for accelerating microfluidic material synthesis, consisting of RL and supervised learning. The framework combines a single-period horizon strategy with a surrogate model. The surrogate model includes, among others, a naive Bayes classifier to determine the feasibility of the experimental parameters and a GP regression-based model to predict the output. The RL approach consists of a belief model, an objective function, and a decision policy. An ENN is used as the belief model and trained to predict three output parameters based on the output of the surrogate model. The objective function uses a multi-objective strategy based on the weighted mean-utility or probability sampling to turn the prediction into a single quality value. This quality value is used to adjust the decision policy based on EI and, thus, new experiments are selected. The obtained experimental results are used to further improve the models.
## 4 Proposed System
### Entrolment  
For a vehicle to receive location-aware service messages by means of trajectory-based message delivery, \(V_{d}\) configures desired socialspots in which \(V_{d}\) is interested for receiving the service messages and selects sRSUs installed at the socialspots denoted as \(SR_{d}=\{sRSU_{d}|1\leq j\leq k\}\subseteq\mathcal{SR}\). Then, to allow each \(sRSUj\in SR_{d}\) to access \(V_{d}\)'s trajectory data under the control of SM using a proxy re-encryption scheme, \(V_{d}\) generates the re-encryption key \(rk_{V_{d}\to sRSUj}\) for each \(sRSUj\) as follows:  
1. Retrieve \(sRSUj\)'s public key \(pk_{sRSUj}\) from the cloud storage.
2. Set a re-encryption key as \(rk_{V_{d}\to sRSUj}\leftarrow\)_clReKeyGen(sk\()_{V_{d}}\), \(pid_{d0}\), \(sRSUj\), \(pk_{V_{d}}\), \(pk_{sRSUj}\))_.
3. Compose a message \(RSM_{d}=\{pid_{d0},pk_{V_{d}},SR_{d},\)\(RK_{d}\}\), where \(RK_{d}=\{rk_{V_{d}\to sRSUj}\mid sRSUj\in SR_{d}\}\).  
\(V_{d}\) entrusts \(RSM_{d}\), consisting of sRSUs list and re-encryption keys, to SM so as for SM to re-encrypt \(V_{d}\)'s trajectory data and delegate decryption right to each \(sRSUj\) when \(V_{d}\) uploads its encrypted trajectory data to SM.