esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhanced Integrated Satellite-Terrestrial NOMA with Cooperative Device-to-Device Communication
## 2 System Model
### Channel Model  
The fading environment for the land mobile satellite (LMS) channel is modeled through the [PERSON]'s model distribution [19], where the power of the line of sight (LoS) component is log-normally distributed with parameters \((M,\Sigma)\), while the power of the multipath component (MP) follows the Rayleigh distribution with the complex channel coefficients being represented by \(h_{i}^{s}\) for each UE\({}_{i}\), assuming zero mean and variance:  
\[\sigma_{s}=\sqrt{0.5\cdot 10^{\frac{MP}{10}}}\sim\mathcal{N}\left(0,\sigma_{s}^{ 2}\right). \tag{1}\]  
Furthermore, pathloss attenuation using the free space pathloss (FSL) model is considered, being denoted as \(L_{FS_{i}}\) for each UE\({}_{i}\). In addition, a maximum Doppler shift of 40 kHz is assumed and modeled with the Jakes model [20].  
Regarding the terrestrial D2D links, the multipath fading is modeled by the Rayleigh distribution with zero mean and unit variance \(\sim\mathcal{N}\left(0,1\right)\). The complex channel coefficient is \(h_{i,j}^{d}\) for each D2D pair of UE\({}_{i}\) and UE\({}_{j}\). Additionally, log-normal shadowing is considered with zero mean and variance \(\sigma_{\lambda}^{2}\) with \(\sim\mathcal{N}\left(0,\sigma_{\lambda}^{2}\right)\). Furthermore, pathloss attenuation of the D2D links, denoted as \(PL(d)_{i,j}\) and expressed in dB for a pair of users being \(d\) km apart from each other, is modeled as in [21]:  
\[PL(d)_{i,j}=127+30\log_{10}\left(d\right). \tag{2}\]  
Moreover, the LMS and terrestrial D2D links are degraded by additive white Gaussian noise (AWGN) \(\sim\mathcal{N}\left(0,\sigma^{2}\right)\). The noise power is calculated as the product of the Boltzmann constant \(k\), the receiver system noise temperature \(T_{g}\), \((g=\{s,d\})\) and the available bandwidth \(B_{s}\) and \(B_{d}\). Thus, the noise power of the satellite and terrestrial receivers is equal to \(N_{s}=kT_{s}B_{s}\) and \(N_{d}=kT_{d}B_{d}\), respectively. Consequently, the corresponding variances for each receiver type are equal to \(\sigma_{s}=\sqrt{N_{s}}\) and \(\sigma_{d}=\sqrt{N_{d}}\).
## 3 Results and Discussion
### Plasmon Modulation Spectrum  
The difference reflection spectrum near the interband transition edge of silver at the time of plasmon excitation is shown in Figure 5, together with the stationary reflection spectrum. The incident angle of white light is 55\({}^{\circ}\); the chopper frequency is 221 Hz; the laser power is 100 mW; and the laser spot diameter is 5 mm. The dip of the reflection spectrum seen on the shorter than 325 nm wavelength side is due to the interband transition. The spectral shape of the change in the reflection spectrum induced by plasmon excitation indicates a broadening of the interband transition edge.
## 4 Results
### Statistical Analysis and Model Assessment
#### 4.2.2 Comparison of Model Accuracy  
The primary difference between the SGWR, OLS, and GWR models is that OLS does not capture the spatial autocorrelation and spatial dependence in the error term. However, the GWR model does not consider the limitations of using local parameters to analyse the driving factors of vegetation cover. The SGWR model is an improvement of the OLS and GWR models, does not ignore the spatial heterogeneity of the variables, and improves the accuracy of the spatial analysis by combining local and global estimates.  
