esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of Coil Misalignment in Data Transmission over the Inductive Link of an EV Wireless Charger
## 2 Overview of a Wireless EV Charger
The works in [22; 23] show how the values of the capacitors are computed in order to verify this condition under specific relative positions between the coils. Firstly, \(C_{2}\) is computed to resonate with the secondary coil at the resonant operational frequency (\(\omega\)). The value of \(C_{2}\) does not depend on the compensation topology. Then, the impedance reflected from the secondary to the primary side is obtained. With the use of the reflected impedance, it is possible to derive the impedance seen by the source by analyzing a non-coupled circuit, which depends on the compensation topology  
Figure 2: Equivalent circuit of an ICPT system. \(C_{1}\) is designed so that the imaginary part of the impedance seen by the source is null. The equations in Table 1 show how the capacitors are computed for each single-resonant compensation topology. It is important to note that the process for defining the values of the capacitors only focuses on obtaining an input impedance with a null reactance. Thus, it does not force impedance matching as conventional communication systems do, because communication systems and wireless power transfer chargers are designed based on two different goals. The goal of a wireless power transfer system is to perform transmission efficiently [24]. In contrast, the impedance matching strategy followed by communication systems derives the maximum power transmission but with an efficiency equal to 50%. As can be observed, the appropriate values for the capacitor, \(C_{1}\), depend on the coils' relative placements, which are modeled by \(M\). The charger is constructed by imposing design conditions, such as the distance between the coils. However, these conditions may not hold because of coil misalignments. The consequences of this effect are explained in the next section.
# Process Mineralogy of Micro/Nano Silicon-Carbon Ore Obtained from Jiangxi, China
## 3 Results
### Particle Size Distribution  
Particle size distribution of the sample is shown in Table 2 and Figure 7. The average particle sizes (D50) of the ore sample, carbon-free sample, and quartz-free sample were 13.80, 6.585, and 11.59 \(\upmu\)m, respectively. Particles in the ore sample in the size ranges of 1-45 and \(<\)1 \(\upmu\)m accounted for 79.92% and 6.48% of all particles, respectively.  
Particles in the carbon removal sample within the size ranges of 1-45, \(<\)1, and \(>\)45 \(\upmu\)m accounted for 83.15%, 11.11%, and 5.74% of the total particles, respectively. Compared with the ore sample particle size distribution (D10, D50, and D90), the particle size of carbon removal sample decreased obviously.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Particle Size** & **\textless{}1 \(\upmu\)m** & **1–10 \(\upmu\)m** & **10–45 \(\upmu\)m** & **\textgreater{}45 \(\upmu\)m** \\ \hline Ore sample & 6.48\% & 34.60\% & 44.60\% & 13.60\% \\ Carbon removal sample & 11.11\% & 50.10\% & 33.05\% & 5.74\% \\ Quartz removal sample & 4.00\% & 41.02\% & 43.76\% & 11.22\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Particle size distributions of ore, carbon-free, and quartz-free samples.  
Figure 6: **(a–c)** SEM images of quartz removal sample and (d) EDS elemental spectrum of area C.  
In the quartz-free sample, the particles were mainly distributed in the range of 1-45 \(\upmu\)m (84.78%). Compared with the ore sample, there were fewer particles <1 \(\upmu\)m in the quartz-free sample (4%). The decrease in particle size was not obvious.
## 2 Materials and Methods  
### Preparation of Ni-Co Binary Hydroixde on Nickel Foam  
Ni-Co binary hydroxide on nickel foam was fabricated by a facile one-step solvothermal reaction. Nickel foam (1 cm \(\times\) 1 cm) was pretreated with 6 M HCl, deionized water, and ethanol, sequentially. Then, a precursor solution containing the desired molar mixture of Ni(NO\({}_{3}\))\({}_{2}\cdot\)6H\({}_{2}\)O, Co(NO\({}_{3}\))\({}_{2}\cdot\)6H\({}_{2}\)O (the feeding concentration ratios of Ni\({}^{2+}\cdot\)Co\({}^{2+}\) were 9:1, 7:3, 5:5, 3:7, and 1:9, respectively, and total molar of cations was 5 mmol), 2 mmol NH\({}_{4}\)F, 6 mmol CO(NH\({}_{2}\))\({}_{2}\), and 50 mL deionized water was prepared. After vigorous stirring for 10 min, the solution and nickel foam were transferred to a Teflon-lined stainless-steel autoclave and maintained at 120 \({}^{\circ}\)C for 8 h. The final products were rinsed with deionized water after cooling down to room temperature naturally. For convenient clarification, the samples were labeled as Ni\({}_{1-\times}\)Co\({}_{\times}\)(OH)\({}_{2}\) (x = 0.1, 0.3, 0.5, 0.7, and 0.9). The loading mass on the nickel foam was approximately 1.5 mg/cm\({}^{2}\).
