esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 5 Limitations of Current AGB Maps and Future Improvements  
### Compilation of Field Biomass Data  
The above efforts to generate accurate forest AGB maps mainly focus on the application of multiple datasets and advanced algorithms, while few efforts have been made to ensure the accuracy of field biomass data. Furthermore, the allometric equations used to calculate field biomass and the representativeness of field biomass are major sources of uncertainties of estimated AGB on a regional scale [112; 113; 114; 115]. Additionally, most AGB maps are not validated due to lack of field biomass data. Therefore, the acquisition and compilation of abundant and highly accurate field biomass data, which would ensure the accuracy of biomass mapping, is of utmost importance.  
Figure 14: Frequency distribution of AGB with 10 Mg/ha bins in eight maps covering South America.  
Figure 15: The average value of the fuzzy numerical index in seven forest biomass maps of evergreen broadleaf forest and savannas over South America.  
Currently, researchers of several international networks such as RAINFOR (the Amazon Forest Inventory Network, [[http://www.rainfor.org/](http://www.rainfor.org/)]([http://www.rainfor.org/](http://www.rainfor.org/))), AfriTRON (African Tropical Rainforest Observation Network, www.afritron.org), and ForestGEO (Global Earth Observatory, [[https://forestgeo.si.edu/](https://forestgeo.si.edu/)]([https://forestgeo.si.edu/](https://forestgeo.si.edu/))) network, have engaged in utilizing long-term permanent sample plots to monitor forest biomass and dynamics [116]. The Forest Observation System (FOS, [[https://forest-observation-system.net/](https://forest-observation-system.net/)]([https://forest-observation-system.net/](https://forest-observation-system.net/))) which includes, but is not limited to data records from these networks, is tasked to coordinate in situ activities in relation to the BIOMASS mission [117]. The biomass plot data will be in unified format, and processed with standardized procedures. With the development of these international networks, sufficient field plots with acceptable accuracy will be available which would improve the calibration and validation of remotely sensed data-based biomass estimation on a large scale.  
Previous studies indicate the possible limitations of using field plots for \"truthing\" due to the existence of plot biases and errors, particularly for heterogeneous landscapes [118]. Therefore, future forest biomass studies could use LiDAR as the sampling technique to complement to field-plot sampling.
# Fracture Threshold Analysis and Parameter Matching of Cut-Out Induced Bolts for Subway Couplers
## 2 Experiment and Simulation Analysis  
### Impact Experiment  
#### 2.1.1 Cut-Out Induced Bol  
Figure 2 shows the tensile break bolt-type overload protection on the coupler. Its specification is M24, and the material grade is 10.9. It can be observed from the figure that a cut-out zone is set on the smooth shank of the bolt. This structure can be described by two structural parameters: width and depth. By varying the width and depth of the cut-out zone, the cross-sectional shape of the bolt can be altered, thereby enabling control over the bolt's impact resistance strength. The currently used bolt cut-out zone has a width of 10.4 mm and a depth of 2.14 mm.  
As shown in Figure 3, the cut-out induced bolts of the subway train coupler are installed between the coupler yoke and the buffer front plate, used to connect the buffer and the coupler yoke. When a collision occurs, the impact force is transmitted through the coupler head, expansion tube, and buffer to the bolts. Due to the constraint of the coupler yoke, a pair of tensile forces is generated on the bolts. When the tensile force exceeds the strength of the bolts, the bolts break, the connection between the coupler and the coupler yoke is invalid, and the coupler is no longer constrained, thus completing the function of overload protection. It is particularly important to note that the fracture threshold of the overload protection device is collectively determined by all four cut-out induced bolts. Should a significant sequential fracture of the cut-out induced bolts occur during impact, the fracture threshold of the overload protection device would substantially decrease. Therefore, the four cut-out induced bolts should maintain maximum consistency in both structure and material properties to ensure simultaneous fracture during impact  
Figure 2: Structural parameters of cut-out induced bolt.  
events. During installation of the cut-out induced bolts, a torque wrench (corresponding tightening torque: 1.92 N-m) was used to apply a 400 N preload force to each bolt.
