esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments and Discussions
### Establishing the Dataset
#### 5.1.3 Data Collecting  
To train a fully connected neural network model for estimating cutting forces, datasets containing machining signals, control signals, and cutting force signals were needed. To estimate the cutting forces under different cutting depths and driven frequencies, the fast tool servo was driven with different frequencies and amplitudes of the trajectories. Virtual voltages with different frequencies and amplitudes were added to the cutting process to simulate cutting forces with different amplitudes. The machining signals and control signals of the device were collected. Furthermore, the datasets for the training of fully connected neural networks were formed through the combination of virtual voltage and collected signals. The frequency and amplitude of tool position and the amplitude of virtual voltage used in the experiment are shown in Table 4. Orthogonal operations were carried out according to the data shown in Table 4. Each operation lasted 0.5 s.  
After collecting the signal data in the experiment, it is necessary to preprocess the data to obtain the characteristic data that can be input into the neural network. The commanded velocity and command acceleration of the tool were obtained by differentiating and secondarily differentiating the command position that controlled the displacement of the diamond tool in the cutting. The actual position collected by the grating scale was differentiated and secondarily differentiated to obtain the actual velocity and acceleration. Because of the interference of high-frequency noise in the actual position and driving voltage signal, the filter of the signal was necessary. The method of directly truncating the high-frequency part of the signal was adopted to avoid the reduction of the amplitude at the specific frequency band caused by low-pass filtering. Fourier transform was used to convert the signal into a signal in the frequency domain, and then all the high-frequency parts of the signal after the specific frequency (190 Hz in the paper) were set to zero. Finally, the truncated signal was restored by the inverse Fourier transform, to avoid the problem of signal attenuation in the low-frequency part of the signal. Similarly, the actual velocity signal and actual acceleration signal obtained by differentiation were also filtered. The signals after differentiation and filtering included seven features, namely, actual position, actual velocity, actual acceleration, command position, command velocity, command acceleration, and driving voltage. Furthermore, these signal features were constructed into the datasets for the training of the neural network model. Part of constructed datasets is shown in Figure 7.
# Estimation of Terrestrial Water Storage Changes at Small Basin Scales Based on Multi-Source Data
## 3 Methods
### Estimation of TWSC Based on the Water Balance Equation  
According to the law of conservation of mass, in the hydrological cycle, for any region and period, the difference between the inflow and the outflow of water must be equal to the change in the water storage, which is known as the basin water balance equation. Therefore, we calculated the terrestrial water flux (the slope of the change in terrestrial water storage over a given period) for the GRB using the basin water balance equation (Equation (2)) based on small-basin-scale precipitation, ET, and runoff. Detailed water balance components are shown in Table 1. The TWSC is obtained by integrating the land water flux.  
\[dS/dt=P-ET-Q \tag{2}\]  
\(dS/dt\) represents the TWSC for a specific period (\(t\)), \(P\) represents precipitation, \(ET\) represents evapotranspiration, and \(Q\) represents runoff.
# Chipless Radio Frequency Identification (RFID) Sensor for Angular Rotation Monitoring
## 2 Cross Polar Features and Chipless RFID Sensor Design
In this case, the second dipole will provide the maximum of its reflected cross polar response at frequency \(f_{2}=7.35\) GHz, since the length of the two-dipole is different (Figure 2b). Therefore, if we consider again two values of \(\phi\) for which the cross polar response at \(f_{1}\) is ambiguous, it is possible to discriminate between \(\phi_{1}\) and \(\phi_{2}\) since they exhibit a different cross polar value at \(f_{2}\), namely XP\({}_{2\text{A}}\) and XP\({}_{2\text{B}}\). Accordingly, the rotation state of \(\phi_{1}\) is encoded by using the couple (XP\({}_{1}\), XP\({}_{2\text{A}}\)), whereas \(\phi_{2}\) is represented by (XP\({}_{1}\), XP\({}_{2\text{B}}\)). Moreover, if the maximum of the reflected cross polar field is considered (Figure 2c), it is apparent that the lowest value is around \(-4\) dB, which can help in retrieving the correct angular rotation state. It is important to point out that only two frequencies are required by the encoding scheme, which is based on the values assumed by the cross polar at \(f_{1}\) and \(f_{2}\). However, despite the adoption of two dipoles, a certain level of ambiguity is still present due to similar values assumed by the cross polar reflection coefficient at the two reference frequencies. It is therefore essential to determine the number of elements that are requested for an unambiguous recovery of the rotation angle, as well as determine a metric for assessing the robustness of the proposed encoding mechanism.
