esa-sceva/satcom-chunk-collection huggingface dataset:
# Analyses of GLONASS and GPS+GLONASS Precise Positioning Performance in Different Latitude Regions
## 2 Evaluation Methods
### Static PPP Processing Strategy  
The static PPP processing was carried out by FUSING (FUSing IN GNSS) software [25], Version 2.0, developed by Wuhan University, Wuhan, China. The Ionosphere-free (IF) linear combination with L1 and L2 was employed. The elevation mask, the precise products, and the ocean tides model, as well as the PCC model, were the same as the DD processing strategy. The GPT2 (Global Pressure and Temperature) [26] model and VMF1 [23] model were used for the tropospheric estimate. PPP static in 24 h window was processed with a forward extended Kalman filter. The processing strategies of PPP are summarized in Table 1.  
DD network and static PPP processing were carried out for different positioning combination modes: GPS stand-alone positioning mode, GLONASS stand-alone positioning mode, and GPS+GLONASS combined positioning mode.
# sRrsR-Net: A New Low-Light Image Enhancement Network via Raw Image Reconstruction
## 4 Experiments
### Generalization Performance Evaluation  
To assess the generalization performance of our model sRrsR-Net, it is firstly trained on the LOL-v1 dataset and then evaluated for its performance on the VE-LOL [37] test set, which includes both real-captured and synthetic low-light images. To effectively simulate human perception of image similarity, the LPIPS evaluation metric was used here. This metric reflects the quality of low-light images enhanced by the model, aiming to assess the model's generalization at the perceptual level in real scenes.  
To evaluate the generalization ability of various models, we considered 10 low-light enhancement models. In Table 3, a vertical comparison reveals the quality of image enhancement across different models. For our low-light enhancement model sRrsR-Net, the higher SSIM value indicates more effective preservation of the original images' structural information and excellent structural similarity. And the experimental indicator LPIPS shows a perceptual difference between the generated images and the ground truth, indicating that the model's generated images are visually closer to the real images. Considering the SSIM, PSNR, and LPIPS results in Table 3, it is evident that sRrsR-Net performs well in LIE tasks. As shown in Table 3, LLFlow exhibits better performance in certain aspects. Specifically, on the LOL-v1 and VE-LOL datasets, LLFlow outperforms sRrsR-Net in SSIM metrics by 0.011 and 0.04, respectively.  
Moreover, when comparing the model's performance across different datasets, the horizontal analysis reveals no significant decline in the test metrics, indicating that our model indeed possesses robust generalization capabilities. But from Table 3, for other methods, there are enormous gaps between the results for PSNR, SSIM, and LPIPS in LOL-V1 and those in VE-LOL.  
Figure 7: Visual comparison of raw domain image reconstruction results using sRrsR-Net and six other methods.  
Here, Figure 8 presents visual results from the generalization test. It is evident that the effects of images enhanced by our model are the best on both the LOL-v1 and VE-LOL datasets, displaying consistent color restoration and retaining complete image details.
# Real-Time Object Detection Based on UAV Remote Sensing: A Systematic Literature Review
## 5 Discussion
### Current Challenges
#### 5.1.4 Other Challenges for Real-Time Object Detection on UAV Remote Sensing  
Since the implementation of real-time object detection for UAVs is a complex task, there are still some neglected issues that need to be taken into account.  
In the detection process of UAVs, remote sensing images are often collected by setting up missions in the system. Mature UAVs have stable flight control systems and autonomous navigation through active or passive sensors [4]. However, in order to achieve the task of real-time target detection, with the potential for UAVs to penetrate deeper into the environment rather than just detecting at high altitudes, consideration needs to be given to whether there is competition for computational resources between increasingly complex autonomous navigation and obstacle avoidance strategies and target detection, as well as the challenges posed by increased energy consumption.  
For UAV real-time target detection, the collected data need to be read onboard. However, commercial UAV manufacturers usually do not open-source their flight control, map transmission, or other systems due to copyright considerations, and only a few UAV series are open to a limited API interface. These studies [138; 139] used the SDKs provided by DJI. This certainly makes secondary development more difficult in situations where the required information is not directly available. Although the option of using open-source flight control, such as PX4 Autopilot ([[https://px4.io/](https://px4.io/)]([https://px4.io/](https://px4.io/)) (accessed on 6 June 2023)), exists, there is also a significant amount of design expertise required to ensure its stability, safety, and legality. This may account for the lack of implementation of real-time object detection on UAVs from a practical operation perspective during this review process.
