esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 BBC Code Design and Optimization
### BBC Optimization
Filter the sequences using a threshold of \(0.5\sigma\), and add those that meet the criteria to Set 2;
4. Iteratively calculate the e and f indicators for all pairs of PRN sequences in Set 2 (e represents even cross-correlation, and f represents odd cross-correlation), obtaining the corresponding \(x_{e}^{n}\) and \(x_{f}^{n}\) values, and store the results in Set 3;
5. Derive the cross-correlation performance matrices \(R_{e}\) and \(R_{f}\) for the code sequences in Set 3 based on the matrix definition in Equation (12);
6. Iteratively search through \(R_{e}\) and \(R_{f}\) matrices for \(N_{3}\times N_{3}\) elements, using their smallest non-zero value as the initial threshold \(T_{h}\);
7. Apply the threshold \(T_{h}\) to the \(R_{e}\) and \(R_{f}\) matrices to make decisions, obtaining the largest connected sets \(A_{e}\) and \(A_{f}\) with values not exceeding the threshold \(T_{h}\), as well as their respective node counts \(M_{e}\) and \(M_{f}\);
8. Compare \(M_{e}\) and \(M_{f}\) with the target code count \(M_{0}\). If they are smaller than \(M_{0}\), the target requirement is not met. Increase the cross-correlation threshold by 1 and return to Step 8 for iteration. Repeat until \(M_{e}\geq M_{0}\) and \(M_{f}\geq M_{0}\), then exit the iteration;
9. Evaluate whether the number of matching node indices between \(A_{e}\) and \(A_{f}\) denoted as \(M_{ef}\), meets the target code count \(M_{0}\). If \(M_{ef}<M_{0}\), the target requirement is not satisfied. Increase the cross-correlation threshold by 1 and return to Step 8 for iteration. Repeat until \(M_{ef}\geq M_{0}\), then exit the iteration, add the results to Set 4, and terminate.
# Optimization of Clustering in Wireless Sensor Networks: Techniques and Protocols
## 6 Recent Advancements in Clustering Optimization Algorithms
### Fuzzy Logic
DFLC ensures the network does not fail when any SN has experienced service failure due to energy depletion. The performance of the DFLC was tested in the NS2 simulator and assessed in terms of energy consumption, number of active nodes, service life of the network, and volume of messages received within five node networks (totaling 100, 200, 300, 400, and 500 nodes). The DFLC aimed to reduce the number of propagated messages, reducing energy consumption. Comparison with LEACH, ACAWT, FCH, and CHEF showed that DFLC performed better based on the evaluation metrics.
# Layer-Wise Compressive Training for Convolutional Neural Networks
## 4 A Greedy Approach for Compressive Training
### Setup  
**Step 1--Pruning**. It consists of a standard magnitude-based pruning applied to both convolutional and fully-connected layers. The user specifies an a priori value for the desired percentage of sparsity of the net. Since such a value is unique for the entire CNN, each layer may show a different pruning percentage. This allows representing the CNN model with a non-homogeneous inter-layer sparsity. We decided to follow this direction under the assumption that each layer influences the knowledge of the CNN differently, i.e., each layer provides a specific contribution to the final prediction. For this reason, the layers do not all keep the same amount of information, but the knowledge is spread heterogeneously among the layers, and hence they keep different percentages of redundant parameters.  
Figure 4: The proposed net compression pipeline.  
**Step 2--Layers Sorting.** It is known that some layers are more significant than others. That means the compression of less significant layers will marginally degrade the overall performance classification. The most significant layers, instead, should be preserved in their original form. As a rule of thumb, we selected the _intra-layer sparsity_ as a measure of significance. More in detail, we argue that layers with lower intra-layer sparsity are those that play a major role in the classification process, whereas those with a higher intra-layer sparsity can be sacrificed to achieve a more compact CNN representation. In other words, we base our concept of significance on the number of activated neurons.  
A significance-based sorted list of layers is generated at first. All layers are processed as they appear in the original model, and then pruned and sorted based on their weights distribution according to the rule _higher-sparsity first-compressed_. A graphical example is reported in Figure 5, where (_i_) the top-most pictures represent the original weight distribution of each layer (numbered L1 to L8) of the AlexNet model trained on the CIFAR10 dataset; (_ii_) the plots in the middle depicts the weight distribution after pruning; and (_iii_) the down-most plots report the layers sorted according to their significance, namely, less important layers are those with a smaller standard deviation, which is directly correlated to their sparsity.
