esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Collection  
Our research utilized knee joint X-ray images from the Chen 2019 study [49], originally sourced from the Osteoarthritis Initiative (OAI)[45]. The OAI, a multi-center study focused on biomarkers for knee osteoarthritis, and included 4796 participants aged 45 to 79. We employed the pre-processed primary cohort data from Chen 2019 [49], which had undergone automatic knee joint detection, bounding, and zoom standardization to 0.14 mm/pixel. This process yielded 8260 images (224 \(\times\) 224 pixels) derived from 4130 X-rays, each containing both knee joints. The images were graded using the Kellgren and Lawrence (KL) system [46], as shown in Figure 2. The KL grade distribution was as follows: 3253 images for Grade 0, 1495 for Grade 1, 2175 for Grade 2, 1086 for Grade 3, and 251 for Grade 4.  
Figure 1: The methodological pipeline for the study. Green represents positive/supportive augmentation, red signifies adversarial augmentations. Blue indicates data processing and purple signifies CNN training. Numeric markers indicate the order of operations.  
Figure 2: Sample images showing various KL grades, ranging from 0 (no OA signs) to 4 (severe OA). From left to right, OA severity increases. Joint space narrowing, denoted as JSN.
## 3 Simulation Results
### Channel Measurements  
In our study, meticulous channel measurements were carried out within the premises of a National Electronics and Computer Technology Center (NETEC) building, National Science and Technology Development Agency (NSTDA), Thailand, encompassing two distinct environments: a corridor and a small meeting room.  
The experimental setup was carefully orchestrated, involving sophisticated equipment to ensure precise data collection. This setup included a vector network analyzer (VNA), two omnidirectional antennas, and a rail positioner. The rail positioner facilitated the dynamic movement of the receiver antenna, allowing for precise adjustments corresponding to different experimental scenarios. To execute these experiments, we leveraged cutting-edge Fieldfox equipment from NECTEC, NSTDA, ensuring high-quality measurements. Our frequency range spanned from 2.5 GHz to 2.8 GHz, with a central frequency set at 2.65 GHz and a bandwidth of 300 MHz. With a meticulous approach, we collected a total of 1001 measurement points to capture the intricacies of the channel.  
Maintaining consistency and accuracy, the output power was carefully controlled, set at 3 dB throughout the experiments. Additionally, the positioning of the antennas played a crucial role. With a standardized height of 1.2 m, the antennas were strategically placed at varying distances ranging from 1.00 m to 3.26 m, with increments of 0.113 m. This meticulous spacing ensured comprehensive coverage and accurate data collection. A total of 21 distinct locations were sampled within the experimental environments, capturing a diverse range of channel conditions. The transmit antenna cable measured 1.5 m in length, while the receive antenna cable extended to 3.0 m, maintaining consistency and minimizing signal degradation. For visual clarity, Figure 4 provides a graphical representation of the experimental setup for channel measurement, illustrating the meticulous attention to detail employed in our data collection process.
## 2 Materials and Methods  
### Overview of Study Area  
The coal panel was located in the south of Shanxi province in China, and had a length of 242 m, width of 118 m, and an average mining depth of 160 m. Figure 1 shows the simplified distribution of overlying strata above the working face. The coal seam was 8 m thick. The characteristics of geological occurrence were as follows: The loess layer was thick (125 m), the bedrock was thin (average thickness, 30 m), and was composed of mudstone and siltstone. The coal seam floor is gritstone with an average thickness of 10 m. The lithology was soft, and the average speed of advance of the working face was 33 m per month. The surface slope was 20\({}^{\circ}\).  
The investigation was carried out above the goal of the working face, and the surface crack was measured by global navigation satellite system real-time kinematic(GNSS-RTK) to obtain its position and shape, and the relationship between the surface crack and terrain and the position of the working face is obtained, as shown in Figure 2. Because of the complex field conditions, the investigators used steel tape to measure the crack width and height, and used an SLR camera to obtain the field crack photos, as shown in Figure 3.  
