esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### The PCO Classifier Application
Age (years): 1 = under 59, 2 = 60–69, 3 = 70–79, and 4 = over 80; T-stage: 1 = T1 (T1b, T1c), 2 = T2a, 3 = T2b, and 4 = T2c-T3; Modified risk level: 1 = [PERSON] score \(\leq 6\) and PSA \(>10\) and PSA \(\leq 20\), 3 = [PERSON] = 7 (3 + 4), 4 = [PERSON] = 7 (4 + 3), and 5 = [PERSON] score \(\geq 8\) or PSA \(>20\); Nomogram points: Low risk = 0–34, Intermediate risk = 35–115, High risk = 116–156, and Total scores = 0–156. Prob. = probability. calculated using the PCO classifier application, and the \(y\)-axis is the actual freedom from BCR for the present patients [10; 25]. The vertical bars represent 95% CI based on bootstrap analysis. Finally, we validated the PCO classifier application in two steps. First, we used the c-index for accuracy, which indicates the discrimination ability of prediction models [26]. The accuracy of the PCO classifier application was also assessed with [PERSON]'s concordance. Next, we compared the PCO classifier and two other classifiers: Kattan nomogram and D'Amico (Figure 3). The c-index of the PCO classifier was 0.75 (training set) and 0.67 (validation set). The c-index of the Kattan nomogram classifier was 0.62 and 0.63, respectively, and the c-index of the D'Amico classification was 0.64 and 0.64, respectively (Table 4). Figure 3: \(Cont\). Figure 2: Calibration of the PCO classifier application. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Risk** & **Predictor** & \multirow{2}{*}{**Treatments**} & \multirow{2}{*}{**Outcome**} & \multirow{2}{*}{**Samples**} & \multicolumn{2}{c}{**Performance** (**c-Index)**} \\ \cline{5-6}  & **Assessments** & & & & & \\ \hline \multirow{3}{*}{1} & \multirow{3}{*}{PCO Classifier Application} & Age, Clinical stage, & & & & \\  & & A modified risk & Stereotactic body radiation & Biochemical & \multirow{3}{*}{809} & 0.75 \({}^{\text{a}}\), \\  & & level (Gleason score & therapy & recurrence & 0.67 \({}^{\text{b}}\) \\  & & + pretreatment PSA) & & & \\ \hline \multirow{3}{*}{2} & \multirow{3}{*}{D’Amico} & PSA, & Radical Prostatectomy, & & & \\  & & Gleason scores, & External Beam Radiation & \multirow{3}{*}{PSA failure} & \multirow{3}{*}{1872} & 0.62 \({}^{\text{a}}\), \\  & & Clinical stage & Therapy, Interstitial & & & \\  & & Radiation Therapy & & & \\ \hline \multirow{3}{*}{3} & \multirow{3}{*}{Kattan-nomogram} & PSA, & & & & \\  & Gleason scores, & Radical prostatectomy & \multirow{3}{*}{} & \multirow{3}{*}{996} & 0.64 \({}^{\text{a}}\), \\  & & Clinical stage & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Risk assessments comparison. Figure 3: BCR (Kaplan–Meier Curve). \(N=809\), Confidence interval = 95%. According to the PCO classifier application, the 5-year BCR-free survival rates for these groups were: low-risk group--98.3%; intermediate-risk group--95.4%; and high-risk group--86.4%. For the entire cohort, the BCR-free survival rate at 5 years was 95.0%.
# Bayesian Optimal Experimental Design for Race Tracking in Resin Transfer Moulding
## 2 Computational Model
### Numerical RTM Model  
The RTM simulation developed here uses a standard multigrid method [25; 26; 27]. This uses control volumes (CVs) that keep track of resin saturation, offset from the triangular finite elements that are used to calculate the pressure through Equation (2). These are illustrated in Figure 1. This paper will refer to these as 'control volumes' (CVs) and 'elements', respectively, to avoid confusion.  
