esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Fuzzing Frameworks
Table 4 summarises the adversarial example generation frameworks described in this section. It is worth mentioning that there are no algorithms for generating universal adversarial perturbations in black-box settings implemented in any of the aforementioned frameworks. Speaking of the white-box attack algorithms, implementations for the majority of them can be found in each framework specified. Furthermore, we are interested in generating adversarial examples not only for neural networks, but also other AI/ML models such as k-NN and decision trees. However, none of the frameworks surveyed has support for k-NN, whereas attacks against decision trees are only mentioned in ART, although in almost all of the frameworks a custom model can be implemented using the base model class. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Module** & **Component** & **Cleverhans** & **ART** & **Foolbox** & **DeepSec** & **Advertorch** & **Advbox** \\ \hline \multirow{3}{*}{Target model} & Tensorflow or Pytorch & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \cline{2-8}  & Skaern, XGBoost, LightGBM or CatBoost & & ✓ & & & & & \\ \cline{2-8}  & Custom model & ✓ & ✓ & ✓ & & & & ✓ \\ \hline \multirow{3}{*}{Whitebox attacks} & Attacks against k-NN or k-means & & & & & & & \\ \cline{2-8}  & Attacks against decision trees & & ✓ & & & & & \\ \cline{2-8}  & Attacks against neural networks & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \multirow{3}{*}{Scorebased attacks} & Single pixel [150] & & & ✓ & & & & ✓ \\ \cline{2-8}  & Local search [150] & & & ✓ & & & & ✓ \\ \cline{2-8}  & GenAttack [152] & & & ✓ & & & & \\ \cline{2-8}  & SimBA attack [153] & & ✓ & & & & & \\ \hline \multirow{3}{*}{Decisionbased attacks} & Substitute model attack [155] & ✓ & & & & & & \\ \cline{2-8}  & Boundary attack [156] & & ✓ & ✓ & & & \\ \cline{2-8}  & HopSkipplump attack [158] & & ✓ & ✓ & & & \\ \cline{2-8}  & ZOO attack [159] & & ✓ & & & & \\ \cline{2-8}  & GeoDA attack [160] & ✓ & & & & & \\ \cline{2-8}  & NES attack [162] & ✓ & & & & & \\ \cline{2-8}  & Pointwise attack [168] & & & ✓ & & & \\ \hline \multirow{7}{*}{Attack utility metrics} & Minimal perturbation size & ✓ & & & & & \\ \cline{2-8}  & Misclassification rate & & & ✓ & & & \\ \cline{2-8}  & Adv. class confidence & & & ✓ & & & \\ \cline{2-8}  & True class confidence & & & ✓ & & & \\ \cline{2-8}  & Adversarial example robustness & & & ✓ & & & \\ \cline{2-8}  & Imperceptibility & & & ✓ & & & \\ \cline{2-8}  & Computation cost & & & ✓ & & & \\ \hline \multirow{7}{*}{Defences} & Adversarial training [19, 137, 187] & ✓ & ✓ & ✓ & & ✓ \\ \cline{2-8}  & Preprocessing [177, 180, 181, 191, 192] & & ✓ & ✓ & ✓ & ✓ & ✓ \\ \cline{2-8}  & Postprocessing [193, 201] & & ✓ & ✓ & & & \\ \cline{2-8}  & Model transformation [189] & ✓ & ✓ & & & & \\ \cline{2-8}  & Adversarial example detection [177, 194, 195] & ✓ & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Frameworks for adversarial example generation.
## 4 Results and Discussions
### Results of Clustering Algorithms
#### 4.1.6 Self-Organizing Map (SOM)  
In order to implement the SOM algorithm, we used SOMPY, which is a Python Library. Next, It was initialized as a 20-by-20 Self-Organizing Map and it used the default parameters of SOMPY, like the initialization and neighborhood method. In Figure 6 we can see also a view of the clusters using the hitmap.  
The results of SOM are 3 cluster:  
* Cluster 0: (1, 6, 7, 8, 9, 10, 14, 22, 23, 25, 29, 30, 33, 35, 36, 37, 41, 42, 43, 48, 49, 50)
* Cluster 1: (0, 2, 3, 4, 12, 13, 15, 24, 27, 28, 31, 32, 38, 47, 52, 54)
* Cluster 2: (5, 11, 16, 17, 18, 19, 20, 21, 26, 34, 39,of each algorithm (generated clusters) and the data used to train the model, the silhouette score is calculated. The results of this analysis are displayed in the bar chart below (see Figure 7 for details).  