Figure 2 shows five main serious cracks C1-C5 extend from about 35-40 m outside the boundary of the coal panel to the inside, and C1-C4 are roughly parallel to the topographic contour. Crack C1: width 50 cm, length 136 m, step height 170 cm, distance to the first cut line 125m. Crack C2: width 50 cm, length 220 m, step height 120-170 cm, distance to the first cut line 178 m. Crack C3: width 40 cm, length 179 m, step height 140 cm, near the stop line. Crack C4: width 20 cm, length 153 m, step height 100 cm, near the stop line. Crack C5: width 15 cm, length 83 m, step height 100 cm, C5 is parallel to the return airway, and the distance is about 53 m. Therefore, the damage range of surface cracks was large, and the surface collapse and cracks were very serious, as shown in Figure 3.  
Figure 1: Simplified distribution of overlying strata above the working face.
[PERSON] [25] adopted the L curve criterion to obtain the optimal regularization parameter for the Moses algorithm. The research results show that the regularized recognition accuracy is significantly better than the original [PERSON] algorithm and insensitive to road surface roughness. The above methods are mostly used for simply supported girders, slabs, steel girders, and truss bridges, and they do not consider the influence of the transverse position of the vehicle, which is a one-dimensional B-WIM system. However, the local stress of the deck is very prominent for the orthotropic deck steel box girder structure. The variation in the transverse position of the vehicle on the bridge deck greatly influences the load's identification due to the local force characteristic of the orthotropic deck, so it needs to locate the vehicle's transverse position [26]. The one-dimensional B-WIM system is not suitable for this type of bridge. [PERSON] [27] investigated the two-dimensional B-WIM algorithm based on the influence surface and conducted experimental studies on integral slab bridges and beam bridges, respectively. [PERSON] [28] proposed an improved two-dimensional B-WIM algorithm for a simply supported T-beam bridge, which considered the transverse distribution of wheel loads on each main girder and calibrated influence lines for each main girder separately in the field. The method achieved superior recognition results after a field test on a US highway. [PERSON] et al. [29] proposed a method of identifying the transverse position, axle weight, and gross weight based on weight sensors. The method took into account the effects of road flatness, vehicle speed, vehicle width, and different measurement points on the recognition accuracy and is validated in field trails. [PERSON] [30] proposed a virtual monitoring method for the fatigue assessment of orthotropic steel decks. He found that using influence surfaces for identifying vehicle loads improved the recognition accuracy. The two-dimensional algorithm can take the effect of vehicle loads into account at different locations laterally. However, it requires the calibration of the influence surface of the structure, which is a troublesome problem in practical engineering, and it needs to locate the transverse position of vehicles on the bridge accurately. This paper applied the B-WIM system to the vehicle's load identification of an orthotropic deck steel box beam bridge. Two types of strain gauges were arranged at the lower edge of the deck to monitor structural strain response. Based on the measured strain response, the vehicle's information, including the number of axles, the wheelbase, and vehicle speed, was identified. An optimized combined strain influence line was obtained by a genetic algorithm based on a proposed index of variable coefficient, which was insensitivity to the transverse position of the vehicle. Then, the unknown vehicle load was identified based on the calibrated combined strain influence line. Finally, a numerical simulation and an experimental test were carried out to validate the effectiveness and anti-noise performance of the proposed method.
## 2 Experimental Design
### In-Situ Experiment  
#### 2.2.1 Physical Setup  
The experiment was carried on in the University of Huddersfield's St Paul's concert hall (Figure 1, RT = 2.1 s; 16 m (W) \(\times\) 30 m (L) \(\times\) 13 m (H)). A Genelec 8040A loudspeaker (Genelec, Lisalmi, Finland) was placed in the centre of the stage and used for exciting the room. An anechoic male speech taken from the Bang and Olufsen's Archimedes database [28] was used as a test signal, due to its broadband frequency nature and controlled ratio of transient and sustained sound. Four positions from the concert hall, with different orientations were tested. A total of ten listening position and head orientation conditions were assessed: four facing towards the source, four facing 90\({}^{\circ}\) from the source and two facing away from the source (Figure 1). P1_0\({}^{\circ}\), P1_90\({}^{\circ}\) and P1_180\({}^{\circ}\) were situated on the room's centre line, at 2 m from the loudspeaker. Similarly, P2_0\({}^{\circ}\), P2_90\({}^{\circ}\) and P2_180\({}^{\circ}\) were situated on the same line at 8 m away from the source. P3_0\({}^{\circ}\), P3_90\({}^{\circ}\), P4_0\({}^{\circ}\) and P4_90\({}^{\circ}\) were situated laterally, 4 m away from the centre line, at the same distances from the stage as the previous groups. For the aforementioned positions, two head orientations, one facing towards the source and one facing 90\({}^{\circ}\) were tested. The measured sound pressure level for each of the positions is presented in Figure 1.  
