esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Selecting Optimal Parameters  
From the selection by geometric characteristic type, i.e., width, depth, and bump volume, the overall combination that allows for the most reliable results must be selected, since the characteristics of potholes in a real application will be determined by a single model. In this way, optimal recommendations in depth, width and volume measurements were analyzed, always seeking to minimize the error of these types of measurement. Table 4 was based on the discussion presented in Section 3.2, the recommended overlap was chosen by severity level, as the use of these recommendations varies depending on the minimum level of severity to be measured. For heights of 2, 5 and 8 m, the experiments were excessively laborious. For these heights, it is difficult to ensure proper positioning and correct overlap between images, as the handling of the UAV at such a low height becomes imprecise due to the sensitivity of the control. In addition, the GPS does not deliver a correct position of the aircraft, which in some cases led to the necessary repetition of the shooting. Although capturing photographs at low altitude allows for more accurate 3D models, in practical terms and in order to optimize the flight and inspection (balance in time, quality, area observed, etc. ), it is not recommended to perform the flights at the indicated heights. Therefore, it is not recommended to plan flights with heights less than 10 m. At 40 m, the error levels  
are maximum, therefore there is no practical justification to make a recommendation for this. For the realization of the case study, the height recommendations of 10 and 15 m were used, which present an acceptable level of error in practice. Table 5 shows longitudinal (B) and transverse distances (A) between photographs captured with the UAV to comply with overlaps; longitudinal (\(p\)) and transverse (q) for each of the heights. These distances are used to calculate the flight lines needed to fully cover the pavement unit to be inspected. To know the number of flight lines for a given road width, Equation (1) should be used. An additional line is added to ensure full width coverage. Additionally, to have a reference of the number of photographs to be taken per flight line, it is recommended to use Equation (2). Four additional photos are added, two for the start and two for the end of the flight line, in order to have the ends properly overlapped. Naturally, the corners will have a lower number of photo coverage compared to the center, as can be seen in an example in Figure 11. That is necessary to guarantee a minimum of photographs in the corners of the pavement to have all the areas covered on the map. \[\mathit{Flight\ line[\#]}=\frac{\mathit{Poad\ width\ [m]}}{A[m]}+1 \tag{1}\]  
_Photography per \(\mathit{flight\ line[\#]}=\frac{\mathit{Pavement\ lenght\ [m]}}{B[m]}+4\)_
# Adaptive Neural Network Control of Zero-Speed Vessel Fin Stabilizer Based on Command Filter
## 2 Problem Formulation and Preliminaries
### Preliminaries  
The radial basis function neural network (RBF NN) is described as follows:  
\[f_{N}(x)=W^{\mathrm{T}}\psi(x) \tag{19}\]  
where \(W=[W_{1},\cdots,W_{m}]^{\mathrm{T}}\in R^{m}\) is the weight vector of the neural network, \(\psi(x)=[\psi_{1}(x),\cdots,\psi_{m}(x)]^{\mathrm{T}}\in R^{m}\) is the basis function vector of the neural network, which is generally represented by the following Gaussian function:  
\[\psi_{j}(x)=\exp(-\frac{\left\|x-\zeta_{j}\right\|^{2}}{2\omega_{j}^{2}}),j=1,\cdots,m \tag{20}\]  
where \(x=[x_{1},\cdots,x_{q}]^{\mathrm{T}}\in\Omega_{x}\subset R^{q}\) is the input vector of the neural network, \(\zeta_{j}=[\zeta_{j,1},\cdots,\zeta_{j,q}]^{\mathrm{T}}\in R^{q}\) is the center of the Gaussian function, \(\omega_{j}>0\) is the width of the Gaussian basis function and \(m\) is the number of nodes in the hidden layer.  
