esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of Software-Defined Radio-Based Adaptable Packet Communication System for Small Satellites
## 1 Introduction
Table 2 compares three different approaches to designing a communication subsystem for small satellites. The traditional system is hardware-based, meaning that developers need to change hardware components in order to change the satellite's characteristics. This paper designs a Raspberry Pi + SDR system. In comparison with the traditional hardware-based systems, this design is more effective in using the available communication channel, and it has the ability to deliver \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**System** & **Flexibility** & **Complexity** & **Efficiency** & **Power Consumption** \\ \hline Traditional & & & & \\ Hardware-based & Low & Low & Low & Low \\ Systems & & & & \\ Raspberry Pi + & & & & \\ SDR Systems & High & Low & High & Low \\ FPGA + SDR & High & High & High & High \\ Systems & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison between different design approaches for communication subsystems in small satellites. more data. That said, the traditional systems are more robust. With specific improvements, this design could be made more reliable. Some SDR-based systems use field-programmable gate arrays (FPGAs) to produce and modulate the signals of SDRs; this design is instead built to be implemented in Raspberry Pi devices, which are easier to program for students and academic researchers. Moreover, Raspberry Pi devices require less power and space, making them preferable for CubeSats and small satellites.
## 3 Results and Discussion  
### Cell Proliferation  
In order to determine the biochemical mechanisms by which our formulation could be used for ED, the current in vitro studies were conducted.  
The capacity of the formulation to improve cell proliferation was evaluated using human muscle epithelial cells as a preliminary test. As can be seen from Figure 1, Icarifil was capable of positively and significantly stimulating cell proliferation. Specifically, different amounts of a 0.5 mg/mL solution were tested. It was found that 100 \(\upmu\)L of the solution was able to increase cell proliferation by about 22% compared to the control, while 200 \(\upmu\)L of the solution was able to increase the degree of proliferation up to 29%; however, the further increase in the amount of the dose of Icarifil did not lead to a significant change in its effect.  
Since good activity was already highlighted at the smallest quantity tested, to avoid toxicity, 100 \(\upmu\)L of Icarifil was used to quantify the proliferation of the murine penile muscle epithelium cells. To better understand which of the components present in Icarifil had greater activity, different combinations of it were tested. As shown in Figure 2, Icarifil was able to increase cell proliferation by about 43% compared to the control, whereas various combinations of the components used, although they still showed a positive action on cell proliferation, never achieved an effect above 29%. Different works have reported that the combination of various nutraceuticalals provides results superior those compared to single agents, probably due to the synergic effect between the components in the mixture [13,14].  
Figure 1: Dose-dependent effect of Icarifil (100, 200, and 300 \(\upmu\)L solution prepared at 0.5 mg/mL) on the proliferative activity of human muscle epithelial cells compared with culture medium and culture medium + Icarifil solvent, used as controls. Data represent mean \(\pm\) SD, significance **\(p\) < 0.05, using the One-WAY ANOVA test followed by Bonferroni test as a post-test.
# Clustering-Based Energy-Efficient Self-Healing Strategy for WSNs under Jamming Attacks
## 2 Related Work
### Contributions  
Clustering techniques with self-healing capabilities have not been completely investigated in the literature. Moreover, previous works only analyze such techniques in scenarios free of attackers. In this article, unlike previous works, we analyze the performance of different strategies in WSNs under jamming attacks. In addition, we propose a novel strategy that combines clustering, power allocation, cooperation, and load-balancing techniques to ensure network self-healing. Using the metrics of residual energy, network lifetime, coverage, and amount of transmitted information, we analyze the potential of combining these techniques under jamming attacks. Thus, the main contributions of this paper are as follows:  
* We analyze different clustering and self-healing techniques with power allocation and cooperation capabilities in scenarios with jamming attacks. For each scenario, we provide a detailed qualitative and quantitative analysis of the algorithms and techniques with their strengths and weaknesses.
* We propose a novel adaptive clustering-based self-healing algorithm that combines power allocation, cooperation, and load balancing. This novel algorithm is tested in the previously ignored presence of a jamming attack, ensuring efficient network operation in such conditions.
