esa-sceva/satcom-chunk-collection huggingface dataset:
# SENTINEL: A Secure and Efficient Authentication Framework for Unmanned Aerial Vehicles
## 8 Security Analysis
### Formal Security Analysis Using ProVerif
```
Stored information: (\(sk_{gs}\), \(pk_{gs}\), \(cert_{gs}\), \(pk_{CA}\)) Process: Groundstation\(\triangleq\) in(ch, (\(id\), \(n_{d}\), received \(cert_{d}\))). new \(n_{gs}\). let concatenated nonce = Conc(received nonce, \(n_{gs}\)) in let signed nonce = SIGN(concatenated nonce, \(sk_{gs}\)) in out(ch, (signed nonce, \(cert_{gs}\)). let \(cert_{d}\) = GetCERT(received \(cert_{d}\)) in let valid \(cert_{d}\) = CheckSIGN_CERT(received \(cert_{d}\), \(pk_{CA}\)) in if \(cert_{d}\) = valid \(cert_{d}\) then let \(pk_{d}\) = GetPKEY(\(cert_{d}\)) in new \(pms_{gs}\). let \(k_{gs}\) = PBRDF(\(pms_{gs}\), concatenated nonce) in let signed \(pms_{gs}\) = SIGN(AENC(\(pms_{gs}\), \(pk_{d}\)), \(sk_{gs}\)) in out(ch, signed\(\backslash\)\(pms_{gs}\)). in(ch, received secret). let decrypted secret = DEC(received secret, \(k_{gs}\)) in event termGS(\(k_{gs}\)). ```  
Listing 3: Procedure performed on ground station \(gs\) for key agreement. Listing 4 shows the queries and verification results in ProVerif. To confirm the flight session key correctly shared, we used two queries. The first query result of RESULT not attacker(s[]) is true means that the attacker failed to obtain secret s[], leading to the secrecy of the flight session key \(k_{d}\). This is because the attacker can finally obtain secret s if the attacker obtains \(k_{d}\). To check that \(d\) and \(gs\) share the same flight session key correctly, we defined query x: key; inj-event(termGS(x))==>inj-event(acceptsDrone(x)) to check whether the key is same or not. The query result of RESULT inj-event(termGS(x_79))==>inj-event(acceptsDrone(x_79)) is true. indicates that \(d\) and \(gs\) successfully share the same flight session key for authentication.
# Cyber Physical Systems Dependability Using CPS-IOT Monitoring
## 4 The CPS-IoT Monitoring Model
### Interference-Aware Clustering with Node Migration Protocol (INMP)
#### 4.4.2 The INMP Protocol  
The INMP protocol is based on the the following key features:  
* The CPS-IoT is subdivided into n clusters, each cluster \(i\in n\) being of size \(k(i)\);
* Each cluster is organised into a m-to-1 model using a collection tree structure with the root being a single sink node \(S(i)\);
* Clusters in an INMP map to collection sets;
* In contrast to the multi-sink structure proposed in [4], node migration can be performed between clusters to restore the CPS-IoT safety and this is triggered by routing updates;
* The routing updates are triggered by either: (i) A beaconing message resulting from visualising the epidemic model or computations using the epidemic curves or (ii) monitoring of the diffusion sets, which reveals that nodes in the sensitive diffusion sets are rapidly moving far from their prescribed epidemic states or that most of the nodes are in an infected epidemic state. This step is carried out at the observation centre (steps 2 to 5 in Figure 2 or step 4 in Figure 4);  
Figure 5: Node-sharing model.  
The number of nodes moving from one cluster to another may depend on many parameters including: (i) The difference in sizes of clusters with the expectation of balancing the load between clusters; (ii) the average and maximum energy consumed by the sink nodes of the clusters; and (iii) the network epidemic state, which reveals the level of attack on the network;
* The inbound surveillance is a two-steps process that starts with the physical environment where the CPS-IoT network performance is evaluated to reveal if some clusters have sink nodes with energy levels above the cluster average. This is used in a second step to trigger the computation of the SAR system of ordinary differential equations in the cyber space to reveal the epidemic evolution in the diffusion sets;
* The outbound actuation is also a two-steps process that starts with a simulation of the optimal/efficient configuration of the CPS-IoT clusters, which is followed by the transfer of the new configuration into the CPS-IoT network in the physical space through parameter adjustments.  
