esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions and Future Directions  
Mobility causes a lot of issues in the overall performance of WBAN. The most important aspect that is affected is data transfer. This work focused on evaluating the behavior of different routing protocols for several different movement patterns exhibited by different nodes in WBAN. Two basic entity mobility models were evaluated for different QoS parameters, and two different categories of routing were considered with three different variants of the same. With the above evaluations, we can clearly see that AOMDV is superior to the other two routing protocols, i.e., AODV and DSDV, in terms of both the mobility models and is highly recommended for WBAN implementations with a varying number of nodes. The evaluation is currently focused on fixed sink node placement; however, sink mobility will be added in the future, and both mobility models will be checked for all routing protocol variants. Additionally, the movement patterns we have evaluated in this work are only for individual nodes, whereas how these nodes behave when grouped together is yet to explore.  
Conceptualization, S.S. and D.P.; methodology, S.S., S.R. and A.S.; validation, F.S.A. and J.A. formal analysis, S.S.; investigation, D.P.; resources. F.S.A. and J.A.; data curation, S.S. and S.R.; writing--original draft preparation, D.P., A.S., S.S. and D.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Taif University Researchers Supporting Project number (TURSP-2020/347), Taif University, Taif, Saudi Arabia.  
Not applicable.  
This research was supported by Taif University Researchers Supporting Project number (TURSP-2020/347), Taif University, Taif, Saudi Arabia.  
The authors declare no conflict of interest.
# Anti-Multipath Performance Improvement of an M-ary Position Phase Shift Keying Modulation System
## 5 Conclusions  
In this paper, the effectiveness of the least mean square time delay estimation method for the estimation of MPPSK-modulated signal delay in a multipath environment is verified by experiments. By analyzing the waveform characteristics of the MPPSK signal, the conditions of multipath separation are given. Using the obtained delay estimation value, the modulation parameters of the system can be selected to achieve the separation of the main path and the multipath. Compared with the multipath separation achieved by the spread spectrum scheme, the multipath separation method proposed by adjusting the MPPSK modulation parameters in this paper has a narrower bandwidth and higher spectrum utilization. The complexity of the whole system is low, and the demodulator reduces the introduced noise while using the multipath information to participate in the decision. The demodulation performance is improved compared with the existing method. The results show that the normalized symbol joint decision method can effectively combat multipath. As the sub-path gain gradually decreases, the advantage of the normalized symbol joint decision becomes more and more obvious. Compared with the previous methods, the method reduces the error rate of the demodulator. Future work will probably study the performance of MPPSK modulation in fading, strong interference environments and the optimization of the demodulator.  
[PERSON] developed the idea, conducted experiments, wrote the original draft, and revised the draft. [PERSON] supervised the process of this work as well as reviewed and revised the draft.  
This work was funded by National Natural Science Foundation of China (Grant No. 61671050).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} LPWAN & Low-Power Wide-Area Network \\ IoT & Internet of Things \\ UNB & Ultra-Narrowband \\ MPPSK & M-ary Position Phase Shift Keying \\ EBPSK & Extend Binary Phase Shift Keying \\ DIF & Digital Impacting Filter \\ IIR & Infinite Impulse Response \\ SIF & Special Impacting Filter \\ HF & High Frequency \\ GCC & Generalized cross-correlation \\ LMSTDE & Least Mean Square Time Delay Estimation \\ \end{tabular}
# Three-Dimension Inversion of Magnetic Data Based on Multi-Constraint UNet++
## 1 Introduction
Multi-component magnetic measurement data were introduced into the training data of the neural network, and the inversion accuracy was improved. The input data of the neural network adopted multiple observation data, including multi-scale data and various other types of data, such as magnetic three-component data, magnetic gradient tensor data, etc., and output 3D models to realize 3D-to-3D mapping. Then, the neural network architecture used 3D convolution to extract the 3D spatial information. The simulated and measured data verified that this method can effectively improve the accuracy of three-dimensional magnetic inversion [9]. In this paper, a multi-constraint DL method based on a UNet++ network is proposed for the 3D inversion of magnetic data. In recent years, deep learning researchers in the geophysical field have principally focused their innovative work on input data and network structure, and this study similarly attempts to perform innovative work on loss functions. In deep learning, the loss function controls the convergence direction of the network. In order to enable the deep learning network to learn in the direction of maintaining the model-fitting effect and narrowing the data-fitting difference, we added another two loss functions on the basis of the conventional loss function MSE, namely, the forward-fitting term \(FFit\) and the \(Dice\) term. Finally, after several experiments, the final mixed loss function was determined. We called this deep learning method using mixed loss functions a multi-constraint DL inversion method. In order to verify the effectiveness of the method, we designed a series of test models to test the effectiveness, noise resistance, and stability of the method. The model-fitting and data-fitting difference were used as the index of the evaluation method, and it was proved that the data-fitting and model-fitting difference in the multi-constraint DL inversion method decrease after using the improved loss function, thus indicating that this multi-constraint DL inversion method has a higher inversion accuracy. Finally, the measured magnetic anomaly data in Suqian City, JiangsuProvince, were inverted and predicted, and three faults distributed in the measured area were successfully identified.
