esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Understanding the use of current land cover, along with monitoring change over time, is vital for agronomists and agricultural agencies responsible for land management. The increasing spatial and temporal resolution of globally available satellite images, such as provided by Sentinel-2, creates new possibilities for researchers to use freely available multi-spectral optical images, with decametric spatial resolution and more frequent revisits for remote sensing applications such as land cover and crop classification (LC&CC), agricultural monitoring and management, environment monitoring. Existing solutions dedicated to cropland mapping can be categorized based on per-pixel based and object-based. However, it is still challenging when more classes of agricultural crops are considered at a massive scale. In this paper, a novel and optimal deep learning model for pixel-based LC&CC is developed and implemented based on Recurrent Neural Networks (RNN) in combination with Convolutional Neural Networks (CNN) using multi-temporal sentinel-2 imagery of central north part of Italy, which has diverse agricultural system dominated by economic crop types. The proposed methodology is capable of automated feature extraction by learning time correlation of multiple images, which reduces manual feature engineering and modeling crop phenological stages. Fifteen classes, including major agricultural crops, were considered in this study. We also tested other widely used traditional machine learning algorithms for comparison such as support vector machine SVM, random forest (RF), Kernal SVM, and gradient boosting machine, also called XGBoost. The overall accuracy achieved by our proposed Pixel R-CNN was 96.5%, which showed considerable improvements in comparison with existing mainstream methods. This study showed that Pixel R-CNN based model offers a highly accurate way to assess and employ time-series data for multi-temporal classification tasks.  
s 2020 10 238 2019 28 2019 28 2020 2019
## 2 Materials and Methods  
### AgriEco Robot  
The mobile robot \"AgriEco Robot\" was developed in our laboratory to be used in a didactical strawberry greenhouse [31]. Its primary objective is to control the pesticide-spraying process, encompassing aspects such as quantity regulation and ensuring precise localized spraying. It consists of a robotic chassis 550 \(\times\) 650 mm in size made of steel, equipped with four-wheel drive. The spraying system is composed of a 10 L bank of polythene, a submerged pump, and two motorized arms. A navigation system, usingHokuyo URG-04 LX-UG01 2D Lidar, from HOKUYO AUTOMATIC CO LTD in Osaka, Japan [32], is installed in the robot's front section. The robot has a vision system using 2 types of cameras, a ZED camera stereo and omnidirectional camera. See Figure 1.  
The robot utilizes the open-source framework Robot Operating System (ROS) [33], which combines CPU and GPU electronic components to execute its diverse range of tasks. For simple tasks like controlling the arms, the pumps, and the wheels, the robot uses Raspberry Pi 4. For deep learning processing tasks, it uses a built-in NVIDIA Jetson TX2 cart from NVIDIA company in the USA [34], equipped with a Quad-core 2.0 Ghz 64-bit ARMv8 A57, a dual-core 2.0 Ghz ARMv8 Denver, a 256 CUDA core 1.3 MHz Nvidia Pascal, and 8 GB memory. To power the robot, a 36 V/30 Ah lithium-ion battery is used. These configurations make it well-suited as an embedded system for this study application.
# Analysis and Experimental Validation of Efficient Coded OFDM for an Impulsive Noise Environment
## 5 Performance Analysis
### Parameters Setting
#### 5.1.2 Frame Format  
The frame that we consider in our simulations is composed of three main parts and can be explained as follows.  
1. RadioType header: This contains all the radio information and has a length of 17 bytes. It is composed of the fields represented in Table 3.  
Figure 5: Measurement scenarios.  
Frame information with a length of 28 bytes is composed of a Frame Control Field (FCF) and Logical Link Control (LLC), as presented in Table 4. LLC contains addressing information consisting of two fields: the Destination Service Access Point (DSAP) and the Source Service Access Point (SSAP), as well as a control field.
3. Data correspond to the useful information that will be encoded. Its length is 316 bytes.  
We run simulations by considering the two scenarios mentioned above. We use the same traffic load, which corresponds to one packet sent every 300 ms. The duration time of each simulation is approximately 120 s.
