esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploring the Potential of Yellow Mealworm (_Tenebrio molitor_) Oil as a Nutraceuticalal Ingredient
## 3 Results and Discussion
### Volatile Compounds  
A total of 68 different compounds were identified in the headspace of TM oil samples, including aromatic hydrocarbons (18), aldehydes (13), hydrocarbons (9), pyrazines (7), ketones (6), terpenes (5), carboxylic acids (3), and furans (1) (Table 4). \begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Batch 1** & **Batch 2** & **Mean \(\pm\) SD** \\ \hline \multicolumn{4}{c}{_Aliphatic Hydrocarbons_} \\ \hline Pentane & 1.96 \(\pm\) 0.22 a & 1.40 \(\pm\) 0.08 b & 1.68 \(\pm\) 0.36 \\ \hline Decane & 0.74 \(\pm\) 0.06 a & 1.26 \(\pm\) 0.06 b & 1.00 \(\pm\) 0.29 \\ \hline (4e)-4-undecene & 0.15 \(\pm\) 0.03 a & 0.36 \(\pm\) 0.07 b & 0.25 \(\pm\) 0.13 \\ \hline (5z)-5-undecene & 0.30 \(\pm\) 0.07 a & 0.21 \(\pm\) 0.03 a & 0.25 \(\pm\) 0.08 \\ \hline Undecane & 0.20 \(\pm\) 0.02 a & 0.57 \(\pm\) 0.01 b & 0.38 \(\pm\) 0.21 \\ \hline
3-methylundecane & 0.11 \(\pm\) 0.02 a & 0.12 \(\pm\) 0.01 a & 0.11 \(\pm\) 0.02 \\ \hline Dodecane & 0.25 \(\pm\) 0.05 a & 0.54 \(\pm\) 0.05 b & 0.39 \(\pm\) 0.17 \\ \hline Tridecane & 0.13 \(\pm\) 0.01 a & 0.23 \(\pm\) 0.02 b & 0.18 \(\pm\) 0.06 \\ \hline Tetradecane & 0.07 \(\pm\) 0.03 a & 0.27 \(\pm\) 0.05 b & 0.17 \(\pm\) 0.12 \\ \hline \multicolumn{4}{c}{_Total_} \\ \hline \multicolumn{4}{c}{_Aromatic hydrocarbons_} \\ \hline Toluene & 0.35 \(\pm\) 0.04 a & 1.25 \(\pm\) 0.17 b & 0.80 \(\pm\) 0.51 \\ \hline Ethylbenzene & 0.26 \(\pm\) 0.02 a & 0.44 \(\pm\) 0.01 b & 0.35 \(\pm\) 0.10 \\ \hline p-Xylene (1,4-dimethylbenzene) & 0.47 \(\pm\) 0.04 a & 0.75 \(\pm\) 0.03 b & 0.61 \(\pm\) 0.16 \\ \hline Styrene (ethenylbenzene) & 0.33 \(\pm\) 0.02 a & 0.27 \(\pm\) 0.02 a & 0.30 \(\pm\) 0.04 \\ \hline O-xylene (1,2-dimethylbenzene) & 0.31 \(\pm\) 0.02 a & 0.32 \(\pm\) 0.00 a & 0.32 \(\pm\) 0.02 \\ \hline Propylbenzene & 0.22 \(\pm\) 0.01 a & 0.24 \(\pm\) 0.01 a & 0.23 \(\pm\) 0.02 \\ \hline
1-Ethyl-4-methylbenzene (toluene, p-ethyl) & 2.15 \(\pm\) 0.19 a & 1.85 \(\pm\) 0.32 a & 2.00 \(\pm\) 0.33 \\ \hline  
5-Hepadiene-3,4-diol & 2.92 \(\pm\) 0.52 a & 1.76 \(\pm\) 0.01 b & 2.34 \(\pm\) 0.75 \\ \hline \multicolumn{4}{c}{_Total_} \\ \hline \multicolumn{4}{c}{_Total_} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Relative content (%) of volatile compounds in _Tenebrio molitor_ oil (n = 6).