## 3 Results and Discussion
### Characterization
#### 3.1.1 FT-IR Technique  
The FT-IR spectra of AMT and the [Cu(L)(NO\({}_{3}\))(H\({}_{2}\)O)\({}_{2}\)]\(\bullet\)H\({}_{2}\)O complex are demonstrated by traces A and B, respectively, in Figure 3. For AMT, a peak due to NH\({}_{2}\) was observed at 3291-3195 cm\({}^{-1}\), aromatic C-H stretching peak was located at 3064-3091 cm\({}^{-1}\), and the aliphatic C-H stretching peak was observed at 2936 cm\({}^{-1}\). A peak corresponding to S-H was observed at 2665 cm\({}^{-1}\). Peaks of C=O, C=C, and C=N were observed at 1660, 1593, and 1618 cm\({}^{-1}\), respectively. Following differences were observed between the spectrum of AMT and that of its complex with Cu\({}^{2+}\). The NH\({}_{2}\) peak in Cu(II)-AMT shifted to approximately 3142 cm\({}^{-1}\) and decreased significantly in intensity. This indicates that the NH\({}_{2}\) group participated in complex formation. The C=O frequency shifted from 1660 to 1705 cm\({}^{-1}\) in reference to coordination of AMT to Cu\({}^{2+}\) through the carbonyl oxygen atom. Peaks due to v(Cu-N), and v(Cu-O) were observed at 530, and 518 cm\({}^{-1}\), respectively. The coordination behavior of the nitrate ion was confirmed through the appearance of characteristic frequencies of bidentate nitrate group at 1545, 1353, and 820 cm\({}^{-1}\) and the absence of the distinctive band of ionic nitrate group at 1766 cm\({}^{-1}\)[31]. Therefore, the nitrate group served as a bidentate ligand with C\({}_{2N}\) symmetry. Table 1 lists the different FT-IR frequencies of AMT and the [Cu(L)(NO\({}_{3}\))(H\({}_{2}\)O)\({}_{2}\)]\(\bullet\)H\({}_{2}\)O complex.  
Figure 3: FT-IR spectra of (A) AMT and (B) [Cu(L)(NO\({}_{3}\))(H\({}_{2}\)O)\({}_{2}\)]\(\bullet\)H\({}_{2}\)O.  
Figure 2: Determination of the stoichiometry of the Cu(II)–AMT complex. Blue lines represent the tangents of the linear portions of the mole ratio curve, while red dashed line shows the molar ratio.
# Virtual Replication of IoT Hubs in the Cloud:
###### Abstract  
In future years, the Internet of Things is expected to interconnect billions of highly heterogeneous devices, denoted as \"smart objects\", enabling the development of innovative distributed applications. Smart objects are constrained sensor/actuator-equipped devices, in terms of computational power and available memory. In order to cope with the diverse physical connectivity technologies of smart objects, the Internet Protocol is foreseen as the common \"language\" for full interoperability and as a unifying factor for integration with the Internet. Large-scale platforms for interconnected devices are required to effectively manage resources provided by smart objects. In this work, we present a novel architecture for the management of large numbers of resources in a scalable, seamless, and secure way. The proposed architecture is based on a network element, denoted as IoT Hub, placed at the border of the constrained network, which implements the following functions: service discovery; border router; HTTP/Constrained Application Protocol (CoAP) and CoAP/CoAP proxy; cache; and resource directory. In order to protect smart objects (which cannot, because of their constrained nature, serve a large number of concurrent requests) and the IoT Hub (which serves as a gateway to the constrained network), we introduce the concept of virtual IoT Hub replica: a Cloud-based \"entity\" replicating all the functions of a physical IoT Hub, which external clients will query to access resources. IoT Hub replicas are constantly synchronized with the physical IoT Hub through a low-overhead protocol based on Message Queue Telemetry Transport (MQTT). An experimental evaluation, proving the feasibility and advantages of the proposed architecture, is presented.  
Internet of Things; edge computing; cloud computing +
Footnote †: journal: Sensor and Actuator Networks
# Comparison of L1 and L5 Bands GNSS Signals Acquisition
## 3 Assessment of Processing Time and Memory Requirements
### Acquisition of a Pilot Channel without Assistance
#### 3.2.5 Use of Non-Coherent Accumulation with Serial Implementation  
This implementation is shown in Figure 9. The processing time is the same as the serial implementation with coherent integration only (Figure 6), because the timing diagram (Figure 7) is similar. Indeed, the number of secondary code periods processed is still \(K/N_{\text{NC}}\times N_{\text{NC}}=K\), even if it is spread between coherent and non-coherent accumulations.  
