esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Glacier movement is an important indicator of climate change, reflecting the quality and state changes in glacier migration and mass balance in the context of global warming. Although accurately estimating glacier surface flow velocity is crucial for various applications, achieving this is challenging due to factors such as low temporal correlation and high noise effects. This paper presents the pixel offset tracking (POT) technology based on Synthetic Aperture Radar (SAR) data for glacier velocity monitoring, with enhanced cross-correlation matching window and noise suppression approaches. In particular, a noise suppression optimization method and a matching window optimization index suitable for wide-area glacier velocity monitoring are proposed. The inter-annual wide-area two-dimensional plane flow velocity of glaciers in the Svalbard archipelago was obtained by using a total of seven Sentinel-1 data sets from two orbits covering the entire Svalbard archipelago in 2021. The results indicate that 25 large glaciers in Svalbard destabilized in 2021, with a peak flow velocity of 6.18 m/day. At the same time, the influence of climate, topography, and other factors on glacier surface velocity is discussed. The wide-area glacier velocity monitoring method and its application demonstrated in this paper will serve as a valuable reference for studying glacier migration in the Arctic Svalbard archipelago and for other large-scale wide-area deformation monitoring efforts.  
Svalbard; glacier flow velocity; SAR offset tracking; noise suppression; wide area deformation Article  
An accepted: 21 November 2022
## 3 RS Applications in Ocean
### Ocean Wave Height (OWH)  
Wind blowing over the ocean surface creates ocean waves with a different range of heights depending on the wind speed, wind duration, and distance. The resulting waves can travel for hundreds or even thousands of kilometers and form swell waves of various heights. Although waves are generally caused by wind, catastrophic waves (e.g., landslide surges, tsunamis, and storm surges) [151] and internal waves (e.g., subsurface waves at the boundary between two water layers) [152] can also generate ocean waves. OWH information is a critical parameter for coastal construction, ship navigation, and human activities in the oceans [153].  
The datasets collected by different RS systems, such as GNSS-R, SAR, altimeter, and marine radar, have been utilized for OWH estimation. In this regard, various models have been applied to retrieve OWH from these datasets [154]. For example, due to the complexity of physical models (e.g., RT models), empirical and semiempirical models have also been developed to estimate OWH [155]. The simplicity of empirical and semiempirical models has also led to the development of ML algorithms [156]. In this regard, DL algorithms, as the most advanced ML models, have received more attention due to their promising performance. For example, [PERSON] et al. [157] proposed a hybrid statistical and a DL model in South China to predict several ocean surface variables, including OWH. In [PERSON] et al. [158], a short-term memory deep network was also proposed to consider the time domain data in OWH estimation. Table 3 summarizes the advantages and disadvantages of each of these RS systems. In the following subsections, the studies that have been conducted to measure OWH based on various RS systems are discussed in more detail.
## 4 Results  
Having understood the theoretical context of smart gate technologies, it is essential to apply these concepts to a real case study to observe their practical implications. In the next case study, a port terminal in Portugal is examined, where these technologies have been implemented to assess their impacts on efficiency and safety.  
This chapter will present the results that will culminate in a smart gate model for the entry and exit processes of a container terminal, in line with the three specific objectives defined, based on a case carried out in one of Portugal's largest seaport container terminals. In addition to the critical components that will determine the key phases of the process representative of the smart gate's activities and operations, the main coupled technologies responsible for capturing and collecting data in operations will also be explored and defined. This study ends with some proposals for indicators that will ensure the sustainability of the new smart gate operating model.  
Before detailing the TO-BE process (the desired or ideal state of the process), we will first present the AS-IS process (the current state of the process without any improvements) (see Table 1).  
Figure 2 illustrates the different locations where the AS-IS process for entering and leaving the terminal is carried out: documentation room (container and lorry access control point), security area (people access control point), entry and exit gates (and their respective barrier and gateway), and road access to the gates.  