## 4 Discussion
As demonstrated by [25], the application of condition-based monitoring in high-precision robotic gearboxes effectively supports early fault detection and long-term operational reliability in demanding industrial environments, further validating the importance of integrating online diagnostics into modern maintenance strategies. Online monitoring can also be part of broader safety strategies, where deviations from normal operation are analyzed to identify potential safety risks. These systems can be implemented as either integrated components of robot control units or standalone software solutions with remote access and control capabilities.
## 2 Background
### Multilingual Pretraining Using Bert  
A few models demonstrated excellent performances on NLU tasks, such as the XL-Net [19] and T5 [20]; however, BERT was still adopted as the baseline model in many studies, as it can be easily applied to fine-tuning, and its performance is comparable to those of other, newer models. In addition, RoBERTa, which affords better performances by supplementing the weaknesses of BERT, has been widely applied in conjunction with BERT in many studies. However, most of these BERT-based models are based on an English-centric design; consequently, researchers have attempted to develop models based on languages other than English.  
Multilingual BERT [19] retains the model structures of BERT but replaces the pretrained corpus attributes with those that include more than 100 languages from only the existing English-centric BERT, resulting in significant performance improvements over the original BERT in natural language comprehension tasks. However, this implies that its vocabulary size is large and that its size increases inefficiently owing to the processing of more than 100 languages, which consequently restricts the memory efficiency.  
Cross-lingual modeling [4] trains a model via unsupervised-learning-based pretraining, where continuous learning in English and other languages is applied simultaneously. BERT pretrained through the cross-lingual method improved the NLU accuracy in multilingual tasks as compared with the original BERT that was pretrained using a dataset containing 100 languages. This method significantly improves symbolic performance in multilingual tasks other than those in English. However, cross-lingual modeling is not comparable to well-preprocessed models in English in terms of accuracy.  
Meanwhile, researchers achieved performances comparable to those of English-based models by completely replacing the pretraining corpus attributes with Korean and excluding English [8; 9]. Because these studies were conducted using different methods in recent years, the pretraining corpus was determined independently and in parallel for each study, wherein the difference was indicated in only the composition of the dataset, thereby rendering performance prediction difficult. For these Korean-based models, various experiments must be conducted using new NLU task-based benchmark datasets insteadof substantially standard task benchmarks such as NSMC [21] (text classification) and KorQuAD [22] (question and answering). Benchmark evaluation is primarily conducted through NSMC in Korean text classification, which involves the classification of two labels; in fact, it is difficult to predict performance using only the data presented in the results.
# Kinematic and Workspace Analysis of RRU-3 RSS: A Novel 2T2R Parallel Manipulator
## 4 Workspace Analysis
### Obtaining the Useful WS
#### 4.2.4 Collisions with the Platform (WS\({}_{\text{platform}}\))  
Another type of collision that we have to consider is the collision between the KCs and the mobile platform. Its calculation must be adjusted depending on the geometry of the mobile platform. We have considered two cases: a mobile platform made of two cross-links and a mobile platform that consists of a planar surface. Note that the methodology is applicable to platforms with other geometries.  
* **Two cross-links**: In Figure 1, the mobile platform consists of two cross-links. These two links coincide with the diagonals of a theoretical rhombus formed by the upper kinematic joints. Hence, the way to calculate the collisions of the KCs with the mobile platform is similar to the one carried out in Section 4.2.3 for WS\({}_{\text{KCs}}\), but includes two new links with which collisions must be checked.  
Figure 15: Translational and rotational WS\({}_{\text{KCs}}\) for different working modes. Blue-colored regions are free of collisions between chains.  
WSplatform is defined as the set of WS regions free of collisions between the KCs and the mobile platform. Similarly to WSKCs, the chance of a collision between the KCs and the platform will vary depending on the working mode. Figure 16 shows some examples of the translational and rotational WSplatform for different working modes.
