esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid STL-Deep Learning Framework for Behavioral-Based Intrusion Detection in IoT Environments
## 2 Related Work  
IDSs form a critical line of defense in modern network security, particularly within the vulnerable and heterogeneous landscape of the IoT. The field has witnessed significant evolution, moving from foundational techniques to sophisticated data-driven approaches [9; 17].  
Historically, IDS development followed several main paradigms. One foundational approach is the signature-based IDS (SIDS), which operates analogously to antivirus software by relying on matching network traffic against a database of known malicious patterns, often referred to as signatures [9]. While this method proves effective for previously identified threats and typically generates few false alarms, SIDSs are fundamentally incapable of detecting novel, polymorphic, or zero-day attacks whose signatures have not yet been cataloged. Consequently, their effectiveness diminishes rapidly when faced with the unseen threats common in dynamic environments like IoT.  
In contrast to signature matching, the anomaly-based IDS (AIDS) aims to model the characteristics of \"normal\" system or network behavior and subsequently identify any deviations from this baseline as potential intrusions [18; 19]. This capability allows the AIDS to potentially detect zero-day attacks. However, accurately defining a precise boundary for normal behavior poses a significant challenge, particularly within diverse IoT networks characterized by dynamic traffic patterns. As a result, AIDSs often suffer from a high FPR, which can overwhelm administrators with spurious alerts [9]. Early iterations of the AIDS primarily relied on statistical modeling, whereas later versions incorporated machine learning techniques to refine the detection process.  
A distinct paradigm is the specification-based IDS, which involves manually defining formal specifications that detail legitimate protocol behavior or system states. Any observed activity deviating from these predefined specifications triggers an alert [20]. Although this method can potentially achieve a low FPR for the specific protocols or systems modeled,the process of creating and maintaining accurate, comprehensive specifications for complex and constantly evolving IoT systems is often prohibitively difficult and labor-intensive.  
The inherent limitations of these traditional approaches, particularly their struggles with zero-day attacks (SIDS) or high FPR (AIDS) and the complexity associated with specification-based methods, necessitate the development of more adaptive and intelligent solutions tailored for the modern IoT threat landscape [5].
## 3 Materials and Methods
### Dataset
After labeling, all images were converted to PNG format and resized to 244 \(\times\) 244 pixels for efficient model training. This dataset serves as a critical resource for advancing precision agriculture. Unlike existing plant disease datasets, which primarily focus on fungal or bacterial infections, this dataset specifically addresses pest-induced damage, making it unique and essential for the development of pest management systems. To ensure that differences observed in severity classification were due to pest damage and not external factors, all images were collected under consistent field conditions (clear, sunny days in the same location) and processed using the same image preparation pipeline. The dataset was designed to include four severity levels (healthy, low, medium, high), which were determined based on expert-defined visual criteria. All preprocessing and labeling steps were applied consistently across all samples, following a structured and controlled image-based classification procedure. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Severity Level** & **Description** & **Visual Indicators** & \begin{tabular}{c} **Estimated Leaf** \\ **Area Affected** \\ \end{tabular} \\ \hline No Infection & \begin{tabular}{c} No visible signs of \\ damage on the leaf \\ \end{tabular} & \begin{tabular}{c} Completely healthy \\ green leaf \\ \end{tabular} & 0\% \\ \hline Low Infection & \begin{tabular}{c} Few scattered \\ white spots limited \\ to a small portion \\ of the leaf surface \\ \end{tabular} & \begin{tabular}{c} Isolated white \\ specks sparsely \\ distributed \\ \end{tabular} & 0–10\% \\ \hline Medium Infection & \begin{tabular}{c} White spots \\ become more \\ prominent and \\ merge into larger \\ patches \\ \end{tabular} & \begin{tabular}{c} Merged dense \\ white lesions \\ \end{tabular} & 10–30\% \\ \hline High Infection & \
\begin{tabular}{c} Merged white \\ lesions develop \\ into necrotic areas \\ covering a larger \\ portion of the leaf \\ \end{tabular} &
\begin{tabular}{c} Large necrotic \\ zones covering the \\ majority of the leaf \\ \end{tabular} & \textgreater{}30\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Severity levels and visual criteria for pest-induced damage in maize leaves.
## 4 Experimental Investigation  
In this section, two case studies with increasing complexity are carried out for experimental investigation to validate the feasibility of the proposed SCEM-UA algorithm. The first case is a shaking test table test of a linear system. This illustrates the capability of the algorithm to identify structural parameters and to infer the posterior target distribution. The second case study is conducted to explore the effectiveness of the algorithm for estimating the parameters of a nonlinear structure, considering the uncertainty of available information.