Based on the limitations identified, such as manual checks and inadequate integration systems, the need for a more automated and efficient system is evident. The following explains the TO-BE process and how the essential technological elements and strategic control points can transform these processes, ensuring greater efficiency, security, and better data flow management in the terminal.  
Figure 2: Layout of current entry and exit gate.
## 3 Results
### Analysis of Voltage and Current Waveform  
As shown in Figure 2, the waveform of the discharge current is similar to that of a common DBD, as it takes the shape of a pointed current spike for every half cycle [18; 19; 20]. The total time span of the current oscillation was a few tens of sub-microseconds. We measured the consumption of electrical energy using a power detector (SJPM-C16, Seojun Electric, Republic of Korea) connected to the input line of the power supply. As shown in Figure 2a-e, the energies consumed by the power supply of the compact air-cooled coaxial DBD system were measured to be 40, 50, 60, 70, and 80 W. The energy for discharge of the plasma was calculated using the electric current method for high accuracy and simplicity [21]. In this study, the discharge power was estimated by integrating the instantaneous current and voltage, as shown in Equation (2) [14; 22].  
\[\mathrm{P(W)=f\times\int_{0}^{T}(V(t)I(t)dt)}, \tag{2}\]  
where f is the frequency, t is the period of the cycle, and \(V(t)\) and \(I(t)\) are the data recorded using an oscilloscope. At applied power consumptions of 40, 50, 60, 70, and 80 W, the measured sinusoidal voltages (V\({}_{\mathrm{rms}}\)) were 3.56, 3.6, 3.74, 3.9, and 4.1 kV, respectively; in addition, the discharge currents (A\({}_{\mathrm{rms}}\)) were 10.95, 12.45, 13.90, 16.20, and 16.80 mA, respectively (Figure 2). The power used for the plasma discharge can calculated based on the sinusoidal voltage and current. The calculated discharge powers at the abovementioned applied powers were 38.98, 44.82, 51.99, 63.18, and 68.88 W, respectively. The difference between the measured and calculated power comes from the efficiency of the power supply used in the test. An FET (field effect transistor) in parts of the power supply discharges heat. The heat also increases as the power increases. Accordingly, the heat becomes power loss. We expect that the difference between the measured power and the calculated discharge power stems from the efficiency of the power supply used in this test, revealing an electrical efficiency of more or less 90%.
## 2 Materials and Methods
### Datasets  
In this research work, two fundus image datasets were used. The first dataset was the APTOS 2019 diabetic retinopathy dataset, which is publicly available online on Kaggle ([[https://www.kaggle.com/c/aptos2019-blindness-detection/data](https://www.kaggle.com/c/aptos2019-blindness-detection/data)]([https://www.kaggle.com/c/aptos2019-blindness-detection/data](https://www.kaggle.com/c/aptos2019-blindness-detection/data)), accessed on 17 February 2022). This dataset was selected among the other publicly available datasets since it is from India, which is close to the Mauritian population in terms of ethnicity. The second dataset was created locally from the images obtained from the hospitals in Mauritius. Each image in the APTOS 2019 dataset was assigned a class label of 0-4 according to the severity of the disease, as shown in Figure 2. Each image from the local cohort was also assigned a class label of 0-4 by a local doctor. The original dataset obtained from Kaggle is termed as the original APTOS dataset. The class distribution of the original APTOS dataset is illustrated in Figure 2.  
