esa-sceva/satcom-chunk-collection huggingface dataset:
# Ultrasonic Deicing Efficiency Prediction and Validation for a Flat Deicing System
## 3 Deicing Experimental Setup and Schemes  
On the basis of FE simulations, the experimental platform was built. In this part, the most efficient configuration 2 (according to simulation results in part 4) was mainly considered to validate the feasibility and rationality of the ultrasonic deicing system.  
Ice adhesion strength varies from icing temperature, humid, surface roughness, measurement error etc. In this study, the ice adhesion strength was measured using a low temperature centrifuge, which is similar to these reported [16; 34]. After completing all settings, the temperature of the low temperature centrifuge was turned to \(-12\)\({}^{\circ}\)C for another 40 min and the centrifuge apparatus started. The rotate speed of the centrifuge was recorded when the ice layer was detached from the sample. In the calculation process, the centrifugal force was written as \(F=mra\omega^{2}\). Then, ice adhesion strength can be calculated based on equation \(\tau=F/A\). \(F\) is centrifugal force (N), \(m\) is ice mass (\(0.00862\pm 0.0002\) kg), \(r\) is radius of centrifuge apparatus (0.135 m), \(\omega\) is speed of rotation (rad/s), \(\tau\) is ice adhesion stress (Pa), \(A\) is icing area (0.00070686 m\({}^{2}\)). Ice adhesion strength was calculated to be \(0.36\pm 0.06\) MPa after 8 experiments.  
All the settings were the same as mentioned in Section 2. In the experiment, a piezoelectric disk with radius 5 mm and thickness 4 mm was used as an actuator to generate ultrasonic waves. The piezoelectric disk was bonded to the aluminum plate by epoxy resin. The setup for the deicing experiments is shown in Figure 6. To eliminate the effect of surrounding temperature on the results, the whole experiment process was conducted in the refrigerator at \(-12\)\({}^{\circ}\)C.  
Three different frequencies 190, 213, and 236 kHz, respectively, were adopted to verify optimal frequency for configuration 2. These three frequencies were optimal frequencies for configuration 7 (\(r=10\) mm), configuration 2 (\(r=5\) mm), and configuration 3 (\(r=6\) mm), respectively. All the boundary conditions were the same for these three deicing experiments (including the size and location of ice layer), except the frequency applied to the PZT actuator. After 90 s testing for these three different frequencies, the final deicing effects can be obtained. Moreover, deicing experiments were also carried out for the remaining six groups to compare their differences of deicing effect and validate the correctness of simulation results. Finally, deicing experiments of four different icing cases for configuration 2 were carried out to verify the rationality of DEF.  
Figure 6: Experimental setup for deicing experiments.
## 4 Experiments and Results
### Evaluation Metrics
#### 4.1.1 PSNR  
To measure the similarity of the pixel values between the denoised reconstructed images and the target images, we used PSNR, which was defined as  
\[PSNR=10\cdot\log\frac{s^{2}}{MSE}, \tag{20}\]  
where \(s\) means the maximum value of the target image and \(MSE\) is defined as  
\[MSE=\parallel f_{\theta}(x)-y\parallel_{2}^{2}. \tag{21}\]
# A Novel Approach of Unit Conversion in the Lattice Boltzmann Method
## 3 Results and Discussions
### Two-Dimensional Examples  
The D2Q9 lattice model is used to solve the Navier-Stokes equations, while the D2Q5 for the energy equation. \(f_{i}\) is a distribution function used for the D2Q9, while \(h_{i}\) for the D2Q5. The models have the following equilibrium distribution function, lattice weights, lattice velocities, and speed of sound.  
