esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Results and Analysis  
#### 4.3.1 Experimental Results of Different Pricing-Strategy Models  
This experiment was designed to evaluate the overall capability of various models to predict pricing strategies under large-scale heterogeneous behavior data. Particular attention was given to their performance in terms of profit growth, behavioral stability, and the consistency of strategy responses, as shown in Table 3 and Figure 4.  
The results indicate that XGBoost demonstrated the weakest performance across all metrics, primarily due to its static modeling approach, which failed to capture the temporal dynamics inherent in complex behavioral feedback. While BERT exhibited some capability in modeling sequential data, its contextual embedding mechanism struggled to generate stable policies in the absence of reinforcement signals. The LSTM-based model performed better in handling continuous time series but still suffered from degradation in capturing long-term dependencies. The transformer-based model significantly improved the efficiency and stability of policy learning through its global attention mechanism. The DQN model, equipped with a reinforcement learning stru  
Figure 4: Visualization of the learned pricing-policy behavior across different models.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Total** & \multirow{2}{*}{**Average**} & **Std. of** & \multirow{2}{*}{**R2**} & **Sharpe** & **Max** \\  & **Profit** & & & & & **Ratio** & **Drawdown** \\ \hline XGBoost [61] & 0.12 & 3.25 & 0.97 & 0.61 & 0.64 & 0.43 \\ BERT [63] & 0.17 & 3.56 & 0.89 & 0.68 & 0.68 & 0.39 \\ LSTM-based [65] & 0.21 & 3.72 & 0.85 & 0.72 & 0.72 & 0.36 \\ Transformer-based [64] & 0.24 & 4.01 & 0.79 & 0.77 & 0.77 & 0.33 \\ DQN [62] & 0.28 & 4.23 & 0.66 & 0.80 & 0.80 & 0.28 \\ Proposed method & 0.34 & 4.78 & 0.41 & 0.88 & 0.86 & 0.21 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results of different pricing-strategy models.  
dynamically and performed well in terms of average return and behavioral smoothness. The proposed method achieved the best results across all evaluation metrics, which can be attributed to its structural design that accommodates the entire modeling pipeline of multi-source behavior data. From a mathematical perspective, the multi-head attention mechanism of the transformer effectively captured long-range dependencies, the delayed-reward estimation module enhanced sensitivity to future gains, and the sparse-feedback-enhancement mechanism mitigated training bias caused by low-frequency behavioral samples. The strategy-generation process incorporated dynamic exploration and value-constrained mechanisms, theoretically constructing a more stable convergence path toward the optimal solution. As a result, the framework succeeded in simultaneously achieving profit maximization and smooth responses under complex behavioral environments.
## 3 Modulation Techniques
### Quadrature Amplitude Modulation  
In QAM, the amplitude and phase of the carrier wave are varied in accordance with the message signal with a capacity of transmitting k bits of information.  
