esa-sceva/satcom-chunk-collection huggingface dataset:
# Lightweight Design and Evaluation of Square Reflector
## 4 Conclusions  
In order to ensure the regular operation of a space remote sensor in a complex vacuum environment, the optical element must have good surface shape accuracy and withstand the harsh mechanical and thermal environment. Based on a square mirror in the off-axis system, this paper focuses on the lightweight design of the mirror blank. It designed the structure of the three-layer sandwich mirror with ultra-low expansion glass as the raw material. The triangular honeycomb structure was to reduce the weight with the premise of ensuring rigidity. The gradual reinforcement structure was proposed through comparative analysis. A simulation model similar to the actual optical component was established by applying the finite element method, and its statics and temperature field were analyzed and calculated. The results show that the surface shape accuracy reached 1/90\(\lambda\) under 1 g gravity and 1/500\(\lambda\) (\(\lambda\)= 632.8 nm) under 15 \({}^{\circ}\)C-25 \({}^{\circ}\)C temperature change. Finally, the mechanical test of the component shows that the first-order mode of the component was 167.8 Hz, which is close to the simulation calculation result of 173.3 Hz, with a gap of less than 3%, which further proves the reliability of the simulation analysis result. The components of the lightweight design of the square mirror studied in this paper have high static and dynamic stiffness and thermal dimensional stability. It shows that the design is reasonable, reliable, and has adaptability to the space environment. At the same time,  
Figure 10: Schematic diagram of random vibration test curve of mirror assembly.  
Figure 9: Schematic diagram of sinusoidal vibration test curve of mirror assembly.  
the lightweight design and test verification method of the square mirror can provide a particular reference for the design and test of similar spatial mirrors and also provide technical guidance for the relevant research of professionals in the industry.  
Conceptualization, Z.Z. and S.D.; methodology, A.T. and B.L.; validation, Z.Z. and B.L.; data curation, X.D.; writing--original draft preparation, S.D., Z.Z. and A.T. All authors have read and agreed to the published version of the manuscript.  
Basic Research (JCKY2020426B009); Shaanxi Provincial Science and Technology Department (2023 KXJ-066).  
Not applicable.  
The data in this research are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
# Optimization of Truck-Drone Parcel Delivery Using Metaheuristics
## 2 Proposed Greedy Randomized Adaptive Search Procedure for TSP-D
### Objective Function  
We aimed to minimize the total delivery time required to serve all customers starting from and returning to the depot, as shown in the objective function (8), where customer \(i\) is where the sub-route starts and customer \(k\) is where the sub-route ends. Customer \(j\) is served by the drone [23].  
\[minimize\sum_{s\in subroute}max\left(C_{D}[i,j]+C_{D}[j,k],\sum_{i}^{k-1}C_{T}[ i,i+1]\right) \tag{8}\]  
The \(CompCost\) procedure, presented in Algorithm 5, computes the cost of the full \(tspd\) tour. Firstly, the \(currentTime\) is initialized to 0. Following that, the nodes in the tour are examined in sequence. If the node is a launch node, the sortie time is computed. A launch location \(i\) and a rendezvous location \(k\) are identified. The sortie time is calculated by taking the maximum time between the \(droneTime\) and \(truckTime\). The \(droneTime\) is calculated by adding the time taken by the drone to move from the launch location to the drone location, to the time taken by the drone to move from the drone location to the rendezvous location. The \(truckTime\) is computed by calculating the time taken by the truck to move from the launch location to the rendezvous location. In line 16, the value of the counter \(i\) is updated to that of the rendezvous location \(k\), as the entire sortie has been processed. Otherwise, if the location is not in a sortie, it is visited by the truck and the cost required by the truck is computed by calculating the time taken by the truck to move from its current location to the following location.  
Figure 8: An example of an invalid \(TwoOpt\) move applied to the graph in (**a**), where origin nodes 2 and 5 are launch nodes and their destination nodes 4 and 7 are rendezvous nodes, resulting in an invalid tour in (**b**).  
Figure 9: An example of an invalid \(TwoOpt\) move applied to the graph in (**a**), where destination node 4 is a launch and rendezvous node for different sorties, resulting in an invalid tour in (**b**).  