# Proof-of-Concept of a Millimeter-Wave Integrated Heterogeneous Network for 5G Cellular
## 2 mmWave-Integrated Cellular Networks
### 60-GHz mmWave WLAN  
At the World Radiocommunication Conference 2015 (WRC-15), ITU-R identified portion(s) of the frequency range between 24.25 and 86 GHz to be considered for allocation to IMT-2020 [48]. However, the 3 GPP LTE standards before Release 13 were designed for frequency bands below 6 GHz [49] and are not optimized for the mmWave frequency bands. In contrast, IEEE 802.11 ad WLAN [29], also known as WiGig (wireless-gigabit) [30], addressed the unlicensed 60-GHz frequency band, which has at least 5 GHz of continuous bandwidth available in many countries [50]. WiGig uses 2.16 GHz-wide continuous bands per channel for transmission and is capable of multi-Gbps transmission. WiGig supports three distinct modulation methods: spread-spectrum, single-carrier (SC) and orthogonal frequency-division multiplex (OFDM) modulations. The spread-spectrum modulation is dedicated to the transmission of control messages. SC modulation is mandatory and is suitable for low-power consumption devices because of the low peak-to-average power ratio (PAPR). OFDM modulation is optional, suitable for relaxed power-consumption devices and more robust against multipaths than SC modulation. An example of the modulation and coding schemes (MCS) for WiGig is shown in Table 1. The physical (PHY) layer achieves a maximum data rate of almost 7 Gbps, and the next generation IEEE 802.11 ay 60-GHz WLAN [51] aims to support a maximum throughput of at least 20 Gbps on the medium access control (MAC) layer by spatial multiplexing or further improvement of MAC efficiency.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**MCS Index** & **PHY Mode** & **Modulation** & **Coding Rate** & **PHY Data Rate (Mbps)** \\ \hline
1 & SC & \(\pi\)/2-BPSK & 1/2 & 385 \\
6 & SC & \(\pi\)/2-QPSK & 1/2 & 1540 \\
9 & SC & \(\pi\)/2-QPSK & 13/16 & 2502.5 \\
10 & SC & \(\pi\)/2-16 QAM & 1/2 & 3080 \\
12 & SC & \(\pi\)/2-16 QAM & 5/8 & 4620 \\
13 & OFDM & SQPSK & 1/2 & 693.00 \\
15 & OFDM & QPSK & 1/2 & 1386.00 \\
18 & OFDM & 16 QAM & 1/2 & 2772.00 \\
22 & OFDM & 64 QAM & 5/8 & 5197.50 \\
24 & OFDM & 64 QAM & 13/16 & 6756.75 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Modulation and coding scheme examples of IEEE 802.11 ad (BPSK: Binary Phase-Shift Keying, QPSK: Quadrature Phase-Shift Keying, QAM: Quadrature Amplitude Modulation, SQPSK: Spread QPSK).  
Figure 1: CP/UP splitting in the C-RAN-based HetNet architecture.  
The latest semiconductor technology brings 60-GHz communication to small handsets. For example, silicon-based CMOS technologies are implementing integrated system-in-package systems including mixers, LNAs and PAs in the mmWave frequency bands [14]. Figure 2 shows examples of IEEE 802.11 ad/WiGig-compliant communication devices.
