esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Clustering of Users in Power Domain NOMA
## 6 Results and Discussions
### Complexity Analysis  
The most complex part of the proposed solution is the search for users in each cluster during the maintenance step, which has the worst-case complexity equal to O(n), where n is the number of clusters formed. As seen in the graphs above, due to the restriction of the SIC, it is favorable that there are more clusters of only two users so that the standard deviation calculation does not negatively affect the complexity of the system.
## 4 Discussion and Analysis
### Differences between Assembled M&E Systems and Conventional M&E Systems
The assembled M&E system is more advantageous in dismantling compared to the traditional building system; the assembled M&E system is a more concise and orderly dismantling process and easy to disassemble due to its modularity, while the traditional M&E system is usually fixed and mixed in the building structure, so the dismantling process is cumbersome. The cost of stripping is high, and the utilization rate is low. With a high reuse rate, the undamaged modules of assembled MEP systems can be directly used in the next system. In contrast, traditional MEP systems must be completely dismantled and rebuilt. The labor cost and time required for dismantling and dismantling the difficulty of assembling M&E systems are relatively low. Taken together, assembled M&E systems have more excellent value in terms of environmental benefits, lower cost of economic benefits through the use of materials, more leading time benefits, and significantly improved flexibility in dismantling and reusing.
## 4 Functionality Benchmark
### Person Following  
This FBM was designed to evaluate the capabilities of robots in effectively following humans.  
The steps for the procedure of the FBM are as follows:  
1. The robot is placed on the starting position in front of a person.
2. The test starts by the RSBB, upon receiving the readiness signal from the robot.
3. A start signal is sent by the RSBB, and the benchmark starts.
4. The person starts to walk and visits a set of locations inside the arena, occasionally stopping and then resuming the walking.
5. The robot is expected to maintain a desired distance with the person while avoiding obstacles and other people that can interrupt the motion of the robot.
6. The benchmark is terminated after a predefined duration, and a timeout signal is sent out by the RSBB.  
The performance of the following behavior is computed with the help of the MoCap system that continuously measures the true pose of the robot and the target at all times. Both the robot and the target person are equipped with MoCap markers. To derive the pose of the robot's odometric center with respect to the origin, the relative pose of the marker set with respect to the robot's center is required. This is simply obtained by the RSBB before the benchmark by placing the robot on the origin of the test bed and measuring the transformation between the marker set and the origin. The three-dimensional poses of the human target and the robot are projected onto the ground plane, and their Euclidean ground distance is continuously computed.  
A performance score is computed based on the following:  
1. \(M_{A}\): The accuracy in following and maintaining the desired relative distance to the target;
2. \(M_{T}\): The total covered distance while following the target. The robot is considered to be following the target if it is within a tolerance range of the desired distance.  
The previous criteria encourage both accurate and fast solutions with minimum interruption requests by the robot. Since this benchmark requires continuous tracking of the robot and the person by the MoCap system, which might be interrupted due to marker occlusions or unexpected movements in regions not captured by the MoCap system, a _benchmark reliability_ metric is also calculated indicating how well the benchmark system was able to capture the trial.  
The three metrics are described in the following equations:  
\[M_{A}=\frac{\sum_{\varepsilon\in S}|D(\text{robot}_{s},\text{person}_{s})-D_{ desired}|}{\#S} \tag{2}\]  
\[M_{T}=\sum_{s\in S}D(\text{r}_{s-1},\text{r}_{s}),\ \ \text{if}\ D_{min}\leq D( \text{r}_{s},\text{p}_{s})\leq D_{max} \tag{3}\]  
\[\text{Benchmark\ Reliability}=\frac{\#S}{\#S+\#F} \tag{4}\]  
where \(S\) and \(F\) are the sets of successful and failed MoCap samples, respectively; the \(D(a,b)\) function is the L2 norm between two 2D positions; and \(D_{min}\), \(D_{max}\) and \(D_{desired}\) are configurable parameters (for the experiments in this work, their values are 0.15 m, 3.5 m and 2 m, respectively).