# Musculoskeletal Rehabilitation: State-of-the-Art
## 2 Technology Advancement
### Instrumental Physical Therapies for Pain Management  
Instrumental physical therapies are currently used as a complementary approach for the management of musculoskeletal pain [9; 10; 18; 19]. In this scenario, transcutaneous electrical nerve stimulation (TENS) is a wide-spread physical therapy, currently used in routine clinical practice. Recently, an umbrella review of systematic reviews has been published [20], assessing the effects of TENS in patients with cervical pain. The authors included eleven systematic reviews that supported the role of TENS in short-term pain relief in both acute and chronic cervical pain.  
Furthermore, another painful condition of rehabilitative interest is complex regional pain syndrome (CPRS), characterized by a continuing regional pain disproportionate in time or degree to the usual course of any lesion [21]. In this context, a recent scoping review performed by [PERSON] et al. [22] assessed the effects of physical therapies in treating CPRS, reporting potential benefits in terms of both pain relief and functional improvement by TENS and pulsed electromagnetic field therapy.  
Moreover, percutaneous electrical nerve stimulation (PENS) has been introduced in the management of mixed chronic pain in musculoskeletal disorders [23]. Our research group recently performed a scoping review [24] assessing the state-of-the-art of this mini-invasive neuromodulation technique. Despite the high heterogeneity of the studies, PENSeffectiveness seemed to be supported by the scientific literature in the management of low back pain (LBP) and osteoarthritis knee pain not responding to conventional therapy.
# Enabling Horizontal Collaboration in Logistics Through Secure Multi-Party Computation
## 3 Optimization Problem
### CFDP Instances and Valid Solutions  
On a high level, the CFDP [3] is defined by a directed weighted graph \(\mathcal{M}:=\{\mathcal{N},\mathcal{E}\}\) specifying a map. The nodes \(\mathcal{N}\) specify locations (e.g., regions/cities, warehouses, etc.) for senders and receivers, while the edges \(\mathcal{E}\subseteq\{(N_{1},N_{2},w)\,:\,N_{1}\
eq N_{2}\in\mathcal{N},w\in\mathbb{N}\}\) represent roads between locations, together with the time \(w\) required to move from theorigin \(N_{1}\) to the destination \(N_{2}\). We assume a complete graph, i.e., every pair of nodes is connected by a unique edge.  
The relevant actors in a CTN are collaborating freight hailiers, each managing a fleet of trucks and a set of customer orders. Hailiers, trucks, and orders all have unique identifiers, modeled as positive integers.  
A _hualier_Haulier is a pair \((\textsc{haulierId},(\textsc{truckId}_{1},\ldots,\textsc{truckId}_{\ell}))\), where the value \(\textsc{haulierId}\in\mathbb{N}\) denotes the hualier identifier and \((\textsc{truckId}_{1},\ldots,\textsc{truckId}_{\ell})\) is the list of identifiers of the trucks that are controlled by the hualier.  
A _truck_Truck is described by a truck identifier \(\textsc{truckId}\in\mathbb{N}\), the identifier \(\textsc{haulierId}\) of the hualier that controls the truck, the current truck position \(\textsc{position}\in\mathcal{N}\), and the truck capacity \(\textsc{capacity}\in\mathbb{N}\). The latter can be thought of as, e.g., the number of pallets that fit on the loading area. Furthermore, each truck has a dedicated starting position (e.g., corresponding to its home base).  