The algorithm can be divided into a series of steps:  
1. Solve finite element equations for pressures at the nodes.
2. Calculate the flow between each control volume.
3. Calculate the time to fill the next control volume.
4. Step forward in time and propagate the resin front forward.  
These steps are repeated until all control volumes are full. This algorithm is available in LIMS [28; 29], which can address complex geometries and 3D flows. However, for this work, the goal was to explore the A-optimality formulation in combination with the Bayesian framework. Once demonstrated, this can be implemented in other RTM simulations. As such, our numerical simulations in this paper use an implementation of this algorithm written in Julia code. Each of the steps of the algorithm is discussed in more detail in the following.
# The Method of Multi-Camera Layout in Motion Capture System for Diverse Small Animals
## 3 Experimental Verification
### Materials and Methods
#### 3.1.3 The Calibration Experiments of the Multi-Camera Layout Patterns  
A. Calibration experiments for different layout patterns  
Four cameras were deployed in three forms: arched, annular and half-annular (The layout parameters are detailed in Supporting Documents S1--Table S2, camera's working distance error does not exceed 30 mm, the angle error was smaller than 5\({}^{\circ}\). Figure 6a-c). The standard calibration bars were shaken separately for each of the three layouts. The markers moved near the surface of the FMA; the motion area of each experiment was guaranteed to cover the entire range of the platform and the movement posture, speed, acceleration and movement amplitude were basically the same.  
B. Calibration experiments for different layout patterns and depth of field  
The three layout patterns of arched, annular and half-annular (The layout parameters are detailed in Supporting Documents S1--Table S3, camera's working distance error does not exceed 30 mm, the angle error was smaller than 5\({}^{\circ}\)) were respectively used to carry out the tests under three different sampling areas. The experimental procedure was the same as that of Experiment A. Each experiment must ensure that the range of motion of the calibration bar is strictly controlled within the length of sampling area \(L\) defined in Table S3.  
C. Experiments for testing the effect of the depth of field (in the half-annular layout)  
In the half-annular layout, the pitch angles \(\alpha\) were set to 30\({}^{\circ}\), 45\({}^{\circ}\) and 60\({}^{\circ}\) (Figure 6d) respectively and the camera angle \(\theta\) was set to 30\({}^{\circ}\) (in line with its definition). The length (300 mm) of the force-measuring array (FMA) in the middle of the motion platform was divided by the ratio of 1:2, which produced the dividing line called the alignment line (_LF_) (Figure 6d). The position of each camera was fixed so that its working distance (_WD_) was 390 mm (specific calculation shown in Support Material S5) and the center point of the dividing line was set to be the camera focus. The coordinate axis was defined on the objects' motion plane (the alignment line (_LF_) was the \(X\)-axis, the \(Z\)-axis was perpendicular to the object plane and intersecting to the midpoint of the alignment line (_LF_) and the \(Y\)-axis could be obtained by the corkscrew rule, as shown in Figure 6d). Then, the motion capture of the calibration bar was carried out the same as the experiment A and the motion range of the calibration bar did not exceed 300 mm during the motion capture.
## 5 Experiments and Results
### Statistical Results and Convergence Speed
From the comparison and statistical results, Rcr-JADE, IWOA, SATLBO, and PGJAYA, together with the DLMVO algorithm in this paper, were the most competitive. Therefore, more experiments are carried out to quantify the convergence of these algorithms. For a scientific comparison, the maximum number of iterations (Max_NFEs) was set to 50,000 for all compared algorithms. The convergence curve of 30 independent runs of five algorithms is shown in Figure 11. As curves, it is evident that the DLMVO had the fastest convergence speed, which indirectly means that the DLMVO can quickly extract the parameters of PV models. Figure 9: Boxplot of best RMSE in 30 independent runs for three models. Figure 11: _Cont._  
Figure 10: Friedman test results of different algorithms.
