esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards Autonomous Bridge Inspection: Sensor Mounting Using Aerial Manipulators
## 5 Motion Planning
### Waypoint Configuration  
When dealing with an aerial manipulator, exerting some contact force inevitably yields a high dimensional waypoint configuration. We define a single waypoint as a set of UAV and joint poses, together with the force reference and motion distribution factor \(\beta\):  
\[\mathbf{w}=\Big{[}\mathbf{q}_{\mathrm{B}}^{T}\quad\mathbf{q}_{\mathrm{M}}^{T} \quad\mathbf{f}_{r}^{T}\quad\beta\Big{]}^{T}\in\mathbb{R}^{13}, \tag{18}\]  
where \(\mathbf{q}_{\mathrm{B}}\in\mathbb{R}^{6}\) and \(\mathbf{q}_{\mathrm{M}}\in\mathbb{R}^{3}\) are generalized coordinates of the UAV and the manipulator defined in Section 3.1. The force reference vector \(\mathbf{f}_{r}=\Big{[}f_{x}\quad f_{y}\quad f_{z}\Big{]}^{T}\in\mathbb{R}^{3}\) and weighing scalar parameter \(\beta\) are required by the impedance controller. Furthermore, the impedance controller assumes a step change of these values. Ideally, the change should occur at the moment of contact since no force can be exerted without contact. Therefore, these values are only changed at the final waypoint.  
Apart from the desired force and the parameter \(\beta\), the final waypoint must contain the UAV position and orientation, as well as the manipulator joint configuration. Specifying these values relies on the blob detection algorithm presented in Section 6. Namely, the algorithm outputs the position and orientation of the detected blob in the world frame. Following the manipulator dexterity and reach analysis described in Section 3.1, the optimal manipulator configuration \(\mathbf{q}_{\mathrm{M}}^{*}\) is obtained based on the provided plane normal. The optimal manipulator configuration is then used as the desired configuration for the final waypoint. This way, during operation, the manipulator never reaches a fully extended or contracted pose, which allows the impedance controller to command both the arm and the UAV to achieve and maintain the desired force.
Second, because we use signals in a time window to predict the load at current time stamp, by sliding the time window along the time axis, the loads can be identified in an online fashion, i.e., a stream of sensor measurements can be inputted into the network to identify the load continuously. The effectiveness of the proposed method is verified by the data collected from the a benchmark structure in the lab. Quantitative comparisons show that more accurate identification results can be obtained from the proposed method would than those from the regularization-based approaches, and those from the time-domain intelligence algorithms, and transform domain intelligence algorithm [30]. The rest of the paper is structured as follows. In Section 2, the proposed load identification method is described. Details of the experimental results are discussed in Section 3. The paper is concluded in Section 4.
## 5 Experimental Results and Discussion
### Experiments of 1# Piezolectric Brick
#### 5.1.1 Peak-to-Peak Voltages from 1#1 PZT4 Patch on Unimorph PCS (Open-Circuit)  
The peak-to-peak voltages from 1#1 PZT4 patch on unimorph PCS of the LPCB were measured by changing the amplitude of footstep excitation and the force arm length. Taking the amplitude (\(F\)) of 500 N and the frequency (\(f\)) of 1 Hz of exerted force as an example, the instantaneous voltages from 1#1 PZT4 patch on unimorph PCS in 8 s were recorded and displayed in Figure 7, in which positive peak voltages are bigger than the negative peak voltages. The reason is that the peak deformation of the PCS of the LPCB in forced vibration (positive voltage section) is larger than that in free vibration (negative voltage section). The peak-to-peak voltages from 1#1 PZT4 patch on unimorph PCS are depicted in Figure 8 in the condition of the excitation frequency of 1 Hz and the amplitude of footstep excitation increasing from 250 N to 500 N; the specific voltages are listed in Table 2. Obviously, the peak-to-peak voltages from 1#1 PZT4 patch linearly increase with the increases in the amplitude of footstep excitation and the force arm length, which all lead to the increase in the surface strain exerted on the PCS (explained as Equations (6) and (9)-(11)). Correspondingly, the peak-to-peak voltage densities from 1#1 PZT4 patch on unimorph PCS have the same change trend as that of voltages with the increase in the amplitude of footstep excitation and the force arm length. This case in which \(A_{p}\) keeps as a constant. Specifically, corresponding to the force arm length of 19.5 mm, 26.5 mm and 33.5 mm, the peak-to-peak voltage densities from 1#1 PZT4 patch on unimorph PCS respectively increases from 0.0128 V/mm\({}^{3}\) to 0.0308 V/mm\({}^{3}\), 0.024 V/mm\({}^{3}\) to 0.0524 V/mm\({}^{3}\), and 0.0472 V/mm\({}^{3}\) to 0.0792 V/mm\({}^{3}\) with the increase in the amplitude of footstep excitation from 250 N to 500 N.  