## 2 Materials and Methods
### Determination of Mycotoxins
#### 2.6.3 Method Validation  
The DLIME method was characterized for the analysis of AFs, OTA, ZEA, ENNs, and BEA in sea bass side streams according to the Commission Decision [30] (Table 2). The analytical parameters determined for method validation were recoveries, repeatability (intraday precision), reproducibility (interday precision), matrix effects, linearity, limit of detection (LOD), and limit of quantification (LOQ). For recoveries at level of 10\(\times\)LOQ, the intraday and interday precision were between 68 and 120%. Matrix effects revealed that there was no significant signal suppression/enhancement (SSE) for the analyzed mycotoxins with SSE values ranging from 65 to 105%. The LODs and LOQs were obtained using the criterion for both transitions predetermined per each analyzed mycotoxin of S/N \(\geq\) 3 for calculating LOD and S/N \(\geq\) 10 for LOQ. LODs values ranged from 0.05 to 5 ug/L and LOQs from 0.2 to 17 ug/L. Regarding the linearity and regression coefficients obtained, all were higher than 0.990.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Mycotoxin**} & \multirow{2}{*}{**Retention Time**} & \multirow{2}{*}{**DP \({}^{\bullet}\)**} & \multirow{2}{*}{**Precursor Ion**} & \multicolumn{4}{c}{**Quantification Ion Q**} & \multicolumn{4}{c}{**Confirmation Ion q**} \\ \cline{5-10}  & & & & CE \({}^{\text{b}}\) & Product Ion & CXP \({}^{\text{c}}\) & CE & Product Ion & CXP \\ \hline AFB1 & 9.13 & 46 & 313.1 & 39 & 284.9 & 4 & 41 & 241.0 & 4 \\ AFB2 & 9.03 & 81 & 315.1 & 33 & 286.9 & 6 & 39 & 259.0 & 6 \\ AFG1 & 8.86 & 76 & 329.0 & 39 & 243.1 & 6 & 29 & 311.1 & 6 \\ AFG2 & 9.37 & 61 & 331.1 & 27 & 313.1 & 6 & 39 & 245.1 & 4 \\ ZEA & 10.40 & 26 & 319.0 & 15 & 301.0 & 10 & 19 & 282.9 & 4 \\ OTA & 10.27 & 55 & 404.3 & 97 & 102.1 & 6 & 27 & 239.0 & 6 \\ ENNA & 12.62 & 76 & 699.4 & 35 & 210.1 & 14 & 59 & 228.2 & 16 \\ ENNA1 & 12.22 & 66 & 685.4 & 37 & 210.2 & 8 & 59 & 214.2 & 10 \\ ENNB & 11.60 & 51 & 657.3 & 39 & 196.1 & 8 & 59 & 214.0 & 10 \\ ENNB1 & 11.89 & 66 & 671.2 & 61 & 214.1 & 10 & 57 & 228.1 & 12 \\ BEA & 12.00 & 116 & 801.2 & 27 & 784.1 & 10 & 39 & 244.1 & 6 \\ \hline \hline \end{tabular}  
* a DP: declustering potential (volts). b CE: collision energy (volts). c CXP: cell exit potential (volts).  