The AIC value of the SGWR model is lower than that of the OLS and GWR models during the study period, and the model fitting accuracy is variably improved. Table 6 shows that the AIC value of the SGWR model was 759.462 in 2015, which was 112.634 and 54.053 lower than that of the OLS and GWR models, respectively. The SGWR model improved the OLS and GWR models better in 2015 than in other years. The results of the SGWR showed that the adjusted R\({}^{2}\) values of the model for the entire period are 0.706, 0.803, 0.820, and 0.829, indicating that the SGWR model explains more than 78% of the fractional vegetation cover dynamics and that the selected driving factors have a significant impact on FVC in Guangdong.
# Self-Supervised Keypoint Learning for the Geometric Analysis of Road-Marking Templates
## 4 Results
### Keypoint-Matching Comparison vs. SIFT/ORB
#### 4.2.1 Evaluation of the IMR  
The IMR metric assesses the quality of the learned keypoint localization by measuring the percentage of the predicted keypoints that fall within the ground-truth segmentation mask of the template in the synthetic test set, as defined in Section 3.7.1. A higher IMR indicates better adherence of the predicted keypoints to the object's shape. Table 3 presents  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Configuration** & **Success Rate (\%)** \\ \hline SIFT + RANSAC & \(N_{min}=3\) & 81.04 \\ \hline SIFT + RANSAC & \(N_{min}=4\) & 81.04 \\ \hline SIFT + RANSAC & \(N_{min}=5\) & 77.48 \\ \hline ORB + RANSAC & \(N_{min}=3\) & 91.76 \\ \hline ORB + RANSAC & \(N_{min}=4\) & 91.76 \\ \hline ORB + RANSAC & \(N_{min}=5\) & 35.64 \\ \hline GeoTemplateKPNet & ResNet-50, K = 3 & 63.36 \\ \hline GeoTemplateKPNet & ResNet-50, K = 4 & 21.56 \\ \hline GeoTemplateKPNet & ResNet-50, K = 5 & 17.28 \\ \hline GeoTemplateKPNet & VGG-16, K = 3 & 60.00 \\ \hline GeoTemplateKPNet & VGG-16, K = 4 & 68.24 \\ \hline GeoTemplateKPNet & VGG-16, K = 5 & 36.84 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Overall average percentages of image pairs with sufficient matches for transformation in the synthetic template test set.  
the overall average IMRs across all the template classes for different GeoTemplateKPNet configurations.  
Table 3 shows the overall average IMRs achieved by GeoTemplateKPNet with different backbones and numbers of keypoints (K). For the ResNet-50 backbone, the IMR is 35.49% with K = 3, 29.99% with K = 4, and 30.98% with K = 5. For the VGG-16 backbone, the IMR is 30.38% with K = 3, 28.55% with K = 4, and 36.84% with K = 5. These overall results indicate that the IMR varies with both K and the backbone type, with VGG-16 at K = 5 achieving the highest overall IMR.  
To further analyze the performance and understand the reasons behind the overall IMR values, we examine the IMR broken down by individual template classes. Table 4 presents the per-class IMRs for the tested configurations.  
Table 4 highlights the significant variations in the IMR across the different template classes for both backbones and K values. Classes 0 and 1 show relatively high IMR values for most configurations. Class 2 exhibits a consistently low IMR across the configurations, although it improves slightly with K = 4 and K = 5 compared to K = 3. Classes 3 and 4 exhibit varying performances depending on K and the backbone, with some configurations (e.g., ResNet-50 with K = 4/5 and VGG-16 with K = 5) achieving notable improvements in the IMR compared to those with K = 3. This per-class analysis reveals that the model's ability to localize keypoints within the template mask is highly dependent on the specific geometry and the chosen configuration.
# Laboratory Testing and Analysis of Clay Soil Stabilization Using Waste Marble Powder
## 2 Materials and Method
### UPV Test  
The ultrasonic pulse velocity test (UPVT) by ASTM C597-16 standards was performed to evaluate the stabilized soil samples' quality and structural integrity [36]. Cylindrical samples were secured between the transmitter and receiver transducers of an ultrasonic pulse velocity instrument. The transmitter transmitted 54 Hz ultrasonic vibrations through the sample, which were detected by the receiver. The instrument recorded the pulse transit time to within 0.1 microseconds. The UPV was calculated by measuring the distance between the transducers. Higher velocities represent denser, more robust soils. Previous research has found that ultrasonic wave frequencies in the 40-80 Hz range effectively evaluate stabilized soils [37].  
