esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Project Description and Data Collection  
In the present study, a granite mine in Penang state, Malaysia, was selected as the subject to research PPV prediction, and thus its blasting operations were investigated. Granite is the most common rock type found in the study area. The top layer is generally less than three feet thick and consists mostly of sandy clay with humus and tree roots. Explosive operations are rather common in this mine, and these operations are repeated at various intervals. Blasting at this location aims to create aggregates for various building projects with annual capacity ranging from 500-700 thousand tons per year, and large quantities of explosives are used in explosive operations. For example, in holes with sizes ranging from 76 mm to 89 mm, explosives weighing 856 kg to 9420 kg are commonly utilized. Concerning the present study, 102 blasting operations were recorded, including the design details of the blast parameters and the PPV values obtained by the Vibra ZEB seismographer. The measured and recorded blast parameters involve the number of holes, hole diameter, hole depth, burden, spacing, stemming length, subdrilling, total charge, powder factor, the maximum charge per delay, and distance from the blast-face to the measuring points. Among them, six parameters were selected as the inputs that will be used to implement the ML modeling, which is in accord with previously published works [1; 48; 49]. The simple statistics of the six parameters are shown in Table 2, including their max/min values, mean values, and standard deviations. It can be seen from Table 2 that the distance from the seismograph to the explosion site was about 285 m-531 m, and the range of the PPV is between 0.13 and 11.05 mm/s.
# Machinery Fault Signal Detection with Deep One-Class Classification
## 4 Experimental Study
### Experimental Results  
Table 7 shows the comparative results of the AUROC values of all OCC methods considered in the current study. We reported the averages and standard deviations of the AUROCs from 10 repetitions of random splitting of training/testing datasets. In this table, the highest average AUROC values are highlighted in bold.
# DLI-Based DP-QPSK Reception Scheme for Short-Range Optical Communication
## 5 Simulations  
Simulations were conducted for a 112 Gbps DP-QPSK signal with a laser at 193.1 THz, modulated on two orthogonal polarization states, and the two sub-MZMs with an extinction ratio of 60 dB in each DP-MZM were configured in parallel. The receiver consisted of a 70 GHz third-order Gaussian optical filter, two tunable DP-DLIs followed by four pairs of balanced photodetectors, and four 21 GHz fourth-order Bessel electrical filters. The simulations were carried out in Optisystem 15 from company Optiwave in Ottawa, Ontario, Canada.
# Buckling-Restrained Bracing System with Ultra-High-Performance Fiber Concrete
###### Abstract  
Recently, buckling-restrained braces (BRBs) have been widely implemented as seismic load resistance systems in buildings to enhance their response against dynamic vibration. However, during catastrophic earthquakes, the steel core in BRB devices fully yields, which causes the BRB to lose its functionality. While the incorporation of various filler materials, such as new high-performance concretes, has the potential to enhance the performance of buckling-restrained braces (BRBs), there remains a notable gap regarding comprehensive research investigating this aspect. Therefore, this study assessed the effect of implementing ultra-high-performance concrete (UHPFRC) as filler material on BRB behavior. For this purpose, the finite element model for the proposed BRB was developed and hysteresis analysis results under incremental cyclic loads were investigated. Then, the prototype of a BRB with UHPFRC concrete was cast and experimentally tested under cyclic loads by using a dynamic actuator. Based on the testing results, a new design for a BRB device named as rubber buckling-restrained brace (BRB) was developed, implementing hyperelastic rubber components between the steel core and UHPFRC as an additional load-bearing mechanism to enhance the device vibration dissipation capacity. Subsequently, a finite element model of the newly proposed rubber buckling-restrained brace (RBRB) was developed to assess the device's performance. The analysis results demonstrate a notable enhancement in load capacity and energy dissipation for the RBRB device compared to conventional BRBs.  
finite element analysis; ultra-high-performance concrete (UHPFRC); hyperelastic rubber; buckling-restrained brace; energy dissipation capacity
## 4 Results and Discussion
### RQ4: Effect of the Size of the Description in the Performance of the Translation
These findings underscore the importance of refining translation strategies based on input length and complexity and open new research opportunities in adaptive translation workflows, hybrid human-AI revision models, and the development of specialized models for long-form technical content.