## 2 Experimental Methodology
### Data Preprocessing  
Scaling is a commonly used data preprocessing technique in machine learning. It is applied to address the issue of significant differences in units or ranges among variables. When variables have different units or ranges, it can make the interpretation of the model difficult. If one variable has a much larger range compared to others, it may have a large impact on the model's predictions. To mitigate this problem, it is necessary to adjust the variables to a consistent scale [67; 68].  
In this study, before applying the scale, the dataset was divided into training and test sets. The dataset consisting of 752 samples was split into a training set, which accounts for 70% of the data, and a test set, which accounts for the remaining 30%, following the methodology employed in previous studies [19; 69; 70]. After splitting the data, standard scaling was applied to the input variables. This approach standardizes the variables by adjusting their means to 0 and standard deviations to 1, aligning them with a standard normal distribution [67].
# Signal Quality Assessment for Transdermal Optical Wireless Communications under Pointing Errors +
## 4 Results and Discussion
For example, for \(\theta=40^{\circ}\) and \(\delta=5\) mm, a 75 dB average SNR is achieved, whereas, for the same \(\theta\) and \(\delta=10\) mm, the average SNR is 63 dB. Therefore, a 16% average SNR degradation occurs when the skin thickness doubles. On the contrary, for a given \(\delta\), the average SNR is inversely proportional to \(\theta\). For instance, for the case of \(\delta=7\) mm, a 13% average SNR increase is achieved when the divergence angle changes from \(40^{\circ}\) to \(20^{\circ}\). This reveals that, by decreasing the divergence angle, we can countermeasure the incurred path loss effect. Figure 4d exhibits the proportionality of the average SNR with respect to the PD effective area, \(A\), for a given divergence angle, \(\theta\). For instance, an 32% average SNR increase can be achieved at \(\theta=20^{\circ}\), by increasing \(A\) from 0.1 to 2 mm\({}^{2}\). On the contrary, the average SNR is inversely proportional to \(\theta\) for a fixed \(A\). For example, an 8% average SNR degradation occurs for \(A=1\) mm\({}^{2}\) when \(\theta\) increases from \(20^{\circ}\) to \(40^{\circ}\). Of note, this figure presents the combined effect of two design parameters, \(\theta\) and \(A\), on the signal's quality, and it can be utilized for appropriately selecting the values of these design parameters. Finally, it is noticed that, regardless of the values of \(\theta\) and \(A\), the average SNR is in the range between 45 and 90 dB, which is extremely high.
## 3 Results
### FTIR Results  
Figure 3 illustrates the FTIR scanning in the wavenumber region of 4000-400 cm\({}^{-1}\) for PS/clays nanocomposite films. The characteristic peaks around 3030 and 2930 cm\({}^{-1}\) of the PS spectra (Figure 3a-c), indicate the stretching of aromatic C-H and stretching vibration of aliphatic, respectively. The bands at 1599, 1496, and 1449 cm\({}^{-1}\) correspond to C=C stretching vibration, the band at 1368 cm\({}^{-1}\) indicates the vibrational mode of the CH\({}_{2}\) group, and the band at 698 cm\({}^{-1}\) is identified as the C-H benzenic stretching vibration. The broadband at 540 cm\({}^{-1}\) corresponds to the out of plane deformation of the phenyl ring [29]. For the PScoMA based films (see Figure 3b) the main differences in vibration bands compared to these of the PS films were caused by the presence of carbonyl group (C=O) of the MA and located at 3630 cm\({}^{-1}\),1858 cm\({}^{-1}\), and 1779 cm\({}^{-1}\)[30].  
In such PS/clay nanocomposites the interaction with clay platelets is indicated by the new bands at 3630, 918, 660, and 470 cm\({}^{-1}\). These bands are accredited to O-H stretching of structural hydroxyl groups, Al-O stretching, Mg-O bond, and Si-O stretching of the tetrahedral silica layers of modified clays, respectively [29; 31]. In our case both the PSNaMt and the PScoMANaMt based films (Figure 2a,b, respectively) exhibit only the band at 470 cm\({}^{-1}\) which corresponds to Si-O stretching. In Figure 2c the PSOrgMt films show the bands at 3630, and 470 cm\({}^{-1}\) which correspond to Al-O and Si-O stretching. This indicates a higher interaction between OrgMt based films and PS matrix compared to the interaction between NaMt based films and PS matrix and compared to the interaction between NaMt based films and PScoMA matrix.