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Scenario** & **Vehicle** & **Distance (m)** & **Time (min)** & **Total Distance (m)** & **Overall Time (min)** & **Computation Time (s)** \\ \hline \multirow{3}{*}{1} & 1 & 1364 & 5.40 & \multirow{3}{*}{5962} & \multirow{3}{*}{9.44} & \multirow{3}{*}{80} \\ \cline{2-2} \cline{5-6}  & 2 & 2239 & 8.96 & & & \\ \cline{2-2} \cline{5-6}  & 3 & 2359 & 9.44 & & & \\ \hline \multirow{3}{*}{2} & 1 & 2718 & 10.87 & \multirow{3}{*}{6239} & \multirow{3}{*}{10.87} & \multirow{3}{*}{81} \\ \cline{2-2} \cline{5-6}  & 2 & 1348 & 5.39 & & & \\ \cline{2-2} \cline{5-6}  & 3 & 2173 & 8.69 & & & \\ \hline \multirow{3}{*}{3} & 1 & 2293 & 9.17 & \multirow{3}{*}{5246} & \multirow{3}{*}{9.17} & \multirow{3}{*}{85} \\ \cline{2-2} \cline{5-6}  & 2 & 1157 & 4.63 & & & \\ \cline{2-2} \cline{5-6}  & 3 & 1796 & 7.18 & & & \\ \hline \multirow{3}{*}{4} & 1 & 1396 & 5.58 & \multirow{3}{*}{6943} & \multirow{3}{*}{6.60} & \multirow{3}{*}{82} \\ \cline{2-2} \cline{5-6}  & 2 & 1649 & 6.60 & & & \\ \cline{2-2} \cline{5-6}  & 3 & 1621 & 6.48 & & & \\ \cline{2-2} \cline{5-6}  & 4 & 1120 & 4.48 & & & \\ \cline{2-2} \cline{5-6}  & 5 & 1157 & 4.63 & & & \\ \hline \multirow{3}{*}{5} & 1 & 2479 & 9.92 & \multirow{3}{*}{5769} & \multirow{3}{*}{10.10} & \multirow{3}{*}{83} \\ \cline{2-2} \cline{5-6}  & 2 \({}^{*}\) & 765 & 3.06 & & & \\ \cline{2-2} \cline{5-6}  & 3 & 2525 & 10.10 & & & \\ \hline \multirow{3}{*}{6} & 1 & 1429 & 5.71 & \multirow{3}{*}{7014} & \multirow{3}{*}{12.64} & \multirow{3}{*}{81} \\ \cline{2-2} \cline{5-6}  & 2 \({}^{*}\) & 549 & 2.2 & & \\ \cline{2-2} \cline{5-6}  & 3 & 3160 & 12.64 & & & \\ \cline{2-2} \cline{5-6}  & 4 \({}^{*}\) & 563 & 2.25 & & & \\ \cline{2-2} \cline{5-6}  & 5 & 1313 & 5.25 & & & \\ \hline \hline \end{tabular}  
* Breakdown vehicles.  
\end{table} 
Table 4: The results from each test scenario. The total distance is the sum of all the vehicle distances for each scenario, and the overall time is the maximum travel time for all vehicles in each scenario. Figure 15: A sample route produced by the lower-level path generation. The path can be seen as the green line, the yellow highlighted area represents the area monitored by the sensor, and the red portions of the path represent segments that were made feasible by the corner-cutting method or the Reeds–Shepp method. An example of the Reeds–Shepp method can be found in the dotted blue circle, and an example of the corner-cutting method can be found in the solid blue circle.
# Improved Patch Packing and Refining Segmentation for the V-PCC Standard
## 3 Proposed Method
### Voxel Selection-Based Refining Segmentation (VS-RS)  
The refining segmentation is aimed at some voxels that cause the patch edge to be not smooth. The characteristic of these voxels is that the selected projection plane is too disarrayed. However, the original method does not consider the differences between voxels, and all voxels are refined segmented. Therefore, the VS-RS is proposed in this paper. Only the voxels with disarray projections are refining segmentation. A reduction of the computation time is required for refining segmentation. Figure 3 shows the overall method of VS-RS. Firstly, based on the Projection Uniformity Index, the voxels with internal projection planes non-uniform are found from all voxels. They are the results of the initial selection of voxels. Then, based on the Expanded Projection Uniformity Index, the voxels that are non-uniform when compared with neighbors are found in the remaining voxels. They are the results of the final selection of voxels. The refining segmentation will only operate on the above-selected voxels. This targeted voxel selection method improves the efficiency of refining segmentation.
