esa-sceva/satcom-chunk-collection huggingface dataset:
# POU-SLAM: Scan-to-Model Matching Based on 3D Voxels
## 5 Experimental Evaluation
### Discussion of the Processing Time  
The operating platform in our experiment is Intel [EMAIL_ADDRESS] GHz with 16 GB RAM. Our method is evaluated by processing KTTI datasets rather than being deployed in an autonomous vehicle. With regarding to real time implementation, which heavily depends on the hardware configuration, we cannot assert if our method is real time or not. However, we can still provide some of the parameters so people can know the runtime performance of our method. First, we split the point cloud into voxels which contain certain numbers of points and calculate the linear-ness values and surface-ness value for these voxels. It takes 0.5 s. Second, we do scan matching for each feature. The time cost depends on the number of features. For KITTI Odometry sequence 01, the runtime is about 1.5 s. Taking both the above factors into consideration, our SLAM runs at 2 s per scan with one thread. For comparison, LOAM runs at 1 s per scan on the same KITTI dataset. The reason is that compared to LOAM which extract two-dimensional curvature features, we construct voxel grids and extract features according to the spatial distribution of the points contained in each voxel. This results in better accuracy (refer to Table 1) with a slight sacrifice of efficiency.
# Landslide Susceptibility Modeling Using a Deep Random Neural Network
## 2 Methodology
### Random Hidden Block  
The landslide sequence factors extracted from the max-pooling layer and the convolution layer are sent to the random hidden block, which is a modified full connection layer. Since the DRNN contains deep structures that may cause overfitting and gradient disappearance problems, we propose a hidden-layer-dropping strategy to encourage regularization. Specifically, we randomly drop several hidden layers and bypass their locations using skip connections during the model training stage. In this case, the dropped layers are inactivated, and their connections with adjacent layers are interrupted. The two adjacent layers of the dropped layer are still available because the skip connection technique establishes a new path to link them, guaranteeing the normal operation of signal propagations. Assuming that the random hidden block has \(L\) hidden layers, the function \(\text{F}_{l}(\cdot)\) represents the original mapping between the \((l-1)\)-th and \(l\)-th layers, where \(l\in[1,L]\). Let \(v_{l}\in\{0,1\}\) be a binary variable which indicates whether the \(l\)-th hidden layer is activated (\(v_{l}=1\)) or inactivated (\(v_{l}=0\)).  
\[H_{l}=v_{l}\times\text{F}_{l}(H_{l-1})+(1-v_{l})\times I(H_{l-1}) \tag{5}\]  
where \(H_{l-1}\) and \(H_{l}\) are the input and output of the \(l\)-th hidden layer. In this study, the number of the dropped layers is controlled by a defined parameter \(R_{layer}\in[0,1]\). \(\text{I}(\cdot)\) denotes identity mapping, indicating that the output of a layer maintains the same values as the input. The original nonlinear mapping from layer \(l-1\) to \(l\) is reduced to identity mapping when the \(l\)-th hidden layer is dropped. This propagation rule is combined with the skip connection strategy, allowing the network to propagate signals back and forth between adjacent or nonadjacent layers.  
During the model inference stage, all layers in the network are activated in order to employ the full capability of the model. The gradient propagation process during inference is expressed as follows:  
\[H_{l}=\text{F}_{l}(H_{l-1}) \tag{6}\]  
As for the nodes in the activated hidden layers, they are randomly dropped using a ratio \(R_{node}\) throughout training. Similarly, we create a random binary mask \(M_{hidden}\in R^{\frac{N-k+1}{P}\times 1}\) to achieve this process:  
\[H_{l}^{{}^{\prime}}=H_{l}\times M_{hidden} \tag{7}\]  
The random strategy of the DRNN can be viewed as injecting noise into the network structure (e.g., nodes of network layers and layer depth), thereby allowing the model to learn robust and generalized representations from limited training data. Due to a random mechanism, the structures involved in the training at each iteration are different. From the ensemble learning perspective, training a single DRNN model with random nodes and random layers can be interpreted as constructing an implicit model ensemble.