\end{table}
Table 1: Spectrometric parameters of liquid chromatography ion trap tandem mass spectrometry (LC-MS/MS-IT).
## 4 FE Analysis Procedure
### Step 2: Rotor Current Computation  
The first step of the procedure ends with the computation of the T model inductances and, applying (8), the proper rotor current is achieved, according to the RFO model.  
A second MS simulation is performed in which the stator current vector is the same as the previous step: only the rotor current is changed:  
\[\vec{i}_{\text{r2}}=-\text{j}\frac{L_{\text{m}}}{L_{\text{r}}}i_{\text{sq}} \tag{14}\]  
Furthermore, in the second step, the rotor current space vector is imposed along the \(q\)-axis, but the amplitude has been modified.  
The second simulation field solution is shown in Figure 5a. The spatial distribution of the current is the same as the previous simulation in Figure 4a. However, the amplitude of the rotor current is imposed according to the RFO model. In Figure 5a, the rotor flux lines are almost parallel to the \(d\)-axis, meaning that the \(q\)-axis component of the rotor flux linkage is close to zero. In Table 1, the procedure is applied on one-pole pairs, \(11\,\text{kW}\) induction motor, at different working points.  
Figure 4: First simulation field solution and related equivalent circuit. The aim of the first analysis is the computation of motor parameters, considering the presence of under-load currents. (**a**) First MS FEA field solution, the imposed current density is shown; (**b**) equivalent circuit related to the first FEA. Stator and rotor leakage and magnetizing indictances are highlighted.  
The equivalent circuit, related to the second MS FEA, is shown in Figure 5b. When the rotor current is inherent with the RFO model, the overall machine leakage flux is related to a single inductance, achieved as:  
\[L_{\rm t}=\frac{\lambda_{\rm sd}}{i_{\rm sq}} \tag{15}\]  
whereas the magnetizing inductance \(L_{\rm\varphi}\) can be derived using the second in (3).  
From this second simulation, the motor torque and slip can be derived using (9) and (10).  
Typically, in IMs, the ratio in (8) is almost in unity, since the leakage component of \(L_{\rm r}\) is a little percentage of the magnetizing component \(L_{\rm m}\).  
Actually, the rotor current imposed in the first step is a close approximation of the correct current according to the RFO model, imposed in the second step of the procedure; see Table 1, in which \(i_{\rm sq}=i_{\rmrq}\) corresponds to the rotor current in the first step and \(i_{\rmrq}\) is the rotor current in the second step.  
For this reason, the parameters \(L_{\rm m}\) and \(L_{\rm r}\) computed in the first FEA, do not change significantly in the second FEA; in fact, the rotor current, computed by (14) and imposed in the second FEA, is already correct to set to zero the \(q\)-axis rotor flux linkage; see the comparison between \(\lambda_{\rmrq}\) in the two steps, reported in Table 1.
## 4 Discussion of Numerical Results
### Low-Mass Floor Sensitivity to Loading Strategy  
When the selected SMD models are applied to the presently explored F#4 system, it is possible to expect a typical response like in Figure 10. In terms of RMS value, see Figure 10a, the trend of acceleration output with the walking frequency is mostly in line for all the SMD approaches, and the RMS acceleration increases with \(f_{p}\). Furthermore, a very close correlation of numerical estimates can be noted, especially for SMD-1 and SMD-3 models (7.4% of their scatter in the range \(f_{p}\) = 1.5 \(\div\) 2 Hz).  