# A Reconfigurable Framework for Hybrid Quantum-Classical Computing
## 3 Proposed Framework
### Quantum Machine Learning Task
#### 3.1.3 Training: Forward Pass Function  
In quantum machine learning, the inability to store and utilize the results of intermediate quantum operations on hardware remains a limitation to using classical ML techniques such as backpropagation to compute gradients and update parameters/weights. Most QML algorithms therefore implement a parameter shift rule mechanism [33] that enables the dynamic updating of circuit parameters during the training process. This is achieved through a circuit forward pass function implemented in the training stage (Figure 2), which takes both input data and variational parameters as arguments. This forward pass function executes three critical operations: parameter binding, circuit execution, and probability extraction. Parameter binding combines input features and trainable weights into a unified parameter vector, which is then used to configure the quantum circuit generated in the previous stage. The next step is to execute the circuit, either on a QPU or a simulator. For this work, we have utilized a state vector simulator running on the host CPU for mimicking the QPU operations in our framework. After executing the quantum circuit, the output probabilities need to be calculated. The forward pass function for simulation is defined in the following listing:  
```
defforward(x,weights,circuit,input_params,weight_params): param_values=list(x)+list(weights) bound_circuit=circuit.assign_parameters(param_values) state=Statevector.from_instruction(bound_circuit) returnstate_to_probs(state.data)
```  
The _Statevector.from_instruction_ method simulates the complete circuit execution, producing a final state vector that encodes the processed information. The _state_to_probs_ function then extracts classification probabilities by computing the squared magnitudes of relevant amplitudes, effectively implementing a quantum measurement process.
## 2 Related Work
### Edge User Allocation  
With the rapid increase in data traffic, CDN has been widely studied [6; 7]. For instance, in [8], the authors proposed an edge cache-based intelligent content delivery solution to improve network performance in information-centric wireless networks. Edge user allocation (EUA) was usually considered as one of the most critical problems in sophisticated edge environments. The study was divided into two categories--static user assignment and dynamic user assignment--according to whether the user's location was moving or not. In static user assignment, for example, this problem was modeled as a variable-size vector boxing problem for utility efficiency [1] or QoE improvement [2]. The authors proposed a decentralized game-theoretic method to select each user's channel and edge server while meeting their resource and data rate requirements [9]. In dynamic user assignment, this problem was modeled as an online decision and evolutionary process and proposed a mobile-aware and migration-supporting approach, called MobMig, for assigning users in real time [10]. An online approach called OL-EUA was proposed for solving dynamic EUA problems in NOMA-based MEC systems [11]. In [12], the authors proposed a centralized training and distributed execution multiagent dueling double deep Q network solution to maximize the total network data rate while minimizing the mobility-induced handoffs. However, none of these methods considered the optimization of the long-term QoE, while assigning users did not select the appropriate video bitrate to the users at the same time.
# Service Level Agreement Violations in Cloud Storage: Insurance and Compensation Sustainability
## 2 Models for Outages
### The Pareto-Lognormal Model  
As the name suggests, in the Pareto-Lognormal model the distribution of operating periods is modelled as a Pareto law, while the lognormal distribution describes the duration of outages.  
The model has been proposed by [PERSON] and [PERSON] [14], who analysed 300+ cloud outage events recorded for an enterprise cloud system (not better identified, but belonging to a small/medium enterprise) over an eighteen-month period. They tested seven models (Pareto, loglogistic, lognormal, gamma, exponential, logistic, and Weibull) through the Anderson-Darling test. In the end, they found the Pareto-Lognormal couple to be the best-fit solution.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Provider**} & \multicolumn{2}{c}{**Exponential**} & \multicolumn{2}{c}{**Pareto**} \\ \cline{2-4}  & **1/\(\Lambda\) [Days]** & \(\beta\) & \(\xi\) \\ \hline Google & 27.53 & 405.29 & 0.39 \\ Amazon & 85.6 & 276.43 & \(-\)0.12 \\ Rackspace & 7.78 & 381.19 & 0.3 \\ Salesforce & 8.56 & 192.47 & \(-\)0.64 \\ Windows Azure & 36.67 & 312.32 & \(-\)0.35 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters in the Exponential-Pareto model.  