**Lemma 1** ([39]).: _If the number of nodes in the hidden layer of the neural network is sufficient, then the RBF neural network can theoretically approximate the continuous function \(f(x)\in R\) defined on a compact set \(\Omega_{x}\subset R^{q}\) with arbitrary precision, namely_  
\[f(x)=W^{*\mathrm{T}}\psi(x)+\varepsilon,\forall x\in\Omega_{x} \tag{21}\]  
where \(W^{*}\in R^{m}\) is the ideal weight vector of the neural network and \(\varepsilon\in R\) is the approximation error. \(W^{*}\) is the case in which \(|\varepsilon|\) is minimized when the condition of \(x\in\Omega_{x}\subset R^{q}\) is satisfied, defined as follows  
\[W^{*}:=\arg\min_{W\in R^{q}}\left\{\sup_{x\in\Omega_{x}}\left|f(x)-W^{\mathrm{ T}}\psi(x)\right|\right\} \tag{22}\]  
**Assumption 4**.: _The approximation error \(\varepsilon\) is bounded, and \(|\varepsilon|\leq\varepsilon\). \(\varepsilon\) is an unknown positive constant._
## 2 Effect of Spoofing Signals on Acquisition
### Noise Floor Estimation
\begin{array}{cc}0\\ 0\end{array} \right],\frac{\sigma_{h}^{2}}{N} \begin{bmatrix}1&0\\ 0&1\end{bmatrix} \bigg{)} \tag{5}\]  
Then, the noise variance estimate can be expressed as [10]:  
\[\left\{ \begin{array}{l}\hat{\sigma}^{2}=\frac{N_{0}}{2 NTs}+\left(\sum \limits_{h=j^{a}}P_{h}^{a}+\sum\limits_{m=j^{a}}P_{m}^{s}\right)\overline{ \sigma}_{\psi,normalization}^{2}\\ \overline{\sigma}_{\psi,normalization}^{2}=\overline{\sigma}_{\psi}^{2}/\text{max} \left(\overline{\sigma}_{\psi,normalization}^{2}\right)\overline{\sigma}_{\psi}^{2} \end{array} \right. \tag{6}\]  
Therefore, the noise variance estimation value is related to the ambient noise power \(N_{0}\), coherent integration time \(T_{c}=NTs_{s}\), variance \(\overline{\sigma}_{\psi,normalization}^{2}\) of the \(I\)/\(Q\) branch normalized spreading code, total power of each spoofing signal \(\sum\limits_{m=j^{a}}P_{m}^{s}\), and total power \(\sum\limits_{h=j^{a}}P_{h}^{a}\) of each channel. In Equation (12), we normalized \(\hat{\sigma}^{2}\) to obtain the estimated noise variance. We add a flow chart for the acquisition and tracking loop structure used in this research to make the derivation in Sections 2.1 and 2.2 more clear. As shown in Figure 1, the correlation results \(i_{E}\), \(i_{P}\), \(i_{L}\), \(q_{E}\), \(q_{P}\) and \(q_{L}\) are coherent integrated to output coherent integration values \(l_{E}\), \(I_{P}\), \(I_{L}\), \(Q_{E}\), \(Q_{P}\) and \(Q_{L}\). Then, after envelope detection and incoherent integration, the coherent integral values \(I_{P}\) and \(Q_{P}\) on the prompt branch are used as inputs to the PLL discriminator, and the coherent integral values of the other two related branches are used as inputs to the DLL discriminator. Finally, the DLL and the PLL filter the respective discrimination results, and adjust the output of code NCO and carrier NCO according to the filtering results, so that the locally output carrier and code are consistent with the received signal. Figure 2 shows the relationship between the total power of spoofing signal and estimation of the noise floor of receiver when \(T_{c}=5\) ms, \(T_{c}=10\) ms, \(T_{c}=20\) ms are used. Figure 2 shows that with the increase of the total power of the spoofing signal, noise floor estimation also increases and gradually exceeds total power of authentic signal.
