esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
To improve the accuracy of material identification under low contrast conditions, this paper proposes an improved YOLOv4-tiny target detection method based on an adaptive self-order piecewise enhancement and multiscale feature optimization. The model first constructs an adaptive self-rank piecewise enhancement algorithm to enhance low-contrast images and then considers the fast detection ability of the YOLOv4-tiny network. To make the detection network have a higher accuracy, this paper adds an SE channel attention mechanism and an SPP module to this lightweight backbone network to increase the receptive field of the model and enrich the expression ability of the feature map. The network can pay more attention to salient information, suppress edge information, and effectively improve the training accuracy of the model. At the same time, to better fuse the features of different scales, the FPN multiscale feature fusion structure is redesigned to strengthen the fusion of semantic information at all levels of the network, enhance the ability of network feature extraction, and improve the overall detection accuracy of the model. The experimental results show that compared with the mainstream network framework, the improved YOLOv4-tiny network in this paper effectively improves the running speed and target detection accuracy of the model, and its _mAP_ index reaches 98.85%, achieving better detection results.  
deep learning; target detection; adaptive self-order piecewise enhancement; multiscale feature optimization
## 6 System Evaluation
### Security Analysis
#### 6.1.1 Security of Encryption  
In our SEPIM model, in order that a secure search request \(u\) is constructed consisting of feature vectors, we use a homomorphic encryption cryptographic scheme \(z_{i}\gets HE_{pk}(u_{i})\), to ensure the security of features. For the search process, all vectors \(V_{i}\), \(VF_{hi}\) associated with the index tree are encrypted through search process by secure dot product based on homomorphic encryption. As proved in the existing homomorphic encryption scheme [33], as long as the secret private key \(pr\) remain confidential to the \(Bob\), it is impossible to obtain the plaintext format of \(V_{i}\) and \(VF_{hi}\), in the known ciphertext homomorphic encryption model. Moreover, every item of \(V_{i}\) has an assigned weight, which differs from image to image contents. Therefore, the feature vectors pertaining to \(Alice\)'s search request and all vectors assigned with the index tree are kept secure by means of homomorphic encryption and the secure dot product based on homomorphic encryption, respectively.  
Theorem 6.1: _The Secure Request and Secure Vectors Will Not Reveal Extra Information Between Two Parties_  
Proof of Theorem 6.1.: In order to quantify the amount of information that is leaked to the \(Bob\) from the secure vectors \(V_{i}\), \(VF_{hi}\) or secure search request \(z_{i}\), we measure the dependency between these secure vectors and secure search request, and their corresponding images dataset \(D\). The rationale informing this method is that low dependency translates into low information leakage. We use the mutual information (MI) entropy [36] to measure the dependency between two entities. MI is defined as follows:  
\[MI(X,Y)=H(X)+H(Y)-H(X,Y), \tag{7}\]  
where \(H(X)\) and \(H(Y)\) are the entropy of the one-way hash function SHA-1 of the original image \(X\) and the secure vector or secure search request \(Y\), respectively. The entropy of the random variable \(Z\) is defined as  
\[H(Z)=\sum_{i=1}^{n}-P(z_{i})logP(z_{i}), \tag{8}\]  
and \(H(X,Y)\) is the join entropy between the two variables \(X\) and \(Y\), defined as  
\[H(X,Y)=\sum_{i=1}^{n}\sum_{j=1}^{m}-P(x_{i},y_{i})logP(x_{i},y_{i}), \tag{9}\]  
where \(P(x)\) and \(P(x,y)\) are the probabilities of occurrence of the outcome \(x\), and the pair\((x,y)\). Throughout our experiments, the secure search request and secure vectors yielded a low MI value equal to 0.0013.
## 1 Introduction  
Evaluations of teaching behaviors in physical education (PE) classrooms are critical components of overall classroom teaching evaluations, which directly impact teaching effectiveness and students' learning experience [1]. However, current evaluations of PE teaching behaviors rely primarily on manual observation and analysis. This approach entails multiple challenges when assessing complex classroom scenarios and attempting to capture specific actions and interactions. Such challenges include inefficiency, subjectivity, and a lack of real-time capabilities [2]. In recent years, rapid advancements in artificial intelligence (AI) technologies, such as computer vision and deep learning, have made automatic recognition and analysis of complex behaviors possible, offering new technological pathways for addressing these issues [3]. Nevertheless, integrating these technologies into PE classroom contexts poses distinct challenges due to the high motion intensity, multi-subject interaction, and spatial variability of physical education settings.  