Regarding the memory, the memory-based accumulators require  
\[\begin{array}{l}M=2N_{\text{P}}B_{\text{C}}+N_{\text{P}}B_{\text{NC}}\text{ bits}\\ =N_{\text{P}}(2B_{\text{C}}+B_{\text{NC}})\text{ bits}\\ =N_{\text{P}}\big{[}3B_{\text{R}}+3\lceil\log{}_{2}(KN_{\text{S}}/N_{\text{NC}} )\rceil+\lceil\log_{2}(N_{\text{NC}})\rceil\big{]}\text{ bits},\end{array} \tag{18}\]  
where \(B_{\text{C}}\) and \(B_{\text{NC}}\) are the same as previously (Equations (16) and (17)).  
Figure 8: Implementation of a pilot channel acquisition, with non-coherent accumulation, in a parallel way (timing diagram available in Figure 5).  
Figure 9: Implementation of a pilot channel acquisition, with non-coherent accumulation, in a serial way (timing diagram available in Figure 7).
# Climate Warming Dominates Vegetation Productivity in the Hanjiang River Basin, China
## 3 Results  
### Vegetation Productivity Characteristics in the HJRB from 2000 to 2020  
Figure 4a depicts the spatial distribution of GPP in the HJRB. The 21-year average of GPP shows the upstream was higher compared to the downstream, and both the southern and northern sides of the basin were higher compared to the valley within the basin. The trend of GPP in the HJRB from 2000 to 2020 is illustrated in Figure 4b, the overall distribution of GPP trends shows higher values in the valley within the HJRB, while the downstream areas exhibit a generally lower trend. Approximately 97.87% of the region demonstrates an increasing trend, with 44.62% of the area exhibiting a significant increasing trend.  
Figure 4: (**a**) Spatial distribution of annual average GPP in the HJRB, (**b**) Spatial distribution of GPP trends during 2000 to 2020 in the HJRB. Regions marked by lines indicate areas that have passed 95% significance testing.  
During the period from 2000 to 2020, the average GPP in the HJRB was 1095.61 gCm\({}^{-2}\)yr\({}^{-1}\), showing a fluctuating upward trend of 12.44 gCm\({}^{-2}\)yr\({}^{-1}\). Figure 5 illustrates the temporal variations in GPP across various vegetation types. In the case of forests, including DBF, MF, and WSA, GPP is significantly higher compared to other vegetation types. The average GPP of DBF in the HJRB during 2000 to 2020 was 1266.97 gCm\({}^{-2}\)yr\({}^{-1}\), higher than the other vegetation types. Grassland had the lowest mean GPP with an average of 804.97 gCm\({}^{-2}\)yr\({}^{-1}\).  
Furthermore, we conducted an analysis of multi-year averages of GPP and climate factors to investigate the seasonality of vegetation productivity in the HJRB, as illustrated in Figure 6. GPP in the HJRB attains its peak in August, with consistently higher values in the upstream region. However, from January to April, downstream GPP slightly exceeds that of the upstream. A noticeable deceleration in the growth of downstream GPP is observed from April to June, while a similar deceleration occurs in the upstream region in June. The seasonal trends of GPP generally align with those of climate factors (Tem, Pre, PET, SSD), rising in conjunction with these factors. Unlike GPP, the peak values of climate factors are recorded in July. WS displays prominent seasonality, steadily increasing from September to October, reaching its zenith in March to April of the subsequent year, and then gradually decreasing. Conversely, the SPEI drought index does not exhibit strong seasonality. In summary, the primary climatic distinction between the upstream and downstream regions in the HJRB can be attributed to temperature fluctuations associated with changes in elevation.
# Optical Element Surface Defect Size Recognition Based on Decision Regression Tree
## 3 The Principle of Defect Size Recognition
### Defect Size Recognition Based on DRT
#### 3.2.1 Generation of DRT
For instance, in the subset \(\mathbf{R}_{\text{left}}\), the width estimation value of all \(v_{i}\) and its error can be, respectively, expressed as \[\overline{w}=\underset{\mathbf{R}_{\text{left}}}{\text{mean}}(w_{i})\] (9) \[\varepsilon_{\text{left}}=\underset{\mathbf{R}_{\text{left}}}{\sum}\left(w_{i}- \overline{w}\right)^{2}\right.\] (10) where \(\underset{\mathbf{R}_{\text{left}}}{\text{mean}}(\ )\) is a function for solving the mean value in the subset \(\mathbf{R}_{\text{left}}\) and \(w_{i}\in\mathbf{R}_{\text{left}}\);
4. Find the optimum \(v_{i}\), where the value of \(\varepsilon_{\text{left}}+\varepsilon_{\text{right}}\) is minimum, and mark this \(v_{i}\) as \(v_{i}\). Take the two subsets \(\mathbf{R}_{\text{left}}\) and \(\mathbf{R}_{\text{right}}\), which are split by \(v_{i}\), as the two new sets \(\mathbf{R}\). Go back to step 2 until the number of the feature parameter is less than 6 (i.e., \(\tau<6\)) or the variance of \(w_{i}\) is less than 0.01 (i.e., \(S^{2}<0.01\)) in new set \(\mathbf{R}\);
5. Take the mean width \(\overline{w}_{k}\) as the prediction width of all \(v_{i}\) in subset \(\mathbf{R}_{k}\) and denote this regression relationship as DRT \(T\). In case the overfitting of DRT \(T\) occurs, DRT \(T\) needs to be further optimized, and this optimization is called the DRT pruning [20], which is illustrated in Section 3.2.2. Figure 4: Flow chart of the defect size recognition.
