esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Verification and Analysis
### Comparative Analysis
#### 4.5.2 Applicability of CAM_MCFCNN under Limited Training Sample Conditions  
Aiming at the problem of the difficulty in obtaining the fault data of a planetary gearbox in actual production, the classification performance of the proposed model was evaluated when the training samples were insufficient. The proportion of training samples was gradually reduced from 80% to 10%, and the model was repeated 10 times on the training set obtained at each proportion. The average diagnostic results of the single-channel CNN and the CAM_MCFCNN model on the test set are shown in the Figure 9.  
It can be observed from the figure that in the four datasets, compared with the single-channel CNN model, the CAM_MCFCNN model significantly improved the classification accuracy in both constant-speed and mixed-speed instances with fewer training samples. In the constant running datasets A, B and C, when 20% of the samples were used for training, the average diagnostic accuracy of the CAM_MCFCNN model was 94.98%, 92.33% and 99.44%, respectively. However, the average diagnostic accuracy of the single-channel CNN model was 86.65%, 78.08% and 98.08%, respectively. For dataset D, containing samples of different rotational speeds, when 10% of the samples were used for training, the diagnostic accuracy of both models was not ideal. However, when the training samples increased to 40%, the average diagnostic accuracy of the CAM_MCFCNN model increased to 93.18%. The accuracy of the single-channel CNN model was 89.61%. The experimental results show that the proposed approach is effective and has a certain value in the industrial environment where training samples are scarce.  
Figure 9: The average diagnostic results of single channel CNN and CAM_MCFCNN model on the test sets under limited training sample conditions.
# Hybrid Representation of Sensor Data for the Classification of Driving Behaviour
## 1 Introduction
(2018), incorporating rules in the context of a hybrid approach. [PERSON] and [PERSON] (2018) used stacked LSTMs for driving behaviour classification, whereas [PERSON] et al. (2018) employed a CNN-based multi-sliding window approach for maneuver classification. Such NN-based approaches depend on the availability of a sufficient amount of data to cope with overfitting. In numerous related works, performance evaluation was performed by means of the UAH dataset, which comprises measurements obtained from mobile phone sensors. In this work, we introduce a hybrid representation approach, combining NN-based encoding and rule-based event detection, in order to classify overall driving behaviour. Aspects of this approach appeared in preliminary works ([PERSON] et al., 2018; [PERSON] et al., 2018). A thorough description, as well as a more complete experimental evaluation, are provided here. Apart from the UAH dataset, experiments are also performed on two datasets obtained by means of telematics sensors. Both datasets comprise samples of aggressive, semi-aggressive, and normal driving behaviour. The rest of this paper is organized as follows: in Section 2, we provide the theoretical background on the NN architectures considered. In Section 3, we present the proposed hybrid representation approach. In Section 4, we present the experimental evaluation, whereas in Section 5 we discuss the main conclusions of this work.
## 2 Role of AI in Dentistry
### Dental Caries Identification and Diagnosis  
The identification and diagnosis of dental caries have been significantly enhanced by the advent of computer vision systems, which utilize advanced techniques such as object identification and semantic segmentation. Dental caries, commonly known as tooth decay, are one of the most prevalent dental health issues worldwide. Traditional diagnostic methods, including visual examination and radiography, can sometimes miss early-stage caries, leading to delayed treatment and potentially more extensive dental work. Computer vision systems offer a more precise and reliable alternative by analyzing dental images with a high degree of accuracy. These systems can detect early signs of caries by identifying minute changes in tooth structure that may not be visible to the naked eye. For instance, object identification algorithms can pinpoint areas of demineralization, which are often the first indicators of caries development. Semantic segmentation further enhances this process by dividing dental images into distinct segments, allowing for a more detailed analysis of the tooth surface and the surrounding tissues. This level of detail is particularly useful in identifying caries that develop in hard-to-see areas, such as between teeth or beneath existing dental restorations. Additionally, AI-driven diagnostic tools can analyze patterns of caries progression, helping clinicians to predict the future development of the condition and plan appropriate interventions. By integrating these advanced techniques into routine dental practice, healthcare providers can offer more timely and effective treatment, reducing the incidence of severe dental caries and improving overall oral health outcomes.
