esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 FaSynRM Optimization Parameters and Results  
### FaSynRM Optimization Parameters  
Figure 11 illustrates the parameters that define the FaSynRM geometry. The rotor has 4 magnetic flux barriers per pole. They are numbered 1, 2, 3, 4, starting from the outer barrier and ending with the inner one. The geometry of the magnetic flux barrier is shown in Figure 11b.  
The integrity of the rotor is ensured by internal ribs with thickness \(h_{in\;\;rib\;i}\) in the middle of each barrier and external ribs with thickness \(h_{out\;\;rib\;i}\), adjoining the air gap. On the surface of the rotor, points at a distance \(\alpha_{i}\) of the barrier symmetry axis are 1 and 1\({}^{\prime}\), which define the barrier geometry. Points 2, 2\({}^{\prime}\), 3, 3\({}^{\prime}\) are marked at a distance \(h\) from the surface of the rotor. The angular distance between points 2 and 3 and between 2\({}^{\prime}\) and 3\({}^{\prime}\) is equal to \(\beta\). A circle of \(455^{\prime}4^{\prime}\) at a depth equal to the thickness of the outer ribs \(h_{in\;\;rib\;i}\) cuts off triangles 1\({}^{\prime}4^{\prime}5^{\prime}\) and 1\({}^{\prime}4^{\prime}5^{\prime}\) from the barrier. For the outermost edge, points 6 and 6\({}^{\prime}\) coincide with points 3 and 3\({}^{\prime}\), that is, line 66\({}^{\prime}\) is drawn between points 3 and 3\({}^{\prime}\). With this exception, the depth \(x_{i}\) of the line is set to 66\({}^{\prime}\), and the length of this line is 2\(y_{i}\). Line 77\({}^{\prime}\) is drawn at depth \((x_{i}+h_{cut\;\;i})\), where \(h_{cut\;\;i}\) is the width of the magnetic flux barrier. The length of this line is 2\(y_{1i}\). Line 77\({}^{\prime}\) of the deepest gap limits the area allocated for cutouts in general. Its angular size \(\epsilon\) is a fixed parameter. Therefore, the parameter \(y_{1i}\) is set only for three external magnetic flux barriers.  
Figure 11: Geometric parameters of the FaSynRM. (**a**) Stator slot; (**b**) Rotor flux barrier, red arrows mark the direction of magnetization of the permanent magnets. Areas occupied by ferrite magnets are marked in yellow.  
In this case, \(x_{i+1}=x_{i}+h_{cut\ i}+h_{st\ i}\), where \(h_{st\ i}\) is the steel thickness deep behind the barrier. Behind barriers 1, 2, 3 are the following barriers. Behind barrier 4 is the circumference of the inner radius of the rotor. To reduce the optimization parameters, we accept the following relations:  
\[\begin{array}{c}\alpha_{i}=a+b(i-1/2),\,i=1,\,2,\,3,\,4;\\ y_{1i}=y_{i}\cdot c,\,i=1,\,2,\,3;\\ y_{i}=y_{1i}\cdot g,\,i=1,\,2,\,3,\,4;\\ h_{cut\ i}=a_{cut\ i}+b_{cut\ i}\,(i-1),\,i=1,\,2,\,3,\,4;\\ h_{st\ i}=a_{st\ i}+b_{st\ i}\,(i-1),\,i=1,\,2,\,3,\,4.\end{array} \tag{5}\]  
The ratio of the widths of the stator slot \(\alpha_{\text{stat1}}/\alpha_{\text{stat2}}\) also does not change. In addition, the variable parameters include the current control angles in operating points 1, 2, 3, 4. Table 7 shows the FaSynRM parameters that are not changed during the optimization. Table 4 shows the FaSynRM parameters that are varied during the optimization.
## 4 Interactive Compression  
If we assume some degree of interaction between the compressor and the decompressor then we can exploit the previous knowledge they both have of the source when data compression is used for data transmission. The price we accept to pay, in some cases, is a low probability of communication errors.  