```
1\(currentTime=0\);
2\(i=0\);
3 while(\(i<len(tspd))\)do
4 if(tspd\([i]\) is launch location for drone)then
5\(truckTime=0\);
6\(launch=tspd[i]\);
7\(done=tspd[j]\);
8\(rendezvous=tspd[k]\);
9\(droneTime=C_{\mathrm{D}}[launch,drone]+C_{\mathrm{D}}[drone,rendezvous]\);
10\(t=i\);
11 while(\(t<k-1\))do
12\(truckTime=truckTime+C_{T}[t,t+1]\);
13\(t=t+1\);
14
15 end if
16\(sortieTime=max(droneTime,truckTime)\);
17\(currentTime=currentTime+sortieTime\);
18\(i=k\);
19 else
20\(truckTime=C_{T}[i,i+1]\);
21\(currentTime=currentTime+truckTime\);
22\(i=i+1\)
23 end if
24
25 end if
26
27 returncurrentTime
```  
**Algorithm 5**\(CompCost\) algorithm
### Extracting Research Gaps from Related Works
#### 1.2.4 Research Gaps  
Analysis using these precise criteria uncovered some significant research gaps in the literature on data-driven diagnosis and prognosis of aeronautical bearings, the most significant of which are given below:  
1. Most of the discussed related works (i) limited their study to a specific set of subsets and working conditions (e.g., [13]), (ii) reduced the problem complexity by turning it into the healthy-non-healthy problem only without in-depth multi-class classification of the seven operating modes (e.g., [10]), or (iii) sub-sampled the dataset by increasing the sliding-window length (e.g., [12]). This considerably reduces the complexity of the problem in general and also reduces the effectiveness of the model in terms of generalization when it comes to real-world application;
2. Data complexity from the noise reduction perspective received a little interest only from a few works (i.e., [9; 11]), while the outlier removal problem has not been considered;
3. Solving the common problem of imbalanced classification by ensuring fair representation of all classes was not discussed in these cases, which is a big problem of model bias to be considered;
4. Most of the time, feature extraction techniques were performed automatically by involving deep learning (i.e., [10; 13; 14]); on the contrary, it is of great importance to explore the spectral nature of the recorded signal before obtaining better representations (e.g., [9; 11; 12; 15]);
5. Most of the time, evaluation procedures were performed by considering training and test sets that were randomly selected and split. This does not guarantee the learning model's generalizability, nor does it prevent the risk of overfitting;
6. Only a few works considered the use of generative modeling and/or domain adaptation transfer learning to overcome the data unavailability issue [13; 14]; however, it is necessary to target such a problem, especially when data are artificially generated (i.e., failure patterns forced to exist and not naturally occurring) and not collected from deterioration or a failure mechanism;
7. Data drift and its dynamics behind the considered system were only addressed in a few works (i.e., [11; 14]);
8. None of these works took into account the second endurance subset, which is tightly linked to the prognosis and severity of bearing damage.  
Overall, these shortcomings lead to important conclusions about the generalizability of the models discussed in this work and the need for ongoing improvements, including the need for our contributions in this work.
# Analysis of the Channel Influence to Power Line Communications Based on ITU-T G.9904 (PRIME)
## 6 Results
### Channel with Abrupt Phase Shift  
Tests are executed according to the method described in Section 5.1 with the particular propagation channel described in Section 4.2.3.  
Given the fact that this type of propagation channel invalidates the subcarrier (or subcarriers) of the abrupt phase shift moment, error correction mechanisms are essential when this type of channel distortions occur. Therefore, only modulation schemes with convolutional encoding are analyzed.  
Figure 13 relates FER and SNR values for each modulation scheme with convolutional encoding.  
Figure 12: Relation FER-SNR with frequency selective channel.  
The SNR necessary for communications with an FER value of \(10^{-2}\) is 5.75 dB for DBPSK with the convolutional encoder, 8.75 dB for DQPSK with the convolutional encoder (3 dB higher than DBPSK), and 14.75 dB for DBPSK with the convolutional encoder (around 9 dB higher than DBPSK).  
To obtain conclusions on how this abrupt phase channel impacts PRIME technology, the results presented in Figure 13 are compared to the flat channel results in Figure 10. The abrupt phase shift introduces a sensitivity loss of approximately 1.75 dB for DBPSK and DQPSK with convolutional encoding. For D8 PSK with convolutional encoding, the penalty obtained in the tests is 3.25 dB.