As we can see from the plot above, the highest values are achieved from two algorithms: K-Means (a value of 0.644) and Mean Shift (with a value of 0.638). indeed, the algorithms that have the lowest values are Agglomerative Clustering and Gaussian Mixture Models Clustering, with a value of 0.54.  
So, based on the theory of silhouette analysis and the results, the best algorithm is K-Means because it has the highest values among other algorithms with the value of 0.644.
# HCLE: Hybrid Clustering Approach for Extending WBAN Lifetime
## 3 Preliminaries
### Path Loss  
Path loss in WBAN refers to the attenuation or reduction in signal power as it travels through the human body or the surrounding environment. It can be captured by different models based on the application and the environment in which sensors are placed [64]. In this work, we use a log-distance path loss model, which considers the effect of obstacles and objects as well as various environmental factors such as attenuation, reflection, diffraction, and scattering. The model is proportional to the logarithm of the distance between the transmitter (e.g., sensor node) and the receiver (e.g., parent or sink node).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Value** & **Units** \\ \hline \(E_{tx-elec}\) & 96.9 & nl/bit \\ \(E_{Rx-elec}\) & 172.8 & nl/bit \\ \(E_{amp}\) & \(2.71\times 10^{-7}\) & J/bit/m\({}^{\eta}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Energy parameters for the Chipcon CC2420 transceiver.  
In Equation (3), the power loss \(PL(d)\) is modeled as a linear function of the distance (\(d\)) between the transmitter and receiver [65]:  
\[PL(d)=PL_{0}-10\eta\log_{10}\frac{d}{d_{0}}+X_{\sigma}, \tag{3}\]  
where \(d_{0}<d\), \(PL_{0}\) is the power loss at the reference distance \(d_{0}\) (defined in Equation (4)), and \(X_{\sigma}\) is the Gaussian random variable with zero mean and standard deviation \(\sigma\).  
In practice, it is difficult to predict the strength of the signal between the transmitter and the receiver [24]. Thus, the random variable (\(X_{\sigma}\)) is used to model the effect of shadowing and multipath fading. In addition, \(PL_{0}\) is used as a reference for comparing the path loss at other distances in the case where there is no shadowing or multipath fading:  
\[PL_{0}=10\log_{10}(\frac{4*\pi*d}{\lambda})^{2} \tag{4}\]  
where \(\lambda\) is the electromagnetic wavelength, \(\lambda=\frac{c}{f}\), \(c\) is the speed of light (\(3*10^{8}\) m/s), and \(f\) is the frequency.
# CRISPR-Cas Systems: Prospects for Use in Medicine
## 4 CRISPR-Cas Implementation to Control Human Infectious Diseases
### Application of CRISPR-Cas in Genotyping  
Bacterial genotyping is an important tool for epidemiological research and is indispensable for investigating outbreaks of infectious diseases, tracking their pathways, and identifying sources. Genotyping is also important for evolutionary research.  
Even before the description of the immune function of the CRISPR-Cas systems, direct repeats were used for genotyping (spoligotyping) of _Mycobacterium tuberculosis_ clinical isolates [96]. This method is based on the analysis of polymorphism of the CRISPR locus (formerly DR): PCR products obtained using primers complementary to DRs are hybridized with a set of synthetic oligonucleotides covalently linked to a nitrocellulose membrane. The hybridization pattern serves as a characteristic (spoligotype) of each strain. Spoligotyping has long been considered the \"gold standard\" in _M. tuberculosis_ genotyping.  
Besides _M. tuberculosis_, spoligotyping is successfully used for genotyping _Corynebacterium diphtherae_, the causative agent of diphtheria [97], _Yersinia pestis_, the causative agent of the plague, _Salmonella_ and _Campylobacter jejuni_[98].  
There are other genotyping approaches based on CRISPR locus analysis. Real-time PCR is used for identification of certain spacers characteristic to the so-called the Big Six _E. coli_ serotypes (O26:H11, O45:H2, O103:H2, O111:H8, O121:H19, O145:H28, and O157:H7), the most clinically significant and common ones in Europe and the USA [99].  
Additionally, typing can be done based on the length of CRISPR arrays, which is directly related to the number of spacers. This approach was used for _Yersinia pestis_ and _Salmonella_[98].
# Evaluation of 5G Positioning Performance Based on UTDoA, AoA and Base-Station Selective Exclusion
## 3 Position Solution and Measurement Exclusion  
This section describes the positioning algorithms considered for observables of time of arrival (i.e., UTDoA and RTT) and the one of angle of arrival (AoA). The position computation of the receiver using UTDoA is done with and without the use of NLoS BS exclusion mechanism. The unknown parameter for all cases is the 3D receiver position. These solutions consider both downlink and uplink positioning approaches. Both derivations are applicable in the same way to the location approach.
