esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Extract Analyses  
The metal study was conducted to see what percentage passes to each extract, analyzing the viability of ingestion avoiding toxicity and its possible damage to health, owing the plant is not consumed directly.  
The samples analyzed contained a significantly lower amount of Cu compared to the standard reference material, NIST 1571 Orchard leaves (Figure 4). The Cu content in _Phoradendron villosum_ passes to the extracts between of 2.5 to 4.8% and the extracts of _Croton dioicus_ in a range of 2 to 5% (Tables 5 and 6). In the case of Cu, it can be consumed up to 3 mg/day (Table 4). Considering the tolerable daily intake, the extracts AL/PVN1,2,3, AQE/PVN1,2,3, HA/PVN1,2, and OE/PVN1,2,3 exceed the limit.  
Figure 4: The graph shows Cu concentrations (mg/kg) in extract samples with standard reference material NIST 1571 Orchard leaves.  
Levels of Ni in the extracts of _Phoradendron villosum_ passed a percentage between 1.6% to 3% and Ni at _Croton dioicus_ were between 2.2% to 4.2% (Tables 5 and 6). The content of Ni in the extracts for both plants is higher than the standard reference material NIST 1571 (Figure 5). Daily consumption is 1.4 mg/day, exceeding by approximately 1 to 3 mg/kg depending on the extract. For the As, it is recommended to consume less than 1 mg/day (Table 4), existing in the extracts of both plants in levels less than 0.6 mg/kg, except the samples OE/CD\({}_{1,3}\). The samples analyzed contained a significantly lower amount of As compared to the standard reference material NIST 1571 (Figure 6). For extracts analyzed, the Pb content value is below the detection limit (Tables 5 and 6). The concentration of Pb is higher in the reference material NIST 1571 than in the samples from both plants (Figure 7).  
The extracts of the investigated plants are consumed in doses of 0.5 to 2 mL of extract dissolved in 200 mL of water, having even less risk of consuming significant amounts of heavy metals. Considering the form of consumption is avoided to a great extent, the intoxication by Ni, being even the levels that pass to the extracts, is considerably low. It is observed that the values of heavy metals in the extracts in _Phoradendron villosum_ and _Croton dioicus_ pass in different percentages depending on the solvent used, lowering the levels,  
Figure 5: The graph shows Ni concentrations (mg/kg) in extract samples with standard reference material NIST 1571 Orchard leaves.  
Figure 6: The graph shows As concentrations (mg/kg) in extract samples with standard reference material NIST 1571 Orchard leaves.  
and considerably avoiding possible toxicity when consumed with supportive treatment in some conditions.  
In addition, no significant difference in the statistical study by ANOVA and [PERSON]'s method was found in the levels of heavy metals between the extracts (Tables 5 and 6). However, there is a marked statistical difference between the content of the heavy metals studied in the plants and what remains in each extract after maceration.
# A Framework for Diagnosing Urban Rail Train Turn-Back Faults Based on Rules and Algorithms
## 2 Literature Review
### Topic Modeling  
The topic model is a statistical model that clusters the hidden semantics in the text through unsupervised learning. It is mainly used by scholars for text mining and text analysis. The latent Dirichlet allocation (LDA) used in this study is one of the typical topic models. It generates each topic by mixing words and each document with mixed topics.  
LDA has been used by many scholars for analysis in the field of fault diagnosis. [PERSON] et al. used data mining for vehicle-mounted Chinese Train Control System (CTCS) equipment on the train to establish a fault information database and used an improved label-LDA to extract the semantics in the work log, classifying and comparing classification accuracy through particle swarm optimization (PSO)-SVM, traditional SVM, and KNN [29]. [PERSON] et al. proposed a text mining method based on two-layer feature extraction; that is, the feature weight \(\chi^{2}\) was used first, and then the traditional LDA model was used. Finally, it was used in the fault diagnosis of railway maintenance data. However, this method is less effective for the classification of unbalanced data [30]. In the current study, LDA was used to analyze the semantics of daily work reports, so there is no need to consider this issue. [PERSON] et al. used the topic modeling method based on LDA to propose a unified topic model for grouping similar TV users through TV descriptors and recommending similar TV programs [31]. Based on semi-supervised non-negative matrix factorization (NMF), [PERSON] et al. proposed a topic modeling visual analysis system called UTOPIAN and compared the results with LDA analysis after applying it to different scenarios [32]. [PERSON] and [PERSON] introduced an entity topic modeling method called EntLDA and combined the semantic concepts in DBpedia with unsupervised learning approaches such as LDA [33].  
