esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In today's increasingly interconnected digital world, cyber threats have grown in frequency and sophistication, making intrusion detection systems a critical component of modern cybersecurity frameworks. Traditional IDS methods, often based on static signatures and rule-based systems, are no longer sufficient to detect and respond to complex and evolving attacks. To address these challenges, Artificial Intelligence and machine learning have emerged as powerful tools for enhancing the accuracy, adaptability, and automation of IDS solutions. This study presents a novel, hybrid ensemble learning-based intrusion detection framework that integrates deep learning and traditional ML algorithms with explainable artificial intelligence for real-time cybersecurity applications. The proposed model combines an Artificial Neural Network and Support Vector Machine as base classifiers and employs a Random Forest as a meta-classifier to fuse predictions, improving detection performance. Recursive Feature Elimination is utilized for optimal feature selection, while SHapley Additive exPlanations (SHAP) provide both global and local interpretability of the model's decisions. The framework is deployed using a Flask-based web interface in the Amazon Elastic Compute Cloud environment, capturing live network traffic and offering sub-second inference with visual alerts. Experimental evaluations using the NSL-KDD dataset demonstrate that the ensemble model outperforms individual classifiers, achieving a high accuracy of 99.40%, along with excellent precision, recall, and F1-score metrics. This research not only enhances detection capabilities but also bridges the trust gap in AI-powered security systems through transparency. The solution shows strong potential for application in critical domains such as finance, healthcare, industrial IoT, and government networks, where real-time and interpretable threat detection is vital.  
security; privacy; cybersecurity; deep learning; machine learning; explainable artificial intelligence; intrusion detection; network security
# A Survey on Moving Target Defense: Intelligently Affordable, Optimized and Self-Adaptive
## 2 MTD Techniques  
### Background  
To have the principles of MTD thoroughly comprehended, it is essential to understand its background. The concept of MTD was first proposed in 2009 as a response to the shortcomings of reactive security defense mechanisms, which were primarily based on techniques such as authentication, access control, information encryption, intrusion detection, vulnerability scanning, and virus prevention. While these measures offered a degree of security, they proved inadequate due to the increasing automation and diversification of attacks. Furthermore, the complexity of modern networking environments places an overwhelming burden on network administrators, who may overlook even minor issues that could lead to serious security risks. In general, the following are key features that differentiate traditional cyber defense mechanisms from MTD:  
* Traditional defenses aim to enhance the defense capabilities of static facilities and minimize their vulnerabilities' exposure. In contrast, MTD concentrates on dynamically shifting the attack surface [3] to increase resilience.
* Traditional defenses often focus on monitoring, detecting, preventing, and remediating attacks on static infrastructure. MTD emphasizes faster and more comprehensive attack detection and timely responses to mitigate potential damages.
* Traditional defenses rely on known attack patterns for defense and may be limited in addressing emerging or novel threats. MTD seeks to proactively address such unpredictable attacks through its dynamic nature.
* Unlike traditional defense mechanisms, which operate in a fixed dimension, MTD adapts and changes constantly to protect against attacks on ever-changing systems. This approach significantly limits attackers' research time and ability to penetrate compromised systems.  
While general defense mechanisms aim to improve system stability, their rigid nature makes it challenging to ensure long-term fortification against the rapidly evolving techniques employed by attackers. In contrast, MTD prioritizes affordable, service-oriented defense [3] to meet three core development points:  
* Minimizing defense costs (e.g., system deployment overhead)
* Maximizing service availability for users
* Maintaining the required defense security levels  
Although MTD is built on the architecture of general defense, it aims to minimize deployment overhead by adopting existing security mechanisms as its base. Introducing new security measures requires intensive analysis and patching efforts quintessentially, which can be time-consuming and impractical. MTD aims to preserve affordability in individual system deployments while maintaining its fundamental principle of providing cost-effective defense, i.e., _affordable defense._
# An Effective Off-Grid DOA Estimation Algorithm Using Difference Coarrays with Limited Snapshots
## 5 Simulation Results
### Estimation Performance Versus Snapshot Number for NLA  
The following analysis examined the RMSE characteristics of the proposed method across varying snapshot conditions ranging from 10 to 10,000. Figure 8 displays the results for the three-source scenario, while Figure 9 presents the corresponding results for the more challenging nine-source case.The same three sources as in Section 5.1 were considered and the same nine sources as in Section 5.3 were considered. Remarkably, the proposed technique reached the theoretical CRB limit in all SNR regimes, maintaining statistical efficiency even under snapshot-constrained scenarios.  
