esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### MLP Results  
Figures 13 and 14 show the results we obtained when using the MLP regression, respectively, without and with the topology clustering. These figures plot the maximum error of the approximation obtained by using the ROM and the maximum displacement of the reference solution, at each state. Clustering does not introduce significant advantages when combined with the MLP regression.  
Furthermore, as we can see in these graphs, some states are approximated with more than 50% of error. It is too much even for a ROM that aims at keeping and ensuring good accuracy.  
Figure 11: Prediction process for the dictionary model.  
Figure 12: ROM generation workflow.  
Figure 15 shows a 3D view of one of the worst approximations we obtained with the MLP regression. As it can be seen in this picture, maximum errors are essentially concentrated at the corners of the metal sheet for the first five states. The prediction quality should be improved to fulfill standard tolerances.  
For that purpose, we searched for better neural network hyper-parameters. Results are reported in Table 1. According to this table, the choice for the neural network hyper-parameters, for this type of architecture and data, seems quite optimal.  
Figure 14: Maximum prediction error and maximum displacement for the MLP regression with topology clustering.  
Figure 13: Maximum prediction error and maximum displacement for the MLP regression without topology clustering.  
Figure 15: MLP approximation of sequence \(\{2,2,2,1,2,1,1,1\}\) compared with the reference solution (displacements \(\times\)10 for visualization).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of** & **Number of** & **Activation** & **Maximum** \\
**Hidden Layers** & **Neurons** & **Function** & **Error** \\ \hline
2 & \((12,12)\) & relu & 2.5 mm \\
2 & \((20,20)\) & relu & 5.5 mm \\
2 & \((40,40)\) & relu & 3 mm \\
2 & \((20,12)\) & relu & 3 mm \\
2 & \((40,20)\) & relu & 2.5 mm \\
2 & \((40,20)\) & logistic & 4.5 mm \\
2 & \((40,20)\) & tanh & 6.5 mm \\
2 & \((40,20)\) & identity & 2.9 mm \\
3 & \((12,12,12)\) & relu & 4 mm \\
3 & \((40,40,40)\) & relu & 4 mm \\
4 & \((12,12,12,12)\) & relu & 2.8 mm \\
4 & \((40,40,40,40)\) & relu & 2.8 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results for different choices of hyper-parameters for the neural network.  
In Figure 17, the maximum error does not exceed 10% of the maximum displacement. Furthermore, the absolute error is always less than 0.7 mm. For a metal sheet of around 300 mm in size, this error could be acceptable in certain applications.  
In addition to this quite reduced absolute error, we can see that the maximum error during the whole sequence seems to stay globally stable, without exhibiting net accumulation.  
Figure 18 shows a 3D view of the worst approximation we obtained with the DMD model. As we can see in this picture, maximum errors are again essentially concentrated at the corners of the metal sheet.
## 4 Engineering Example Validation
### Data Collection
#### 4.1.2 Data Segmentation  
According to the road design information, accident data information and meteorological data information collected and organized in the input layer, the meteorological event carrier division and meteorological event division are carried out in the division layer.  
(1) Meteorological event carrier division. Considering the domestic previous research and calculation needs, the average road section method is selected as the weather event carrier division method, and 1 km is selected as the length of the weather event carrier [34], with a total of 131 weather event carriers, numbered as LD_i, where i is the number of the route carrier, which is arranged sequentially from the starting point to the end point of the route. Based on the division mode of the average section method, the meteorological event carriers of SC2 highway are obtained, and the division results are shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Serial** & **Time of** & **Location of** & **Weather** & **Accident Pattern** & **Accident Type** \\
**Number** & **Accident** & **Accident** & & & **Vehicle Type** \\ \hline
1 & 2018/6/3 & K26+200 & Rainy & Collision Vehicle & Small Car \\
2 & 2018/6/3 & K49+000 & Rainy & Collision Objects & Small Car \\ \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) \\
1001 & 2018/8/11 & K38+500 & Cloudy & No Records & Small Car \\ \hline \hline \end{tabular}
\end{table}
Table 4: Data of traffic accidents on SC2 highway.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Serial** & **Make** & **Horizontal** & **Vertical** & **Roadway** \\
**Number** & **Number** & **Alignment** & **Alignment** & **Structure** \\  & **Number** & **Elements** & **Elements** & **Elements** \\ \hline
1 & K1+000 & Circular Curve & Sag Curve & Standard Section \\
2 & K2+000 & Straight & Crest Curve & Bridge \\ \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) \\
132 & K132+000 & Spiral Curve & Straight & Highway Tunnel \\ \hline \hline \end{tabular}
\end{table}
Table 3: Road design data collection example.  