## 4 Validation of Proposed Method  
In this section, we investigate whether the frame synchronization and blind detection processes are functioning as designed and determine the appropriate level for the embedding strength. The correct functioning of the frame synchronization process is evaluated by carrying out a frame synchronization test. The correct functioning of the blind detection process is evaluated by carrying out bit detection tests. The appropriate embedding-strength level is determined by examining the trade-off between PESQ, LSD, and BER.
## 3 Proposed Methodology  
Improved efficient data aggregation in Hexagonal grids with energy optimization (IEDA-HGEO) in UWSNs is proposed. The features of our suggested protocol are as follows: The first is to cover as much ground as possible. To cover most nodes, we divided our WSN into hexagonal areas. The best cell shape for clustering in a network is hexagonal. One node in each cell serves as the cell's CH, and the cluster head is selected using a unique method that involves picking the node in smaller cells with the highest residual energy and closest location to the base station (BS) to handle data aggregation as well as transmission.
# Integrated Fusion Network for Hyperspectral, Multispectral and Panchromatic Data Fusion
## 4 Experimental Results  
The performance of the proposed fusion approach is initially assessed by qualitative and quantitative evaluations on simulated datasets. Next, we focused on an ablation analysis to examine the impact of different parameters on the network.
## 6 Discussion and Summary
### Physics-Based SHM Highlights  
* Physics-based SHM systems produce a high level of accuracy of structural response prediction; however, they require domain expertise to define an FEM model with correct boundary conditions, constraints and structural properties.
* For long-term monitoring systems and in operation, a strategy for updating the FEM model should be defined in order to have a model that reflects and simulates the current state of the structure. This strategy should define the update method, which parameters to update and when to update the structural parameters.
* To define the initial FEM model of the structure of interest, a stage of identification of the structural parameters must be considered, using estimation algorithms that include wear effects, operating conditions, the uncertainty of material properties, as well as the possibility of underlying structural damage. Using a data-driven method for the identification of these parameters, through a regression task by a machine-learning algorithm is recommended.
* For real-time SHM applications based on physical models, optimization algorithms in the solution of the numerical computation problem of an FEM model must be implemented to guarantee the responsiveness and availability of the SHM system to an event or in the continuous monitoring task.  
Derived from the analysis of the works shown above in Tables 8-10, the most relevant observations of the application of ML techniques are presented below.
## 3 Three-Dimensional Finite Element Modeling
Since the focus of this paper is on the influence mechanism of the left tunnel excavation (the following tunnel) on the right tunnel (preceding tunnel), the soil displacement will be cleared away after the completion of the excavation of the right tunnel. 5. The left tunnel is excavated, 4 m at each step, and relevant pressure is applied until the construction is completed. Furthermore, the established numerical model was calibrated by adopting the theoretical formula on surface settlement of paralleled twin tunnels presented by [PERSON] et al. [37]. Considering the disturbance caused by preceding tunnel excavation, the disturbance correction factor \(\eta\) is introduced as follows:  
\[\eta(x)=M(e^{\frac{-x^{2}}{2}}-1)(1-e^{\frac{-W^{2}}{8}})^{-1}+1+M \tag{1}\]  
where \(1+M\) is the maximum disturbance correction factor, \(W\) is influence range of the surface subsidence, \(x\) is the horizontal distance between the point and the axis of the preceding tunnel. With \(\eta\), the equation of the surface settlement caused by the following tunnel construction considering the influence of the preceding tunnel excavation is proposed [37]:  
\[S(x)=\eta(x)\cdot\frac{V}{\sqrt{2\pi i}}\exp\left(\frac{-(x-d)^{2}}{2i^{2}}\right) \tag{2}\]  
Figure 3: Finite element model for construction simulation of paralleled twin tunnels. where \(V\) is the stratum loss, \(i\) is the width of settlement trough, and \(d\) is the distance between paralleled twin tunnels. Figure 4 presents the comparison between the numerical and analytical results, in which Equations (1) and (2) were adopted to predict ground settlement caused by excavation of twin tunnels in unreinforced stratum, i.e., \(t\) = 0.00 \(D\). It can be seen that the trends of the two curves are consistent, and the \(R^{2}\) (goodness of fit) is about 0.936. Both of the results show that the maximum settlement point at the ground surface is not located on the axis of the following tunnel but is offset by about 0.3 \(D\) to the preceding tunnel.
