esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
### DEGs Related to Glucoraphanin Biosynthesis in Different Organs  
Glucoraphanin is a major GSL in broccoli [25]. Previous studies have shown that _BCAT_ and _MAM_ are crucial enzymes in delamination and transamination during side chain extension [27; 28; 29]. In this study, we found that _BCAT2_ was differentially expressed in the stalk, and _BCAT4_, _MAM1/2_ in the leaf and floret (Figure 2D-F). The upregulated expression of them is an important factor in glucoraphanin accumulation [33]. For the formation of core structure, _CYP_, _GST_, _UGT_, _SOT_ family genes played a crucial role in a series of biochemical reactions [5; 6]. In our study, _CYP97F1_ was differentially expressed in all three plant parts, while _CYP83A1_ was only differentially expressed in stalk (Figure 8). Moreover, we also detected some _GST_ or novel homologous (_GSTF2_, _GSTF3_, _GSTT3_ and _GSTU_) and _SOT_ (_SOT15_, _SOT17_ and _SOT18_) genes in different organs (Figure 8). Previous studies have shown that the accumulation of _FMOGS-OX_ catalyzed the conversion of glucoerucin into glucoraphanin [34]. In this study, we also found that _FMOGS-OX2_ and a homologous gene _FMOGS-OXL8_ were differentially expressed in stalk and leaf, respectively (Table S6). Thus, the expression of these DEGs related to glucoraphanin was tissue and spatiotemporal-specific (Figure 8). This explained the different levels of glucoraphanin in different organs. In addition, we also found some novel homologous genes to functional genes, which participated in the process of conjugation of the activated aldoximes to a sulfur donor, and the conversion from S-oxygenated to aliphatic glucosinolates.  
Figure 7: Connection network between DEGs (red ovals) and DAMs (blue ovals) of glucoraphanin biosynthesis in stalk (**A**) and floret (**B**). Blue and red ovals represent metabolites and genes, respectively. Solid line represents positive correlation, and dash line represents negative correlation.
## 4 Experimental Results and Analysis
### Effective Stiffness  
To evaluate the seismic performance of each specimen based on the test results, the effective stiffness was calculated according to the literature [17]. Effective stiffness is defined as the slope of the maximum load and displacement in the positive and negative directions of each cycle. The effective stiffness calculated by this method for each test specimen according to the drift ratio is summarized in Table 4; the rate of reduction in the effective stiffness when compared to the initial stiffness is shown in Figure 12. For the NRC test specimen without seismic details, the initial effective stiffness of 22.31 kN/mm decreased by approximately 24% at a drift ratio of 1%, where initial cracks occurred. In the case of the WCFST specimen, the initial effective stiffness was 43.24 kN/mm, about twice that of the NRC test specimen. The effective stiffness was reduced by about 15% at a drift ratio of 1% and 22% at a drift ratio of 2.5%, where failure of the specimen occurred. As a result of the experiment, the initial stiffness of the WCFST specimen attached to CFST was approximately twice that of the existing reinforced concrete test specimen. Furthermore, the WCFST specimen showed more than twice the effective stiffness of the NRC specimen when failure occurred. When the effective stiffness was equal to half of the initial effective stiffness, the drift ratios were 0.75% and 1.5% for the NRC and WCFST test subjects, respectively, indicating a greater reduction in the effective stiffness of the NRC specimens. It was confirmed that the WCFST system significantly increased the effective stiffness when compared with the existing reinforced concrete frame, and decreased the effective stiffness more smoothly after cracking occurred.  
Figure 11: Strain at the end of the column: (**a**) strain of the concrete at the end of the column, (**b**) strain of the longitudinal steel at the end of the column, (**c**) strain of the transverse steel at the end of the column, and (**d**) strain of the CFST frame at the end of the column.
# Future Industrial Applications: Exploring LPWAN-Driven IoT Protocols
## 3 Wireless Protocols Architecture
### Sigfox  
Sigfox is an open-source technology and the first of its kind. Sigfox was designed to focus on an even longer range and lower data rate compared to LoRaWAN, in which it utilizes ultra-narrow-band (UNB) frequency [43]. It was designed using a variety of low-power IoT devices, including several sensors and M2M applications [13]. It delegates the complex processing to the cloud instead of the individual nodes to reduce resource consumption [29]. Star topology can be implemented with Sigfox [41].  