Meteorological event division. Based on the meteorological data, each meteorological event carrier has been divided into meteorological events. Since weather conditions such as sunny, cloudy, and foggy are not the main meteorological factors affecting driving safety, meteorological events such as sunny, cloudy, and cloudy that pose less risk to driving are uniformly set up as a control meteorological event group.
# Single-Channel Blind Source Separation of Spatial Aliasing Signal Based on Stacked-LSTM
## 5 Experiment
### Case Study 1: Effect under Pure Environment
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Parameter Configuration** & **Value** \\ \hline ICA & Iteration number & 100 \\ \hline NMF & Iteration threshold & \(1\times 10^{-8}\) \\  & Iteration number & 100 \\ \hline \multirow{4}{*}{TasNet} & Base signal number \(N\) & 128 \\  & Frame length \(L\) & 64 \\  & LSTM hidden layer unit number & 128 \\  & LSTM-block number \(X\) & 2 \\ \hline \multirow{4}{*}{Wave-U-Net} & Kernel size \(P\) & 5 \\  & Block number \(X\) & 5 \\  & Channel number & 16-32-64-128-256 \\ \hline \multirow{4}{*}{Conv-TasNet} & Encoder filter number \(N\) & 512 \\  & Frame length \(L\) & 16 \\  & Bottleneck layer channel number \(B\) & 128 \\  & Kernel size \(P\) & 3 \\  & 1D-block channel number \(H\) & 512 \\  & 1D-block number \(X\) & 8 \\  & Repeat number \(R\) & 3 \\ \hline \multirow{4}{*}{Stacked-LSTM} & The number of expected features in the input \(N\) & 512 \\  & The number of features in th hidden state \(h\) & 512 \\ \cline{1-1}  & The number of hidden channels & 256 \\ \cline{1-1}  & Encoder and decoder kernel size \(P\) & 16 \\ \cline{1-1}  & Block number \(X\) & 6 \\ \cline{1-1}  & The length of chunk \(K\) & 200 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameter configuration of five methods. separation accuracy is: Stacked-LSTM > Conv-TasNet > Wave-U-Net > TasNet > ICA > NMF algorithm, the loss was \(-18.67\) dB < \(-16.05\) dB < \(-13.97\) dB < \(7.93\) dB < \(4.09\) dB. In addition, we found that the performance of the three deep learning methods under high signal-to-noise ratio was significantly better than traditional ICA and NMF, and the separation performance of Stacked-LSTM was the best method in deep learning methods. The Stacked-LSTM network had three main characteristics, so its performance was superior. First, the LSTM block only needed to be stacked at six times, while the 1-D CNN block of ConvTasNet was stacked at eight times, and this operation was repeated three times, for a total of 24 blocks. In terms of model size, the Stacked-LSTM network was smaller. Second, the Stacked-LSTM method was based on the three-level structure of ConvTasNet, replacing the CNN block with a Bi-LSTM-based block. Multi-dimensional long sequence features were segmented and stacked into a 3-D tensor as input, which could fully model the long-term dependence of signal blind source separation, while the CNN block in Stacked-TCN had a limited receiving domain, so the Stacked-LSTM network could solve the problem of Stacked-TCN of restricted receiving domain. Third, Stacked-LSTM applied the attention mechanism of the SE module to the LSTM block. A simple mechanism was used to achieve the effect of capturing important global information.
## 3 Results and Discussion
### Neutralisation Method
#### 3.1.2 Two-Step Neutralization Test on Semi Industrial Plant  
The real AMD sample (about 1800 L) from the Robule Lake was used for the test operation. Characteristics of AMD that were sampled from a flow of the Robule Lake are given in Table 5. The AMD was sampled by a pump from the Robule Lake in two IBC containers (1000 L volume per one) and transported by a track to the laboratory in the Mining and Metallurgy Institute Bor where semi industrial plant for the two-step neutralization treatment is located.  
During the two-step neutralization treatment, the following parameters were measured and controlled: pH value in the Tanks for pH control, AMD flow rate, lime milk, and flocculant flow rates. Process duration was 6 h.  