## 2 Materials and Experiment
The nominal compositions of the base metals were analysed using LECO TCH 400 instrument (LECO Corporation, St. Joseph, MI, USA). The etched samples were observed under a Nikon SMZ745T stereomicroscope (Nikon Instruments Inc., New York, NY, USA) to capture the macrostructure of the weld. Microstructural features of base metals and welds were analysed by Leitz optical microscope (Leitz GMBH & CO. KG, Leitzstrasse 2, Oberkochen, Germany). The microstructure of the welds and fracture surfaces of tensile tested specimens were examined in a scanning electron microscope (VEGA 3 LMV, TESCAN ORSAY HOLDING, Brno, Czech Republic), equipped with an Oxford energy dispersive X-ray spectrometer (EDS) (Oxford instruments, TubneyWoods, Abingdon, UK) for line scanning. The welds were subjected to X-ray diffraction (XRD) on PANalytical (Malvern, UK, X'pert powder XRD) using copper K\(\alpha\) radiation to identify various phases in the weld region and base metal. Microhardness measurements were conducted according to ASTM E384 standard (West Conshohocken, PA, USA) across the weld region at intervals of 0.25 mm at 500 gm load for 15 s on as-welded and PWHT conditions using a Vickers hardness tester (MMT-X Matsuzawa, Akita Prefecture, Kawabetoshima, Japan) equipped with a diamond pyramid indenter. For tensile testing, sub-sized transverse weld specimens with a gauge length of 25 mm, gauge width of 6 mm and thickness of 5 mm were fabricated from linear friction welded samples. Tensile tests for base metals as well as welds were carried out on a Universal Testing Machine (Jinan WDW-100S, Jinan, China) according to ASTM E8 standard at a cross speed of 0.5 mm/min.
## 4 Results
### Model Testing
In particular, Model 3 shows better adaptability and accuracy. It can not only clearly select the damaged area but also accurately identify high-detail damage, such as cracks, showing strong scale robustness. This result shows that under conditions of high resolution of the acquired image and clear brick surface features, the damage features learned in the model training can be fully activated, and the detection effect is more ideal. In addition, the three models generally confuse damage with stain in this batch of test images, especially in images 2, 3, and 5 of Figure 12, indicating that there is strong uncertainty in the boundaries between such labels. According to field investigations and manual labeling experience, this type of confusion is mostly caused by the use of cement mortar or lime for local repairs during the maintenance of Macau's gray bricks. The repaired area has both the structural properties of material replacement and the characterization characteristics of significant color difference and material discontinuity. Therefore, it has the dual semantics of \"damage\" and \"stains\" visually. Even manual identification is difficult to label consistently, resulting in recognition conflicts in model training and testing. In comprehensive comparison, Model 3 still shows more stable performance, especially in image no. 7 of Figure 12, which accurately captures all cracks and broken bricks, reflecting its discrimination ability in high feature density images. In long-distance images (such as images 1, 4, and 8 of Figure 12), the three models all show high uncertainty and deviation, further confirming that the model in this study is more suitable for close-range or medium-short-range detection scenarios. Practical applications should avoid directly deploying this model in high-viewing environments like long-distance monitoring or large-scale drone scanning. It is recommended to combine the close-range image acquisition strategy to give full play to the advantages and reliability of the model for brick damage detection. Figure 9: Model heatmap test based on the training dataset. (Image source: drawn by the authors). Figure 10: Model heatmap testing: based on a new dataset. (Image source: drawn by the authors). Figure 11: Model damage quantification ability test based on the training set data. (Image source: drawn by the authors). Figure 12: Model damage quantification ability test based on a new dataset. (Image source: drawn by the authors).
# Evaluation of IEEE802.15.4g for Environmental Observations
## 4 Analysis
### Advanced Metering Infrastructure Scenario  
For the nodes located within 215 m, the PDR values of almost all radio settings are close to 100%. Taking one of those 4 locations would have similar results to those exposed in Section 4.3.1. For the RX nodes at 350 m and 400 m, PDR values decay and we approach to the limits of the radio link. Therefore we present the analysis for those 2 RX locations. Several buildings are between TX and those 2 RX nodes, making it a challenging environment to the radio link.
# Signal Source Localization of Multiple Robots Using an Event-Triggered Communication Scheme
## 4 Simulation Results
### Simulation Environment  
This subsection briefly describes the simulation environment where a static electromagnetic signal field is used. Correspondingly, due to different noise errors, two cases are considered.  