Figure 2 reveals that, despite the data belonging to five different classes, the number of samples in each class varied substantially, resulting in an unbalanced dataset. As discussed in [27; 28; 29], an unbalanced dataset leads to a high misclassification rate and sub-optimal performance. To mitigate this challenge, we applied data augmentation, which is one possible solution to this problem. Traditional data augmentation techniques, namely horizontal and vertical flipping and changes in the brightness range [30], were applied to the original APTOS datasets to produce the augmented APTOS dataset. Figure 1: Workflow of our proposed system. Table 1 shows the total number of images for each class in the original APTOS dataset, the augmented APTOS dataset and the local Mauritian dataset. We divided both the APTOS dataset and the augmented APTOS dataset into a training set and testing set. There were 3662 images in the original Aptos dataset, whereby 70% (2563 images) were considered for training and 30% (1099 images) were taken for the testing phase. For the augmented APTOS dataset, data augmentation was performed on the training set only as performed by [PERSON] et al. [22]. Only the data from classes 1, 3 and 4 were augmented since the model could not correctly classify these 3 classes in the original APTOS dataset. All the images in these 3 classes were augmented. In this paper, we used two sets for testing data, one which is made up of fundus images from the APTOS 2019 dataset (the remaining 30% of which were not used as training data) and the second being the Mauritian dataset composed of fundus images obtained from a local hospital in Mauritius. Table 1 presents the image count for each class in the training and testing data for the original and augmented APTOS datasets as well as the Mauritian dataset. Figure 3 presents the number of images in each of the 5 classes after the application of data augmentation on the original APTOS dataset. It can be observed that the augmented dataset was more balanced.
# The Effects of Loaded Plyometries and Short Sprints in U19 Male Soccer Players in Tunisia
## 3 Results
### Effect of Training on Balance  
For all balance parameters significant interaction effects were observed as an indication of the effectiveness of the intervention (Table 6). Partial eta squared (\(\eta_{\rm p}\)\({}^{2}\)) consistently moved on a very high level (from 0.78 to 0.95). Y-balance test right leg pld (\(p\) = 0.034, \(\eta_{\rm p}\)\({}^{2}\) = 0.13), left leg ad (\(p\) = 0.094, \(\eta_{\rm p}\)\({}^{2}\) = 0.09) and pmd (\(p\) = 0.488, \(\eta_{\rm p}\)\({}^{2}\) = 0.02), were not different between groups preintervention. The effect sizes in for improvement in EG ranged from d = 1.11 (right leg pld) to d = 2.54 (left leg pmd) and were greater than in the CG (d\({}_{\rm max}\) = 0.64, left leg pmd). Relationships between Anthropometric and Performance Parameters  
Meaningful correlations (r \(\geq\) 0.5) were not observed between anthropometric and performance parameters. The largest relevant relationship was detected for height and right leg pl (r = 0.475). \begin{table}
\begin{tabular}{l l c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**EG (n = 18)**} & \multicolumn{3}{c}{**CG (n = 16)**} & \multicolumn{3}{c}{**Variance Analysis \(p\) (\(\eta_{\rm p}\)\({}^{2}\))**} \\ \cline{3-10}  & & **Test** & **Retest** & **d** & **Test** & **Retest** & **d** & **Group** & **Time** & **Group \(\times\) Time** \\ \hline \multirow{4}{*}{**S90\({}^{\circ}\)**} & ad (cm) & 95.5 \(\pm\) 5.26 & 107 \(\pm\) 6.16 & **2.01** & 92.5 \(\pm\) 5.49 & 94.4 \(\pm\) 4.98 & 0.36 & **\textless0.001 (0.36)** & **\textless0.001 (0.96)** & **\textless0.001 (0.92)** \\  & pmd (cm) & 115 \(\pm\) 5.45 & 127 \(\pm\) 4.02 & **2.53** & 114 \(\pm\) 5.27 & 117 \(\pm\) 5.40 & 0.56 & 0.003 (0.24) & **\textless0.001 (0.96)** & **\textless0.001 (0.91)** \\  & pld (cm) & 80.4 \(\pm\) 7.99 & 89.6 \(\pm\) 8.62 & **1.11** & 79.2 \(\pm\) 3.99 & 80.9 \(\pm\) 3.82 & 0.44 & 0.034 (0.13) & **\textless0.001 (0.88)** & **\textless0.001 (0.78)** \\  & pld (cm) & 95.2 \(\pm\) 8.09 & 107 \(\pm\) 7.95 & **1.47** & 95.7 \(\pm\) 5.39 & 98.0 \(\pm\) 5.35 & 0.43 & 0.094 (0.09) & **\textless0.001 (0.97)** & **\textless0.001 (0.94)** \\  & pmd (cm) & 115 \(\pm\) 4.70 & 126 \(\pm\) 3.96 & **2.54** & 119 \(\pm\) 3.35 & 121 \(\pm\) 2.86 & 0.64 & 0.488 (0.02) & **\textless0.001 (0.97)** & **\textless0.001 (0.92)** \\  & pld (cm) & 79.7 \(\pm\) 4.95 & 89.9 \(\pm\) 4.87 & **2.08** & 74.2 \(\pm\) 5.29 & 76.5 \(\pm\) 5.11 & 0.44 & **\textless0.001 (0.48)** & **\textless0.001 (0.98)** & **\textless0.001 (0.95)** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Y-Balance test performances depending on groups (EG vs. CG) before and after 8-week intervention. Anterior direction = ad; Posteromedial direction = pmd; Posterolateral direction = pdd; Meaningful effects (criteria: \(p\) \(<\) 0.05 and \(\eta_{\rm p}\)\({}^{2}\) \(>\) 0.15 and d \(>\) 0.80) and the largest effect sizes for each parameter are highlighted in bold.