This test was designed to establish a correlation between UPV and UCS. Critical parameters such as sample dimensions, transducer contact, wave frequency, and data acquisition were controlled to ensure test repeatability.
## 3 Results  
In this section, we wish to highlight the results obtained by only analysing a patient. So, in Figure 2A,B, we show an example of regeneration of the treated area, as observed, for an Italian white male patient aged 54, affected by bilateral open angle glaucoma, more severe on the left eye, since he was 45. Moreover, the patient has no other pathology and he is treated with timolol (twice a day) and dorzolamide (three times a day). All the patients reported an improvement of the visual sensation with particular reference to the recovery of depth. In Figure 2, the OCT of this patient is represented as an example. We can highlight that:  
* In relation to nerve fibre layer, the thickness map is a topographical display of RNFL, an hourglass shape of yellow and red colours, typical of normal eyes. In relation to this patient, we can observe a reduction in nerve fibre in the exam of March 2020, but an improvement in the exam in July 2020;
* The key parameters in the table show average RNFL thickness for the patient: in March 2020 the right eye measured 55 \(\upmu\)m and the left eye measured 44 \(\upmu\)m, while in July 2020 the right eye measured 64 \(\upmu\)m and the left eye 44 \(\upmu\)m;
* RNFL quadrant and clock hour: characteristic parameters are temporal, superior, nasal, and inferior quadrants; in relation to the patient considered, we can highlight:
* In March 2020: the right eye presents a reduction in fibres in the superior, nasal and inferior quadrant, and normal values in the temporal quadrant, while the left eye presents a reduction in fibres in all quadrants;
* In July 2020: the right eye presents an improvement of fibres in the damaged areas, in particular in the nasal and inferior quadrants, a smaller improvement in the superior quadrant, and unchanged fibres in the temporal quadrant, while the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Wavelength (nm)** & **sRGB** & **Hex Triplet** \\ \hline
491.41 & (0,255,255) & \#00 ffff \\
497.49 & (207,52,118) & \#cf3476 \\
549.13 & (255,0,255) & \#ff00 ff \\
570.47 & (255,255,0) & \#ffff00 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Table of colours used during the therapy.  
Figure 1: An example of treatment. Coloured circles map the screen at the border of the visual field lost.  
left eye presents an improvement in the nasal quadrant, an unchanged number of fibres in the superior and temporal quadrant, but a reduction in fibres in the temporal quadrant.  
The results here summarised represent a starting point for a further experimental analysis, but it is needed to highlight an experimental evidence for the confirmation of the thermodynamic results and the design of a possible photobiomodulation complementary light therapy for glaucoma.
# A New Method of Angle Measurement Error Analysis of Rotary Encoders
## 1 Introduction
The coaxiality error and radial error motion were measured, and the verification experiment was carried out by the use of an optical polygon and an autocollimator to verify the theory. Then [PERSON] proposed an embedded sensor system for real-time measurement of two radial and three angular error motions of a rotary stage. The geometrical relationship between the four scanning heads and radial error motions was proposed and verified. The rotational angle and radial error motions of the shaft were measured by using an encoder with multiple scanning heads [13; 14]. In this article, an in-depth study of the angle measurement error due to the installation eccentricity of the grating disk and the radial error motion of the rotating shaft is presented, a novel error model is built. The radial error motion of the rotating shaft is measured by the inductive probes with precision test sphere. A charge-coupled device (CCD) camera is used to get the images of radial grating, and both the eccentricity value and direction are detected by using image processing technology. Then the angle measurement error is obtained by putting the data of the radial error motion and eccentricity into the analytical model. At last, the verification experiment is carried out to verify the error model. The angle measurement error can be predicted if the installation eccentricity and radial error motion are known.