# Handover Decision-Making Algorithm for 5G Heterogeneous Networks
###### Abstract  
The evolution of 5G small cell networks has led to the advancement of vertical handover decision-making algorithms. A mobile terminal (MT) tends to move from one place to another and, as the 5G network coverage is small, user network access will change frequently and lead to a high probability of unnecessary handover, which is a waste of network resources and causes degradation of service quality. This paper aims to reduce the number of unnecessary handovers in 5G heterogeneous networks by proposing a handover decision-making algorithm that integrates the dwelling time prediction technique and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The proposed algorithm reduces the number of unnecessary handovers by estimating the connection time to a small cell network using the dwell time prediction technique. The TOPSIS evaluates the network quality and chooses the best network based on user preference. The result shows that the proposed handover algorithm reduces the number of unnecessary handovers to small cell networks in high-speed scenarios. It also saves the network connection cost by up to 27.51% compared with the TOPSIS-based handover algorithm. As for throughput achievement, the proposed algorithm yields an improvement of 5.12%. The proposed algorithm significantly reduces the number of unnecessary handovers in the high-speed scenario while fulfilling user preferences.  
vertical handover; TOPSIS; 5G Networks; unnecessary handover; dwelling time prediction
# Anti-Aging Performance and Action Mechanism of Asphalt Modified by Composite Modification
## 3 Results and Discussion
### Mean square displacement  
The Mean Squared Displacement (MSD) curves for the different asphalt types were plotted for the time interval of 10 ps to 80 ps, as shown in Figure 31. From the analysis of Figure 31, it can be observed that the MSD values for all four asphalt types increase with time, likely due to the rising temperature of the asphalt models, which makes the asphalt molecules more active. Comparing the MSD curves of the different asphalt types, the matrix asphalt has the smallest MSD value. The MSD values for the matrix asphalt and the UV absorber-modified asphalt are similar, which might be due to the good compatibility between the UV absorber and asphalt. In contrast, the MSD values for the two types of modified asphalt with hydrotalcite show a greater rate of change over time. This could be attributed to the layered structure of hydrotalcite, which provides more space for the movement of asphalt molecules.  
During the 30-75 ps time period, the MSD values rank as follows: ULMA > LMA > UMA > OMA. This indicates that the composite-modified asphalt has the highest mobility, reducing the impact on asphalt molecules during aging. The MSD values further confirm that the composite-modified asphalt exhibits the best aging resistance.  
Based on the Mean Squared Displacement (MSD) curves for each asphalt model, the diffusion coefficients were calculated as shown in Figure 32. The matrix asphalt has the smallest diffusion coefficient. After adding modifiers, the diffusion coefficients of the asphalt molecules increased, indicating a faster diffusion rate of the asphalt molecules. The UV absorber has the smallest impact on the increase in diffusion coefficient, improving it by about 15.90%. The addition of hydrotalcite increases the diffusion coefficient by approximately 51.28%. The composite modifier has the most significant effect on the diffusion coefficient, increasing it by about 65.13%. This indicates that the combined effect of the UV absorber and hydrotalcite can enhance the diffusion rate of asphalt molecules in the colloidal system, thereby reducing the impact of UV radiation on asphalt molecules during aging and improving the asphalt's resistance to UV aging.  
Figure 31: Mean square displacement curves of the different types of asphalt and their differences.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Modifier** & **Ultraviolet Absorber** & **Hydrotalcite** & **Compound Modifier** \\ \hline Free volume fraction change (\%) & 3.55 & 1.69 & 2.81 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Free volume fraction of the model after the addition of different types of modifiers.