# Designing Procedure Execution Tools with Emerging Technologies for Future Astronauts
## 3 Ground Procedures
### Research  
For this project, we conducted a literature review of repair, maintenance, and servicing tasks at NASA, contextual inquiry within primary and analogous domains, subject matter interviews, needs identification and validation for a primary user group, and formulation of requirements for a proof-of-concept technology interface. Twelve subject matter interviews were conducted at NASA KSC with TOSC field service technicians, engineers who author procedures, as well as facility managers. Despite the fact that there are a host of stakeholders involved in the execution of ground procedures, technicians became the target user for the project based on an evaluation of challenges (\"pain points\"). Our research revealed the following challenges for technician execution of ground procedures: current procedure documents do not provide sufficient foresight to upcoming tasks relevant for efficient team management, planning, or organization, team communications are not linked, tied, or associated with specific work steps, comments are not captured within the execution workflow and are typically communicated among collaborators via text message, and supplementary materials (manuals and resource information) are not inherently linked to the PDF procedure (and may be missing from TOSC-specific workflows). Technicians typically need their hands free to execute work, yet our research observed that they often carry large manuals or tablets with reference materials. Our research also indicated that a significant challenge of collaborative ground procedure execution included that buoyf tended to be difficult using paper copies of procedures in the field. Ultimately, ensuring task safety, efficiency, reliability, and troubleshooting were identified as significant areas of concern and potential areas for improvement. A mixed reality interface was proposed to overlay a checklist within a hands-free interface. Our team hypothesized that a mixed reality interface would: improve WAD execution efficiency, provide additional foresight into upcoming tasks, and enable the user to quickly access task-specific information.
# A Hybrid STL-Deep Learning Framework for Behavioral-Based Intrusion Detection in IoT Environments
###### Abstract  
The rapid proliferation of Internet of Things (IoT) devices presents significant security challenges due to inherent vulnerabilities and increasing cyberattacks. Effective intrusion detection systems (IDSs) are crucial for securing IoT environments, requiring high detection accuracy while efficiently handling large data volumes and critically minimizing both false negatives (FNR), which miss real attacks, and false positives (FPR), which cause unnecessary alarms. Traditional methods and standalone deep learning models often struggle to achieve an optimal balance between these requirements. This paper proposes a novel hybrid IDS framework designed to enhance IoT network security by integrating time series analysis with deep learning. Specifically, we leverage seasonal-trend decomposition using Loess (STL) as an intelligent pre-filtering mechanism to isolate potentially anomalous traffic, which is then classified using sophisticated deep learning models, with a particular focus on long short-term memory (LSTM) networks compared against recurrent neural networks (RNN) and dense neural networks (DNN). The proposed framework performance was rigorously evaluated using the comprehensive and realistic BoT-IoT dataset. The methodology involved principal component analysis (PCA) for dimensionality reduction and careful hyperparameter tuning. The experimental results demonstrate the effectiveness of the hybrid approach, with the STL-LSTM variant achieving superior performance. It attained an overall accuracy of 98.5% and a macro F1-score of 98.49%, while significantly reducing the overall false negative rate to 5.2% (FNR \(\approx\) 0.24) and the overall false positive rate to 0.276% (FPR \(\approx\) 0.11) across five Attack/Normal classes. These results represent a substantial improvement over standalone deep learning models (standalone LSTM FNR = 0.302, FPR = 0.185) and compare favorably to state-of-the-art benchmarks reported in the literature, particularly in minimizing critical detection errors. The findings indicate that the proposed hybrid STL-LSTM framework presents a robust and viable solution for high-stakes IoT network security, effectively balancing high detection accuracy with exceptionally low error rates, making it well-suited for real-time deployment in protecting critical IoT infrastructure.  
Internet of Things (IoT); intrusion detection system (IDS); deep learning; LSTM; time series analysis; anomaly detection; STL decomposition; network security; cybersecurity; feature selection; BoT-IoT dataset +
Footnote †: journal: Applied Sciences
## 2 Ballistic Impact Test  
### Test Specim  
The composite target was braided with 12k T700 carbon fiber in the form angle of (\(\pm 60^{\circ}/0^{\circ}\)). Figure 1 shows the process for manufacturing triaxial braid composites: First, a triaxial braider, mounted with the axis horizontal, created a braid tube using 12k flat tow fibers in both the \(\pm 60^{\circ}\) (bias) and \(0^{\circ}\) (axial) directions (Figure 1a). The tube was then flattened into a double-walled tape (Figure 1b). Next, the three layers of flattened tapes were piled up with the \(0^{\circ}\) fibers aligned. Triaxial braided composite panel was fabricated using the resin-transfer molding technique, whereby epoxy resin was infused into multiple layers of dry braided preform. This transfer molding method has the advantages of good surface finish, dimensional stability, and good mechanical properties [49]. The fiber volume was the same in each direction so that both the individual plies and the entire lay-up were quasi-isotropic. From a large panel, 250 mm \(\times\) 250 mm composite targets were cut out, each 4.3 mm thick.  