\[\mathrm{k}=\log_{2}\mathrm{M};\ \mathrm{where,\ k}=16,64,256\ \mathrm{bits}\]For 16-QAM, \(\text{M}=16\), \(\text{k}=\log_{2}16=4\text{bits}\)  
BER of GMSK is given by:  
\[\text{P}_{\text{QAM}}=\frac{3}{2\text{k}}\text{erfc}\left(\sqrt{\frac{\text{k}E _{b}}{10N_{0}}}\right) \tag{12}\]
# Software-Defined NB-IoT Uplink Framework--The Design, Implementation and Use Cases
## 6 Analysis of the Results and the Use Cases
Basing on the knowledge concerning CP attachment (a theoretical copying), the highest execution times can be misleading and should be definitely explained for the purposes of the teaching process. In general, the approach of just copying and appending the last part of the OFDM symbol onto the front of it is not the only process that is performed in that module. In addition, there is a need to maintain the phase continuity between successive samples by shifting the frequency of the signal by half of the subcarrier spacing values (for ensuring spectrum symmetry with respect to the DC component) and performing window function for the PAPR control. The process of designing the proper filtration of the signal at the output is yet another important case. PAPR minimization can be crucial, especially in the context of green sensors utilization and power efficiency, and thus it may be a good opportunity to include practical aspects and a learn-by-practice approach in the educational process. The IFFT operation, with the length of 128 or 512 points (dependent on the subcarrier spacing) in the NB-IoT interface, may be asymmetrically reduced and/or processed parallel, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Min** & **Quartile 1** & **Median** & **Quartile 3** & **Max** \\ \hline CRC & 0.01 & 0.02 & 0.05 & 0.1 & 1.61 \\ Rate matching & 0.03 & 0.11 & 0.26 & 0.78 & 3.73 \\ Turb encoder & 0.01 & 0.03 & 0.06 & 0.15 & 0.92 \\ Interleaving & 0.01 & 0.04 & 0.08 & 0.48 & 1.09 \\ Scrambling & 0.15 & 0.39 & 0.92 & 2.79 & 9.67 \\ Modulation & 0.01 & 0.02 & 0.03 & 0.09 & 0.5 \\ Transform precoding & 0.05 & 0.09 & 0.15 & 0.68 & 1.78 \\ DMRS & 0.06 & 0.14 & 0.33 & 0.95 & 4.34 \\ Mapping & 0.5 & 0.7 & 0.96 & 1.2 & 5.66 \\ \hline Phase rotation & 3.65 & 5.0 & 9.3 & 19.2 & 20.8 \\ IFFT & 23.9 & 26.2 & 30.7 & 33.0 & 40.3 \\ CP attachment & 31.9 & 52.8 & 54.3 & 56.4 & 64.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Normalized execution times of the framework modules and decomposed functions. Figure 9: Normalized execution time of the OFDM generation decomposed functions. which determines designing and development of the multithreaded environment. These are the potential topics of the students' research activity in the Masters or Bachelors degrees, either during laboratories or qualification works. The phase rotation module, based on its description introduced in Section 5.8.1, can also be optimized by the usage of the parallel processing and exponential values tables for the proper configurations in the device static memory.
## 2 Principle and Construction of the Settlement Prediction Model
### Improved VMD Based on the SSA
This leakage incorporates future information into the decomposition process, diverging from the principles of time series forecasting and potentially resulting in misleadingly high model accuracy. To mitigate this issue, this study utilizes rolling decomposition. Unlike traditional methods that analyze all data points at once, rolling decomposition processes the data sequentially, preventing future information from influencing the current analysis. Rolling decomposition adds data points one by one during the decomposition process, as shown in Figures 3 and 4. Figure 3: Original sequence data analysis. Figure 3 illustrates the decomposition process of a primitive sequence, starting from the bottom and presenting different levels of decomposition sequentially upward. The links between the layers of the primitive sequence are established through modal decomposition, reflecting the layer-by-layer decomposition structure. In time series analysis, the decomposition process helps analyze the intrinsic structure and trends in the data. Figure 4 illustrates the structure of a sequence decomposition, where the whole sequence is divided into subsequences, and the decomposed fragments of the different subsequences are hierarchical and cover the range of the sequence from the beginning to the end. This decomposition method can be used to extract features of different frequencies or modes in the data to help identify and analyze patterns of variation in the sequence. The structure of the corresponding rolling decomposition algorithm is shown in Figure 5[37]. As shown in Figure 5, the time series is first input into the VMD algorithm, which decomposes to obtain a set of subsequences. These subsequences are subsequently used as input data for the model to train the prediction model constructed on the basis of the SSA-VMD method. Predictions are made on the test set data one by one, and the prediction results corresponding to the nth test set data are obtained. By repeating the above process, the predictions of all the test set data are eventually aggregated to generate the final predicted output corresponding to the entire test set. After model construction and theoretical analysis, the actual performance and applicability of the model need to be evaluated by engineering examples. Moreover, to intuitively compare the accuracy and reliability of the proposed prediction models, we compared several types of common neural network models and their combination models for observation in future work. Figure 4: Subsequence data analysis. Figure 5: Architecture of the rolling decomposition algorithm.