## 2 Materials and Methods
### Dimensional Analysis  
Calculation of upstream energy head (\(E_{max}\)) was done using Equation (1). Additionally, Equations (2) and (3) were used for calculation of downstream energy head (\(E_{1}\)) and relative energy dissipation (\(\Delta E/E_{max}\)), respectively. In these equations, \(g\) is the acceleration due to gravity, \(H_{dam}\) is the height of spillway, \(y_{0}\) is the upstream water depth, \(V_{0}\) is the approach velocity, and \(q\) is the discharge rate per unit width. The conjugate depth (\(y_{2}\)) was used to calculate flow depth (\(y_{1}\)) as given by Equation (4).  
\[E_{max}=H_{dam}+y_{0}+\frac{{{V_{0}}^{2}}}{2g}=H_{dam}+y_{0}+\frac{q^{2}}{2g{ \left(H_{dam}+y_{0}\right)}^{2}} \tag{1}\]  
\[E_{1}=y_{1}+\frac{{{V_{1}}^{2}}}{2g}=y_{1}+\frac{q^{2}}{2g{y_{1}}^{2}} \tag{2}\]  
\[\frac{\Delta E}{E_{max}}=\frac{E_{max}-E_{1}}{E_{max}}=1-\frac{E_{1}}{E_{max}} \tag{3}\]  
\[y_{1}=\frac{y_{2}}{2}\Big{(}\sqrt{1+8{{F_{7}}^{2}}}-1\Big{)} \tag{4}\]  
where \(F_{7}\)\({}_{2}\) is Froude number (\(F_{72}=V_{2}/\sqrt{gyz}\)), \(y_{2}\) and \(V_{2}\) are water depth and velocity after hydraulic jump, respectively. Generally, effective parameters in the energy dissipation process can be written as Equation (5). In this equation, \(h\), \(l\), and \(N\) are step height, step length, and step number, respectively. Both of \(y_{1}\) and \(y_{2}\) were calculated in corresponding locations that are shown in Figure 1, but using Equations (1)-(4), only \(y_{2}\) is used in calculations of energy dissipation.  
\[\frac{\Delta E}{E_{max}}=f(q,l,h,g,N,H_{dam}) \tag{5}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of Steps** & **Spillway Height over Critical Depth** & **Spillway Slope** & **Spillway Height** \\
**(N)** & \(H_{dam}\)/\(d_{c}\) & \(\alpha\) (Degrees)** & \(H_{dam}\) **(m)** \\ \hline
4–75 & 1.9–37.3 & 10–50 & 0.6–1.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical models’ parameters and their values.  
In summary, the most effective parameters in hydraulic of stepped spillways are divided in two groups: geometrical parameters and hydraulic parameters. Step length (\(l\)), the spillway height (\(H_{dam}\)), spillway slope (\(\alpha\)), step height (\(h\)), and steps number (\(N\)) are the geometrical effective parameters. Additionally, upstream energy head (\(E_{max}\)), downstream energy head (\(E_{1}\)), and discharge per unit width of canal (\(q\)) can be classified as hydraulic effective parameters. The Buckingham \(\pi\) theorem was used to present relative energy dissipation as provided in Equation (6):  
\[\Delta E/E_{\max}=f(H_{dam}/d_{c},l/h,N) \tag{6}\]  
Equation (6) can be rewritten as follows:  
\[\Delta E/E_{\max}=f(H_{dam}/d_{c},\alpha,N) \tag{7}\]  
where, \(d_{c}\) is critical depth (\(d_{c}=(\text{q}^{2}/\text{g})^{1/3}\)) and \(\alpha=tg^{-1}(h/l)\) is spillway slope in degrees.