The SMD-4 formulation, see Figure 10a, generally tends to overestimate the effect of pedestrian (+43.5% compared to other SMD models for the examined \(f_{p}\) range), while the SMD-2 approach underestimates the other modelling strategies, especially for low walking frequencies \(f_{p}\) (\(-\)10.7% for the RMS acceleration). In terms of acceleration peak trend, as in Figure 10b, a similar dynamic response can be noted for the F#4 system. As a significant consequence, the corresponding CREST factor from Equation (20) also has marked sensitivity to different SMD models and input biodynamic parameters.  
Figure 10: Numerical results for the glazed F#4 system, based on different SMD or equivalent-force calculations: (**a**) RMS acceleration and (**b**) peak acceleration, as a function of walking frequency \(f_{p}\), with (**c**,**d**) corresponding SMD (average \(\pm\) standard deviation) to RU percentage scatter (ABAQUS).  
On the other side, it should be reminded from Table 1 that the F#4 slab has a relatively low mass but still high frequency. Figure 10c,d, in this regard, shows the corresponding average (\(\pm\)standard deviation) of SMD averages towards the RU model estimates in percentage scatter. Under RU assumption and high frequency, the SMD approach overestimates the RU predictions in the same order of magnitude of F#1 and F#3 floor systems. Conversely, the magnitude of percentage scatter is significantly reduced compared to Figure 9, less than 15%, thus suggesting a more stable trend of various SMD estimates with \(f_{p}\).
###### Abstract  
The objective of this study is to present a new coupling method in order to measure the electrical properties of titanium alloy pedicle screws used in spinal surgery and to compare it with other common methods of measurement. An experimental setup was devised to test the electrical resistance of two specimens of pedicle screws using four methods for coupling the sensing leads, including the use of multimeter probes, alligator clips, wrapped wires and encapsulation with thermo-retractable sleeves. The electrical resistance of the pedicle screw under testing was measured at a current of 10 mA for each coupling method, and the results compared. Our findings show that although widely used in electrical analysis, the alligator clips do not perform as well as the other methods, such as simple wrapping of wires around the screw or the direct application of multimeter probes. The use of thermo-retractable sleeves provides the lowest resistance and inter-quartile range and is closer to the tabled values for the screw's titanium alloy. Additionally, only this method allows the measurement of identical resistivity values between different screw models manufactured with the same titanium alloy. We then concluded that the use of wrapped wires encapsulated with thermo-retractable sleeves allow more accurate measurements of the pedicle screw's electrical properties.  
Electrical resistance; pedicle screw; electrical stimulation; intraoperative monitoring; spine surgery; pedicle instrumentation 2021
# Research on the Path Planning of Unmanned Sweepers Based on a Fusion Algorithm
## 3 Method
### Extract Key Points Based on Raster Map Boundaries and Obstacles  
A* algorithm will produce many redundant points in the addressing process, which will affect the planning efficiency. Therefore, the expansion nodes in path planning were screened and the key points in the path were extracted to improve the algorithm. The redundant nodes in the node expansion process of the A* algorithm were removed, and only key nodes were retained as expansion points for path planning.  
In this paper, a large number of redundant points were excluded and the number of unnecessary nodes was reduced by searching the key points in the path based on raster  
Figure 2: Minimum heap sort process.  
map boundaries and obstacles. Two key points were connected to realize the jump between two points, so as to improve the efficiency of path search. The key point search process is as follows:  
Step 1: In the OpenList table, select a node with the minimum generation value or select a starting node to start the search.  
Step 2: Search search direction in the line or oblique search only. The search ends if a node is found, an obstacle is encountered, or a raster map boundary is hit. Add the searched nodes to the OpenList table.  
Step 3: If the search is not completed in the oblique direction, step forward in the oblique direction and repeat the above process.  
Step 4: If the comprehensive search has been completed, the current node is considered to have been completely searched, and the current node is removed from the OpenList table and added to the ClostList table.  
Step 5: Repeat the search for the node with the lowest weight in the ClostList table until the OpenList table is empty or the final point is found. The points stored in the CloseList table are the key points to search for.  