Figure 6: RMSE of the four methods versus SNR with 10 snapshots.  
Figure 7: RMSE of the four methods versus SNR with 100 snapshots.
# Angular Circle Array Multiple Input Multiple Output Underwater Optical Wireless Communications +
## 4 Analysis and Discussion of Data Results
### Path Loss  
From Figure 10, we can observe that the path loss curves for the SISO communication system and the transceiver pairs of the planar circle array, as well as the transceiver pairs at the center of the angular circle array, are essentially the same. However, the side transceiver pairs of the angular circle array experience greater path loss compared to the other structures. This is because the side transceivers of the angular circle array are tilted outward at an angle to achieve a larger communication range. This tilting breaks the LOS link, resulting in a reduction in the number of photons received and consequently increasing the path loss.  
Next, the non-line-of-sight (NLOS) case is considered. From Figure 11, we can observe that the path loss at the same communication distance for all system settings is greater than that for the LOS link. This increase in path loss is due to the break in the transceiver alignment link caused by the deflection. From Figure 10, we can also observe that the path loss for the transceiver pair \(T_{5}-R_{5}\) of the angular circle MIMO communication system is less than that of other transceiver pairs and is close to the path loss in the line-of-sight case. This is attributed to the unique structure of the angular circle MIMO system, where the side transceivers are outward at a certain inclination angle, mitigating the effect of the NLOS link.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value/Unit** \\ \hline \((x_{t},y_{t},z_{t})\) & (0,0,0) \\ \(P_{t}\) & 50 mw \\ \(\theta_{div}\) & 20\({}^{\circ}\) \\ \(w_{0}\) & 0.001 m \\ \(\lambda\) & 514 nm \\ \(A_{t}\) & 0.15 m \\ \((x_{r},y_{r},z_{r})\) & \((0,0,L)\) \\ FOV & 60\({}^{\circ}\) \\ \(A_{r}\) & 0.15 m \\ \hline \end{tabular}
\end{table}
Table 1: Transceiver parameters.  
Figure 10: Path loss under different communication system settings in the LOS link: (**a**) planar circle array and (**b**) angular circle array.
# Experimental Study on Permeability Characteristics of
## 3 Results and Discussion
### Permeability Variation Law of the CBB
#### 3.2.3 The Influence of Cement Dosage on the Permeability of the CBB  
Figure 10 shows the curve of the permeability of the CBB with a particle size range of 0-5 mm under different additions of cement dosages. As the cement dosage increases, the permeability of the CBB with a particle size range of 0-5 mm gradually decreases. As the  
Figure 9: Effect of particle size range on permeability of the CBB.  
cement dosage gradually increases, the decreasing amplitude in permeability also becomes greater. This can be explained as follows: (1) gaps in the CBB can be filled by cement particles with a micrometer-scale particle size; (2) bonding products are generated by the cement hydration, resulting in a more tightly interconnected solid material within the CBB. The addition of cement reduces the permeability of the CBB by 1-2 orders of magnitude. For example, t 0-5 mm, the CBB without cement exhibits a permeability of 5.12 \(\times\) 10\({}^{-15}\) m\({}^{2}\) under an axial pressure of 2 MPa, while the CBB with a particle size range of 0-5 mm and cement dosage of 6% has a permeability of 9 \(\times\) 10\({}^{-17}\) m\({}^{2}\) at an axial pressure of 8 MPa. This indicates that cement exerts a significant optimization effect on the waterproof performance of the CBB.
###### Abstract  
The transmission impairments of a Raman amplified link using dual-polarization 16-quadrature amplitude modulation (DP-16 QAM) are experimentally characterized. The impact of amplitude and phase noise on the signal due to relative intensity noise (RIN) transfer from the pump are compared for two pumping configurations: first-order backward pumping and bi-directional pumping. Experimental results indicate that with increased Raman backward pump power, though the optical signal-to-noise ratio (OSNR) is increased, so is the pump-induced amplitude and phase noise. The transmission performance is firstly improved by the enhanced OSNR at a low pump power until an optimum point is reached, and then the impairments due to pump-induced noise start to dominate. However, the introduction of a low pump power in the forward direction can further improve the system's performance.  
Raman amplification; coherent communications; fiber optics communications +
Footnote †: journal: _photonics_
## 3 Results
### Optimization Process
#### 3.1.5 ANN Surrogate Models  
The ANN model is a Single-layer Feed-Forward Back Propagation network in this paper. The ANN model consists of an input layer, a hidden layer with 10 neurons, and an output layer. The signal is transferred forward layer by layer. The result of the output layer would be compared with the experimental results, and the error would be transferred back to the hidden layer. The schematic diagram, training parameters, and structural parameters of the ANN models are shown in Figure 4 and Table 5.  