## 3 Material and Methods
### DL-Based Text Classification
#### 3.2.2 Embedding  
The embedding stage is designed to transform textual inputs into dense, context-rich vector representations that preserve both syntactic and semantic information. In our proposed approach, we utilize advanced word embedding techniques to represent textual data in a manner that effectively captures semantic and syntactic relationships.  
Embeddings play a crucial role in DL-based NLP by transforming words and phrases into continuous vectors within a lower-dimensional space. For this study, we focus on embeddings derived from the 12-layer RoBERTa transformer model, which is central to our text classification framework.  
Unlike traditional embedding methods such as Word2 Vec and ELMO, which rely on predefined static representations or task-specific training with limited context, the embeddings generated by RoBERTa are dynamically adjusted during fine-tuning. This enables our model to capture deeper contextual nuances and relationships between words, making it highly effective for propaganda detection in complex multi-label classification tasks. The RoBERTa-based embeddings not only enhance feature representation but also contribute to improved classification accuracy by integrating context across longer text spans.  
By focusing on RoBERTa embeddings, our approach emphasizes the advantages of transformer-based architectures in capturing rich text representations. This ensures that our propaganda detection framework is equipped to handle the intricate patterns and subtle signals present in COVID-19-related public discourse. Figure 5 illustrates the embedding process within the 12-layer RoBERTa model, showcasing how textual data are transformed into a structured representation optimized for classification tasks. This embedding strategy is tailored to align with the specific requirements of our study, underscoring the main contribution of leveraging state-of-the-art techniques to enhance propaganda detection and classification.
## 3 FE Analysis for the Flange Segment
### FE Analysis for the Flange Gap I  
The influence of the flange gap I on bolt fatigue is also analyzed. Here, it is hypothesized that the opening height \(h_{\text{I}}=1\) mm remains constant while the tower wall is subject to varying loads \(Z\). The partial results reflecting the nonlinear relationship between \(Z\) and \(F_{\text{VS}}\) are given in Table 1 and plotted in Figure 14.  
As shown in Figure 14, when the flange is tensed, the bolt's internal force decreases with increasing \(l_{\text{I}}\). When the flange is compressed, the curve representing the variation in bolt internal force stays flat due to the clearance compensating for deformation.  
Secondly, the length along the flange gap \(l_{\text{I}}\) is set as \(l_{\text{I}}=100\) mm while changing \(h_{\text{I}}\). The partial results by varying tower wall loads \(Z\) are included in Table 2 and demonstrated in Figure 15.  
As illustrated in Figure 15, a pattern similar to that seen in Figure 14 emerges when the parameter \(l_{\text{I}}\) is fixed. This is due to the fact that an increased opening height results in greater gap at the bolt hole, exposing the flange to more adverse effects under tension; the compensating effect under compression causes the bolt forces to increase continuously in the opposite direction.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Tower Wall Load \(Z\)** & \(F_{\text{VS}}\) (\(l_{\text{I}}=80\) mm) & \(F_{\text{VS}}\) (\(l_{\text{I}}=90\) mm) & \(F_{\text{VS}}\) (\(l_{\text{I}}=100\) mm) \\ \hline \(-600\) & 937.1 & 944.7 & 959.3 \\ \(-400\) & 935.0 & 941.9 & 953.4 \\ \(-200\) & 932.7 & 937.9 & 937.0 \\ \(0\) & 926.1 & 926.1 & 926.1 \\ \(200\) & 993.8 & 992.1 & 979.1 \\ \(400\) & 1087.9 & 1075.3 & 1036.9 \\ \(600\) & 1198.9 & 1174.9 & 1157.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of external load and internal force \(F_{\text{VS}}\) for bolts (unit: kN).  
Figure 14: The nonlinear relationship between \(Z\) and \(F_{\text{VS}}\) for flange gap I.