The INMP protocol is an implementation of the Interference aware clustering with Node Migration Algorithm (INMA) described in Figure 6.  
Note that as presented in this paper, the INMA algorithm contains a loop that includes some steps of the MAPE-K control loop, introduced by IBM [33] and adopted in different works on autonomic computing and self-organising networking [34; 35; 33]. It monitors and collects details such as topology information, metrics (e.g., offered capacity and throughput), configuration property settings, etc. from managed resources, then performs complex data analysis and reasoning on the symptoms (information) collected.
# Interaction Patterns for Staggered Assembly of Fibrils from Semiflexible Chains
## 3 Results
### Sequence Optimization
For the bead compositions that we chose, we find that the most probable value for \(E_{rad}\) is negative. Even random sequences with this bead type composition might therefore have a tendency to stick to each other and bundle up, albeit probably not in an ordered array. This we will check later using Langevin Dynamics simulations for a sequence \(S_{nmd}\) was randomly chosen from the set of sequences with interaction energy close to the peak of the energy distribution (given in Figure 5a). Figure 4: Sequence optimization for staggered hexagonal packing in \(P_{1}\) lattice. (**a**) Distribution of rod interaction energies \(E_{rad}\) (units of \(k_{B}T\)) from simple random sequence search (\(10^{6}\) steps). A representative random sequence \(S_{nmd}\) is selected randomly from the sequences with energy for which the distribution peaks (vertical dotted line). (**b**) Low energy tail of the distribution of rod interaction energies, obtained with Metropolis Monte Carlo sequence search (\(10^{6}\) steps). Figure 3: Lowest interaction energies \(E_{rad,min}\) (units of \(k_{B}T\)) found in Monte Carlo sequence optimization for chains of \(L=40\) beads (\(30\%\) hydrophobic, \(30\%\) hydrophilic, \(40\%\) charged) arranged in staggered hexagonal packings \(P_{1}\)–\(P_{8}\). For finding the tail of the distribution at the side of low interaction energies, the simple random search is not efficient enough, and we need the Monte Carlo sampling, which zooms in on all sequences up to a certain energy above the minimum, as controlled by the parameter \(\Delta E\). The tail of the distribution found from a Monte Carlo search of \(10^{6}\) steps is shown in Figure 4b, the 22 lowest energy sequences are illustrated in Figure 5a. The interaction energy is invariant with respect to exchange of the positive and negative beads, so in fact there are only 11 distinct minimum sequences for packing the chains into the \(P_{1}\) packing, that we label in order of ascending rod interaction energy, \(S_{0}\)-\(S_{10}\) The striking feature of these minimum sequences, compared to the random sequence, is that all show clustering of similar bead types. The two lowest energy sequences, \(S_{0}\) and \(S_{1}\), exhibit the most regular and simple patterns of stretches of bead types. The packing of chains with the sequence \(S_{0}\) in the \(P_{1}\) lattice is illustrated in Figure 5b,c. Many of the low energy sequences \(S_{0}\)-\(S_{10}\) might correctly assemble into the designed \(P_{1}\) structure also in the presence of thermal motion. A possible reason for exploring multiple sequences might be that the physical properties of \(P_{1}\) fibrils, such as the mechanical properties, need not be the same. This, however, we leave as a topic for further research. Here, we restrict ourselves to establishing that designed sequences indeed assemble into the designed structures even if thermal motion is switched on. This we do for the lowest energy sequence \(S_{0}\).