The cumulative distribution function of operating periods is then  
\[F_{S}(x)=\left\{\begin{array}{ll}1-\left(\frac{h}{x}\right)^{\alpha},&\text{if }x \geq h,\\ 0,&\text{otherwise}.\end{array}\right. \tag{5}\]  
The distribution of outage duration is instead  
\[F_{D}(x)=\left\{\begin{array}{ll}\text{G}\left(\frac{\ln(w)-\mu}{\sqrt{2} \sigma}\right),&\text{if }x\geq 0,\\ 0,&\text{otherwise}.\end{array}\right. \tag{6}\]  
The reliability indicators (MTTF and MTTR) are related to the parameters of the two distributions by the following formulas:  
\[\begin{split}\text{MTTF}&=\frac{\alpha}{\alpha-1}h,\\ \text{MTTR}&=e^{\mu+\frac{\sigma^{2}}{2}}.\end{split} \tag{7}\]  
The values found by [PERSON] and [PERSON] for the parameters of the two distributions are reported in Table 2 (where the abbreviation SME--Small/Medium Enterprise--stands for the company whose cloud has been monitored during the measurement campaign, but whose name has been undisclosed); they lead to  
\[\begin{split}\text{MTTF}&=2445\text{ min},\\ \text{MTTR}&=227\text{ min}.\end{split} \tag{8}\]
## 5 Results and Discussion  
### Analysis of Registration Result  
In order to analyze the accuracy of the point cloud registration, the No. 1 supporting block is selected for testing. Figure 14 displayed the registration deviation information between point cloud data and standard model, and the deviation value is in the range of \(-\)1 mm to 1 mm. As can be seen from Figure 14, the deviation of registration results for most point clouds is \(\leq\)\(\pm\)0.300 mm, and the deviation is small in general. According to Table 4, among the 3 million points involved in registration calculation. The points with deviation within \(\pm\)0.26 mm are about 79% of the total points, and the points with deviation within double standard deviation (\(\pm\)0.5 mm) are about 96% of the total points.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Point Cloud Registration Deviation Statistics**} \\ \hline Point Number & 3,314,649 \\ Average deviation & 0.091 mm \\ Standard deviation & 0.257 mm \\ Points in +/– (1 \({}^{\star}\) Standard deviation) & 2,615,584 (78.910\%) \\ Points in +/– (2 \({}^{\star}\) Standard deviation) & 3,192,525 (96.316\%) \\ Points in +/– (3 \({}^{\star}\) Standard deviation) & 3,249,480 (98.034\%) \\ Points in +/– (4 \({}^{\star}\) Standard deviation) & 3,283,100 (99.048\%) \\ Points in +/– (5 \({}^{\star}\) Standard deviation) & 3,310,660 (99.880\%) \\ Points in +/– (6 \({}^{\star}\) Standard deviation) & 3,314,440 (99.994\%) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistical results of registration bias.  
Figure 13: Dimensions of the area measured to extract the slope.
## 3 Generation of a Cachexia-Induced Phenotype
### Hormones  
Glucocorticoids are major inducers of skeletal muscle atrophy involving increased proteolysis, alterations in gene expression, and inhibition of muscle growth and differ  
Figure 1: Summary of inducers of a Cachexia-induced phenotype on in vitro skeletal and adipose models includes secreted factors by tumour cells, pro-inflammatory cytokines (TNF-\(\alpha\), IL-6, IL-8), hormones (glucuronoids, angiotensin, myostatin), anticancer chemotherapeutic agents (doxorubicin, cisplatin) and gene regulation by miRNAs.  
entiation [58; 117]. Dexamethasone is a synthetic glucocorticoid widely applied for the nutritional treatment of cancer cachexia to improve patient appetite. However, in high doses or long-term use, the hormone promotes side effects including muscle atrophy by decreasing protein synthesis and breakdown of muscle proteins [118]. Dexamethasone activates the UPS system promoting the expression of Atrogin-1 and MuRF1 disrupting the autophagy flux and increasing cell apoptosis, inducing in vitro and in vivo muscle atrophy [118; 119]. Dexamethasone induces skeletal muscle atrophy without disturbing cell viability in a serum-free culture medium [120].  