Figure 2 gives a simple overview of the network architecture of IoT technologies that utilize the free-of-cost ISM frequency spectrum to establish and maintain wireless M2M connectivity. In this framework, the data flow from the user end (UE) devices to the IoT base station over the associated wireless protocol: LoRa, Sigfox, or Z-Wave. For example,  
Sigfox uses its proprietary UNB technologies while utilizing the open-source sub-GHz ISM band spectrum. LoRaWAN also employs a sub-GHz ISM band spectrum for wireless connectivity with end devices. The end device can be an actuator, a temperature-measuring device, a sensor, etc. The base station then acts as the gateway to the respective protocol's IoT server over an ethernet connection. Gateways have point-to-point connectivity with the servers through an IP network, and the data are passed on through the proprietary IoT cloud to the servers. Servers are placed in the network according to requirements, following the specifications supported by the topologies that are being used. Both Sigfox and LoRaWAN support star topology, while Z-Wave can implement mesh topology [41; 42].
# Tabular Data Models for Predicting Art Auction Results
## 3 Results
### XGBoost  
XGBoost outperformed all other models across NoImg and SVD datasets, achieving the lowest sMAPE scores of 55.11% and 54.83% and consistent results in other datasets presented in Figure 4.  
The authors believe it to be due both to its precise optimization and robust regularization capabilities. The core strength of XGBoost comes from its gradient boosting framework, where trees are built sequentially to minimize a regularized objective function. The objective function includes both the gradient (first derivative) and the Hessian (second derivative), allowing for more precise optimization. The function is approximated as follows:  
\[\mathrm{L}(\theta)\approx\sum_{i=1}^{n}\left[\mathrm{g}_{i}(\theta)+\frac{1} {2}\mathrm{h}_{i}\left(\theta^{2}\right)\right]+\Omega(f) \tag{6}\]  
where \(\theta\) represents parameters of the model, \(g_{i}\) is the gradient, \(h_{i}\) is the hessian, \(\Omega(f)\) is a regularization term.  
The second strong point of XGBoost is a solid regularization term that is based on both L1 (Lasso) and L2 (Ridge) techniques that control model complexity. The complete regularization term is described by  
\[\Omega(f)=\lambda\sum_{j=i}^{m}\theta_{j}^{2}+\alpha\sum_{j=1}^{m}\left| \theta_{j}\right| \tag{7}\]  
where \(\lambda\) controls the strength of L2 regularization (penalizing large weights), and \(\alpha\) controls the strength of L1 regularization (encouraging sparsity by setting less important weights to zero). In auction datasets, L1 regularization helps focus the model on key features  
Figure 4: Bar chart comparing the sMAPE metric of XGBoost model across the datasets.  
Figure 3: Bar chart comparing the sMAPE metric of RandomForest model across the datasets.  
(e.g., ARTIST or TECHNIQUE) by eliminating irrelevant ones, simplifying the model and improving interpretability. L2 regularization, on the other hand, controls the size of the remaining feature weights by penalizing large coefficients. This prevents any single feature from dominating the model and helps reduce overfitting, making the model more stable and robust, especially in noisy auction datasets. Together, L1 and L2 regularization allow XGBoost to balance model complexity and generalization.