## 1 Introduction
The proposed paper's primary contributions are as follows;  
* The power optimization problem is coordinated through multi-input parameters such as distance (\(d\)), signal-to-noise ratio (\(SNR\)), and foliage depth (\(d_{f}\)) for each user in PD-NOMA;
* The optimization problem for equity power allocation is solved through an FIS-based system that guarantees user-fairness in terms of the optimal power distribution among PD-NOMA users;
* In realistic 5G micro-cells, the Weissberger model is being used to analyze the impact of foliage on channel conditions between the gNodeB and users;
* The proposed methodology employs the decode and forward (D&F) cooperation relaying mechanism at the cell-center level to improve BER performance at the cell-edge level, where a maximum ratio combining (MRC) is used for detection. The rest of the paper is organized as follows. Section 2 presents the system model, optimal PA and cooperative relaying PD-NOMA is discussed in Section 3, respectively. Section 4 provides the simulation results. Finally, conclusions are drawn in Section 5.
# Academic Proposal for Heritage Intervention in a BIM Environment for a 19 th Century Flour Factory
## 4 Project Proposal: Heritage Intervention Results & Discussion  
### Phase 1. First Step: Original Condition  
To reconstruct the original building, the work team searched for documentation in the Simancas Archive and at town hall, with practically no results. Basic graphic documentation was located in a publication on flour factories in the province of Valladolid; the document provided an initial orientation for the survey of the building [31].  
As described by [PERSON] [31], stone, wood, brick, and ceramic tiles are, among others, the constituent materials of this factory, present in the foundation, interior structure, enclosures, and roof, respectively (Figure 4). Stone is used as the structural material, forming the foundation of the building, which has 1.20 m-thick walls. In the lower part of the building, three water channels run through arcades that are built with ashlars and continually exposed to erosion. In the interior structure, wood is used by means of insulated supports to support the weight transmitted by slabs of the same material. These supports are called \"piedroits\", which usually rest on a stone block and, at the top, are finished off with a wooden capital. However, wood acquires more importance in the architecture of the flour factory in the slabs for different floors. The beams are combined with filler ties, and planks are nailed on top, leaving the wooden floors visible. The roof, which is also made of this material, is defined as a row-to-row. Its use is rare because balance can be an issue; although it can be supported on wing walls, the central row or beam is generally supported by pairs of crossbeams. Wood is also used in the corbels of the roof eaves [31].  
Aided by building documentation, the first step of the work methodology involved reconstructing the original condition of the building; notably, that there was no annex (Figure 5). The annexed warehouse was a later addition to the initial construction, which helps to differentiate the styles between the two parts of the complex. This addition is also a reflection of the probable changes that the then-established company underwent, assuming an increase in production that required an increase in warehouse surface.
# Seismic Performance of Ductile Column with Rectangular Hollow Cross-Section in RC Building
## 4 Discussion
### Flexural and Shear Deformation Distribution  
To investigate the difference in deformation patterns between solid- and hollow-sectioned columns, the flexural and shear deformation distributions were calculated as follows. As mentioned earlier, the column height was divided into seven or five sections, and the rotation angle (\(\theta\)) by flexural deformation and the shear strain (\(\gamma\)) by shear deformation, in each section, were calculated. Based on Figure 10, the rotation angle (\(\theta\)) and the shear strain (\(\gamma\)) were calculated using Equations (2) and (3), respectively.  
\[\theta= \frac{\delta_{1}-\delta_{2}}{x} \tag{2}\]  
\[\gamma= \frac{\sqrt{a^{2}+b^{2}}}{2 ab}(\delta_{3}+\delta_{4}) \tag{3}\]  
where \(x\) is the distance between measurement points by LVDTs, \(\delta_{1}\) and \(\delta_{2}\) are the variation value in each LVDT in the vertical direction, \(a\) and \(b\) are the horizontal and vertical distance between measurement points by LVDTs, and \(\delta_{3}\) and \(\delta_{4}\) are the variation value in each LVDT in the diagonal direction, respectively.  
