esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Multi-Scale Feature Extraction Using Swin Transformer
#### 3.3.1 Patch Embedding  
In SwinT, patch embedding is performed as a preprocessing step to convert an input image into a sequence of token embeddings. Specifically, given an input image \(\mathbf{X}\in\mathbb{R}^{H\times W\times C}\), it is divided into non-overlapping patches of size \(P\times P\):  
\[x_{p}=\mathbf{X}[p_{h}:p_{h}+P,p_{w}:p_{w}+P,:]\in\mathbb{R}^{P\times P\times C} \tag{19}\]  
where \(p=(p_{h},p_{w})\) denotes the top-left coordinate of the patch. Each patch \(x_{p}\) is then flattened and projected to a \(D\)-dimensional vector through a linear projection layer:  
\[\mathbf{z}_{p}^{0}=\text{Linear}(x_{p})\in\mathbb{R}^{D} \tag{20}\]In addition, a learnable 1D position encoding is added to retain spatial information:  
\[\mathbf{z}_{p}=[\mathbf{z}_{p}^{0};\mathbf{e}_{p}]\in\mathbb{R}^{D+1} \tag{21}\]  
where \(\mathbf{e}_{p}\) denotes the position encoding for patch \(p\). The sequence of patch embeddings \(\{\mathbf{z}_{p}\}\) are then input to the subsequent Transformer encoder layers.  
According to the above process, we partition the CWD of each radar jamming signal into non-overlapping patches of size \(4\times 4\), and obtain patch embeddings. These patch tokens capture local time-frequency information. We then feed the patch sequence into the hierarchical modules of SwinT. The network can learn time-frequency characteristics at different scales, and integrate multi-scale information for powerful graph representation, thereby achieving effective identification of radar jamming signals.
## 3 Results and Discussion
### Effective Mode Area and Non-Linear Coefficient  
In most non-linear optical processes, one of the key factors to determine the efficiency of the non-linear medium is the non-linear coefficient [2]. The non-linear coefficient \(\gamma\) in an optical fiber is defined as [2,7]  
\[\gamma=\frac{2\pi n_{2}}{\lambda A_{eff}}, \tag{7}\]  
where \(n_{2}\) is a non-linear refractive index of the core material and \(A_{eff}\) is an effective mode area of the fundamental guided mode in the fiber core, defined as [2,7]  
\[A_{eff}=\frac{\int\int(|E|^{2}dA)^{2}}{\int\int(|E|^{4}dA)}. \tag{8}\]  
The effective area and non-linear coefficient of a C-PCF and E-PCF with \(d=0.692\), \(\Lambda=0.867\), \(a=1\)\(\upmu\)m is shown in Figure 8.  
As seen in Figure 8, the effective mode area increases gradually with an increase in excitation wavelength and consequently, the non-linear coefficient decreases with an increase in excitation wavelength. It is evident that extremely large non-linear coefficients of 15 to 45 W\({}^{-1}\) m\({}^{-1}\) can be achieved in our proposed composite PCFs with both circular and elliptical cores. These values are more than three orders of magnitude larger than that of a regular silica-based HNL-PCF and more than one order of magnitude larger than that of a liquid-filled HNL-PCF [7]. The combined effect of tight mode confinement in an As\({}_{2}\)S\({}_{3}\) core and large \(n_{2}\) of chalcogenide glasses, particularly \(3\times 10^{-18}\) m\({}^{-1}\) W\({}^{-1}\) for As\({}_{2}\)S\({}_{3}\)[13], leads to the large non-linear coefficients in our proposed PCFs. The proposed fiber with such a huge nonlinearity, tunable dispersion, and large birefringence can have applications in different nonlinear processes where phase matching is important (for example wavelength conversion  
Figure 7: Birefringence of C-PCF with circular-core and elliptical-core as a function of wavelength. The C-PCF parameters are: \(d/\Lambda=0.692\), \(d_{c}/\Lambda=0.876\), \(\Lambda=0.867\)\(\upmu\)m, and E-PCF parameters are \(b/a=0.75\) and \(a=1\)\(\upmu\)m. The inset show effective modal indices of fundamental guided mode of E-PCF for x-polarized and y-polarized light. Note that the birefringence of circular-core PCF is enhanced by three orders of magnitude to facilitate visualization.  
via four-wave mixing), and applications where the polarization maintaining is important (like sensing via a Sagnac interferometer).