The solution of the communication problem, in this case, is an interactive protocol that allows Sender and Receiver to take advantage of the knowledge they have of the source and that exploits the interaction to minimize the communication cost. In real life this could be used in situations in which finite size files are transmitted over a bidirectional communication line, like a telephone line, internet, a satellite link or any other line between two systems that have already available a large number of files of the same type.  
As an example consider an internet user that has to frequently download the same kind of file (a newsletter, an image, a report, _etc._) from the same source, or a system manager that has to download repetitively an update file for a program or for an antivirus, or an ftp mirroring site that has to be periodically brought up to date, _etc._The compression algorithms involved in the communication might be static or dynamic dictionary methods or, in the case of images, Vector Quantization.
## 3 Results
### Capacity of Microalgae to Remove Heavy Metals  
The capacity of _Chlamydomonas_ sp. MA\({}_{\text{a1}}\) and _Navicula_ sp. MA\({}_{\text{n1}}\) strains to remove cadmium were tested (Figure 5).  
Both tested strains exhibited high removal efficiencies of cadmium from water at all tested concentrations. In fact, the obtained percentages of removed cadmium vary between 89.8% and 99.38%.
## 3 Main Results
### Integrated Algorithm Design for Vehicle Speed and Distance Control  
The vehicle speed-prescribed performance control takes speed as the system output, without considering objective 1. When the vehicle approaches the front vehicle, it needs to meet objective 2. Therefore, the entire process of vehicle adaptive cruise control needs to meet the following additional conditions:  
* When the vehicle enters the safe-distance error constraint range, i.e., \(z(k)-z^{L}(k)+d_{safe}(k)+\psi_{d}(k)<\psi_{d}(k)\), the speed-prescribed performance control should automatically switch to distance-prescribed performance control;
* When distance-prescribed performance control is in effect, try to ensure \(v(k)<v_{ref}(k)+\psi_{v}(k)^{-1}\). If \(v(k)>v_{ref}(k)+\psi_{v}(k)^{-1}\) (leader deceleration), it should not switch back to the speed-prescribed performance control.  
The problem of the integrated control algorithm for vehicle speed and distance can be summarized as follows: when \(z(k)=z^{L}(k)-d_{safe}(k)\), \(\kappa_{d}^{1}(k)\
earrow\infty\), \(\kappa_{d}^{2}(k)\
earrow\infty\); when \(v(k)=v_{\text{ref}}(k)-\phi_{v}(k)^{-1}\), \(\kappa_{v}(k)\
earrow\infty\). For this purpose, in the intersection area of speed- and distance-prescribed performance, i.e., \((k,\epsilon_{v}(k))\in\mathcal{R}_{\phi_{d}}\wedge(k,\epsilon_{d}(k))\in \mathcal{R}_{\phi_{d}}\), the control input is set as \(\min\{-\kappa_{v}(k)\epsilon_{v}(k),-\kappa_{d}^{1}(t)\epsilon_{d}(k)-\kappa_ {d}^{2}(t)\epsilon_{k}(k)\}\), see Figure 2.  
Figure 2: Integrated speed and distance controller structure (dashed lines represent the 0 axis).  