## 6 RF Feature Classifiers
### Threshold-Based Classification  
The threshold-based classification is also known as a traditional hypothesis testing and can be implemented through well-known algorithms such as likelihood ratio testing (LRT) or Gaussian likelihood ratio testing (GLRT) [83; 84]. The traditional hypothesis testing problem is a problem of distinguishing between two hypotheses, namely \(\mathcal{H}_{0}\) and \(\mathcal{H}_{1}\):  
\[\left\{\begin{array}{ll}\mathcal{H}_{0}&\text{spoofer is absent}\\ \mathcal{H}_{1}&\text{spoofer is present}\end{array}\right. \tag{22}\]  
If the feature-extraction transform outputs scalar or vector values (instead of N-dimensional matrices with \(N\geq 2\)), a classification can be envisaged via a simple threshold, for example, by comparing the scalar value or the vector statistics (mean, minimum, maximum, median, etc.) to a certain pre-defined threshold. If the data are in N-dimensional form, then LRT/GLRT methods with Gaussian multivariate modelling can be employed.  
The challenging part in this approach is choosing a suitable threshold, when no a priori knowledge about the genuine and spoofing signals is available. Such a threshold can be determined based on theoretical assumptions (e.g., kurtosis transform is known to be close to 3 for Gaussian-distributed variables) or by using an initial training base with genuine and spoofing signals and derive a threshold based on the training database. Another challenge in this threshold-based approach is that most of the transmitter features are 'hidden' and not distinguishable through classical hypothesis testing, as the probability distribution functions of \(\mathcal{H}_{0}\) and \(\mathcal{H}_{1}\) hypotheses from Equation (22) would overlap.  
In our work, we used the optimal false alarm rate from ML-based classification to calculate the detection rate. By comparing this detection rate with the optimal one from ML-based classification, we evaluate the performance of the threshold-based method.
## 2 Solid Oxide Fuel Cell Control Method
### Ladrc  
The ADRC can classify all uncertainties acting on the controlled object as unknown disturbances and then estimate and compensate for them. A second-order LADRC is applied to the SOFC system as an example, and the structure is shown in Figure 3. In this paper, a linearly expanding state observer (LESO) is used to compensate for the \"total disturbance\" of the system with real-time feedback. \(u\) is the control signal, and \(b_{0}\) is the estimate of input gain \(b\).  
Let the second-order system expression be  
\[\ddot{y}=g(\dot{y},y,w,t)+bu \tag{17}\]  
where \(g(\dot{y},y,w,t)\) is the total effect of internal and external disturbances in the system, and \(b\) contains the gain information, which can also be expressed as  
\[\ddot{y}=f+b_{0}u \tag{18}\]  
where \(f=g+(b-b_{0})u\) such that \(x_{1}=y,\ x_{2}=\dot{y},\ x_{3}=f\), and then Equation (18) can be transformed into a state space expression as  
\[\left\{\begin{array}{l}\dot{x}_{1}=x_{2}\\ \dot{x}_{2}=x_{3}+b_{0}u\\ y=x_{1}\end{array}\right. \tag{19}\]  
Figure 3: Block diagram of second-order LADRC controller structure.  