## 2 Results and Discussion
### Dye Adsorption  
Methylene blue (MB) dye adsorption was carried out in aqueous media to gain insight on the role of the textural properties (surface area and porosity) of the treated and untreated flax fibers and the relative MB uptake for the various fiber systems. As shown in Figure 9, analysis of dye uptake by the raw FFs revealed enhanced MB dye adsorption, as compared with the treated samples. MB adsorption by the F-T15-ALK and FT-15 samples was reduced by 75% and 50%, respectively. The greater MB uptake by raw samples can be related to the higher abundance of lignin, pectins, hemicellulose, and ionizable groups (-COOH, -OH, etc.) that favor electrostatic adsorption of the MB cationic dye [41; 42]. In the case of treated FFs, removal of non-cellulosic fiber components such as lignin, pectin, and hemicellulose occurred, which resulted in a reduced level of ionizable groups that have lower affinity to MB dye due to enrichment of cellulose content at the expense of the other components with ionizable groups. The results concur with the removal of non-cellulose biomass fractions by the treatment techniques, resulting in fibers with reduced polar and/or ionizable functional groups (cf. Figures 5 and 7). Parallel conclusions were drawn by [PERSON] & [PERSON] [40] for flax fiber composites that contained incremental levels of chitosan. The decreased uptake of MB was related to greater formation of complexes between the lignin components of FFs as the chitosan loading increased.
# Anomaly Detection Method for Unknown Protocols in a Power Plant ICS Network with Decision Tree
## 4 Discussion  
In Section 2.2, we present a decision tree-based model for anomaly detection in the control network of a power plant. We could have provided more details on how decision trees work and how we applied them in our study. Specifically, decision trees are a type of supervised learning algorithm that recursively partition data based on the most significant features to make a series of binary decisions that lead to a prediction or classification. In our study, we used decision trees to classify traffic as normal or abnormal based on a set of features, such as packet size, port number, and protocol type. We trained the decision tree using a labeled dataset and tested it on unseen data to evaluate its performance.  
Our experimental results showed that the decision tree-based model achieved superior detection efficiency, a lower false-positive rate, and a faster detection processing time compared to those of existing solutions. This suggests that applying machine learning to the control network of power plants is crucial for ensuring system stability in unpredictable environments. The proposed method also allows for the early detection of anomalies, which can prevent human error and the normalization of abnormalities.  
We acknowledge that in a real-world operating environment, additional indicators and verification factors need to be applied to improve anomaly detection. However, the proposed method enables the separate collection and analysis of control system network data without affecting the control system's operation, facilitating the control of abnormal symptoms.
# Towards Sentiment Analysis for Romanian Twitter Content
## 3 Data Processing Methodology  
Given the specific advice provided in [2] with respect to building a SA classifier, in this section, we present our approach to carrying out this task. Figure 1 summarizes all performed steps. At the top of the diagram, the automatic translation process of the dataset from English to Romanian is highlighted. The preprocessing step consists of various procedures and are grouped in two different abstract pipelines. The one on the left will generate texts which are fit for the TF-IDF variants, Doc2 vec and Word2 Vec techniques while the one on the right will generate texts as expected by the pretrained BERT encoder. The feature extraction step highlights the transformation of the preprocessed text into numeric features which can be later used to train the ML models. The TF-IDF variants and Doc2 Vec are grouped together to highlight that the output of all these methods are in the same form. To be more specific, a preprocessed text is transformed into a vector of length \(N\) while Word2 Vec will transform a preprocessed text into a \(NxM\) matrix where the number of rows will be equal to the number of words within the text and the number of columns will be equal to the word embedding size, i.e., the number of values set to represent a single word/token. BERT contextual encoding will represent texts using multiple vectors.  
Figure 1: Architecture of the sentiment analysis system.  
In model training and tuning, we can see that for all the selected ML approaches, with the exception of the BERT classifier, the evolutionary hyperparameter optimization methodology is used to identify the optimal set of parameters. Due to the high training times of BERT, we opted for a classic fine tuning process using the recommended parameters. The Bernoulli NB, Linear SVM, Logistic Regression, Random Forest and DNN are grouped together to highlight the many-to-many relation of this group with the TF-IDF variants and Doc2 Vec features. This means that any feature from this group can be used by any model mentioned previously. LSTM and CNN are grouped together in order to highlight that both use the Word2 Vec features, while the BERT classifier uses the specific BERT encodings. The prediction of any trained model will denote the sentiment polarity of the input texts.  
In the following subsections, we introduce the dataset and provide details on the following: text preprocessing, feature extraction, dimensionality reduction, and classifier selection. Many transformations over the text indicated below were implemented with the help of SpaCy [57] ([[https://spacy.io/](https://spacy.io/)]([https://spacy.io/](https://spacy.io/)), accessed on 1 September 2022). Model construction and performance evaluation are presented in Section 4.