###### Abstract  
In the present study, a combined treatment strategy involving the addition of rhamnpid, rhamnpid-producing bacteria (_Shewanella_ sp. BS4) and a native soil microbial community for the remediation of hydrocarbon-contaminated soil under pilot-scale conditions was adopted. The isolate BS4 (rhl+), demonstrating the highest emulsification activity and surface tension reduction efficiency, was identified based on 16 S rDNA sequencing as _Shewanella_ sp. strain. Growth conditions for rhamnpid production were optimized based on Central Composite Design (CCD) as 2.9% crude oil, a \(54\times 10^{6}\) CFU g\({}^{-1}\) inoculation load of soil, a temperature of 30.5 \({}^{\circ}\)C, and a pH of 6.7. In situ bioremediation experiments, conducted using hydrocarbon-contaminated soil treated with the combination of rhamnpid and rhamnpid-producing bacteria, showed that the inoculated _Shewanella_ sp. BS4, along with the indigenous soil microbial community, supported the highest hydrocarbon-degrading bacterial population and soil respiration activity, and this treatment resulted in 75.8% hydrocarbon removal efficiency, which was higher compared to contaminated soil devoid of any treatment.  
bioremediation; biosurfactant; hydrocarbon-contaminated soil; rhamnpids; _Shewanella_ sp.
# Dynamics of Monoterpene Formation in Spike Lavender Plants
## 3 Discussion
### \({}^{13}\)CO\({}_{2}\) Labelling in Transgenic HMGR5 Plants
Results herein do not clearly support that the increased monoterpene production in HMGR overexpressing spike laverder plants are due to a transport of MVA-derived units from the cytoplasm to plastids. It is true that analysis of metabolic flux based on \({}^{13}\)CO\({}_{2}\) labelling does not constitute a direct method for the investigation of the cross-talk between both pathways [57]. Nevertheless, the results indicate that the MEP/monoterpene pathway is regulated by a product of the MVA-route at transcriptional, posttranscriptional, translational and/or posttranslational level [13]; therefore, the increased monoterpene production in HMGR overexpressing spike laverder plants could also be explained by feedback MEP pathway gene-regulation in response to HMGR overexpression. As an example, MVA-derived prenylated proteins were shown to regulate the expression of genes in the early steps of monoterpenoid biosynthesis in _Catharantlus roseus_ cells [58]. In future experiments, the expression levels of selected enzymes, specifically HMGR before the pulse period, and during/after the chase periods should be monitored in connection with \({}^{13}\)C-labeling experiments. Secondly, in vitro experiments with [U-\({}^{13}\)C\({}_{6}\)]glucose in combination with inhibitors of the MVA (e.g., thevinoline) and MEP pathways (e.g., fosmidomicine) might help to further clarify the role of a potential crosstalk between the terpene pathways. In any case, it will be important to include other terpen such as sesquiterpenes, sterols and carotenoids into future studies, in order to finally clarify the distribution of MEP vs. MEV building blocks in the terpenes from _L. latifolia_.
# Effect of PVA/PVAc Based Polymer Coating on Dust Reduction in Playground
## 2 Experimental Section
### Soil Coating Procedure  
PVA film and PVAc gel polymer were used as the coating materials in this study. First, pumice sand and sandy loam soil particles were mixed thoroughly at a ratio of 3:7 to obtain a representative playground soil. A coating solution was prepared using a combination of PVA solution and PVAc gel polymer. PVA film and PVAc-based gel polymer were added in water at a ratio of 1:100. For instance, 1 g of PVA film and 1 g of PVAc gel polymer were added in 100 g of water to obtain the coating solution. PVAc gel polymer was added to prevent the washout of PVA film from the surface of soil particles during precipitation. Later, coating solution was poured in the mixer containing soil particles and mixing was carried out for five minutes. The particles were coated with 7%, 10%, and 12% of coating solution by weight of particles. Consequently, the coated soil samples were placed in a laboratory-scale oven for 3 h at 80 \({}^{\circ}\)C.