## 3 Methods  
This study is primarily divided into four parts, including data collection, data processing, modeling and optimization, and validation, as shown in Figure 2. Firstly, data was collected from various aspects such as remote sensing, natural environment, urban construction, and social perception. These geospatial data often originate from different  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**NO.** & **Reclassified Label** & **Examples** & **Quantity** \\ \hline
1 & poi\_administration & Authority, federation, committee, fire station, etc. & 103,208 \\
2 & poi\_company & Factories, farms, bases, warehouses, business units, etc. & 168,788 \\
3 & poi\_education & Kindergartens, schools, art institutions, conference centers, training institutions, etc. & 73,534 \\
4 & poi\_hospital & Hospitals, pharmacies, health stations, emergency centers, clinics, etc. & 107,438 \\
5 & poi\_hotel & Inns, hotels, apartments, villas, homestays, clubs, etc. & 53,831 \\
6 & poi\_lifestyle services & Maintenance site, logistics distribution, beauty salon, service center, etc. & 371,364 \\
7 & poi\_residence & Community, dormitory, villa, residential area, etc. & 54,992 \\
8 & poi\_restaurant & Restaurant, restaurant, noodle shop, bakery, tea house, etc. & 406,194 \\
9 & poi\_scenery & Ethnic areas, scenic spots, ancient towns, temples, resorts, old sites, squares, etc. & 12,716 \\
10 & poi\_shopping & Supermarkets, shopping malls, specialty stores, building materials markets, hardware markets, etc. & 921,792 \\
11 & poi\_sportleisure & Swinming pools, gymnasiums, clubs, gyms, theaters, living halls, etc. & 60,976 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The details of POI categories.  
sources and have varying resolutions. In order to unify the geographic reference information during the modeling process and facilitate the sampling of features from different layers at the same locations, operations such as calibration, projection, and resampling were performed on these data. Additionally, to ensure that data features can be directly used for spatial modeling, vector data needed to be transformed into quantifiable raster layer information. Furthermore, by combining various feature selection methods, features that more effectively capture the spatial distribution of the population were chosen as input data for the model. Random Forest (RF) was chosen as the modeling method. Simultaneously, various optimization algorithms like random search, grid search, genetic algorithm, simulated annealing, and Bayesian optimization were applied to optimize the model parameters, aiming to build the best model. Finally, using the random forest model with optimized parameters, we predicted and modeled the spatial distribution of the population in Sichuan Province for the year 2020. We obtained the population spatialization dataset with 1 km resolution. Subsequently, the obtained population grid dataset was compared with GPW, LandScan, and WorldPop at the township scale for analysis and accuracy verification.  
Figure 2: Flowchart of population spatialization.
## 3 Performance Analysis
### OP of Imperfect SIC  
Up to now, the MU MISO system with the proposed method has been analyzed based on the assumption of perfect SIC, however, in practice, it is very difficult to achieve the ideal SIC. The imperfect SIC causes the error in decoding the received superposition code at users. Investigation of imperfect SIC concerns in designing NOMA system, therefore we analyze the proposed method in case of imperfect SIC hereafter.  