# A Survey of Big Data Archives in Time-Domain Astronomy
## 4 Astronomical Data Mining
Numerous methods have been proposed, such as cleaning, integration, transformation, and reduction of data sets [50]. * **Selection of Attributes:** Some properties of an object are not necessary for its proper functioning. To maximize performance, it is possible to use all the qualities of the object. Several low-density habitats and gaps have been created because of this. It is difficult to extract new ideas from data. As a result, dimension reduction is essential for retaining as much information as possible while using fewer attributes. Several algorithms are hampered by the presence of unnecessary, redundant, or otherwise unimportant features [48]. Filters and wrappers are prominent phrases used to describe the nature of the metric used to evaluate the value of attributes in a categorization. The accuracy estimates produced by the real target learning method are used by wrappers to evaluate attributes. Filters, on the other hand, work independently of any learning process and use generic properties of the data to evaluate attributes [51]. * **Use of Machine Learning Algorithms:** Machine learning algorithms are usually classified into supervised, semi-supervised, and unsupervised methods. Semi-supervised approaches use two sets of objects for which the target property, such as classification, is known with confidence. The algorithm was trained on these objects and then applied to others without the target characteristics. The test set included these additional items. In most astronomy cases, a photometric sample of objects can predict qualities that ordinarily require a spectroscopic sample. The parameter space spanned by the input attributes must span the time for which the algorithm is employed. This may appear restricted initially, but may often be overcome by merging data sets [48]. The research, development, and validation of algorithms for web service-based (possibly grid-based or peer-to-peer) classification and mining of distributed data are required. A combined text-numeric data mining algorithm may be the most effective, and thus has to be explored for these algorithms to be successful [45]. We explored the vast survey databases produced by numerous NASA missions while discussing the possibility of distributed data mining to assist astronomical research. GALEX conducts all-sky surveys in the near-UV and far-UV regions at various depths. Large-scale infrared surveys are being carried out by the Spitzer Space Telescope, which includes areas of the sky that have already been extensively investigated by the Hubble Space Telescope (optical), Chandra X-ray Observatory, and other observatories [52]. The WISE mission, launched in 2009, conducted an infrared survey of the sky. Millions of stars and galaxies in the near-infrared region have been cataloged by the 2-Micron All-Sky Survey (2 MASS). Numerous classes of astrophysical specimens can be studied with the using these wavebands [45].
## 3 Results
### Showcasing the Results through User Experience
Note that during the session, the users interact with each other by communicating verbally, explaining details of the actions and training provided. This can be observed at various moments in the demonstration video. It is worth noting that, to enhance usability and ensure a satisfactory user experience when interacting with the proposed industrial models, a tray with only four grip positions has been implemented. These positions are indicated by gray handles on the tray, as illustrated in examples in Figure 12. When the user grasps the object at one of these positions on the tray, it automatically aligns so that the handgrip and the handle coincide, replicating the experience with a real handle. Additionally, the tray consistently positions itself to ensure that the model is always visible on top of it, facilitating continuous user engagement with the model (refer to Figure 13e). This design approach eliminates awkward viewing and gripping scenarios, preventing user distractions and maximizing the focus on the training content. Qualitatively, users provided feedback on the application. Overall, it was noted that the application was user-friendly and did not necessitate prior knowledge, aside from initial instructions on navigating the environment. Socially, the realistic facial expressions of the avatars enhanced the interaction, fostering a greater sense of empathy. While interacting with objects was generally straightforward, there were occasional instances of objects being lost. This occurrence was attributed to limitations in the tracking system of the devices used.
# Remote Image Capture System to Improve Aerial Supervision for Precision Irrigation in Agriculture
## 2 Materials and Methods
### Description of the Remote Image Capture System (RICS)
#### 2.1.1 Communication Module of the RICS Node  
The communication module is based on a low-power XBee 868 LP ZigBee module (Digi International Inc., Minnetonka, MN, USA). It is configured as a transmission gateway of the images captured by the image module of the A20/A6C subsystem. This device also enables the measuring nodes to be part of the wireless network, by sharing the same connection. To cover extensive distances from the local to the remote nodes, and to reduce the effect of signal attenuation caused by the vegetation, which is higher in fruit crops [47], wireless networks that operate in lower frequency bands should be used [4]. The nodes contain 2 dBi omnidirectional antennas to reach distances in outdoor/line-of-sight range up to 800 m, and with 12 dBi five-element Yagi antennas to allow for distances up to 1500 m. These maximum distances were tested in the experimentation described in Section 3.  