* **Infinite plane**: Depending on the application, the end-effector may require a planar surface of the mobile platform. Hence, the implemented code in MATLAB also includes the option to calculate this WS subset, considering the mobile platform as an infinite plane that contains all the upper kinematic joints.  
For its calculation, we have to take into account the relative position between \(\mathbf{A}_{i}\mathbf{B}_{i}\), \(\mathbf{A}_{i}\mathbf{C}_{i}\) and the normal vector of the mobile platform (\(\mathbf{w}\)). The collision between the KC and the mobile platform will occur at those positions where Equation (32) is verified, except when both terms of the equation are zero (which happens for the first and third KCs when a \(90^{\circ}\)\(\theta\) rotation is performed). In Figure 17, some schematic representations of non-collision and collision positions are depicted.  
\[sign\Big{(}\mathbf{A}_{i}\mathbf{B}_{i}\textsuperscript{{}^{T}}\cdot\mathbf{w}\Big{)}\
eq sign \Big{(}\mathbf{A}_{i}\mathbf{C}_{i}\textsuperscript{{}^{T}}\cdot\mathbf{w}\Big{)} \tag{32}\]  
In Figure 18, some examples of the translational and rotational WSplatform are depicted for different working modes. For both Figures 16 and 18, the non-gray areas colored in the plots represent the positions and orientations belonging to WSplatform.  
Figure 16: Translational and rotational WSplatform for a mobile platform that consists of two cross-links. Yellow-colored regions are free of collisions with the platform.
# Study on Abnormal Pattern Detection Method for In-Service Bridge Based on Lasso Regression
## 4 Monitoring Data Analysis
### Structural Frequency Analysis  
During the service of bridge structures, they are significantly influenced by the on-site environment, and their dynamic characteristics also change over time. In this paper, the NexT + ERA algorithm is used to identify the vertical bending frequency. The results are shown in Figure 5, where the second-order vertical bending frequency represents the structural vertical fundamental frequency.  
From Figure 5, it can be seen that the structural frequency varies due to the effects of the on-site environment, loading conditions, and structural performance. The interaction between piles and soil significantly influences the structural frequency [30]. However, this paper fails to address the impact of pile-soil interaction on the structural frequency because the main focus of this paper is the changes in structural frequency. Furthermore, the magnitude of the changes in different-order frequencies over time is not the same. It is difficult to determine the structural state in practical engineering by using a fixed threshold.
## 4 The Energy Balance Problem
### Basic Definitions - Preliminaries  
We aim at calculating probability \(p_{i}\) for each \(i\) in order to ensure the energy balance property. Using simple geometry, one can easily prove the following Lemmas.  
**Lemma 6**: The area size, \(S_{1}\), of the ring sector \(T_{1}\) is \(S_{1}=\frac{\phi}{2}\cdot R^{2}\)  
**Lemma 7**: The relation between the area size of the ring sector \(T  
Let us now consider sector \(T_{i}\).  
**Definition 5**: An area \(T_{i}\)_\"handles\"_ an event generated in ring sector \(j\) if either the message was generated in the area \(T_{i}\) (i.e. \(j=i\)) or the message was propagated to \(T_{i}\) from the ring sector \(T_{i+1}\).  
**Definition 6**: Let \(h_{i}\) be the number of the messages that are \"handled\" by the area \(T_{i}\).  
We now define energy \(\epsilon_{ij}\) spent for message \(j\) when sector \(i\) handles it.  
**Definition 7**: Let \(\epsilon_{ij}\) a random variable which measures the energy that dissipates the sector \(T_{i}\) so as to handle the message \(j\). For \(\epsilon_{ij}\) we have that:  
\[\epsilon_{ij}=\left\{\begin{array}{ll}cR^{2}&\mbox{with probability }p_{i}\\ c(iR)^{2}&\mbox{with probability }1-p_{i}\end{array}\right.\]  
where \(cR^{2}\) is the energy dissipation for sending a message \(j\) from \(T_{i}\) to its adjacent ring sector \(T_{i-1}\) and \(c\) is a constant.  
