esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Ablation Study
#### 4.3.1 Effect of MFF  
Figure 3 illustrates the thermal comparison map depicting the UAV image both before and after MFF processing. Initially, the feature information of targets is obscured by significant noise and background data. However, after applying MFF, the feature information is extracted with a much higher accuracy, which indicates that MFF successfully reduced noise and background interference.  
VisDrone2019 contains numerous small targets. These targets occupy only a few pixels and are characterized by similarity between categories, which leads to poor detection of small targets. The MFF component fuses deep features into shallow feature maps, which increases the accuracy of the model by 5.4%. ARH2000 contains only one type of target (rat hole). The shallow features of the targets, such as shape, color, and texture, are highly prominent. The improved detector achieves an accuracy of 86.2% and runs 3.8 times faster than on the former dataset. The addition of MFF increases the accuracy by 4.5%, which is less than on VisDrone2019. Figure 4 provides a visualization of the two UAV datasets.
## 3 Results  
### Function and Gait Analysis Results  
The general characteristics of the patients included are reported in Table 1. Age, body mass, height, and BMI showed no significant differences between the two groups.  
Measurements from gait analysis demonstrated that cadence and gait speed were significantly higher in the Study Group, while stance time in the operated side and cycle time were significantly higher in the Control Group (Table 2). The other time-distance parameters showed no significant differences.  
Regarding kinematic measurements, there were no significant differences in pelvic obliquity and hip range of motion between the Study Group and the Control Group (Table 3).  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Study Group** & **Control Group** \\ \hline Sample Size & 12 & 6 \\ Gender & 11 female/1 male & 5 female/1 male \\ Follow-up (months) & 7 (1) & 7 (1.5) \\ Age (years) & 58.2 (9.4) & 53 (6.8) \\ Body Mass (kg) & 63 (8.8) & 66 (15) \\ Height (m) & 1.6 (0.04) & 1.59 (0.13) \\ BMI (kg/m\({}^{2}\)) & 24 (3.1) & 23.3 (3.9) \\ \hline \hline \end{tabular}
\end{table}
Table 1: General characteristics of the patients (Median (IQR)).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Study**} & \multicolumn{2}{c}{**Control**} & \multicolumn{1}{c}{**Mann–Whitney**} \\  & **Group** & & **Group** & **U-Test** \\ \hline  & Median & IQR & Median & IQR & \(p\)-values \\ \hline
**Stance time** & & & & \\
**operated side** & 61.1 & 2.4 & 62.5 & 5.5 & 0.037 \\
**(\% stride)** & & & & & \\
**Stance time** & & & & \\
**contralateral side** & 62.2 & 3.3 & 62.9 & 4.5 & 0.786 \\
**(\% stride length)** & & & & \\
**operated side** & 72.9 & 9.2 & 67.9 & 9.2 & 0.062 \\
**(\% height)** & & & & \\
**contralateral side** & 72.5 & 11.3 & 70.2 & 7.3 & 0.270 \\
**(\% height)** & & & & \\
**Cycle times** & 1.13 & 0.17 & 1.17 & 0.26 & 0.007 \\
**Cadence** & & & & \\
**(stride/m)** & 52.6 & 7.8 & 52.2 & 10.4 & 0.009 \\
**Speed** & & & & \\
**(cm/s)** & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of time–distance parameters.
## 3 Results and Discussion
### Intra-Particle Diffusion  
The mechanisms and rate controlling steps the kinetics of adsorption is investigated through the intraparticle diffusion model from data (Figure 9b). The model proposes that a surface diffusion mechanism governs adsorption, in a first stage while the pore diffusion mechanism may occur in second stage, according to the following Equation (10) [45; 56]:  
\[q_{t}=k_{int}\sqrt{t}+C \tag{11}\]  
where \(k_{int}\) is the intra-particle diffusion rate constant (mg\(\cdot\)g\({}^{-1}\cdot\)min\({}^{-1/2}\)), which can be analyzed from the slope of the linear plot of \(q_{t}\) versus \(t^{1/2}\) and \(C\) is the intercept of the graph, which gives the thickness of the boundary layer.  
The curves do not cross zero value (Figure 9b) indicating that some other adsorption mechanism like surface diffusion in combination with intraparticle diffusion is occurring at rate controlling step, as shown in Table 7.  