Figure 11a shows the flexural and shear deformation distributions of N-series specimens at \(R=4.0\%\). From the flexural deformation distribution, it is confirmed that the inflection points of both specimens were generally located in the middle of the column height. Comparing the solid-sectioned CM1 and the hollow-sectioned CM2, the deformation at the hollow parts was expected to increase in CM2; however, the flexural deformation distribution was almost the same as that of CM1. The shear deformation at both column ends tended to become larger than other sections due to the damage concentration, and their distribution was also found to be almost the same in both specimens. Although the moment of inertia and cross-sectional area decreased at the hollow cross-sections, higher strength concrete with a higher Young's modulus was employed in CM2, and it is considered to result in similar deformation patterns in both specimens.  
Figure 11b shows the flexural and shear deformation distributions of S-series specimens at \(R=2.0\%\). For the flexural deformation, CM12 and CM13 had deformation distributions that were almost equivalent to that of the solid-sectioned CM11 in the same way as the N-series. Moreover, for the shear deformation distributions, those of hollow-sectioned  
Figure 10: Calculation method of deformation: (**a**) rotation angle by flexural deformation; (**b**) shear strain by shear deformation.  
specimens were almost equal to that of the solid-sectioned one, as observed in the N-series. As mentioned above, this similarity can be attributed to the concentration of damage on the column ends, and the use of higher strength concrete in hollow-sectioned specimens (CM2, CM12, and CM13). Also, it is concluded that, as long as severe damage on the hollow sections can be avoided, the deformation patterns will not be changed.
# Performance and Requirements of GEO SAR Systems in the Presence of Radio Frequency Interferences
## 2 Ground RFI Impacts on GEO SAR Signal
### Point-Like RFI Source  
If the ground RFIs are contributed by the point-like RFI sources (strong, localized and continuous emitting) [8; 33], the powers of all the received point-source RFIs have to be summed together. Referring to Figure 3, the total power of RFI at the receiver of GEO SAR can be expressed as  
\[P_{RFI}=\frac{A_{t}}{4\pi R^{2}}\cdot\sum_{i=1}^{N}P_{e,i}P_{e,i}F(\theta_{e,i },\psi_{e,i}), \tag{5}\]  
where \(P_{e}\) is defined as the average Effective Isotropic Radiated Power (EIRP) of each single point-like RFI source, \(p\) is the probability of each point-like RFI source, \(i\) is the index, \(N\) is the number of the point-like RFI sources, \(\theta_{e}\) and \(\psi_{e}\) indicates the position of each point-like RFI source. Because (5) relates to the antenna pattern, the position of the point-like RFI source will impact the power of \(P_{RFI}\)  
Figure 3: Sketch of GEO SAR system in the presence of RFI.  
A high-power point-like RFI source located far away from the centre of the beam of GEO SAR may have a smaller impact than a lower power point-like RFI source which is near the centre of the beam of GEO SAR.