D2Q9:  
\[f_{i}^{\rm eq}=w_{i}\rho\Bigg{[}1+\frac{\mathbf{e}_{i}\mathbf{u}^{\rm eq}}{C_{\rm g}^{ 2}}+\frac{\left(\mathbf{e}_{i}\mathbf{u}^{\rm eq}\right)^{2}}{2C_{\rm s}^{4}}-\frac{ \left(\mathbf{u}^{\rm eq}\right)^{2}}{2C_{\rm s}^{2}}\Bigg{]}, \tag{84}\]  
\[w_{i}=\left\{\begin{array}{ccc}4/9&i=0\\ 1/9&i=1,2,3,4\\ 1/36&i=5,6,7,8\end{array}\right., \tag{85}\]  
\[\mathbf{e}_{i}=\left[\begin{array}{cccccccc}0&1&0&-1&0&1&-1&-1&1\\ 0&0&1&0&-1&1&1&-1&-1\end{array}\right], \tag{86}\]  
and  
\[C_{\rm s}^{2}=\frac{1}{3}. \tag{87}\]  
D2Q5:  
\[h_{i}^{\rm eq}=w_{i}\rho\bigg{[}1+\frac{\mathbf{e}_{i}\mathbf{u}^{\rm eq}}{C_{\rm s}^ {2}}\bigg{]}, \tag{88}\]  
\[w_{i}=\left\{\begin{array}{ccc}2/6&i=0\\ 1/6&i=1,2,3,4\end{array}\right., \tag{89}\]  
\[\mathbf{e}_{i}=c\bigg{[}\begin{array}{cccc}0&1&0&-1&0\\ 0&0&1&0&-1\end{array}\bigg{]}, \tag{90}\]  
and  
\[C_{\rm s}^{2}=\frac{1}{3}. \tag{91}\]
## 4 Proposed Suppression System Model
### Adaptive IR NF
#### 4.2.2 Optimal Notch Depth, Maximizing the Output SNR
\begin{gathered}\sigma_{w_{o}}^{2}=E[(u(n)+k_{od}k_{1}u(n-1)+k_{1}u( n-2))^{2}]\\ \sigma_{w_{o}}^{2}=\left\{1+k_{od}^{2}k_{1}{}^{2}+{k_{1}}^{2} \right\}R_{uu}(0)+\left\{2k_{od}k_{1}(1+k_{1})\right\}R_{uu}(1)+2k_{1}R_{uu}(2 )\end{gathered}  \tag{31}\]  
Substituting Equations (26)-(28) into Equation (31), the \(\sigma_{w_{o}}^{2}\) becomes:  
\[\sigma_{w_{o}}^{2}=\frac{1+k_{1}^{2}-2\beta^{2}k_{1}^{2}}{\left(1-\beta^{2}k_ {1}^{2}\right)}\sigma^{2} \tag{32}\]  
Moreover, since \(\sigma_{w_{o}}^{2}=E\left[w_{o}^{2}(n)\right]=\sigma^{2}\sum_{k=0}^{\infty}h_ {k}^{2}\), we have:  
\[\sum_{k=0}^{\infty}h_{k}^{2}=\frac{1+k_{1}^{2}-2\beta^{2}k_{1}^{2}}{\left(1- \beta^{2}k_{1}^{2}\right)} \tag{33}\]  
\({\sigma_{l_{o}}}^{2}\) in Equation (24) is the interference power after the second IIR NF, since \(J_{o}(n)\) CWI with center frequency \(\omega_{o}\), the variance of \(J_{o}(n)\) is given as follows:  
\[{\sigma_{l_{o}}}^{2}=\frac{A^{2}}{2}\left|H\left(e^{j\omega_{o}}\right)\right| ^{2} \tag{34}\]  
When the adaptive algorithms in Equation (6) and Equation (11) are successfully converged, \(k_{od}=-2\) cos(\(\omega_{o}\)), and Equation (34) becomes:  
\[{\sigma_{l_{o}}}^{2}=\frac{A^{2}}{2}\frac{(1-k_{1})^{2}}{\left(1-\beta k_{1} \right)^{2}} \tag{35}\]  
Hence, substituting Equations (32), (33), and (35) into Equation (24) yields the described \(SNR_{o}\)\(dB\) in terms of filter parameters, as follows:  
\[SNR_{o}dB=10\log_{10}(\frac{1}{(1+\sigma^{2})\left(\frac{1+k_{1}^{2}-2\beta^{ 2}k_{1}^{2}}{\left(1-\beta^{2}k_{1}^{2}\right)}\right)+JSR_{i}\left(\frac{ \left(1-k_{1}\right)^{2}}{\left(1-\beta k_{1}\right)^{2}}\right)-1}) \tag{36}\]  
where \(\sigma^{2}\) is the variance of AWGN, and \(JSR_{i}=\frac{A_{i}^{2}}{2}\). To maximize \(SNR_{o}\)\(dB\) in Equation (36), the optimal value \(k_{1}\) needs to be found. After some modifications, the denominator in Equation (36) can be rewritten as a function of \(k_{1}\):  
\[f(k_{1})=\left(\frac{1+k_{1}^{2}-2\beta^{2}k_{1}^{2}}{\left(1-\beta^{2}k_{1}^{ 2}\right)}\right)+G_{i}\left(\frac{(1-k_{1})^{2}}{\left(1-\beta k_{1}\right)^ {2}}\right)-\frac{1}{\left(1+\sigma^{2}\right)} \tag{37}\]  
where \(G_{i}=\frac{JSR_{i}}{\left(1+\sigma^{2}\right)}\). To find the optimal \(k_{1}\), we differentiate \(f(k_{1})\) and solve \(f^{\prime}(k_{1})=0\) for all possible roots of \(k_{1}\), as shown in Equation (37):  