## 4 Results
### La Salud North  
The geophysical models for La Salud North profile are depicted in Figure 8. The resistivity profile (Figure 8a) shows striking lateral variations on the resistivity. The CZ is interpreted by high resistivity (\(\rho>200\)\(\Omega\)m) reaching the surface in the NW and SE parts of the line profile. This is consistent with the Quaternary formation's expected high resistivity, which is formed by alluvial gravel and siltstone deposits. The medium resistivity (50-150 \(\Omega\)m) corresponds to the Tortonian-Messinian formation with marl and interlayered sand and gypsum along the NW-facing profile path at a depth of more than 50 m. A low resistivity \(\rho<50\)\(\Omega\)m at a distance of ca. 450-900 m is interpreted as the Messinian-Pliocene formation with sandy and marl siltstone and interlayered laminated gypsum and marl. The low resistivity contact is interpreted as a fault zone (defined by the fault planes F1 and F2 in Figure 8) that corresponds to the S-AMF strand. This low resistivity was also clearly identified in the resistivity model obtained by inversion of magnetotelluric data reported by [PERSON] et al. (2020) [48]. The authors interpret this area as an alternation of resistors and conductors (50 \(\Omega\)m/5000 \(\Omega\)m) produced by the alternation of shallow levels of very dry colluvial materials cut by vertical faults.  
The Vp and Vs profiles (Figure 8b and Figure 8c, respectively) show a low seismic velocity area supbarallel to the surface. The CZ is characterized by low seismic velocity (Vp \(<\) 1300 m/s and Vs \(<\) 600 m/s), which corresponds to the Quaternary formations. On the NW-SE board, the thickness of the CZ varies from 30-40 m at a distance of ca. 100m (h1), increases by 40-50 m at a distance of ca. 350 m, very close to the fault zone F1 and F2 (h2), and re-thins by 30-40 m at a distance of ca. 750 m (h3). The bedrock is characterized by a velocity layer of Vp \(>\) 1300 m/s and Vs \(>\) 600 m/s. In turn, the fault zones F1 and F2 are indicated by the low-velocity contrast of Vp which is well correlated with the ERT results. Meanwhile, another fault is predicted from the geophysical interpretation that is located at a distance of ca. 120 m.
## 5 Discussion  
Lattice and cellular structures are proven to be suitable options for tailoring properties when they are used to fill parts and components. However, when characterizing their effective properties, the lattice samples are fabricated with simple macro geometries, such as cubes and prisms. Components in real applications have far from simple macro geometries. The need is then for these structures to conform to complex macro geometries. The first step toward this is to conform them to non-planar surfaces [6]. The algorithm developed in this work supports this general objective found in the literature by allowing specific trajectories (generated parametrically following recursive or repetitive patterns). Furthermore, it contributes to the manufacturing of continuous filament deposits on a non-planar surface, such as those needed for lattices (printing layer-by-layer affects the performance of structure) or to deposit conductive material (stair-stepping effect would cause discontinuity) or any second material on the surface of a substrate.  
The results presented in this work demonstrate that the algorithm's precision depends on the resolution polygonal mesh of the target structure. For example, a saddle's geometry was chosen to use two different approximations: the first with low-resolution and 33,240 triangles, and the second with an approximately three times higher resolution (99,816 triangles). Based on the data collected and condensed in Figure 12, the mean of the absolute relative error found for the low-resolution polygonal mesh was 0.025853, with concentrated highest values of error at the saddle surface's concave zones and a maximum value of error of 0.287034%. To reduce the error, the number of triangles (vertices) must be increased to increase the polygon mesh. Here, when the resolution of the previous low-resolution polygonal mesh of saddle target was improved with approximately a three-times higher resolution geometry, the mean of the absolute relative error decreased to 0.007551, which represents closely the same proportion \((1/3)\) of change, and the error  
Figure 17: Simulation of a hexagonal pattern projected on an arbitrary tessellated target structure.  