Therefore, the integrated control for vehicle speed and distance control can be expressed as  
\[F(k) = \left\{\begin{array}{rcl}-\kappa_{v}(k)\epsilon_{v}(k),\epsilon_{d }(k)\leq-\phi_{d}(k)^{-1}\wedge(k,\epsilon_{v}(k))\in\mathcal{R}_{\phi_{v}}\\ -\kappa_{d}^{1}(k)\epsilon_{d}(k)-\kappa_{d}^{2}(k)\epsilon_{v}(k),\epsilon_{v }(k)\leq-\phi_{v}(k)^{-1}\wedge(k,\epsilon_{d}(k))\in\mathcal{R}_{\phi_{d}}\\ \min\{F_{d}(k),F_{v}(k)\},(k,\epsilon_{v}(k))\in\mathcal{R}_{\phi_{v}}\wedge(k,\epsilon_{d}(k))\in\mathcal{R}_{\phi_{d}}\\ \end{array}\right.\] \[\kappa_{v}(k) = \frac{1}{1-\phi_{v}(k)^{2}\epsilon_{v}(k)^{2}}\] \[\epsilon_{v}(k) = v(k)-v_{\text{ref}}(k)\] \[\kappa_{d}^{1}(k) = \frac{\phi_{d}(k)}{1-\phi_{d}(k)|\epsilon_{d}(k)|},\kappa_{d}^{2} (k)=\frac{\phi_{v}(k)}{1-\phi_{v}(k)|\epsilon_{v}(k)|}\] \[\epsilon_{d}(k) = z(k)-z^{L}(k)+d_{\text{safe}}(k)+\psi_{d}(k) \tag{14}\]
## 2 Materials and Methods
### System Optimization and Commissioning
#### 2.2.2 Threshold Value Limitation  
At the BL08U1A beamline of the SSRF, M2 is the main beamline mirror that affects the beam center during the Slit 2 during experiment, which is driven by a step motor with a minimum moving precision of 200 nm. Additionally, the return difference of the M2 motor also needs to be considered during it movement. In order to prevent the M2 position from vibrating around the optimal position due to return difference, a threshold value limitation is defined as follows:  
\[S_{V}=abs\left(\frac{S_{R}-S_{L}}{S_{R}+S_{L}}+offset\right), \tag{2}\]  
where the _offset_ is a correcting value according to the gold mesh optimal signal value. A suitable threshold value V\({}_{0}\), which measures the deviation in the slight signals due to M2 movement, should be tested so that the feedback system is only activated when \(S_{V}>\) V\({}_{0}\).  
Figure 7 shows the change in the position of S\({}_{\text{RL}}\) as the M2 moves in steps of 200 nm at an incident X-ray energy of 920 eV. The \((S_{\text{R}}-S_{\text{L}})/(S_{\text{R}}+S_{\text{L}})\) is collected with the M2 change before the experiment and these data are fitted with a straight line (see Figure 7). The slope of the fitted straight line represents the percentage change of \((S_{\text{R}}-S_{\text{L}})/(S_{\text{R}}+S_{\text{L}})\) after 1000 nm, and multiplying the slope by 0.2 gives the percent change of \((S_{\text{R}}-S_{\text{L}})/(S_{\text{R}}+S_{\text{L}})\) after 200 nm. It can be observed that the relative change in the position of S\({}_{\text{RL}}\) in each motor step is approximately 0.0475; this is obtained by calculating the slope in the central linear area around the optimal M2 position. For the BL08U1A beamline, the incident X-ray energy ranges from 250 eV to 2000 eV. Therefore, we systematically tested the entire energy range and calculated the threshold values, which are listed in Table 1. In real experiments, V\({}_{0}\) is set to 0.05 for simplicity and the automatic feedback procedure works very well.  
Figure 6: Calibrate slit signals using PD at 540 eV. The left dotted vertical line marks the first M2 position of \(S_{\text{L}}=S_{\text{R}}\). The right dashed vertical line marks the second M2 position of the maximum PD value. For the second position, SRL is calculated to be \(-2.677\%\). However, the light flux of the first position is 99.12% of the second position. Considering that the M2 stepping motor with a 200 nm minimum step size cannot distinguish between the two positions, the difference in the two light fluxes can be ignored; thus, the offset value is set to 0 in practical applications.