An _order_Order consists of an identifier \(\textsc{orderId}\in\mathbb{N}\), its origin and destination \(\textsc{origin}\
eq\textsc{dest}\in\mathcal{N}\), its volume \(\textsc{vol}\in\mathbb{N}\), and the identifier \(\textsc{haulierId}\) of the hualier that the order is assigned to (including \(\bot\) for \"unassigned\").  
Finally, a _truck drive_Drive is a tuple of the form \((\textsc{truckId},(\textsc{origin},\textsc{dest}),(\textsc{orderId}_{1},\ldots, \textsc{orderId}_{k}))\), where \(\textsc{truckId}\) is the identifier of the truck assigned to the drive, \(\textsc{origin}\in\mathcal{N}\) is the drive departure node, \(\textsc{dest}\in\mathcal{N}\) is its destination, and the list \((\textsc{orderId}_{1},\ldots,\textsc{orderId}_{k})\) denotes orders that need to be carried from the departure node to the destination node.  
Intuitively, a valid solution to a collaborative freighter delivery problem instance is now a set of consistent truck drives respecting the trucks' maximum capacity, such that each order is picked up at its origin and delivered to its destination address. We omit the formal modeling of CFDP instances and valid solutions here, and refer the interested reader to [3; 5].
## 2 Materials and Methods
### Agricultural Drought Index: Soil Moisture Anomalies  
SM anomalies have been successfully used in many works [70; 71; 72] and have been proven to be a good indicator of agricultural drought [73; 74]. In the present study, monthly SM anomalies at the provincial scale in the root zone were used to analyze the impact of agricultural drought on cereal yields. For this purpose, first, a provincial-scale spatial average of SM in the selected cereal areas was obtained. Then, provincial-scale SM anomalies were calculated as follows:  
\[\text{SM anomalies}=\text{SM}_{\text{t}}-\overline{SM}/\delta_{\text{SM}}, \tag{1}\]  
where \(\text{SM}_{\text{t}}\) is the monthly SM series for year t, \(\overline{SM}\) is the monthly average using the entire study period and \(\delta_{\text{SM}}\) is the standard deviation of SM in the study period at the provincial scale [70; 71; 72].
# Research on a Point Cloud Registration Method Based on Dynamic Neighborhood Features
## 3 Results
(**a**) Registration result of indoor data, (**b**) registration result of the Dragon data, (**c**) registration result of the storage box data, and (**d**) registration result of the chair data. Figure 6 provides a comparative visualization of registration results. Three datasets are used: the Stanford Bunny, the Stanford Dragon, and a chair dataset captured with LiDAR (arranged from top to bottom). The columns display, from left to right: the original, unregistered point clouds, the point clouds registered using K4 PCSs, the point clouds registered using a KD-tree-based improved ICP, the point clouds registered using 3D-NDT, and the point clouds registered using the proposed method. The more overlap of point cloud data, the better the registration effect, which can usually be measured by root mean square error. Root mean square error is the relationship between the distance difference between source point cloud data and target point cloud data after matrix transformation [27], as shown in Equation (28):  
\[E=\sqrt{\frac{1}{n}{\sum\limits_{i=1}^{n}{(A\cdot p_{i}-q_{i})}^{2}}} \tag{28}\]  
where A is the transformation matrix; \(p_{i}\) represents source point cloud; \(q_{i}\) is the target point cloud. The smaller the value of \(E\), the smaller the registration error and the higher the accuracy. The registration effect of each method is measured by the time taken for the registration of point cloud data and the root mean square error, as shown in Tables 2 and 3. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Registered Data** & **Time/s** & **Standard Deviations** \\ \hline \multirow{3}{*}{K4 PCSs method} & [PERSON] & 4.9 & \multirow{3}{*}{6.976} \\  & [PERSON] & 14.11 & \\  & [PERSON] & 22.07 & \\ \hline \multirow{3}{*}{KD-ICP method} & [PERSON] & 11.86 & \multirow{3}{*}{10.15} \\  & [PERSON] & 8.64 & \\ \cline{1-1}  & [PERSON] & 31.5 & \\ \hline \multirow{3}{*}{3D-NDT method} & [PERSON] & 8.7 & \multirow{3}{*}{2.206} \\  & [PERSON] & 7.61 & \\ \cline{1-1}  & [PERSON] & 12.78 & \\ \hline \multirow{3}{*}{Proposed method} & [PERSON] & 5.7 & \multirow{3}{*}{2.46} \\ \cline{1-1}  & [PERSON] & 4.69 & \\ \cline{1-1}  & Chair & 10.36 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Registration time for point cloud data. Figure 6: Comparison of point cloud data registration results. Table 2 shows the registration time for each method. K4 PCSs achieved the shortest registration time for the Bunny dataset (4.9 s), while the proposed method had the shortest registration times for both the Dragon (4.69 s) and chair (10.36 s) datasets. Table 3 shows the root mean square error (RMSE) for the registration of each method. K4 PCSs achieved the lowest RMSE for the Bunny dataset (0.599 mm), while the proposed method yielded the lowest RMSE for both the Dragon (0.964 mm) and chair (1.115 mm) datasets.
