esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Results
### Simulation Results
Besides, we can find that when the task size is small, the task can be processed successfully even without local computing resources. Furthermore, the simulation results indicate that the successful task processing ratio decreases with the increase in the number of tasks under the given local computing resources. Figure 12 depicts the comparison of the successful task processing ratio with respect to the vehicle speed under different task intervals. This figure shows that the successful task processing ratio increases with the task interval. This is because our proposed LBTO algorithm is based on IEEE 802.11p protocol, which adopts the carrier sense multiple access protocol with collision avoidance for data transmission. Therefore, a higher task frequency will lead to more collisions, which will decrease the successful task processing ratio. It is also noticed that the increase in the successful task processing ratio is not significant when the task interval is increased from 30 s to 40 s. Therefore, from this figure, we can know that the proper utilization of the task interval can effectively improve the MEC-enabled UAV-assisted VANETs performance comprehensively. Figure 11: Comparison of the successful task processing ratio with respect to the local computation resources under different task sizes. Figure 12: Comparison of the successful task processing ratio with respect to the vehicle speed under different task intervals. Figure 13 presents the comparison of the successful task processing ratio with respect to the number of vehicles under different task maximum allowed latencies. This figure shows that when the number of vehicles is larger than some values (referred to as turning points), the successful task processing ratio will decline sharply, which means the task cannot be processed in time. It can be seen that the turning point of task maximum allowed latency = 5 s is the smallest and that of the task maximum allowed latency = 20 s is the largest. This indicates that increasing the task maximum allowed latency can provide better task processing capability for the vehicles. In addition, from examining Figures 12 and 13, we realize that there is a trade-off between the task interval and the task maximum allowed latency in terms of the successful task processing ratio. The successful task processing ratio optimization by jointly considering the task interval and the task maximum allowed latency is an important problem for future research.
## 4 Proposed Empirical Models for Weather Factors
### Particulate Matter  
To calculate the coefficients for particulate matter, a similar approach as that for dust was chosen. However, it is very uncommon to describe the impact of particulate matter with visibility; therefore, the calculations are based on the more common parameter, the total suspended particle mass \(TSP\). The particle density function was retrieved from [38], and the refractive index \(n\) from [48]. The calculation below was analogous to the backscattering coefficient \(\beta_{\text{PM2.5}}\):  
\[\alpha_{\text{PM2.5}}(TSP)=\frac{\pi}{8}\int_{0}^{\infty}D^{2}Q_{ext}(D)N( TSP)PDF(D)\,dD \tag{21}\] \[\beta_{\text{PM2.5}}(TSP)=\frac{\pi}{8}\int_{0}^{\infty}D^{2}Q_{buck }(D)N(TSP)PDF(D)\,dD \tag{22}\]  
Figure 11 shows both coefficients in relation to the total suspended particle mass. As the refractive index \(n\) of soot has a large imaginary part, and hence a very large absorption coefficient, the backscattering coefficient \(\beta_{\text{PM2.5}}\) is one order of magnitude lower than the extinction coefficient \(\alpha_{\text{PM2.5}}\). By applying a curve fit with an inverse power law to 1000 calculated data points of Equations (21) and (22) in Figure 11, the following empirical models could be retrieved, and both show a coefficient of determination of \(R^{2}=1\),  
\[\alpha_{\text{PM2.5}}(TSP) =9.50\,10^{-4}\,TSP \tag{23}\] \[\beta_{\text{PM2.5}}(TSP) =3.89\,10^{-5}\,TSP \tag{24}\]
# A Lightweight YOLOv8 Model for Apple Leaf Disease Detection
## 3 Results
### Model and Algorithm Test  
To further assess the YOLOv8n-GGi model's effectiveness, an ablation experiment was performed using a modified version of YOLO v8n, as detailed in Table 5.  
The outcomes of the experiments demonstrate that the proposed enhancements in this paper significantly improve detection precision while reducing both the number and size of model parameters. After integrating the GhostNet module, there was a 6.1% reduction in FLOPs, a 12.5% decrease in the model's parameter count, and a 14.3% reduction in weight size. There was a slight decline in accuracy and recall. Further inclusion of the C3 Ghost module led to additional decreases in precision and recall. However, this was accompanied by reductions in computational complexity and parameter count. Subsequent addition of the GAM module resulted in a precision increase from 80.0% to 84.9%, with minimal changes in computational complexity and parameter count. Finally, introducing the improved BiFPN module saw precision rise from 84.9% to 86.3%, representing a 1.4% increase, and recall from 77.9% to 80.2%, a 2.3% increase. The [EMAIL_ADDRESS] surged from 83.4% to 86.9%, a 3.5% increase. Additionally, FLOPs decreased by 17.9%, the parameter count dropped by 29.2%, and the model size shrank to just 3.8 MB, showcasing significant enhancements in both detection capabilities and the model's light weight.  