## 2 Existing CF Approaches
### One-Way Pearson Correlation-Based Approaches  
The Pearson correlation-based approach can use either user-item similarities or item-user similarities, where either the user-based CF or the item-based CF is considered. For the user-based CF, \(\mathbf{V}=\left(\mathbf{v_{1}},\ldots,\mathbf{v_{j}},\ \ldots,\mathbf{v_{m}}\right)= \left(v_{ij}\right)\), (\(i=1,2,\ldots,n\); \(j=1,2,\ldots,m\)) represents the binary user-item matrix shown in Figure 0(a), which comprises ones (representing purchased items) and zeros (representing non-purchased items). [PERSON] and [PERSON] (1988) expressed the predicted score for an active user \(a\), for an item \(j^{\prime}\), \(P_{aj^{\prime}}\) by  
\[P_{aj^{\prime}}=k_{a}\sum_{i=1}^{n}(w(a,i)v_{ij^{\prime}})\,, \tag{1}\]  
where  
\[\overline{v}_{i}=\frac{1}{m}\sum_{j}v_{ij},\ \overline{v}_{a}=\frac{1}{m}\sum_{j }v_{aj},\]  
\[w(a,i)=\frac{\sum_{j}\left(v_{aj}-\overline{v}_{a}\right)\left(v_{ij}-\overline {v}_{i}\right)}{\sqrt{\sum_{j}\left(v_{aj}-\overline{v}_{a}\right)^{2}\sum_{j }\left(v_{ij}-\overline{v}_{i}\right)^{2}}}\]  
and  
\[k_{a}=\frac{1}{\sum_{i=1}^{n}\left|w(a,i)\right|}\,.\]  
Here, the Pearson correlation denoted by \(w(a,i)\) represents a user-item similarity for the user-based CF. On the contrary, we can consider the binary item-user matrix as  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Description** \\ \hline \(n\) & number of users \\ \hline \(m\) & number of items \\ \hline \(w(a,i)\) & similarity between users \(a\) and \(i\) \\ \hline \(w(b,j)\) & similarity between items \(b\) and \(j\) \\ \hline \(P_{aj^{\prime}}\), \(P_{bi^{\prime}}\) & predicted scores by user-based and item-based CFs \\ \hline \(\overline{v}_{j}\) & predicted scores by regression \\ \hline \(P_{P(p_{j^{\prime}},p_{bi^{\prime}})}\) & Pearson correlation-based score \\ \hline \(P_{rsq(\overline{v}_{j},\ \overline{u}_{i})}\) & RF R-square-based score \\ \hline \(P_{P(\overline{v}_{j},\ \overline{u}_{i})}\) & RF Pearson correlation-based score \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations.  
Figure 1: Two types of matrices for the CF. (A: existing users, B: active users, C: existing items, D: active items).  
illustrated in Figure 1b, where item-user similarities are used for the item-based CF [20]. Then, the predicted voting score for an active item \(b\), for a user \(i^{\prime}\), \(P_{bi^{\prime}}\) is denoted by  
\[P_{bi^{\prime}}=k_{b}\sum_{j=1}^{m}(w(b,j)v_{ji^{\prime}}), \tag{2}\]  
where  
\[\overline{v}_{j}=\frac{1}{n}\sum_{i}v_{ji},\ \overline{v}_{b}=\frac{1}{n}\sum_{i }v_{bi},\]  
\[w(b,j)=\frac{\sum_{i}(v_{bi}-\overline{v}_{b})\big{(}v_{ji}-\overline{v}_{j} \big{)}}{\sqrt{\sum_{i}(v_{bi}-\overline{v}_{b})^{2}\sum_{i}\big{(}v_{ji}- \overline{v}_{j}\big{)}^{2}}}\]  
and  
\[k_{b}=\frac{1}{\sum_{j=1}^{m}\lvert w(b,j)\rvert}\.\]  
Here, the Pearson correlation denoted by \(w(b,j)\) represents an item-user similarity.
# Tracking and Registration Technology Based on Panoramic Cameras
## 3 Method  
### Network Architecture  
Due to the difficulty in obtaining pose data for panoramic image sequences and the limited availability of public datasets, a panoramic camera pose estimation model based on unsupervised deep learning is proposed to reduce the training complexity and overcome the dependency on pose labels, while fully leveraging panoramic sequences for training, as shown in Figure 1. To enable unsupervised learning through a closed-loop constraint, the model includes not only a pose estimation sub-network but also introduces a depth estimation sub-network. The depth sub-network predicts dense panoramic depth maps, which, together with the pose information estimated by the pose sub-network, are used to reconstruct panoramic views. The reconstructed panoramic image serves as a supervision signal, and a spherical photometric consistency loss between the reconstructed and input panoramas is constructed to enable the joint unsupervised training of both sub-networks.  