Angiotensin II has been associated with cardiac and cancer, which is involved in the pathogenesis of associated cachexia. Clinical studies indicate elevated levels of plasma in these patients [121]. C2C12 myotubes treated with angiotensin II presented a reduced size in myotube diameter by increasing Atrogin-1 and Murf-1 expression [122]. Moreover, Angiotensin II is induced by iPPARy decrease, which is an upstream regulator of miR-29b involved in muscle atrophy [123]. Also, in primary rat cardiomyocytes and H9C2 cells, it was demonstrated that angiotensin II and endothelin-1 promote a hypertrophic response [65].  
Myostatin (GDF-8) and Activin A belong to the TGF-\(\beta\) family and are negative regulators of skeletal muscle growth, by one hand inhibiting protein synthesis and the other potentiating protein degradation dependent on p38\(\beta\) MAPK-activated signalling [124; 125]. Muscle cells treated with myostatin presented a lower expression of myoD and pax3, two genes involved in myogenesis, and increased expression of UPS-related genes (atrogin-1, MuRF-1, and E214) [126]. 3T3-L1 cells treated with myostatin demonstrated a significant reduction in the intracellular lipid content compared with non-treated cells [127].
## 2 Materials and Methods
### Model Parameterization  
The current study applied a 10-fold cross validation method with the aim of minimizing the root mean square error (RMSE) function to set the optimal parameters of the ELM, SVR, and LSTM models for the training phase. The optimal hyperparameters were used in river runoff prediction for the validation phase, and Python was used to write the codes.  
[PERSON], [PERSON] and [PERSON] [32] recommended the ELM model owing to its small training error and norm of output weights compared with single-hidden-layer feedforward networks (SLFNs). In the ELM model, the number of neurons in hidden layer was randomly selected from 2 to 100 to achieve a model with the highest possible accuracy. A three-layer ELM model with a sigmoid activation function for the hidden layer was established. Optimal parameters were determined through a 10-fold cross-validation method with the aim of minimizing the root mean square error (RMSE) function for the training phase.  
In the SVR model, the main parameters of the RBF kernel function, including the structural parameter (\(\gamma\)), penalty coefficient (\(C\)), and tolerance threshold (\(\epsilon\)-precision), were optimized. For this purpose, 30 \(\gamma\) values from 0.0001 to 10,000, 30 \(C\) values from 0.0001 to 10,000, and 20 \(\epsilon\)-precision values from 0.001 to 1 were chosen and evaluated. Similar to the ELM model, a 10-fold cross-validation approach was applied to find the optimized parameters.  
In the LSTM model, the Keras framework with the TensorFlow back-end was used. The hyperparameters of the LSTM model include neurons, dropouts, learning rates, epoch ranges, and batch ranges. Random generation was used to tune the LSTM model. For this purpose, different neurons (10 values between 10 and 500), dropouts (10 values between 0 and 1), learning rates (10 values between 0.00001 and 0.01), epoch ranges (10 values between 10 and 1000), and batch ranges (10 values between 10 and 1000) were evaluated. Similar to the ELM and SVR models, a 10-fold cross-validation approach was applied to determine the optimized parameters of the LSTM model.  
The selected parameters calculated for all three ML models consisting of the ELM, SVR, and LSTM models were applied to three coupled models: HEC-HMS-ELM, HEC-HMS-SVR, and HEC-HMS-LSTM. The optimal parameters for each ML model are presented in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Initial Parameters** & **Values** \\ \hline \multirow{4}{*}{LSTM} & neurons & 297 \\  & dropout & 0.053 \\  & learning rate & 0.007 \\  & epochs & 671 \\  & batch size & 199 \\ \hline \multirow{3}{*}{SVR} & tolerance threshold & 0.159 \\  & structural parameter & 0.0001 \\  & penalty coefficients & 1034.48 \\ \hline \multirow{2}{*}{ELM} & sig-neurons * & 25 \\  & rbf-neurons * & 41 \\ \hline \hline \end{tabular}
* sig: sigmoid, rbf: radial basis function.  
\end{table}
Table 2: Optimized values of the ML models.