As mentioned in the Introduction part, there are three types of projectiles which are commonly used or have potential application for fan blade out events of aero-engine, i.e., cylindrical gelatin projectiles, rectangular titanium alloy projectiles, and composite blade-like projectiles. In the current study, these three projectiles were designed with the same mass. The geometry and dimensions of three types of projectile were shown in Figure 2. The gelatin projectiles were shaped as flat-ended cylinders with 30 mm diameter and 52 mm height. The cylindrical gelatin projectile has a density of 950 kg/m\({}^{3}\) and a porosity of 10%. The CFRP and titanium-alloy projectiles were both rectangular, 70 mm long, and 40 mm wide. Considering the differences in density, the CFRP projectile was 6 mm thick while the titanium-alloy projectile was 2 mm thick. Because the current research is aimed at a specific engine containing casing as the research object, the design of the projectile is based on the engine rotor blades, which is usually a thin sheet with a three-dimensional twisted blade shape. Therefore, the thickness of 2 mm of the titanium alloy blade herein is a suitable thickness. The thickness of the CFRP material body is determined based on the density difference between the titanium alloy material and the carbon fiber composite material. The CFRP projectile is a 12-layered woven fabric-reinforced laminate. The rectangular projectiles have the same equivalent diameter as the gelatin cylinder.
###### Abstract  
Data imbalance is a frequently occurring problem in classification tasks where the number of samples in one category exceeds the amount in others. Quite often, the minority class data is of great importance representing concepts of interest and is often challenging to obtain in real-life scenarios and applications. Imagine a customers' dataset for bank loans-majority of the instances belong to non-default class, only a small number of customers would be labeled as defaulters, however, the performance accuracy is more important on defaulters labels than non-defaulter in such highly imbalance datasets. Lack of enough data samples across all the class labels results in data imbalance causing poor classification performance while training the model. Synthetic data generation and oversampling techniques such as SMOTE, AdaSyn can address this issue for statistical data, yet such methods suffer from overfitting and substantial noise. While such techniques have proved useful for synthetic numerical and image data generation using GANs, the effectiveness of approaches proposed for textual data, which can retain grammatical structure, context, and semantic information, has yet to be evaluated. In this paper, we address this issue by assessing text sequence generation algorithms coupled with grammatical validation on domain-specific highly imbalanced datasets for text classification. We exploit recently proposed GPT-2 and LSTM-based text generation models to introduce balance in highly imbalanced text datasets. The experiments presented in this paper on three highly imbalanced datasets from different domains show that the performance of same deep neural network models improve up to 17% when datasets are balanced using generated text.  
GPT-2; transformer; text generation; imbalanced datasets; LSTM; deep neural network; deep neural language model +
Footnote †: journal: Applied Sciences
# Impact of Chitosan-Genipin Films on Volatile Profile of Wine along Storage
## 2 Materials and Methods
### Analysis of Volatile Compounds
#### 2.4.3 Determination of Volatile Compounds in Wines  
The analyses of volatile compounds of white and red wines were performed by HS-SPME combined with comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry (GC \(\times\) GC-ToFMS). A gas chromatograph, LECO Pegasus 4D (Leco, St. Joseph, MI, USA) GC \(\times\) GC system consisted of an Agilent GC 7890 gas chromatograph (Agilent Technologies, Inc., Wilmington, DE) with a dual stage jet cryogenic modulator (licensed from Zoek) coupled to mass spectrometer equipped with a time-of-flight (ToFMS, 5976 N MS (Agilent Technologies) analyser. The volatile compounds were separated through the combination of two columns: HP-5 (30 m \(\times\) 0.32 mm i.d. \(\times\) 0.25 um film thickness) in the first-dimension (\({}^{\text{1D}}\))) and DB-FFAP (0.79 m \(\times\) 0.25 mm i.d. \(\times\) 0.25 um film thickness in the second-dimension (\({}^{\text{2D}}\))). Both columns purchased from J&W Scientific (Folsom, CA, USA).  