# A Virtual Reality Soldier Simulator with Body Area Networks for Team Training
## 6 Experimental Results
### Discussion  
When the participants executed the rescue mission, the activities involved in the experiment included detecting enemies, engaging enemies, moving inside the building and rescuing the hostages. The results reveal significant differences in several respects, including experience, quantity, and communication, and show that compared with the inexperienced participants, all experienced participants who had done the same training in a live situation took less time to complete the rescue mission. The wireless BANs of the participants are able to work accurately in the virtual environment for experienced participants. Tactical skills (e.g., moving through built-up areas, reconnoiter area, reacting to contact, assaulting, and clearing a room) absolutely require team work, demanding that wireless BANs interact with each other perfectly in terms of connection and accuracy. Without proper BANs, participants may feel mismatched interaction with their virtual avatars, and may feel uncomfortable or sick in the virtual environment.  
The experimental results show that a larger sized group of participants took less time to complete the rescue mission than a smaller sized group of participants. Moreover, a group of multiple participants had a lower death rate compared with that of a single participant. This is due to the fact that, as the group size of participants increases, team movement is more efficient and fire power is greater in the virtual environment, which is similar to a real world mission. Furthermore, when the VoIP communication function was disabled, whether participants were experienced or not, the rescue mission time in the experiment consequently increased. As we know, in the real rescue mission, team coordination is important in the battlefield. In the system, all participants are able to interact with each other through hand signal tracking and voice communication. As a result, multiple-user training may become a key feature of the system.
## 10 **Ul/l (UpLink/DownLink) Signal Quality**  
This indicator, which has been introduced in the previous sections as one of the reasons for success or failure in the most important network quality assurance indicators, calculates the quality of the sent signal through the base transmitter and receiver station to the user (the downlink signal), as well as the sent signal from the user to the station (the uplink signal). If the received signal strength by the user is less than the amount specified in the existing settings of each operator, the user's call is disconnected. In addition to signal quality affecting the increase or decrease of the call disconnection rate, other factors. such as access to the traffic channel and signaling interrupted in the signaling channel, etc. can also play a part.
# The Role of Surveillance Imaging in Patients with Advanced Laryngeal Cancer
## 3 Results  
### Patients  
A total of 289 patients were treated for laryngeal SCC during the study period. Of them, 123 had advanced-stage disease (stage 3 or 4) and were included in the initial study cohort. Ninety-four patients were male (76.4%). The mean age at diagnosis was 63.1 years. A total of 93 patients were active smokers at diagnosis (75.6%), five were past-smokers (ceased smoking for at least three months) (4.1%), and 25 never smoked (20.3%). The mean follow-up time after initial diagnosis was 64.5 months (range: 2.8-221 months). Figure 1 describes the inclusion process for patients in the study.  