# Can Livestock Farming Benefit from Industry 4.0 Technology? Evidence from Recent Study
## 4 Results
### Pig Fattening  
Germany is a net exporter of pig meat. About 11.2 million fatteners (above 50 kg) live in about 16,700 farms. (European Commission 2022) Farm sizes vary from an average of 300 to 400 fatteners in the southwest of Germany, and 38% of the fatteners are growing in farms with more than 2000 individuals. The majority of pigs are fattened in farms sized from 1000 to 2000 fatteners. In recent years, the pig population in Germany has been declining [53]. In particular, small- and medium-sized farms have been dropping out of production. [54; 55]. Overall, a ten-year comparison of fattening and breeding pigs in Germany shows that the number of pigs decreased by 20.8% or 5.8 million animals since 2012, while the number of farms decreased by as much as 41.0%. One reason for this is probably the social discussion in Germany on animal husbandry [56].  
The main focus of fattening pig production in Germany is in Lower Saxony and North Rhine-Westphalia with over 50%. The rest is evenly distributed across all other federal states.  
Germany ranks second in the European Union behind Spain in fattening pig production. The group of the five largest producers furthermore includes France, Denmark, and the Netherlands. The development of the herds between 2020 and 2022 shows that there were moderate increases in Spain between the individual years. While a clear reduction in stocks over the three years could be observed in Germany, there was only a moderate decline in the other large producing countries.
## 4 Results and Discussion  
### Setting Time  
As demonstrated in Figure 3, the setting time of the slurry was influenced by various factors. The present study investigated the effect of different dosages of zeolite powder on the setting time of the slurry. The findings indicated a direct correlation between the increased dosage of zeolite powder and the rise in setting time. The initial setting time increased from 20 min to 36 min, an 80% increase, while the final setting time rose from 62 min to 109 min, indicating an increase of 75.81%. The phenomenon was due to the porous nature of zeolite powder. This characteristic facilitated the absorption of water molecules, which were then introduced into the internal pores. Consequently, the hydration reaction rate of the slurry was inhibited, resulting in a prolonged setting time.  
Figure 3b shows that when the CL dosage was set at 5%, the initial and final setting times of the slurry were maximized to 100 and 298 min, respectively, with an increased rate of 488.24% and 396.67%. This is because the CL molecule contains a large number of hydrophilic groups (e.g., sulphonic acid groups and hydroxyl groups), which promote the interaction between the CL molecules and the surface of solid particles [29; 30]. When mixed with slurry containing solid particles, such as zeolite powder and GGBFS, the CL molecules were adsorbed onto the surface of the solid particles by chemisorption. During this process, a complexation reaction occurred between the CL molecules and Ca\({}^{2+}\), forming  
Figure 3: Effects of different factors on setting time: (**a**) zeolite powder: GGBFS; (**b**) CL dosage; (**c**) NaOH dosage; (**d**) water–binder ratio.  
a gelling film. This film hindered the direct contact of zeolite powder and GGBFS with water molecules. Consequently, the hydration reaction was slowed down.  
It was established that as the quantity of NaOH increased, the setting time correspondingly decreased. The initial and final setting times decreased by 55.07% and 74.37%, respectively. Notably, the slurry remained unsolidified at a NaOH dosage of 1%. This outcome demonstrates that the alkaline environment was not sufficient to stimulate the production of a gelatinous substance in the slurry when the NaOH dosage was set at 1%, as per the mix ratio established in Group 11. An increased water-binder ratio resulted in an increased setting time for the slurry. Furthermore, when the water-binder ratio was between 0.7 and 0.8, the rate of increase in the slurry setting time was accelerated, with a rise of 19.05% and 58.19%, respectively. The hydration reaction of the slurry was delayed as the free water molecules in the slurry increased, leading to an increased setting time.  