Compared with the above literature, the current research combined domain knowledge and used the LDA model to assist in analyzing the characteristics of the three reentrant failures in daily work reports, focusing on the semantic analysis of the text and the interpretability of the algorithm.  
However, the above articles lack an in-depth analysis of the three types of faults during the turn-back faults of urban rail transit, and little attention has been paid to the data combined with the train communication code and the daily work report of testers.
# Building Greibach Normal Form Grammars Using Genetic Algorithms
## 3 Proposed Algorithm of Grammatical Evolution for Greibach Context-Free Grammars
### Advantages of the GNF Encoding  
The GNF encoding offers a robust rule of thumb of whether the use of the crossover operator may lead to a grammar which is not in the GNF: the chromosome of any grammar in the GNF cannot have two consecutive terminal symbols. That means that we actually have a priori knowledge of whether the genetic operators lead to a valid GNF. For the mutation operator we only need to check whether the neighbouring symbols in the chromosome are leading to two consecutive terminal symbols in the new chromosome. So the validity of an offspring can be determined simply and robustly in fixed time. In cases in which an invalid mutation occurs, we can retry a different mutation to a specific chromosome.  
GNF representation also eliminates the probability of new chromosomes not being a valid GNF with the crossover operator. An invalid offspring would be a chromosome with two consecutive terminal symbols, something that can not occur in a GNF. This probability for such cases is only \(1/(\overline{R}^{2})\) whereas \(\overline{R}\) is the average length of a production rule counting all symbols in the head and the body of a production rule. This probability is easily derived, as for a grammar to be invalid the first subvector used in the crossover must end with a terminal symbol (\(p_{1}=1/(\overline{R})\) in the GNF) and the second subvector must start with a terminal symbol (\(p_{2}=1/(\overline{R})\) in the GNF). In these cases, our method chooses to reapply the crossover operation in different locations of the original chromosomes. With this method, the invalid offsprings probability is zero.  
Finally, the use of GNF, facilitates a robust mapping of each grammar in a linear structure with no auxiliary data needed except from the mapping of the terminal and non terminal symbols.  
The advantages of the GNF representation discussed offer a huge advantage in the proposed GNF inference method, because different CFG formulations tend to increase significantly the probability to introduce an invalid offspring through the crossover operator. This means, that, in our case, a new pool of candidate solutions always contains valid grammars adhering to the GNF structure and no parsing of chromosomes and discarding is needed before the evaluation step. Typical cases where invalid offsprings are created through genetic operations can be met in the bibliography in other forms of CFGs such as the Backus-Naur form. For example, if the average length of a production rule in a grammar is reported to be equal to 5, then the average probability, through the population, of two chromosomes' crossover leading to an invalid offspring is 4%. Using the GNF constraints during crossover, these cases are discarded. Finally, the complexity of the proposed method is equal to \(O(SGA)\times O(parse(data))\) whereas an efficient top-down parser such as the one presented can significantly drop the computational complexity.
## 3 Challenges and Related Recommendation
### Future Research Challenges  
6G networks approaching challenges by using hybrid RF-Optical networks are discussed in this section in addition to space-air-ground-sear (SAGS) networks, integrated sensing and computing networks, digital twins' networks, and AI-enabled networks.
# A Multi-Machine and Multi-Modal Drift Detection (M2D2) Framework for Semiconductor Manufacturing
## 5 Illustrative Example
### Phase 4: Multi-Modal Interpretability and Root-Cause Analysis  
Anomaly detection alone offers limited practical benefit unless operators can pinpoint the primary causes. Phase 4 implements interpretability techniques--particularly SHAP--to map detected anomalies back to the most impactful features, encompassing both numeric sensor values and textual maintenance logs. In one instance, during the short-term alert on 17 June 2024, SHAP revealed that a sharp rise in \"current\" alongside repeated mentions of \"_current fluctuations_\" in the logs were the main contributors, as shown in Figure 5. Thisconsistent alignment between numeric sensor data and textual entries steered engineers to inspect the electrical supply infrastructure and measurement modules, reducing speculation and the time to resolution. Moreover, if \"current fluctuations\" repeatedly emerges as a top driver in future alerts, process parameters tied to current regulation can be reevaluated to forestall additional incidents.