The mean square error (_MSE_) and coefficient of determination (\(R^{2}\)) were applied to evaluate the prediction performance of the ANN model, as shown in Equations (24) and (25).  
\[MSE=\frac{1}{n}\sum\limits_{i=1}^{n}{{{({f_{i}}-{y_{i}})}^{2}}}, \tag{24}\]  
\[R^{2}=1-\frac{\sum\limits_{i=1}^{n}{{{({y_{i}}-{f_{i}})}^{2}}}}{\sum\limits_{i= 1}^{n}{{{({y_{i}}-{\overline{y_{i}}})}^{2}}}}, \tag{25}\]  
\(n\) is the \(N\), the sample number, in Section 3.1.4. \({f_{i}}\) is the predicted value, \({y_{i}}\) is the actual value, and \({\overline{y_{i}}}\) is the average value of \({y_{i}}\). A small value of \(MSE\) and approximate value of \(R^{2}\) to 1 indicated precise prediction performance of the ANN model.  
The comparison of the predicted value and the actual value showed good prediction performance, as shown in Figure 5. The _MSE_ and \(R^{2}\) were 1.3606 \(\times\) 10\({}^{-5}\) and 0.9069, respectively, which means that the surrogate model trained by ANN was sufficient to predict the \(P_{net}\) in the actual model.
# A New GNSS-Derived Water Vapor Tomography Method Based on Optimized Voxel for Large GNSS Network  
[PERSON]  
1 School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China; [EMAIL_ADDRESS] (C.L.); [EMAIL_ADDRESS] (C.X.)1 School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China; [EMAIL_ADDRESS] (C.L.); [EMAIL_ADDRESS] (C.X.)1 School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China; [EMAIL_ADDRESS] (C.X.)1 School of Geodesy and Ge  
been developed by [PERSON] et al. [18] in 2000, numerous challenges still exist. The main drawbacks in GNSS WV tomography, particularly in a large GNSS network (such as the Nanjing area), are ill-posed problems caused by null-ray voxels or sparse station [18; 24; 27; 28], and complex computation for the inverse of the coefficient matrix as it is large and sparse [19; 21].  
Numerous studies have been carried out to overcome these problems, which have led to remarkable results. For example, [PERSON] et al. [18] imposed constraints on tomographic equations that significantly increased the rank score of the tomographic coefficient matrix. This measure is beneficial for the tomographic equation solution and thus, was widely used by other scientists [17; 23; 25; 26; 27]. [PERSON] et al. [25] maximized the usage of GNSS observations by including the signals leaving the side face of the tomographic zone in the tomographic process by a global pressure and temperature 2 wet (GPT2w) model, in addition to the general effective signals (i.e., signals penetrating the top layer of the tomographic zone to the receivers). Similarly, [PERSON] et al [29] used the side signal by a novel height factor model (HFM). [PERSON] et al. [26] and [PERSON] et al. [17] improved the utilization of effective signals by combining multi-GNSS observations (global positioning system (GPS), Beidou, global navigation satellite system (GLONASS), and Galileo). Furthermore, they verified the approach in, respectively, Guiyang and Hong Kong, China. In the aspect of ill-posed tomography model resolving, [PERSON] et al. [30] adopted an improved ridge estimation (IRE) method to revise the singular in the tomographic coefficient matrix, and numerical results indicate its superior performance compared to the commonly used singular value decomposition (SVD) method. Notably, the above WV tomographic experiments rely on constrains, and massive constraints tend to decrease the role of GNSS observations in the tomographic results [24].  
To completely avoid the dependence on these constraints generated from empirical functions, in this study, an optimized tomographic algorithm with voxel redistribution is proposed, based on the principle of elimination of all the null-ray-passed voxels. We establish a new constraint that originates from GNSS-derived PWV that fits the actual distribution of WV (Section 2). The optimized tomographic method is validated in the Nanjing area (Section 3). A conclusion is presented in Section 4.