Figure 1: Flow chart describing the study cohort and different recurrence diagnostic modalities.  
Forty-two patients (34.2%) were treated surgically; out of them, 35 (83.3%) received adjuvant radiotherapy and 27 (64.3%) also received adjuvant chemotherapy. Eighty patients (65%) received radiotherapy as their primary treatment; out of them, 68 (85%) were also treated with chemotherapy. One patient (0.8%) died before receiving any treatment and was excluded from further analyses. A total of 35 patients (28.7%) experienced disease recurrence after their initial therapy. Demographic, clinical, and pathological features are presented in Table 1.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Number of** & **Number of** \\
**Variable** & **Patients—All Stages** & **Patients—Advanced** \\  & **(N = 289)** & **Stage (N = 123)** \\  & **(\%)** & **(\%)** \\ \hline Gender & & \\ Male & 239 (82.7) & 94 (76.4) \\ Female & 50 (17.3) & 29 (23.6) \\ \hline Age at diagnosis, years & 65.2 \(\pm\) 11 & 63.1 \(\pm\) 11 \\ \hline Smoking & & \\ Current & 192 (66.1) & 93 (75.6) \\ Past & 31 (10.7) & 5 (4.1) \\ Never & 67 (23.2) & 25 (20.3) \\ \hline Alcohol & & \\ Yes & 15 (5.2) & 3 (8.6) \\ Mean follow-up time, months (range) & 68.2 (2.8–221) & 64.5 (2.8–221) \\ \hline Origin Site & & \\ Glottis & 224 (77.5) & 68 (55.3) \\ Supraglottis & 64 (22.2) & 54 (43.9) \\ Subglottis & 1 (0.3) & 1 (0.8) \\ \hline T stage & & \\ In situ & 9 (3.1) & 0 \\
1a & 78 (27) & 1 (0.8) \\
1b & 20 (6.9) & 0 \\
2 & 65 (22.5) & 5 (4.1) \\
3 & 78 (27) & 78 (63.4) \\
4 & 39 (13.5) & 39 (31.7) \\ \hline N stage & & \\
0 & 242 (84) & 76 (61.8) \\
1 & 14 (4.8) & 14 (11.4) \\
2a & 10 (3.5) & 10 (8.1) \\
2b & 10 (3.5) & 10 (8.1) \\
2c & 12 (4.2) & 12 (9.8) \\
3 & 1 (0.4) & 1 (0.8) \\ \hline M stage & & \\
0 & 287 (99.3) & 121 (98.4) \\
1 & 2 (0.7) & 2 (1.6) \\ \hline Treatment modality & & \\ Surgery & 122 (42.2) & 42 (34.2) \\ Adjuvant radiotherapy & 76 (62.3) & 35 (83.3) \\ Adjuvant chemotherapy & 45 (36.8) & 27 (64.3) \\ Radiotherapy & 165 (57.1) & 80 (65) \\ Chemo-radiotherapy & 70 (42.4) & 68 (85) \\ No treatment & 2 (0.7) & 1 (0.8) \\ \hline Recurrence & 76 (26.3) & 35 (28.7) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data of the total 289 patients and the 123 patients with advanced-stage tumor treated in our institution with laryngeal cancer between 2000 and 2020.