# Adjacency Maps and Efficient Graph Algorithms
## 1 Introduction
The outgoing dictionary corresponding to vertex \(v\) contains the mappings \((v,(v,w))\) for all edges \((v,w)\in E\) going out of vertex \(v\), for all vertices \(v\in V\). The adjacency map representation of a directed graph is also illustrated in Figure 1. The small collection of \(11\) abstract operations can also be implemented using the adjacency map representation to take \(O(1)\) expected time, with the exception of \(G.del\_vertex(v)\), which takes \(O(\deg(v))\) expected time, as follows:  
* \(G.vertices()\) are the keys in dictionary \(D\). * \(G.edges()\) are the values \(D[v].O[w]\) for all keys \(v\) in dictionary \(D\) and for all keys \(w\) in dictionary \(D[v].O\). Figure 1: Adjacency list (**top left**) and adjacency map (**bottom**) representation of a directed graph (**top right**). Source and target vertices of each edge not shown. * \(G\)_incoming\((v)\)_ are the values \(D[v].I[u]\) for all keys \(u\) in dictionary \(D[v].I\). * \(G\)_outgoing\((v)\)_ are the values \(D[v].O[w]\) for all keys \(w\) in dictionary \(D[v].O\). * \(G\)_adjacent\((v,w)\)_ is true if \((w,e)\in D[v].O\), where \(e=D[v].O[w]\), and false otherwise. * \(G\)_source\((e)\)_ is the source vertex associated with edge \(e\). * \(G\)_target\((e)\)_ is the target vertex associated with edge \(e\). * \(G\)_new_vertex\(()\)_ is implemented by inserting an entry in dictionary \(D\), with the key as a new vertex \(v\) and value a pair of empty dictionaries \(D[v].I\) and \(D[v].O\), and returning vertex \(v\). * \(G\)_new_edge\((v,w)\)_ is implemented by setting to \(v\) the source vertex associated with a new edge \(e\), setting to \(w\) the target vertex associated with edge \(e\), inserting an entry in dictionary \(D[v].O\) with key \(w\) and value \(e\), inserting an entry in dictionary \(D[w].I\) with key \(v\) and value \(e\), and returning edge \(e\). * \(G\)_del_vertex\((v)\)_ is implemented by performing \(G\)_del_edge\((e)\)_ for each entry with key \(u\) and value \(e\) in dictionary \(D[v].I\) and for each entry with key \(w\) and value \(e\) in dictionary \(D[v].O\), and then deleting the entry with key \(v\) from dictionary \(D\). * \(G\)_del_edge\((e)\)_ is implemented by deleting the entry with key \(w\) from dictionary \(D[v].O\) and deleting the entry with key \(v\) from dictionary \(D[w].I\), where \(v=G.\textit{source}(e)\) and \(w=G.\textit{target}(e)\). Similar to the adjacency list representation, the adjacency map representation of a graph \(G=(V,E)\) with \(n\) vertices and \(m\) edges also takes \(O(n+m)\) space. In addition to the low space requirement, the main advantage of the adjacency map representation is the support of the adjacency test in \(O(1)\) expected time, when dictionaries are implemented using hashing. In this article, we compare the performance of three graph algorithms on a large benchmark dataset of random directed graphs, when implemented with an adjacency-list and an adjacency-map representation, and we show that they run faster on the average with adjacency maps than with adjacency lists.
## 7 Conclusions  
In this study, the analysis of three approaches for damage detection, the Cascade Classifier (CC), the Convolutional Neural Network (CNN), and the Visual Question Answering (VQA), were performed. The first model analyzed was the CC, in which an algorithm was developed in order to detect roof damages using aerial footage. Next, the CNN-based detection model was analyzed, and similarly to the CC model, an algorithm to detect  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Overall Accuracy** & **Accuracy for Yes/No** & **Accuracy for Count** \\ \hline
**Argo Hall** & 92\% & 94\% & 90\% \\
**Martin Hall** & 93\% & 96\% & 89\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Accuracy of VQA Analysis.  
Figure 12: Examples 4–6: VQA analysis of post-disaster footage.  
-roof damages in aerial footage was developed. Finally, a VQA model was designed by combining an image classification model (CNN) with a language model (Bag of Words), with the goal of not only detecting any damage, but also being able to answer different questions that might arise in post-disaster scenarios. In order to demonstrate their performances, the algorithms of the three approaches were tested on the videos recorded from an Unmanned Aerial Vehicle (UAV) flying over the University of West Florida (UWF) campus after Hurricane Sally in 2020. The results of the overall accuracy performance of each method were presented. The developed VQA algorithm combination of the CNN and BoW model obtained over 90% accuracy validation, specifically, the overall accuracy for Argo Hall was 92%, and the overall accuracy for Martin Hall was 93%, where the CNN had an accuracy of 85% and 88%, and the CC had 48% and 55%, for Argo Hall and Martin Hall, respectively. The VQA analysis results support the feasibility of the use of the developed VQA framework for damage detection in post-disaster aerial footage.  