The expression for the linear expansion state observer can be obtained from Equation (20) as follows:  
\[\left\{\begin{array}{l}e=z_{1}-y\\ \ddot{z}_{1}=z_{2}-\beta_{1}e\\ \ddot{z}_{2}=z_{3}-\beta_{2}e+b_{0}u\\ \ddot{z}_{3}=-\beta_{3}e\end{array}\right. \tag{20}\]  
where \(z_{1}\), \(z_{2}\), and \(z_{3}\) can be observed by LESO. \(b_{0}\) are the estimates of the coefficients of the system inputs \(\beta_{1}\), \(\beta_{2}\), and \(\beta_{3}\), which are the correction coefficients and have a significant impact on the final effect. The characteristic equation of Equation (20) is  
\[s^{3}+\beta_{1}s^{2}+\beta_{2}s+\beta_{3}=0 \tag{21}\]  
The characteristic equation is simplified to \((s+\omega_{0})^{2}=0\) where the equations \(\beta_{1}=3\omega_{0}\), \(\beta_{2}=3\omega_{0}^{2}\), \(\beta_{3}=\omega_{0}^{3}\), and \(\omega_{0}\) are the observer bandwidths whose values affect the observation accuracy of the linear expansion state observer.  
The perturbation compensation link of the second-order LADRC is  
\[u=\frac{u_{0}-z_{3}}{b_{0}} \tag{22}\]  
When \(f\approx z_{3}\), \(\ddot{y}=u_{0}\) can be obtained, and when the system estimate is accurate, similar to above, the characteristic equation is  
\[s^{2}+k_{d}s+k_{p}=0 \tag{23}\]  
The characteristic equation is simplified to \((s+\omega_{c})^{2}=0\), which leads to \(k_{p}=\omega_{c}^{2}\) and \(k_{d}=2\omega_{c}\).  
After the analysis of the basic structure of the LADRC, only \(\omega_{c}\), \(\omega_{0}\), and \(b_{0}\) are known. These three parameters need to be rectified, and the parameters will be sought later by the improved firefly algorithm based on inertia weights to ensure the reliability of the SOFC system.
# Digital Shearography for NDT: Phase Measurement Technique and Recent Developments
## 4 Recent Developments of Shearographic NDT
### Shearographic NDT on Specular Surface Materials  
In general, shearography is suitable for the measurement of rough surfaces because the speckle pattern can be generated by diffuse reflection on the object surface, which is a basic requirement for  
Figure 11: (**a**) The dual-shearing direction SPS-DS system; (**b**) measurement result of circular flaw and bar flaw in x direction; (**c**) measurement result of circular flaw and bar flaw in y direction.  
shearography. However, if the object surface is specular or quasi-specular, a speckle pattern cannot be created and digital shearography cannot be used for measurement. In 2014, [PERSON] et al. proposed a new shearographic setup to achieve measurement for a specular surface material [51] in which a rough plane M is embedded into the optical path to produce the speckle pattern required for shearographic measurement. In 2018, [PERSON] et al. improved the shearographic optical path of specular surfaces [52]. The rough surface was placed at the front portion of the optical path to improve the problem of the flaw location on the phase map being shifted when the area has high local slope. Moreover, the conjugate of the specular surface and the phase map makes it easy to determine the positional correspondence between the interferogram and the internal flaws. This setup is shown in Figure 12a, and the experimental results of cone-shape deformation and irregular deformation are shown in Figure 12b,c, respectively.