###### Abstract  
This study's aim was to examine the effect of non-Newtonian fluid (NN) shoe and ethylene vinyl acetate (EVA) shoe on human lower limb biomechanics and muscle activation during running in hot temperatures. Thirty-five men utilizing a rearfoot strike ran 5 km at a self-selected tempo at an average summer temperature of 41.7 \(\pm\) 1.0 \({}^{\circ}\)C and relative humidity of 80.7 \(\pm\) 3.5%. The kinematics, kinetics, and muscle activation of the right leg were monitored from landing until the pedal was off the ground. A two-way repeated-measures ANOVA was conducted to investigate the main effects of the shoe condition, temperature, and interaction effect. Wearing NN at high temperature resulted in increased hip range of motion (ROM) (\(p\) = 0.001). The knee torque increased significantly when wearing EVA and NN shoes after the temperature increased (\(p\) = 0.006). When wearing EVA and NN, the ground reaction force (GRF) and loading rate (LR) increased significantly after the temperature increased (\(p\) = 0.001; \(p\) = 0.009). When wearing NN after running for 5 km at a high temperature, the displacement range of center of pressure (COP) was significantly reduced (\(p\) < 0.001), while the EVA was significantly increased (\(p\) < 0.001). Neither pair of shoes substantially altered muscle activity. After excluding the factor of fatigue, the increase in temperature not only changed the properties of the material inside the shoe, but also changed the parameters of the biomechanics of the human lower limbs. After the temperature increases, the shoes made of non-Newtonian fluid materials can quickly stabilize under the condition of increased shear stress and reduce the displacement of the human body. Thus, it indicated that non-Newtonian fluid shoes may lower the risk of injury when running in extremely hot conditions.  
non-Newtonian fluid; EVA; high temperature; footwear; biomechanics 2023
## 3 Results
### Final Head Posture in Space  
Final posture of the head in space reflected the spatial position of targets in both heading and pointing (see Figure 2, filled circles). Movement towards North targets was associated with posterior tilt and South targets with anterior tilt; East targets produced right axial rotation with left lateral tilt and West targets left rotation with right lateral tilt (see Table 1). ANOVA confirmed that AP tilt, LA tilt and RO values varied with the designated target (F56,7 = 56.8, 52.7 and 52.3 respectively; \(p<0.001\)). Figure 2 shows larger modulations of the antero-posterior and axial rotation for heading than for pointing. However, the mean across targets were in both cases very small without differences between tasks at the ANOVA. A difference between heading and pointing movements was observed for lateral tilt only (F56,1 = 15.4, \(p=0.004\)). The statistical effect of task can only be asserted by the significant task-target interactions for each Euler angle (F = 23.9, 15.0 and 14.4 for AP tilt, LA tilt and RO, respectively; \(p<0.001\)).
## 2 Preliminary
### Tensor-Ring Decomposition  
Tensor ring decomposition is proposed on the basis of tensor train decomposition [16]. The main advantage of the tensor ring decomposition is that the tensor is used in the form of a ring. With the first rank equal to the last rank, so that there are more optimal rank choices. Assuming that \(\mathcal{T}\) is a d-dimensional tensor, denoted by \(\mathcal{T}\in\mathbb{R}_{1}^{t_{1}\times t_{2}\times\ldots\times t_{d}}^{t_{ 1}\times t_{2}\times\ldots\times t_{d}}\). After TR decomposition, \(\mathcal{T}\) can be expressed as a series of low rank tensors \(\mathcal{Z}\in\mathbb{R}_{v}^{k_{\times}\times k_{k+1}}^{t_{1}\times t_{2} \times\ldots\times t_{d}}\), \(k=1,2,\ldots,d\), which can be expressed as, according to [16],  
\[\mathcal{T}(i_{1},i_{2},\ldots,i_{d})=\mathcal{T}\{Z_{1}(i_{1})Z_{2}(i_{2})...Z _{d}(i_{d})\}, \tag{15}\]  
where \(\mathcal{T}(i_{1},i_{2},\ldots,i_{d})\) represents the \((i_{1},i_{2},\ldots,i_{d})\)th element of the original tensor \(\mathcal{T}\). \(Z_{k}(i_{k})\) represents the \(i_{k}\)th slice matrix of the original tensor \(\mathcal{T}\). Tensor ring decomposition can also be expressed in the index form:  
\[\mathcal{T}(i_{1},i_{2},\ldots,i_{d})=\sum_{a_{1},\ldots,a_{d}=1}^{r_{1}, \ldots,r_{d}}\prod_{k=1}^{d}Z_{k}(a_{k},i_{k},a_{k+1}). \tag{16}\]  
where \(k\) is the index of the tensor dimension, \(a_{k}\) is the k-th rank, and \(i_{k}\) is the index of data dimensions. We can also write Equation (16) in tensor format, given by  
\[\mathcal{T}=\sum_{a_{1},\ldots,a_{d}=1}^{r_{1},\ldots,r_{d}}z_{1}(a_{1},a_{2} )\circ z_{1}(a_{2},a_{3})\circ\ldots\circ z_{d}(a_{d},a_{1}). \tag{17}\]  
where the mathematical notation \(\circ\) indicates the outer product of vectors; \(z_{k}(a_{k},a_{k+1})\in\mathbb{R}^{k_{k}}\) indicates the \((a_{k},a_{k+1})\)th mode-2 fiber of tensor \(\mathcal{Z}_{k}\). There is \(\mathcal{O}(dIR^{2})\) spatial complexity of TR format, which is linear to the order of the tensor \(d\). TR satisfies the property of circular dimensional permutation invariance, which is the property of TR decomposition different from that of TT decomposition.  