## 2 Numerical Model
### Mathematical Equations
#### 2.1.4 Computation of the Flow Rate  
The surface of the basin where the runoff is supposed to take place can be assumed to be a rectangular channel, with \(l\) as the width, D-Ds as the height, and a slope of S. The flow can be computed using the Manning equation, as follows:  
\[Q=\frac{1.49}{nm}S^{0.5}R_{H}^{2/3}A_{S} \tag{24}\]  
where:  
* nm is the roughness coefficient;
* \(A_{s}\) is the surface of the basin crossed by the runoff (\(A_{s}=l*(D-Ds)\);
* \(R_{H}\) is the hydraulic radius (\(R_{H}=D-Ds\));
* A is the surface of the basin or catchment;
* D is the height from the impervious area in the soil until the surface runoff;
* Ds is the thickness between the soil surface and the impervious area of the soil.
* At this point, the flow rate can be determined using the following equation.
* If (D \(>\) Ds):  
\[q=\frac{1*49}{A.nm}l*S^{0.5}*(D-Ds)^{5/3}\,. \tag{25}\]  
In the case where (D \(\leq\) Ds), then \(q=0\).
# On the Impact of the Rules on Autonomous Drive Learning
## 6 Conclusions  
We investigated the impact of imposing traffic rules while learning the policy for AI-powered drivers in a simulated road traffic system. To this aim, we designed a road traffic model that allows analyzing system-wide properties, such as efficiency and safety, and, at the same time, permits learning using a state-of-the-art RL algorithm.  
We considered a set of rules inspired by real traffic rules and performed the learning with a positive reward for traveled distance and a negative reward that punishes driving behaviors that are not compliant with the rules. We performed a number of experiments and compared them with the case where rules compliance does not impact on the reward function.  
The experimental results show that imposing the rules during learning results in learned policies that gives safer traffic. The increase in safety is obtained at the expense of efficiency, i.e., drivers travel, on average, slower. Interestingly, the safety is also improved after the learning--i.e., when no reward exists, either positive or negative--and despite the fact that, while training, rules are not enforced. The flexible way in which rules are taken into account is relevant because it allows the drivers to learn whether to evade a certain rule or not, depending on the current situation, and no action is prohibited by design: rules stand hence as guidelines, rather then obligation, for the drivers. For instance, a driver might have to overtake another vehicle in a situation in which overtaking is punished by the rules, if this decision is the only one that allows avoiding a forthcoming collision.  
Our work can be extended in many ways. One theme of investigation is the robustness of policies learned with rules in the presence of other drivers, either AI-driven or human, who are not subjected to rules or perform risky actions. It would be interesting to assess how the driving policies learned with the approach presented in this study operate in such situations.  
From a broader point of view, our findings may be useful in the situations where there is a trade-off between compliance with the rules and a greater good. With the ever increasing pervasiveness of AI-driven automation in many domains (e.g., robotics and content generation), relevance and quantity of these kinds of situations will increase.  
Conceptualization, E.M. and J.T.; Methodology, E.M.; software, J.T.; investigation, J.T.; writing-original draft preparation, J.T.; writing-review and editing, A.B. and A.D.L.; supervision, E.M.; All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflicts of interest.
# Bridge Model Updating Based on Wavelet Neural Network and Wind-Driven Optimization
## 2 Wavelet Neural Network  
In this section, the inception and concepts of wavelet neural network (WNN) are introduced, the structure of WNN is discussed, the training process of WNN is demonstrated conceptually, and finally, the shortcomings of WNN and measures to improve WNN are discussed.  
Backpropagation (BP) neural network is the training process of feeding error rates back through a neural network to make it more accurate. The excitation function selected by the BP neural network is generally the Sigmoid function, which makes it difficult to guarantee the uniqueness of the solution, and the Sigmoid functions do not interact with each other; therefore, the learning convergence is slow. The RBF neural network needs to determine the center and width of the radial basis, and then, the linear optimization method is employed to determine the parameters, which are difficult to solve, and the radial basis function is non-orthogonal, which cannot guarantee the uniqueness of the approximation function. Wavelet neural network (WNN) was first proposed in 1992 [34] and was gradually developed into a mathematical modeling analysis method. WNN originated from the BP neural network [35]; however, it replaces the original excitation function Sigmoid function of the BP neural network with the wavelet function and obtains a new neural network similar to RBF neural network [36]. The excitation function of WNN is orthogonal or approximately orthogonal, which guarantees fast convergence and high fitting accuracy [37].  
In addition, WNNs have the following characteristics [38]:  
(1) The WNN can select different excitation functions for different fitting functions to improve the fitting;  
(2) WNN can describe the function mutation stepwise, resulting in the function's better fitting performance;  
(3) Determining wavelet function and the whole network structure has a reliable theoretical foundation, which avoids blindness in network design.