# Trends and Challenges in Network Covert Channels Countermeasures
## 4 Covert Channel Detection  
In this section, we present the main detection techniques with emphasis on the limits and the challenges to be faced in order to be deployed in real-world scenarios. First we will present mechanisms targeting the two fundamental injection families at the basis of a wide range of hidden communication paths (i.e., those allowing the creation of timing and storage channels). Then, we will address techniques intended for specific protocols. In more detail, we will investigate the detection of covert channels targeting IPv6, which will be expected to become widespread in the near future and thus appealing from the viewpoint of an attacker. In addition, we will also investigate the various methodologies used to reveal hidden communication paths in VoIP conversations as well as DNS and HTTP tunnels. Such protocols have in common a great popularity and the ubiquitous availability, hence their presence in a network is seldom perceived as an anomaly and defenders typically need to deals with non-negligible traffic volumes.
# Modeling Shadow with Voxel-Based Trees for Sentinel-2 Reflectance Simulation in Tropical Rainforest
## 3 Results
### Reflectance Simulation under Several SZA
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**SZA (Degree)** & **Group1** & **Group2** & **Meandiff** & **p-adj** & **Lower** & **Upper** & **Reject** \\ \hline \multirow{8}{*}{10} & C & E & 0.0002 & 0.9 & \(-\)0.0019 & 0.0023 & False \\  & C & HE & 0.0 & 0.9 & \(-\)0.0021 & 0.0021 & False \\  & C & IHE & \(-\)0.0 & 0.9 & \(-\)0.0021 & 0.002 & False \\  & E & HE & \(-\)0.0002 & 0.9 & \(-\)0.0023 & 0.0019 & False \\  & E & IHE & \(-\)0.0002 & 0.9 & \(-\)0.0023 & 0.0019 & False \\  & HE & HE & \(-\)0.0 & 0.9 & \(-\)0.0021 & 0.002 & False \\ \hline \multirow{8}{*}{20} & C & E & \(-\)0.0015 & 0.0056 & \(-\)0.0026 & \(-\)0.0003 & True \\  & C & HE & \(-\)0.0024 & 0.001 & \(-\)0.0035 & \(-\)0.0012 & True \\  & C & IHE & \(-\)0.0 & 0.9 & \(-\)0.0012 & 0.0011 & False \\  & E & HE & \(-\)0.0009 & 0.1661 & \(-\)0.002 & 0.0002 & False \\  & E & IHE & 0.0014 & 0.008 & 0.0003 & 0.0025 & True \\  & HE & IHE & 0.0023 & 0.001 & 0.0012 & 0.0034 & True \\ \hline \multirow{8}{*}{30} & C & E & \(-\)0.0018 & 0.0212 & \(-\)0.0034 & \(-\)0.0002 & True \\  & C & HE & \(-\)0.0036 & 0.001 & \(-\)0.0053 & \(-\)0.002 & True \\  & C & IHE & 0.0 & 0.9 & \(-\)0.0016 & 0.0016 & False \\  & E & HE & \(-\)0.0018 & 0.0191 & \(-\)0.0034 & \(-\)0.0002 & True \\  & E & IHE & 0.0018 & 0.0181 & 0.0002 & 0.0035 & True \\  & HE & IHE & 0.0037 & 0.001 & 0.0021 & 0.0053 & True \\ \hline \multirow{8}{*}{40} & C & E & \(-\)0.0026 & 0.0153 & \(-\)0.0049 & \(-\)0.0004 & True \\  & C & HE & \(-\)0.0057 & 0.001 & \(-\)0.008 & \(-\)0.0035 & True \\  & C & IHE & 0.0007 & 0.8461 & \(-\)0.0016 & 0.0029 & False \\  & E & HE & \(-\)0.0031 & 0.0027 & \(-\)0.0054 & \(-\)0.0008 & True \\  & E & IHE & 0.0033 & 0.0011 & 0.0011 & 0.0056 & True \\  & HE & IHE & 0.0064 & 0.001 & 0.0041 & 0.0087 & True \\ \hline \end{tabular}
\end{table} 
Table 4: Multiple comparisons using [PERSON]’s HSD test for reflectance simulation results for different canopy shapes in each SZA (C: cylinder, E: ellipsoid, HE: half ellipsoid and IHE: inverted half ellipsoid). \(p\) value is 0.05. Figure 6: Relative reflectance simulated at SZA from 10\({}^{\circ}\) to 40\({}^{\circ}\) for each tree canopy shape. Base reflectance is mean reflectance at SZA 10\({}^{\circ}\) for each canopy shape.