## 4 Results
### Application of Hybrid Models  
Compared with a single model, a hybrid model has higher predictive ability when dealing with high-dimensional problems [94; 95; 96]. In this study, the SysFor algorithm was combined with FR and IoE models. During the modeling process, the following parameters were used for hybrid FR_SysFor and IoE_SysFor models: separation, 0.3; confidence, 0.25; goodness, 0.3; number of trees built in the forest, 500.  
The hybrid integration of LR with FR and IoE was also applied as a benchmark model to build landslide susceptibility maps. A forward stepwise LR was adopted, and the analysis results are given in Table 3. The landslide occurrence probability \(P\) of FR_LR and IoE_LR models can be expressed using Equations (13)-(16), respectively.  
\[\begin{array}{ll}Z_{\text{FR-}LR}=&(Elevation_{\text{FR}}*0.596)+(Profile \ curvature_{\text{FR}}*0.005)+\\ &(Plan\ curvature_{\text{FR}}*1.390)+(Slope\ angle_{\text{FR}}*0.182)+\\ &(Slope\ aspect_{\text{FR}}*0.633)+(SPI_{\text{FR}}*0.370)+(TWI_{\text{FR}}*0. 729)+\\ &(STI_{\text{FR}}*0.381)+(Distance\ to\ roads_{\text{FR}}*0.436)+\\ &(Distance\ to\ rivers_{\text{FR}}*(-0.722))+(Distance\ to\ faults_{\text{FR}}*(-0.0 60))+\\ &(Lithology_{\text{FR}}*(-0.138))+(Rainfall_{\text{FR}}*0.607)+( Soil_{\text{FR}}*(-0.030))+\\ &(NDVI_{\text{FR}}*0.275)+(Landuse_{\text{FR}}*0.399)-5.702\end{array} \tag{13}\]  
\[\begin{array}{ll}P_{\text{FR-}LR}=\frac{e^{Z_{\text{FR-}LR}}}{1+e^{Z_{\text{ FR-}LR}}}\\ Z_{\text{IoE-}LR}=&(Elevation_{\text{IoE}}*1.978)+(Profile\ curvature_{\text{ IoE}}*(-0.626))+\\ &(Plan\ curvature_{\text{IoE}}*72.424)+(Slope\ angle_{\text{IoE}}*1.042)+\\ &(Slope\ aspect_{\text{IoE}}*9.670)+(SPI_{\text{IoE}}*66.354)+(TWI_{\text{ IoE}}*1.570)+\\ &(STI_{\text{IoE}}*7.543)+(Distance\ to\ roads_{\text{IoE}}*5.933)+\\ &(Distance\ to\ faults_{\text{IoE}}*(-4.887))+(Lithology_{\text{IoE}}*(-1.090))+\\ &(Rainfall_{\text{IoE}}*5.141)+( Soil_{\text{IoE}}*0.008)+\\ &(NDVI_{\text{IoE}}*5.007)+(Landuse_{\text{IoE}}*1.260)-6.400\end{array} \tag{14}\]  
\[\begin{array}{ll}P_{\text{IoE-}LR}=\frac{e^{Z_{\text{IoE-}LR}}}{1+e^{Z_{\text{ IoE-}LR}}}\end{array} \tag{15}\]
# Effects of Polypropylene Fiber on the Liquefaction Resistance of Saturated Sand in Ring Shear Tests
## 5 Conclusions  
Using a ring shear apparatus, a series of tests were carried out to evaluate the effects of polypropylene fiber on the liquefaction resistance of saturated sand, considering the effects of fiber content and sand density. Two shearing methods, STC and CTC, were considered. The energy method was used to analyze the test results. According to the test results and analysis, we can draw the following conclusions:  
1. The liquefaction resistance of fiber-reinforced sand is influenced by both the sand density and amount of fiber used in all the tests. The presence of fibers increases the shear strength and shearing time of saturated sand in STC tests. It also clearly increases the cycles required for liquefaction in CTC tests, and the number of cycles increases with the increasing sand density and fiber content. The stress paths show a similar trend for the same density specimen, while the pore pressure generation of fiber-reinforced specimens is slower than the unreinforced specimen.