Against this backdrop, this study aimed to design and implement an AI-driven system that enables comprehensive, real-time, and accurate evaluation of teaching behaviors in secondary school PE classrooms. The system targets observable behaviors aligned with China's curriculum standards, assessing teacher, student, and interactive actions through a framework incorporating entropy, redundancy, ratio, and temporal analysis [4]. Specifically, the study seeks to answer the following research question: Can an AI-based system, leveraging the CogVLM2-Video model, accurately recognize and automatically evaluate teaching behaviors in PE classrooms? Answering this question is expected to provide robust scientific support and practical guidance for improving the quality of physical education teaching.
# Fuzzy Logic-Based Performance Enhancement of FSO Systems Under Adverse Weather Conditions
## 1 Introduction  
The growing demand for higher data rates has significantly increased the need for advanced solutions in wireless data transmission. Furthermore, the emphasis on green communication has further driven research directions toward innovative technologies. Optical wireless communication (OWC) has emerged as a compelling candidate, offering a sustainable and efficient approach to address modern communication requirements. FSO is a subclass of OWC that has gained significant attention in the realm of wireless data transmission. FSO has many positive attributes which present a viable and efficient alternativeto the traditional radio frequency (RF) communication links [1]. FSO utilizes optically modulated signals and enables the wireless transmission of data signals in free space between precisely aligned transmitter and receiver units. This technology offers numerous benefits, including high-speed data transmission, a broad bandwidth operating in an unlicensed spectrum, resistance to electromagnetic interference, energy efficiency, and suitability for deployment in urban environments [1; 2]. Future communication networks, including QKD-based optical systems, demand precise modeling of environmental effects on optical signal propagation. In [3], the authors emphasize this need in the context of converged satellite/fiber optical infrastructure. Atmospheric attenuation and atmospheric turbulence are the two main factors that degrade the strength of an optical signal during beam propagation in the FSO channel. Atmospheric attenuation in FSO communication systems arises from various weather conditions, including rain, fog, haze, snow, and dust. These factors cause signal degradation primarily through scattering and absorption of an optical signal. Fog and haze typically exert the most significant impact due to Mie scattering from their high concentration of small particles [1; 2]. Rain and snow further cause both Mie and geometric scattering [4; 5]. Absorption is influenced by the interaction of light with water molecules and other atmospheric components. The relative impact of these factors varies greatly depending on the intensity of the weather event and the wavelength of the optical signal [6]. Heavier rainfall can significantly reduce signal strength with higher attenuation [5]. Atmospheric turbulence is caused by fluctuations in the refractive index of air, which result from uneven heating and cooling. These fluctuations produce optical beam defocusing, signal strength variations (scintillation), and increased beam divergence. Turbulence-induced effects, such as signal distortion and spreading, further degrade the quality of the received signal [7]. This study focuses on atmospheric attenuation caused by fog and rain conditions.
## 4 Autonomous Shovel Technology
### Shovel Trajectory Planning  
After establishing an accurate three-dimensional ore pile model, the starting position for ore shovel loading will be independently determined and the optimal trajectory will be planned as well, theoretically based on the maximization of loading efficiency and minimization of energy consumption [50]. Based on the controlling model, bucket loading trajectory planning methods can be divided into the one determined by resistance force and the other directed by self-learning.