It is to be noted here that the listening positions are situated between the stage and the seating area--situated approximately 10 m away from the stage--for a number of reasons: the underlaying motivation of this research relies in finding salient perceptual characteristics of a space, to be used in an XR scenario. Consequently, a space where one can freely move from one position to another is desirable for future experiments to be carried out in a VR scenario. It was also decided that other rooms, such as lecture theatres or classrooms will be measured and tested therefore changes caused by different heights in the seating area were undesirable for the present research.  
Figure 1: The top view of the St. Paul’s concert hall and the listener positions and orientations used for the elicitation experiment.
## 3 Materials and Methods
### Phenology-Aware Module (PAM) Module
#### 3.3.5 Fusion Quality Check and Stage Feature Weighting  
To address the common issues of noise or missing values in remote-sensing data, which can affect the effectiveness of feature fusion, and to enhance the model's robustness, we introduce a fusion quality check mechanism. Specifically, the time attention weight is adjusted by calculating the variance of the fused feature \(F_{\text{fused}}^{t}\). This ensures that the model reduces its attention on time steps with lower data quality, thereby focusing on more reliable data and providing a more stable foundation for subsequent attention generation (see Formula (11)).  
\[\beta_{t}^{\prime}=\beta_{t}\cdot\left(1-\text{Var}\left(F_{t}^{\text{fused}} \right)\right) \tag{11}\]  
After obtaining the time attention weights \(\beta_{t}\), we perform a weighted sum of the spatiotemporal features to generate the final output of the PAM module (see Formula (12)):  
\[F_{\text{PAM}}=\sum(t=1)^{1}2\beta_{t}^{\prime}.F_{t}^{\text{PAM-spatial}} \tag{12}\]  
This step integrates the spatiotemporal information of the 12 times steps into a comprehensive feature representation, preserving both temporal dynamics and spatial context, thus providing high-quality input for the subsequent segmentation task.  
As shown in Figure 7, specifically, the PAM module achieves dynamic perception of different growth stages by transforming crop phenological information into a continuous feature space and combining it with the temporal features from remote-sensing data. The phenological information of non-food crops is mapped into a continuous vector representation, where each stage's information is mapped to the feature space using one-hot encoding and linear transformation. These pieces of information are fused with the temporal features of the remote-sensing data and dynamically adjusted through an adaptive attention mechanism to shift the model's focus. Compared to traditional methods based on fixed time windows, PAM can automatically adjust its focus according to the crop's growth stage, ensuring efficient utilization of features during the crop's key growth stages, such as flowering and maturation.
# Authorship Attribution in Less-Resourced Languages: A Hybrid Transformer Approach for Romanian
## 3 Method  
Our approach introduces a hybrid Transformer and a baseline ensemble learning approach as illustrated in Figure 1. Both methods were evaluated on two sets of texts with different lengths: full texts (FT) and paragraphs (PP) containing around 200 words each, preserving paragraph integrity. In the hybrid Transformer method, the input text is tokenized and then processed by a pre-trained BERT model to generate text embeddings. Concurrently, linguistic features are extracted from the texts to serve as numerical input features. Feature selection is carried out using the Kruskal-Wallis mean rank, retaining the top features for each set. Subsequently, the text embeddings are combined with the numerical features to form a hybrid vector, which is afterward fed to a classification layer for authorship prediction. In contrast, the classical ML method leverages the Readerbench framework to extract linguistic features, which are then employed as features for a set of 7 machine learning models in an ensemble learning approach with a soft voting technique for authorship identification.  