* We describe the advantages and disadvantages of each technique in jamming scenarios in terms of residual energy and transmitted information. The exhaustive analysis of routing paths, energy efficiency, and transmission powers insightful information on the behavior of the network against jamming attacks.  
In Table 1, we highlight those related works that have mostly addressed the issues dealt with in this paper. Only the method proposed here addresses jamming with a multidimensional perspective, exploring clusters to efficiently control the transmit power, to self-heal and to maintain persistent operation despite the presence of jamming.
## 3 Results and Discussion
Similar considerations hold had we started by circularly polarized light with opposite helicity, leading to a change of \(\Delta M_{L}=-1\). In short, for two-photon ionization, only the ionization path \(S\to P\to D\) is allowed. Accordingly, for three-photon ionization, only the \(S\to P\to D\to F\) ionization channel will occur. Now the crucial observation is that the transition amplitudes by circularly and linearly polarized light for these ionization paths differ only by the total magnetic quantum number, \(M_{L}\). For linearly polarized light, it is \(\Delta M_{L}=0\), while for circularly polarized light, it is \(|\Delta M_{L}|=1\). Straightforward angular momentum algebra for these transition amplitudes (\(T_{\rm C}(T_{L})\) for circular(linear)-polarized light) of two-electron states shows that they differ only by a proportional factor; namely, \(|T_{\rm C}(D)/T_{L}(D)|=\sqrt{3/2}\) for two-photon ionization and \(|T_{\rm C}(F)/T_{L}(F)|=\sqrt{5/2}\), for three-photon ionization, where \(D\) and \(F\) denote the final angular momentum channel for two-photon and three-photon ionization, respectively [31]. Nevertheless, note that circularly polarized light does not guarantee that the total ionization cross section will provide higher rates relative to the ionization by linearly polarized light; the final rate depends on the number of available ionization paths available and the electric field intensity and, in fact, high \(N\)-photon ionization by linearly polarized light is more effective. Figure 6: Three-photon ionization partial cross sections of Ar\({}^{16+}\) from its ground state with linearly polarized light.
# Aerospace
## 4 Single Small Satellite to Mars Orbit as Secondary Payload
### Aerocapture Entry Corridor and Nominal Vehicle Trajectory  
The aerocapture entry corridor is the range of EFPA within which the drag modulation vehicle can enter and achieve the target apoapsis altitude at the atmospheric exit by jettisoning the drag skirt at the appropriate time. The overshoot limit is the shallowest the vehicle can enter, and keeping the low-ballistic configuration \(\beta_{1}\) (i.e., with the drag skirt on) for the entire duration of the flight, is able to achieve the target apoapsis altitude post aerocapture. The undershoot limit is the steepest the vehicle can enter, and keeping the high-ballistic configuration \(\beta_{2}\) (i.e., the drag skirt jettisoned) for the entire duration of the flight, is able to achieve the target apoapsis altitude post aerocapture. The drag-modulation aerodynamic entry corridor is thus bounded by the undershoot (steep) and overshoot (shallow) EFPA limits and the width of the corridor is the TCW. Table 2 shows the overshoot and undershoot EFPA limits for the Mars aerocapture vehicle computed using the entry state in Table 1.  
Figure 7 shows the evolution of altitude, vehicle speed, deceleration, and stagnation-point heat rate for the Mars aerocapture vehicle with \(\beta_{1}\) = 20 kg/m\({}^{2}\), \(\beta_{2}\)/\(\beta_{1}\) = 7.5, \(R_{N}\) = 0.235 m entering at atmosphere-relative EFPA = \(-\)9.25 deg. The sharp change in the deceleration at 2.5 min after entry indicates the drag skirt jettison event. The peak deceleration is about 2.4 g, and the peak stagnation-point heat rate is 20 W/cm\({}^{2}\). The timing of the jettison event is critical, as keeping the drag skirt on for just a few more seconds can result in the vehicle bleeding too much speed and not being able to exit the atmosphere.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Overshoot limit, deg. & \(-\)8.78 \\ Undershoot limit, deg. & \(-\)9.86 \\ TCW, deg. & 1.08 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mars SmallSat aerocapture entry corridor.  