Water samples for chemical analysis were taken from the Thickener A on each hour during the test. Sampling from the Thickener B started 3 h after starting the outflow of AMD neutralized in the second step. The results for chemical composition of the neutralized AMD during the two-step neutralization treatment are presented in Table 5.  
Figure 4: Total lime milk consumption during two neutralization steps of real AMD from the Robule Lake.  
The obtained results revealed that concentration of all elements, except for calcium, decreased. Increase in Ca concentration was due to the input of lime milk used as a neutralizer. Fe concentration was reduced during the first neutralization step below the maximum allowable value [24]. During the first neutralization step for a test duration of 6 h, the Fe (ferric iron) concentration was gradually reduced. Fe removal degree was in the range of 96.5 mass % to 99.85 mass %. The results of chemical analysis for Cd, Co, Ni, and Zn concentration were almost same as the values for starting sample. Cu and Mn concentration was decreased during the process in the range from 11.7 mass % to 27.4 mass %, and in the range from 11 mass % to 20.7 mass % for Mn and Cu, respectively.
# Deep Learning for Joint Adapatations of Transmission Rate and Payload Length in Vehicular Networks
## 4 Deep Learning for AMCPLS in IEEE 802.11p V2V Networks  
In this section, we present our deep learning approach to tackle the AMCPLS problem in IEEE 802.11p V2V networks. Generally speaking, a machine learning algorithm can learn the input-output relationship of a certain system directly from the previous observations (training data) without any previous knowledge about the model of the underlying system. If the adopted machine learning algorithm is sufficiently powerful, it can accurately learn the model of the underlying system utilizing the training data. Deep learning with deep nerual networks [22] has emerged as a powerful machine learning tool and has achieved impressive performance in many fields such as computer vision [23], speech recognition [24], and many other fields. Recently, the wireless communication community pays a considerable attention to deep learning in order to harness its capabilities in designing future wireless communication systems. In this work, we present a deep convolutional neural network (DCNN) approach for implementing AMCPLS in V2V networks. We formulate the AMCPLS problem as a multiclass classification problem and solve it by training the DCNN under the framework of supervised learning. For the multiclass classification problem, a certain class, \(a\in\mathbb{A}\), defines a combination of a MCS \(i\) and a payload length \(l\), where \(\mathbb{A}=\{0,1,2,...,A-1\}\) is the set of the possible classes and \(A=I\times L\) is the number of classes. The features are extracted from the estimated channel state information and estimated noise standard deviation. The target of our approach is to increase the effective throughput and satisfy a predefined FER constraint.
# NOMA or OMA in Delay-QoS Limited Satellite Communications: Effective Capacity Analysis
## 1 Introduction
The performance analysis and suboptimal power control policy for delay-limited NOMA-based satellite networks were, respectively, studied in works [23; 24]. Taking transmission latency minimization as the target, the authors in [25] incorporated a multi-agent deep deterministic policy gradient algorithm to solve a joint trajectory and power optimization problem in a NOMA-based satellite aerial network. Although works [23; 24; 25] have proven that the NOMA scheme can provide a higher delay-limited system performance than that provided with the OMA scheme at some certain transmission power, they did not study whether the performance of each user achieved with the NOMA scheme can always be higher than that achieved with the OMA scheme in any condition. In other words, a comparative study on the advantages and disadvantages of NOMA and OMA schemes for different satellite users under various transmission conditions has not been investigated. Motivated by these observations, this paper studies the delay-QoS limited capacity for downlink satellite networks. Specifically, the analytical expressions of effective capacities for each user achieved with the NOMA and OMA schemes are first studied. Then, approximated effective capacities achieved in some special cases, exact closed-form expressions of users' achievable effective capacity, and capacity difference between NOMA and OMA schemes are derived. The simulation results are finally provided to validate the theoretical analysis and show the advantages and disadvantages of NOMA and OMA schemes for different users at different transmission powers. Moreover, the influences of delay requirements and key parameters on user selection strategy and system performance are also shown in the simulations. The remainder of the paper is organized as follows. Section 2 presents the system model. In Section 3, we derive the theoretical expressions for the effective capacities of each user achieved with the NOMA and OMA schemes in downlink satellite networks. In Section 4, simulation results and discussions are provided and conclusions are finally drawn in Section 5.