# Micro-Nanobubble Technology in Surface Cleaning and Defouling
###### Abstract  
The physicochemical characteristics of micro- and nanobubbles (MNBs) have attracted considerable interest owing to their potential use in various industries, such as water treatment, agriculture, healthcare, and environmental remediation. This review focuses on the functions of MBs and, mainly, NBs in cleaning and defouling applications by thoroughly examining the mechanics of their stability, generation, and interaction with surfaces. Wastewater treatment, biofilm removal, and membrane fouling avoidance are cutting-edge techniques that use MNBs to improve cleaning effectiveness. Notably, this review highlights that microbubbles and nanobubbles can be used together synergistically or applied separately based on specific application needs. This review emphasizes how MNB technology can be integrated with other systems, such as bioremediation and sophisticated oxidation processes, to address challenging cleaning issues. The capacity of MNBs to lower operating costs; their impact on the environment; and their synergistic effects with chemical, biological, and physical agents are highlighted. To maximize the use of MNBs in environmentally friendly cleaning technologies, this review offers insights into the new horizons of MB and NB applications by synthesizing recent discoveries and suggesting directions for further studies and industrial-scale deployment.  
nanobubbles; microbubbles; stability; surface cleaning; defouling; reactive oxygen species +
Footnote †: journal: Applied sciences
## 1 Introduction
The benefits of these sorts of converted systems are fast response speed and powerful strength to outer disturbance, which are enhanced through some progressed control techniques such as sliding mode control (SMC). In comparison with our system, even though they are different in structures and used devices, they can be implemented in radar systems and cellular communications. In this paper, we study, for the first time, novel electro-optical up-converters in a differential modulation schema based on a real and simulated SOA-MZI sampling mixer. The sampling signal is injected into the active zones of both SOAs. Moreover, the IF electrical subcarrier modulates the electrical ports for both SOAs. The proposed electro-optical sampling mixer shows features including broad bandwidth and high conversion gains. In order to confirm the simulation results, experimental setups by using a sampling SOA-MZI from CIP (40G-2R2-ORP) are built to compare with the simulation results. The major benefits of our study are the novel concept that allows us to reach the highest, to our knowledge, frequency range of 195.5 GHz, observe EVMs at the data rate of 5 Gbit/s, and obtain positive conversion gains. This novel concept may pave the way toward mm-wave and microwave applications and wireless access applications [25]. The paper is arranged into several sections in addition to the introduction. In the second section, we demonstrate in detail the technique of the electro-optical up-converter in the modulation schema of the differential mode. In the third section, simulation setups are given, simulation results for the electro-optical up-converter spectrum are achieved, conversion gains are obtained, and then EVMs of QPSK data are presented. The real results are achieved and compared with simulation results in the fourth section. Finally, in the last section, the conclusions are discussed.
## 4 Classification of Routing Protocols
### Temperature-Aware Routing Protocols
#### 4.2.4 Least Total Route Temperature (LTRT)  
In [56], [PERSON] _et al._, proposed a thermal aware routing protocol named least total route temperature, designed to find the routes with minimum temperature as well as to address the problem of redundant hops. This protocol selects routes with total least temperature from the source node to the destination node and also saves the bandwidth of the network by decreasing the hop count. Single Source Shortest Path (SSSP) algorithms from graph theory (for example: Dijkstra's Algorithm) are used to calculate the route with total least temperature and then use these routes for data communication. This scheme monitors the communication activities of the neighbor nodes to collect their temperature information and accordingly assign the estimated temperature of each node as weight of that node. The objective of LTRT can be achieved by the following steps.  
* Transfer the node's weight to outgoing edges weight of that node which results in a weight graph to calculate all possible routes from source node to the destination node.
* Apply Single Source Shortest Path (SSSP) algorithm to the weights graph of the nodes in order to figure out the routes with total least temperature from the source node to the destination node.
* Periodically update the routes to avoid the excessive temperature rise of the nodes.  