# DPtracer: Integrating Log-Driven Accountability into Data Provision Networks
## 7 Comparative Analysis  
In this section, we compare DPTracer with existing tamper-evident logging systems: History Tree [12], Concurrent Authenticated Tree (CAT) [23], and Accountability-oriented Tree (AoT) [24]. History Tree introduced a versioned Merkle tree to support tamper-evident logging, which Process Tree extends with input-referencing and leaf-grouping. The Merkle-tree-based structure is advanced by adding chameleon hashing in CAT to enable concurrent log additions for large-scale distributed systems. AoT, also based on the Merkle tree, focuses on node-level accountability for IoT devices. In contrast, DPTracer provides tamper-evident logging specifically for DPNs by enforcing mutual verification based on log records and integrating these verification steps directly into the protocol. Table 2 compares the main features of DPTracer with those of the existing systems. Each scheme is designed for different target applications and operates under different design principles. For example, History Tree prioritizes efficient log verification, CAT is optimized for high concurrency, and AoT focuses on node-level accountability. In comparison, DPTracer targets DNNs, focusing on data-flow transparency for reconstruction and mutual auditing. The following unique features of DPTracer are highlighted in contrast to existing systems:  
* **Data flow tracking and reconstruction**: DPTracer provides an end-to-end view of data flows with DPLog based on Process Tree. This allows for the reconstruction of data flows and the identification of misbehaving nodes, which is crucial for data provisioning networks. * **Mutual auditing**: DPTracer integrates mutual audits with interval proofs and commitments for tamper detection during provisioning, while others support one-way audits on log integrity. AoT also integrates periodic audits of protocols, but at coarser intervals than DPTracer. However, DPTracer has limitations on scalability and overhead due to its focus on DPN features:  
* **Scalability**: While DPTracer can scale to large DPNs, it incurs higher overhead due to data-flow tracking and mutual auditing mechanisms. Each scheme is optimized for a specific application; for example, CAT is optimized for high-concurrency systems, while AoT is optimized for power-constrained IoT devices. * **Storage requirements**: DPTracer requires more storage to serialize the Process Tree in DPLog. However, group collapsing (as described in Section 4.4) can improve storage efficiency depending on the focus of inspection. AoT is the most storage-efficient among the existing schemes. It may limit the storage overhead into constant size of several KBytes per hour per device.
# Review of Image-Based 3D Reconstruction of Building for Automated Construction Progress Monitoring
## 6 Discussion
### Practical Guidance  
Through the analysis of related technologies, it is found that the image-based method is the development trend of construction progress monitoring in the future. The image-based method includes multiple branches and processes. In practice, different methods can be integrated, such as image-based modeling, perspective-based method, time-delay photography, target detection, and so on. For different scenarios, appropriate methods can be flexibly selected in terms of equipment, data form, registration method, schedule reasoning method, etc. For example, in the concrete pouring site of high-rise buildings, the latest construction progress is blocked by templates, scaffolds, and protective nets. Both point cloud-based and perspective-based methods fail in such a chaotic scene. Therefore, the target detection technology can be used to infer the construction progress through the context information in the image. In short, in the actual construction process, flexible technology combination should be adopted.
## 4 Controller Design
### Design of Explicit MPC Controllers  
This section elaborates on the design of the explicit model predictive controller, including the controller structure and the process of determining the weighting matrices \(Q\), \(R\), and \(Q_{y}\), are shown in the following Table 2. In this section and the next section, the superscript \(C\) refers to the crane and the superscript \(T\) refers to the trolley.  
In this paper, the EMPC controller, designed using the MPT toolbox 3.0 [31], generates an optimal input while satisfying the system's constraints. The performance of the EMPC  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Predicted horizon \(N_{p}\) & \(N_{p}^{C}=12\), \(N_{p}^{T}=6\) \\ Control horizon \(N_{c}\) & \(N_{c}^{C}=N_{c}^{T}\)=2 \\ State weighting matrix \(Q\) & \(Q^{C}=Q^{T}=\left[\begin{array}{cc}0.00001&0\\ 0&0.00001\end{array}\right]\) \\ Output weighting matrix \(Q_{y}\) & \(Q_{y}^{C}=Q_{y}^{C}=10,000\) \\ Input weighting matrix \(R\) & \(R^{C}=R^{T}=0.08\) \\ Sampling time \(T_{s}\) & \(5\) ms \\ \hline \hline \end{tabular}
\end{table}
Table 2: EMPC Controller design parameters.  