## 4 Results.
### Optimal Model Selection  
Each model listed in Table 5 underwent a comprehensive evaluation utilizing a dataset comprising 57,704 records. The derived results, systematically arranged in descending order based on their respective evaluation scores, are detailed in Table 6. Notably, every model demonstrated superior performance compared to the median of the Mean Absolute Error (MAE) observed during 2015-2022. However, selecting the optimal model requires an in-depth assessment beyond mere evaluation scores. This includes a detailed analysis of the models' hourly MAE fluctuations, as depicted in Figure 4. Additionally, a critical review of each model's computational efficiency, particularly their execution times during both the GridSearch process and subsequent training phases, is essential. This aspect of model performance is thoroughly explicated in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Hyperparameters** & **Test Score** & **Train Score** & **Gap** \\ \hline RandomForest & max depth = 45, & 7.825020 & 2.923992 & 4.901028 \\ Execution time: 2600 & max features = 25. & & & \\ \hline StandardScaler \(\rightarrow\) SVR (RBF) & C = 100, & & & \\ Execution time: 29,672 & gamma = 0.01. & 7.845238 & 3.350762 & 4.494476 \\  & C = 10, & & & \\  & gamma=0.001. & 8.757496 & 8.652644 & 0.104852 \\ StandardScaler \(\rightarrow\) SVR (poly) & C = 100, & & & \\ Execution time: 104,571 & degree = 3. & 9.062664 & 5.348223 & 3.714441 \\  & C = 10, & & & \\  & degree=2. & 11.562271 & 10.979601 & 0.582670 \\ MinMaxScaler \(\rightarrow\) SVR (RBF) & C = 100, & & & \\ Execution time: 22,465 & gamma = 0.1. & 7.785040 & 6.806308 & 0.978732 \\  & C = 100, & & & \\  & gamma = 0.01. & 8.700342 & 8.617988 & 0.082355 \\ MinMaxScaler \(\rightarrow\) SVR (poly) & C = 100, & & & \\ Execution time: 1,705,128 & degree = 3. & 7.925763 & 6.892926 & 1.032837 \\  & C = 10, & & & \\  & degree=4. & 8.060671 & 7.198237 & 0.862434 \\ \cline{2-6}  & learning rate = 0.1, & & & \\ GradientBoosting & max depth = 10, & & & \\ Execution time: 42,584 & max features = 12, & & & \\  & n estimators = 300. & & & \\  & learning rate = 0.1, & & & \\  & max depth = 9, & & & \\  & max features = 11, & & & \\  & n estimators = 300. & & & \\ \hline \hline \end{tabular} The grid search was executed using n\({}_{\text{,jobs}}\) = 20 option; the execution time unit is in seconds.  
\end{table}
Table 5: Gridsearch results.
## 3 Results and Discussion
### The Total BaPTE Value Prediction  
Comparing the total _BaPTE_ value data between the experimental results and the prediction values, the first-degree model had a better fit in predicting than the zero-degree model; this result was confirmed by the higher mean of the coefficient of determination (Figure 11a,b), respectively. The mean of the coefficient of determination, or R\({}^{2}\), was between 0.9728 and 0.9875, which is satisfactory (Figure 11b). Hence, the response of both models presented an increasing trend of the total _BaPTE_ values as the POSS bio-char combustion period progressed.  
For the cost side in the production process, the POSS bio-char produced by this technique had a greater cost than the normal technique by about 1.5%. It was referred by the Department of Alternative Energy Development and Efficiency, Ministry of Energy [52].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Order of the Reaction Rate Constant**} \\ \cline{2-5}
**Velocity (m/s)** & \multicolumn{2}{c}{**Zero**} & \multicolumn{2}{c}{**First**} \\ \cline{2-5}  & **A (h-1)** & **E (J/mol)** & **A (h-1)** & **E (J/mol)** \\ \hline
0.1 & \(48.8\times 10^{8}\) & 96,597.4 & \(47.1\times 10^{8}\) & 94,216.8 \\
0.2 & \(29.2\times 10^{8}\) & 91,982.9 & \(25.6\times 10^{8}\) & 85,858.6 \\
0.3 & \(15.4\times 10^{8}\) & 88,376.8 & \(11.2\times 10^{8}\) & 83,287.9 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The activation energy and frequency factor values of the expressed rate constant.