For Case 1, the electromagnetic signal field can be established by using the following function.  
\[f_{1}(x,r)=10\times\log(0.001)-1.96\times\log(\|x-r\|)+\sqrt{5}\times rand \tag{33}\]  
where \(x\) is any position in the search environment; \(r\) is the position of the signal source; \(rand\) is a random number in \([0,1]\).  
For Case 2, a big noise is considered where the electromagnetic signal field can be established by applying the following function.  
\[f_{2}(x,r)=10\times\log(0.001)-1.96\times\log(\|x-r\|)+\sqrt{8}\times rand \tag{34}\]  
The simulation environment is built in MATLAB, where the search space is a square area with 30 m \(\times\) 30 m, and other parameters can be found in Table 1.
## 3 Proposed CE Method  
### Sbl-Based CE  
In the SBL-based CE framework, we assume the Gaussian likelihood model as  
\[p(\mathbf{y}_{i}|\mathbf{h})=\frac{1}{(\pi\sigma_{n}^{2})^{N_{path}}}e^{- \frac{||\mathbf{y}_{i}-\mathbf{\Phi}_{i}\mathbf{h}||^{2}}{\sigma_{n}^{2}}} \tag{9}\]  
where \(\mathbf{\Phi}_{i}=\mathbf{X}_{i}\mathbf{A}\). The objective is to estimate each tap of the CIR, and it is assumed that each tap of the CIR follows independently and is identically distributed (i.i.d.) with a complex Gaussian distribution as  
\[h(l)\sim\mathcal{CN}(0,\gamma_{l}), \tag{10}\]  
where \(\gamma_{l}\) is the prior variance corresponding to the \(l\)-th tap of the CIR, which is treated as a deterministic but unknown hyperparameter. The parameterized Gaussian prior is given by  
\[p(\mathbf{h};\,\mathbf{\Gamma})=\prod_{l=0}^{N_{path}-1}\frac{1}{\pi\gamma_{ l}}e^{-\frac{|\mathbf{h}_{l}|^{2}}{\gamma_{l}}} \tag{11}\]  
where \(\mathbf{\Gamma}=\text{diag}\Big{(}\gamma_{0},\,\gamma_{1}\,\cdots,\,\gamma_{ N_{path}-1}\Big{)}\) is the diagonal matrix of hyperparameters. The hyperparameters can be estimated by performing ML optimization of the marginalized probability density function (pdf) \(p(\mathbf{y}_{i};\mathbf{\Gamma})\)[17]:  
\[\tilde{\mathbf{\Gamma}}=\arg\max_{\mathbf{\Gamma}}p(\mathbf{y}_{i};\mathbf{ \Gamma}). \tag{12}\]where the marginalized pdf is given by  
\[\begin{split} p(\mathbf{y}_{i};\mathbf{\Gamma})&=\int p (\mathbf{y}_{i}|\mathbf{h})p(\mathbf{h};\mathbf{\Gamma})d\mathbf{h}\\ &=\pi^{-N_{path}}|\Sigma_{y}|^{-1}e^{-\mathbf{y}_{i}^{\|}\frac{ \mathbf{y}_{i}-1}{\mathbf{y}_{i}}\mathbf{y}_{i}}.\end{split} \tag{13}\]  
The ML optimization in (12), which is called type-II ML, cannot be solved in closed form. Accordingly, in the SBL-based CE framework, the expectation maximization (EM) algorithm is employed to maximize \(p(\mathbf{y}_{i};\mathbf{\Gamma})\)[17; 18; 19]. Note that the EM algorithm guarantees convergence to local optima and low complexity [20]. The EM algorithm treats the CIR vectors \(\mathbf{h}\) as hidden variables and estimates \(\mathbf{\Gamma}\) in an iterative way. The \(E\) and \(M\) steps of the EM algorithm for the \(k\)-th step is given as  
\[\begin{split} E\  
Regarding convergence, a maximum a posteriori estimate of \(\mathbf{h}\) is given as \(\mathbf{\hat{h}}=\mu\).
# Mechanical and Microstructural Studies of High Performance Concrete with Condensed Silica Fume
## 3 Results and Discussion
### Tensile Splitting Strength  
The studies were performed according to PN-EN 12390-6:2011 [51] in order to obtain the split tensile strength of HPC. Cubic samples were aged in water at 20 \({}^{\circ}\)C, until examined after 28 days. Figure 4a shows the split tensile strength versus the CSF content. Strength increased as CSF replacement increased to 10%, and as CSF replacement further increased from 10% to 25%, HPC tensile strength decreased.  