As shown in Figure 3, the red grid X is the starting point, the green grid Y is the end point, the blue X5 and X9 are the key points, and the path connected by nodes X \(\rightarrow\) X\({}_{5}\)\(\rightarrow\) X\({}_{9}\)\(\rightarrow\) Y is the final path planned by the algorithm. Compared with the traditional A* algorithm, the improved A* algorithm only leaves the starting point, the ending point, and the two key points X\({}_{5}\) and X\({}_{9}\); deletes a large number of redundant points; and adds the key points to the OpenList table for cost evaluation and path planning. The improved A* algorithm can make long-distance connections through key points, and it takes less time to reach the target point, which greatly improves the planning efficiency.
# Effect of Preheating Treatment before Defatting on the Flavor Quality of Skim Milk
###### Abstract  
Skiim milk has a poor flavor due to the lack of fat. Finding ways to improve the flavor quality of skim milk has attracted the attention of more and more researchers. The purpose of this study was to create a skim milk product with good flavor by processing. Briefly, raw milk was treated by preheating at pasteurization (85 \({}^{\circ}\)C, 15 s) and ultra-high temperature (UHT) instantaneous sterilization (137-141 \({}^{\circ}\)C, 4 s). Subsequently, the sample was centrifuged to remove fat and obtain two kinds of skim milk, namely, PSM (skim milk obtained by preheating at 85 \({}^{\circ}\)C, 15 s) and USM (skim milk obtained by preheating at 137-141 \({}^{\circ}\)C, 4 s). The results showed that the intensity of the main sensory attributes (overall liking, milk aroma, etc.) and the concentrations of the key flavor compounds (2-heptanone, 2-nonanone, decanal, hexanoic acid, etc.) were significantly higher in the USM (\(p<0.05\)) than that of the PSM and RSM (skim milk without preheating). Principal component analysis (PCA) with E-Nose (electronic nose) showed that the RSM had significant differences in the milk aroma compared with the PSM and USM. Furthermore, it was found that there were good relationships between volatile compounds and sensory attributes by partial least squares regression (PLSR) analysis. These findings provided insights into improving the flavor quality of skim milk by preheating treatment instead of any flavor additives.  
skim milk; preheating treatment; sensory analysis; e-nose; volatile compounds +
Footnote †: journal:
## 2 Materials and Methods
### Experimental Data Normalization  
Data preprocessing is a crucial step in model training, as it enhances accuracy and accelerates the learning process [36; 37]. Among various preprocessing techniques, we applied input data normalization to optimize model performance by adjusting the data range and distribution. This approach improves training speed, enhances learning accuracy, and ensures model stability. Normalization is widely used in image processing and numerical data models [38]. Because the training and test datasets did not follow a normal distribution, we applied min-max normalization, which scales the input data to a range of \([0,1]\) based on data size. The transformation is defined in Equation (7).  
\[x_{nm}=\frac{x_{i}-x_{min}}{x_{max}-x_{min}}. \tag{7}\]  
Here, \(x_{nm}\) represents the normalized input data; \(x_{i}\) is the input data; and \(x_{min}\) and \(x_{max}\) are the minimum and maximum values in the input data, respectively.  
For effective machine learning with neural networks, the emission and engine data used in the training model were scaled within the range of \([0,1]\), as shown in Table 7.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Spearman’s Correlation (\(p\))** &
\begin{tabular}{c} **Pearson** \\ **Correlation (R\({}^{2}\))** \\ \end{tabular} & **Rank** \\ \hline Fuel flow & 0.9850 & 0.995201 & 1 \\ M/E T/C RPM & 0.9791 & 0.985149 & 2 \\ Main engine load & 0.9772 & 0.997298 & 3 \\ M/E scavenge air & 0.9767 & 0.991914 & 4 \\ receiver in press & 0.9755 & 0.997075 & 5 \\ Shaft torque & 0.9750 & 0.990015 & 6 \\ M/E A/C C.W outlet & 0.8610 & 0.9672 & 23 \\ temperature & & & \\ \hline \hline \end{tabular} A/C: Air cooler; C.W: Cooling water.  