## 4 Simulations and Results
### Simulation Results
Mixed ANOVA assumes that the data come from normally-distributed populations with a homogeneous variance and similar covariance and sphericity (differences between all possible pairs of groups are equal). Some of the twelve data groups of our performance results violate some of these assumptions, especially normal distribution and homogeneous variance. However, our test results can be considered reliable, because ANOVA is robust to these assumptions [23], especially under the balance number of elements in each group [24]. When there is not a significant dependency between the factors in the mixed ANOVA, as happens in our study between attenuation models and vehicle density for the percentage of packet losses, average end-to-end delay and average number of hops, the _post hoc_ tests, _i.e._, repeated measures ANOVA [21], to study the attenuation model effects do not need to differentiate between the vehicle densities. On the other hand, the _post hoc_ tests for the average number of neighbors metric have to be done for each vehicle density independently. According to mixed ANOVA results, the differences among these metrics depend on the vehicle density used in the simulation. Due to the _post hoc_ test (ANOVA with repeated measures), it is required to meet the same assumptions of mixed ANOVA, and even when this test is robust to violations of these assumptions, we decided to apply the equivalent non-parametric tests to assess the difference among the attenuation models, because our data conform to their requirements. For our data, both kinds of tests, parametric and non-parametric, agree with the same decision in all of the _post hoc_ tests performed in this work. There  
is not a well-accepted non-parametric statistical test equivalent to mixed ANOVA. Table 3 shows the _p_-values obtained from the Friedman test [22], which is the non-parametric version of ANOVA for repeated measures. It is used to know if there are statistically significant differences among the building attenuation models. As the reader can notice, none of the _p_-values of Table 3a (Barcelona scenario) are higher than the significance threshold of 0.05, except the average number of neighbors for a vehicle density of 100 cars/km\({}^{2}\). Hence, this means that there is a statistically significant difference among the distribution of the results obtained when the simulation uses different building attenuation models, excluding the average number of neighbors for a vehicle density of 100 cars/km\({}^{2}\). All of these differences, _i.e._, the percentage of packet losses, average end-to-end delay and average number of neighbors, are not detected in the performance results of the Tarragona scenario. It can be seen in Table 3b that the _p_-values show significant differences in average delay and average number of hops. We would like to point out that even when the _p_-value of mixed ANOVA (to test the interaction between the attenuation models and the vehicle density in the Tarragona scenario; see Table 2b) is significant, the Friedman tests performed per
## 2 Materials and Methods
### Proposed Machine Learning Models
#### 2.4.1 BPNN Model  
A BPNN was used in this study because it is the most widely used and effective neural network learning algorithm [45]. The preliminary architecture of the BPNN was determined as 4-\(h\)-1, where 4 is the number of input layer neurons representing \(R\_max\), \(P\_max\), \(\omega\), and \(d\). The number of hidden layer neurons is represented by \(h\), and 1 represents the number of output layer neurons. To eliminate the effects of coupling among dependent variables on the modeling performances of the model, we built BPNN prediction models for \(c\) and \(\varphi\) separately.  
However, the number of hidden layer neurons greatly affects the accuracy of BPNN [31]. So far, there is no exact formula to calculate the number of hidden layer neurons, but its range can be deduced using an empirical formula. A commonly used formula is as follows [33]:  
\[h=\sqrt{n+p}+\alpha \tag{7}\]  
where \(h\) is the number of neuron nodes in the hidden layer, \(n\) is the number of input nodes, and \(p\) is the number of output nodes, respectively; \(\alpha\) is a coefficient from 1 to 10. Since \(n\) is 4 and \(p\) is 1, \(h\) can be assigned a value between 3 and 12.  
In this paper, the coefficient of determination in the calibration set (\({R_{c}}^{2}\)) is taken as the evaluation index, and the trial-and-error method is adopted to select the appropriate number of hidden layer neurons within the above-calculated \(h\) range. The specific optimization steps are as follows:  
1. For each \(h\) value within the range, a BPNN model is built, and the calibration set is tested 10 times by the BPNN.
2. The \({R_{c}}^{2}\) corresponding to each test is calculated, and the average value of \({R_{c}}^{2}\) corresponding to each \(h\) value is counted.
3. The optimal \(h\) value of modeling is determined according to the average value of \({R_{c}}^{2}\). The larger the \({R_{c}}^{2}\), the better the model.  
In order to facilitate the construction of the BPNN model, it was created based on the newff function in the MATLAB toolbox in our study. The maximum number of iterations is set as 1000 times, the learning rate is 0.01, and the target error is 0.001. It is worth clarifying that in BPNN modeling, hidden layer neurons were an S-shaped transfer function tansig, while output layer neurons were a linear transfer function purelin.