# Quantification of Liver Fibrosis--A Comparative Study
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author/Year** & **Dataset/Histological Stain** & **HAI** & **Image Analysis Method** & **Results/Remarks** \\ \hline \multirow{4}{*}{[PERSON] et al. 2009 [35]} & 218 samples (ALD patients) & & FibrometerA, Hepascove vs. \\  & Stains: H&AF-Suffran, MT, & METAVIR & - & FibroTest fibrosis (AUROC = 0.83 \(\pm\) 0.03), v. APR-forms-PH4 \\  & picro-Sirus red & & & 0.03), v. APR-forms-PH4 \\ \hline \multirow{4}{*}{[PERSON] et al. 2012 [36]} & 179 samples (CHB patients) & & Manual areas exclusion & Morphometry correlation. \\  & Stain: Masson’s trichrome & & & (Aperio ImageScope). & FibroTest (\(r=0.392\), \(p=0.03\)), \\  & Stain: Masson’s trichrome & & Batch analysis (Pielectourt 9.0) & HBeAg-positive (\(r=0.492\), \(p=0.001\)) \\ \hline \multirow{4}{*}{[PERSON] et al. 2014 [37]} & 282 samples (CHC patients) & & Image enhancement. & \\  & Stain: Masson’s trichrome & & Background removal. & DIA—[PERSON], METAVIR correlation (\(r=0.95\), 0.92, \(p<0.001\)) \\  & 142 samples (CHB patients) & & & \\  & Stair: Sirius red, & & & \\  & Masson’s trichrome & & & \\ \hline \multirow{4}{*}{[PERSON] et al. 2014 [39]} & 79 samples, 7 normal & [PERSON] & \multirow{4}{*}{ROI volume assessment} & MR–Histology fibrosis correlation (\(r=0.68\), \(p<0.0001\)). \\  & as controls & (modified) & & Fibrosis AUROC (0.94) \\ \hline \multirow{4}{*}{[PERSON] et al. 2014 [40]} & 101 samples (HCV patients & & \\  & after LT) & & - & MT—SR stains \\  & Stain: H&AF, MT, Sirius red & & SF correlation (\(p<0.01\)) \\ \hline \multirow{4}{*}{[PERSON] et al. 2015 [41]} & 137 samples (CHC patients) & & Threshold setting (Adobe & \\  & Stair: Masson’s trichrome & & & \\  & & & & \\ \hline \multirow{4}{*}{[PERSON] et al. 2015 [42]} & \multirow{4}{*}{78 samples (CHB patients)} & & Intensity adjustment. Random selection of non-overlapping & \\  & & & visual fields. & \multirow{2}{*}{PSME correlation: CPA (\(r=0.82\), \(p\) \(<0.05\)), Scheuer (\(r=0.473\), \(p<0.05\)),} \\  & & & CPA assessment (Image-Pro plus 6.0) & \\ \hline \multirow{4}{*}{[PERSON] et al. 2016 [43]} & 99 samples (chronic & & Samples digitization & \\  & ALD patients) & Ishak & (Nanozomer 2.0 HT). & \\  & Stain: Sirius red & & & Image analyzing & \\  & & & & \\  & & & (VISIONDAPH 4.36) & \\ \hline \multirow{4}{*}{[PERSON] et al. 2018 [44]} & 61 samples (ALD patients), & & CPA quantification (Qwin & Mean CPA in ALD (?1.1\%, \(p=0.17\)), Fibrosis-HVG correlation \\  & 9 normal as controls & - & Leica Q50 HW) & (\(p=0.02\)) \\  & Stain: picro-Sirus red & & & \\ \hline \multirow{4}{*}{[PERSON] et al. 2012 [45]} & 11 samples (SNP-pretreated mice) & - & Fibrosis areas quantification & \multirow{2}{*}{Fibrosis SNP-MSc (\(0.4\pm 0.3\)),} \\  & & & (Image J) & MSc (\(1.9\pm 1.5\)), \\ \cline{1-1}  & Stain: Sirius red & & & SNP (\(1.3\pm 0.4\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Studies comparing histopathological imaging with non-invasive imaging modalities.
## 4 Numerical Analysis and Results
### Case Study I  
We will consider Auckland Central as shown in Figure 11 as an area for data analysis and as the locations to show the vehicle's distribution among different bus stops. We have considered four different locations: City Center, Britomart, Wellesley Street, and Auckland Hospital. All of these bus stops are equipped with local storage for data storage to upload or download onto buses on that route. Furthermore, all users' profiles are checked, as per the source and destination location of the data transmission, and buses are selected based upon that.  
We will evaluate the performance of the AHP method using simulations over MATLAB, based upon different utility values for all attributes. We simulate for our goal to have optimal network selection based upon different criteria and alternatives. User preferences play an important role in the selection of the best available network in a heterogeneous environment. The proposed method for determining the user's preference is based upon the basic idea of AHP.