## 1 Introduction  
Independently disclosed in 2002 by the groups of [PERSON] and of [PERSON] [1, 2], the copper-catalyzed azide-alkyne cycloaddition reaction (CuAAC) has become an essential component of the _click-chemistry_ toolbox [3, 4, 5]. Driven by many applications [6, 7], the strong interest in CuAAC stimulated an intense search for improved catalytic systems [3, 4, 5, 8]. The main goals of these efforts were the stabilization of the Cu(I) active form and reduction of catalyst loading [9, 10, 11], the suppression of side-reactions [12], and the lowering of metal contamination in 1,2,3-triazole products. Concerning the last aim, the immobilization of copper species on insoluble supports is one of the most actively investigated options [13; 14; 15; 16]. Broadly speaking, this task has been pursued by either preparing nanostructured Cu(0), Cu(I), or Cu(II) (pre)catalysts on insoluble materials, or by anchoring a discrete Cu(I) or Cu(I) complex to a suitable support.  
In principle, with the second choice, an advantage can be produced by the capacity of some ligands, such as tris[(1-benzyl-1\(H\)-1,2,3-triazole-4-yl)methyl]amine (TBTA, **1** in Figure 1), to stabilize the catalytically active Cu(I) form and to increase the CuAAC reaction rates [9; 10]. In fact, a supported version of **1** on TentaGel\({}^{\text{TM}}\) resin was developed in 2007 by [PERSON] and [PERSON] (**P2**) [17], and made commercially available a few years later, albeit at a relatively high price (\(\epsilon\)133.00 for 250 mg with a 0.17 mmol g\({}^{-1}\) content of TBTA units) [18]. After loading with [Cu(MeCN)\({}_{4}\)]PF\({}_{6}\), the resulting Cu(I) complex proved active in the CuAAC of model substrates [19], with a remarkable capacity to work in both protic and aprotic reaction media and a good stability upon recycling [17].  
Variants of **P2** were reported in the following years to include soluble macromolecular analogs [20; 21; 22] and surface-immobilized TBTA units in microfluidic devices [23]. However, to the best of our knowledge, no supported TBTA material suitable for continuous-flow chemistry at a scale of truly preparative interest has been described to date (for recent examples of CuAAC in flow with other supported catalytic systems, see [13; 14; 15; 16; 24; 25; 26; 27; 28; 29; 30; 31; 32]).  
For this reason, due to our continuing interest in immobilized copper catalysts for selective transformations [33; 34; 35; 36; 37; 38], we recently tackled the problem of designing a new polymeric TBTA ligand fit for use in mini- or meso-fluidic reactors.  
Eventually, these efforts allowed a route for the convenient synthesis of the known TBTA monomer **3**, as well as the conditions for its immobilization in bead-type or monolithic macroporous polystyrene resins, to be established. After charging with Cu(I) or Cu(II) salts, the resulting materials proved to be competent catalysts for batch and continuous-flow CuAAC reactions.
## 3 System Design
### Structured Output for Easier Analysis  
To validate the robustness, responsiveness, and scalability of the blockchain-based access control system, an extensive simulation involving 50 unique users and 30 distinct resources was executed. This setup was designed to mirror the interaction complexity found in real-world multi-user environments such as mining governance, industrial IoT systems, and decentralized data platforms [27; 35].  
1. User-Resource Interaction Matrix  
A combinatorial evaluation is performed, whereby every user is assessed against every resource to determine access eligibility. This resulted in a total of  
\[\text{Total Evaluations}=50\text{ users}\times 30\text{ resources}=1500\text{ access queries}\]  
Each query simulates a blockchain smart contract invocation that either grants or denies access based on the dynamic evaluation of attribute-based policies.  
2. System Execution Flow  
The simulation is organized into three sequential phases:  
* User Registration: All 50 users are registered on the blockchain network via smart contract invocations.
* Policy Creation: Policies are dynamically created and mapped to resources, ensuring access conditions varied in complexity.
* Access Evaluation: Each user-resource pair is evaluated individually through smart contract queries, with interactions logged systematically for later analysis.  
The execution flow of the simulation script is illustrated in Figure 4.  
Figure 4: Simulation Execution Flow: Register Users \(\rightarrow\) Create Policies \(\rightarrow\) Evaluate Access for 1500 Cases \(\rightarrow\) Log Results.  
Performance and Latency Considerations  
Performance metrics such as transaction latency and access decision speed are implicitly captured via blockchain event timestamps. Although detailed profiling (e.g., endorsement time, commit latency) is not performed, the system demonstrates capability to handle large query volumes without error or deadlock under the test configuration.  