Figure 8: Peak-to-peak voltages from 1#1 PZT4 patch on unimorph PCS of 1# piezoelectric brick versus the amplitude of footstep excitation (\(f\) = 1 Hz).  
Figure 7: Reported voltages from 1#1 PZT4 patch on unimorph PCS of 1# piezoelectric brick (\(F\) = 500 N, \(f\) = 1 Hz). (**a**) \(L\) = 19.5 mm; (**b**) \(L\) = 26.5 mm; (**c**) \(L\) = 33.5 mm.
## 2 Materials and Methods
### PV Output Interval Prediction
#### 2.2.2 Quantile Regression  
The quantile regression method can well analyze the quantile relationship between a set of explanatory variables and the explained variables. Compared with the least-squares method, the quantile regression method is more robust when dealing with outlier data and reflects more comprehensive data information [24; 25]. Assuming a set of sample sequence  
Figure 1: LSTM network structure diagram.  
explanatory variables and explained variables, the parameters of the linear regression model are obtained by Equation (2).  
\[\min\sum\limits_{i=1}^{n}\left(Y_{i}-X_{i}\beta\right) \tag{2}\]  
where \(\beta\) is the regression coefficient, which changes with the change of the quantile \(\tau(0<\tau<1)\). The estimation of the \(\beta\) can be transformed into an optimization problem, which can be obtained by Equation (3).  
\[\begin{array}{l}\min\limits_{\beta}\sum\limits_{i=1}^{n}\rho_{\tau}(Y_{i}-X _{i}^{\prime}\beta)\\ =\min\limits_{\beta}\sum\limits_{i|Y_{i}\geq X_{i}^{\prime}\beta}\tau\big{|}Y_ {i}-X_{i}^{\prime}\beta\big{|}+\sum\limits_{i|Y_{i}<X_{i}^{\prime}\beta}(1- \tau)\big{|}Y_{i}-X_{i}^{\prime}\beta\big{|}\end{array} \tag{3}\]  
where \(X_{i}^{\prime}\) denotes the explanatory variable with quantile condition; \(i|Y_{i}\geq X_{i}^{\prime}\beta\) denotes that the actual value of the \(i\)-th explained variable is not less than the estimated value of linear regression; \(\rho\) is the optimization function--when \(\rho\) is the smallest, the optimal estimate of \(\beta\) is obtained. The calculation formula is shown in Equation (4), where \(I(\cdot)\) is the indicative function.  
\[\begin{cases}\rho_{\tau}(u)=u[\tau-I(u)]\\ I(u)=\begin{cases}1&u<0\\ 0&u\geq 0\end{cases}\end{cases} \tag{4}\]  
It can be seen from Equation (3) that for each different quantile \(\tau\), the corresponding parameter estimate \(\beta(\tau)\) can be calculated. When \(\tau\) is continuously valued in the (0,1) interval, the complete range of the explained variable and its conditional distribution can be obtained, and the conditional density prediction can finally be obtained after the condition density is obtained.