Figure 6: Approach trajectory of the Mars small satellite aerocapture vehicle. The white ring and the solid line indicate the equatorial plane and the direction of the north pole, respectively.
## 4 FTN Detection Scheme in Multipath Channels Based on Neural Network
### Complexity Analysis  
\(M\) represents the modulation order, and \(L_{ISI}\) represents theength of the FTN signaling affected by the ISI considered by the algorithm. It should be noted here that the ideal theoretical value of \(L_{ISI}\) should be as the sequence, that is, ISI interference comes from the entire sequence. However, existing algorithms are generally set at a fixed value to reduce the difficulty of implementation, which reduces performance to a certain extent.  
The existing traditional algorithms includeinear-level complexity algorithms and exponential-level complexity algorithms. Theower complexity is the frequency domain equalization algorithm, whose complexity is \(\mathcal{O}(N)\), while the complexity of the MAP algorithm is \(\mathcal{O}\left(L_{ISI}NM^{2L_{ISI}+1}\right)\). For data-driven algorithms, the training process is completed offline in advance and does not occupy computing resources, so only the complexity of online operations needs to be considered. Since the CNN model used is determined to be twice the dimensionality of the input data when processing two-dimensional input, the complexity measurementet of the convolutionalayer operation is the same for the same dimensional input, which is both \(\mathcal{O}(N)\). For the DMD-JD algorithm, the computational complexity of the online training stage is \(\mathcal{O}\left(N^{2}\right)\), but in the real-time detection stage, as there is only simple forward reasoning, the computational complexity will be reduced to \(\mathcal{O}(N)\). Although the calculation amount of Deep-Bi-LSTM is four times that of LSTM, it still has ainear relationship with the sequencelength.  
What needs to be emphasized here is that the complexity of data-driven algorithms does notie in their operations, but transfers the complexity to model adaptability. Specifically, due to the time-varying characteristics of the channel, the data characteristics of different channels, different moving speeds, and even different signal-to-noise ratios are different, and targeted model training needs to be performed according to specific application scenarios, which willead to higher complexity. For the problem of generalization performance, we present targeted work in Section 5.2.3 to make up for this defect.
# Machine Learning- and Deep Learning-Based Multi-Model System for Hate Speech Detection on Facebook
## 3 Methodology
### Data Annotation and Labeling  
The tagger used specific labeling criteria to classify the comments as \"Hate Speech\" or \"Not Hate Speech\". Comments were classified as \"Hate Speech\" if they contained (i) direct insults, threats, or slurs against a group or individual based on an identity (religion, ethnicity, or gender); (ii) implicit or explicit incitement to violent or discriminatory action; or (iii) dehumanizing or derogatory language. Comments indicating criticism, disagreement, or emotional reactions without the target being an identity were considered as not hate speech. Annotators were taught examples from related work, and conflicts were resolved by majority among the three bilingual annotators.