To corroborate these results, the deviation at the origin can be attributed to a variation of mass transfer at initial and final stages during Acid Red 66 adsorption process [56].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Adsorbent**} & \multicolumn{3}{c}{**Intraparticle Diffusion**} \\ \cline{2-4}  & \(k_{int}\) & \(\mathbf{R^{2}}\) & \(\mathbf{S}\)D \\ \cline{2-4}  & **(mg\(\cdot\)g\({}^{-1}\cdot\)min\({}^{-1/2}\))** & **R\({}^{2}\)** & **SD** \\ \hline Fe & 0.427 & 0.831 & 0.183 \\ FeC & 1.367 & 0.687 & 0.234 \\ Fe/FeC & 1.542 & 0.647 & 0.244 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Intraparticle diffusion kinetic parameter at 25 \({}^{\circ}\)C for Acid Red 66 adsorption on the solids.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{3}{c}{**Crystallite Size (nm)**} & \multicolumn{2}{c}{**Energy Band Gap (eV)**} \\ \cline{2-5}  & **Anatase** & **Rutile** & **Brookite** & \\ \hline TiO\({}_{2}\) before & 6 & - & 3 & - \\ calcination & 6 & - & 4 & 3.10 \(\pm\) 0.03 \\ TiO\({}_{2}\)-300 & 7 & - & 4 & 3.10 \(\pm\) 0.03 \\ TiO\({}_{2}\)-400 & 8 & - & 5 & 3.29 \(\pm\) 0.04 \\ TiO\({}_{2}\)-500 & 12 & 4 & 5 & 3.28 \(\pm\) 0.03 \\ TiO\({}_{2}\)-600 & 20 & 19 & 6 & 3.16 \(\pm\) 0.02 \\ TiO\({}_{2}\)-650 & 23 & 25 & - & 3.08 \(\pm\) 0.02 \\ TiO\({}_{2}\)-700 & 30 & 32 & - & 3.07 \(\pm\) 0.02 \\ TiO\({}_{2}\)-800 & - & 52 & - & 3.05 \(\pm\) 0.01 \\ P25 & 17 & 28 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Crystallite size and energy band gap of synthesized TiO\({}_{2}\) particles before and after calcination and commercial P25 TiO\({}_{2}\). Figure 3: X-ray diffraction (XRD) patterns of TiO\({}_{2}\) particles before and after calcination at different temperatures ranging from 300 \({}^{\circ}\)C to 800 \({}^{\circ}\)C.  
agreement with the reports of [PERSON] et al. [26], [PERSON] et al. [33] and [PERSON] et al. [34]. A mixture of the anatase and the rutile phases was found between 650 \({}^{\circ}\)C and 700 \({}^{\circ}\)C which was consistent with the study of [PERSON] et al. [35]. The anatase and brookite phases were completely converted to the rutile phase at 800 \({}^{\circ}\)C because the rutile phase provided thermal stability at high temperature [34]. Absorption edge of TiO\({}_{2}\) with different calcination temperatures was investigated by DRS as shown in Figure 4b. It was observed that absorption edge of TiO\({}_{2}\) between 320 nm and 440 nm showed red shift when calcination temperature was increased. The higher wavelength of absorption edge provides the lower energy band gap. Energy band gap of TiO\({}_{2}\) with direct band gap structure was determined using Tauc analysis as shown in Table 1. Bulk TiO\({}_{2}\) is an indirect band gap and direct transitions are forbidden by selection rules. However, due to the finite size of the TiO\({}_{2}\) nanoparticles, the origin of the transition does not necessarily reflect that occurring in the bulk. Therefore, direct or indirect transitions in TiO\({}_{2}\) nanoparticles can be presented [36; 37]. It was observed that the mixture of three phases provided the energy band gap between 3.16 eV and 3.28 eV. While the mixture of anatase and rutile phases (3.07-3.08 eV) showed a lower energy band gap than the mixture of anatase and brookite phases (3.10-3.29 eV). This result agreed well with many studies in which rutile has been reported to have a band gap of about 3.0 eV and brookite has been reported to have a band gap between 3.0 eV and 3.6 eV. Anatase has been reported to have a band gap about 3.2 eV. The lowest energy band gap of TiO\({}_{2}\)-800 composed of only the rutile phase is 3.05 eV and well correlated with the report of [PERSON] et al. [20] who also found the energy band gap of the rutile phase was 3.05 eV.