To visually display the main regional differences that the model focuses on in the detection process before and after the improvement, we randomly selected four apple leaf disease images for heat map visualization, as shown in Figure 15. Darker colors indicate more crucial regions to the model in the heat map. Comparing the heat maps from YOLOv8n and YOLOv8n-GGi reveals that the enhanced YOLOv8n-GGi model significantly boosts detection capabilities. The heat map of YOLOv8n-GGi exhibits more  
Figure 13: Comparison of indices before and after improvement: (**a**) precision, (**b**) recall, (**c**) [EMAIL_ADDRESS], and (**d**) [EMAIL_ADDRESS].  
precise and focused regions of interest, highlighting its superior performance in identifying lesion areas. While the YOLOv8n heat map also points out the same regions, its markings are less distinct and blurrier than those of the YOLOv8n-GGi model. This indicates that our method can accurately identify apple lesions and effectively reduce the interference from intricate background noise on the model.
# From Patient to Musician: A Multi-Sensory Virtual Reality Rehabilitation Tool for Spatial Neglect
## 8 Discussion
### Findings
#### 8.1.2 Scales Task  
For the _Scales_ task, two patterns are observed. First, for all patients, except Patient 1, the time to play a key gradually decreased the further it was towards the neglected side. Second, Patient 1 needed significantly more time to play the ultimate key. The reduction in the time to play the following key can be explained by the cognitive effort required to play the first key compared to the keys following it. Once the participant has found the pattern, i.e., playing one key after the other, this action can be performed faster and with less cognitive effort. This behaviour can most clearly be seen for Patients 2 and 4. The other patients and the test users also had the same pattern but less pronounced. A possible explanation for the second pattern is that the different nature of the ultimate key impacts the perception of the users on the neglected side. Specifically, the last keys play a sound that is not congruent in pitch compared to the other keys. The patient could be conditioned not to play the last note as it does not belong to the scale. However, this appears to only happen for tasks of the highest difficulty (nine keys). This explanation is in line with the findings of [PERSON] et al. [17], which stated that individuals performed better under conditions with congruent sound feedback compared to random sound feedback. In this case, patients needed more time to play the last key, which is the only key to produce a sound not part of the musical scale, i.e., random sound feedback, compared to the rest of the keys, which each produce a pitch part of the musical scale, i.e., congruent sound feedback.
## 2 System Model
### Users-to-RLs Uplink Channel Model  
We denote the received signal of \(\mathrm{RU}\ (i,r)\) by \(y_{i,r}\) which can be written under flat-fading channel model as  
\[y_{i,r}=\sum_{k\in\mathcal{K}}h_{i,r,k}x_{i,k}+\sum_{k\in\mathcal{K}}g_{i,r,k}x _{i,k}+z_{i,r}. \tag{1}\]  
Here, \(x_{i,k}\) denotes the transmit signal of user \((i,k)\) and satisfies a transmit power constraint \(\mathbb{E}[|x_{i,k}|^{2}]\leq P\) with \(P\) denoting the power budget of each user. \(h_{i,r,k}\) represents the channel coefficient from user \((i,k)\) to \(\mathrm{RU}\ (i,r)\), \(g_{i,r,k}\) is the channel coefficient from user \((\vec{i},k)\) to \(\mathrm{RU}\ (i,r)\), and \(z_{i,r}\) indicates the noise signal at \(\mathrm{RU}\ (i,r)\) with \(z_{i,r}\sim\mathcal{CN}(0,\sigma_{z}^{2})\). On the right-hand side (RHS) of Equation (1), the first term indicates the desired signal transmitted by the in-cluster users \((i,k)\), \(i\in\mathcal{K}\), and the second term represents the interference signals from the neighboring cluster's users \((\vec{i},k)\), \(k\in\mathcal{K}\).