## 3 Error Introduced by LEO Satellite Assistance
### LEO Positioning and Timing Error  
For a static receiver, taking the Iridium satellite as an example, the position error obtained by LEO satellite through Doppler positioning is within 1 km, and the timing accuracy is about 1 \(\upmu\)s [19]. It is obvious that the influence of timing error is within one code chip for GPS. However, for the positioning error, an error of 1 km corresponds to three code chips. The position error of the receiver affects not only the search range of the receiver's code phase but also the accuracy of the estimated Doppler shift caused by the satellite dynamic. The relative movement between the receiver and the satellite causes the Doppler shift of the satellite signal. Under the assumption that the receiver is stationary, the Doppler shift is mainly caused by satellite motion. In the process of positioning solutions using LEO and GNSS signals, the receiver could estimate Doppler shifts through signal processing correctly. However, the initial position deviation of the receiver will cause errorsin the estimated Doppler projection to the GNSS signal LOS. This frequency error caused by the projection satisfies the following expression:  
\[f_{e}=v\times\left(1-cos\left(\frac{Z}{H}\right)\right)\times\frac{f_{sat}}{c} \tag{5}\]  
where \(v\) and \(H\) represent the orbital speed and altitude of the satellite respectively. \(Z\) indicates the position error of the receiver. \(c\) is the speed of light. For LEO-assisted GNSS receivers, the estimated Doppler deviation of LEO signal after projection will be absorbed into the receiver clock frequency deviation. However, the Doppler frequency shift of the GNSS signal will be affected by the position error after LOS projection. Figure 5 shows the effect of receiver position error on Doppler shift estimation. It can be seen from Figure 5 that the Doppler estimation errors caused by the position error on Iridium and GPS dynamics are both small enough to be ignored.
## 4 Discussion
### Highlight from the Overview
However, to maximize their potential, future research must addresstheir long-term impacts, integration into healthcare practices, and ethical implications, ensuring their place as effective, ethical tools in improving patient care.
# Robust Feature Selection-Based Speech Emotion Classification Using Deep Transfer Learning
## 4 Experimental Result and Analysis
### Emotion Datasets
#### 4.1.1 Tess  
Many researchers have used the Toronto Emotional Speech Set, a publicly accessible speech emotion database, to classify emotions. The speech samples were captured in 2010 at Northwestern University Auditory Laboratory [39]. Two actresses were requested to recite a few of the 200 words during the impromptu event, and their voices were recorded. During the scene, seven different emotions were recorded (happy, angry, fear, disgust, pleasant, surprise, sad and neutral). There are 2800 files in all that show human emotion.
## 2 Geological Setting  
The Qaidam Basin, located on the northeastern Tibetan Plateau, is an intermontane basin surrounded by the Altun, Qilian, and Kunlun Mountains (Figure 1a). The average elevations of the basin and the surrounding mountains are 2800 m and 4000-5000 m, respectively [18]. Covering an area of 12 \(\times\) 10\({}^{4}\) km\({}^{2}\), the basin is situated on Proterozoic crystalline and Paleozoic folded basements. Its tectonic evolution has occurred in four distinct stages: rifting from the Early to Middle Jurassic, compression from the Late Jurassic to Cretaceous, local strike-slip and regional compression from the Paleogene to Late Pliocene, and compressional folding and depression from the Late Pliocene to the Quaternary [19]. The basin's evolution provides important insights into the tectonic processes of the Tibetan Plateau, and the plateau has influenced Quaternary climate of the Northern Hemisphere [20].  
