esa-sceva/satcom-chunk-collection huggingface dataset:
# Advancing Image Spam Detection: Evaluating Machine Learning Models Through Comparative Analysis
## 2 Related Work
### Comparative Analysis of Machine Learning Models
Numerous Kaggle Machine competitions have highlighted and included the use of XGBoost for classification. As XGBoost is based on the gradient boosting framework, it continuously adds new decision trees to fit a value to improve accuracy and performance. It has gained popularity due to its scalability and accuracy, and incorporates regularization techniques to prevent overfitting. The research by [24] integrated CNNs with gradient-boosting techniques to enhance detection accuracy and resulted in 88% F1-score. Furthermore, XGBoost and LightGBM have consistently achieved top results in many structured data competitions. VGG16 is a deep learning model and was selected for this research due to its strong and consistent performance in image classification tasks. Its straightforward layer structure makes it effective at learning patterns from image data, which is important for detecting spam images. In the study by [13], VGG16 achieved the second-highest accuracy and AUC, showing that it performs well compared to other models. This makes VGG16 a reliable choice for comparing it with both traditional and ensemble-based models in our analysis. Logistic Regression is a highly successful machine learning algorithm that calculates probabilities using discrete and continuous data and classifies newly entered data [25]. Based on probability, it is decided whether that feature vector belongs to a specific class or not. Logistic Regression demonstrated outstanding performance in spam detection. In the comparison given in [26], it achieved an accuracy of 0.981 and a precision score of 0.972, indicating its strong ability to correctly identify spam images while minimizing false negatives. Also, Logistic Regression has been effectively applied in spam detection frameworks, particularly in multi-modal architectures where it serves as a probabilistic fusion layer. In [27], authors proposed a CNN-LSTM-based spam filter, integrating outputs via logistic regression, achieving over 98% accuracy on hybrid image/text datasets. These results highlight Logistic Regression as a competitive traditional model, offering both simplicity and reliability. Therefore, this model is included in our comparative analysis. These models were selected to represent a range of classification approaches: traditional (SVM, Logistic Regression), deep residual (ResNet50), ensemble-based (XGBoost, LightGBM), and deep learning (VGG16). This allows for a balanced evaluation across algorithmic families. By incorporating these models, the research aims to compare their accuracy, false positive rates, and effectiveness in identifying true positives, which are key metrics for evaluating the reliability and robustness of image spam detection.
# A Review of Fault Diagnosing Methods in Power Transmission Systems
## 7 Fault Location Finding Methods
### FL Methods for Hybrid TLs  
Hybrid TLs consisting of both underground cables and overhead (OH) TLs show discontinuity at joints, where the reflection of voltage and current signals are produced. Velocities of traveling waves are different in cables and OH lines. Conventional approaches need improvements so that they could be implemented for hybrid transmission systems [131]. Traveling wave velocities can also be employed for fault location estimation because of different TW velocities within hybrid transmission systems. [PERSON] et al. proposed a TW-based fault lactation technique using transients caused by the circuit breaker operation instead of fault-induced transients [132]. Arrival time of traveling-wave components is measured by WT and fault zone is estimated via polarity of reflections. Wave speed is also calculated and the double-end traveling wave method is employed to locate the fault. The dc offset is removed through a finite impulse response (FIR) filter. DWT plays a significant role in gathering details of wavelets and voltage signal coefficients. Such details are then fed as input to the neuro-fuzzy system. This helps in the determination of fault location (either on an overhead transmission line or underground cables) [133]. The time-reversal method is also applicable for fault location finding [134].  
Figure 10: FL algorithm for series compensated transmission lines.  
The maximum energy point is extracted by comparing all the energies of different points and treated as fault points.