Figure 3: Dense HPC microstructure of with 25% CSF substitution.  
The condensed silica fume has a noticeable impact on the tensile splitting strength. 10% CSF causes an average strength increase of about 26% after 28 days. The results also designated that strength was evidently decreased with CSF replacement at the level of 20-25%, compared to 10% CSF content. A slightly different trend was reported by [PERSON] [13], who observed that tensile splitting strength did not increase meaningfully with growing CSF content. Similar to the compressive strength, slight reductions in tensile strength were gained when the CSF replacement was augmented at the level of 16-20%. Figure 4b presents the ratios of tensile splitting to compressive strength in relation to CSF substitution. It has been noted that growing the CSF replacement from 10% to 25% leads to a decrease in the split tensile to compressive strength ratio. On the other hand, from 0% to 10% CSF, there was a gradual increase in this ratio. This could not have been predicted, as growing CSF content usually leads to greater compressive strength, and these are related to lower ratios. [PERSON] [13] observed that for an increase in compressive strength in the range of 75-100 MPa, the ratio of tensile splitting strength to compressive strength falls from 6% to 4% with increasing compressive strength. This ratio initially increases from 5.8% to 6.5%, and then falls from 6.5% to 5.5% with increasing compressive strength, for compressive strengths described in this paper in the range of 96.1-109.7 MPa.
# Solar Irradiance Forecasting with Transformer Model
## 2 Dataset and Methods  
The following section is divided into two main parts. Firstly, a detailed description of the used dataset is provided, including solar irradiance signals, positions of the used regions on the Earth, where the signals were used, and their weather data, together with the necessary preprocessing. Secondly, the Transformer model for solar irradiance is specified, with its encoder and decoder parts, and spatial-temporal encoding. The limiting factor in forecasting solar irradiance is primarily weather, which strongly influences the performance, although the Transformer model used here is not directly focused on weather forecast. Though India is only ranked fifth in the publication _Solar Power Capacity by Country_[17], there have been many recent works predicting solar irradiance in this country [9; 18; 19; 20]; hence, its dataset has been used in this paper to enable comparison with state-of-the-art prediction methods.
## 2 Materials and Methods
### Leaf Area Index Data  
The Moderate Resolution Imaging Spectroradiometer (MODIS) MOD15A2 leaf area index (LAI) product was used to drive the LUE models. This dataset is an 8-day composite with a 1 km spatial resolution and was downloaded from the website ([[https://ladsweb.modaps.eosdis.nasa.gov/search/order/4/MOD15A2H/](https://ladsweb.modaps.eosdis.nasa.gov/search/order/4/MOD15A2H/)]([https://ladsweb.modaps.eosdis.nasa.gov/search/order/4/MOD15A2H/](https://ladsweb.modaps.eosdis.nasa.gov/search/order/4/MOD15A2H/))). MOD15A2 LAI data from a 2-year period--from 1 January 2015 to 31 December 2016--were used. The projection of these data is sinusoidal, and the MODIS Reprojection Tools (MRT) was used to reproject them into a Universal Transverse Mercator/ World Geodetic System 1984 (UTM/WGS 84) projection. The resulting LAI time series was further smoothed using the locally adjusted cubic-spline capping (LACC) method [58] to remove unrealistically short-term abrupt changes in the LAI caused by contamination of the measurements by clouds.