In the Sanhu Depression of the Qaidam Basin, tectonic activity and the Quaternary drying climate have resulted in thick sediment accumulation during the Pleistocene and Holocene [21]. Sediments deposited since 2.8 Ma BP are divided into standard zones from K13 to K0\({}^{\prime\prime}\) upward. The Pleistocene is further divided into three subunits: Lower Pleistocene [Q1, K13 (2.8 Ma BP) to K0 (0.73 Ma BP)], Middle Pleistocene [Q2, K0 (0.73 Ma BP) to K0\({}^{\prime}\) (0.12 Ma BP)], and Upper Pleistocene [Q3, K0\({}^{\prime\prime}\) (0.12 Ma BP) to K0\({}^{\prime\prime}\) (0.01 Ma BP)]. The Holocene corresponds to Q4 [K0\({}^{\prime\prime}\) (0.01 Ma BP) to the present]. Drilling in the Sanhu Depression has revealed over 3000 m of Quaternary mudrock, carbonaceous dark grey mudrock, grey siltstone, and greyish-green argillaceous siltstone, with evaporites (i.e., anhydrite and halite) present in shallow zones and at the surface (Figure 1b). The lithologies are lose and weak diagenetic. Water parameters indicate the strengthened sealing ability in Qigequan Formation [22]. During the formation of the sediments in the Taidong area, northwestern of Sanhu Depression and western of Sebei-1 gas field, the lake level was shallow in the K13 to K10 (delta front facies) and K5 to K3 (delta front to prodelta facies) intervals, while it was deep in the K9 to K6 (lacustrine facies) and shallow K2 to K0 (lagoon to salt pan facies) intervals. The last period of shallow lake levels coincided with the emergence of an evaporite lagoon at west part of Taidong area [6]. The evolution of sedimentary facies and lithologies from K13 to K0, particularly the presence of halite in K0\({}^{\prime}\) (Upper Pleistocene) and K0\({}^{\prime\prime}\) (Holocene), reflects the extreme arid conditions that characterize the region, in line with the regional climate changes in the Qaidam Basin [23]. The change of water level and climate may influence the sedimentary facies in Taidong area.  
Figure 1: Location of Guidance Basin, data points in the basin (a), and Quaternary lithological column in Sanhu Depression (b).
## 5 Results and Discussion
### Evaluation Criteria  
We evaluated the performance of different detection models using five commonly used evaluation metrics: Precision (\(P\)), Recall (\(R\)), \(F_{1}\) Score (\(F_{1}\)), mean Average Precision (\(mAP\)), the number of frames transmitted per second (Frames Per Second, FPS), and the number of floating-point operations (FLOPs). The formulae for \(P\), \(R\), \(F_{1}\), and \(mAP\) are defined as follows:  
\[P=\frac{T_{P}}{T_{P}+F_{P}} \tag{4}\]  
\[P=\frac{T_{P}}{T_{P}+F_{N}} \tag{5}\]  
\[F_{1}=\frac{2*P*R}{P+R} \tag{6}\]  
\[AP_{i}=\int_{0}^{1}P(R)dR \tag{7}\]  
\[mAP=\frac{1}{N}\sum_{i}^{N}AP_{i} \tag{8}\]  
where \(F_{P}\) (false positive) and \(F_{N}\) (false negative) represent the number of incorrect and missed detected objects, respectively, while \(T_{P}\) (true positive) indicates the number of correctly detected objects. \(T_{P}+F_{P}\) represents the total number of objects detected, while \(T_{P}+F_{N}\) represents the total number of actual objects. \(F_{1}\) score indicates the harmonic mean of \(P\) and \(R\). \(AP\) refers to the area under the precision-recall curve (PR curve), and \(mAP\) is the average of APs for different classes, and \(N\) is the number of classes in the test sample. In object detection tasks, a higher precision value indicates fewer false detection results, while a higher recall value denotes fewer missed detection results.
# A Low-Brightness Image Enhancement Algorithm Based on Multi-Scale Fusion
###### Abstract  
Images captured in low-brightness environments typically have low brightness, low contrast, and high noise levels, which significantly affect the overall image quality. To improve the image quality, a low-brightness image enhancement algorithm based on multi-scale fusion is proposed. First, a novel brightness transformation function is used for the generation of two images with different brightnesses. Then, the illumination estimation technique is used to construct a weight matrix, which facilitates the extraction of advantageous features from each image. Finally, the enhanced image is obtained by the fusion of two images using the weight matrix and the pyramid reconstruction algorithm. The proposed method has a better enhancement effect as shown by the experimental results. Compared to other image enhancement algorithms, it has lower evaluation values in the natural image quality evaluator (NIQE) and lightness order error (LOE) indices. The lowest average NIQE value of the proposed algorithm in each dataset is 2.836. This further demonstrates its superior performance.  
brightness enhancement; image fusion; image processing; Laplacian pyramid Article