The term of \(\mathbf{h}_{m,n}\mathbf{w}_{m}\sum\limits_{k=1}^{n-1}\sqrt{a_{k}P_{S}}x_{n-1}\) in Equation (6) depends on the quality of SIC operation, and it is unequals to zero in the case of imperfect SIC. Thus, the instantaneous SINR and the OP of the \((m,n)\)th user in this case are respectively represented in Equations (25) and (26), where \(\Delta_{k}=\xi\sum\limits_{k=1}^{n-1}a_{k}P_{S}\) is the remained power of interference signal and \(\xi\) presents the quality coefficient of SIC operation.  
\[\gamma_{m,n}^{impSIC}=\frac{P_{S}a_{m,n}|\mathbf{h}_{m,n}\mathbf{w}_{m}|^{2}}{ \sum\limits_{i=n+1}^{N}P_{S}a_{m,i}|\mathbf{h}_{m,n}\mathbf{w}_{m}|^{2}+| \mathbf{h}_{m,n}\mathbf{w}_{m}|^{2}\xi\sum\limits_{k=1}^{n-1}a_{k}P_{S}+ \sigma_{m,n}^{2}}, \tag{25}\]  
\[\text{OP}_{m,n}^{impSIC}=\text{Pr}\Big{(}\gamma_{m,n}^{impSIC}\leq\gamma_{ \text{th}}\Big{)}=\text{Pr}\left(\frac{P_{S}a_{m,n}|\mathbf{h}_{m,n}\mathbf{ w}_{m}|^{2}}{\Big{(}\sum\limits_{i=n+1}^{N}P_{S}a_{m,i}+\Delta_{k}\Big{)}| \mathbf{h}_{m,n}\mathbf{w}_{m}|^{2}+\sigma_{m,n}^{2}}\leq\gamma_{\text{th}} \right). \tag{26}\]  
Based on the order of channel gains of the users which is represented as in Equation (3) and from Equation (26) we can rewrite the OP of the \((m,n)\)th user as in Equation (27):  
\[\text{OP}_{m,n}^{impSIC}=1-\text{Pr}\left(\begin{array}{c}X_{m,n}>\frac{ \gamma_{\text{th}}}{P_{S}a_{m,n}-\gamma_{\text{th}}b_{k}}=\alpha_{k}\end{array} \right), \tag{27}\]where \(b_{k}=\sum_{i=n+1}^{N}P_{\text{S}}a_{m,i}+\Delta_{k}\), \(\forall k\in\{n:\,1\div N\}\), and  
\[\alpha_{k}=\max_{n:\,1\div N}\Big{\{}\frac{\gamma_{\text{th}}}{P_{\text{S}}a_{m, n}-\gamma_{\text{th}}b_{k}},\forall k\in\{n<k<N\}\Big{\}}.\]  
As shown in Equation (27), the outage always occurs if \(P_{\text{S}}a_{m,n}<\gamma_{\text{th}}b_{k}\), it means that the transmit power which is allocated to the \((m,n)\)th user, should be large enough. Furthermore, the PDF function of \(X_{m,n}\) is depicted by Equation (6.58) [21] in Equation (28):  
\[f_{X_{m,n}}(x)= \frac{N!}{(N-n)!(n-1)!}f_{X_{k}}(x_{k})\Big{[}F_{X_{k}}(x_{k}) \Big{]}^{n-1}\Big{[}1-F_{X_{k}}(x_{k})\Big{]}^{N-n}\] \[= \frac{N!}{(N-n)!(n-1)!}\sum_{j=0}^{N-n}(-1)^{j}\binom{N-n}{j}f_{X _{k}}(x_{k})\Big{[}F_{X_{k}}(x_{k})\Big{]}^{n+j-1}. \tag{28}\]  
Using Equation (28), we can rewrite the OP in the case of imperfect SIC as follows in Equation (29):  
\[\text{OP}_{m,n}^{impSIC}=\frac{N!}{(N-n)!(n-1)!}\sum_{k=0}^{N-n}\frac{(-1)^{k }}{n+k}\binom{N-n}{k}\Bigg{[}1-\exp\Big{(}-\frac{\alpha_{k}}{P_{\text{S}} \Omega_{m,n}}\Big{)}\Bigg{]}^{n+k}. \tag{29}\]  
where \(n\leq k\leq N\).
# Optical Frequency Comb Expansion Using Mutually Injection-Locked Gain-Switched Lasers
## 2 Mutually Injection-Locked Gain-Switched Laser
### Operational Principle
Additionally, the average OFC power remains unchanged across the tuning range, thus overcoming the reduction of comb output power for low FSRs [31].