# Efficient Implementation of PRESENT and GIFT on Quantum Computers
## 2 Related Work  
### PRESENT Block Cipher  
The compact symmetric key cryptography, namely PRESENT block cipher, was presented in CHES'07 [15]. The PRESENT is a block cipher using the Substitution Permutation Network (SPN) method and consists of 31 rounds. The PRESENT has a block size of 64 bits and supports 80-bit and 128-bit key sizes. In the PRESENT block cipher, each round consists of three steps: AddRoundKey, Sbox, and Permutation. The encryption algorithm of PRESENT block cipher is described in Figure 1. Each round consists of AddRoundkey, Sbox, and Permutation in the order. When the plain text is entered, the round is repeated by 31 times. In the end, AddRoundkey is performed.
# Authentication-Based Secure Data Dissemination Protocol and Framework for 5G-Enabled VANET
## 3 Interactive Model and Architecture of IoV
### IoV Architecture
#### 3.2.3 Cognition Layer  
A cognitive engine relates to a physical data space and the cognition layer divides into cognition and resource cognitive engines at the control layer. The data cognitive engine processes and analyzes data flows using, for example, machine and deep learning techniques, data mining, and pattern recognition with complex event processing [36]. In network space, the data cognitive engine sends data analysis results to resources cognitive engine to guide network resource allocation. The vehicle areas are deployed to edge unit and non-vehicle areas are deployed to cloud unit. In network data space, the data cognitive engine can realize dynamic cognition of data such as computing, storage, and network resources. If there is any delay in a specific task, then the edge will check whether it can complete or not [15].
## 2 Materials and Methods
### Toxicity Tests  
Toxicity tests were performed on OLM and its DPs were isolated from chlorination experiments at initial concentrations of 5 mg/L. Acute toxicity was carried out on _A. fischeri_ (NRRLB-11177) bacterium supplied by MicroBioTest, Belgium, according to ISO 11348-3:2007. The bioluminescence inhibition test was performed as previously described [24]. OLM and its DPs were tested in three replicates, and the inhibitory effect was measured after 30 min of exposure. Chronic toxicity tests were carried out on _R. subcapitata_ and were performed in 24-well microplates according to [PERSON] et al. [29]. The algal inoculum was taken from an exponentially growing culture according to ISO 8692 [30], and it was added to OLM and its DPs to reach a final algal concentration of 104 cells/mL. Compounds were tested in three replicates. The algal growth was measured after 72 h at 670 nm (HachLange DR 5000). The acute and chronic results were processed using Prism 8 (GraphPad Inc., Version 8.4.2, San Diego, CA, USA) and analyzed statistically using ANOVA and the Tukey _post hoc_ test, with \(p\) < 0.05 considered to be significantly different.
## 4 Syntax about LR-SP-IBBE
### Formalization of LR-SP-IBBE  
Based on the work in [62; 68], the formal definition of LR-SP-IBBE is given below. The LR-SP-IBBE was composed of the following algorithms:  
**Initialization.****Set\((\zeta,t)\rightarrow(P\ P,MK)\)**. This inputs the security parameters \(\zeta\) and the upper bound \(t\) of users. The algorithm produces the public parameter \(PP\) and the master private key \(MK\). \(PP\) is open to all users. \(MK\) is kept as a secret.  
**Private key generation.****KG\((PP,MK,ID)\to SK_{ID}\)**. The algorithm inputs the public parameters \(PP\), the master private key \(MK\), and one user's identity \(ID\). It generates one private key: \(SK_{ID}\)= \((SK_{ID,0,1},SK_{ID,0,2})\).  
**Private key update.****KU\((PP,SK_{ID,k})\to SK_{ID,k+1}\)**. This inputs \(SK_{ID,k}\) and \(PP\) and outputs the updated private key \(SK_{ID,k+1}\).  
**Encryption.****EN\((PP,M,S)\to CT\)**. This takes \(PP\) and one identity set \(S=\{ID_{1},\ldots,ID_{d}\}\) (\(d\leq t\)) as the input and outputs \((Hd,K)\), where \(Hd\) is the headers and \(K\) is a symmetric key that is used to encrypt the plaintext \(M\). If the broadcaster is about to send the ciphertext corresponding to the plaintext \(M\), they encrypts \(M\) with \(K\), which generates the ciphertext \(CM\) and broadcasts \((CM,Hd,S)\).  