# DMLU-Net: A Hybrid Neural Network for Water Body Extraction from Remote Sensing Images
## 1 Introduction
DySample realizes adaptive feature repositioning and sub-pixel alignment by learning spatial offset parameters, which can overcome the boundary-blurring problem caused by traditional interpolation upsampling, thereby preserving the continuity and integrity of complex structures, such as tributaries and lakes. Compared with other water body extraction studies, our model simultaneously features an advanced multi-scale feature extraction module and an enhanced sampling module. Additionally, all of the above-mentioned modules are first applied to extract water bodies. A systematic evaluation of two high-resolution public remote sensing datasets in China--GID and LoveDA [22]--is conducted to assess the effectiveness and generalization capacity of the proposed model in remote sensing applications. Experiments show that DMLU-Net demonstrates superior performance on the two public datasets, significantly outperforming the mainstream methods in the water body detection field in recent years in terms of multiple indicators such as the IoU and F1-score, demonstrating good accuracy, robustness, and cross-scene adaptation abilities. The subsequent sections of this paper are structured as follows: Section 2 presents the DMLU-UNet model, offering a detailed description of each component. Section 3 describes the public datasets, experimental configurations, and various comparative and ablation experiments conducted in the study. Finally, Section 4 summarizes the experimental findings and provides a comprehensive discussion.
## 3 Results
### Temperature Distribution within the Process Zone
#### 3.4.4 Melt Pool Size  
The last evaluation method of the temperature distribution is the global histogram in order to quantify the melt pool size. Figure 19 confirms the impression of the previous analysis: that the melt pool at the cut front differs significantly for static beam shaping and beam oscillation. The increased hot parts of the melt pool are especially remarkable. The chart shows the cumulated regions of equal temperature, which are averaged for a period of 500 ms. The following example explains the reading method: for static beam shaping (set  
Figure 18: Temporally resolved temperature sequences at the position of the submatrix \(\overline{T_{\text{P,P}}}\)(t) in Figure 16 of 12 mm stainless steel cut with: **(a)** Set #1: Static beam shaping; and different values of the longitudinal, linear beam oscillation parameters: **(b)** Set #2: 300 \(\upmu\)m amplitude and 500 Hz frequency; **(c)** Set #3: 550 \(\upmu\)m amplitude and 500 Hz frequency; **(d)** Set #4: 300 \(\upmu\)m amplitude and 2000 Hz frequency.  
#1), a temperature of 2000 \({}^{\circ}\)C or above is reached within an area of 2000 \(\upmu\)m\({}^{2}\). For beam oscillation, 2000 \({}^{\circ}\)C and higher temperatures are measured at an area of 58,000 \(\upmu\)m\({}^{2}\) (set #2), 83,000 \(\upmu\)m\({}^{2}\) (set #3), and 36,000 \(\upmu\)m\({}^{2}\) (set #4). This clearly indicates that high temperatures are only present in a small area of the static beam shaping melt pool. In contrast, beam oscillation exhibits these high temperatures over a larger melt pool area.  
In addition, the regions around the melting temperature were considered to acquire the whole melt pool size. This is especially important for static beam shaping, as the temperature distribution illustrates low values at a wide area of the cut front. To identify the borders of the melt pool, an appropriate threshold was defined. Corresponding to Section 2.1.3, the uncertainty analysis is mandatory for this part of the evaluation. Calculating with an emission coefficient of e = 0.25, the melt pool size corresponds very well with the geometric view of Figure 10. Considering Figure 4, this emission coefficient requires a temperature threshold of 1250 \({}^{\circ}\)C for the melt pool evaluation. Consequently, as a result, the melt pool size of static beam shaping set #1 is measured out of Figure 19 with 0.65 mm\({}^{2}\). Applying beam oscillation, the melt pool size is reduced. Set #3 achieves a melt pool size of 0.48 mm\({}^{2}\), which is a reduction of \(-\)26%. Comparable values for the melt pool size are measured for set #2 and set #4, due to the similar amplitude and comparable focal plane. Set #2 achieved 0.37 mm\({}^{2}\) and set #4 achieved 0.36 mm\({}^{2}\), which is a reduction of \(-\)43% respectively \(-\)45% to static beam shaping. Nevertheless, the temperature characteristic and the cutting result differ significant for set #2 and set #4.