Thus, the expected energy dissipation in sector \(i\) for handling a message is  
\[E[\epsilon_{i,j}]=cR^{2}\cdot[i^{2}-p_{i}(i^{2}-1)] \tag{1}\]  
**Note**: The expected energy above is the same for all messages; we use \(j\) just for counting purposes.  
**Definition 8**: Let \(\mathcal{E}_{i}\) the _total energy_ spent by sensors in \(T_{i}\). Clearly:  
\[\mathcal{E}_{i}=\sum_{j=1}^{h_{i}}\epsilon_{ij} \tag{2}\]  
Energy balance is defined as follows:  
**Definition 9**: The network is energy balanced if the average per sensor energy dissipation is the same for all sectors, i.e. when  
\[\frac{E[\mathcal{E}_{i}]}{S_{i}}=\frac{E[\mathcal{E}_{j}]}{S_{j}}\quad i,j=1, \ldots,n \tag{3}\]
# Semantic Image Segmentation Using Scant Pixel Annotations
## 5 Experimental Results and Discussion
### Discussion
parameter value may be varied based on different datasets based on expert assessments of model predictions. From the above experimental results, we can conclude that the best model constructed from the SSPA approach achieved high prediction accuracy with a mix of over 94% pseudo pixel labels generated from the MC-WS ensemble that were iteratively improved using select expert annotations. The terminating condition we employed also worked well in practice by stopping the constructing of new models when mean entropy increases with increased expert annotations. The additional methods--heatmaps and oscillating pixels--were valuable in understanding the behavior of the SSPA approach. They provided insights on which pixels are hard for a model to learn and how the scant annotations provided in each iteration contributed to the mean entropy of model outputs and the accuracy of the models. From these methods, we observed that the SSPA method may not always assign the same label as the expert to a pixel consistently. Therefore, in the final model, certain pixels may be assigned incorrect labels, though they were assigned correct labels in earlier models. The SSPA is a general purpose segmentation method that should be applicable to several datasets. The segmentation performance of the SSPA method evaluated through IOU and Dice scores does not depend on the percentage of the pixels to be relabeled. The percentage of pixels to relabeled is related to the manual labeling effort. No specific threshold values are used to identify images with HE and LE values. Top-k HE (or LE) images are chosen for annotation. Similarly, pixels with most uncertain predictions (probability value \(0.5\pm\delta\)) are examined by the experts and labeled. Two parameters that need to be chosen in order to apply the SSPA are (1) the number of images to be analyzed in each iteration (the value j), and (2) the uncertainty range delta for pixels. For each dataset, experiments can be run based on on both LE and HE values, and the resulting models can be compared and chosen.
# Estimation of the Noise Source Level of a Commercial Ship Using On-Board Pressure Sensors
## 2 Method and Validation
### Methodology
At frequency \(f\), the measured signals at the array are \(y_{1}\) and \(y_{2}\). The propagation vectors can be calculated using Equation (3), which are expressed as \(\mathbf{a}\) and \(\mathbf{a}^{\prime}\). For the true source point, Equation (1) reduces to  
\[\mathbf{y}=s\mathbf{a}. \tag{10}\]  
Accordingly, the relation between the matrices \(\mathbf{Y}\) and \(\mathbf{A}\) can be expressed as  
\[\mathbf{Y}=|s|^{2}\mathbf{A}. \tag{11}\]  
Substituting Equation (11) into Equation (8) yields  