# Mapping Gaps in Sugarcane Fields in Unmanned Aerial Vehicle Imagery Using YOLOv5 and ImageJ
## 2 Literature Review  
### Computer Vision and Object Detection  
Computer vision is a multidisciplinary research area within artificial intelligence and machine learning that aims to enable computers to interpret and understand visual information [16]. Object detection, a critical aspect of computer vision, has garnered significant attention [17; 18].  
Deep neural networks (DNNs) are a type of neural network that is widely employed in computer vision, primarily for identifying objects in images. Convolutional neural networks (CNNs), a specialized form of DNNs, are frequently used to process and analyze visual data [16]. CNNs excel at automatically extracting features from images, which are then used to create models for object classification [19].
## 5 Discussion
### Distillation Methods
#### 5.2.3 Weighting of Distillation Loss  
The total loss function of our proposed method consists of two components: the building polygon prediction loss and the distillation loss. The weight of the distillation loss \(\eta\) is a hyperparameter that controls the importance of the distillation process. In this study, we conducted experiments with different values of the distillation loss weight \(\eta\) to explore the optimal value for this hyperparameter.  
Table 5 presents the experimental results with different values of the distillation loss weight. As the value of the weight \(\eta\) increased, the model's performance metrics steadily improved, indicating the crucial role of knowledge distillation in our proposed method. Particularly, when the weight value \(\eta\) was set to 1, the model achieved the best performance, demonstrating the effectiveness of the knowledge distillation approach.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Distillation Temperature** & **OA** & **IoU** & \(\mathbf{P_{50}^{IoU}}\) & \(\mathbf{P_{50}^{Boundary}}\) \\ \hline \(T=0.02\) & 91.25 & 63.21 & 53.86 & 51.84 \\ \(T=0.50\) & 88.76 & 60.13 & 47.31 & 45.05 \\ \(T=0.70\) & 92.05 & 65.97 & 54.96 & 52.81 \\ \(T=1.00\) & **92.28** & **66.54** & **56.01** & **54.19** \\ \(T=1.50\) & 90.85 & 62.76 & 52.24 & 50.96 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental results for different distillation temperatures T.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{4}{c}{**Distillation Feature**} & & & \\ \hline \(\mathbf{F_{seg}}\) & \(\mathbf{F_{line}}\) & \(\mathbf{F_{ver}}\) & **OA** & **IoU** & \(\mathbf{P_{50}^{IoU}}\) & \(\mathbf{P_{50}^{Boundary}}\) \\ \hline \(\surd\) & & & 91.33 & 64.43 & 54.01 & 52.93 \\  & \(\surd\) & & 92.08 & 66.31 & 55.35 & 53.34 \\  & & \(\surd\) & 92.04 & 66.26 & 54.92 & 53.86 \\ \(\surd\) & \(\surd\) & & 92.13 & 66.32 & 55.62 & 53.65 \\ \(\surd\) & & \(\surd\) & 92.16 & 66.37 & 55.56 & 54.12 \\ \(\surd\) & & \(\surd\) & 92.21 & 66.45 & 55.61 & 53.74 \\ \(\surd\) & \(\surd\) & \(\surd\) & **92.28** & **66.54** & **56.01** & **54.19** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Distillation feature ablation experiments. \(\surd\) denotes the feature is selected for knowledge distillation.  
From Table 6, it can be found that although the method framework and backbone of Frame-field and baseline are different, both Frame-field and baseline are multi-task learning models, which are able to apply our proposed multi-feature distillation. After applying the proposed distillation method, the Frame-field was also able to improve significantly in all indicators. Therefore, our proposed distillation method can be applied to other building profile extraction models based on a multi-task learning approach. Due to the limitation of the distillation method, it is difficult to apply the proposed distillation method to the building polygon extraction model that does not adopt the multi-task learning method.