## 2 Materials and Methods
### Landslide Conditioning Factors
#### 2.3.4 Anthropogenic Factors  
Among anthropogenic influences, proximity to roads plays a significant role in landslide occurrence [30]. Road construction, excavation, and maintenance activities often disturb slope stability and trigger failures [31; 32]. The anthropogenic conditioning factors are illustrated in Figure 8a-c.  
Additionally, land use and land cover (LULC) changes modify natural surface conditions, altering slope response to external stresses such as rainfall or seismic shaking [33]. For example, deforestation reduces root reinforcement and slope cohesion, increasing landslide vulnerability [34]. To assess these impacts, we used Landsat 8 satellite imagery to derive the Normalized Difference Vegetation Index (NDVI) in ArcGIS Pro, providing a proxy for vegetation cover and slope stability [35]. High NDVI values indicate denser vegetation and generally greater slope stability. Road network data were sourced from the International Centre for Integrated Mountain Development (ICIMOD) ([[https://www.icimod.org/](https://www.icimod.org/)]([https://www.icimod.org/](https://www.icimod.org/)) (ac  
Figure 7: Hydrologic factors: (**a**) drainage proximity, (**b**) rainfall, (**c**) stream power index (SPI), and (**d**) topographic wetness index (TWI).  
cessed on 25 January 2025)). Multiple buffer zones were generated around road segments to calculate proximity for each point in the study area. The LULC data for the year of the earthquake (2015) were obtained from the Forest Research and Training Centre (FRTC), Nepal. Together, these layers enable a nuanced understanding of human-induced influences on landslide susceptibility.
###### Abstract  
This work was developed as part of the European H2020 ONION (Operational Network of Individual Observation Nodes) project, aiming at identifying the technological opportunity areas to complement the Copernicus space infrastructure in the horizon 2020-2030 for polar region monitoring. The European Earth Observation (EO) infrastructure is assessed through of comprehensive end-user need and data gap analysis. This review was based on the top 10 use cases, identifying 20 measurements with gaps and 13 potential EO technologies to cover the identified gaps. It was found that the top priority is the observation of polar regions to support sustainable and safe commercial activities and the preservation of the environment. Additionally, an analysis of the technological limitations based on measurement requirements was performed. Finally, this analysis was used for the basis of the architecture design of a potential polar mission.  
Earth Observation (EO); satellite; sensors; platform; microwave radiometer; SAR; GNSS-R; optical sensors; polar; weather; ice; marine 2020 10 1098 10.3390/rs10071098
## 4 Experimental Results and Discussion  
### Experimental Setup  
In order to verify the effectiveness of the proposed DTSMC, the controller is deployed on the DEAP actuator for the high-precision trajectory tracking control. The block diagram and the experimental platform of the DEAP actuator are shown in Figures 8 and 9, respectively. The actuator is manufactured by Danfoss PolyPower A/S Inc. Because DEAP materials are flexible materials, the DEAP actuator needs to be suspended vertically in the experimental platform to generate a certain pre-tension force. Considering the real time executions and the simplicity of the controllers development, a personal computer running Windows 10 operating system and Matlab 2017a software is employed. A high resolution split LVDT displacement sensor is utilized to measure the output displacement of the DEAP actuator. Considering the driving voltage of DEAP materials is 0-2500 V, a high voltage source S15-3P is employed. For realizing the data interaction between MATLAB 2017a and the DEAP actuator, a data acquisition card PCI-1710 is used. In this work, the sampling time \(T\) is set to 0.01 s, and a white Gaussian noise is added to the controller as the external disturbance.  
Figure 8: Block diagram of the DEAP actuator.
# A Target Detection Method of Distributed Passive Radar without Direct-Path Signal
## 2 The Scene of Without-Direct-Path Signals  
Based on modern detection theory, this paper studies the distributed target detection problem of passive radar based on without-direct-path signals, derives the generalized likelihood ratio test for this problem, determines the distribution of test statistics under two assumptions, and uses its distribution characteristics to achieve target detection. Its structure diagram is shown in Figure 2, including \(N_{r}\) receivers and an illuminator. Each  
Figure 1: The condition of without-direct-path signals.  