\[\|\mathbf{Y}-E^{2}\mathbf{A}\|_{F}^{2}=|s|^{4}\text{tr}(\mathbf{A}^{\text{H}} \mathbf{A})-\frac{|s|^{4}\text{tr}(\mathbf{A}^{\text{H}}\mathbf{A})^{2}}{ \text{tr}(\mathbf{A}^{\text{H}}\mathbf{A})}=0. \tag{12}\]  
Figure 1: Example of the beamforming problem. As the left-hand side of Equation (8) is a non-negative value, Equation (12) shows that for the true source location, the cost function is minimized. For the false source location, this can be written as  
\[\|\mathbf{Y}-E^{2}\mathbf{A}\|_{F}^{2}=\mathrm{tr}(\mathbf{Y}\mathbf{Y}^{ \mathrm{H}})-E^{2}\mathrm{tr}(\mathbf{Y}^{\mathrm{H}}\mathbf{A})-E^{2} \mathrm{tr}(\mathbf{Y}\mathbf{A}^{\mathrm{H}})+E^{4}\mathrm{tr}(\mathbf{A} \mathbf{A}^{\mathrm{H}}). \tag{5}\]0  
It can be seen that the second term of the right-hand side of Equation (13) depends on the propagation vector \(\mathbf{a^{\prime}}\), and thus, the cost function is always greater than zero. In the actual application, the cost function is calculated at each point in the source region and the point where the cost function is minimum is determined. The corresponding source strength \(E\) is then calculated from Equation (7). The whole procedure is summarized in \[\|\mathbf{Y}-E^{2}\mathbf{A}\|_{F}^{2}=\mathrm{tr}(\mathbf{Y}\mathbf{Y}^{ \mathrm{H}})-E^{2}\mathrm{tr}(\mathbf{Y}^{\mathrm{H}}\mathbf{A})-E^{2} \mathrm{tr}(\mathbf{Y}\mathbf{A}^{\mathrm{H}})+E^{4}\mathrm{tr}(\mathbf{A} \mathbf{A}^{\mathrm{H}}). \tag{5}\]1 It should be noted that the method works well when the actual cavitation noise sources are concentrated within a single region because a single monopole source at a single point is assumed at each frequency.
# Machine Learning Techniques to Detect a DDoS Attack in SDN: A Systematic Review
## 5 Available Benchmarked DDoS Datasets and Classes of Attacks in Datasets  
The datasets and attack class types utilized by the studies that were examined for DDoS attack detection are listed in Table 4. eight datasets (KDD Cup99, Kyoto 2006+, NSL-KDD, UNSW-NB15, CIC-IDS2017, CSE-CIC-IDS2018, SCX2012, and CICDDoS2019) were utilized across the majority of studies. Following is a description of these datasets. **KDD Cup99:** One of the most well-known and often used datasets for IDS is KDD Cup99. It contains about five million and two million recordings for training and testing, respectively. Each recording has 41 distinct characteristics or properties, and is classified as an attack or as normal data. Four categories of attacks are established for the recordings, namely, Denial of Service (DoS), Probe, Remote to Local (R2L), and User to Root (U2R) [80].
# Methodology for CubeSat Debris Collision Avoidance Based on Its Active ADCS System
## 2 State of the Art
### STK Software  
Ansys STK is a mission analysis software widely used in the aerospace industry, with a physics-based modelling environment for analyzing platforms and payloads visually and analytically. STK allows propagation of objects in orbit using the best-known propagators such as J2, J4, or Standard General Perturbations Satellite Orbit Model 4 (SGP4) and incorporates proprietary propagators such as High-Precision Orbit Propagator (HPOP), which uses numerical integration of the differential equations of motions to generate ephemeris.  
The STK software (version 12.1.0) incorporates the Lifetime tool which estimates the amount of time a satellite can be expected to remain in orbit, before atmospheric drag and other perturbations cause it to decay. When using Lifetime, a report can be obtained with orbital element variations in each orbit until re-entry [21].