**Decryption1.****D\(1(PP,SK_{ID,k,1},S,CT)\to CT^{\prime}\)**. This inputs \(PP\), private keys \(SK_{ID,k,1}\), user identity sets \(S\), and ciphertext \(CT\). First, it divides \(CT\) into \((CM,Hd)\). If \(ID_{i}\in S\), the algorithm calculates the part plaintext \(CT^{\prime}\) of \(CT\).  
**Decryption2.****D\(2(PP,SK_{ID,k,2},S,CT^{\prime})\to M\)**. This inputs \(PP\), private keys \(SK_{ID,k,2}\), user identity sets \(S\), and ciphertext \(CT^{\prime}\). First, it divides \(CT\) into \((CM,Hd)\). If \(ID_{i}\in S\), the algorithm uses \(Hd\) to calculate the symmetric key \(K\). Then, the plaintext message is recovered by \(CT^{\prime}\).  
**Semifunctional private key generation.****KSF\((PP,MK,ID)\rightarrow\widetilde{SK_{ID}}\)**. The algorithm inputs \(PP\), \(MK\), and one identity \(ID\). It generates the semifunctional private key \(\widetilde{SK_{ID}}\).  
Figure 4: The relations of the algorithms of the proposed scheme.  
**Semifunctional encryption.**\(\mathbf{ESF}(PP,M,S)\rightarrow\widehat{CT}\). This inputs \(PP\), \(S\), and \(M\). It gains the semifunctional ciphertext \(\widehat{CT}\).  
The first three algorithms were run by the key generation center (KGC), and the other algorithms were run by the user. The last two algorithms were only used for the security proof.
# Resilient Modulus Characterization of Compacted Cohesive Subgrade Soil
## Materials and Methods
The specimen is fixed to the pedestal at the bottom end, and the other end is connected to the drive plate, while the top cap remains free. This system is provided with a testing unit (testing chamber), control computer, back pressure system, cell pressure controller, resonant column controller, and a data acquisition box [48; 49; 50]. Immediately after the first mode is found, the measurements of the resonant frequency (F\({}_{r}\)) of the vibration amplitude are made. The sinusoidal torsional vibration at variable frequency is applied in a rotary manner by a device which causes such excitations. Subsequently, these measurements are combined with the specimen size and equipment characteristics in order to determine the shear wave velocity (\(V_{S}\)), shear modulus (\(G\)), and shearing strain amplitude (\(\gamma\)). Based on the elastic wave propagation, the fundamental data-reduction equation, Equation (4), can be established:  
\[\frac{I}{I_{0}}=\left(\frac{\omega_{r}L}{V_{S}}\right)\tan\left(\frac{\omega_{ r}L}{V_{S}}\right), \tag{4}\] \begin{table}
\begin{tabular}{c c c} \hline \hline
**Properties** & **Symbol** & **Value** \\ \hline Skeleton density & \(\rho_{s}\) (g-m\({}^{-3}\)) & 2.64 \\ Volume density & \(\rho_{d}\) (g-m\({}^{-3}\)) & 2.09 \\ Natural moisture & \(w_{n}\) (\%) & 12.82 \\ Liquid limit & \(LL\) (\%) & 18.9 \\ Plasticity limit & \(PL\) (\%) & 10.3 \\ Plasticity index & \(PI\) (-) & 8.6 \\ Void ratio & \(e_{0}\) (-) & 0.41 \\ Optimum moisture content & \(W_{opt}\) (\%) & 10.2 \\ Maximum dry unit density & \(\gamma_{d\ max}\) (g/cm\({}^{3}\)) & 2.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Physical and mechanical properties of sandy clay in this study.
# Energy-Efficient Partial LDPC Decoding for NAND Flash-Based Storage Systems
## 6 Hardware Architecture
### Dedicated Syndrome Check Module  
LDPC decoders that utilize soft-information are generally required to perform the first decoding iteration. This approach is adopted because generating the soft-information itself consumes a significant amount of latency, thus making it more advantageous in several aspects to proceed with an initial decoding iteration rather than performing a separate syndrome check. However, the proposed decoding algorithm, which also uses soft-information, requires decoding with partial H-matrices of various sizes. To accommodate this, a separate syndrome check module is incorporated. Employing an independent syndrome check module can significantly reduce decoding latency, especially in good channel conditions.  