## 4 Conclusions  
This paper has presented experiment results of high-power MMW/THz pulse detection using electronic and optical GDD detectors. These results proved that GDD green lamp type N521 can be used to detect high-power MMW/THz pulses. An MMW accelerator of Ariel University was used as a source of MMW/THz pulses. This accelerator has unique features such as high radiation power of 0.1-5 KW, radiation frequency of 95-110 GHz, and pulse duration of 1-50 \(\upmu\)s. The NEP of the Schottky diode is lower as \(10^{-10}\)W/\(\sqrt{\upmu}\)[10], but they are much less sensitive from GDD, and it's not appropriate to high power radiation. Results demonstrated the priority use of GDD detectors instead of Schottky diode due to the shorter response times, wide bandwidth, low cost of the GDD detectors. Results showed that the Schottky diode had a longer response time than the GDD detector did. Due to the characteristic of the discharging capacitor of the Schottky diode, it has a longer decrease time than that of the GDD. We also presented a comparison between the frequency change response of the Schottky diode and the GDD detectors. Results showed that, due to the high bandwidth of the GDD, the Schottky diode was more sensitive to the frequency change in the MMW/THz pulse. Further, the responsivity of the optical detection of the GDD was better than that in electronic detection. Optical detection is much quieter than electronic detection is. In electronic detection, the incident pulse is an electromagnetic wave, and an electrical current is an output, i.e., parasitic capacitance and inductance of the electronic circuit of the GDD limit the response time. The upconversion method monitors the optical change of the GDD; therefore there is no parasitic impedance at the output, and response time is due to the response time of the photodetector. The sensitivity of electronic GDD detection is limited by the plasma noise of the GDD. However, in the upconversion method, sensitivity is limited by photodetector noise, which is less than plasma noise. The comparison of the response time between the upconversion and the electronic method was limited because of the photodetector bandwidth. Using a faster and more sensitive photodetector can improve the upconversion method.  
Conceptualization, A.H.Y., Investigation D.B., Methodology, A.K. and A.H.Y., Data curation, N.B., supervision, A.F., Writing--original draft, A.H.Y., All authors have read and agreed to the published version of the manuscript.  
This research was supported by the [PERSON] family center for compact accelerators, Ariel University, Israel.  
Not applicable.  
Data is contained within the article.  
The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 4 Q3: What Are the Methodologies Used to Overcome the Challenges in UIoT?
### Methods to Overcome the Technical Challenges of UIoT
#### 4.1.3 Unreliable Data Transmission Methods  
In [68; 69; 70], the existing techniques for solving the unreliable data communication in UIoT are discussed, and some methods are indicated herewith. In [68], [PERSON], [PERSON] et al. show that unreliable channels cause propagation delays. Therefore, three aspects of solving this problem suggested reducing unnecessary routing detection, routing distance between relay nodes and retransmission. In [69], [PERSON] recognized the need for an optimal design to provide reliable end-to-end transmission. Thus, a reliable transmission control was systematically reviewed, focusing on the data link, network and transport layers. Finally, in [70], [PERSON] et al. discussed the impact of noise from underwater environments on reliable data transmission, and based on this, link reliability was an essential consideration for data transmission to achieve the rate of high transmission in real-time scenarios.