The final results showed that, among the 18 groups of ratios, the initial and final setting times of the slurries in Groups 1-7, 11, and 14-16 did not meet the requirements of the Chinese technical specification standard GB 175-2023 (initial setting time was not less than 45 min and final setting time was not more than 600 min).
## 2 Materials and Methods
### Resampling Techniques
#### 2.4.1 Synthetic Minority Oversampling Technique (SMOTE)  
SMOTE stands out as an oversampling technique designed to generate synthetic samples for the minority class. Unlike random oversampling, which can lead to overfitting, SMOTE operates within the feature space, creating new instances through interpolation between closely positioned positive instances.  
Initially, the total number of oversampled observations, denoted as N, is determined. Typically, this value is selected to attain a balanced binary class distribution of 1:1, although adjustments can be performed based on specific requirements. The process initiates by randomly selecting a positive class instance, followed by identifying its K-nearest neighbors, often set to 5 by default. Subsequently, N instances from this set of K neighbors are chosen to generate new synthetic instances. This is achieved by calculating the difference in distance between the feature vector and its neighboring instances using a chosen distance metric. The difference is then multiplied by a random value in the range (0, 1) and added to the original feature vector [38].
## 2 The Proposed Video QoE Metric for Dynamic Adaptive Streaming Services
### Extraction of Deep Spatial-Temporal Features of Video  
Many works have proved that 3D CNN and LSTM exhibit highly accurate results while studying the deep spatial-temporal features of the content of a video. The research results show that the 3D CNN can effectively extract the spatial and short-term temporal features of a video, and a \(3\times 3\times 3\) convolution kernel can help in achieving optimum performance in all layers. However, the disadvantage of 3D CNN is that it is unable to extract the long-term temporal features of a video. This paper proposes to use 3D CNN to extract the spatial features and short-term temporal features of a video, and LSTM to extract the long-term features of a video to obtain the deep spatial-temporal features of the video.  
Figure 2 shows the network structure which combines 3D CNN and LSTM together to extract the deep spatial-temporal features as mentioned above. The 3D CNN comprises of five convolution layers (Conv1-Conv5), five pooling layers (Pool1-Pool5) and two fully connected layers (F1 and F2). Softmax is used as a classifier whose output are the values between 0-1, indicating the probability of each category. The input video frames are normalized to be the size of \(112\times 112\). Usually, low convolutional layer of the 3D CNN can extract low-level visual features such as edges and textures, whereas the high convolutional layer can extract more distinguishing high-level semantic features.  
Figure 1: The framework of QoE assessment model. Deep spatial-temporal features and DASH based feature parameters are combined to generate the input feature parameters vector, and the ridge regression method is used to map the QoE model between the input feature parameters vector and MOS value, then the model is used to predict the user’s QoE. QoE: Quality of Experience; DASH: Dynamic Adaptive Streaming over HTTP (HyperText Transfer Protocol); MOS: Mean Opinion Score.  
Figure 2: Network Structure combines 3D CNN and LSTM together. 3D CNN aims to extract low-level visual features and LSTM aims to model the long-term temporal dependency of a video.  
LSTM aims to model the long-term temporal dependency of a video. As opposed to the 3D CNN, LSTM can simulate the dynamic evolution of the state of the video content through a series of memory units, which can represent the long-term temporal characteristics of a video.  
In this paper, the second convolutional layer features of the 3D CNN is extracted and used as the input to LSTM network after being reshaped (denoted as ReshapedFeature1 in Figure 2). The output generated from the first layer of LSTM is extracted and used as the deep spatial-temporal feature of the video.  
The 3D CNN network parameters are set as follows: the size of convolution kernel is set as \(3\times 3\)\(\times\) 3. The MaxPool3D's kernel size of the first layer is \(2\times 2\times 1\) and other layer's kernel size is \(2\times 2\times 2\).  
The LSTM network parameters are set as follows: the size of the input tensor is reshaped as [16, 200704] and the dimension of the output of the first layer is 50.