# An Effective Wide-Bandwidth Channel Access in Next-Generation WLAN-Based V2X Communications
## 3 Proposed Scheme and Its Variants
### Alternative Channel Access Schemes for 20-MHz Transmission  
Since there might be hardware restrictions in adding the proposed simplified receiver, several variant 20-MHz frame transmission schemes with a single transceiver are also proposed. Without the proposed simplified receiver, both detection and decoding, i.e., ED and PD, of received 10-MHz frames in the secondary channel are difficult to implement. Therefore, with a single transceiver, only ED sensing is performed in the secondary channel. To the best of our knowledge, today's common WLAN implementation already employs such ED sensing in secondary channels for wide-bandwidth channel operation. Since fairness must be considered more seriously in OCB communication environments than BSS communication environments, new back-off procedures with ED sensing in secondary channels need to be designed. Since fairness and medium access delays have a trade-off relationship, in the design of the proposed variant schemes, the joint optimization of both aspects must be considered.
## 2 Dense Feature Extraction and Matching Theory and Mismatch Cause Analysis  
In machine-vision-based structural displacement measurement methods, structural displacement is measured by extraction and tracking of feature points. Dense feature extraction can enhance the quantity of detection points, facilitate the measurement of subtle changes in structural global displacement, and detect minor defects in structure health monitoring. This section introduces the basic theory of the SIFT and FLANN feature matching algorithms and analyzes the causes of feature mismatches.
# Enhancing Autonomous Driving Navigation Using Soft Actor-Critic
## 5 Experiments
### Training and Testing Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model** & **Dens.** & **Avg. Re** & **Avg. Time** & **Avg. Speed** & **Avg. Dis** & **Avg. Steps** & **Succ.** & **Coll.** \\  & & & **(s)** & **(m/s)** & **(m)** & & **(\%)** & **(\%)** \\ \hline \multirow{4}{*}{**SAC**} & \(AVs=120\) & **93.01** & **6.70** & **3.77** & **31.58** & **31.64** & **98** & **2** \\  & \(AVs=140\) & **92.99** & **7.48** & **3.78** & **31.70** & **32.91** & **98** & **2** \\  & \(AVs=160\) & **91.79** & **8.50** & **3.64** & **31.72** & **35.06** & **96** & **2** \\  & \(AVs=210\) & **85.07** & **10.36** & **3.75** & **31.63** & **30.80** & **94** & 6 \\ \hline \multirow{4}{*}{**PPO**} & \(AVs=120\) & 62.1 & 7.01 & 3.60 & 32.51 & 36.01 & 88 & 12 \\  & \(AVs=140\) & 60.8 & 8.02 & 3.68 & 32.63 & 37.20 & 86 & 14 \\  & \(AVs=160\) & 57.2 & 8.70 & 3.55 & 32.72 & 38.10 & 83 & 17 \\  & \(AVs=210\) & 50.1 & 10.5 & 3.70 & 32.51 & 36.20 & 80 & 20 \\ \hline \multirow{4}{*}{**DQN**} & \(AVs=120\) & 35.74 & 7.89 & 3.58 & 33.48 & 47.13 & 70 & 30 \\  & \(AVs=140\) & 24.98 & 9.28 & 3.52 & 33.35 & 47.81 & 64 & 36 \\  & \(AVs=160\) & 29.40 & 8.63 & 3.45 & 33.57 & 49.0 & 66 & 34 \\  & \(AVs=210\) & 1.86 & 10.88 & 3.63 & 33.54 & 47.69 & 52 & 48 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Quantitative evaluation of SAC performance compared to DQN. Best results, indicated by bold data. Figure 5: The average reward of SAC, PPO, and DQN during episodes.