# Helicopter Takeoff and Landing Point Location in Cities for Emergency Services
## 4 Optimization of Selection Based on the Greedy Algorithm
### Location Optimization
#### 4.3.2 Promotion of Optimized Location  
From past experimental data, the average cost of constructing a take-off and landing platform is approximately 500,000 yuan [23], and because there aren't many helicopters in China, it is difficult to achieve full coverage for large-scale low-altitude rescue in the short term. In order to further improve the utilization rate of helicopters and reduce the initial construction funds, this paper expands the rescue radius. We explore the possibility of carrying out cross-city rescue by increasing the rescue range of helicopters. On the premise of not affecting the survival rate of victims, the rescue radius is expanded to 50 km. After algorithm iteration, only two take-off and landing bases are needed, which can basically cover the whole area of Nanjing. As shown in Table 7.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Possible Location** & **Longitude/\({}^{\circ}\)** & **Latitude/\({}^{\circ}\)** \\ \hline
1 & 118.7191 & 32.0662 \\
2 & 119.0066 & 32.2029 \\
3 & 118.4501 & 32.0317 \\
4 & 118.8749 & 31.8023 \\
5 & 118.3416 & 31.3601 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Optimized locations within a 30 km rescue radius.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Possible Location** & **Longitude/\({}^{\circ}\)** & **Latitude/\({}^{\circ}\)** \\ \hline
1 & 118.9172 & 32.0416 \\
2 & 119.0301 & 31.3250 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Optimized locations with 50 km rescue radius.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Greedy Algorithm Framework for Location** \\ \hline Step 1: & Input all initial location coordinate sets \(\Omega\), constraint function \(\Gamma\) \\ Step 2: & Set the number of nodes k \\ Step 3: & Set the initial location point coordinate set \(\text{A}_{0}\in\Omega\) \\ Step 4: & for \(\text{i}\text{c}\text{1},2,\ldots,\text{k}-1\) do \\  & (1) \(\omega\)\(\text{argmax}_{\text{j}\in\Omega\backslash\Lambda_{i}}\Gamma(\text{A}_{i}\cup\{\} \{\})-\Gamma(\text{A}_{i})\) \\  & (2) \(\text{A}_{i+1}=\text{A}_{i}+\{\omega\}\) \\ Step 5: & end for \\ Step 6: & Coverage \(\subseteq\) 95\%? \\  & (Yes) return Step7 \\  & (No) \(\text{p}=\text{p}+1\), return Step2 \\ Step 7: & Output coverage C and optimization results \(\text{A}_{\text{k}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Basic greedy algorithm framework.
## 4 Results
### Wave Tank Experiments
#### 4.1.2 Ka-Band Radar Probing of Wind Waves  
Wind Wave Spectra  
Typical spectra of wind waves at low wind velocities of 2 m/s and 2.5 m/s are shown in Figure 9.  
The radar contrasts as functions of OLE concentration at two wind speeds are presented in Figure 10 at the non-resonance and resonance between the IW and the film drift.  
Figure 8: The relative variations of surface concentration at the resonance and the non-resonance. Black solid line—phase of maximum surface concentration.  
Figure 9: Wave height spectra (1—clean water, 2.5 m/s; 2—OLE concentration 0.6 mg/m\({}^{2}\), 2.5 m/s; 3—clean water, 2 m/s; 4—OLE concentration 0.6 mg/m\({}^{2}\), 2 m/s).  
From Figure 10, one can conclude that the mean contrast characterizing the suppression of radar backscatter increases with surfactant concentration, but is not practically affected by a relationship between the IW phase velocity and film drift. This could be expected, particularly for the case of not-too-strong variations of radar backscatter due to IW when the radar return modulation can be described as sinusoidal, which is removed after the radar backscatter averaging.  