Figure 7: CLRV’s motion trajectory and speeds in the \(XY\) directions for desired path four. The initial position of the CLRV is (\(-40\), \(0\)). This path mainly comprises five parts: a uniformly accelerated straight motion part, two turning curves, uniform straight motions, and a uniformly decelerated straight motion part.  
scheme can be tuned by appropriately adjusting the weighting matrices \(Q\), \(R\), and \(Q_{y}\), and by a suitable choice of the prediction horizon \(N_{p}\) and \(N_{c}\). The EMPC controller for the crane moving system has 993 critical regions, whereas the EMPC controller for the trolley moving system has 685 critical regions. Figure 8 shows the feasible critical regions of the trolley moving system's controller.
## 4 Discussion
### Challenges and Limitations
#### 4.4.2 Privacy and Confidentiality  
Tracking products at a granular level can conflict with privacy, especially if linked to consumers. The ethical dimensions of consumer data protection in circular economy traceability systems warrant scrutiny, given the sensitive nature of return patterns and usage behaviors. Consumer return data inherently reveals personal consumption habits, product satisfaction levels, and disposal preferences that could be exploited for discriminatory pricing or targeted marketing if inadequately protected. Our framework addresses these concerns through a multi-layered privacy architecture that separates personal identifiers from product lifecycle data. Specifically, we propose that consumer returns be linked to anonymized order identifiers rather than individual customer profiles, with personal data maintained exclusively in retailers' off-chain systems under existing data protection regulations (GDPR [45]). This approach enables statistical analysis of return patterns for supply chain optimization while preventing individual consumer profiling.  
Competitive information sharing presents equally complex challenges, particularly regarding yield rates, defect patterns, and supplier performance metrics that constitute valuable trade secrets. The permissioned blockchain architecture with channel-based access control provides granular visibility management, allowing participants to share necessary traceability data while protecting competitively sensitive information. For instance, a textile manufacturer can verify recycled content authenticity without exposing proprietary blend ratios or quality control processes. However, power imbalances in buyer-driven commodity chains may compel smaller suppliers to share more data than larger retailers, creating potential for asymmetric information exploitation. Governance frameworks must therefore establish reciprocal transparency requirements and ensure that participation benefits are proportional to data contributions. Future implementations should consider privacy-preserving technologies such as zero-knowledge proofs to enable verification of compliance claims without revealing underlying operational details, thereby maintaining both transparency and competitive confidentiality.
## 3 Methodology
### Feature Selection  
Feature selection is the process of removing non-informative or redundant input characteristics from the dataset. Feature selection decreases the computational complexity of prediction algorithms. This minimizes prediction uncertainty and improves the model's overall efficacy.  
The Chi-squared test is a non-parametric statistical technique used to examine the relationship between two variables [41]. The approach generates a number that quantifies the relationship between the input characteristics and the projected result. The greater the value, the stronger the connection between the input and output characteristics, and features with values less than the critical value are removed. As the Chi-squared approach  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**S.no** & **Parameters** & **Mean** & **Standard Deviation** & **Minimum** & **Maximum** \\ \hline
1. & Pregnancies & 3.84 & 3.36 & 0 & 17 \\
2. & Glucose & 121.6 & 30.46 & 44 & 199 \\
3. & Blood-pressure & 74.8 & 16.68 & 24 & 142 \\
4. & Skin Thickness & 56.89 & 44.51 & 7 & 142 \\
5. & Insulin & 139.42 & 87.24 & 14 & 846 \\
6. & BMI & 33.63 & 12.22 & 18 & 142 \\
7. & Diabetes-Pedigree & 0.47 & 0.33 & 0 & 2.42 \\
8. & Age & 33.24 & 11.76 & 21 & 81 \\
9. & Outcome & 0.34 & 0.47 & 0 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistical analysis of PIMA Indian dataset.  
Figure 3: Box plot representation filtered data.  
operates on categorical data, the numerical values of the features in this dataset were discretized depending on their frequency of occurrence.  
Extra trees apply several randomized decision trees to different subsets of the total dataset [42]. In the tree building process, the input variables and cut-off values are chosen at random to divide a node so that they are fully independent of the output variable. Each tree leads to a different model, which was trained with subsets of data, and the algorithm evaluates the relevance of the contributing features using a criterion known as the Gini index.  
LASSO is an L1 regularization approach for feature selection that is used to facilitate dataset interpretation [43]. In this technique, regression analysis is used to estimate parameters and pick models at the same time, minimizing feature variability by lowering coefficients of noncorrelation characteristics to zero. Table 5 shows the essential characteristics chosen by each technique, along with their ranking measures. The Chi-squared test employs the chi-score, extra trees use the Gini index, and LASSO employs regression coefficients. The characteristics of glucose, insulin, BMI, and age were consistently scored as high in relevance and were chosen by each of the techniques used. After experimenting with the characteristics, it was discovered that removing the skin fold thickness and diabetes pedigree features enhanced the model's overall performance.