## 3 Outage Analysis of Blind-RIS-NOMA Uplink System
### Outage Probability of the Blind-RIS-SR-NOMA System for Uplink Transmission  
#### 3.1.1 System Model  
A sample scenario of a blind-RIS-SR-NOMA uplink system with two users is shown in Figure 1. Blind RIS, which does not perform any phase compensation, is deployed in the environment and serves as an SR. The BS is located far from the users. Due to this, users can communicate with the BS via two hops. Both users and the BS are equipped with single antenna. \(x_{1}\) and \(x_{2}\) are the unit average energy symbols corresponding to users 1 and 2, respectively. The channel between the \(j\)th user and the \(i\)th RIS element is represented as \(g_{ji}\). The channel is assumed to be zero mean circularly symmetric complex Gaussian with unit variance. The channel between the \(i\)th RIS element and BS is \(h_{i}\). \(\mu_{1}\) and \(\mu_{2}\) are the power fractions allotted to users 1 and 2, respectively, where \(\mu_{1}\ +\mu_{2}\ =1\).  
Figure 1: A sample scenario of the blind-RIS-SR-NOMA uplink.  
The signal from users received at the \(i\)th RIS element is given by  
\[r_{i}=g_{1i}\sqrt{\mu_{1}\ \kappa_{s}}x_{1}+g_{2i}\sqrt{\mu_{2}\ \kappa_{s}}x_{2},\ \ i=1,2\ldots N \tag{1}\]  
where \(\kappa_{s}\) is the transmit power. The superposed signal received at the BS is [30; 31; 32]  
\[y_{BS}=\sum_{i=1}^{N}h_{i}r_{i}+n_{BS} \tag{2}\]  
where \(n_{BS}\in\mathbbm{N}(0,N_{0})\) is the complex Gaussian noise added at the BS. Substituting (1) in (2) gives  
\[y_{BS}=\left(\sum_{i=1}^{N}h_{i}g_{1i}\right)\sqrt{\mu_{1}\ \kappa_{s}}x_{1}+ \left(\sum_{i=1}^{N}h_{i}g_{2i}\right)\sqrt{\mu_{2}\ \kappa_{s}}x_{2}+n_{BS} \tag{3}\]  
Let  
\[L=\sum_{i=1}^{N}h_{i}g_{1i}\in\mathbbm{N}(0,\ N) \tag{4}\]  
\[M=\sum_{i=1}^{N}h_{i}g_{2i}\in\mathbbm{N}(0,\ N) \tag{5}\]  
The BS decodes \(x_{1}\) first, with \(x_{2}\) being handled as interference. The SINR of user 1 at BS is given by [18]  
\[v^{x_{1}}=\frac{\mu_{1}\ \kappa_{s}|L|^{2}}{\mu_{2}\ \kappa_{s}|M|^{2}+N_{0}}= \frac{\mu_{1}\ \kappa|L|^{2}}{\mu_{2}\ \kappa|M|^{2}+1} \tag{6}\]  
where \(\kappa=\frac{\kappa_{s}}{N_{0}}\) is the transmit SNR. Let \(\alpha_{1}=|L|^{2}\) and \(\alpha_{2}=|M|^{2}\). The corresponding probability density functions (pdf) are given by  
\[f_{\rm A_{1}}(\alpha_{1})=\frac{1}{{N{v_{1}}^{2}}}exp\bigg{(}-\frac{\alpha_{ 1}}{{N{v_{1}}^{2}}}\bigg{)} \tag{7}\]  
\[f_{\rm A_{2}}(\alpha_{2})=\frac{1}{{N{v_{2}}^{2}}}exp\bigg{(}-\frac{\alpha_{ 2}}{{N{v_{2}}^{2}}}\bigg{)} \tag{8}\]  
In the above expressions, \({N{v_{1}}^{2}}\) and \({N{v_{2}}^{2}}\) are the average channel gains of user 1 and user 2, respectively, i.e., \(E\{\alpha_{1}\}={N{v_{1}}^{2}}\) and \(E\{\alpha_{2}\}={N{v_{2}}^{2}}\).
## 1 Introduction  
Barrel end-milling, also known as Parabolic Performance Cutting (PPC), represents a further development of milling with ball-nosed end mills. This is a relatively new discipline, also called barrel tool milling or tangential milling. Barrel tools are circular segment end mills and turbine end mills, used for machining complex parts. PPC involves the use of a modern 5-axis milling machine and CAM software that allows the tool geometries to be processed as a data model, thus supporting the PPC process.  