## 3 Optimization of Cutting Parameters under Optimal MQL Condition
### Results and Discussion
It identifies that feed rate and depth of cut are the most influential factors related to surface roughness in the linear regression model. The \(P\) values (\(<\)0.05) feed rate, depth-of-cut, and workpiece hardness mean that the effect of these factors has statistical significance. When the P value of the model is less than 0.05, the linear regression model is considered to be statistically significant. With \(R\)-Sq = 85.09%, it expresses that 85.09% of the variability in surface roughness is explained by the factors of cutting conditions. The Probability plot of surface roughness under MQL conditions shown in Figure 4 represent that the data points of \(R\)a-mql lay reasonably close to a center straight line. It proves that the terms chosen in model have significant influence on the responses provided. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Source**} & **Degrees of Freedom** & **Sequential Sums** & **Adjusted Sums** & **Adjusted Mean** & \multirow{2}{*}{**F**} & \multirow{2}{*}{**P**} & \multirow{2}{*}{**PC\%**} \\  & **Freedom** & **of Squares** & **of Squares** & **of Squares** & & & & \\ \hline Regression & 4 & 0.165730 & 0.165730 & 0.041433 & 31.3870 & 0.000 \({}^{\text{a}}\) & - \\ \(\upsilon\) & 1 & 0.004576 & 0.004576 & 0.004576 & 3.4666 & 0.076 & 2.349 \\ \(f\) & 1 & 0.102756 & 0.102756 & 0.102756 & 77.8421 & 0.000 \({}^{\text{a}}\) & 52.757 \\ \(d\) & 1 & 0.042438 & 0.042438 & 0.042438 & 32.1484 & 0.000 \({}^{\text{a}}\) & 21.789 \\ \(H\) & 1 & 0.015961 & 0.015961 & 0.015961 & 12.0911 & 0.002 \({}^{\text{a}}\) & 8.195 \\ Error & 22 & 0.029041 & 0.029041 & 0.001320 & - & - & - \\ Total & 26 & 0.194771 & - & - & - & - & - \\  & & & \(S\) = 0.0363325 \(R\)-Sq = 85.09% \(R\)-Sq(adj) = 82.38\% & & & \\ \hline \hline \end{tabular} \({}^{\text{a}}\) Significant.  
\end{table} 
Table 12: Analysis of variance for the linear regression model. Figure 4: The probability plot of surface roughness under MQL conditions. A series validation experiments were carried out to examine the accuracy of the linear regression model. The validation experiment is different from the initial experiment. The result of validation experiments is shown in the Table 13.  
Comparing the surface roughness of the experiment and surface roughness of the model, the percentage differences are less than 10%. It proved that the accuracy of the model is of value.