# Adaptive Semi-Active Suspension and Cruise Control through LPV Technique
## 3 Modeling and Control Synthesis of the Suspension System
Hence, these performance weighting functions related to passenger comfort and road holding are selected in a second-order proportional form as follows:  
\[W_{p_{a}} =\rho\frac{\alpha_{1}s+1}{T_{1}s+1} \tag{15a}\] \[W_{p_{d}} =(1-\rho)\frac{\alpha_{2}s+1}{T_{2}s+1}\] (15b) \[W_{p_{t}} =(1-\rho)\frac{\alpha_{2}s+1}{T_{2}s+1}, \tag{15c}\]  
where \(\alpha_{1,2,3}\) and \(T_{1,2,3}\) are designed parameters. Note, that weighting functions \(W_{p_{d}}\), \(W_{p_{a}}\), \(W_{r}\), \(W_{n}\) and \(W_{w}\) are all given in similar linear and proportional form without containing the scheduling variable \(\rho\). The LPV performance problem is to choose a parameter-varying controller, which guarantees quadratic stability for the closed-loop system while the induced \(\mathcal{L}_{2}\) norm from the disturbance \(\omega\) to the performances \(z\) is smaller than the value \(\gamma\), as described in Reference [22]. Therewith, the minimization task is given as:  
\[\inf_{K}\sup_{\varrho\in\mathcal{F}_{P}}\sup_{\|w\|_{2}\
eq 0,w\in\mathcal{L}_{2}} \frac{\|z\|_{2}}{\|w\|_{2}}\leq\gamma. \tag{16}\]  
The solution of an LPV problem is directed by the set of infinite-dimensional LMIs being satisfied for all \(\rho\in\mathcal{F}_{\mathcal{P}}\), thus it is a convex problem. In practice, this problem is set up by gridding the parameter space and solving the set of LMIs that hold on the subset of \(\mathcal{F}_{\mathcal{P}}\), see Reference [23]. The existence of a controller that solves the quadratic LPV \(\gamma\)-performance problem can be expressed as the feasibility of a set of the LMIs that can be solved numerically. The closed-loop LPV system is exponentially stable with its \(\mathcal{L}_{2}-\)gained less than \(\gamma\), if there exists an \(X(\rho)>0\) satisfying the following linear matrix inequality for all \(\rho\), where \(x^{T}X(\rho)x\) is a parameter-dependent Lyapunov function for the closed-loop system for all \(\rho\) if a solution exists. The proof of the existence of the solution is available in study of Reference [24]. \[ \begin{bmatrix}A_{cl}^{T}X+XA_{cl}^{T}+d/dt(X)&XC_{cl}&\gamma^{-1}C^{T}\\ B_{cl}^{T}X&-I&\gamma^{-1}D_{cl}^{T}\\ \gamma^{-1}C_{cl}&\gamma^{-1}D_{cl}&-I.\end{bmatrix} \]  
The result of the presented design is a reconfigurable LPV controller, \(\rho=1\) stands where passenger comfort is preferred, whereas \(\rho=0\) represents a controller considering stability and road holding of the vehicle, see Figure 5. When \(\rho\) is between these edge values, a combination of performances is guaranteed by the controller. Since the modeled MR damper has limitations on the control input force \(u=F\), a semi-optimal solution is given as follows:  
\[F=\left\{ \begin{array}{l l}F_{opt},&\quad\text{if}&\dot{q_{1}}(\dot{q_{1}}- \dot{q_{2}})>0\\ F_{pas},&\quad\text{if}&\dot{q_{1}}(q_{1}-\dot{q_{2}})<0\end{array} ,\right. \tag{17}\]  
where \(F_{opt}\) is optimal force and \(F_{pas}\) is passive suspension force. Figure 5: Scheduling variable scale.
## 3 Current Research and Applications
### Emergency Response and Disaster Management  
Unexpected events can significantly damage and disrupt the transportation system. For example, road accidents and traffic rule violations can lead to traffic congestion. Restoring normal traffic flow after such events can be challenging, as the transportation system requires a quick response. Incorporating autonomous UAVs into the system could reduce emergency response times and help mitigate potential traffic congestion caused by disasters. [PERSON] et al. [93] developed a framework for a UAV-assisted emergency network. This framework considers both trajectory scheduling and communication network design. To address the limitations of traffic policing and emergency response systems, [PERSON] et al. [94] proposed a solution that is intelligent, autonomous, and UAV-enabled. Most applications of UAVs in this sector could be related to hazard monitoring, disaster response, relief, and recovery [95]. UAVs can play a vital role in search and rescue operations, as they can monitor broader areas easily compared to humans [96]. Additionally, UAVs are employed to deliver supplies and recover hazardous materials, including dry food, medicines, and water. In addition, UAVs can be deployed to enable communication when communication channels are destroyed, or transportation infrastructures are damaged [97]. UAVs could also be used during medical emergencies [98].