receiver receives only the echo signals of the target. The location of the illumination source is indicated by \(\mathbf{d}\), the location of the \(j\)th receiver is indicated by \(\mathbf{r}_{j}\), and the location of the target is indicated by \(\mathbf{t}\). The number of arrays of each receiver is \(M\), the distance between the array is \(\mathbf{d}\), \(\theta_{jk}\) is the direction of arrival relative to the \(j\)th receiver, then the difference of phase \(\varphi_{jk}^{(i)}\) is given by  
\[\varphi_{jk}^{(i)}=\frac{2\pi d\sin\theta_{jk}}{\lambda},i=1,2\cdots,M. \tag{1}\]  
Assume that the signal is independent of the noise. Then, the guide vector \(\mathbf{a}_{j}(\theta_{k})\) is given by  
\[\mathbf{a}_{j}(\theta_{k})=\left[e^{i\varphi_{jk}^{(1)}},e^{i\varphi_{jk}^{(2) }},\cdots e^{i\varphi_{jk}^{(M-1)}},e^{i\varphi_{jk}^{(M)}}\right]. \tag{2}\]  
Suppose the target position is \(\mathbf{t}\), and let \(R_{1}=\|\mathbf{t}-\mathbf{d}\|\) and \(R_{2j}=\|\mathbf{r}_{j}-\mathbf{t}\|\). Then the target-path propagation delay is \(\tau_{j}=\left(R_{1}+R_{2j}\right)/c\), \(c\) is the speed of light. \(v_{j}\) is the speed of the target. Similarly, \(f_{dj}=2v_{j}/\lambda\) is the target-path Doppler shift, where \(\lambda=c/f_{c}\) is the wavelength of the transmitter, and \(f_{c}\) is the carrier frequency of the transmitter.  
And assuming that the signal accumulation time is \(T\), the sampling rate is \(f_{s}\), and the signal length is \(L=Tf_{s}\), the signal received by the \(k\)th array in the \(j\)th receiver is \(s_{j}^{k}\), given by  
\[s_{j}^{k}=\gamma_{j}e^{i\varphi_{jk}^{(k)}}D_{j}\mathbf{u}+\mathbf{n}_{j}^{k},s_{j}^{k}\in\mathbb{C}^{L*1}, \tag{3}\]  
Where \(\mathbf{u}\in\mathbb{C}^{L*1}\) is the complex baseband signal transmitted by the illuminator; \(i\) is the imaginary unit; \(D_{j}=D\left(\tau_{j},f_{dj}\right)\in\mathbb{C}^{L*L}\) is a Doppler operator considering delay and Doppler shift. \(\gamma_{j}\) is the channel coefficient, \(\mathbf{n}_{j}^{k}\) is a complex Gaussian noise distribution of \(CN\left(0,\sigma^{2}\right)\), where \(\sigma^{2}\) unknown.  
Then, concatenates the time series vector of all \(M\) elements from the \(j\)th receiver  
\[s_{j}=\gamma_{j}\left(D_{j}\otimes\mathbf{a}_{j}\right)\mathbf{u}+\mathbf{n} _{j^{\prime}}s_{j}\in\mathbb{C}^{ML*1}, \tag{4}\]  
where \(\otimes\) is the Kronecker product, \(\mathbf{n}_{j}\in\mathbb{C}^{ML*1}\), \(\mathbf{a}_{j}\in\mathbb{C}^{M*1}\), \(\mathbf{u}\in\mathbb{C}^{L*1}\), \(s_{j}\in\mathbb{C}^{ML*1}\).
## 3 Method
### Model Architecture for Temperature Anomaly Reconstruction
The integration of these diverse features ensures that the model considers all relevant factors, from surface conditions to deeper oceanic responses, in its reconstruction of the subsurface temperature anomalies. The training of the FCT model is grounded in high-quality observational data from Argo floats, specifically temperature profile data collected from the Western Pacific region between 2011 and 2020. This dataset is particularly valuable as it spans a full decade, providing a comprehensive view of temperature variations across different ocean depth levels, ranging from 5 m to 1000 m across 46 distinct depth layers. The data underwent rigorous quality control and filtering processes to ensure its accuracy and reliability, which are critical for the model's ability to learn and generalize effectively. By utilizing this extensive and carefully curated dataset, the FCT model was able to learn the complex characteristics of ocean temperature variations across different depths, enhancing its capability to accurately reconstruct three-dimensional thermal structures,  
Figure 1: Flowchart of STA reconstruction based on FCT. even under the disruptive influence of typhoons. The final model output successfully reconstructed the three-dimensional thermal structure characteristics of the Western Pacific region. To validate the accuracy and reliability of the model's inversion results, the reconstructed temperature profiles were compared against independent Argo observations from regions directly affected by typhoons. These validation data were selected based on precise typhoon location information, ensuring that the evaluation of the model's performance was both rigorous and contextually relevant.