The chromatographic conditions were the same as reported by [PERSON] et al. [27], such as the injector temperature (250 \({}^{\circ}\)C), the temperature program for primary (40 \({}^{\circ}\)C (1 min) to 230 \({}^{\circ}\)C (2 min) at 10 \({}^{\circ}\)C/min) and secondary (70 \({}^{\circ}\)C (1 min) to 250 \({}^{\circ}\)C (3 min) at 10 \({}^{\circ}\)C/min) oven, using helium (2.5 mL/min) as carrier gas. The modulator was offset by +20 \({}^{\circ}\)C above in relation to primary oven and the modulation time was 5 s. The mass spectrum parameters were: the transfer line temperature at 250 \({}^{\circ}\)C, detector voltage of \(-\)1786 V, at 70 eV with ion source temperature at 250 \({}^{\circ}\)C, mass range of 33 to 350 \(m/z\), and acquisition rate of 125 spectra s\({}^{-1}\). Total ion current chromatograms (TIC) were processed using data processing software ChromaTOF(\({}^{\text{\textregistered}}\) (LECO).  
For the identification of the compounds, the first criteria is the co-injection of standards (when available). However, it is possible to identify the compounds even in the absence of a standard that consists in a combination of three criteria, namely mass spectrum similarity (provided by commercial libraries Wiley 275 and US National Institute of Science and Technology (NIST) V. 2.0, Mainlib and Replib), relation of calculated linear RIcal (according to the van [PERSON] and Kratz equation [28]) with reported in literature (Rllit) for 5% phenyl-methylsiloxane (or equivalent column), and the presence of a structured chromatogram (in which similar structures are located in similar spaces due to orthogonal separation mechanism that provided the combination of a HP-5 with a DB-FFAP columns) [29]. The use of GC \(\times\) GC peak area, expressed as arbitrary units (a.u.), allowed to estimate the relative content of each volatile component in wine.
# Integrating a Path Planner and an Adaptive Motion Controller for Navigation in Dynamic Environments
## 4 Experiments and Results  
JPS-IA3C combines a geometrical path planner and a learning-based motion planner to navigate robots in dynamic environments. To verify the performances of the path planner, we evaluate JPS+ (P)'s ability to find subgoals on complex maps, and then compare it with the ability of A*. Then, to evaluate the effectiveness of the motion planner, we validate the capabilities of the LSTM-based network architecture and the novel reward function framework in the training process. Finally, the performance of JPS-IA3C will be evaluated in large-scale and dynamic environments. To acquire the map data of tested environments, we firstly constructed grayscale maps via real data from laser sensors, and then transform them to grid maps (if a cell is occupied by an obstacle, this cell is set to be untraversable; otherwise, it is traversable). The distance measurement accuracy can be improved by optimization methods [36]. The dynamics of environments are caused by moving obstacles that imitate the simple behaviors of people (i.e., linear motion with constant speed).  
For simulation settings, we adopt numerical methods to compute the robot's position via kinematic equations. Although smaller steps can achieve more precise results, they also lead to more computational consumptions and slower computation speed. To acquire high efficiencies, we select the step of simulation time as 0.1 s.  
The discounted factor \(\gamma\) is 0.99. The learning rates of the actor-network and critic-network are 0.00006 and 0.0004, respectively. The unrolling step is 7. The subgoal tolerance is 1.5, and the final goal tolerance is 1.0. Since subgoals roughly guide the robot, the subgoal tolerance is relatively large. In the training, we set the time interval of action choice to 2 s, while the time interval decreases to 0.5 s in the evaluation.  
We run experiments on a 3.4-GHz Intel Core i7-6700 CPU with 16 GB of RAM.