## 3 Results and Discussion  
### Evolution of UCS with Time  
The results obtained during the unconfined compressive strength tests (UCS) are shown in Figure 8. The results for mixtures with SHS varied from 400 kPa to 5000 kPa and 155 to 5000 kPa without SHS. These values are the average of three tests, with each individual strength value not deviating more than \(\pm\) 10% from the average. A strength increase with curing time can be observed, up until day 120. This behavior is related to the  
Figure 7: Load application system (**a**) reaction structure; (**b**) load application; (**c**) measurement of the vertical displacement.  
pozzolanic capacity of the waste glass, which reacts with the calcium hydroxide [Ca(OH)\({}_{2}\)] available in the CL, forming a bonding matrix. These pozzolanic reactions are known to develop for periods well beyond the 28-days period usually considered for the assessment of OPC-based binders. Moreover, relevant is the higher short-term strength produced by the alkali-activated material compared with the material hydrated with water. Since the pozzolanic reactions depend on the development of a strongly alkaline environment, the presence of the activator creates such an environment immediately after the materials are mixed. On the contrary, the CL is less effective at increasing the pH of the mixture, resulting in lower dissolution rates of the silica (from the glass). After 120 days, the differences are practically null, as the hydrated material had time to develop similar volumes of binding gel.  
UCS ratio between the stabilized and natural specimens, which initially was approximately 2.6 (i.e., more than double), decreased to 1 after the longest curing period considered. According [28], the biggest difference between the pozzolanic reactions and alkaline activation is that the alkaline metal (NaOH) is more effective than the alkaline earth metals (calcium) due to the higher initial pH that the activating solution provides, thus dissolving and breaking the original structure more effectively.
# An Opportunistic Network Routing Algorithm Based on Cosine Similarity of Data Packets between Nodes
## 3 An Efficient Forwarding Strategy Based on Cosine Similarity between Nodes  
For the problems in the process of forwarding information in the OppNet, this paper analyzes the relationships among nodes in the network and proposes an efficient forwarding strategy, cosSim, which is based on the cosine similarity of data packets between nodes. First, the algorithm calculates the cosine similarity of the data packets between nodes to define the relationship between nodes. Then according to the degree of similarity between the nodes to select the next hop for information forwarding.
## 3 Methodology
### Performance Evaluation Metrics  
In this study, accuracy, F-measure, and area under the curve (AUC) were used to assess the detection performances of Android malware models. The selection of these assessment measures was based on current research, which shows widespread and regular use of these evaluation metrics for Android malware detection.  
1. Accuracy measures the overall rate at which the actual labels of all instances are correctly predicted [69]. It is calculated using Equation (2): \[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{FP}+\text{TN}+\text{ FN}}\] (2) ii. F-measure is the weighted average of both the recall (R) and precision (P) metrics. It emphasizes how good a classifier is at maximizing both the precision and recall values simultaneously. F-measure can be computed as defined in Equation (3): \[\text{Fmeasure}\,=\frac{2\times\,\text{TP}}{2\times\,\text{TP}+\text{FP}+\text{ FN}}\] (3) iii. The area under the curve (AUC) plots the FP rate on the \(X\)-axis and the TP rate on the \(Y\)-axis. AUC is not susceptible to majority class bias and does not ignore the minority class during its evaluation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Number of Features** & **Number of Instances** & **Number of Benign** & **Number of Malware** \\ \hline Drebin & 215 & 15,036 & 9476 & 5560 \\ Malgenome & 215 & 3.799 & 2539 & 1260 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Details of Android malware datasets.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Value Family** & **Number of Instances** \\ \hline DroidKungFu3 & 309 \\ AnserverBot & 187 \\ BaseBridge & 122 \\ DroidKungFu4 & 96 \\ Geinimi & 69 \\ Pjapps & 58 \\ KMin & 52 \\ GoldDream & 47 \\ DroidDreamLight & 46 \\ DroidKungFu3 & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Top 10 malware families in the Malgenome dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature Name** & **Feature Category** & **Number of Features** \\ \hline WRITE\_SETTINGS & & & \\ SET\_TIME & & & \\ ADD\_VOICEMAIL \(\dots\) & & & \\ \hline URL ClassLoader & & & \\ PathClassLoader & API Calls & 73 \\ \(\dots\) & & & \\ \hline Remount & & & \\ Chown & & & \\ \hline Intent.action.BOOT\_COMPLETED & & & \\ Intent.action.TIME\_SET & & & \\ Intent.action.Action\_SHUTDOWN & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Feature details of malware in Malgenome and Drebin datasets.