\[f^{\prime}(k_{i})=\beta^{2}G_{i}k_{1}^{3}+\left[2\beta G_{i}-\beta^{2}G_{i}- \beta^{2}-\beta\right]k_{1}^{2}+[1+\beta+G_{i}-2\beta G]k_{1}-G_{i}=0 \tag{38}\]  
Equation (38) has at least one real root in the [0 1] range that gives the optimal \(k_{1}\) as a function of \(JSR_{i}\).
## 3 Outage Analysis of Blind-RIS-NOMA Uplink System
### Outage Probability of Blind-RIS-AP-NOMA System for Uplink Transmission
\[\mu_{1}\leq\frac{\kappa\alpha_{2}-D_{2}}{\kappa\,\alpha_{2}} \tag{32}\]  
The detailed derivation of (32) is highlighted in Appendix G. By combining the conditions (31) and (32),  
\[\underbrace{\frac{D_{1}(1+\kappa\alpha_{2})}{\kappa(\alpha_{1}+\alpha_{2}D_{1 })}}_{\mu_{1 min}}\leq\mu_{1}\leq\underbrace{\frac{\kappa\alpha_{2}-D_{2}}{ \kappa\,\alpha_{2}}}_{\mu_{1 max}} \tag{33}\]  
The above equation can be written as  
\[\frac{D_{1}(1+\kappa\alpha_{2})}{\kappa(\alpha_{1}+\alpha_{2}D_{1})}\leq \frac{\kappa\alpha_{2}-D_{2}}{\kappa\,\alpha_{2}} \tag{34}\]  
Simplifying (34) for \(\kappa\) results in  
\[\kappa\geq\frac{D_{1}}{\alpha_{1}}+\frac{D_{2}}{\alpha_{2}}+\frac{D_{1}D_{2}} {\alpha_{1}} \tag{35}\]  
The simplification of (34) is given in Appendix H. The condition in (35) is the minimum SNR required to meet the QoS constraints of both the users. The optimal power to be allocated for user 1 is the maximum value of \(\mu_{1}\). \[\mu_{1}^{opt}=\frac{\kappa\alpha_{2}-D_{2}}{\kappa\,\alpha_{2}} \tag{36}\]  
The remaining power is allocated to user 2  
\[\mu_{2}^{opt}=1-\mu_{1}^{opt} \tag{37}\]
# Remote Sensing Image Target Detection Method Based on Refined Feature Extraction
## 2 Materials and Methods  
### Overall Structure of DCN-YOLO Network  
This chapter introduces a DCN-YOLO algorithm, which is an improvement built upon the foundation of YOLOv7. The overall structure of the DCN-YOLO model is illustrated in Figure 1. First, the original convolutional block is replaced with a new basic block that utilizes DCNv2 [25] reconstruction. Additionally, a residual structure is incorporated into the new block, which effectively improves the network's ability to adapt to geometric changes in objects, and leads to a more accurate estimation of target spatial deformation. To address the issues of detecting dense targets and reducing missed detections and false alarms, the algorithm incorporates SimAM at the lateral connections during multi-scale feature fusion. SimAM allows for the targeted detection of feature information in a 3D space without increasing the model's training parameters. This approach reduces the impact of high semantic noise during contextual semantic fusion and enables the extraction of more powerful features. Moreover, the traditional distance metric is influenced by the limited number of pixels, which can adversely affect the detection of small targets. To mitigate this issue, the paper introduces the NGWD loss function. This loss function helps compensate for the impact of small targets with a small number of pixels during the calculation of the regression loss, thereby improving the detection performance of small targets. The DCN-YOLO algorithm is evaluated using various datasets, including DOTAv1.0 and DIOR, to assess its performance and effectiveness. Experimental results demonstrate its capability to effectively detect small targets in challenging scenarios.  