2. The energy approach is used to evaluate the soil liquefaction resistance. In ring shear rests, the shear energy is dependent on the shear stress and shear displacement. The total shear energy increased with the increase of both sand density and fiber content. The shear energy mainly consumed from the shear failure was triggered to liquefaction or a final steady.
3. For two shearing methods, all the unreinforced loose specimens collapsed completely and the dense one collapsed partly after performing shearing tests; but the fiber-reinforced specimens still maintain structural stability. It is hence that the lateral failure of the sand induced by liquefaction can be limited or even prevented by means of mixing the fibers.
4. Fiber reinforcement might provide a three-dimensional structure among sand grains via interlinking, overlapping and crossing, and increase the stability microstructures, thereby requiring more energy to overcome the resistance during the shearing process. The test results demonstrated that the addition of fiber can be used as an available technology to enhance the anti-liquefaction ability of sand.  
Conceptualization, J.L. and Y.B.; data curation, J.L. and C.Q.; formal analysis, F.B. and C.Q.; investigation, F.B. and W.Q.; methodology, Y.B. and Z.S.; software, Z.S. and C.Q.; writing--original draft, Y.B. and Z.S.; writing--review and editing, J.L., Y.B. and W.Q. This research was funded by the National Natural Science Foundation of China (Grant No.41472241&41877212), Fundamental Research Funds for the Central Universities (Grant No. 2019B17314), and Water Conservancy Science and Technology Project of Jiangsu Province, China (Grant No.2017010). The authors gratefully acknowledge [PERSON] and [PERSON] from the Research Center on Landslides, Disaster Prevention Research Institute, Kyoto University, Japan, for their help and comments on this work. The authors declare no conflict of interest.
# A Congestion Control Strategy for Power Scale-Free Communication Network
## 3 Congestion Control Strategy for the Power Scale-Free Network
### Integrated Routing Strategy
#### 3.3.1 Shortest Path Routing  
Open shortest path priority protocol (OSPF) based on the Dijkstra algorithm has been widely used in the power communication network. Given that the power communication network topology is \(\mathrm{G}\ =\ (\mathrm{V},\ \mathrm{E},\ \mathrm{\Omega})\), the adjacency matrix \(\mathrm{\Omega}\) is used to represent the weighted network, and the weight of each edge in the network is \(\mathrm{\omega_{ij}}\). When the edges are connected between the two power communication nodes, \(\mathrm{\omega_{ij}}=1\), or \(\mathrm{\omega_{ij}}=\infty\), a routing table can be constructed on the shortest path tree (SPT) and points out a route with less cost to each destination in the routing area. Additionally, the fixed routing table is obtained, which is calculated by topology and link weights. As long as the topology of the power communication network is not changed, the routing table will not change. The forwarding of I-level packets is based on the fixed routing table.  
The shorter average transmission distance is obtained, and the I-level packets can reach the destination quickly when the network is in the free flow state. Therefore, the effective delivery of I-level packets is guaranteed.  
The load of nodes is an important parameter to evaluate the network congestion. The total number of packets within the network is denoted as Np(t), and the total load of hub nodes is denoted as n*(t) at each time step, that is the sum of the queue lengths of each hub node. The maximum transmission time <Tmax> and the average transmission time <Tavg> are also important indexes to measure the efficiency of network transmission. The maximum time and average time for a packet to deliver from its source to its destination are denoted as <Tmax> and <Tavg> [19; 20].