# Neural Approximation Enhanced Predictive Tracking Control of a Novel Designed Four-Wheeled Rollator
## 3 Predictive Controller Design of the Rollator Based on Neural Approximation
### Predictive Controller Development  
The tracking error displayed in Figure 10 can be addressed as  
\[\dot{x}_{e} =\left(\dot{x}-\dot{x}_{d}\right)=-v_{d}\sin\varphi_{d}\left(x-x_ {d}\right)+\cos\varphi_{d}\left(v-v_{d}\right), \tag{18}\] \[\dot{y}_{e} =\left(\dot{y}-\dot{y}_{d}\right)=v_{d}\cos\varphi_{d}\left(y-y_{ d}\right)+\sin\varphi_{d}\left(v-v_{d}\right),\] \[\dot{\varphi}_{e} =\left(\dot{\varphi}-\dot{\varphi}_{d}\right)=\frac{\tan\delta_{ d}}{L}\left(v-v_{d}\right)+\frac{v_{d}}{L\cos^{2}\delta_{d}}\left(\delta- \delta_{d}\right).\]  
To transfer the error function, the model can be discretized as:  
\[\widetilde{\mathcal{X}}(k+1)=\mathcal{H}_{k,t}\widetilde{\mathcal{X}}(k)+ \mathcal{K}_{k,t}\tilde{u}(k). \tag{19}\]  
Among them, \(\mathcal{H}_{k,t}=\left[\begin{array}{ccc}1&0&-v_{d}T\sin\varphi_{d}\\ 0&1&v_{d}T\cos\varphi_{d}\\ 0&0&1\end{array}\right],\mathcal{K}_{k,t}=\left[\begin{array}{ccc}T\cos \varphi_{d}&0\\ T\sin\varphi_{d}&0\\ \frac{\tan\delta_{d}}{L}T&\frac{v_{d}}{L\cos^{2}\delta_{d}}T\end{array}\right]\) and \(T\) is the sampling time.  
To effectively grasp the desired trajectory reliably and smoothly, it is essential to constrain the state error and control parameter:  
\[\mathcal{S}(k)=\sum_{j=1}^{N}\widetilde{X}^{T}(k+j|k)\mathcal{L}(k+j)+\tilde{ u}^{T}(k+j-1)\mathcal{M}\tilde{u}(k+j-1), \tag{20}\]  
where \(\mathcal{L}\) and \(\mathcal{M}\) are weighting factors; \(N_{P}\) is the prediction horizon, and \(N_{e}\) is the control horizon.  
Considering the safety and stability of the rollator [56; 57], it is necessary to restrict the control limit and control increment, as follows:  
Figure 10: Trajectory tracking error.  
\[u_{\min}(t+k)\leq u(t+k)\leq u_{\max}(t+k), \tag{21}\] \[\Delta U_{\min}(t+k)\leq\Delta U(t+k)\leq\Delta U_{\max}(t+k),\]  
where \(k=1,2,\cdots,t+N_{c}-1\). The control horizon can be applied as:  
\[\Delta U_{t}^{*}=\left[\Delta u_{t}^{*},\Delta u_{t+1}^{*},\cdots,\Delta u_{t+ N_{c}-1}^{*}\right]^{T}. \tag{22}\]  
In addition, the actual control input increment can be presented as  
\[u(t)=u(t-1)+\Delta u_{t}^{*}. \tag{23}\]  
Therefore, the neural approximation based predictive controller framework of four-wheeled rollator for trajectory tracking is displayed in Figure 11.
## 2 Materials and Methods
### Global Average Pooling Classifier  
After features were extracted by the convolutional network module, a classification module based on global average pooling method was used instead of the traditional fully connected layer classifier. Conventionally, in a convolutional neural network, convolutional layers are usually followed by several fully connected layers to vectorize the feature extracted by convolutional layers and to accomplish the classification task via a softmax logistic regression layer. However, fully connected layers involve many weights to be trained, which increase the cost of computing and reduce the convergence speed of the network. On the other hand, the increment of weights will also increase model complexity, which may easily lead to overfitting. Effective techniques have been proposed to avoid overfitting, such as dropout [21; 22]. Using global average pooling (GAP) instead of fully connected layers to classify different categories directly from feature maps is a revolutionary innovative improvement made to traditional convolutional neural networks [23]. Instead of adding fully connected layers on top of the feature maps from convolutional layers, GAP generates one feature map for each corresponding category to be classified, vectorize the features by global average pooling, and feed the vectors directly into the final softmax classifier, as shown in Figure 2. Compared to traditional fully connected layers, GAP had enforced the correspondences between feature maps and categories. Besides, the GAP didn't introduce extra weights to be optimized for the network, which had reduced the prone of overfitting.