## 4 Conclusions  
In this study, the thermo-mechanical process and thermo-hygral process of lightweight concrete slabs exposed to high temperatures were analyzed under different experimental variables. The combination of thermal stress and vapor pressure induced the spalling of some specimens. Based on the test results, the following conclusions can be drawn:  
* Under the same test variables, the temperature distribution trend of the LWAC slab was different from that of the NWC slab. At a target temperature of 600 \({}^{\circ}\)C and a heating rate of 5 \({}^{\circ}\)C/min, the oven-dried LWAC slab had a maximum temperature of 368.2 \({}^{\circ}\)  
Figure 29: The time at which the maximum pore pressure occurred under different test conditions: (**a**) LWAC in an oven-dried state; (**b**) LWAC in an air-dried state; (**c**) NWC in an oven-dried state; (**d**) NWC in an air-dried state.  
at a depth of 10 mm, while the oven-dried NWC slab had a maximum temperature of 338.6 \({}^{\circ}\)C at a depth of 10 mm. This shows that the maximum temperature of the LWAC slab at a depth of 10 mm was 8.7% higher than that of the NWC slab;
* At 10 mm from the heated surface, the maximum pore pressure of the LWAC slabs was generally higher than that of the NWC slabs, while there was an opposite trend at 30 and 50 mm from the heating surface. At a target temperature of 600 \({}^{\circ}\)C and a heating rate of 5 \({}^{\circ}\)C/min, the maximum pore pressure at a depth of 10 mm for the LWAC slab was 102 KPa, while that of the NWC slab was 85 KPa. This shows that the maximum temperature of the LWAC slab at a depth of 10 mm was 20% higher than that of the NWC slab;
* As the heating rate increased, a higher pore pressure was generated inside the concrete and the pressure rose more rapidly. The peak value of the measured pore pressure was reached in the vaporization temperature range. This result suggests that the vaporization of water into the concrete was responsible for the increase in the pore pressure;
* When the moisture content of the specimen increased, the maximum pore pressure of the LWAC slabs occurred at a deeper position from the heated surface, while it occurred closer to the heated surface in the NWC slabs;
* As the concrete specimen was exposed to high temperatures, a higher moisture content and a faster heating rate increased the probability of spalling. In particular, at a target temperature of 800 \({}^{\circ}\)C and a heating rate of 10 \({}^{\circ}\)C/min, the corner spalling phenomenon appeared on the air-dried LWAC slab.  
This research was funded by the National Science and Technology Council of Taiwan, grant number NSC 97-2221-E-230-020.  
Not applicable.  
The data presented in this study are available from the corresponding author upon request.  
The authors are grateful to the Department of Civil Engineering of National Chung-Hsing University for providing the experimental equipment and technical support.  
The author declares that there are no conflict of interest.
# A Comprehensive Survey on Knowledge-Defined Networking
## 8 Discussion
### Challenges
#### 8.2.5 Scalability  
One of the strongest scalability issues found in the KDN architecture is the amount of data collected for generating knowledge. KDN systems must obtain information from a variety of sources, including sensors, network devices, etc. The amount of data that has to be gathered and evaluated grows as a network becomes bigger. Due to the enormous quantity of data, the knowledge plane could find it difficult to develop knowledge quickly, which might cause a performance bottleneck. Machine learning models require significant computational and memory resources to run, even at low data sizes. As the size of the data increases, the machine learning algorithms may take a long time to process and may have decreased accuracy. Furthermore, KDN inherits all scalability issues found in the SDN paradigm, such as the controller scalability issue where the controller becomes overloaded in large networks, the switch scalability issue where the flow table size increases beyond the switches' memory capacities, etc.  