## 2 Preliminaries and Problem Description
### Dynamic Model of MHSTs
\tag{6}\]  
Let  
\[\left\{ \begin{aligned} &\mathbf{V}_{N_{i}}(\ell+1)=[v_{i}(\ell+1), \cdots,v_{i}(\ell+N)]^{\mathsf{T}},\\ &\Delta\mathbf{F}_{N_{i}}(\ell)=[\Delta F_{i}(\ell),\cdots, \Delta F_{i}(\ell+N-1)]^{\mathsf{T}},\\ &\mathbf{E}(\ell)=[1,1,\cdots,1]^{\mathsf{T}},\\ &\mathbf{A}_{i}(\ell)=\left[\begin{array}{cccc}\phi_{i}(\ell) &0&0&0\\ \phi_{i}(\ell)&\phi_{i}(\ell+1)&0&0\\ \vdots&\vdots&\ddots&\vdots\\ \phi_{i}(\ell)&\phi_{i}(\ell+1)&\cdots&\phi_{i}(\ell+N-1)\\ \vdots&\vdots&\cdots&\vdots\\ \phi_{i}(\ell)&\phi_{i}(\ell+1)&\cdots&\phi_{i}(\ell+N-1)\\ \end{array}\right]_{N\times N}\end{aligned} \right.\]  
where \(\mathbf{V}_{N_{i}}(\ell+1)\) represents the N-step forward prediction vector of the system's output, and \(\Delta\mathbf{F}_{N_{i}}(\ell)\) denotes the control input increment. Equation (6) can be simplified as follows:  
\[\mathbf{V}_{N_{i}}(\ell+1)=\mathbf{E}(\ell)v_{i}(\ell)+\mathbf{A}_{i}(\ell) \Delta\mathbf{F}_{N_{i}}(\ell) \tag{7}\]  
the parameter \(N_{u}\) specifies the control horizon. Under the condition that \(\Delta u_{i}(k+m-1)=0\) for \(N_{u}<m<N\), the prediction model described by Equation (7) can be reformulated accordingly. \[\mathbf{V}_{N_{i}}(\ell+1)=\mathbf{E}(\ell)v_{i}(\ell)+\mathbf{A}_{ui}(\ell) \Delta\mathbf{F}_{N_{ui}}(\ell) \tag{8}\]where  
\[\mathbf{A}_{ui}(\ell)=\left[ \begin{array}{cccc}\phi_{i}(\ell)&0&\cdots&0\\ \phi_{i}(\ell)&\phi_{i}(\ell+1)&\cdots&0\\ \vdots&\vdots&\ddots&\vdots\\ \phi_{i}(\ell)&\phi_{i}(\ell+1)&\cdots&\phi_{i}(\ell+N_{u}-1)\\ \vdots&\vdots&\cdots&\vdots\\ \phi_{i}(\ell)&\phi_{i}(\ell+1)&\cdots&\phi_{i}(\ell+N_{u}-1)\\ \end{array} \right]_{N\times N_{u}}\]  
Equation (8) presented above constitutes an \(N\)-step ahead predictive data model for train \(i\).
## 5 Experimental Evaluation
### Evaluation Metrics  
In our experimental setup, we partitioned the dataset into training and test sets. The training set was utilized to train the APGCN-CF model, while the test set was employed to evaluate its recommendation performance. During the test set construction, we retained two previously purchased items from each user as validation data. For performance evaluation, we generated personalized recommendation lists for target users and assessed them using two primary metrics: precision and recall. We implemented a top-N recommendation strategy, where N denotes the length of the recommendation list. Notably, precision@N and recall@N exhibit an inherent trade-off relationship, where improving one metric typically leads to a degradation in the other. To provide a comprehensive evaluation that balances both metrics, we adopted the F1-score as a unified measure of recommendation quality.  
We utilized the following formula to calculate the recommendation precision (which measures the proportion of recommended items that users are actually interested in):  
\[\text{Precision}=\frac{|\mathcal{R}(u)\cap\mathcal{T}(u)|}{|\mathcal{R}(u)|}, \tag{6}\]  
where R(u) represents the set of items recommended for user u, and T(u) denotes the set of items actually purchased by user u (specifically, the set containing two items from the validation data). Consequently, \(|\mathcal{R}(u)\cap\mathcal{T}(u)|\) represents the number of correctly recommended items within the recommended item set, while \(|\mathcal{R}(u)|\) denotes the total number of recommended items. Given that the recommended item set has a length of N, and each user in the test data possesses a set of two actually purchased items, the maximum achievable precision under these conditions is \(2/N\).  
We employ the following formula to calculate the recommendation recall (which measures the proportion of items that users are actually interested in that were successfully recommended):  
\[\text{Recall}=\frac{|\mathcal{R}(u)\cap\mathcal{T}(u)|}{|\mathcal{T}(u)|}, \tag{7}\]  
where \(|\mathcal{T}(u)|\) represents the total number of items actually purchased by the user.  