## 4 Experiment
### Main Results
#### 4.2.2 Optimized Gather-Scatter  
We use the easy network mentioned above to demonstrate the acceleration effect. We use the Pytorch profiler tool to evaluate the acceleration effect. The network is repeated \(100\times\) with \(100\,k\) inputs, and then, we calculate its average time consumption in Gather, Scatter, and GEMMs operations on CPU, as shown in Tables 4 and 5. The proportion of each component is shown in Figure 10. Compared to the original Gather-GEMMs-Scatter data flow, the optimized Gather-GEMMs-Scatter data flow demonstrates a significant speedup. This is because the former operations of Gather and Scatter use OMP multithreading, where the overhead of creating new threads is much more significant than the computation time on each thread. This means that the required data need to be moved from memory to the corresponding thread each time, resulting in the majority of time spent on this part. The speedup ratio decreases as the network depth increases, caused by the supported number of bits in the SIMD instruction set. For example, the 256-bit SIMD instruction can simultaneously process eight 32-bit single-precision floating-point datasets.  
However, as the number of channels increases, the number of operations to be processed increases, decreasing the speedup. As shown in Tables 4 and 5, when the number of channels is doubled, the decrease in the speedup ratio is also proportional. The decrease in the speedup ratio is slightly smaller in channel (4, 8) than in channel (8, 16). This is because channels (4, 8) use a 128-bit SIMD instruction, while channels (8, 16) use a 256-bit SIMD instruction, which is faster than  
Also, we compared SIMD's time consumption with that of its pruned version in Table 6. With optimized Gather and Scatter operations, each inference time consumption on the pruned and unpruned models is vastly decreased. On the nuScenes-LiDARSeg dataset, MinkeUnet (SIMD) and MinkUnet (prune+SIMD) achieve 7.98\(\times\) speedup and 7.06\(\times\) speedup compared to MinkUnet and MinkUnet (prune). SPVCNN (SIMD) and SPVCNN (prune+SIMD) achieve 6.12\(\times\) speedup and 5.51\(\times\) speedup compared to SPVCNN and SPVCNN (prune). On the SemanticKITTI dataset, MinkeUnet (SIMD) and MinkUnet (prune+SIMD) achieve 9.00\(\times\) speedup and 9.16\(\times\) speedup compared to MinkUnet and MinkUnet (prune), and SPVCNN (SIMD) and SPVCNN (prune+SIMD) achieve 6.56\(\times\) speedup and 6.31\(\times\) speedup compared to SPVCNN and SPVCNN (prune).
## 2 Related Work  
### Traffic Signal Control  
Signal control based on vehicle arrival data can improve traffic sustainability and traffic efficiency. For signal optimization problems based on CAV arrival data, including green light duration optimization and phase sequence adjustment [16; 17; 18; 19; 20; 21; 22], the input for signal optimization is usually vehicle arrival information. In order to optimize the signal timing and phase sequence in a connected car environment, [PERSON] et al. [17] suggested a real-time adaptive phase adjustment technique based on vehicle speed and spatial data. The experimental results show that the proposed algorithm has the ability to significantly reduce traffic delay. [PERSON] et al. [18] included additional traffic data to use vehicle trajectory data as input for signal optimization, such as intersection waiting duration and traffic demand, including breakdowns and jumps. The signal optimization model is typically built using the National Electrical Manufacters Association (NEMA) industry standard traffic signal structure optimizer [17; 22]. In addition, Macroscopic Fundamental Diagram (MFD)-based traffic performance metrics is also used as input information for signal optimization [23]. To identify junction signal timing techniques, including signal cycle length and red and green light durations, [PERSON] et al. [22] used a phase-controlled optimization model. The experimental results showed that the suggested optimization strategy could increase traffic efficiency while maintaining high standards. Other variables such as fuel consumption and driving comfort are frequently seen as goals of optimization problems in addition to vehicle delay. It has been demonstrated that approaches such as dynamic programming and mixed-integer linear programming can be utilized to solve these issues [24]. However, these studies only consider the optimization of traffic signals, ignoring the optimization of vehicle trajectories, especially the random model problems [25; 26; 27] caused by the random arrival of vehicles, which may affect the performance of relevant strategies.