In order to increase scalability, organizations may employ several strategies. Machine learning models such as deep learning may be used, which can process large amounts of data quickly [554]. Instead of a physically centralized knowledge plane, a physically distributed knowledge plane at the network edge may be employed to distribute the workload among multiple machine learning models whose output may be combined to generate the final output. Furthermore, management and control planes may also be implemented as a distributed approach using one of the distributed architectures discussed in Section 5.3.1 in order to improve the scalability of the KDN paradigm.
# An Insightful Overview of the Wiener Filter for System Identification
## 7 Simulations
### Iterative Wiener Filter with \(\ell_{1}\)-Norm Regularization  
The iterative \(\ell_{1}\)-norm regularization from (26) can improve the Wiener solution for sparse system identification. This is supported in the next set of experiments, where the iterative \(\ell_{1}\)-norm regularization is compared to the conventional \(\ell_{2}\)-norm regularization of the Wiener filter.  
In Figure 5, different SNRs are set and a large quantity of data is used to estimate the statistics, i.e., \(N=5000\). The conventional Wiener filter uses \(\delta=10^{-8}\), while the iterative \(\ell_{1}\)-norm regularization involves different values of the parameter \(\delta_{1}\) from (26). As it can be observed in Figure 5 (where \(N\gg L\)), for some values of \(\delta_{1}\), the iterative \(\ell_{1}\)-norm regularization leads to better performance. Details of these results [focusing on the overlapping curves in Figure 5a-d] are shown in Figure 6a-d.  
This is more apparent for low values of \(N\), e.g., when \(N=L\), as illustrated in Figure 7. In this case, the conventional \(\ell_{2}\)-norm regularization is clearly outperformed by the iterative \(\ell_{1}\)-norm version, for all the values of \(\delta_{1}\).  
The SNR also influences the performance of the iterative \(\ell_{1}\)-norm regularization, and the  
On the other hand, in moderate or low SNR environments (e.g., \(0\leq\text{SNR}<20\) dB), another option could be  
\[\delta_{1}|_{\text{SNR(dB)}<20\text{dB}} =10^{-\left(2+\log_{10}\text{SNR}\right)} \tag{66}\] \[=10^{-\left(2+\frac{\text{SNR(dB)}}{10}\right)}.\]  
Figure 6: Details of Figure 5 (focusing on the overlapping curves), showing the misalignment of the conventional Wiener filter with \(\ell_{2}\)-norm regularization using \(\delta=10^{-8}\) and the Wiener filter with iterative \(\ell_{1}\)-norm regularization using different values of \(\delta_{1}\), for \(N=5000\) and (**a**) \(\text{SNR}=30\) dB, (**b**) \(\text{SNR}=20\) dB, (**c**) \(\text{SNR}=10\) dB, and (**d**) \(\text{SNR}=0\) dB.  
Figure 7: Misalignment of the conventional Wiener filter with \(\ell_{2}\)-norm regularization using \(\delta=10^{-8}\) and the Wiener filter with iterative \(\ell_{1}\)-norm regularization using different values of \(\delta_{1}\), for \(N=500\) and (**a**) \(\text{SNR}=30\) dB, (**b**) \(\text{SNR}=20\) dB, (**c**) \(\text{SNR}=10\) dB, and (**d**) \(\text{SNR}=0\) dB.  
The validity of these recommended choices are verified in Figure 8, in which the iterative \(\ell_{1}\)-norm regularization is compared with the conventional \(\ell_{2}\)-norm regularization in various SNR conditions and for different values of \(N\). Here, \(\delta=\delta_{1}\), where \(\delta_{1}\) is computed based on (65) and (66), depending on the SNR. As it can be inferred, the iterative \(\ell_{1}\)-norm regularization works significantly better in all the cases, but its gain is more apparent for low SNRs. Furthermore, the results indicate a good behavior for low values of \(N\), especially for the critical case when \(N\approx L\).