## 3 Adaptive Filtering Queueing
In other words, memory can be approximately reduced to two-thirds. To compare the fairness, we define the Normalized Bandwidth Ratio (NBR) for a specific flow based on Equation (8). NBR\({}_{\text{j}}\) denotes NBR; D\({}_{\text{j}}\) denotes the mean departure rate; and r\({}_{\text{j}}\) denotes the Mean Arrival Rate (MAR), all related to flow \(j\). Additionally, f denotes the Max-Min Fair Share Rate (MMFSR). \[\text{NBR}_{\text{j}}\ =\ D_{\text{j}}/\min(\text{r}_{\text{j}},\text{f}) \tag{8}\]  
If \(\sum_{\text{j}\ =\ 1}^{\text{n}}\text{r}_{\text{j}}>\text{C}\), f can be derived from \(\sum_{\text{j}\ =\ 1}^{\text{n}}\min\left(\text{r}_{\text{j}},\text{f} \right)\ =\ \text{C}\), where n denotes the number of active flows. Otherwise, \(\text{f}\ =\ \max(\text{r}_{\text{i}}),\text{i}\in\text{n}\). If a scheme achieves the optimal fairness, then the NBR of each flow is equal to 1.
# Impact of Disruption on Ship Emissions in Port: Case of Pandemic in Long Beach
## 3 Model and Methodology
### Navigational Status  
Emissions might change based on the navigational status of a ship, as a result of the many engines operated. Typically, the ship's main engine (ME) creates propulsion during cruising and manoeuvring, while the auxiliary engine (AE) ensures the ship's power, heating, cooking, etc. In turn, the boilers safeguard facilities such as fuel heat and cargo heating. Typically, the main engine is turned off during times when the ship is moored and anchored; however, the ship's AE and boiler operations continue, in order to maintain normal crew life and cargo security ([PERSON] et al., 2016).  
Before estimating emissions, it is necessary to identify and analyse the ship's motion status. The \"status\" information in the AIS data can be utilised to examine the status of most ships, and the National Automatic Identification System (NAIS) Code specifies the state code. Moreover, the shipping status can be determined based on the distance between neighbouring AIS reporting sites for each ship over the same period. For ships with missing Status data, we used velocity to estimate the ship's status, as shown in Table 1.  
Moreover, it is essential to consider that some of the sailing statuses in the AIS report may be inaccurate, because the crew must manually update the information when assessing a ship's operational condition. Therefore, the determination process requires a mix of the operating conditions provided by the AIS data and the vessel's speed. In addition, the ship's speed is modified by weather, waves, currents, etc. Consequently, we accept a margin of error of 50%, as do others' research ([PERSON] et al., 2016).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Speed** & \(\leq\)**1 nm from Port** & \(\leq\)**1 nm from Coast** & **1–5 nm from Coast** & \(\leq\)**5 nm from Coast** \\ \hline \(<\)1 knot & Berth & Anchor & Anchor & Anchor \\
1–3 knots & Anchor & Anchor & Anchor & Anchor \\
3–5 knots & Manoeuvring & Manoeuvring & Manoeuvring & Cruising \\ \(>\)5 knots & Manoeuvring & Cruising & Cruising & Cruising \\ \hline \hline \end{tabular}
\end{table}
Table 1: Judging the operating mode of ships, based on their speed.
# Collision Avoidance Adaptive Data Rate Algorithm for LoRaWAN
## 6 Results and Discussion  
An evaluation of the proposed algorithm is presented in this section based on the results derived from the simulations. The performance analysis of the LoRaWAN network primarily focuses on four key metrics: confirmed packet success rate (CPSR), uplink packet delivery ratio (UL-PDR), total energy consumption (ET), and interference ratio (IR). To benchmark our proposed ADR implementation, we differentiate it against the standard ADR protocol, the ns3-ADR protocol implemented in the ns-3 LoRaWAN module, and the fuzzy logic-based ADR scheme known as FL-ADR [42]. In the first evaluation scenario, we vary the application data packet rate while keeping the number of end devices constant at 100. In the second evaluation scenario, we vary the number of EDs while maintaining a constant application data packet rate of 600 s. The main findings of our study indicate that our proposed ADR model surpasses the standard ADR and ns3-ADR algorithms in relation to the packet delivery ratio, while also demonstrating a reduced interference rate and energy consumption. However, it is essential to note that the FL-ADR scheme surpasses our developed framework in relation to energy utilisation and interference rate reduction, although it lags behind in achieving higher PDR.