# Effective Implementation of Edge-Preserving Filtering on CPU Microarchitectures
## 1 Introduction
The filtering parameters are as follows: \(\sigma_{r}=4\), \(\sigma_{s}=6\), \(r=3\sigma_{s}\) and \(h=\sqrt{2}\sigma_{r}\). The template window size is \((3,3)\), and the search window size is \((2r+1,2r+1)\). The image size is \(768\times 512\). In (**b–e**), the ratios of denormalized numbers in all weight calculations are \(2.11\%\), \(3.26\%\), \(1.97\%\) and \(3.32\%\), respectively. A fast implementation requires effective utilization of the functionalities in CPUs and GPUs. In the present study, we focus on a CPU-centric implementation. Existing CPU microarchitectures are becoming complex. The architectures are based on multi-core architectures, complicated cache memories and short vector processing units. Single-instruction, multiple-data (SIMD) [41] instruction sets in vector processing units are especially changed. The evolution of the SIMD instructions has taken the form of the increased vector length [42], increased number of types of instructions and decreased latency of instructions. Therefore, it is essential to use SIMD instructions effectively for extracting CPU performance. In the edge-preserving filtering, execution of the weight calculation is the main bottleneck. Thus, the vectorization for weight calculation has a significant effect. In the present study, we focus on two topics: the influence of denormalized numbers and effective vectorized implementation on CPU microarchitectures in the edge-preserving filtering. For the first, we verify the influence of the denormalized numbers on the edge-preserving filtering, and then, we propose methods to accelerate the filter by removing the influence of the denormalized numbers. For the second, we compare several types of vectorization of bilateral filtering and non-local means filtering. We develop various implementations to clarify suitable representations of the latest CPU microarchitectures for these filters. The remainder of this paper is organized as follows. In Section 2, we review bilateral filtering, non-local means filtering and their variants. Section 3 describes IEEE standard 754 for floating point numbers and denormalized numbers. In Section 4, we present CPU microarchitectures and SIMD instruction sets. We propose novel methods for preventing the occurrence of the denormalized numbers in Section 5. In Section 6, we introduce several types of vectorization. Section 7 presents our experimental results. Finally, in Section 8, we show a few concluding remarks.
# In Situ Neutron Radiography Investigations of Hydrogen Related Processes in Zirconium Alloys
## 3 Examples of In Situ Neutron Radiography Investigations
### Investigation of the Reaction of Zry-4 in Steam, Nitrogen/Steam, and Air/Steam Atmospheres
#### 3.3.2 Data Analysis  
The data analysis was much more complex than in the previous examples because three elements (H, O, and N) were added to the pristine zircaloy. The amount of these elements changes during reaction with time nonmonotonically because not only oxidation and nitriding but also reoxidation of zirconium nitrides occur when the temporary oxygen or steam starvation is finished. Additionally, a strong swelling of the sample occurs. Under this condition, referencing to the initial sample state is difficult. Therefore, a quantitative analysis of the data was not possible in each case. However, even a qualitative analysis of the data provided new insights in the time scale of changes in the reaction kinetics.  
Where possible, the following correlations were applied for a quantitative analysis:  
* For hydrogen, \[\Delta\Sigma_{total}^{H}=2.6617\;cm^{-1}\;\frac{N^{H}}{N^{Zr}}+0.168\;cm^{-1}\] (3) with N as the number density of hydrogen and zirconium, respectively.
* For oxygen, the correlation of Equation (2) was used.
* For nitrogen, the contribution to the total neutron cross section \(\Delta\Sigma_{total}^{N}\) was estimated by \[\Delta\Sigma_{total}^{N}=6.0\;cm^{-2}\overline{\delta_{ZrN}}\] (4) \[\overline{\delta_{ZrN}}\] is the mean thickness of the nitride-containing layer. This value can only be determined destructively after the neutron radiography in the sample. Therefore, a parallel sample set was produced for the determination of this parameter.  
As already mentioned, the samples changed their shape by swelling. Therefore, two ways of referencing were used: referencing by the initial state of the sample and by the empty furnace at the test temperature.
## 3 Encapsulation Protocol
### Icmp  
#### 3.6.1 Introduction  
**Fingerprint**  
Among the traditional detection methods (in Table 8), most papers focus on detecting whether ICMP tunnel traffic is present, without paying much attention to fine-grained classification. A simple string scan of \"passwd\", \"root\", \"tmp\", \"ls\", and \"dir\" was used to complete [PERSON]'s [70] preliminary detection in the unencrypted ICMP tunnel. While this method has low overhead and high speed, there are a few disadvantages to its use, including the need to maintain a database of suspicious strings regularly, as well as a high false negative rate. As characteristics of ICMP tunnel traffic matching, [PERSON] [22] defined 89 field types (such as AS MPLS label, IPv6 address, and AS number related to data) and found that hiding data in ICMP tunnels is also common practice by using byte of payload.  
**Statistic Feature**  
ICMP tunnel traffic can be identified by marking abnormally large and often persistent or burst packets to proxy nodes. According to [PERSON] [23], ICMP tunnel traffic can be detected by detecting traffic congestion in gateways according to bandwidth utilization, processing all unreachable messages from hosts and networks in the network, establishing a protocol for unreachable messages, etc. The paper presents only theory, not experimental data. To identify normal traffic or ICMP tunnel traffic, [PERSON] [24] first compared the number of packet bytes, then checked the ICMP message rate, ICMP sequence number, and fast random pattern matching in the feature library or MD5 hash verification.