Barrel end-milling (PPC) is used on 5-axis CNC machine tools. In the absence of a 5-axis programmable CNC machine tool, it is necessary to position the workpiece relative to the milling cutter for testing, so that during the cutter's movement, the position of its axis respects the programming angle. While theoretical aspects of barrel tool performance are well-studied, adapting these tools for 3-axis CNC machines remains a practical hurdle. To use barrel tools on a 3-axis CNC machine tool, the machining of an inclined plane surface will be programmed. The inclination of the machined plane will be equal to the programming angle of the barrel tool. Thus, the cutting edge of the tool will be tangent to the machined surface.  
Machining a plane surface can be performed with barrel tools and with the help of a CNC machine tool. However, performing barrel tool milling tests on a 3-axis CNC machine, unlike a 5-axis machine, requires a specific positioning of the workpiece machined surface relative to the cutter.  
For each programming angle of the barrel tools, a well-defined position of the workpiece is necessary. This workpiece position for machining can be ensured by creating a fixture with interchangeable locating elements, depending on the programming angle.  
The objectives of this research are  
Designing a fixture to orient the workpiece in accordance with the programming angle of the cutter and the motion axes of the 3-axis CNC machine tool.  
Ensuring workpiece clamping during machining.  
Providing probing surfaces for defining the workpiece-fixture coordinate system, in which the machining program is written.  
The fixture must orient the workpiece in accordance with the programming angles of the tools used for testing (18\({}^{\circ}\), 20\({}^{\circ}\), and 42.5\({}^{\circ}\)).  
Regarding the originality and importance of the topic, this research proposes a novel and practical method to utilize barrel tools, typically intended exclusively for 5-axis machines, on a more accessible 3-axis CNC machine tool, through the design of a specialized fixture. The fixture ensures correct workpiece positioning for machining inclined planes, thereby uniquely compensating for the lack of additional axes of the machine tool. This approach allows for complex machining operations to be performed on simpler and more accessible machine tools, having a potentially significant impact in various industries that may not have access to 5-axis equipment.
# Spatiotemporal Image Fusion in Remote Sensing
###### Abstract  
In this paper, we discuss spatiotemporal data fusion methods in remote sensing. These methods fuse temporally sparse fine-resolution images with temporally dense coarse-resolution images. This review reveals that existing spatiotemporal data fusion methods are mainly dedicated to blending optical images. There is a limited number of studies focusing on fusing microwave data, or on fusing microwave and optical images in order to address the problem of gaps in the optical data caused by the presence of clouds. Therefore, future efforts are required to develop spatiotemporal data fusion methods flexible enough to accomplish different data fusion tasks under different environmental conditions and using different sensors data as input. The review shows that additional investigations are required to account for temporal changes occurring during the observation period when predicting spectral reflectance values at a fine scale in space and time. More sophisticated machine learning methods such as convolutional neural network (CNN) represent a promising solution for spatiotemporal fusion, especially due to their capability to fuse images with different spectral values.  
Keywords:data fusion; time series satellite images +
Footnote †: journal: Remote Sensing
###### Abstract
These have focused on microalgae species screening [21], wastewater type testing [18; 22], culture process optimization [23], trophic mode comparison [24] and lipid composition analysis [13]. For example, [PERSON] et al. [22] found that the initial biomass concentration significantly influenced lipid accumulation when _Chlorella vulgaris_ was cultivated using seafood wastewater effluent. [PERSON] et al. [24] confirmed that simultaneously adding optimal amounts of iron and acetate to municipal wastewater increased the biomass concentration of _Chlorella pyrenoidosis_, lipid content and lipid productivity, respectively, by 185%, 248% and 671%. These results indicated that iron and acetate supplements can greatly improve lipid productivity. _C. pyrenoidosis_ is often used for wastewater treatment, such as swine wastewater, municipal and industrial wastewater [24; 25; 26]. Moreover, several studies demonstrated that _C. pyrenoidosis_ has a strong capability to remove the COD, TN, NH\({}_{3}\)-N and TP from wastewater, and simultaneously accumulate a large amount of lipid [27; 28]. In the present study, we mixotrophically cultivated _C. pyrenoidosis_ in a mixture of swine wastewater (SW) and diluted furfural wastewater (DFW) to evaluate the feasibility of simultaneous treatment of swine and furfural wastewater combined with microalgal lipid production. Microalgae were cultivated in wastewater with different mix ratios of swine to furfural wastewater, and microalgal growth, nutrient removal and lipid accumulation were evaluated.