# Study of In-Plane Mechanical Properties of Novel Ellipse-Based Chiral Honeycomb Structure
## 1 Introduction
### FEM Simulations  
Based on the representative volume element (RVE) method, the FEM analysis software (COMSOL Multiphysics 6.0) was used for analysis. The parameters of the E-antitet cell were set as follows: \(l_{x}=30.00\) mm, \(l_{y}=30.00\) mm, \(a=10.00\) mm, \(b=5.00\) mm, \(t=3.00\) mm and \(d=10.00\) mm, which means \(\delta=2.00\), \(\alpha_{ax}=3.00\), \(\alpha_{xy}=3.00\), \(\alpha_{bx}=6.00\), \(\alpha_{by}=6.00\), \(\beta_{a}=0.30\), \(\beta_{b}=0.60\), \(\gamma_{a}=1.00\) and \(\gamma_{b}=2.00\). \(l_{cx}\) and \(l_{cy}\) can be respectively calculated by Equations (10) and (11): \(l_{cx}=22.86\) mm and \(l_{cy}=11.67\) mm. The material is set to polylactic acid (PLA) with Young's modulus \(E=3255.43\) MPa [56]. The models were developed using 3D structural solid hexahedral elements, and the hypothesis of small strain was also adopted in the FEM analyses. The cross-section boundary corresponding to the ligament \(l_{x}\) in the \(x\)-direction is set as a symmetric boundary (free displacement), and the boundary corresponding to the ligament \(l_{y}\) in the \(y\)-direction is set as a symmetric boundary (normal force \(F=-100\) N), and at this time, the \(v_{xy}\) and \(E_{y}\) of the E-antitet can be obtained. For the \(v_{yx}\) and \(E_{x}\) analysis of the E-antitet, the two boundary conditions need to be exchanged.  
Figure 3 shows the stress and displacement cloud diagrams of the E-antitet cell under compression. The axial ligament \(l_{y}\) always bears the larger stresses and displacements. The [PERSON]'s ratios \(v_{xy}\) and \(v_{yx}\) and [PERSON]'s modulus \(E_{x}\) and \(E_{y}\) of the E-antitet can be obtained from the FEM simulations either in the \(x\)-direction or \(y\)-direction.  
The analytic solutions derived theoretically are compared with the numerical solutions by FEM, as shown in Table 1. The two are relatively close, and the errors are between 9.52% and 14.29%, with the average errors of 14.29% for [PERSON]'s ratio and 10.36% for [PERSON]'s modulus, and the total average errors are 12.32%. The errors may be caused by the fact that, when the elliptical node ring is twisted to wrap the ligament, the ligament will deflect, instead of being perfectly wound on the node ring. This will lead to a lager strain in the numerical solutions, i.e., the magnitude of [PERSON]'s ratio and [PERSON]'s modulus in the numerical solution will be smaller. It is worth noting that [PERSON]'s ratio and Young's modulus obtained from both analytical and theoretical solutions satisfy the reciprocal relationship, i.e., \(E_{x}v_{yx}=E_{y}v_{xy}\), which proves the correctness of the results.
# Influence of Degree of Saturation on Soil-Pile Interactions for Piles in Expansive Soils
## 3 Laboratory Testing and Results
### Oedometer Tests  
For expansive soils, two basic types of oedometer tests, including the consolidation-swell (CS) test and the constant volume (CV) test, are commonly performed to measure the swelling pressure of expansive soils in the oedometer ring under fully wetted conditions [5; 9; 14; 20; 30; 36]. Figure 1 presents the typical consolidation-swell curves determined from the CS and CV tests for expansive soils. As shown in Figure 1, the consolidation-swell swelling pressure, \(\sigma_{cs}^{\prime\prime}\), and the percent swell, \(\varepsilon_{ss}\), will be determined from the CS test, whereas the constant-volume swelling pressure, \(\sigma_{cv}^{\prime\prime}\), will be determined from the CV test. A heave index, \(C_{H}\), shown in Figure 1, can be determined based on the inundation pressure, \(\sigma_{i}^{\prime\prime}\), the consolidation-swell swelling pressure, \(\sigma_{cs}^{\prime\prime}\), and the percent swell, \(\varepsilon_{ss}\). The heave index, \(C_{H}\), is a critical parameter in predicting soil heave [37; 38]. The CS and CV tests were conducted in this study to determine the percent swells and swelling pressures of the soil specimens under fully wetted conditions. The oedometer test results for the soil specimens conducted for various initial degrees of saturation (_DOS_) under fully wetted conditions are presented in Table 3. The results of the oedometer tests shown in Table 3 indicated that lower initial saturation led to higher swelling pressure, as the soils with the same dry density but a higher degree of saturation exhibited lower swelling potential.  