Scalability to Real-World Scenarios  
The simulation framework ensures system scalability through the following settings.  
JSON-formatted payloads for flexible input scaling.  
Dynamic attribute-based smart contracts for adaptable evaluations.  
Structured CSV/JSON logs suitable for easier analysis with minimal preprocessing.  
Access evaluation results are stored in structured, machine-readable logs with the following fields:  
Requester (user ID)  
Resource (TNO/license ID)  
Status (Granted/Denied)  
LatencyMs (response time)  
Timestamp (ISO 8601 [36])
## 2 Problem Statement  
Planning the maintenance of nuclear power plants is not a pure scheduling problem: the impact of maintenance decisions is calculated with the expected production cost to fulfill the power demands. Maintenance operations for nuclear power plants imply an outage, a null production phase during several weeks. Planning the outages is crucial in the overall production costs: the part of nuclear production in the French power production is around 60-70%, and the marginal costs of nuclear production are lower than the ones of other thermal power plants [18; 19]. Furthermore, maintenance planning must imply the feasibility of low-level technical constraints for productions and fuel levels. The optimization problem was formulated using 2-stage stochastic programming in [14]. Power demands, production capacities, and costs are uncertain and modeled using discrete stochastic scenarios. Refueling and maintenance decisions for nuclear power plants are taken, as a tactical level. The operational level optimizes production plans implied by the previous decisions for each stochastic scenario, to minimize the average production cost. Notations are presented in Table 1 for the sets and indexes and in Table 2.  
\begin{table}
\begin{tabular}{l l} \hline \(c\in\mathcal{C}_{\mathcal{C}\Gamma}\) with \(CT\in[14,21]\) & Scheduling constraints denoted CT14 to CT21 in [14]. \\ \(i\in\mathcal{I}=[1,l]\) & Nuclear power plants (Type 2, T2). \\ \(j\in\mathcal{J}=[1,l]\) & Flexible (Type 1, T1) power plants. \\ \(k\in\mathcal{K}=[0,K]\) & Cycles related to T2 units, \(k=0\) for initial conditions. \\ \(s\in\mathcal{S}=[1,S]\) & Stochastic scenarios. \\ \(t\in\mathcal{T}=[1,T]\) & Production time steps, index \(t\) denotes period \([t,t+1]\) \\ \(w\in\mathcal{W}=[1,W]\) & Weekly time steps to plan outage dates. \\ \(\mathcal{W}_{c}\) & Subset of time periods involved in constraint \(c\). \\ \(\mathcal{Z}_{c}\) & Subset of outages involved in constraint \(c\). \\ \hline \end{tabular}
\end{table}
Table 1: Definitions and notations—the sets and indexes.
## 5 Simulation and Results  
In this section, we evaluate the performance of our proposed algorithm (FMSTC) by numerical simulation. The simulation is programmed in Visual C\(+\) language. We produced the connected network graphs by randomly distributing nodes in a \(500\times 500\) square board. We considered the networks with various numbers of nodes, _i.e._, 10, 20, 30, 40, 50, 60, 70, 80, 90 and 100 nodes. The power range (or transmission range) was fixed before the simulation was started, and different power ranges were considered. Here, we used the following power ranges: three, six, nine, 12 and 15 units. Once the simulation is started, a sink node is randomly selected. As mentioned in an earlier section, other researchers have tried to solve the convergecast problem. However, most of them make different assumptions and take different approaches than ours. No direct comparison with their work is possible. We first report the number of time slots required by the proposed algorithm and then represent the results, which are compared to the optimal scheduling algorithm in the linear topology developed by [PERSON] _et al._[3]. In Section 5.1, we show the results of the optimal solution for a linear topology. The simulation results of the proposed algorithm for the convergecast scheduling time in the general topology were shown in detail in Section 5.2. The performance evaluation follows in Section 5.3.