## 2 Description of the Assumption of the Driver Model
### The Steering Submodel  
The steering submodel takes into account the driver's avoiding of the obstacle using the steering wheel angle \(\delta\) and depends mainly on the potential in the \(y\)-axis direction.  
When the vehicle, or more precisely the safety zone of the vehicle, approaches the safety zone of an obstacle in the \(y\)-axis direction, the potential \({U^{\prime}}_{y}\) is increased significantly.  
\[\delta(t)=w_{4}*{U^{\prime}}_{y}+w_{5}\frac{{U^{\prime}}_{y}}{dt} \tag{19}\]  
where  
\(\delta(t)\)--vehicle steering wheel angle;  
\({U^{\prime}}_{x}\)--potential towards \(x\)-axis;  
\({U^{\prime}}_{y}\)--potential towards \(y\)-axis;  
\(w_{4}\)--model coefficient related to the potential towards the \(y\)-axis;  
\(w_{5}\)--model coefficient related to the rate of change in the lateral position of the obstacle-change of potential towards the \(y\)-axis.  
An increase in the potential along the \(y\)-axis \({U^{\prime}}_{y}\) can result in a possible adjustment of the vehicle's position (avoidance manoeuvre) against the steering wheel angle \(\delta\). Changing the vehicle's position as a result of avoiding the obstacle also results in a change in the potential in relation to the edge of the road \({U^{\prime}}_{B}\). As a result of the analyses, it was noted that the value of the steering wheel angle is affected not only by how close the safety zones of the vehicle and the obstacle have approached during driving (in the \(y\) axis direction), but also by the speed of these changes. Hence, the component is related to the rate of change in the potential value in the \(y\) axis direction. There are driver models (not created based on the method of potential fields), in which there are derivative terms. In robotics, models (created based on the method of potential fields) perform a movement ensuring avoidance of an obstacle, but at low speeds. Preliminary analyses have shown that in the case of cars moving at much higher speeds, adding the term with the time derivative gives better trajectory courses.
## 4 Experimental Results and Discussion
### Experimental Results  
During the training process, the dataset was divided into a training set and a test set, with the ratio selected as 0.7/0.3. As shown in Figure 6, the training set and the test set loss function of the four models can all converge within a limited epoch.  
The comparison between the predict values and the actual values of the four models is given by the Figures 7 and 8, Figure 7 demonstrates the results in the testing stage, and Figure 8 illustrates the results in training stage, where the REP denotes real error points. The error denotes the actual value minus the predicted value. Figures 9 and 10 illustrate the results in summer and winter. It can be seen from the figures that the overall charge in summer is high, and there are some no charging periods in the winters. Such difference directly affects the model performance. It can be seen from the figure that the GRU model has the best performance in this case. In Figure 8, the peak load reached at 5 p.m. of the day, and the REP is larger than other points. In Figure 7, the peak load reached at 11 a.m., the REP is also the largest. Although all four models show poor predictive effects on peak loads, the GRU model achieves a comparatively lower REP.  
During the experimental process, we modified the number of hidden layers and nodes of the four models, and the performance comparisons are shown in Table 1. It can be seen from the Table 1 that the training process of the deep neural networks (DNN) model was the fastest, costing 68.88 s, whereas the NRMSE and NMAE of the one hidden layer GRU model were minimum, which were as small as 1.48% and 0.47% in training stage respectively, 2.89% and 0.77% in testing stage. However, the multi-hidden layers model had the poorest performance compared to the one hidden layer; it can be seen from the results that although the number of network layers increased, if the other parameters are not changed, not only the training speed is lowered, but also the accuracy is lowered.  
Figure 6: Loss curve of the four models.  
Figure 8: Predictions and REP of the training stage.  
Figure 7: Predictions and real error points (REP) of the test stage.  
Figure 9: Predictions and REP of the summer scenario.