## 3 DSM Use Case
### Use Case Description
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Function Name:**} & \multicolumn{3}{c}{**Load Management Based on Demand Response/Demand Side Management**} \\ \hline \multirow{2}{*}{**Step No.**} & \multirow{2}{*}{**Event**} & **Description of** & **Information** & **Information** & **Information** & **Technical** \\  & & **Process/Activity** & **Producer** & **Receiver** & **Exchanged** & **Requirements** \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{\begin{tabular}{c} SM sends metering \\ data to LNAP \\ \end{tabular} } & The smart meter forwards the \\  & & The metric data to the LNAP \\  & & (smart metering gateway) & \\ \hline \multirow{2}{*}{2} & \multirow{2}{*}{\begin{tabular}{c} LNAP sends metering \\ data to NNAP \\ \end{tabular} } & The LNAP (smart metering \\  & & gateway) forwards the data to the NNAP (data concentrator) & LNAP & NNAP & Metering data & - \\ \hline \multirow{2}{*}{3} & \multirow{2}{*}{\begin{tabular}{c} NNAP sends \\ metering data to HES \\ \end{tabular} } & The NNAP forwards the \\  & & metering data to the HES \\  & & & (Head End System) & \\ \hline \multirow{2}{*}{4} & \multirow{2}{*}{\begin{tabular}{c} HES sends metering \\ data to MDMS \\ \end{tabular} } & The HES forwards the metering \\  & & data to the MDMS (Meer Data \\  & & Management System) & \\ \hline \multirow{2}{*}{5} & \multirow{2}{*}{\begin{tabular}{c} The MDMS Operator \\ agathers the \\ data from the MDMS \\ \end{tabular} } & The MDMS gathers data from \\  & & smart meters for a specific time \\  & & duration. Then the data is \\  & & processed by the MDMS \\  & & & processed to extract \\  & & & Operation user profiles. \\ \hline \multirow{2}{*}{6} & \multirow{2}{*}{\begin{tabular}{c} Actor B receives \\ information about the \\ consumption of the \\ end-users \\ \end{tabular} } & Data is sent to Actor B about \\  & & & the consumption profiles of the \\  & & & end-users. \\ \hline \multirow{2}{*}{7} & \multirow{2}{*}{\
\begin{tabular}{c} Actor A receives \\ information about the \\ end-users \\ \end{tabular} } & Data is sent to Actor A about \\  & & & the consumption profiles of the \\  & & end-users \\ \hline \multirow{2}{*}{8} & \multirow{2}{*}{
\begin{tabular}{c} Actor A sends information \\ feedback to the \\ consumer application \\ \end{tabular} } & Data is sent to Actor A about \\  & & the consumption profiles of the \\  & & end-users & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Step by Step Analysis of Use Case. Figure 3: Message Sequence Chart.
## 3 Material and Methods
### Study Area and Stakeholders  
HMP-Coffee model is based on the operation of the coffee farm \"_La Sultana_\", belonging to the Universidad del Cauca. The farm is located in Timbio, Cauca, Colombia (222228.517 \(\mathrm{N}\), 76\({}^{\circ}\)43\({}^{\circ}\)31.89 \(\mathrm{\SIUnitSymbol{W}}\)), at 1700 m.a.s.l. with a total area of 13.5 hectares of which 4.9 ha are produce coffee [23; 24; 25]. In 2006, La Sultana migrated towards a sustainable coffeeproduction crop through an ecological processing system and by-products management. Besides, the production, management, and transformation of coffee are developed under good practices to produce quality coffee under environmental, social, and economic sustainability [26]. _La Sultana_ meets three essential characteristics addressed by the HMP-Coffee model: (i) small-scale coffee production, (ii) incorporation of good practices, mainly to improve the economic sustainability; (iii) located at a developing country (Colombia; see Figure 1).  
Several stakeholders played a key role to construct the HMP-Coffee model. They contributed, with their knowledge, to define the input, aggregate, and output attributes of the model and the corresponding qualitative values. The stakeholder's team comprises six people from the agribusiness area. The role of each stakeholder is detailed in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Stakeholder Id** & **Area of Expertise** & **Years’ Experience** & **Experience** & **Organization** \\ \hline \multirow{2}{*}{1} & Agro-industrial & \multirow{2}{*}{28} & The agro-industrial transformation and the economy coffee activity. & \multirow{2}{*}{Public university} \\  & Economic Problems & & & \\ \hline \multirow{2}{*}{2} & Agribusiness & \multirow{2}{*}{10} & Strategies for the production and & \multirow{2}{*}{Experimental coffee} \\  & & & & \\ \hline \multirow{2}{*}{3} & Agricultural management & \multirow{2}{*}{20} & Implementation of sustainability & \multirow{2}{*}{Experimental coffee} \\  & Agricultural management & & & \\ \end{tabular}
\end{table}
Table 1: The stakeholder’s team involved in the HMP-Coffee conception. Stakeholder Id: identifier of the expert. Area of expertise: Academic training of the members of the expert panel. Years’ experience: Number of years of experience working on topics associated with coffee. Experience: Work areas, and Organization: Academic and business organizations to which they belong.  