## 5 Parametric Modeling of System Characteristics
### Energy Consumption
The total energy consumption per bit for local communication can be obtained as follows:  
\[E_{b}^{l}=\frac{P_{PA}^{l}+P_{c}^{l}}{R_{bi}} \tag{14}\]  
\(P_{PA}^{l}\) is the power of each amplifier of DAN during local communication and its value can be obtained by using Equations (10) and (11) and substituting the parameters \(\bar{E}_{b}^{L}\), \(R_{b}\), \(G_{t}\), \(G_{r}\), \(d^{L}\) with \(\bar{E}_{b}^{l}\), \(R_{bi}\), \(G_{ti}\), \(G_{ri}\), \(d^{l}\), where \(\bar{E}_{b}^{l}\) is the required energy per bit for a given BER \(\bar{P}_{b}^{l}\) at DGN side, \(R_{bi}\) is the bit rate of each individual node \(i\), \(d^{l}\) (\(\ll d^{L}\)) is the distance between DAN and DGN, \(G_{ti}\) is the antenna gain of each DAN, \(G_{ri}\) is the antenna gain of DGN. Circuit power consumption for local  
Figure 5: Transmitter and receiver architecture for WOFDM (analog). Figure 6: Transmitter and receiver architecture (In-Phase/Quadrature-Phase) for FOFDM, QAM, DQPSK, and OQPSK (analog). communication \(P_{c}^{l}\) can be calculated using Equations (12) and (13) by replacing \(N_{r}=N_{t}=1\). The total energy per bit per node (RF section) can be calculated using the following equation:  
\[E_{b\_Analog}=\frac{E_{b}^{L}+(N_{r}-1)E_{b}^{l}}{N_{t}+N_{r}} \tag{15}\]  
The energy efficiency (EE) can be calculated by taking the inverse of Equation (15). _Base Band (Digital) Energy Consumption_: The number of CPU cycles of a processing block is estimated by using Odyssey prediction model [30]. To calculate the base band energy consumption of a block, the TelosB mote [31] energy consumption per CPU cycle [32] value is multiplied by the estimated number of CPU cycles. The energy consumed per bit by CPU during modulation (\(E_{Mod}\)), which also represents the base band energy consumption in transmit (T\({}_{x}\)) mode, can be calculated by multiplying the estimated number of CPU cycles of the modulation processing block with energy consumption per CPU cycle and dividing it with total number of bits. The energy consumption per bit by CPU during demodulation (\(E_{Dmod}\)) and V-BLAST detection (\(E_{Det}\)) can be calculated similarly, and their sum represents the base band energy consumption in receive (R\({}_{x}\)) mode. With \(N_{t}\) transmitters (DSNs) and \(N_{r}\) receivers (DANs and DGN), the total base band energy consumption per bit per node can be calculated as:  
\[E_{b\_digital}=\left(\frac{N_{t}E_{Mod}+N_{r}E_{Dmod}+E_{Det}}{N_{t}+N_{r}}\right) \tag{16}\]
## 6 Results and Discussion
### Random-Forest Model Results
The red asterisks in the figure represent misclassified samples and the blue circles represent correctly classified samples. The samples in the blue boxes in Figure 9 represent close voting results and are prone to misclassification. Therefore, the closer the sample points are to the coordinates (2000, 0) and (0, 2000), the better is the performance of the model. It can be seen that, in the figure, most of the samples are concentrated at the two coordinates mentioned above, which indicates that the proposed random-forest model has a good classification performance. From a quantitative perspective, the total number of samples in the blue box is 7, accounting for 0.92% of the total testing set; this is a small percentage of the data, indicating that this random-forest model has a good classification effect. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Category** & \(mtry=1\) & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline Class 1 & 98.18 & 96.63 & 96.58 & 97.83 & 97.50 & 96.58 & 97.28 \\ Class 2 & 91.51 & 92.89 & 95.59 & 93.75 & 91.46 & 90.69 & 91.87 \\ Average & 94.85 & 94.76 & 96.09 & 95.79 & 94.48 & 93.64 & 94.58 \\ \hline \hline \end{tabular}  
* (Note: Class 1 stands for the normal state of the track slab and Class 2 for the deformed state of the track slab).  
\end{table} 
Table 3: Classification accuracy (%) with different number of split features. Figure 9: Results of random-forest classification.