Figure 11, redrawn from [15], show any example which explains that how LRTR works.
## 2 Preliminary  
### Problem Formalization  
#### 2.1.1 Graph Definition  
An undirected graph is represented as \(G=\{V,\ E,\ X\}\), where \(V=\{v_{i}\}_{i=1}^{N}\) consists of a set of nodes with \(|V|=N\). A graph signal \(x:V\rightarrow\mathbb{R}\) is a real-valued function on the nodes regarded as a vector \(x\in\mathbb{R}^{N}\), where \(x_{i}\) is the value of \(x\) at the \(i^{th}\) node. \(E\) is a set of edges, and \(A=\left\{a_{i,j}\right\}\in\mathbb{R}^{N\times N}\) is the adjacency matrix with \(a_{i,j}=a_{j,i}\) representing the connection relationships between node \(v_{i}\) and node \(v_{j}\). The graph Laplacian matrix is denoted as \(\mathcal{L}=D-A\), where \(D\) is the diagonal degree matrix with \(D_{i,i}=\sum_{j}A_{i,j}\), and the normalized graph Laplacian is defined as \(L=I_{N}-D^{-\frac{1}{2}}AD^{-\frac{1}{2}}\). Since \(L\) is a real symmetric positive semidefinite matrix, it can be eigendecomposed as \(L=U\Lambda U^{T}\) where \(U=[u_{1},u_{2},\ldots,u_{N}]\in\mathbb{R}^{N\times N}\) is a complete set of orthonormal eigenvectors known as graph Fourier modes and \(\Lambda=\text{diag}\{\lambda_{i}\}_{i=1}^{N}\) are ordered real nonnegative eigenvalues associated with \([u_{i}]_{i=1}^{N}\), which are identified as the frequencies of the graph.
# A Real-Time Semi-Supervised Log Anomaly Detection Framework for ALICE O\({}^{2}\) Facilities
## 2 Literature Review
### Latent Semantic Analysis (LSA)  
Latent semantic analysis (LSA) [27] is a widely recognized method for dimensionality reduction and topic modeling in text data. In the context of anomaly detection, LSA analyzes the underlying structure of text data by mapping them into a lower-dimensional semantic space. This representation allows the identification of anomalies by detecting deviations from established patterns in the semantic space.  
[PERSON] et al. [28] utilized LSA for decomposition and cluster representation in network traffic logs, demonstrating its capability to enhance model performance by reducing irrelevant features and effectively classifying anomalies. Similarly, [PERSON] and [PERSON] [29] applied LSA to establish behavioral baselines for process anomaly detection, showing its effectiveness in distinguishing normal behavior from anomalous activities by constructing LSA matrices from the normal training set. Additionally, [PERSON] et al. [30] further highlighted the utility of LSA in analyzing free text data by detecting changes in social groups through email analysis, emphasizing its strength in uncovering hidden patterns in structured textual datasets.  
The primary strength of LSA lies in its ability to handle high-dimensional log data and reveal latent patterns, making it especially useful for understanding the underlying structure of log messages. However, LSA faces several drawbacks [31]. It assumes a joint Gaussian distribution between words and documents, making it less consistent with the observed data. The factorized matrix also contains negative values that lack a clear meaning in the context of word distributions. Moreover, choosing the number of latent dimensions in LSA typically relies on ad hoc heuristics, rather than formal statistical methods, leading to a less systematic approach to determining model complexity.