Figure 1: The input to the panoramic camera pose estimation model consists of three temporally consecutive panoramic images, \(I_{l-1}\), \(I_{t}\), and \(I_{t+1}\). Among them, the image \(I_{t}\) is fed into the depth estimation sub-network to produce the corresponding depth map \(D_{l}\). The three images \(I_{t}\), \(I_{t-1}\), and \(I_{t+1}\) are concatenated along the channel dimension and input to the pose estimation sub-network, which outputs the relative camera poses \(T_{t,l-1}\) and \(T_{t,t+1}\) between the central frame and its neighbors, as well as the occlusion mask \(M\). Using the view synthesis module, the images \(I_{t-1}\) and \(I_{t+1}\), predicted depth map \(D_{t}\), and relative poses \(T_{t,l-1}\) and \(T_{t,l+1}\) are used to reconstruct the corresponding panoramic views \(I^{\prime}_{t-1}\) and \(I^{\prime}_{t+1}\), which serve as supervisory signals during training. The occlusion mask \(M\) is incorporated to compute the spherical photometric consistency loss between the reconstructed panoramic images \(I^{\prime}_{t-1}\) and \(I^{\prime}_{t+1}\) and the input image \(I_{t}\), thereby enabling joint training of the depth and pose estimation sub-networks over the panoramic image sequence. Although these two sub-networks are trained jointly, they can be used independently during inference.
## 2 Rockburst Monitoring Data Processing
### Rockburst Data Processing
#### 2.2.1 Rockburst Data Preprocessing  
During coal mine production, there are problems of monitoring data missing and abnormal monitoring data due to suspension of construction, failure to monitor, or abnormal monitoring equipment. In the data preprocessing process, the small amount of missing monitoring data is compensated with the before-and-after mean method \(x_{i}=(x_{i-1}+x_{i+1})/2\), and abnormal data are replaced using the same method.  
Different rockburst monitoring systems cause different magnitudes and units of the data. Suppose the analysis is performed directly with multiple original indicators, among which one feature is more significant in magnitudes, such as energy sum, energy max, and daily output. In contrast, other features are smaller, such as tons of coal release energy and stress increase max. In that case, the final results of model prediction will be dominated by the more prominent feature in magnitude, and the influence of the other features will be weakened. To ensure the role of each feature, the maximum value normalization is performed with all data mapped between 0 and 1. The datasets X1, X2,..., and Xn of each feature are transformed by Equation (1).  
\[x_{scale}=\frac{x-x_{\max}}{x_{\max}-x_{\min}} \tag{1}\]  
In the equation: \(x\) represents the data value of the indicator before nondimensionalization, and \(x_{\max}\) and \(x_{\min}\) represent the maximum and minimum values of the indicator in these sample data. In the normalization process, the differences between the factor feature magnitudes and their effects on the model are reduced, and a better quality rockburst feature magnitude dataset is obtained.
## 4 Varying Noise Level--Speech Command Results
The result, as presented here, does not give additional information on which of the four words are failing, and whether these are primary keywords, primary modifiers, secondary keywords, or secondary modifiers' parameters. Note that the failure of the two primary parameter/words could be considered as the failure of the control command. However, a more general approach is used to address this, by investigating the overall word failure frequency in a later section. Figure 5e,f shows the frequency map and trendline for the eighth speech command (SC8), \"Go Backward One Metre\", another four-word speech command. Figure 5g,h shows the frequency map and trendline for the ninth speech command (SC9), \"Hover One Metre\", a three-word command. \[y(x)=\frac{\sum_{i=0}^{n}d_{i}(x)f_{i}(x)}{\sum_{i=0}^{n}f_{i}(x)}, \tag{1}\]7 Frequency map and trendlines III. (**a**) SC10 frequency map, (**b**) SC10 trendline, (**c**) SC11 frequency map, (**d**) SC11 trendline, (**e**) SC12 frequency map, (**f**) SC12 trendline. Figure 6 shows the results of the remaining three speech commands, SC10-SC12. Figure 6a,b shows the frequency map and trendline of the tenth speech command (SC10), \"Step Left Half Metre\", a four-word speech command with over 75% and about 50% recognition accuracy rate at 75 dB and 80 dB, respectively. Figure 6c,d shows the frequency map and trendline of the eleventh speech command (SC11), \"Step Right One Metre\", another four-word speech command. Similar to SC4 \"Step Right\", SC11 had a good performance with a recognition accuracy of 92% and 67% at 75 dB and 80 dB, respectively. Both SC4 and SC11 share the same base keywords of \"Step Right\", the recognition of which seems to be highly successful in all cases. This would be investigated further when breaking down the speech command constituents, in a later section investigating speech command keyword selection. Figure 6e,f shows the frequency map and trendline of the tenth speech command (SC12), \"Stop\", a one-word speech command with 91% and 64% recognition accuracy rate at 75 dB and 80 dB, respectively.