Figure 18: Actual 3D printing of hexagonal pattern projected on an arbitrary tessellated target structure.  
zones remained at the concave zone, as can be observed in Figure 13. It can be assumed that the error decreases as the tessellated surface resolution improves at the same proportion. The algorithm proposed here allows for projecting complex printing trajectories to achieve structures with different densities of material as the Hilbert curve sample shown in Figure 14a, where the length of the unitary cell is changed to simulate a different density distribution. In Figure 19, the curved lattices printed were detached from the target structure, using adhesive blue painter tape to cover the target structure before printing the lattices.
## 4 Experiments and Results  
### Datasets  
Annotated data of Arabic tweets is important to build a system for Arabic sentiment analysis in the context of COVID-19. To our knowledge, SenWave is the only available dataset that can be used for emotion detection of Arabic COVID-19 tweets. This dataset consists of 10,000 tweets collected from 1 March 2020 to 15 May 2020. Each tweet is labeled by 1 or more emotions, from a total of 11 emotions. However, this dataset is highly imbalanced and we generated another dataset, SenAIT, by combining the common classes of SenWave with AIT dataset. These two datasets are used to train and evaluate the proposed models. Each dataset is divided into 80% for training and 20% for testing. More information about these datasets and the augmentation techniques used to enrich them can be found in Section 3.5.
# A Novel Dynamic Approach for Risk Analysis and Simulation Using Multi-Agents Model
## 8 Simulation Testbed  
In literature, many software languages and tools specifically focused on ABMS development have become established as open-source ABMS platforms, such as NetLogo, Swarm [109], AnyLogic, Repast [110], JADE [111], and MASON [112]. A comparison between those simulators is presented in Table 6 according to many criteria.  
GDABM was simulated using the Repast Simphony Simulator tool.  
The simulation of this model provides the following additional features: (1) identifying and analyzing the risk among system components, (2) studying the risk propagation among these components, and (3) performing the risk evaluation process.
## 3 Mitigation Scheme for SAR
### Notch Filtering  
The intuitive way to perform RFI detection and mitigation is to apply notching filtering in the range spectrum of the radar echoes, which simply excises the samples that are beyond the energy threshold. Since RFI is usually narrowband when compared with the transmitted pulse bandwidth, and presents as narrow spikes in the frequency domain. These spikes are usually many dB stronger than the surrounding signal level. This method is quite simple and efficient and, thus, widely adopted in real airborne SAR and space-borne SAR systems, such as the E-SAR system [20] and Phased Array type L-band Synthetic Aperture Radar (PALSAR) [21]. The ITU-R RS.1749 report also provided a similar strategy suggestion for the Earth exploration-satellite service (EESS) services [33]. [PERSON] et al. [34] proposed a sub-band spectral cancellation method, in which the NBIs are estimated and cancelled by subtracting different range sub-band spectra of the SAR image.  
The range spectrum notching filtering should be performed on raw-data whenever possible. When raw data are undisclosed and only focused single look complex (SLC) images are accessible, it is required to remove the remaining RFI from focused data. [PERSON] et al. [30] and [PERSON] [35] proposed posteriori filtering methods to excise interference for focused image. [PERSON] et al. [36] proposed a filtering method in the range-time and azimuth-frequency domain utilizing the Doppler difference for intermittently transmitted wideband (ITWB) RFI.  
In these cases, the latent fundamental assumption is the RFI is narrow-band, and the signal loss after interference mitigation can be tolerated. However, in some practical cases, the radar echoes may be corrupted by many narrow-band sources or interference with larger bandwidth. The deficiency of notch filtering method is that if the notching exceeds 2% of the waveform, it will induce the degradation in spatial resolution, as well as serious degradation in the waveform side-lobe levels [37]. Since it is an incoherent elimination process, it is not phase preserving. The aperture synthesis in SAR, polarization synthesis in radar polarimetry, and interferogram generation in interferometry SAR (InSAR) would also be affected if large portion of the samples that are notched. To deal with this deficiency, [PERSON] et al. [38] and [PERSON] et al. [39; 40; 41] studied the reconstruction methods based on spectral estimation, with the help of cross-information between coherent data pairs.