It should be noted that the measured swelling pressures provided in Table 3 represent the vertical swell pressures that develop when the soil is allowed to be fully wetted in the laboratory. However, expansive soils may not be fully wetted during the soil heaving process in the field [22; 39]. As a result, the swelling pressures provided in Table 3 may not directly represent the in situ soil conditions. Therefore, these swelling pressures may need to be adjusted to account for partially wetted conditions.  
the swelling pressure determined using the procedure outlined in [PERSON] (2007) [40] for the soil specimens under partly wetted conditions are presented in Table 4. These parameters were used in the calculations of total pile uplift force.
## 5 Overview of 5G-Enabling Technologies
### Key 5G-Enabling Technologies
#### 5.1.13 Mobile Femtocells (MFemtocells)  
In the mobile femtocell idea, the moving network (or mobile relays) is a viable solution for boosting the spectral efficiency of next-generation networks. MFemtocells are small cells that can be easily put in moving buses, trains, and vehicles, allowing the BS to execute data sending and receiving with onboard consumers [29]. The deployed femtocells frequently move, forcing them into areas with poor eNB signal strength. Thus, they demand continuous handovers with neighboring base stations, which must be exceedingly rapid to offer smooth wireless access to the end user as they move. Furthermore, due to the frequency of connection change requests and the necessity to accomplish the requests within a limited amount of time, MFemtocells complicate the entire handover process, causing signaling load and frequent connection drops in the network. The MFemtocell concept tremendously benefits wireless cellular networks since it dramatically boosts spectrum efficiency and average throughput. With a few added capabilities, MFemtocell manages traffic as if it were a single UE (or transceiver).
# STGAN: Swin Transformer-Based GAN to Achieve Remote Sensing Image Super-Resolution Reconstruction
## 4 Experiments  
### Datasets  
In this study, we used the RRSSRD [43] public dataset as the training set, as shown in Figure 7. This dataset extensively covers a wide range of typical remote sensing scenarios, such as airports, bare ground, beaches, etc., with a total of 19 different categories, consisting of 4047 pairs of high-resolution (HR) reference images (Ref) in RGB bands, and the detailed dataset information is shown in Table 1.  
To evaluate the performance of the model across different sources and geographic locations, we constructed four independent test datasets, each containing 40 pairs of HR-Ref images. The Ref images in all test sets were obtained from Google Earth 2019, maintaining a consistent spatial resolution of 0.6 m. The LR images were obtained by four-fold double-triple downsampling of the corresponding HR images, ensuring a uniform size of 120 \(\times\) 120 pixels. To match the size of the HR image, the reference image was adjusted to 480 \(\times\) 480 pixels. This setup ensured consistency and validity during the training and testing phases.  
Figure 7: Selected RSSRD public datasets.
## 3 Parametric Study
### Effect of Stationary Vehicle Frequency
#### 3.1.2 Vehicle Frequency Close to the Bridge's Fundamental Frequency (Resonance Case)  
To show the contact-point response reliability in the case of resonance, a stationary vehicle with a frequency of 1.95 Hz was selected. The vehicle stiffness was 30,000 N/m and the mass was 200 kg. The stationary vehicle frequency was very close to the bridge fundamental frequency of 2.08 Hz. The speed and properties of the moving vehicle remained unchanged from the previous case. Although the stationary vehicle response was vastly amplified, its contact-point response agreed well with the true bridge response, as shown in the acceleration responses in Figure 6a. The acceleration response spectra in Figure 6b also show good agreement between the bridge and contact-point responses; however, there was a small but noticeable difference between the bridge and contact-point response spectra in the amplitude of the first frequency mode. This difference only occurred in the case of resonance, unlike the other cases where the stationary vehicle frequency is far from the bridge's fundamental frequency. The stationary vehicle acceleration spectrum showed only one frequency at 2 Hz, which was the frequency of both the bridge and the vehicle. In contrast, the contact-point response spectrum shows the third mode frequency of the bridge at around 18 Hz.  