For future work, in order to improve the accuracy, more training data are planned to be used. The custom dataset created for this study, the Sally-UWF dataset, was mainly focused on roof damages. To expand the proposed approach to any type of damage, a larger dataset with different types of damages will be utilized. Furthermore, we plan to expand the experimental validation of data in different regions, as well as compare with other models such as you only look once (YOLO), as another future work.
# A Real-Time Detection of Pilot Workload Using Low-Interference Devices
## 5 Results and Discussion
### Comparison with Other Studies  
In addressing physiological differences between individuals, some studies [88; 89; 90] have optimized machine learning to uncover shared features to mitigate the impact of inter-subject variability on workload recognition outcomes. Inspired by their algorithmic ideas, this study also experimented with a workload detection model based on a fully connected network and adversarial domain generalization (FCN-DG), incorporating an adversarial domain generalization module to enhance robustness and generalizability by extracting common features, as shown in Figure 11.  
Figure 10: The receiver operating characteristic (ROC) curves.  
Figure 9: The confusion matrix of the model results.  
However, the shared features extracted through confrontation were suboptimal for classification purposes, achieving only 65% accuracy via this model, while it costs more time training the adversarial generalization models. And the highest accuracy of other scholars' adversarial generalization models only reached 69%. This may be due to the following factors: (1) the association between the shared physiological features identified and workload is derived from group-level analyses, representing the commonality of all individuals, which are not strong enough to assess an individual's workload at a specific moment accurately; (2) physiological responses that are consistent within some individuals may vary across single individuals such that some variables that are non-responsive to pilot workload at the group level may be valuable for assessing workload at the individual level [26].  
Therefore, the proposed model proves more advantageous in terms of both efficiency, accuracy, and generalizability.  
Table 6 lists several other studies from recent years that have also used machine learning models for workload detection. It is evident that although categorizing into the same three classes, the proposed model surpasses most models built on traditional physiological measurement techniques in terms of accuracy through its low-interference detection approach.
## Appendix B  
By Huygens-Fresnel diffraction integral Equation (9), the field of the coaxial double-position power Gaussian vortex in free space at the receiving plane is given by [31; 33]  