## 3 Results and Discussion
### Seismic Performance Analysis for Other Structural Variables
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Soil Class** & **Parameter** & **Reference** & **Model I** & **Model II** & **Model III** & **Model IV** \\ \hline \multirow{4}{*}{ZA} & Period (s) & 0.696 & 0.5971 & 0.7845 & 0.7765 & 0.63257 \\ \cline{2-7}  & Base Shear (kN) & 6693.49 & 6515.39 & 4898.13 & 6924.58 & 7203.13 \\ \cline{2-7}  & K-class (kN/m) & 129,717 & 153,480.00 & 110,111.44 & 121,184.71 & 152,322.00 \\ \cline{2-7}  & K-eff (kN/m) & 66,112.1 & 74,950.30 & 58,385.54 & 62,009.67 & 78,548.80 \\ \hline \multirow{4}{*}{ZB} & Period (s) & 0.696 & 0.5971 & 0.7845 & 0.7765 & 0.63257 \\ \cline{2-7}  & Base Shear (kN) & 6693.49 & 6515.39 & 4898.13 & 6924.58 & 7203.13 \\ \cline{2-7}  & K-class (kN/m) & 129,717 & 153,480.00 & 110,111.44 & 121,184.71 & 152,322.00 \\ \cline{2-7}  & K-eff (kN/m) & 66,112.1 & 74,950.30 & 58,385.54 & 62,009.67 & 78,548.80 \\ \hline \multirow{4}{*}{ZC} & Period (s) & 0.696 & 0.5971 & 0.7845 & 0.7765 & 0.63257 \\ \cline{2-7}  & Base Shear (kN) & 6693.49 & 6515.39 & 4898.13 & 6924.58 & 7203.13 \\ \cline{2-7}  & K-class (kN/m) & 129,717 & 153,480.00 & 110,111.44 & 121,184.71 & 152,322.00 \\ \cline{2-7}  & K-eff (kN/m) & 66,112.1 & 74,950.30 & 58,385.54 & 62,009.67 & 78,548.80 \\ \hline \multirow{4}{*}{ZD} & Period (s) & 0.696 & 0.5971 & 0.7845 & 0.7765 & 0.63257 \\ \cline{2-7}  & Base Shear (kN) & 6693.49 & 6515.39 & 4898.13 & 6924.58 & 7203.13 \\ \cline{1-1} \cline{2-7}  & K-class (kN/m) & 129,717 & 153,480.00 & 110,111.44 & 121,184.71 & 152,322.00 \\ \cline{1-1} \cline{2-7}  & K-eff (kN/m) & 66,112.1 & 74,950.30 & 58,358.54 & 62,009.67 & 78,548.80 \\ \hline \end{tabular}
\end{table} 
Table 9: Comparison of the results obtained for structural models. stiffness values remained the same in all structural models. The independence of these variables from structural properties did not change the results. In addition, the change in local soil conditions did not change these results. For Model I, where the number of storeys in the structure was reduced, the period value decreased with the increase in the stiffness value. This decrease was lower than the reference building for this model. Structural Model II, which was created by increasing the ground storey height and was relatively created, caused this negativity in the structure to decrease the stiffness in the structure and therefore to increase the period value. For Model III, which was created by increasing the frame opening, the period value increased compared to the reference building, while the stiffness values decreased. For Model IV, where the height of each storey in the structure was reduced, the period value decreased depending on the increase in the stiffness value. In general, for the ZD soil class: Recommend higher damping ratios, smaller frame spans, and reinforced foundation designs. For ZB/ZC soil classes: Increase use of base isolators or shear walls to counteract soil softening effects.
## 5 Discussion on Potential Applications of the Data Products
### Spaceborne GNSS-R from Different GNSS Constellation and Frequency Bands
In fact in [71], by using the complex waveform products, it is clearly shown that the SNR of the reflected signal can be further improved by up to 1.2 dB for Galileo E1 B/C and BDS-3 BIC signal by increasing the coherent integration time to 4 ms. However, it still needs further investigation how the coherent integration times for different GNSS-R signals can be further optimized as the functions of GNSS-R geometry (e.g., elevation and azimuth angles) and reflecting surface (e.g., open ocean, land, sea ice or ice sheet). * The complex waveform products from the other GNSS constellations can be used to assess the performance of these new GNSS signals in different applications (e.g., in [27; 71; 72]). Such assessments are of great importance for the definition and optimization of mission and instrument specifications for future spaceborne GNSS systems, e.g., in establishing the link budget for reflected signals from different GNSS constellations. Moreover, the combinations of these simultaneous GNSS-R measurements at different frequencies (e.g., as shown in Figure 6d,e) can also be further investigated, which can improve the performances of the GNSS-R retrievals. For example, the combination of the L1 and L2 delay observations (phase delay or group delay) can be used to estimate the ionosphere delay for precise GNSS-R altimetry [18] or ionospheric total electron content measurement [73]. In addition, the combination of their power or amplitude observations at different frequencies can provide a more robust way to estimate the surface bistatic radar cross section or reflectivity, which can be also attempted in future studies.