The structure of DCN-YOLO can be divided into four main modules, namely, the input end, backbone, neck, and head. In the backbone module, we adopted the CBS structure, which includes convolution, batch normalization, and SiLU activation function, as well as the D-ELAN module and D-MP module. Detailed explanations regarding this part can be found in Section 2.2. As for the neck module, we employed the SPAN structure, and a comprehensive description of the SPAN structure can be found in Section 2.3.
## 3 System Model Design
### Message Forwarding Strategy in Social Opportunity Networks  
In the previous section, we described the process of selecting the appropriate relay node. From the first step, this mechanism collects the information of the nodes in the network, calculates the similarity between the nodes through the neighbor nodes and the moving trajectory, and narrows the range of the nodes by a single threshold. The message forwarding strategy of the traditional opportunistic social networks is basically that the source node carries the message, and searches for a relay node with a large forwarding and forwarding probability to forward the message.  
The message carrier selects a message forwarding preference, which is usually judged by the social similarity of a pair of nodes; and selects a suitable node for message forwarding. However, the message forwarding strategy proposed by us here is different. When we calculate the social attribute distance between all nodes and the destination node, according to the larger the distance and the smaller the similarity, we not only seek a node with the smallest distance. Our approach is to average the social attribute distances of all compute nodes, and then use nodes with social attribute distances below the mean as relay nodes. The main purpose of this is to be afraid of carrying lost messages and a friendlier transmission between communities.  
As shown in Figure 3, which is a model diagram of a message forwarding policy in a social opportunity network, three circles are respectively communication ranges of nodes that carry messages differently at three times. At time \(t_{1}\), the message carrying node forwards the message to nodes B and C whose social attributes are below average. At time \(t_{2}\), node B moves to another communication area, and the message is forwarded to nodes D and E. At time \(t_{3}\), node E moves to the communication area containing the destination node, and finally transmits the message to the destination node. \(W\_M(S_{\text{A}},S_{\text{B}})\) represents the social attribute distance between node A and node B. The blue node indicates that the social attribute distance between nodes is greater than the average distance. Green indicates that the social attribute distance between the two nodes is less than the average distance, so the message is forwarded to the green node in the figure.  
\[Avg_{node}=\frac{1}{n}\sum_{i=1}^{n}Weig\_MinkV(S\_attr_{u},S\_attr_{i}) \tag{22}\]  
where \(Avg_{node}\) represents the social attribute distance of the average node, and \(Weig\_MinkV(S\_attr_{u},S\_attr_{i})\) represents the social attribute distance between the node \(u\) and other nodes.