It should be noted that the configuration of the XBee node in ZigBee AT mode makes it possible to dispense with the implementation of a microcontroller in the structure of the remote node, since the requests of the coordinator node are transferred transparently by the XBee module and processed by the images capture module A20/A6C. As a disadvantage, the coordinating system must make a modification in the configuration of the local XBee communication module by adjusting the appropriate link address (i.e., the DL register: destination address) to the remote node with which it is desired to establish a communication link. This task does not involve significant delays or drawbacks since the configuration time is only a few seconds.
# Research on Remaining Useful Life Prediction of Bearings Based on MBCNN-BiLSTM
## 4 Experimental Study
### Noise Resistance Test  
To explore the validity of the model to predict the RUL in a strong noise environment, B1_1, B1_2 and B1_4 of working condition 1 are selected as the training set and B1_3 as the test set. Meanwhile, three models are compared and analyzed with CNN, BiLSTM and CNN-BiLSTM. The model is trained using the training set, Gaussian white noise with different signal-to-noise ratios is added to the test set, the trained model is used to predict the RUL of the test set, and the signal-to-noise ratio is calculated as below:  
\[SNR_{dB}=10\lg\left(\frac{P_{s}}{P_{n}}\right) \tag{17}\]  
where \(P_{s}\) is the effective power of the signal, and \(P_{n}\) is the finite power of the noise.  
In this paper, the test sets are constructed as 0 dB, 2 dB, 4 dB, 6 dB, 8 dB and 10 dB, respectively. The performance of MAE, RMSE and Score of each model in different noises are illustrated in Figures 13-15, respectively.  
As observed in Figures 13-15, compared to other comparison models, the model proposed in this paper performs best in different noises and has stronger stability. The prediction accuracy of the model decreases with the increase of noise, but the model still has a high accuracy, which fully illustrates that the model can still make a more effective prediction of the RUL of the bearing in the face of the noise in the actual industrial environment.
# Adhesion Evaluation of Asphalt-Aggregate Interface Using Surface Free Energy Method
## 2 Raw Materials and Methodology  
The raw materials in this study include asphalt binder, aggregate, organic additives. Two types of asphalt binders were used: SK-70 and SK-90. The organic additives were Sasobit and RH, and the aggregates were limestone and basalt. The organic additive-modified asphalt was produced by adding 3% Sasobit and RH by weight of the base asphalt in the asphalt matrix. The sessile drop method was conducted to test the surface free energy components of base asphalts, organic additives, organic additive-modified asphalts and aggregates. After that, the adhesion models of asphalt-aggregate and organic additive-asphalt-aggregate in dry and wet conditions were established, and their adhesion works were calculated subsequently according to the surface free energy theory. Energy ratios were established to evaluate the adhesion of asphalt-aggregate and organic additive-asphalt-aggregate interfaces under dry and wet conditions. Finally, the influence of organic additives and water on asphalt-aggregate adhesion was evaluated based on the adhesion works and energy ratios obtained.
# Crop Disease Diagnosis with Deep Learning-Based Image Captioning and Object Detection
## 3 Materials and Methods
### Structure of the Crop Disease Diagnosis Solution
#### InceptionV3-Transformer  
Figure 6: Structure of Inception Modules.  
Figure 7: Structure of Transformer Model.  
The process of creating captions for images is shown in Figure 8. First, when the image goes as an input into InceptionV3, various features are analyzed in the image. For example, if an image of a red pepper infected with Anthracnose is loaded as an input, the model analyzes the shape, the number, and the color of the affected area. Features extracted from InceptionV3 become an input into the Decoder of Transformer to generate captions. Meanwhile, the Transformer model itself has an Encoder-Decoder structure. First, the features of the image were input into Transformer's encoder, along with the location information of each feature, and then analyzed through the Self-Attention process. The analyzed information is then entered into Transformer's decoder, which also includes a pre-made disease diagnostic sentence for model training. Finally, features of the image and disease diagnostic sentences are comprehensively analyzed by the Self-Attention process in the decoder of Transformer, resulting in generated captions predicted by the model.