# Thermal Conductivity of Nanofluids: A Review on Prediction Models, Controversies and Challenges
## 3 Parameters That Affect Thermal Conductivity
### Nanofluids
#### 3.3.5 Sonication Time  
The sonication treatment is very common when preparing NFs. For most fluids, the increase of the sonication time contributes to an increase of the thermal conductivity since sedimentation is mitigated and the Brownian motion as well as the particle uniformity improved. Nevertheless, if the sonication time is too long, aggregation decreases and so is the thermal conductivity [5; 26]. [PERSON] et al. [65] reported increasing values of thermal conductivity with increasing sonication time until 50 min for ethylene glycol-based iron NFs. Higher sonication values corresponded to a decrease in thermal conductivity. Also, excessive sonication can break NPs with high aspect ratio such as nanotubes [66] and disrupt the effect of the addition of dispersants to the NFs [67].
# Phase Compensation Technique for Effective Heat Focusing in Microwave Hyperthermia Systems
## 1 Introduction
However, the nanocapsule must be injected into the treatment area [23], and the power source unit is too complex to provide the signals required for multiple-frequency excitation [24]. In this paper, effective EM focusing with a novel phase compensation technique for multifocal microwave hyperthermia is proposed. A narrowband signal at 2.45 GHz is applied to the array antenna elements to organize a simple power supply circuit. The antenna array configuration is optimized for application to a small female breast and for an effective electric field (E-field) concentration on the target area. To deliver a useful therapeutic result, each antenna excites an equal polarization signal, which requires elaborate control of the input phase. Depending on the tumor condition in the breast phantom, the input phases of each antenna are calculated for single and multiple targets with phase delay compensation. For multifocal breast cancer, the energy localization on each tumor cell is important for the minimization of damage to normal cells. To enhance the EM energy focused on multiple targets, the sub-arrays are designated near each tumor location for maximum energy transformation. Therefore, sub-array antennas with a phase compensation technique applying a modified phase are proposed for multicentric breast carcinoma. To demonstrate the thermal treatment effects under single and multiple tumor conditions, the SAR distributions and temperature changes are obtained via simulations and experiments with a breast phantom.
## 5 Conclusions  
In this study, a prefabricated prestressed supporting structure was used in a field prototype test in Beijing, China. The performance of the new structure was investigated by monitoring the lateral earth pressure, the lateral displacement in the surrounding soil, and the stress of the supporting structure. The following conclusions may be drawn:  
(1) The change in the earth pressure and structure stress can be divided into four stages during construction: Rapid attenuation, fluctuation, slow change, and stabilization. Both the earth pressure and structure stress completed the major attenuation within three days of the applied prestress and eventually stabilized. The values of the stable attenuance were 0.32-0.45 and 0.09-0.26 for earth pressure and structure stress, respectively.  
(2) The surrounding soil at the supporting position apparently moves toward the outside of the shaft with the supporting structure construction. This behavior indicates that the new prestressed structure can realize active support and has a significant effect on controlling shaft deformation.  
(3) The displacement value is connected with the prestressing value and the physical properties of the soil. The larger the prestress is and the softer the soil is, the larger the lateral displacement will be. The lateral displacement will increase with the prestress attenuation, and both tend to be stable when the lateral displacement and prestress achieve balance.  
(4) The monitoring data of the field prototype tests show that both the stress and deformation are less than the warning value. It can also be concluded from the shaft construction that the new supporting structure is safe, convenient, environmentally friendly and recyclable. The mutative rules of prestress loss and soil deformation provide guidance for the construction of the new prefabricated prestressed structure.  
Conceptualization, G.Z. and Y.Y.; Data curation, G.Z. and C.G.; Formal analysis, S.M.; Investigation, C.G. and Y.Y.; Methodology, G.Z., S.M. and Y.Y.; Project administration, C.G. and Y.Y.; Writing--original draft, G.Z. and S.M.; Writing--review and editing, G.Z. and Y.Y.  
This research was funded by the National Key Research and Development Program of China, grant number 2017 YFC0805008 and the National Nature Science Foundation of China (NSFC), grant number 41472278.  
The authors declare that there are no conflicts of interest.