# Focusing 3D Small Objects with Object Matching Set Abstraction
## 3 Methodology
### Focus-Based Sampling  
The point clouds of small objects often exhibit obvious sparse characteristics. The key points cannot be sampled by the FPS effectively, and the feature cannot be learned capably, owing to the neglect of semantic information. Semantics-guided FPS assigns higher weights to the foreground points [26]. The corresponding probability is significantly enhanced. Inspired by this work, a comprehensive focus-based sampling (FocS) approach is presented, which incorporates an additional MLP to calculate scores for small objects, further increasing the corresponding sampling probability. Suppose we have selected \(M\) points: \(\{\mathbf{x}_{i},\mathbf{x}_{2},\,\ldots,\mathbf{x}_{M}\}\). For each 3D point \(\mathbf{x}_{i}\), we determine the shortest distance, \(d_{i}^{\text{R}}\), between it and the remaining points within this subset, shown in Figure 2. Subsequently, we design a comprehensive score, \(score_{i}\), to evaluate each point:  
\[score_{i}=s_{F}+\lambda s_{so} \tag{1}\]  
where \(s_{F}\) is the score of the foreground, \(s_{so}\) is the score of the small object, and \(\lambda\) is the balance factor. The rectified focus-based distance \(d_{i}^{\text{FocS}}\) is the following:  
\[d_{i}^{\text{FocS}}=score_{i}*d_{i} \tag{2}\]  
where \(score_{i}\) is the correction parameter. Subsequently, we perform FPS with the focus-based distance. Such a design effectively samples small object points by increasing the sampling probability of these points. This approach effectively mitigates the information degradation of small objects throughout successive downsampling operations, consequently enhancing the model's capability.
# Object-Based Time-Constrained Dynamic Time Warping Classification of Crops Using Sentinel-2
## 2 Materials and Methods  
### Study Areas and Datasets  
#### 2.1.1 Study Areas  
The two study areas were chosen due to their complexity of agricultural practices, land parcel geometry, and the large number of crop types and cropping cycles, which together with the irregular temporal sampling of images would challenge most of the existing image classification methods.  
The first test area (TA1) is in Southern California, in central Imperial County (33 deg00'N and 115 deg30'W) (Figure 1). The area covers 58,890 ha, with an extent of \(23.36\times 25.21\) km. Situated at low altitudes with a hot desert climate, the area is characterized by high agricultural productivity, especially for hay (alfalfa), onions, and lettuce [40]. The agricultural landscape is intensively managed to produce irrigated crops, with one or more cropping cycles. The crops occupying more than 2% of the study area (according to CropScape--Cropland Data Layer which is provided by the United States Department of Agriculture, National Agricultural Statistics Service [41; 42]) were considered for classification, and include durum and winter wheat, alfalfa, other hay/non-alfalfa, sugarbeets, onions, sod/grass seed, fallow/idle cropland, vegetables (mostly lettuce, plus carrots, broccoli and greens), together with the water class.  
The second test area (TA2) is in Northwestern Texas, near the border with New Mexico and Oklahoma, in the southern part of Dallam County (36 deg10'N and 102 deg35'W) (Figure 1). The area covers 59,400 ha, with an extent of \(27\times 22\) km. The semi-arid climate with hot summers and cool and dry winters highly influences the agricultural practices, characterized by center-pivot irrigation. The main crops cultivated according to CropScape [41; 42] and occupying more than 2% of the study area are corn, cotton, winter wheat, alfalfa, fallow/idle cropland, grass/pasture, and double crop (winter wheat, followed by corn).
## 3 Results
### Prediction of Membrane Wetting with Sodium Dodecyl Sulfate  
#### 3.2.1 Surface Tension  
The surface tension of the aqueous solution of 35 g/L sodium chloride decreases with increasing concentrations of sodium dodecyl sulfate (SDS) (Figure 2). As described in the literature, the surface tension decreases strongly with increasing concentration of SDS up to 50 mg/L, after which an asymptotic level is reached at higher concentrations of sodium dodecyl sulfate [33; 34; 35]. This point correlates with the critical micelle concentration (CMC) of the surfactant. For pure water, a the critical micelle concentration of 2560 g/L is reported for sodium dodecyl sulfate systems, whereas it is also observed that for salt-containing systems this value is much lower [35]. This means that salinity is an important aspect when studying the wetting behavior of a solution containing surfactants.  
The critical surface tension for wetting, defined as the surface tension required to wet the membrane, is also indicated in the figure. Based on this figure, the PE membrane is expected to be wetted by the feed liquid if it contains 50 mg/L SDS or more. The surface energy of the PTFE and PES membranes is below the asymptotic value of the surface tension of the liquid up to 150 mg/L and therefore these membranes might be more suitable for treatment of surfactant containing waste streams.