\end{table}
Table 6: Results of the data correlation analysis.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Time**} & \multirow{2}{*}{**CO2**} & \multirow{2}{*}{**RPM**} & \multirow{2}{*}{**Torque** (N-m)**} & \multirow{2}{*}{**Power** (kW)**} & \multirow{2}{*}{**Cylinder**} & **No. 1 Cylinder** & \multirow{2}{*}{\(\cdots\)} \\  & & & & & & **(bar)** & & **Temperature** & \\ \hline
1 & 11:06:19 & 0.6366 & 0.9971 & 0.9070 & 0.8968 & 0.5680 & 0.9970 & \(\cdots\) \\
2 & 11:06:20 & 0.6371 & 0.9971 & 0.9060 & 0.8959 & 0.5675 & 0.9967 & \(\cdots\) \\
3 & 11:06:21 & 0.6381 & 0.9971 & 0.9050 & 0.8950 & 0.5670 & 0.9965 & \(\cdots\) \\ \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) \\
3253 & 12:05:20 & 0.6393 & 0.9821 & 0.8969 & 0.8865 & 0.5493 & 0.9954 & \(\cdots\) \\ \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) \\
21,879 & 21:35:40 & 0.8131 & 0.9752 & 0.8541 & 0.8385 & 0.5355 & 0.9876 & \(\cdots\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Normalization result of the experimental data.
## 3 Results and Discussion
### Modeling
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Factors**} & \multicolumn{4}{c}{**Protein Yield (\%)**} \\ \hline \multirow{2}{*}{**Run**} & **Temperature** & **Time** & **Alkaline** & \multirow{2}{*}{**Experimental \({}^{*}\)**} & **Predicted \({}^{**}\)** & **Difference** \\  & **(\({}^{\circ}\)C)** & **(min)** & **(Coded)** & & & \\ \hline
1 & 25 & 100 & 2 & 31.51 \(\pm\) 1.83 & 33.16 & 1.65 \\
2 & 47.5 & 25 & 1 & 23.19 \(\pm\) 1.36 & 24.80 & 1.60 \\
3 & 25 & 62.5 & 3 & 45.56 \(\pm\) 1.16 & 45.51 & 0.04 \\
4 & 70 & 25 & 2 & 44.03 \(\pm\) 2.28 & 42.38 & 1.65 \\
5 & 47.5 & 62.5 & 2 & 41.29 \(\pm\) 1.20 & 38.57 & 2.71 \\
6 & 47.5 & 100 & 3 & 49.86 \(\pm\) 2.31 & 48.26 & 1.60 \\
7 & 25 & 62.5 & 1 & 20.39 \(\pm\) 1.44 & 16.79 & 3.59 \\
8 & 47.5 & 25 & 3 & 48.77 \(\pm\) 1.96 & 46.82 & 1.94 \\
9 & 25 & 25 & 2 & 21.04 \(\pm\) 1.66 & 23.03 & 1.99 \\
10 & 70 & 62.5 & 3 & 45.88 \(\pm\) 1.42 & 49.47 & 3.59 \\
11 & 47.5 & 100 & 1 & 33.43 \(\pm\) 2.72 & 35.37 & 1.94 \\
12 & 47.5 & 62.5 & 2 & 38.26 \(\pm\) 1.11 & 38.57 & 0.30 \\
13 & 70 & 62.5 & 1 & 43.23 \(\pm\) 2.74 & 43.28 & 0.04 \\
14 & 47.5 & 62.5 & 2 & 36.17 \(\pm\) 1.58 & 38.57 & 2.40 \\
15 & 70 & 100 & 2 & 46.25 \(\pm\) 1.23 & 44.26 & 1.99 \\ \hline \hline \end{tabular} \({}^{*}\)Mean of three replicates, ** Calculated according to Equation (4).  
\end{table} 
Table 4: Experimental and predicted values of the protein yield after the extraction process according to the Box-Behnken design. Figure 1: Protein yield (%) from experimental and predicted values.