# GIS-Based Optimal Route Selection of Submarine Cables Considering Potential Seismic Fault Zones
## 3 Results
### Case Study 1: Submarine Telecommunication Cable Connecting Crete with Libya
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Criteria**} & \multicolumn{6}{c}{**Weight-Influence (\%)**} & \multirow{2}{*}{**Sub-Criteria**} & \multirow{2}{*}{**Rating Score**} \\ \cline{2-2} \cline{5-8}  & & & **Scenarios** & & & \\ \cline{2-8}  & **1** & **2** & **3** & **4** & **5** & & \\ \hline \hline \multirow{4}{*}{Seabed Inclination (\({}^{\circ}\))} & \multirow{2}{*}{82} & \multirow{2}{*}{9} & \multirow{2}{*}{9} & \multirow{2}{*}{33} & \multirow{2}{*}{30} & \multirow{2}{*}{7} & 0–100 & 1 \\  & & & & & & 100–20 & 2 \\ \cline{1-1}  & & & & & & 20–30 & 3 \\ \cline{1-1}  & & & & & & 30–40 & 4 \\ \cline{1-1}  & & & & & & \(>\)40 & 5 \\ \hline \multirow{4}{*}{Bathymetry (m)} & \multirow{2}{*}{9} & \multirow{2}{*}{82} & \multirow{2}{*}{9} & \multirow{2}{*}{33} & \multirow{2}{*}{7} & 0–1000 & 1 \\  & & & & & & 1000–2000 & 2 \\ \cline{1-1}  & & & & & & 2000–3000 & 3 \\ \cline{1-1}  & & & & & & 3000–4000 & 4 \\ \cline{1-1}  & & & & & & \(>\)4000 & 5 \\ \hline \hline \multirow{4}{*}{Seismic Fault Zones} & \multirow{2}{*}{9} & \multirow{2}{*}{9} & \multirow{2}{*}{82} & \multirow{2}{*}{34} & \multirow{2}{*}{63} & \multirow{2}{*}{63} & \multirow{2}{*}{63} & \multirow{2}{*}{63} \\  & & & & & & & & \\ \cline{1-1}  & & & & & & & & \\ \cline{1-1}  & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Weight-influence and rating score of each of the adopted criteria and sub-criteria for Case Study 1. Figure 10: Proposed alternative routes and seismically active fault zones: (**a**) with the bathymetry as a background and (**b**) with the seabed inclination as a background. Since submarine cables are often fully embedded in the seabed due to prevailing sea conditions [32], a burial depth of the cable has been considered equal to 0.5 m, measured from its centerline (H\({}_{\text{b}}\) = 0.5 m). The cable-soil interaction, which has been simulated with PSI elements, is based on the analytical expressions of ALA guidelines [69], in terms of the peak friction angle, \(\varphi_{\text{P}}\), which has been set equal to 30\({}^{\circ}\), the coefficient of lateral earth pressure at rest, K\({}_{0}\) = (1 \(-\) sin\(\varphi\)) = 0.5, as well as reduction factor f, which is taken as equal to 0.6 (smooth polypropylene surface of the cable). The friction between the seabed and the cable has been evaluated considering the effective soil unit weight equal to 5.89 kN/m\({}^{3}\). Figure 12a presents the cable deformations in terms of absolute maximum axial strains at the middle of the cable section for net bedrock displacement equal to 2 cm, 4 cm, 6 cm, 8 cm, 16 cm and 32 cm, for dip angle 60\({}^{\circ}\), whereas Figure 12b displays the actual displacement of the cable.
# Margin and Shared Proxies: Advanced Proxy Anchor Loss for Out-of-Domain Intent Classification
## 3 Proposed Method
### Classic Proxy-Anchor Loss  
Proxy-Anchor loss randomly initializes one proxy for each class and uses the created proxy as an anchor to learn the distance to all samples within a batch. The equation of Proxy-Anchor loss is as follows:  
\[\mathcal{L}=\frac{1}{|P^{+}|}\sum_{p\in P^{+}}\log\biggl{(}1+\sum_{x\in X_{p} ^{+}}e^{-\alpha(s(x,p)-\delta)}\biggr{)}+\frac{1}{|P|}\sum_{p\in P}\log\biggl{(} 1+\sum_{x\in X_{p}^{+}}e^{\alpha(s(x,p)+\delta)}\biggr{)}, \tag{2}\]  
where \(\delta>0\) and \(\alpha>0\) are hyperparameters that respectively refer to the margin and scaling factor. \(P\) is a set of all proxies, and \(P^{+}\) is a set of the positive proxies of the samples in a batch. Moreover, \(X\) is a set of the embedding vectors of all samples in a batch, \(X_{p}^{+}\) is a set of the positive samples of each proxy \(p\), and \(X_{p}^{-}=X-X_{p}^{+}\). The similarity function \(s\) denotes the cosine similarity function.  
The first term of Proxy-Anchor loss, which consists of two terms in total, intuitively brings the distance of positive samples closer to each proxy in the embedding space, while the second term makes negative samples further away. Here, based on the calculation of \(s(x,p)-\delta\), the cosine similarity between the positive sample and the proxy is made larger than the margin (i.e., the sample is made to be located inside the decision boundary). The margin of Proxy-Anchor loss is set directly by the researcher to be the same for all proxies, which has limitations in terms of reflecting the specific characteristics of each IND intents.