Specifically, it employs a triple-branch heterogeneous CNN design to capture fine-grained and diverse local features, a dual-path pyramid self-learning fusion module (PSFM) with learnable weights for adaptive multi-scale fusion, and a novel graph fusion module (GFM) based on GCNs to model spatial topological relationships in high-dimensional feature space. These modules collaboratively improve semantic continuity, small object recognition, and long-range contextual understanding, addressing core limitations such as boundary fragmentation and semantic inconsistency in complex remote sensing scenes. To summarize, our main contributions include:  
1. Heterogeneous Multi-Branch Local Feature Extraction: The proposed model establishes a multi-scale local feature extraction network through three parallel convolutional branches. Specifically, the feature extractors perform diverse processing operations within different architectures to better capture comprehensive representations such as high-frequency textures, mesoscale structures, and detail representations for the remote sensing images. By doing this, the aligned and concatenated composite features preserve both sub-pixel edge responses and regional semantic correlations, effectively addressing the feature representation limitations of single convolutional kernels. 2. Hierarchical Window Attention with Pyramid Pooling: To mitigate the transformer's local detail loss, we propose an enhanced customized architecture, termed the pyramid self-learning fusion module, by constructing four-level feature pyramids via patch merging. The dynamic shifted window mechanism enables cross-scale context modeling. Innovatively, dual pyramid pooling modules perform multi-granular spatial pooling on local and global features. After bilinear upsampling and convolution-based channel control, this strategy significantly improves vegetation classification by fusing local details with global semantics. 3. Spatial Graph Convolution-Guided Feature Propagation: The proposed graph fusion module maps local, global, and fused features into high-dimensional graph nodes, constructing spatial topology using four-neighbor adjacency matrices. Three GCN pathways facilitate feature propagation, with learnable weights dynamically aggregating improved node features. This design goes beyond traditional grid limitations, establishing long-range dependencies in the reconstruction space to address semantic discontinuities caused by high-rise occlusions. In the next section, we discuss some related works in remote sensing image segmentation. Subsequently, the proposed framework is detailed in Section 3, followed by a presentation of all experimental results in Section 4. Finally, in Section 5, we discuss future research directions and summarize this work.
# A Novel Dynamic Approach for Risk Analysis and Simulation Using Multi-Agents Model
## 7 Case Study: Modelling Chemical Reactor/Operator Using GDABM
### Agent Operator  
_The agent operator has two attributes. The first one is the Gas Concentration Threshold (_\(\gamma\)_) which is the maximum value of the gas concentration above which it is considered to be toxic and needs immediate attention. The second one is the_ \(exposureTime\) _(_\(\tau\)_) that represents the maximum exposure time of an operator to a toxic Gas release before being out of order (irreversible state). It has also four variables:_  
1. _Input_\({}_{1}\)_: It is the maximum quantity of products to be loaded from_ ProductionLine_\({}_{1}\)_._
2. _Input_\({}_{2}\)_: It is the maximum quantity of products to be loaded from_ ProductionLine_\({}_{2}\)_._
3. _Output_\({}_{3}\)_: It is the maximum quantity of products to be unloaded from the reactor._
4. _State 52: It describe the state of the reactor that can be Idle, Inactive, or Out of order._  
_Initially, the operator and the reactor agents are assumed to be functioning properly in their associated nominal modes._  
_The operator's nominal mode has four activities:_  
1. _Load: The load activity is the process of filling the reactor's production lines_ ProductionLine_\({}_{1}\) _and_ ProductionLine_\({}_{2}\) _with quantities_ \(P_{1}\) _and_ \(P_{2}\) _respectively._  
_The products' incoming rates to the production lines are assumed to be_ \(d_{v_{1}}\) _and_ \(d_{v_{2}}\) _respectively._  
_The load activity is executed with consideration of the following: the total quantity of the products to be added to the reactor (_\(P_{1}\)_+_\(P_{2}\)_) in addition to the quantity of products inside the reactor (_\(V\)_) is less than or equal to_ \(V_{max}\) _as shown in Equation (_10_)._ \[P_{1}+P_{2}+V<=V_{max}.\] (10)
2. _Unload: The unload activity is the process of pumping out an amount_ \(P_{3}\) _through_ ProductionLine_\({}_{3}\) _with outgoing rate_ \(d_{v_{3}}\)_._
3. _Wait1: This activity represents the process of waiting for the reactor to be Unlocked. It is a pre-process of the load activity in a Locked reactor._  
Figure 10: Transform-Product Activity.  
Wait2: This activity represents the process of waiting for the chemical reaction to be performed in time \(\tau\). It is an intermediate process between the load and the unload activities.  
The Load/Unload activities are only enabled when the state of the reactor is Unlocked. In the nominal mode, whenever the state of the reactor is locked, a Wait activity is triggered.