Radar Modulation Transfer Function  
The magnitude and phase of RMTF as well as the coherence are shown in Figure 11 for two case studies: resonance and non-resonance modulation of surfactant concentration. Remarkably, the obtained dependencies for RMTF magnitude and phase are completely different. For the case when the IW phase velocity is significantly larger than the film drift velocity, the RMTF magnitude weakly depends on the presence of surfactants being practically constant up to 0.6 mg/m\({}^{2}\) and is slightly reduced at higher OLE concentrations. The RMTF phase is also practically constant, and is close to \(-\pi/2\); therefore, the radar return maxima are located over the rear slopes of IW pycnolcine elevations. The coherence values exceeded 0.5, which is a rather high correlation, so a confidence interval in our cases is less than 25%. On the contrary, the RMTF magnitude for \"slow\" IWs, which are in resonance with film drift velocity, grows with surfactant concentration being rather small at zero/low \(\Gamma\) values, but exceeding the non-resonance RMTF magnitudes at sufficiently high surfactant concentration values. The RMTF phase for clean surfaces at the resonance is close to zero, i.e., the maximum of the radar return is over the IW crests, the phase of modulation increases with surface concentration, and the maximum radar return is shifted to the forward slope (2 m/s) or to the trough (2.5 m/s).  
Figure 10: Contrast vs. surface concentration of OLE for a non-resonance (**left**) and resonance (**right**) case when the IW and film drift velocities are close to each other. Wind velocities are of 2.5 m/s (black) and 2 m/s (red).
## 2 Wireless Communication for Maritime Applications
### Communication to On-Shore Base Station
As previously mentioned, data from sensor nodes can either be sent to a coordinator which then relays it to the onshore main station or the sensor node can send it directly to the main station. These approaches need to be considered bearing in mind the use case. For example, if the sensor node is close to the shoreline or it is employed in a network with coordinator modules, data communication can be achieved with protocols such as LR Wi-Fi. On the contrary, when the sensor node is truly remote, satellite communication will need to be employed, at the cost of high energy expenditure and, consequently, a smaller lifetime of the sensor. Therefore, it is advisable, when applicable, to have several sensor nodes communicate with a coordinator node through low energy expenditure protocols and gift the coordinator with a larger battery and an energy harvesting module, such as a solar panel, to extend the lifetime of all the sensors.
## 3 Background and Related Concepts
### Access Control Components  
This section explains the essential access control components [7], briefly described below:  
* **Policy Enforcement Point (PEP):** receives requests for entry to resources and forwards them to the Policy Decision Point (PDP), where access decisions are made. PEP executes the access decisions that PDP has transmitted. It ensures the fulfillment of obligations that PDP might include in access determinations.
* **Policy Decision Point (PDP):** examines user access and makes decisions about access based on attributes that describe the parts of the access request (subject, resource and action) and relevant access control policies.
* **Policy Information Point (PIP):** is the provider of characteristics PDP requires to assess access requests against ABAC policies. PIP captures information on the subject, relevant resource and environment where access requests occur and presents these attributes to the PDP via the context handler during access decision-making.
* **Policy Administration Point (PAP):** enables policy management features, including adding, removing and updating access policies. Additionally, it stores the access policies that the policy administrator has set.
* **Attributes:** these differentiate the underlying entities in access control. They consist of an attribute name, a value and types of attributes, shown in the following diagram (subject, resource, operation and environment). The \"subject\" attribute describes the access requester's attributes. The term \"resource\" refers to the informational characteristics of a currently available resource. \"Operation\" indicates the operation attribute when defining how a subject interacts with a resource. The environment attribute describes the environment for access control, such as time, location, etc.
* **Policies:** a policy on access control describes the rules governing the access of subjects to resources. It is a physical expression of the subject's authorised behaviour towards the resource, also known as access control policy (subject-tuple, resource-tuple, operation-tuple, environment-tuple, state). This state determines whether access is allowed or refused.  
* **Access Request:** describes the user, the resource and the requested action. One way it can be carried out is with a formal request for access, which could be a subject-, resource-, or operation-tuple.
* **Access Decision-Making:** The final decision on whether users should be granted or refused access to an associated object will be based on the specific policy evaluation context.
## 2 Fisher-Snedecor \(\mathcal{F}\) Fading Channel Model  
In this section, we first give the statistical characteristics of the univariate Fisher-Snedecor \(\mathcal{F}\) distribution. Then, based on these characteristics, the joint PDF of the bivariate Fisher-Snedecor \(\mathcal{F}\) composite envelope with arbitrary fading parameters is derived. Finally, the joint PDF and the joint cumulative distribution function (CDF) of the instantaneous SNR over the correlated Fisher-Snedecor \(\mathcal{F}\) composite fading channels are obtained, respectively.