# RF Frequency Selective Switch by Multiple PMIM Conversions
## 3 Results  
### The Full-Pass RF Frequency Response  
First, we only observe one of the optical WSS output, and control the optical frequency response of optical WSS in this channel programmatically, and obtain a single-channel programmable microwave photon filter. As an example, we set the optical WSS \"all on\" in this channel, that is, to control the optical WSS so that all comb teeth of the optical frequency comb enter the output through this optical channel. Then we obtain a full-pass RF frequency response from 9.5 to 10.5 GHz. As shown in Figure 3a, the experiment generates a flat top, near rectangular RF amplitude frequency response. Note that the rectangular amplitude frequency characteristics can be obtained by setting all tap weights of 1, rather than traditional FIR filters, where the weights need to be set to sinc shapes with different heights and positive and negative distributions.  
When the optical WSS is set so that only the central tooth of the optical frequency comb is output through this channel, we can obtain a filter response with a bandwidth of about 50 MHz, as shown in Figure 3b. This is the same as the conventional PMIM conversion filter, because we only use one of many PMIM conversions. An interesting phenomenon is that in traditional FIR filters, all taps must be ON to obtain the narrowest frequency response; To obtain the widest frequency response, only one tap needs to be turned on. This is the opposite of our scheme.  
Figure 3: (**a**) The frequency response when all of the comb teeth are on, which shows an all-pass response from 9.5 GHz to 10.5 GHz. (**b**) The response when only one comb tooth is on, which shows a narrow 50 MHz bandpass filter.  
Figure 2: The experimental setup. Optical frequency comb boosted by EDFA is phase-modulated and then notch-filtered by a ring resonator. After optical WSS, each optical branch is recovered to a RF signal. The RF channels are separated by multiple PMIM conversions, while they are directed according to the optical WSS.
## 2 Background and Problem Formulation
### The WGA and WAA Fusion Method  
Given the fusion densities \(f_{i}(\mathbf{X}),i=1,2,\cdots,N\) with a fusion weight of \(w_{i}\geq 0\), \(\sum_{i=1}^{N}w_{i}=1\), where \(N\) denotes the total number of sensors, WGA and WAA can be derived using the weighted sum of the directional KLD between the fusion densities and the fused result as follows:  
\[f_{\text{WAA}}(\mathbf{X}) =\arg\min_{g(\mathbf{X})}\sum_{i=1}^{N}w_{i}D_{\text{KL}}\big{(} f_{i}(\mathbf{X})||g(\mathbf{X})\big{)}, \tag{18}\] \[f_{\text{WGA}}(\mathbf{X}) =\arg\min_{g(\mathbf{X})}\sum_{i=1}^{N}w_{i}D_{\text{KL}}\big{(} g(\mathbf{X})||f_{i}(\mathbf{X})\big{)}, \tag{19}\]  
where \(g(\mathbf{X})\) denotes the fused result, and \(D_{\text{KL}}(\cdot||\cdot)\) denotes the KLD. The corresponding implementations for WGA and WAA are expressed as follows:  
\[f_{\text{WAA}}(\mathbf{X})=\sum_{i=1}^{N}w_{i}f_{i}(\mathbf{X}). \tag{20}\]  
WGA can be expressed as follows:  
\[f_{\text{WGA}}(\mathbf{X})\frac{f_{i}(\mathbf{X})^{w_{i}}}{\int\prod_{i=1}^{N }f_{i}(\mathbf{X})^{w_{i}}d\mathbf{X}}. \tag{21}\]  
For MB class a posteriori density fusion, due to the non-closability of MB fusion, additional processing is required during information fusion. Specifically, the Bernoulli components from one sensor must be associated with those from other sensors to enable the closure of fusion with respect to the fusion of the Bernoulli process.