\[\begin{array}{ll}E(r,\theta,z)&=-\frac{ik}{2\pi z}\exp\Big{[}i\Big{(}kz+ \frac{k^{2}}{2z}\Big{)}\Big{]}\int_{0}^{\infty}\int_{0}^{2\pi}E(r^{\prime}, \theta^{\prime},0)\\ &\times\exp\Big{\{}i\frac{k}{2z}\Big{[}r^{\prime 2}-2 rr^{\prime}\cos( \theta-\theta^{\prime})\Big{]}\Big{\}}r^{\prime}dr^{\prime}d\theta^{\prime}\\ &=-\frac{ik}{2\pi Nz}e^{i(kz+\frac{k^{2}}{2z})}\int_{0}^{\infty}r^{\prime|_{0 }+1}e^{-(\frac{1}{w_{0}}-\frac{ik}{2})r^{\prime 2}}\\ &\Big{\{}\frac{|\alpha|-1}{2}\int_{0}^{2\pi}e^{-i[l_{0}\theta^{\prime}+\frac{ kkr^{\prime}}{z}\cos(\theta-\theta^{\prime})]}d\theta^{\prime}+\frac{|\alpha|+1}{2} \int_{0}^{2\pi}e^{-i[-|\theta^{\prime}+\frac{kr^{\prime}}{z}\cos(\theta- \theta^{\prime})]}d\theta^{\prime}\Big{\}}dr^{\prime}\end{array} \tag{15}\]  
Consider integrals [32]  
\[\int_{0}^{2\pi}e^{-i[0\theta^{\prime}+\frac{kr^{\prime}}{z}\cos(\theta- \theta^{\prime})]}d\theta^{\prime}=(-i)^{l_{0}}2\pi I_{l_{0}}\Bigg{(}\frac{ krr^{\prime}}{z}\Bigg{)}e^{-il_{0}\theta} \tag{16}\]  
\[\int_{0}^{2\pi}e^{-i[-l_{0}\theta^{\prime}+\frac{kr^{\prime}}{z}\cos(\theta- \theta^{\prime})]}d\theta^{\prime}=(i)^{l_{0}}2\pi I_{l_{0}}\Bigg{(}\frac{ krr^{\prime}}{z}\Bigg{)}e^{-il_{0}\theta} \tag{17}\]  
Equation (11) becomes  
\[\begin{array}{ll}E(r,\theta,z)&=-\frac{ik}{2 Nz}\exp\Big{[}i\Big{(}kz+\frac{ k^{2}}{2z}\Big{)}\Big{]}\\ &\times\Big{\{}\Big{[}(1-|\alpha|)(-i)^{l_{0}}e^{-il_{0}\theta}+(1+|\alpha|)(i )^{l_{0}}e^{il_{0}\theta}\Big{]}\\ &+\int_{0}^{\infty}r^{\prime|_{0}+1}\exp\left[-\left(\frac{1}{w_{0}^{2}}-\frac {ik}{2z}\right)r^{\prime 2}\right]I_{l_{0}}\Big{(}\frac{krr^{\prime}}{z}\Big{)}dr^{ \prime}\Big{\}}\end{array} \tag{18}\]  
Further, consider the following integrals further [32]  
\[\int_{0}^{\infty}r^{\prime\prime}e^{-ar^{\prime 2}}I_{\
u}\big{(}br^{\prime} \big{)}dr^{\prime}=\frac{b^{\
u}\Gamma\Big{(}\frac{\
u+\mu+1}{2}\Big{)}}{2^{ \
u+1}a^{\frac{1}{2}(\
u+\mu+1)}\Gamma(\
u+1)}{}_{1}F_{1}\bigg{(}\frac{\
u+\mu +1}{2};\
u+1;-\frac{b^{2}}{4a}\bigg{)},\,\text{Re}a>0 \tag{19}\]Equation (A4) becomes  
\[\begin{array}{ll}E(r,\theta,z)&=-\frac{ik}{2 Nz}\exp\left[i\left(kz+\frac{kr^{2}}{ 2z}\right)\right]\left[(1-|\alpha|)(-i)^{l_{0}}e^{-il_{0}\theta}+(1+|\alpha|)(i )^{l_{0}}e^{il_{0}\theta}\right]\\ &\times\frac{zk^{0}w_{0}^{2(l_{0}+1)}l_{0}}{\left(2z-ikw_{0}^{2}\right)^{l_{0} +1}}F_{1}\left(l_{0}+1;l_{0}+1;-\frac{k^{2}r^{2}w_{0}^{2}}{2z\left(2z-ikw_{0}^{ 2}\right)}\right)\end{array}\] (A10)  
where \(\Gamma\left(\frac{\
u+\mu+1}{2}\right)\) is gamma function, and \({}_{1}F_{1}\left(\frac{\
u+\mu+1}{2};\
u+1;-\frac{k^{2}}{4\alpha}\right)\) is confluent hypergeometric function. \(k\) is the wave number, and \(\lambda\) is the wavelength of light in the vacuum.
## 1 Introduction
### Compressive Sensing  
A typical IoT framework with a CS block for remote ECG monitoring is given in Figure 1. The accurate reconstruction of ECG signal from digital compressed information is very vital to meet the requirement [14; 15]. The emergence of compressive sensing (CS) or Compressed Sampling is acknowledged as a defining moment in the field of signal processing for sensing and reconstructing a digital signal at very low sampling rates [16; 17]. CS has gathered a significant identity due to its potential to reconstruct signals from sampled data below the Nyquist rate [18; 19]. CS works against the conventional signal compression algorithm principles [20; 21; 22]. The efficient l\({}_{1}\)-minimization based CS methodto reconstruct signal using a smaller number of samples is discussed in reference [23] with mathematical background and fundamental formulation of CS framework.  