# Quantification and Analysis of Carrier-to-Interference Ratio in High-Throughput Satellite Systems
## 3 Quantification Method of CIR
### User Downlink  
The schematic diagram of co-channel interference in the user downlink is shown in Figure 6. The green continuous line represents the downlink of the useful signal. The green dotted line represents the connection between the center of the cell, where the target user is located, and the satellite. The red continuous line represents the downlink of the \(i\)-th interference signal; the red dotted line represents the connection between the center of the cell, where the \(i\)-th interfering user is located, and the satellite; \(P_{\mathrm{I}}\) represents the on-board transmitting power to the target user; \(L_{\mathrm{I}}\) represents the path loss of the downlink of the useful signal; \(\theta_{\mathrm{I}}\) represents the deviation angle of the downlink of the useful signal from its own cell center; \(G_{\mathrm{I}}(\theta)\) represents the on-board antenna transmitting gain function for the downlink of the useful signal; \(P_{\mathrm{I}}\) represents the on-board transmitting power to the \(i\)-th interfering user; \(L_{i}\) represents the path loss of the downlink of the \(i\)-th interfering user; \(\theta_{i}\) represents the deviation angle of the downlink of the \(i\)-th interfering signal from its own cell center; \(G_{i}(\theta)\) represents the on-board antenna transmitting gain function for the downlink of the \(i\)-th interfering signal; and \(\psi_{\mathrm{c}i,1}\) represents the deviation angle of the downlink of the useful signal from the cell center of the \(i\)-th interfering user.  
The CIR of the downlink for the target user is:  
\[\left(C/I\right)_{fd}=\frac{\left(C\right)_{fd}}{\sum\limits_{i}(I_{i})_{fd}} =\frac{P_{\mathrm{I}}G_{\mathrm{I}}(\theta_{\mathrm{I}})/L_{\mathrm{I}}}{ \sum\limits_{i}P_{\mathrm{G}}G_{\mathrm{I}}(\psi_{\mathrm{c}i,1})/L_{\mathrm{I }}}, \tag{16}\]  
Figure 6: Schematic diagram of co-channel interference in the user downlink.  
where \((I_{i})_{fd}\) is the power of the \(i\)-th interference signal received by the target user and \((C)_{fd}\) is the power of the useful signal received by the target user.  
In the downlink, due to the same propagation path of the useful signal and interference signals, \(L_{1}\) can be eliminated in Equation (16) and then we have:  
\[(C/I)_{fd}=\frac{P_{1}G_{1}(\theta_{1})}{\sum_{i}P_{i}G_{i}(\psi_{ ci,1})}. \tag{17}\]  
It can be seen that the interference mechanism in the downlink is different from that in the uplink. The downlink interference at the user's receiver comes from beams pointed at other users. Thus, the interference occurs when these spurious signals fall into the receiving antenna of the target user. Therefore, the CIR of the downlink for the target user is not related to the positions of the interfering users but instead only to the position of the target user.
## 2 Materials and Methods
### Datasets  
ALOS-2 PALSAR-2 Mosaic Data  
The ALOS-2 PALSAR-2 L-band imaging radar, operated by the Japan Aerospace Exploration Agency (JAXA), acquires imagery across the tropical belt in ScanSAR mode with a 350 km swath every 42 days as per the PALSAR-2 Basic Observation Scenario [36]. The data used in this study were provided through JAXA's ALOS Kyoto & Carbon (K&C) Initiative project [37] in 1 \(\times\) 1 degree tiles (\(n\) = 1794), processed to the CEOS Analysis Ready Data standard [38] expressed as Gamma Nought \(\gamma^{0}\). The HH and HV backscatter, as well as the Local Incidence Angle images, are used in the image classification, and in addition, the Normalised Difference of HH and HV backscatter, creating a Normalised Polarisation Difference Index band (NPDI) defined as (HH-HV)/(HH+HV) [23].  
As all SAR imagery is susceptible to speckles resulting from random signal-target interaction, a 3 \(\times\) 3 pixel window Lee Filter was applied to the input imagery as a means of reducing the speckle within the imagery. All available 1 \(\times\) 1 degree tiles intersecting the Amazon Basin boundaries (defined by HydroBASIN), between 2019 and mid 2023 were used for image classification.