Typically, a full H-matrix is not necessary for syndrome checking to verify the integrity of a codeword; it only needs to cover the entire message. Therefore, the size of the dedicated syndrome check module can be very compact and implemented with minimal effort. Table 1 shows the gate count for syndrome check logic of LDPC codes of various sizes in 65 nm CMOS process. For a commonly used 4 KB LDPC code with a rate of 0.9, it only requires 22 k equivalent gates, which is about 1% of the total decoder area. Therefore, incorporating  
Figure 6: The theoretically calculated number of iterations for the various partial H-matrices of the (149, 61, 6) array LDPC code.  
this logic into an existing decoder incurs minimal overhead and can be easily applied to any decoding architecture.
## 4 Conclusions  
Several backscattering signals from objects in different positions and experiments with different schemes were presented and processed with Prony, matrix pencil method, Cauchy, and VCM in order to find the CNRs and then compare the reconstruction fidelity, number of samples used, and execution time, with the intention of having a better insight before selecting or designing a hardware platform for a particular study.  
If high reconstruction accuracy, i.e., reliable CNRs extracted, is desired, MPM is the most appropriate choice with low execution time (assuming a previous identification of the late-time response). If a reduced number of samples to process is required by the signal acquisition hardware (or if the hardware capabilities are limited), then frequency domain methods should be considered instead. Additionally, VCM should be selected if buried targets are to be studied; this is because the reconstruction fidelity is better in experimental scenarios.  
[PERSON]'s method has a low \(R^{2}\) coefficient with experimental signals, probably due to a low SNR, even though it employs the same number of samples as MPM, and for this reason, it is not recommended above the other methods studied here. However, according to the first approach of the HSF, [PERSON]'s method should be implemented in hardware first.  
If the hardware implementation of this signal processing technique requires a real-time operation or a portable device, such as a portable GPR, two approaches could follow. The first one is to increase the reconstruction quality of frequency domain methods, as the sample set to be processed is small, and also reduce the execution time. The second approach is to reduce the number of samples or MPM computation time (or hardware resources needed); however, these improvements are still to be studied. In future work, the hardware-suitability factor, including computational complexity, has to be tested in a digital platform, which can also include hardware acceleration.  
Conceptualization, A.G.; Validation, A.G.; Resources, F.R.; Writing--original draft, A.G.; Writing--review & editing, A.G. and F.R.; Funding acquisition, F.R. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable. This research was supported by the EMC-UN research group, which provided us with all the necessary tools for this paper. We offer special thanks to [PERSON] and [PERSON], who provided us with expertise for experimental setups and sandbox signals for buried objects.  
Figure 21: Average number of samples per HSF in different scenarios.