Figure 1: La Sultana Colombian Coffee Farm location (2\({}^{\circ}\)22\({}^{\prime}\)28.51\({}^{\prime\prime}\) N, 76\({}^{\circ}\)43\({}^{\prime}\)31.89\({}^{\prime\prime}\) W). Left map depicts the Colombian Departments and the right map the location of Colombia in South America. Image source [proyectomapamundil].
# A Novel Method for Wheat Spike Phenotyping Based on Instance Segmentation and Classification
## 4 Conclusions  
Facing the present problems of wheat spike phenotyping, a rapid and accurate method was proposed to obtain the parameters of spikelets and grains based on instance segmentation and classification in this study. The YOLOv8-seg model was used to realize the instance segmentation of the frontal wheat spikes, and the classification of wheat varieties and growth periods. Furthermore, 52 image features were extracted for each spikelet to classify the number of spikelet grains and the total number of spike grains. The YOLOv8-seg model achieved excellent segmentation and classification performance, and its precision, recall, AP@[0.50], and AP@[0.50:0.95] reached 0.994, 0.979, 0.993, and 0.858, respectively. The SVM model also had good classification performance for the number of grains in spikelets, and its accuracy, precision, recall, and F1 score reached 0.855, 0.860, 0.865, and  
Figure 13: Regression results for counting grains in the single wheat spike. The blue dot reflects the difference between the predicted number and true number. The dot on the dotted line means the prediction is correct. The red line is a line that fits all the dots.  
0.863, respectively. MAE and MAPE were as low as 1.04 and 5% when counting the total number of grains in the frontal wheat spike. In future research, continuous improvement of wheat spike phenotyping is needed. Concretely, increasing the varieties and growth periods of wheat samples and building a complete database and analysis models to serve spike phenotyping might be a promising approach.  
Conceptualization, Z.Q.; Methodology, Z.N. and N.L.; Validation, Y.H. and S.S.; Formal Analysis, C.X.; Investigation, Z.Q.; Resources, Z.Z.; Data Curation, Z.N.; Writing--Original Draft Preparation, N.L.; Writing--Review and Editing, Z.N.; Visualization, C.X.; Supervision, Z.Q.; Project Administration, Z.Z.; Funding Acquisition, Z.Q. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the China National Key Research and Development Plan Project (2023 YFD2000101).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# Enhancing Robotic-Assisted Lower Limb Rehabilitation Using Augmented Reality and Serious Gaming
## 3 Serious Gaming Application Developed for LegUp
### Game Modes
#### 3.3.1 Football Mode  
This mode simulates kicking a ball, where the robot performs the rehabilitation movements to enable the action. Goniometers placed on the lower limb send the angles associated with each movement to the application, allowing the patient to visualize the exact movements executed by their leg. This type of application offers the following benefits:  
**1. Improvement of Eye-Foot Coordination**  
Kicking the ball requires precise synchronization between what the patient sees and how they move their foot. This can help restore eye-foot coordination, which is essential for many daily activities.  
**2. Specific Muscle Activation**  
The movements involved in kicking a ball (lifting the foot, extending the knee, flexing the ankle) activate specific muscle groups in a controlled and repetitive manner. This strengthens the muscles involved and improves stability and motor control.  
**3. Enhancement of Flexibility and Range of Motion**  
Kicking the ball requires movements that increase the range of motion of the foot joints, such as the ankle, knee, and hip. Repetitive and controlled movements help maintain or improve flexibility in these joints.  
**4. Increase in Strength and Endurance**  
Repeated kicking exercises promote muscle strength and endurance over time. This is essential for complete rehabilitation and relapse prevention.  
Figure 8: **(a) Football Mode: the patient interacts with a ball, simulating a football scenario; user interface (left); the range of motion of the robot-hip duction/abduction (red area). (b) Color Mode: the patient interacts with colors. The task requirements are displayed as a message and are also communicated via audio (in the HoloLens device).**