CS comprises of three process, sparse representation, sampling, and reconstruction of the signal [24]. A basic CS block diagram is shown in Figure 2. In 2004, CS obtained a new entity as [PERSON], [PERSON], [PERSON] and [PERSON] came with a fundamental findings on a CS mathematical basis [25].
## 2. Mature Technologies for Water and Wastewater Remediation
### Ultraviolet Radiation  
#### 2.3.1 Applications of UV Radiation  
UV radiation is frequently used for water recycling systems in the electronics, pharmaceuticals and cosmetics industries. These industries do not often introduce organic contaminants into their water, which makes UV treatment feasible. On the other hand, European water treatment plants that do make use of UV are only certified to purify water that is visibly clear, and not wastewater [30].
# Multi-Lens Array Full-Field X-ray Microscopy
## 2 Materials and Methods
### Multi-Lens Array Fabrication  
A large effective aperture, of course, is also crucial when using the multi full-field principle. The effective aperture of a CRL is maximized when using suitable lens material showing low X-ray absorption. Most X-ray transparent materials for CRLs are low atomic Z-number materials such as lithium, beryllium, or carbon [17]. Lithium is very reactive with humidity in the air, and fabricating Be-CRLs via imprinting [18] limits the possible geometries of CRLs. In recent years, additive manufacturing processes that use direct laser exposure have been tested for the production of CRLs [19; 20]. This manufacturing process is still slow [21] and, due to the voxel-wise exposure, provides quite rough surfaces compared to lithographic processes.  
Using lithographic fabrication methods allows the fabrication of an aligned array of slightly tilted CRLs with high quality and precision. For these reasons, we fabricated multi-focus CRLs via deep X-ray lithography. Deep X-ray lithography is a crucial part of the LIGA technology (German acronym for lithography, electroplating, and molding), invented and developed at KIT/IMT [22; 23]. It is used to structure a resist of up to several millimeters in height [24]. For CRLs, the resist mr-X (micro resist technology GmbH, Berlin, Germany), an epoxy-based negative resist, is used. A rectangular layout area of 20 mm \(\times\) 60 mm is exposed perpendicular to the substrate via an X-ray absorption mask. In this way, up to about 40 line focus CRLs with a height of up to 2 mm are fabricated in one exposure. After post-exposure bake and development, the CRLs are separated with a wafer saw. To fabricate point focus CRLs, two corresponding line focus CRLs are mounted under 90\({}^{\circ}\) in an interdigitated way (Figure 2). Currently, point focusing optics with apertures of up to 1.8 mm \(\times\) 1.8 mm are fabricated.
# Adaptive DecayRank: Real-Time Anomaly Detection in Dynamic Graphs with Bayesian PageRank Updates
## 5 Experiments
### Accuracy and Speed
#### 5.3.3 AUC-ROC  
Figure 5 presents the ROC curves for Adaptive-DecayRank, AnomRank, and DecayRank on the DARPA dataset. The ROC curve illustrates the trade-off between the _true positive rate (TPR)_ and the _false positive rate (FPR)_ at various thresholds. Adaptive DecayRank achieves the highest AUC score of **0.90**, indicating its superior ability to distinguish between normal and anomalous patterns. This represents a significant improvement over AnomRank (AUC: 0.86) and DecayRank (AUC: 0.83), demonstrating the effectiveness of the adaptive decay factor and Bayesian updating mechanism employed by Adaptive-DecayRank, particularly in capturing sudden changes in dynamic graph structures. Meanwhile, Figure 4d plots accuracy (AUC) vs. running time (log scale, in seconds, excluding I/O), further showcasing Adaptive-DecayRank's efficiency and scalability compared to the baselines, including SedanSpot, which achieves an AUC of 0.64.  
This represents a **4.65**% improvement in AUC over AnomRank and an **8.43**% improvement over DecayRank. Additionally, compared to SedanSpot (AUC: 0.64), Adaptive-DecayRank shows a **40.63**% improvement, further highlighting its superior performance in anomaly detection. In [23], the AUC score for DynAnom was not implemented; hence, it is missing from our Figure 5.