# Post-Quantum Digital Signature: Verkle-Based HORST
## 10 Security Proof
* Generate a Verkle proof required to reconstruct the root: \[\text{verkleProof}_{i}=\text{ProveVerkle}\Big{(}\text{leaf}_{\text{index}_{1 }},\dots,\text{leaf}_{\text{index}_{k}}\Big{)}\] (30)
* The final signature is as follows: \[\sigma_{i}=\left(\text{sigHORS}_{i},~{}\text{verkleProof}_{i}\right)\] (31)  
\(\mathcal{S}\) returns \(\sigma_{i}\) to \(\mathcal{A}\). 4. **Forgery Extraction**  
After \(Q\) signing queries, \(\mathcal{A}\) outputs a forgery \(\left(m^{\star},\sigma^{\star}\right)\). \(\mathcal{S}\) parses \(\sigma^{\star}\) to extract the revealed secret key elements and the Verkle proof. * Parse \(\sigma^{\star}=(~{}\text{sigHORS}^{\star},~{}\text{verkleProof}^{\star}\,)\). * Compute leaf\({}^{\star}_{i}=H(\text{ sk}^{\star}_{\text{index }_{i}})\). * Verify the Verkle proof (not sibling path reconstruction): \[\text{valid }=\text{ VerifyVerkle}\Big{(}\text{pkVerkleHORST, leaf}^{\star}_{1},\dots,\text{ leaf}^{\star}_{k},\text{ verkleProof}^{\star}\Big{)}\] (32) If \(\text{valid }=\text{True}\), \(\mathcal{S}\) succeeds. Otherwise, \(\mathcal{S}\) aborts. * **Probability Analysis** The probability that \(\mathcal{S}\) extracts a legitimate signature is at least \(\epsilon\), the success probability of \(\mathcal{A}\). If \(\epsilon\) is non-negligible, then \(\mathcal{S}\) can break either the collision resistance of \(H\) or the binding property of _Commit_ with non-negligible probability, challenging certain cryptographic primitives' hardness assumptions. Under the presumption of collision-resistant hash functions and binding vector commitments, the Verkle-based HORST signature method is safe from EUF-CMA assaults since S can use A's forgery to violate either the binding property of _Commit_ or the collision resistance of H. We reach the conclusion that the security of the Verkle-based HORST signature scheme is still strong since both of these presumptions are thought to be computationally impossible to violate, even in the case of quantum adversaries. As a result, the Verkle-based HORST signature scheme provides a dependable foundation for digital signatures in the quantum era while also offering a safe and effective solution for post-quantum cryptography applications.
# A Categorization of Digital Twin and Model-Based System Engineering Interactions
###### Abstract  
The main goal of this study was to provide a new categorization of the different types of interactions between model-based system engineering (MBSE) and digital twins (DTs). To achieve this goal, an overview of the relationships between these two concepts was obtained based on a representative set of articles. The search identified 444 unique and valid records, of which 16 were selected for analysis based on article screening and eligibility assessments. The selected articles were then analyzed to identify the types of DT-MBSE relations and the area of the case study. As a result, the types of relationships were classified into two main categories: MBSE-based DTs and DTs that use MBSE system models. Finally, we present a case study of the Perception system, a system of systems designed to monitor and generate strategic assets through satellite data collection, further developing the capabilities established by the AlfaCrux satellite mission. Specifically, the case study focused on collecting data from a tower with micrometeorological instrumentation in the Brazilian Amazon Rainforest. The modeling was performed on the Capella software using the Arcadia method. In the case study, the system and the digital twin were designed in parallel based on a five-dimensional DT model.  
digital twin; model-based system engineering; MBSE; Capella; Arcadia Article
# Development of a Deep Learning-Based Algorithm to Detect the Distal End of a Surgical Instrument
## 2 Materials and Methods
### Algorithm and Evaluation of Distal End Detection  
The proposed algorithm to detect the distal end of a surgical instrument used the central coordinates of the predicted bounding boxes, and was completed with YOLOv2. The predicted bounding box had a square shape around the center point of the distal end of the surgical instrument. Therefore, we can use coordinate information from the center of the bounding box as a mark to display the distal end of the surgical instrument. The evaluation of whether the calculated coordinates of the center point were normally displayed was confirmed in order to compare the position of the supervised coordinates. The detection rate of the distal end of the surgical instrument was defined as including the calculated coordinates of the center point of \(8\times 8\) or \(16\times 16\) pixels among the valid bounding boxes (Figure 6). We calculated the detection rate of the distal end using the test data of 512 image. We calculated these processes with MATLAB software. We presented all the results as mean and standard deviation (SD) according to the number of nine-fold subsets.  
Figure 5: Data augmentation of the training datasets. The original images and supervised bounding boxes were rotated from \(-90^{\circ}\) to \(90^{\circ}\) in \(5^{\circ}\) steps.
## 4 Methodology
### Smart Farms  
The framework allows the user to define farms, deploy sensors and configure the data collection process through UAVs. Here, we assumed that the farms are large-scale and comprise flat and mountainous terrain. Therefore, due to the uneven terrain, ground-to-ground communication is not a suitable option [67]. Therefore, data collection through UAVs are more suitable for uneven farming lands.