# Deep Learning Stranded Neural Network Model for the Detection of Sensory Triggered Events
## 2 Related Work  
Industrial maintenance levels are mainly identified by monitoring parameters during operation using sensors. Different operation responses can be determined according to the measurement probing, variability, or limits set by the machinery manufacturers. Usually, the industrial concentrators receive a series of time-framed measurements per machine. Then, these measurements are split into measurement intervals called measurement batches and are driven to a detection model as input. Existing detection models include classifiers or regressors. The size of the measurement batches as model inputs signifies the model's detection-monitoring granularity. For this reason, based on the data input and sensor probing intervals, we have different types of machinery monitoring cases:  
* critical events include sensory measurements of precise machinery operations. The sensory input in such cases is of multiple sensors of the same locality measuring different machinery parameters per second, setting the detection response interval to no more than a few minutes (1-3 min);
* events include sensory measurements where the malfunction detection can be extended to a few minutes' response due to the extended intervals of measurement acquisition or degraded sensors' accuracy. In such cases, the detection response intervals can be of a few minutes (3-15 min);
* maintenance events or checks include batches of sensory measurements of hourly or daily intervals (15 min/30 min/hourly/daily). Such detection tests can be automatically performed during operation or post-processing. These are targeted maintenance checks that detect deviations or malfunctioning patterns concerning past machinery operational behavior.  
Following this categorization that requires different handling approaches per event type, the authors set real-time events of short observation intervals that require immediate actions and periodic long term observation intervals for planning future maintenance tasks. The following subsections summarize the existing methods used for each type.
## 2 Data Processing
### Normalization Processing  
Due to the various dimensions and magnitudes among various types of data (such as train speed, train axle temperature, and train load), the direct analysis of diverse data would affect the analysis results. Similarly, if there is a significant difference in magnitude between the data used, it would greatly affect the analysis results. To guarantee precise data analysis and reliable data modeling results, it is therefore crucial to first preprocess the raw axle temperature data by addressing missing values and outliers and then applying normalization processing. Normalization processing is able to standardize data of various types on the same scale. In the present investigation, the normalization method applied to the axle temperature data matrix after missing value handling is standard deviation normalization, which makes the data conform to a standard normal distribution with a mean of 0 and a standard deviation of 1 [24]. For the data matrix that has not been normalized, normalization processing is conducted to obtain the normalized data matrix \(X_{p}\):  
\[X_{p}=\frac{X_{s}-\mu}{\sigma} \tag{6}\]  
Figure 3: After data processing of the outlier axle temperature.  
Figure 2: Before data processing of the outlier axle temperature.
## 4 Simulation Results
### Multiple Congested Links  
We now analyze how the NBRs of five flows with smaller MARs are affected by other flows with larger MARs under three congested links. We performed two cases based on the topology shown in Figure 3b. Flows 1 to 5 send at 0.2, 0.4, 0.6, 0.8 and 1 Mbps, and the others send at 10 Mbps. The capacity of each link is 10 Mbps so that each link between routers is congested. We consider the NBRs of flows 1 to 5 here because their fairness can be damaged by other aggressive flows. The simulation results are illustrated in Figure 8, and the MMFSR is 0.363 Mbps at the last router. The NBRs of flows 1 to 5 are all close to 0 in CHOKe and FIFO. In other words, they both fail to provide fairness. When the number of traversing routers increases, CSFQ gradually loses precision on estimates of the fair share rate and MAR of each flow. As a result, AFQ performs better than RPQ, while RPQ performs much better than CSFQ. AFQ achieves the best fairness because it can dynamically filter the arriving packets according to traffic variations.  
In the second case, we increase double traffic burstiness to flows 1 to 5, and the MBR of flows 6 to 28 change to 8 Mbps. Figure 9 illustrates the NBRs of flows 1 to 5, and the MMFSRs are the same as in  
Figure 8: The NBR achieved by flows 1 to 5 with lower ON-OFF burstiness, with other flows all consisting of 10 Mbps.  
the first case. Repeatedly, AFQ proves to be the scheme with the best fairness. Although CSFQ is worse than RPQ, it is still much better than CHOKe and FIFO. AFQ, RPQ and CSFQ degrade their fairness compared with the first case because the traffic burstiness of flows 1 to 5 is double. According to our simulation results, we conclude that AFQ can achieve the best degree of fairness under different network topologies and various traffic conditions.