The steps to decompose the TR tensor in detail are the same as those of TT discussed in Section 2.3. The only difference is that when verifying rank, \(R_{0}=R_{N}\) in TR decomposition, So, these tensor cores form a ring. \(R_{0}=R_{N}=1\) in TT decomposition is a special case of TR decomposition, so that TR decomposition subsumes TT decomposition, and its rank selection is more than that of TT decomposition. Thus, there are more chances of obtaining the optimal core sequence tensors \(\mathcal{Z}\in\mathbb{R}^{n_{\times}k_{\times}n_{k+1}}\), \(k=1,2,\ldots,d\) of optimal rank in TR decomposition.
## 2 Materials and Methods  
### Inclusion and Exclusion Criteria  
To ensure the replicability of the study, strict inclusion and exclusion criteria were established. Studies addressing PIPJ stiffness, regardless of the underlying etiology, were included. Various methodological approaches were considered, including randomized controlled trials (RCTs), quasi-experimental studies, observational and retrospective studies, systematic reviews, case studies, and expert opinions. To guarantee the clinical applicability of the findings, studies in which PIPJ stiffness was not the primary condition were excluded. Furthermore, included studies were required to report outcome measures related to joint mobility and functionality, such as range of motion (ROM), DASH and QuickDASH scores, pain levels, and treatment-related complications. Studies that lacked clearly specified outcome measures were excluded to prevent bias in data interpretation. Initially, studies in English and Spanish were considered. However, publications in Spanish were excluded due to a lack of methodological rigor in the clinical guidelines and available literature on PIPJ. Priority was given to studies published in indexed journals and internationally recognized databases. The analysis included orthopedic treatments with customized orthoses, surgical interventions, and conservative strategies such as manual therapy and dynamic orthoses. The effectiveness of the treatments was not directly compared; rather, the validity and coherence of their methodological designs were assessed. The observed heterogeneity hindered comparisons between studies and highlighted the need for standardized criteria in future research. The broad inclusion criteria initially resulted in the identification of numerous studies. However, the application of strict methodological standards reduced the dataset to those of higher quality and comparability, reflecting the challenges of systematically reviewing an area with diverse clinical approaches.
# Classification of OCT Images of the Human Eye Using Mobile Devices
## 4 Experimental Selection of Neural Networks  
### Training and Test of the Selected NN Models  
The CNN was run with the data split described in Section 2.1, and training was performed for 100 epochs. The run utilized the Google Colab platform, Google Compute Engine backend with Python 3.10, and a V100 GPU with 16 GB RAM. Table 7 lists setup parameters for the conducted initial experiments. Furthermore, the following data augmentation techniques (applied randomly) were proven to have a positive impact during training only for Joint Attention Network + MobileNetV2 and OpticNet-71 architectures: horizontal flip, rotation (\(\pm 40^{\circ}\)), translation (\(\pm 20\%\)), shear (\(\pm 20\%\)), and zoom (\(\pm 20\%\)).  
The results of this experiment are shown in Table 8. The best result of 89% for all metrics is achieved for the Joint Attention Network with the MobileNetV2 model. The lowest performance of 64% was obtained with the three-layer CNN architecture. Its low efficiency, resulting from the simple and uncomplicated model structure, confirms the need for advanced methods for the task of classifying human eye OCT images. All models had the highest prediction accuracy for the VMT class and the lowest for the DRUSEN and  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** &
\begin{tabular}{c} **3-Layer** \\ **CNN** \\ \end{tabular} & **VGG16** & **InceptionV3** & **Xception** & **JAN + MobileNetV2** & **OpticNet-71** \\ \hline Input image & \multirow{2}{*}{\(224\times 224\)} & \multirow{2}{*}{\(224\times 224\)} & \multirow{2}{*}{\(150\times 150\)} & \multirow{2}{*}{\(150\times 150\)} & \multirow{2}{*}{\(150\times 150\)} & \multirow{2}{*}{\(224\times 224\)} & \multirow{2}{*}{\(224\times 224\)} \\  size & & & & & & \\ Learning rate & 0.00005 & 0.00005 & 0.00001 & 0.00001 & 0.0001 & 0.0001 \\ Batch size & 64 & 32 & 64 & 64 & 8 & 8 \\ Loss function & \multicolumn{3}{c}{Categorical Cross-Entropy (CCE)} & \multicolumn{3}{c}{CCE and MSE} & \multicolumn{1}{c}{CCE} \\ Optimizer & \multicolumn{3}{c}{RMSprop} & \multicolumn{3}{c}{Adam} & Adam \\ \hline \hline \end{tabular}
\end{table}
Table 7: Training parameters for the utilized NN models.  