# Enhancing Energy Efficiency in Retail within Smart Cities through Demand-Side Management Models
## 2 Literature Review
[25] proposed a short-term electricity demand prediction method based on seasonal double exponential smoothing. This method can accurately predict future electricity demand without the need for any external factors, while also handling complex seasonal trends. [PERSON] et al. reviewed the application of deep learning techniques in electricity demand prediction [26]. After comparison and analysis, the authors found that deep learning algorithms can improve the accuracy and efficiency of electricity demand prediction, but there are also some challenges and limitations. [PERSON] et al. [27] proposed the development of an integrated grey relational analysis and multi-layer functional link neural network for retail sales forecasting. According to the research of [PERSON] and [PERSON] [28], artificial neural networks have significantly better predictive capabilities and efficiency than traditional statistical methods and other related reasoning prediction models [29; 30; 31]. In this study, we first observe international DSM practices and develop a suitable DSM plan. We then use a questionnaire to understand the retail industry's perception and willingness to participate in the DSM plan, and propose a prediction method to aggregate all users' electricity usage information and predict electricity demand.
## 3 Near Field Models of Reflectarray and Transmitarray Antennas
### Unit Cell Characterization  
In order to be able to successfully perform a wideband direct layout optimization, the chosen unit cell should be able to provide a large enough phase-shift range as well as enough degrees of freedom for the optimization. In this regard, a unit cell comprised of only one rectangular patch would not provide enough phase-shift and degrees of freedom for its use in a wideband optimization procedure [32]. However, by stacking two rectangular patches, the range of the phase-shift can be increased. Thus, the chosen unit cell consists  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Time (s)** \\ \hline FF contributions & 1.40 \\ NF (\(N_{x}=N_{y}=1\)) & 24.65 \\ NF (\(N_{x}=N_{y}=3\)) & 168.45 \\ NF (\(N_{x}=N_{y}=5\)) & 455.89 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Computational performance of each algorithm in the computation of the near field in a total of 22,500 points employing an Intel i9-9900 CPU working at 3.1 GHz. Computations are parallelized with OpenMP [30].  
Figure 5: Proposed scenario for indoor femtocell coverage. A reflectarray provides a coverage on top of an office desktop with a maximum desired magnitude ripple of \(2\,\mathrm{dB}\). Picture adapted from [31].  
of two stacked rectangular patches backed by a ground plane as shown in Figure 6a. Both layers employ the same substrate, the Rogers DiClad 870 with relative permittivity \(\varepsilon_{r}=2.33\), loss tangent \(\tan\delta=0.005\), and thickness \(h=30\,\mathrm{mil}\). A CuClad 6250 bonding film between the two layers with the same electrical characteristics as the DiClad 870 is considered in the simulations. The substrate has been chosen such that a large enough phase-shift range is achieved.  
For the initial layout design after the POS (see Section 4.3), a fixed relative size of the stacked patches is kept, with \(T_{x_{1}}=0.77T_{x_{2}}\) and \(T_{y_{1}}=0.77T_{y_{2}}\). The scaling factors were found after performing a parametric study to obtain a smooth variation of the phase-shift for \(T_{x_{1}}=T_{y_{1}}\) at several frequencies and angles of incidence. Figure 6b shows the phase response of the unit cell at \(28\,\mathrm{GHz}\) for normal and oblique incidence. The total phase-shift provided by the cell with the selected substrate is around \(500^{\circ}\), and it can be seen that it is stable with the angle of incidence. Conversely, Figure 6c shows the unit cell phase response at three different frequencies for oblique incidence with \((\theta=40^{\circ},\varphi=30^{\circ})\). From these curves, it can be seen that the unit cell also provides around \(500^{\circ}\) of phase-shift at several frequencies, making it suitable for a multi-frequency optimization.