The selection of our models was motivated by the objective of establishing a baseline for comparison with prior research in Romanian authorship attribution. Considering the limited number of studies (only two known to date), our goal was to fill this gap by presenting a comprehensive analysis. Unlike previous approaches that primarily relied on standalone machine learning (ML) models and a basic BERT-based approach for authorship attribution, we introduced novel methodologies. These methods combine the strength of seven ML models and introduce a hybrid BERT-based model. Our hybrid model not only uses text input but also incorporates linguistic features for predictions. As a result, our proposed approach surpasses the state-of-the-art performance of prior studies.
# In Vitro Accuracy of Two Different Electronic Apex Locators Under Various Conditions
## 4 Discussion
It can be concluded that all canal irrigant solutions have a greater or lesser impact on the accuracy of the EAL. Some authors suggest that for clinicians, the most appropriate root canal irrigation protocol with the least negative impact on the accuracy of EALs could be the combination of 17% EDTA after NaOCl or CHX; this is in contrast to the use of NaOCl alone due to its high electroconductivity [44]. Solutions with high electrical conductivity such as saline, anesthetic solution and hypochlorite may affect the accuracy of fourth-generation EALs. 1% hypochlorite with an electrical conductivity of 66 ms and pH 11.72 and EDTA with an electrical conductivity of 40 ms and pH 7.1 shortened the canal length measured with the EAL. CHX 2% with an electrical conductivity of 1 ms and pH 6.5 caused a longer canal length. We can conclude that electroconductive solutions significantly reduce the canal impedance and can lead to shorter measurements due to high electroconductivity [45; 46]. Therefore, based on the results of our study, we agree that the different electroconductivity of root canal irrigation solutions, among other factors, affects the accuracy of EAL measurements. We must keep in mind that this and other in vitro studies were performed under ideal conditions; therefore, the accuracy of EALs may be significantly different from that in clinical work, mainly due to factors such as a complex root canal morphology, debris and pulp remains, saliva contamination, and other factors. Most commercial canal irrigant solutions have an impact on measurements due to the material conductivity. An explanation could be that the complex composition of these solutions has an influence on hard dental tissue as well as on the EAL reading. Correct and detailed chemical compositions of canal irrigants are scarce, and most canal irrigant manufacturers consider it confidential data, making it hard to analyze or determine which chemical compounds have an influence on EALs. Also, the clinician should know which generation of EAL he is working with, in order to know its performance when used alongside different canal irrigants.
## 3 Result and Discussion
### UV-Vis Absorption Properties
Because the anchor spacing of **AP12** is similar to **AP6**, which did not show a significant shift of absorption onset on TiO\({}_{2}\) relative to in solution, no significant change was expected for **AP12** on the TiO\({}_{2}\) films. As predicted, the onset for **AP12** on TiO\({}_{2}\) was very similar to that in solution. Additionally, it is particularly noteworthy that all of these dyes suffered from poor molar absorptivities ranging from 12,000 to 5500 M\({}^{-1}\) cm\({}^{-1}\). **AP8**, **AP9**, and **AP12** all had molar absorptivities \(\leq\)8000 M\({}^{-1}\) cm\({}^{-1}\). Low molar absorptivities can be problematic in DSC devices.