# Agglomerative Clustering with Threshold Optimization via Extreme Value Theory
## 4 Approach
### ImageNet-Based Evaluation Protocols  
**In-, Out-, Mixed- Distribution Protocol:** We base our new in/mixed/out-of-domain protocols on the ILSVRC partitions of 2010 and 2012 [62] from the ImageNet dataset, using the data splits of [63]. While previously these splits were used for classification, we adapt them to a novel clustering protocol. Since many available pre-trained deep networks were trained on the 1000 classes of the 2012 dataset, we use the validation set of the 2012 dataset as our in-distribution data. We consider out-of-distribution data to be the 166 classes from the 2010 dataset that have not been re-used in the 2012 dataset. Finally, the mixed-distribution data are a mix of the in- and out-of-distribution data containing images from a total of 1166 classes.  
**Out-of-Domain Protocol:** We use a network that is pre-trained on ILSVRC 2012 training set and apply it to face identity clustering, showing that the clustering properties  
Figure 4: _Diagnm of Overall System Function._ This figure shows the overall pipeline of PEACH. First, we extract deep features, then we compute the initial pairs, median{d(S)} and max{d(S)} for the EVT Estimation to compute the threshold \(\tau_{r}/\tau_{w}\). Next, we use the nearest neighbors to generate initial clusters and the \(\tau_{r}/\tau_{w}\) will be used for merging to obtain final clustered data.  
of deep features transfer across domains. In particular, we use the Labeled Faces in the Wild (LFW) dataset for testing and call this protocol Out-of-domain LFW (OLFW). Labeled Faces in the Wild (LFW) [64] contains mostly frontal facial images from 5749 people, while only 1680 of them have multiple images. The remaining subjects are singletons and should not be clustered with other subjects--they produce many outliers for our theory. Since performance on OLFW is not comparable to prior work that used in-domain face features for clustering on LFW, we report those in-domain experiments in the supplemental material.  
**Unsupervised Network Training Protocol:** The furthest out-of-domain, with the greatest uncertainty of the feature space and similarity, occurs for unsupervised network training or deep clustering. Initially, the features are random and all data are out-of-distribution. As the networks learn from the samples, they start moving toward in-distribution. However, there is no independent concept of correct classes/clusters and, thus, no practical way to estimate clustering parameters. We follow the protocol of [10,11], which requires a fully autonomous clustering subsystem, but where that past work guessed an overestimate of the number of clusters.
## 4 Discussion
Under normal conditions, approximately 97% of glucose is reabsorbed in the PCT [48]. The administration of a dose-dependent manner of _A. muricata_ extract in alloxan-induced mice performed a refinement on the thickness of the PCT of left and right renal in this study. A study conducted on knock-out mice found that the sodium-glucose co-transporter 2 (SGLT2) is responsible for glucose reabsorption, especially in the initial segment of the PCT. In addition, sodium-glucose co-transporter1 (SGLT1) was responsible for glucose reabsorption of approximately 40% in subsequent segments in the PCT. Studies conducted on Zucker rats (in obesity-induced diabetes) found an increase in the expression of SGLT1 and SGLT2, which are primarily responsible for glucose reabsorption in the proximal tubular convolution [48]. The early onset of DM is characterized by cellular proliferation, hypertrophy, and aging of tubular cells in the PCT. The hyperglycemia condition in DM also induces structural changes in the PCT, including the thickening of the PCT due to an increase in the reabsorption capacity of proximal contours [49; 50]. A normal distribution of left renal glomerular tubularization was reported in this study. This is a novel finding since no studies have previously examined the effect of ethanol extract of _A. muricata_ leaves on glomerular tubularization in Swiss Webster mice treated with alloxan. Glomerular tubularization is a critical indicator for determining the severity of kidney damage [51]. Glomerular tubularization is the retrograde growth or proliferation of PCT cells around Bowman's luminal surface [52]. The ethanol extract of _A. muricata_ leaves is expected to prevent glomerular tubularization in DM, as this extract lowered the blood glucose and lipid levels in alloxan-induced Wistar rats [53]. The ethanol extract of _A. muricata_ leaves contains several active substances that have the antidiabetes and antioxidant potential. _A. muricata_ leaves contain phenolics, catechine, epicatechin, routine, other flavonoid glycosides, and chlorogenic acid, which are known to have antioxidant effects. The antioxidant activity of _A. muricata_ leaves can suppress the production of ROS and reduce oxidative stress, which is a major cause of the renal damage in DM characterized by alterations in renal morphology and glomerular tubularization [25; 39; 41; 44].