## 2 Models
### Single-Air-Route Planning Model of Logistics UAV  
In single-air-route planning, the objective is to ensure efficiency and safety, keeping the travel distance and flight risk of the UAV as low as possible. Based on the modeling results of the operating environment, the air route can be split according to the grids traversed  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Geographical Region** & **Safety Barrier Factor** \\ \hline open area & 0 \\ shrubbery & 0.25 \\ buildings below 20 m & 0.5 \\ buildings above 20 m & 0.75 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Surface safety barrier factor.  
when quantifying route costs. The set of grids through which the route passes can be stated as \(G=\{g_{i}|i=1,2,\ldots,n\}\) of n grids. The travel distance of the UAV within the air route can be defined as the air range of the air route, reflecting the efficiency of the result. The flight risk of the UAV can be expressed as the product of the travel distance of the UAV in each spatial grid from \(G\) and the risk value of the grid, reflecting the safety of the result. Single-air-route planning is mainly limited by UAV performance, which is reflected in the setting of maximum air range and maximum turning angle of the air route.  
The required parameters for the single-air-route planning mathematical formulation are presented in Table 3.  
1. Objective function Based on the parameters stated, the problem can be formulated as a minimization problem as \[\text{min}C_{R}=\sum_{i=1}^{n}\left(1+D_{i}+S_{i}+L_{i}\right)\sqrt{\left(x_{ i}-x_{i-1}\right)^{2}+\left(y_{i}-y_{i-1}\right)^{2}}\] (7)  
Function (7) indicates that the single-route planning results should minimize the air range, collision risk, crash risk, and noise risk of the route.  
2. Constraints \[\sum_{i=1}^{n}\sqrt{\left(x_{i}-x_{i-1}\right)^{2}+\left(y_{i}-y_{i-1}\right)^{2 }}\leq AR_{\text{max}}\] (8) \[0\leq\theta_{i}=\arccos\left[\frac{\left(x_{i}-x_{i-1}\right) \left(x_{i+1}-x_{i}\right)+\left(y_{i}-y_{i-1}\right)\left(y_{i+1}-y_{i}\right) }{\sqrt{\left(x_{i}-x_{i-1}\right)^{2}+\left(y_{i}-y_{i-1}\right)^{2}}\sqrt{ \left(x_{i+1}-x_{i}\right)^{2}+\left(y_{i+1}-y_{i}\right)^{2}}}\right]\leq \theta_{\text{max}}\] (9)  
Equation (8) refers to the maximum air-range constraint, indicating that the route air range should be less than the maximum travel distance of the UAVs. Equation (9) refers to the maximum turning-angle constraint, indicating that there should not be a turning angle exceeding the UAV's performance.
# Exergaming-Based Rehabilitation for Lateral Trunk Flexion in Parkinson's Disease: A Pilot Study
## 3 Results
### Clinical Assessment  
The assessed clinical outcomes included the Berg balance scale (BBS), timed up and go test (TUG), and trunk inclination measurements (both lateral and anterior/posterior). With regard to BBS, it showed a statistically significant improvement (\(p\) = 0.0078; Cis 95% +38.4/+50.7), with BBS scores increasing from 44.5 (SD \(\pm\) 8) to 49.1 (SD \(\pm\) 5.7). TUG test scores decreased from 13.7 (SD \(\pm\) 4) to 12.5 (SD \(\pm\) 4.3), indicating a performance improvement; this reduction is statistically significant (\(p\) = 0.03; Cis 95% +10.7/+16.7). As regards the lateral trunk inclination, it decreased from 5.8\({}^{\circ}\) (SD \(\pm\) 2.4\({}^{\circ}\)) to 3.1\({}^{\circ}\) (SD \(\pm\) 1.6\({}^{\circ}\)), showing a statistically significant reduction (\(p\) = 0.0039; Cis 95% +3.9/+7.6). Finally, the anterior/posterior inclination of the trunk reduced from 9.9\({}^{\circ}\) (SD \(\pm\) 7.3\({}^{\circ}\)) to 6\({}^{\circ}\) (6.5\({}^{\circ}\)), also a statistically significant improvement (\(p\) = 0.0039; Cis 95% +4.2/+15.5).