## 2 Methodology
### Concrete Damaged Plasticity Model (CDPM)
#### 2.1.2 Input of Plasticity Parameters  
The plasticity parameters determine the yield function and the flow rule. In particular, the yield function of Abaqus CDPM is defined in Equation (12) to account for different tension and compression strength evolution.  
\[F=\frac{1}{1-\alpha}(\overline{q}-3\alpha\overline{p}+\beta\langle\dot{ \overline{\sigma}}_{\text{max}}\rangle-\gamma\langle-\dot{\overline{\sigma}}_ {\text{max}}\rangle)-\overline{\sigma}_{\text{c}}\leq 0. \tag{12}\]  
\[\alpha=\frac{\sigma_{b0}-\sigma_{c0}}{2\sigma_{b0}-\sigma_{c0}};\beta=\frac{ \overline{\sigma}_{\text{c}}}{\overline{\sigma}_{\text{t}}}(1-\alpha)-(1+ \alpha);\gamma=\frac{3(1-K_{\text{c}})}{2K_{\text{c}}-1}. \tag{13}\]  
In Equations (12) and (13), \(\overline{q}\) represents the Mises equivalent effective stress and \(\overline{p}\) is the effective hydrostatic pressure. As the typical experimental values of the ratio between biaxial and uniaxial compressive strength \(\sigma_{b0}/\sigma_{c0}\) range between 1.10 and 1.16, the coefficient \(\alpha\) can vary from 0.08 to 0.12. \(\dot{\overline{\sigma}}_{\text{max}}\) stands for maximum principal effective stress, \(\overline{\sigma}_{\text{t}}\) and \(\overline{\sigma}_{\text{c}}\) are the effective tensile and compressive cohesion stresses (as explained in Section 2.1.3), respectively. The coefficient \(\gamma\) enters the yield function only for stress states of triaxial compression, which can be determined by comparing the yield conditions along the tensile and compressive meridians. \(K_{c}\) is the ratio of second stress invariant on tensile and compressive meridians that could be calculated by referring to the literature [7].  
Regarding the flow rule, the Drucker-Prager hyperbolic function, as defined in Equation (14), is chosen for the flow potential  
\[G=\sqrt{\left(\varepsilon f_{\text{t}}\tan\psi\right)^{2}+\overline{q}^{2}}- \overline{p}\tan\psi, \tag{14}\]  
where \(\psi\) is the dilatancy angle measured in the p-q plane (deviatoric plane) at high confining pressure. \(f_{\text{t}}\) is the uniaxial tensile stress at failure. \(\varepsilon\) is referred to as the eccentricity of the plastic potential surface.  
According to Equations (12)-(14), the spatial stress behavior of concrete depends on four plasticity parameters \(K_{\text{c}}\), \(\psi\), \(\sigma_{\text{10}}/\sigma_{\text{c0}}\), \(\varepsilon\), whose value must be specified as input. The default values for the four parameters in Abaqus are 2/3, 35\({}^{\circ}\), 1.16, and 0.1 [9], and users can adjust parameters according to specific loading conditions.
## 6 Conclusions and Future Work  
In this paper, we presented V2 SOM as the basis of a human-friendly cobot in compliance with safety level 1 on the AIS. V2 SOM displays two complementary working modes:  
* In normal working conditions, characterized by high stiffness, the proposed mechanism limits the storable elastic energy that increases the absorbed kinetic energy in case of a sudden HR shock. Moreover, this high stiffness limits the elastic deflection to near zero, which eases the robot control.
* In the case of a blunt HR impact, V2 SOM stiffness promptly decreases, allowing fast absorption of the collision energy.  
From a safety perspective, as shown via the collision simulations, two complementary safety criteria were adopted for internal and external damage evaluation of the human body. Based on these two criteria, HIC and ImpF, important improvements were observed for the V2 SOM profile in contrast to an equivalent CS profile. Especially in terms of brain concussion quantification, HIC was reduced by over 70% relative to the results of the CS profile. Moreover, V2 SOM presented a high inertia decoupling capacity (i.e., large variation in rotor-side inertia slightly impacts the evaluation of safety on a HIC basis).  
The experimental implementation, through the HR collision tests, shows the decoupling capacities of V2 SOM. In case of collision, the deflection is detected, and the kinetic energy absorbed, which guarantees safe physical human-robot interaction.  
Thus, the present work is focused on an illustrative case study of a one degree-of-freedom robot arm. In future work, different HR collision scenarios will be investigated by considering a generic multi-degrees of freedom (DoFs) robot with V2 SOM joints, its dynamics, and the impact location on the robot arm.
## 2 Previous Work  
Our main goal was to design a good quality model that evaluates our project best. Accordingly, following the indications of the most referenced network architectures reviewed by [PERSON] et al. ([PERSON] and [PERSON], 2011) and the references therein), who provided an in-depth literature review on Water Quality Monitoring Systems based on Internet-of-Things (IoT-WQMS), in ([PERSON], 2011), we already developed a performance comparison of the most suitable communication technologies.  