# MASA: Multi-Application Scheduling Algorithm for Heterogeneous Resource Platform
## 4 Multi-Application Scheduling Algorithm (MASA) Based on DRL
### Network Training
#### 4.2.1 Reward Dynamic Alignment Method
* Step 2: At the beginning of the action, memorize the clock signal \(\mathrm{clk}_{\mathrm{start}}\), and at the end, memorize the clock signal \(\mathrm{clk}_{\mathrm{end}}\). Use Equation (19) to calculate \(\mathrm{r_{t}}\). \[\mathrm{r_{t}}=\sum_{\mathrm{clk}=1}^{\mathrm{clk}_{\mathrm{end}}}\mathrm{r^{ \prime}_{clk}},\mathrm{-}\sum_{\mathrm{clk}=1}^{\mathrm{clk}_{\mathrm{start} }}\mathrm{r^{\prime}_{clk}},\mathrm{t}=\sum_{\mathrm{clk}_{\mathrm{start}}} \mathrm{r^{\prime}_{clk}},\] (19) In summary, the RL process based on the RDA method can be summarized in Equation (20). \[\left\{\mathrm{s_{t}},\{\mathrm{a_{n,t}}\}_{\mathrm{n=1}}^{\mathrm{n^{\prime}}},\{\mathrm{r_{n,t}}\}_{\mathrm{n=1}}^{\mathrm{n^{\prime}}}\right\}_{\mathrm{t =1}}^{\mathrm{T}}\rightarrow\left\{\mathrm{s_{t}},\{\mathrm{a_{n,t}}\}_{ \mathrm{n=1}}^{\mathrm{n^{\prime}}},\left\{\sum_{\mathrm{clk}_{\mathrm{start}}} \mathrm{r^{\prime}_{clk}},\mathrm{a_{t}}\right\}_{\mathrm{n=1}}^{\mathrm{n^{ \prime}}}\right\}_{\mathrm{t=1}}^{\mathrm{T}} \tag{20}\]  
In this way, the correct empirical data can be obtained using the RDA method during the interaction between SCHEDULER and the environment, no matter how dynamically the set of actions is changing.
## 6 The Proposed Clustering Routing Protocol
### Data Transmission Phase  
After the clusters are formed, data transmission phase could start. In every cluster, the CHN allocates time slots through the time division multiple access mechanism for its CMNs, and the CMNs transmit data to the CHN based on the time slots through a single hop, thereby decreasing collisions. After transmitting the data for this round, the CMNs go into sleep mode so as to save energy. Once the CHNs receive the data, they fuse the data by using the improved BPNN algorithm and forward the processed data to the SN by employing the carrier sense multiple access with collision detection scheme. Each effective multi-hop transmission path to the SN is identified through the enhanced GA. If one CHN is close to the SN, it sends data towards the SN through one hop. It is noted that the training processes of the BPNN algorithm are conducted by the SN due to its energy supplies. Once clusters are formed, the SN transmits the trained wights and biases of the BPNN to the CHNs. Based on the trained model, the CHNs fuse the data, eliminate the redundancy, extract the features, and then send the processed data to the SN. In addition, the searching process of the multi-hop transmission paths is also completed in the SN. After the CHN selection phase, all the CHNs transmit the message packets with information such as the node ID, the node energy, and the node location to the SN. Then the SN figures out the optimal multi-hop transmission paths by employing the GA and sends the routing path information to the CHNs.  
After the SN receives the data from all the CHNs, one round ends. If the remaining energy of every CHN is over half of the average residual energy of all the nodes, the CHNs of the next round remain unchanged, thereby saving time and energy. Hence, the next round directly begins with the data transmission phase. Otherwise, the next round starts with the CHN selection phase.
## 3 Analysis of the Selected Case Studies
### The Case Study Description
#### 3.2.4 Sana'a Old City  
A residential district in Sana'a old city features organic and compact planning, similar to the rest of the Islamic region cities, which are built around Islamic centers--the most important of which is the Great Mosque.  
The houses in Sana'a are characterized by vertical buildings centered around a shared garden (orchard). Those houses that define tower houses are a result of development through the history of design, to take maximum advantage of the sunlight and natural ventilation. The tower houses are divided into six to nine floors. The utilization of natural energy entails excessive use of the southern facades, such that all living rooms and bedrooms on the south side contrast service rooms on the northern facades.