## 4 Future Work  
The results of this work also provide a foundation for future work in radio wave propagation in the vegetation fields for smart agriculture communication scenarios. One area for future work is combining the knowledge gained about radio wave propagation in the corn field (R3 stage) with that without vegetation or in other corn growth stages. We also plan to examine how the x-polarization of the antennas will impact the coverage area of a wireless sensor network for IoT-based smart agriculture. Another area involves performing a measurement campaign collecting received signal-strength indicator (RSSI) and packet loss from sensors (from a wireless sensor network) placed at the same position as the transmitting antenna.
# Dynamic Packet Duplication for Industrial URLLC
## 6 Conclusions  
Many solutions have been proposed for URLLC connectivity in order to achieve high reliability and low latency; however, these typically come at a cost in terms of resource usage. This paper proposes a scheme to predict the E2E latency in order to determine if the PD technique is required for downlink transmissions (dynamic PD) and, thus, reduce the resource wasting.  
The proposed algorithm uses a ML approach, where an estimator is running in the MN. The algorithm was implemented and tested in a 5G simulator, showing high accuracy for determining whether or not to activate PD for a packet transmission.  
Moreover, the PD technique was evaluated using the predictor (once the model is already trained). The results obtained show that the proposed dynamic PD based on the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**PD Technique**} & **Number of** & **Latency below** & **Average (Packet)** & **PD** \\  & **Packets Duplicated** & **Threshold Rate** & **Latency Reduction Rate** & **Reduction** \\ \hline Always PD & 59,940 & 95.7891\% & 25.0917\% & Not applicable \\ PD via & 11,376 & 95.7541\% & 86.5506\% & 81.0211\% \\ Random Forest & 11,376 & 95.7541\% & 86.5506\% & 81.0211\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison results between static and dynamic PD.  
Figure 7: ECDF of the latency gain when the predictor activates PD.  
E2E latency prediction is more efficient than always duplicating, obtaining a high PD reduction rate (81%) and maintaining the same level of latency below the threshold.  
In addition, when activating PD based on the predictor, the latency reduction is higher than when using always duplicating technique, that is, there are so many unnecessary packets duplicated, where the latency constraint can be fulfilled by a single connection. A comparison between PD techniques and a single connection was performed, where PD techniques demonstrate that help to achieve URLLC latency constraint.  
As a future line of work, some of the implementation aspects will be studied. A major milestone toward the real implementation of the system will be the study of FL for the creation and enrichment of a model. Another important aspect will be the study of the required model update frequency, which may also be reduced due to FL.  
Conceptualization, D.S. and E.J.K.; methodology, D.S. and E.J.K.; software, D.S.; validation, D.S. and E.J.K.; formal analysis, D.S. and E.J.K.; investigation, D.S. and E.J.K.; writing--original draft preparation, D.S. and E.J.K.; writing--review and editing, D.S., E.J.K. and R.B.; visualization, D.S.; supervision, R.B.; project administration, R.B.; funding acquisition, R.B. All authors have read and agreed to the published version of the manuscript.  
This work has been partially funded by Junta de Andalucia (Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades, Proyecto de Excelencia PENTA, P18-FR-4647 and EDEL4.0, UMA18-FEDERJA-172) and ERDF.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Simulation and Results Analysis
### The Comparative Test under Constant Current Discharge Conditions
The calculation time for various SOC estimation algorithms [7; 9; 17] was performed in MATLAB 2014b (The MathWorks, Natick, Massachusetts, USA) on a PC equipped with an Intel Core i5-8300H CPU @ 2.3 GHz and 8 GB RAM. Table 3 listed the calculation results under BBDST in the entire SOC area (0-100%). It can be seen that the calculation time of the proposed algorithm was relatively small and the accuracy was relatively high. Therefore, the proposed algorithm improved the estimation accuracy without increasing the complexity, which was very suitable for the actual use of EVs.