In our previous work, with the help of the WinProp software simulation framework ([PERSON], 2011), we compared the performance of three long-range communications technologies to support an IoT-based network reporting data from the sensing devices spread along rivers of the Abruzzo region in Italy to a remote central station, where they are collected and analyzed. More in detail, we jointly assessed the radio signal coverage and the maximum achievable data rate for (i) 4G/LTE, (ii) NB-IoT, and (iii) LoRa communications technologies. In particular, the transmitting antennas' were placed at different heights above the ground or the river's water level.  
Our results demonstrated that 4G/LTE outperforms the other two technologies since it achieves the highest throughput and allows adding value services, such as video surveillance over simple data chunks reporting. Nevertheless, 4G/LTE is limited in coverage: the best performance results are achieved near the base stations, i.e., in urban/suburban areas, while the rural regions need more radio signal quality. On the contrary, the LoRa and NB-IoT technologies achieve outstanding connectivity for large regions far from the base stations. However, this performance is paid with a reduced capacity of the medium to support the mentioned added value service.  
Considering all the above, we believe, in this paper, that the most exciting scenarios to study are identifying pollutants in urban and suburban areas, i.e., where most people live. Accordingly, the suitable solution for our project is to focus on the 4G/LTE communication technology ([PERSON], 2011) due to its maturity, its overall coverage, i.e., the availability of base stations, and in general its capacity in terms of guaranteeing radio connections of good quality (low packet loss rate and latency, and generally high throughput). We then assess the system's performance to support different network topologies.
###### Abstract  
Optical coherence tomography angiography (OCTA) is a popular medical imaging technology that can quickly establish a three-dimensional model of the fundus without dye injection. However the number of images in a model is quite large, so finding the lesions through image processing technology can greatly reduce the time required for the judgment of the condition. This paper proposes a method for finding choroidal neovascularization (CNV) in OCTA images. Among the several characteristics of CNV, the larger turning angle of blood vessels is a relatively clear feature, so we will use this property to find out whether there is CNV in an OCTA image. We will transform the color space to CIELAB space, and extract the L-channel prior to preceding to the next step. We will then use some image segmentation methods to find the clearer vessel region. Finally, we will detect the CNV through certain morphology methods. The experimental result shows that our proposed method can effectively find the CNV in the OCTA image, meaning that we can make automated judgments through this method in the future and reduce the time necessary for human judgment.  
Keywords:optical coherence tomography angiography; choroidal neovascularization; image segmentation; morphological image processing +
Footnote †: journal: Applied Sciences
# Intelligent Transmission Control Scheme for 5G mmWave Networks Employing Hybrid Beamforming
###### Abstract  
Hybrid beamforming plays a critical role in evaluating wireless communication technology, particularly for millimeter-wave (mmWave) multiple-input multiple-out (MIMO) communication. Several hybrid beamforming systems are investigated for millimeter-wave multiple-input multiple-output (MIMO) communication. The deployment of huge grant-free transmission in the millimeter-wave (mmWave) band is required due to the growing demands for spectrum resources in upcoming enormous machine-type communication applications. Ultra-high data speed, reduced latency, and improved connection are all promised by the development of 5G mmWave networks. Yet, due to severe route loss and directional communication requirements, there are substantial obstacles to transmission reliability and energy efficiency. To address this limitation in this research we present an intelligent transmission control scheme tailored to 5G mmWave networks. Transport control protocol (TCP) performance over mmWave links can be enhanced for network protocols by utilizing the mmWave scalable (mmS)-TCP. To ensure that users have the stronger average power, we suggest a novel method called row compression two-stage learning-based accurate multi-path processing network with received signal strength indicator-based association strategy (RCTS-AMP-RSSI-AS) for an estimate of both the direct and indirect channels. To change user scenarios and maintain effective communication constantly, we utilize the innovative method known as multi-user scenario-based MATD3 (Mu-MATD3). To improve performance, we introduce the novel method of \"digital and analog beam training with long-short term memory (DAH-BT-LSTM)\". Finally, as optimizing network performance requires bottleneck-aware congestion reduction, the low-latency congestion control schemes (LLCCS) are proposed. The overall proposed method improves the performance of 5G mmWave networks.  