## 3 Results
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Subjects**} & \multicolumn{3}{c}{**A**} & \multicolumn{3}{c}{**B**} & \multicolumn{3}{c}{**Average**} \\ \hline \multicolumn{1}{c}{\multirow{2}{*}{**Methods**}} & \multicolumn{4}{c}{**Repetitions**} & \multicolumn{4}{c}{**Repetitions**} & \multicolumn{4}{c}{**Repetitions**} \\ \cline{2-13}  & **1** & **5** & **10** & **15** & **1** & **5** & **10** & **15** & **1** & **5** & **10** & **15** \\ \hline Proposed method & 23 & 66 & 86 & 98 & 44 & 81 & 95 & 97 & 33.5 & 73.5 & 90.5 & 97.5 \\ CNN-1 [34] & 16 & 61 & 86 & 97 & 35 & 79 & 91 & 92 & 25.5 & 70.0 & 88.5 & 94.5 \\ MCNN-1 [34] & 18 & 61 & 82 & 97 & 39 & 77 & 92 & 94 & 28.5 & 69.0 & 87 & 95.5 \\ MCNN-3 [34] & 17 & 63 & 85 & 97 & 34 & 74 & 90 & 92 & 25.5 & 68.5 & 87.5 & 94.5 \\ MsCNN-TL-ESVM [20] & 24 & 60 & 86 & 96 & 40 & 79 & 97 & 96 & 32.0 & 69.5 & 91.5 & 96.0 \\ MsCNN-ESVM [20] & 16 & 46 & 81 & 89 & 37 & 74 & 94 & 96 & 26.5 & 60.0 & 87.5 & 92.5 \\ PCA-EWSVM [20] & 20 & 52 & 82 & 95 & 35 & 72 & 88 & 94 & 27.5 & 62.0 & 85.0 & 94.5 \\ SSAE-ESVM [20] & 17 & 63 & 81 & 96 & 39 & 77 & 93 & 95 & 28.0 & 70.0 & 87.0 & 95.5 \\ CMC-CVN-CNN-ESVM [20] & 18 & 52 & 80 & 90 & 36 & 73 & 92 & 95 & 27.0 & 62.5 & 86.0 & 92.5 \\ ESVM [35] & 16 & 72 & 83 & 97 & 35 & 75 & 91 & 96 & 25.5 & 73.5 & 87.0 & 96.5 \\ DRBM [36] & 15 & 62 & 84 & 99 & 35 & 76 & 92 & 93 & 25.0 & 69.0 & 88.0 & 96.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Character recognition accuracy (%) for subject “A” and “B” from BCI competition III dataset II using different previous studies and our proposed method in four repetitions.
# A Range-Extension Method for an Indoor Standard Device for Large-Scale Length Measurement
## 5 Conclusions  
The work research proposed delved into the range-extension methodology applied to the indoor standard device for large-scale length measurement and established the measurement traceability of measurements in laser interferometers. Experimental findings revealed that the specific Renishaw XL-80 laser interferometer exhibited a maximum measurement error of \(-13.02\)\(\upmu\)m within a 104 m range, at the 102 m distance to be exact. The measurement errors remained within the MPE range. Furthermore, a comparative analysis between measurements with and without the range extension was conducted within 50 m to assess their impact on measurement uncertainty, and the results substantiated the feasibility of the proposed approach. The range extension of the indoor standard device for large-scale length measurement can, to some extent, act as a viable alternative to its outdoor equivalents, facilitating the comparison of instruments with long measuring ranges within shorter distances. We first report the comparison measurement of laser interferometers in the world. Future research may explore range-extension traceability methods for other similar laser measuring instruments.  
[PERSON] and [PERSON] designed the experiments; [PERSON] helped to perform measurements; [PERSON] wrote the paper; [PERSON] revised the paper. All authors have read and agreed to the published version of the manuscript.  
The research was funded by the National Key Research and Development Program of China (2021 YFA1401100), the Innovation Group Project of Sichuan Province (20 CXTD0090), and the National Natural Science Foundation of China (52202165).  
Figure 9: A diagram of the simulated optical path for the range-extension measurement of a rangefinder.