We utilize the following formula to calculate the recommendation F1-score (the harmonic mean of precision and recall, which comprehensively considers both metrics to provide a measure of overall performance):  
\[F1\text{-}Score=\frac{2\times\text{Precision}\times\text{Recall}}{\text{ Precision}+\text{Recall}}. \tag{8}\]
## 3 Discussion and Conclusions  
The possibility using nanophononic cavities as a suitable platform to study topological phenomena is in fact an interesting perspective of this work. Two parameters are usually used to engineer the performance of acoustic phonon devices, the thickness of the layers and the material composition of these heterostructures. In this work, we showed how it is possible to predict the confinement characteristics of a standard Fabry-Perot resonator from topological considerations based on the SSH model. Although main predictions such as the existence of edge and interface modes are confirmed, the strict assignment of Zak phases to the bands enclosing the considered minigap is not possible in the phononic case. In particular, the band bounding the first minigap from above is not isolated, i.e., the second acoustic minigap is closed for a \(\lambda/4\) superlattice.  
In a previous work [27], it was demonstrated that the band inversion principle can be implemented in nanophononic systems by changing the thickness of the composing materials. Using this technique, it is however only possible to induce a band inversion for the second and higher order minigaps since the first minigap (at the Brillouin zone edge) is open for any thickness ratio, such that the symmetries of the Bloch modes at the minigap edges cannot be swapped by a band inversion.  
In contrast, we showed in this work that, by tuning the composition of the materials, while keeping the acoustic length of each layer fixed at the specific value of \(\lambda/4\), it is indeed possible to close and reopen the first minigap with exchanged Bloch mode symmetries. Depending on the chosen way to concatenate two DBRs, it is furthermore possible to form the equivalent of a monomer and the trimer states predicted by the SSH model, which correspond to the modes of a Fabry-Perot resonator with either a high-index or low-index spacer.  
The authors acknowledge funding through the ERC Starting Grant No. 715939 Nanophennec; a public grant overseen by the French National Research Agency (ANR) as part of the \"Investissements d'Avenir\" program (Labex NanoSaclay, reference: ANR-10-LABX-0035); and the \"Initiative de Recherche strategique\" of the Universite Paris-Saclay through the IQUPS project.  
[PERSON] and N.D.L.K. contributed equally to this work.  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results
## 4 Control Analysis Based on Sensitivity
### Reference Tracking  
\(S(s)\) can be used to assess the reference tracking performance quantitatively. The error dynamic behaviour, \(\varepsilon(s)\), in the absence of any other input rather than the reference \(R(s)\), can be defined as  
\[\varepsilon(s)=R(s)-Y(s)=\cdots=S(s)R(s) \tag{17}\]  
where \(Y(s)\) designates the transfer function of the time-dependent output magnitude defined by \(y(t)\). Unfortunately, the final value theorem used to analyse the steady-state error from \(S(0)\) is not feasible. This is due to the pure imaginary poles of a sinus signal reference.  
The error tracking study is then delegated to the complementary sensitivity, \(T(s)\), i.e., to the close-loop response. Figure 7 compares the close-loop bode diagram for PR, PRHC (with third and fifth harmonic compensators) and FPR controllers. In this figure, the green line illustrates the current inner loop bode diagram, denoting that it has enough bandwidth to avoid relevant cross-coupling effects between control loops. It can also be seen that all three controllers present close design \(\phi_{m}\) at \(\omega_{c}\) although the \(s^{\alpha}\) term has been approximated according to Section 3-step 4. Also, in Figure 7a, it can be observed that the \(\alpha\) value modifies the behaviour of the gain tracking, being the controller with \(\alpha=0.75\) the worst one (wider excitation region) and \(\alpha=1.5\) the best one. However, Figure 7b shows that \(\alpha=0.75\) results in better phase tracking. Paying attention to the PRHC case, it can be concluded that it is better at tuning harmonic components, but it produces a cross-coupling effect, and the excitation regions are extended.