# Multilayered Network Model for Mobile Network Infrastructure Disruption
## 3 Proposal of Multilayered Network Model for Mobile Network Infrastructure Disruption
### Inter-Layer Communication
Based on the size of the MANET network, using clustering algorithms, it is possible to divide MANET nodes into individual logical subnets in which one Cluster Head (CH) will be selected. This CH will serve as MANET gateway for other MANET nodes in the cluster when urgent data needs to be sent to the DRONET layer. The information about clustering and MANET gateway selection will be discussed in Section 5.1. Besides the Wi-Fi standard, UAV uses the WiMAX IEEE 802.16 standard for communications among other UAVs and the dock. All UAVs needs to use dual protocol stack in the same way as WSN gateway sensors, which is depicted in Figure 5.  
The first protocol stack implemented in UAV uses IEEE 802.11 Wi-Fi standard on the Data Link OSI layer and IPv6 MANET routing protocol on the Network layer. The second protocol stack used IEEE 802.16 WiMAX standard on the Data Link OSI layer and appropriate WiMAX routing protocol  
Figure 5: The example of dual protocol stack used in MANET node and UAV. on the Network layer. Based on this approach, the UAV is able to communicate with MANET nodes and other UAV, which is depicted in Figure 6.  
The scenario depicted in Figure 6 describes WSN source node that sends urgent data to AP_1. Since the link is disrupted, urgent data are transferred to the MANET layer through the WSN gateway sensor. MANET node is also unable to deliver urgent data to the same AP_1. Therefore, urgent data are transferred to DRONET layer through the MANET gateway in order to find another MANET subnet with functional AP_2.
## 3 Results and Discussion
### Tracing the Origin of Groundwater within the EDB Using Deuterium Excess
Some regional flow relates to the major rivers which cut across the entire basin, with several tributaries and discharge water into the ocean through the delta plain. The hydrochemical model shows Na-Cl as a dominant water type followed by Ca-HCO\({}_{3}\) with other mixed water types, such as Ca-Cl, Na-SO\({}_{4}\), Na-Cl-HCO\({}_{3}\), and Ca-SO\({}_{4}\), which typifies an early stage of rock-water interactions and possible anthropogenic influence. This conceptual view explains the lateral and vertical variations in groundwater \(\delta^{18}\)O, \(\delta\)D, and D-excess concentrations, controlled by factors such as high humidity, static water level within the basin, and significant anthropogenic influence from urbanisation, industrialisation, and agricultural irrigation in most parts of the study area. For example, the D-excess results show comparatively shorter residence time for groundwater moving through the vadose zone to the shallow aquifers of the basin, especially within the alluvium and coastal plain sands geologic units (Figures 16-18). Figure 16: A plot of \(\delta^{18}\)O vs. D-excess for the groundwater samples: (**a**) wet season; (**b**) dry season. Observed hydrochemical characteristics across the basin generally reflect the geology and mineralogy of the area, while the lack of consistencies in groundwater isotope values and water type distribution could be attributed to heterogeneity and anthropogenic impacts along the flow paths. Furthermore, as illustrated in the schematic diagram in Figure 18, water recharged in the northern parts of the basin has to travel deeper as the vadose zone thickness is higher in this area. A certain amount of this infiltrated water recharges local rivers as baseflow while the remaining one recharges the groundwater in the shallow aquifers of the basin. Figure 17: Map of the eastern Dahomey Basin showing contour line of static water level and rivers drainage with profile line AB along which the geological profile section was generated for the conceptual description of hydrogeochemical characterisation of the shallow aquifers underlying the eastern Dahomey Basin. Figure 18: A sketch of a conceptual description of stable isotopes of \(\delta^{2}\)H and \(\delta^{18}\)O in groundwater samples along profile line A-B in North-South direction of the eastern Dahomey Basin.