## 3 Results and Discussion
### Characterization of Polystyrene Nanoparticles  
TEM and DLS were used to determine the diameter of the polystyrene nanoparticles. As expected, the hydrodynamic radius (determined by DLS) is larger than the physical radius (determined by TEM), as the high surface charge causes an extended hydration shell, which makes the particle appear larger in light scattering experiments. The mean diameter as calculated by evaluating 61 particles in the TEM image is \(84\pm 8\) nm (Figure 2), while DLS measurements showed a hydrodynamic diameter of 92 nm with a polydispersity index of 0.14. A zeta potential of \(-51.6\) mV was calculated from the electrophoretic mobility data and proves the expected highly negative surface charge due to the presence of sulfonate groups in the polymer.  
For the sensor application presented herein, it is desirable that the nanoparticle dispersions reflect visible light, so as to enable a visual readout. We therefore tuned the size and surface charge of the particles, such that the films display a strong violet reflectance. As proven in earlier studies [26], the particles also show a long-term stability of better than one year.  
Figure 1: Cross-section of the photonic crystal (PhC)-based sensor for potassium ions. Bottom layer: microscope glass slide (width 2.5 cm). Center layer: a partially hydrolyzed wet polyacrylamide film (size \(\sim\)1 cm\({}^{2}\)) containing polystyrene nanoparticles. Top layer: potassium-selective plasticized PVC membrane (diameter 4 cm). The sample containing K\({}^{+}\) ions is placed on the top layer.  
Figure 2: TEM image (**left**) and corresponding size distribution (**right**) of monodisperse nanoparticles consisting of styrene, divinylbenzene and sodium styrene sulfonate.
# An Information Theoretic Approach to Privacy-Preserving Interpretable and Transferable Learning
## 2 Mathematical Background
### Membership Mappings for Classification  
A classifier (i.e., Definition 8) and an algorithm for its variational learning (stated as Algorithm 4) follows from [15; 22].  
**Definition 8** (A Classifier [15; 22]).: _A classifier, \(\mathcal{C}:\mathbb{R}^{p}\to\{1,2,\cdots,C\}\), maps a vector \(y\in\mathbb{R}^{p}\) to \(\mathcal{C}(y)\in\{1,2,\cdots,C\}\) such that_  
\[\mathcal{C}(y;\{\mathcal{P}_{c}\}_{c=1}^{\mathcal{C}})=\arg\min_{c\in\{1,2, \cdots,C\}}\ \|y-\widehat{\mathcal{WD}}(y;\mathcal{P}_{c})\|^{2} \tag{36}\]_where \(\widehat{\mathcal{WD}}(y;\mathcal{P}_{c})\), computed using (34), is the output of the \(c\)-th wide CDMMA. The classifier assigns to an input vector the label of that class whose associated autoencoder best reconstructs the input vector._  
```
0: Labeled dataset \(\mathbf{Y}=\left\{\mathbf{Y}_{c}\mid\mathbf{Y}_{c}=\left\{y^{i,c}\in\mathbb{R}^ {p}\mid i\in\{1,\cdots,N_{c}\}\right\}\right\}\), \(c\in\{1,\cdots,C\}\}\); the subspace dimension \(n\in\{1,\cdots,p\}\); ratio \(r_{max}\in(0,1]\); the number of layers \(L\in\mathbb{Z}_{+}\).
1:for\(c=1\) to \(C\)do
2: Build a wide CDMMA, \(\mathcal{P}_{c}=\left\{\mathcal{M}_{c}^{s}\right\}_{s=1}^{S_{c}}\), by applying Algorithm 3 on \(\mathbf{Y}_{c}\) for the given \(n\), \(r_{max}\), and \(L\).
3:endfor
4:return the parameters set \(\left\{\mathcal{P}_{c}\right\}_{c=1}^{C}\).
```  
**Algorithm 4** Variational learning of the classifier [15; 22]