## 3 Results and Discussion
### Model Comparison Experiments  
The effectiveness of the proposed SWSDS-Net for imaging logging fracture segmentation was verified through comparison experiments conducted with four well-established models, namely, UNet, UNetPlus, UNet++, and 3D-UNet, all under identical conditions. In Table 2, in terms of P, the average value of the UNet baseline model is 0.694, that of 3D-UNet is 0.636, that of UNetPlus is 0.639, that of UNet++ is 0.664, and that of SWSDS-Net is 0.712. SWSDS-Net demonstrates the best performance in P, followed by UNet, UNet++, UNetPlus, and 3D-UNet. Regarding R, the average value of the UNet baseline model is 0.510, that of 3D-UNet is 0.570, that of UNetPlus is 0.334, that of UNet++ is 0.653, and that of SWSDS-Net is 0.608. SWSDS-Net demonstrates the best performance in R, followed by UNet++, 3D-UNet, UNet, and UNetPlus. Concerning I, the average value of the UNet baseline model is 0.663, that of 3D-UNet is 0.670, that of UNetPlus is 0.584, that of UNet++ is 0.672, and that of SWSDS-Net is 0.676. SWSDS-Net also demonstrates the best performance in I, followed by UNet++, 3D-UNet, UNet, and UNetPlus. Finally, with respect to D, the average value of the UNet baseline model is 0.578, that of 3D-UNet is 0.594, that of UNetPlus is 0.411, that of UNet++ is 0.650, and that of SWSDS-Net is 0.649. SWSDS-Net again demonstrates the best performance in D, followed by UNet++, 3D-UNet, UNet, and UNetPlus. Overall, SWSDS-Net exhibits the optimal performance in all parameters of the evaluation metrics. The experimental results demonstrate that UNet++ outperforms other models in terms of P, R, I, and D. However, due to its complex network structure, large number of parameters, and high computational cost, it is not suitable for on-site deployment on mobile devices for imaging logging fracture segmentation. In contrast, the proposed SWSDS-Net achieves high accuracy while being lightweight without introducing additional parameters or significantly increasing computational costs. It effectively improves small target detection accuracy. Although 3D-UNet has the capability to handle 3D data and capture 3D features, it does not perform well in this specific scenario. UNetPlus reduces the number of channels to enhance model efficiency but sacrifices some accuracy. In summary, SWSDS-Net effectively enhances the performance of fracture segmentation tasks in imaging logging by introducing the SWS and DSCN modules. These modules establish a closed loop between the encoder and decoder of UNet, where the feature maps
# Research of Modal Analysis for Impeller of Reactor Coolant Pump
## 2 Modal Analysis of the Impeller
### Modal Experiment  
Due to limitations of the test environment, a 0.1 scale RCP impeller (maximum diameter of 92 mm and height of 53 mm) was taken as the research object. In order to measure natural frequencies of the impeller in water, it is necessary to transmit rotating vibration signals to collectors. This study installed the motor upside down, and immersed the shafting in the tank. The test schematic diagram is shown in Figure 1. Upper and lower bearings with end caps are used to ensure stable rotation of the spindle. The motor is inverted above the frame. A rigid coupling connects the upper end of the spindle to the motor output shaft. The signal testing equipment mainly includes waterproof, magnetic, piezoelectric accelerometers [12], charge amplifiers, NI (National Instruments) Data Acquisition Card, slip rings and a computer for monitoring data. This test platform is being applied for a Chinese patent. In addition to measuring natural frequencies, it can be used to test the blade vibration when the interaction occurs between rotor and stator.  
In our test, the transient excitation method [13] was applied to the static impeller in the air (also in water). The excitation point was arranged at the inlet edge of the impeller hub. Furthermore, the spectral analysis method [14] was applied to the rotating impeller in water. Magnetic piezoelectric acceleration sensors were attached to the surfaces of blades [15], the slip ring exported the electric vibration signal on the surface of the impeller to the charge amplifier, and then the NI Data Acquisition Card read vibration signals. It should be noted that a vortex generated by a high speed will make the impeller incompletely immersed in water, and the slip ring also has a limit on the rotational speed, so the experimental rotational speed is set to 100 r/min.  
On this bench test, natural frequencies of the static impeller in the air (Condition A), the static impeller in water (Condition B) and the rotating impeller in water (Condition C) were measured.  
By Fast Fourier Transform Algorithm (FFT) analysis [16] and filter process (filtering shaft frequency and its multipliers), spectrograms of measured signals under these three conditions are shown in Figure 2. Natural frequencies of the impeller are 2653.6 Hz, 2111.5 Hz and 2216.0 Hz, respectively.