digital and analog beam training with long-short term memory (DAH-BT-LSTM); low-latency congestion control schemes (LLCCS); 5G mmWave; hybrid beamforming (HB)
## 4 Results and Discussion
### Energy Potential Analysis
These deviations suggest that the model occasionally struggles with accurately predicting the solar hydrogen output, leading to instances of both overprediction and underprediction. These errors might stem from the model's inability to fully account for sudden or extreme changes in environmental conditions, such as unexpected weather events that alter solar irradiance. Additionally, the model may have been trained on data that did not sufficiently capture the full range of possible conditions, leading to less accurate predictions in outlier scenarios. These issues highlight the importance of ongoing model refinement. To mitigate these errors and improve the model's predictive accuracy, several approaches could be considered. First, incorporating more comprehensive environmental data, such as real-time weather information or atmospheric conditions, might help the model better anticipate fluctuations in solar energy production. Secondly, enhancing the model's architecture or employing techniques like ensemble learning could improve its robustness and ability to generalize from diverse datasets. Lastly, a thorough analysis of the periods of significant deviation could provide insights into the specific factors that the model currently overlooks, guiding targeted improvements. By addressing these limitations, the Transformer model could be further optimized to provide more reliable and accurate forecasts of the university's solar hydrogen energy potential.
## 3 Methods and Data
### Methods
#### 3.1.3 Vegetation Vulnerability  
1. Data standardization  
Due to the units and properties being different, the original data were standardized for easier subsequent calculations. The positive indexes (2) and negative indexes (3) were calculated by different standardized calculation formulas:  
\[Z_{i}=\frac{X_{i}-X_{min}}{X_{max}-X_{min}} \tag{2}\]  
\[Z_{j}=\frac{X_{max}-X_{i}}{X_{max}-X_{min}} \tag{3}\]  
where \(Z_{i}\) and \(Z_{j}\) are the standardized values of the index \(i\). \(X_{i}\) is the original value \(i\). \(X_{max}\) is the maximum value of \(i\), and \(X_{min}\) is the minimum value.
2. Vegetation vulnerability  
Principal component analysis can maximize the retention of information reflected by multiple variables; it was used to calculate vegetation vulnerability. The vegetation vulnerability was calculated by:  
\[EV=a_{1}F_{1}+a_{2}F_{2}+\cdots+a_{n}F_{n} \tag{4}\]  
where \(EV\) is the vegetation vulnerability, \(a_{i}\) is the importance of index \(I\) and \(F_{i}\) is the index \(i\).
3. Spatial cluster of vegetation vulnerability  
To analyze the spatial cluster of vegetation vulnerability in the MRB, global Moran's \(I\) was used to measure whether there was a spatial cluster of vegetation vulnerability, and local Moran's \(I\) was used to measure the spatial cluster pattern of vegetation vulnerability.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Factors** & **Index** & **Unit** & **Property** \\ \hline \multirow{4}{*}{Natural factors} & Precipitation & mm & – \\  & Temperature & \({}^{\circ}\)C & – \\  & Elevation & m & + \\  & Vegetation ecological water & m\({}^{3}\) & – \\ \hline \multirow{2}{*}{Human factors} & Population density & person/km\({}^{2}\) & + \\  & Land use & \% & + \\ \hline \end{tabular}
\end{table}
Table 1: Vegetation vulnerability factors.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Farmlands** & **Forests** & **Grasslands** & **Shrubs** \\ \hline
0.71 & 0.17 & 0.29 & 0.29 \\ \hline \end{tabular}
\end{table}
Table 2: Land use degree parameters (\(P_{i}\)) for different land use types in the MRB.  
Global Moran's \(I\) can be calculated as:  
\[I_{g}=\frac{\sum_{i=1}^{n}\sum_{j=1}^{n}w_{ij}(x_{i}-\overline{x})\big{(}x_{j}- \overline{x}\big{)}}{\sum_{i=1}^{n}\sum_{j=1}^{n}w_{ij}(x_{i}-\overline{x})^{2}} \tag{5}\]  
Local Moran's \(I\) can be calculated as:  
\[I_{l}=\frac{(x_{i}-\overline{x})}{S^{2}}{\sum_{j=1}^{n}w_{ij}}(x_{j}-\overline {x}) \tag{6}\]  
where \(x_{i}\) and \(x_{j}\) are the vulnerability values of the \(i\) and \(j\) regions, \(\overline{x}\) is the average vulnerability value of all regions. \(w_{ij}\) is the spatial weight matrix. \(S\) is the sum of the elements of the spatial weight matrix. \(n\) is the number of regions. A Local Indicators of Spatial Association (LISA) of vegetation vulnerability can be obtained by clustering the local Moran's \(I\). The LISA has five cluster patterns, namely high-high cluster (H-H), high-low outlier (H-L), low-high outlier (L-H), low-low cluster (L-L), and not significant.