# Role of Radio Telescopes in Space Debris Monitoring: Current Insights and Future Directions
## 3 Radio Telescope Measurements with Active Transmitters
### BIRALES
Orbit determination results were computed to obtain a re-entry epoch prediction, and a novel method for estimating the ballistic coefficient in a stochastic manner, which can be used in orbit determination and re-entry analysis, was also discussed by the authors. The predicted re-entry epoch was found to be consistent with the reference provided by the U.S. Space Command. A unique orbit determination pipeline was considered for the two-sensor scenario. Using the first set of observations from BIRALES, researchers calculated the initial orbit determination (IOD) with an adaptive beamforming approach [47]. Subsequently, predictions were made using an Unscented Kalman Filter (UKF) to propagate to the next observation set at the MFDR-MR tracking radar [54]. At the end of the measurements at MFDR-MR, the states were propagated again to the BIRALES sensor to obtain a second set of measurements to perform the refined orbit determination again. Table 1 presents the Keplerian parameters of the orbit determination results obtained, along with the reference parameters derived from the most recently available TLE. Note that BIRALES 1 and BIRALES 2 correspond to the first and second sets of measurements, respectively, and \(a(m)\), \(e\), \(i\), \(\Omega\), \(\omega\), and \(\upsilon\) represent the semimajor axis, eccentricity, inclination, longitude of the ascending node, argument of periapsis, and true anomaly, respectively. Overall, BIRALES demonstrates notable efficiency in detecting small objects owing to its large receiving area and real-time radar data processing backends [55]. This enables it to identify items just a few centimeters in size while significantly conserving energy. Future plans include upgrading 64 parabolic cylindrical reflectors on the N-S arm, totaling 256 receivers and over 10,000 m\({}^{2}\) of the collecting area, and a new transmitting antenna for observation flexibility of any kind [56]. Maximum sensitivity is achieved when all reflectors are aligned in the same direction, enabling BIRALES to detect debris a few centimeters in size in LEO. This configuration reduces the field of view from 90\({}^{\circ}\)\(\times\) 6.6\({}^{\circ}\) to 5.7\({}^{\circ}\)\(\times\) 6.6\({}^{\circ}\) compared with independent cylinder pointing. This mode will be effective in specific cases, such as monitoring fragment clouds after satellite explosions and to prevent collisions with operational satellites.
## 3 Results and Analysis
### Measurement Results
While absolute values may differ slightly due to losses or imperfections, the measured S21 remains a reliable proxy for comparing the polarization response of the system. To compare the simulation and measurement results, both datasets were normalized so that their maximum values correspond to the same reference point. This normalization allows a direct visual comparison of the voltage trends as a function of the polarization angle. As shown in Figure 10, the measurement results confirm the simulation results that the square patch metasurface energy harvesting using one via and one resistor has polarization-dependent characteristics. This can be observed from the change in voltage value against the polarization angle, which is consistent with the measurement and simulation results. Therefore, the key outcome is the agreement achieved between the measurement and simulation results. Both the simulation and measurement results exhibit a voltage dip at the same polarization angle for each design; however, the dip observed in the measurement is not as deep as that in the simulation. The difference in polarization angular characteristics, particularly in terms of voltage dip, can be attributed to practical factors present in the measurement setup. These include edge effects from the finite metasurface array, parasitic elements from coaxial cable connections, minor misalignments during rotation, and the influence of measurement noise. The simulation assumes an ideal infinite array with perfect symmetry and noiseless conditions. A small shift in the frequency of the maximum voltage was observed between the simulation and measurement results for each metasurface model, ranging from 174 MHz to 234 MHz. Specifically, for the designs with via positions \(\varphi_{\text{P}}=0^{\circ}\), \(30^{\circ}\), \(45^{\circ}\), and \(60^{\circ}\), the simulated maximum voltage frequency was consistently 5.114 GHz, while the corresponding measured frequencies were 4.88 GHz, 4.91 GHz, 4.90 GHz, and 4.94 GHz, respectively. The shift in maximum voltage frequency may arise from fabrication-related imperfections, such as slight deviations in via placement, soldering inaccuracies in the lumped resistor connections, and the connector. Furthermore, material properties such as the dielectric constant and loss tangent of the FR-4 substrate may be different from the ideal values used in simulations, contributing to the observed frequency offset.
## 3 Results and Discussion
### Model Monitoring Results and Analysis
#### 3.4.1 Evaluation Criteria
The core idea is to repeatedly divide the dataset into the training set and the validation set, and avoid the bias caused by a single division through multiple rounds of training. This study uses a 5-fold cross-validation method:  
1. Data Partitioning  
The preprocessed remote sensing dataset (including images and labels) is randomly divided into 5 subsets (Subset 1-5) of similar size, ensuring sample independence and uniform distribution across subsets. Note: If the data contains spatio-temporal correlation (e.g., multi-temporal images of the same region), spatial stratified sampling is used to avoid allocating samples from the same region to different subsets. 2. Multi-Round Training and Validation  
1. Train the model using Subsets 2-5, validate with Subset 1, and record validation metrics (e.g., overall accuracy _CPA_, _F1-score_). 2. Train with Subsets 1, 3-5, validate with Subset 2, and repeat metric recording. This process is repeated for 5 rounds to obtain 5 sets of metric results. 3. Result Analysis  
Calculate the mean (to measure the model's average performance) and standard deviation (to measure stability) of the metrics.