Figure 5: Application screen interface where image classification takes place.  
DME classes. The obtained models' overall insufficient accuracy can be attributed to the small size and imbalance of the dataset (only 4467 images) and data characteristics differing between the OCT manufacturers (e.g., image brightness and contrast).
# LoRa Mobile-To-Base-Station Channel Characterization in the Antarctic
## 3 Measurement Results  
### Trajectories  
In total, data were collected during five expeditions. Two of them applied the dual-frequency setup. At the transmitter side, the GPS locations and their timestamps were recorded, whereas at the receiving side, timestamps and SNRs were recorded for both the 434.1 and 868.1 MHz bands. During both expeditions in which dual-band measurements were performed, the snowmobile drove along nearly straight lines from the Princess Elisabeth station (71.949960\({}^{\circ}\) S 23.347503\({}^{\circ}\) E) to the first mountain in the northern direction, named Vesthaugen Nunatak (72.700392\({}^{\circ}\) S 23.554936\({}^{\circ}\) E), at a distance of approximately 30 km, as shown in Figure 8. The expeditions include both a Line-of-Sight (LoS) and a Non-Line-of-Sight (NLoS) path due to the slope of the terrain. No other mountains that could generate reflections are nearby. The main effects are probably caused by changing terrain elevation, often blocking the direct signal path.  
In order to know the exact distance between the LoRa transmitter and the LoRa receiver, a GPS unit was installed next to the Lora transmitter on the vehicle. To convert its coordinates to the distance between the antennas, the open-source library Gdal [17] (Geospatial Data Abstraction Library) is used, which translates the WGS 84 angular coordinates, delivered by the Garmin GPS in a GPX format, into distances. For Antarctic regions, we use the projection UTM 34S, containing a valid model for the Earth's shape in this area.
# Comparative Study of the Seismic Response Characteristics of Three Special Soils
## 3 Selecting the Input Ground Motion  
On 18 May 1940, an earthquake with a magnitude of 7.1 occurred in Imperial Valley, United States. The complete seismic waveform of the earthquake was recorded at the El Centro Strong Seismological Station located in the basement of the El Centro Substation of the Nancy Ala Power Company in southern California, United States. This earthquake is of special significance in the history of strong earthquake seismology. For the first time, a high peak acceleration record was obtained during this earthquake. This strong earthquake wave, known as the El Centro wave, is a typical strong motion record and is widely used in seismic engineering research and structural seismic analyses. The duration of the El Centro wave is 53.71 s; the peak acceleration of the horizontal north-south component is 341.7 cm/s\({}^{2}\); the peak acceleration of the horizontal east-west component is 210.1 cm/s\({}^{2}\); and the peak acceleration of the vertical component is 206.3 cm/s\({}^{2}\). It should be emphasized that this strong earthquake record has made an important contribution to the development of seismic engineering, and many scholars have conducted many structural and site dynamic response analyses using this strong earthquake record as the input seismic motion history.  
Due to its undisputed acceptance by the seismic community, this section selects El Centro waves as the input seismic motion time history for a soil layer response analysis, with vertical incidence from the bedrock. The amplitude of the seismic waves is adjusted to 0.05 g, 0.1 g, 0.2 g and 0.4 g (corresponding to the acceleration time history of seismic intensities VI, VII, VIII and IX); the first 15 s containing strong seismic motion are intercepted; and baseline correction is performed. Four input seismic waves (seismic waves A, B, C and D) are formed, as shown in Figure 1. For the semi-infinite space problem of elastic uniform media, the seismic field at the free surface is twice the value of the incident wave field. Based on this theoretical result, the actual problem is approximated, and it is specified that the seismic wave at the seismic input interface should be input as half of the seismic time history of the free bedrock.