Figure 5: Dynamic response of the stationary vehicle at 1.59 Hz, along with its contact-point and bridge (**a**) acceleration responses at the mid-span location and (**b**) the acceleration spectra.  
Figure 6: Dynamic response of the stationary vehicle at 1.95 Hz, along with its contact-point and bridge (**a**) acceleration responses at the mid-span location and (**b**) the acceleration spectra.
# A Sparse Autoencoder-Based Unsupervised Scheme for Pump Fault Detection and Isolation
## 1 Introduction
Thus, a couple of improved AEs have been derived, e.g., multi-level-denoising AE [24], sparse autoencoder (SAE) [26]. Among them, the SAE is an effective one, which solves the overfitting problem by putting a sparsity constraint on the hidden units [23; 26]. A successful application of SAE for anomaly detection in turbomachinery can be found in [27]. To address the aforementioned challenges, we developed a fault detection and isolation scheme based on the unsupervised machine learning method, SAE. Mahalanobis distance (MD) is employed to measure the distance between the condition monitoring data and the data under healthy condition. The MD merged multiple variables into one system-wide health indicator. Fault isolation can be achieved by using a two-dimensional contribution map. The variables with larger contributions are responsible for the fault. The performance of the fault detection model was evaluated by the receiver operating characteristics (ROC) curve and the area under the ROC curve (AUC value), considering both false alarm rate and fault detection rate. The following of the paper is organized as follows. Section 2 explains the fault detection and isolation scheme and the algorithms employed in this paper. Then, the experimental data from an industrial factory are explained in Section 3. The performance of fault detection models on the multivariate industrial data are evaluated and discussed through two case studies in Section 4. Finally, conclusions are drawn in Section 5.
# Recognition of American Sign Language Gestures in a Virtual Reality Using Leap Motion
## 3 Experiment and Results
### Evaluation  
We have achieved 86.1% accuracy of recognition of ASL signs. These results are in the range of accuracy achieved by other authors as indicated by a survey in Reference [50]. Note that the subjects participating our study were not experienced users of sign languages, therefore, the quality of sign  
Figure 11: Linear regression analysis of relationship between gesture typing performance and error rate with linear trend and 95% confidence ellipse shown.  
Figure 10: Gesture typing error rate of all subjects.  
gesturing could have adversely affected the accuracy of recognition. Other authors used stand-alone letters or words in ASL for training, whereas we used complete sentences (pangrams), which has been a more difficult task for subjects. Moreover, the view of gestures was presented to subjects as 3D models of hands using a head-mounted Oculus Rift DK display, so the subjects were not able to view their physical hands during the experiment and this could have made the gesturing task more difficult as well.  
After analysing the recorded gesture recognition data, we have observed that there are problems in detecting the gap between the fingers. Small (one centimetre or less) gap is poorly understood and, for example, it is difficult to discriminate between the gestures of C and O signs. Gestures that require a precise thumb position are also more difficult to determine. Thumbs are often covered with other fingers, which decreases the accuracy of the recognition of the E, M, N, T, H, K, S, V and X signs. The recognition of some gestures requires a very precise 3D image of the hand. This is evident in the gesture of the P sign, when the fingers are only partially folded (not scrambled in the fist) but the device from the palm of the hand has identified the fingers as completely curved. This problem also occurred with the letter R gesture, that is, the fingers have to be crossed but they are presented as a concave and such a gesture corresponds to the letter U in the sign language. In some cases, partially folded fingers are treated as completely folded. Our study revealed the gaps in the algorithm of the Leap Motion device used for gesture analysis. Problems occur when the Leap Motion device does not see some fingers. Then the fingertip positions cannot be captured and the gestures are identified incorrectly.  
The gesture recognition was implemented as a microservice over the Internet. The sending of data from the Leap Motion device over the network to the microservice does not significantly increase the duration of the gesture recognition. On average, the size of Leap Motion gesture data batch ranges from 500 to 1500 bytes. Transmission of this amount to a network service does not require a lot of resources or speed. The greatest slowdown occurs at the network service itself by filtering these data and performing the gesture recognition functions. The entire process takes no more than 200 ms.