esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Theoretical Solution and Numerical Validation of Mechanical Parameters
### Parameter Solving and Validation
#### 4.3.2 Longitudinal Equivalent Bending Stiffness
In mode 2, the joint is compressed, at which time only the segment is stressed, which can be regarded as a uniformly continuous model. In conventional studies, the longitudinal stiffness efficiency typically overlooks axial force effects, aligning the resulting longitudinal bending stiffness with the \(EI_{0}\) value corresponding to \(e=0\) in mode 3. Therefore, the longitudinal stiffness efficiency in the conventional sense is the ratio of \(EI_{0}\) to \(EI_{2}\), i.e., \(\eta=\frac{EI_{0}}{EI_{2}}\). After the analysis in this chapter, the longitudinal stiffness efficiency is changing with \(e\) all the time, and the trend is shown in Figure 11.  
In conclusion, when the axial pressure significantly influences the contact status between rings more than bending moments do, the joint interfaces between the rings completely close. At this point, the longitudinal stiffness efficiency is 1.0, indicating that the longitudinal aspect of the shield tunnel can be considered a continuous reinforced concrete structure. As the axial pressure continues to decrease and transforms into gradually increasing axial tension, the longitudinal stiffness efficiency initially decreases rapidly and then gradually stabilizes. When the influence of axial tension on the contact status between rings is much greater than that of bending moments, the joint interfaces between the rings are completely open. At this point, the longitudinal stiffness efficiency becomes constant, indicating that longitudinally, the shield tunnel can be considered a discontinuous reinforced concrete structure formed by \"segmental rings--fully tensioned bolts\". Figure 10: Comparison of analytic and numerical solutions for \(EI-e\) curve.
## 2 Related Work
### Statistical-Based Speech Enhancement  
The traditional statistical-based speech enhancement method operates in short time Fourier transform (STFT) domain. Let's denote \(\sigma_{X}^{2}(t,k)\), \(\sigma_{S}^{2}(t,k)\) and \(\sigma_{N}^{2}(t,k)\) for power spectral density (PSD) of noisy speech, clean speech and noise signal, respectively. The relationship of these PSD is:  
\[\sigma_{X}^{2}(t,k)=\sigma_{S}^{2}(t,k)+\sigma_{N}^{2}(t,k) \tag{2}\]  
where \(t\) and \(k\) are the time frame index and the frequency bin index, respectively.  
Figure 2 illustrates the process of typical statistical-based speech enhancement. Firstly, the noisy speech is transformed into the time-frequency domain. Then, an estimation of \(\sigma_{N}^{2}(t,k)\) is obtained by means of a noise estimator, such as the improved minima controlled recursive averaging (IMCRA) [2]. After that, the speech spectrum is estimated by speech estimator, e.g., the minimum mean square error (MMSE). Finally, inverse short time Fourier transform (ISTFT) is applied to get the estimated time-domain speech signal. It should be noticed that the phase spectrum of the noisy speech is not being processed.
## 3 Results
### ANN Model
#### 3.1.3 Error Distribution  
After evaluating the accuracy of the ANN models when compared to reference data, it is time to assess the error distribution for every hour heating or cooling systems are working.  
Concerning to the cooling energy demand error distribution, Figure 9 presents the range of errors from 8:00 to 17:00, corresponding to the working hours of the cooling systems.  
Analyzing the boxplot for cooling energy demand error (Figure 9), the highest deviations occur between 10:00 and 14:00, corresponding to the higher outside temperatures verified in that period. The median is located near 0 for every hour, with a higher percentage of the error above the median, meaning that the reference values tend to be higher than the values forecasted by the ANN models.  
The boxplot with the error distribution for heating energy demand, for the working hours of the heating systems, is presented in Figure 10.  
The error range for the heating energy demand, presented in Figure 10, is much lower than that for the cooling period. The highest deviations occur early in the morning and during the afternoon. As in Figure 9, the median is also located near 0 for every hour in which systems are working. From 07:00 to 18:00, the higher percentage error is located above the median, meaning that ANN models tend to underpredict heating energy  
Figure 8: Daily load profile (19 December) comparison between ANN model H4 and reference for the heating period.  
Figure 7: Daily load profile (13 September) comparison between ANN model C2 and reference for the cooling period.  
demand. Towards the end of the day, the opposite happens, as the forecasted values tend to be higher than the reference values.
# Real-Time Pre-Identification and Cascaded Detection for Tiny Faces
## 1 Introduction
The first sub-network of PMCD and the pre-identification mechanism generate a set of RoI candidates, defined by the pedestrian area in the image. Then, the RoI candidates are resized to different scales to build an image pyramid as the input of the following network. As the input of the second sub-network has greatly reduced the irrelevant area, a shallow network can learn enough features and guarantee real-time performance. We tested PMCD on a self-collected dataset and Caltech Pedestrian [18] dataset. Due to the scarcity of the tiny-face detection dataset, we collected 1370 images, 2450 faces in total and most faces were less than \(20\,\times\,20\). In order to test in different situations, the size of 562 faces were larger than \(20\,\times\,20\), while the rest were all smaller than the size in the self-collected dataset. As the results showed, compared with other state-of-the-art methods, PMCD achieves impressive performance in tiny-face detection and it can achieve real-time detection. To this end, this paper aims to alleviate the issues discussed above which are tiny-face detection and keeping real-time features. * We propose a new pre-identification mechanism to obtain a set of face candidates as the input for the face detector, which contains higher face proportion than that in the original image. The mechanism greatly reduces miss rate of tiny-face detection and leads to the robustness of PMCD. * We propose a novel cascade neural network called PMCD for real-time tiny-face detection. The first sub-network is a deep pedestrian detector as a part of the pre-identification mechanism, and the second sub-network is designed based on a shallow multi-task CNN to detect faces in the RoI candidates. The rest of this paper is as follows. Section 2 introduces related works on face detection. Section 3 details the framework including two sub-networks and the pre-identification mechanism. Section 4 mainly describes the performance of PMCD and compares it with other state-of-the-art methods. Section 5 concludes and looks forward to future works.
## 4 Results
### AD Definition as a Data Preprocessing Method (Substudy 1)
#### 4.1.5 Effects of y-outliers on the Model Performance  
Concerning the y-outliers still present in the dataset after the elimination of X-outliers and Model-outliers with Layout 3 (in red in Figures 10d and 12), they were not eliminated, as this did not lead to a significant improvement in the performance of the models, as shown in Table 2. In fact, the removal of y-outliers does not necessarily improve the statistical parameters, since a y-outlier is not necessarily a Model-outlier. Indeed, Figures 10d and 12 show that y-outliers have low standardized residuals. More generally, a y-outlier can have more or less impact on the overall model's performance. If a molecule is both a y-outlier and a X-outlier, its thermodynamic property can be both well and poorly predicted: it will depend on if the ML model manages to extend its response surface to include this molecule. If a molecule is a y-outlier but not a X-outlier, this molecule represents a case of property cliff (i.e., similar structures but very different property values). In this case, the response surface can be pulled toward this molecule which can deteriorate the model's performance in varying amounts. In this study, the model's performance was improved for H after y-outliers removal, but not for S (cf. Table 2). It is difficult to qualitatively explain  
Figure 12: Data set after the elimination of outliers with Layout 3 for the **entropy**.  
this difference between H and S, as the elimination of each individual y-outlier can have a positive or negative effect on the model's performance. Accordingly, the overall trend can be different in each case. Finally, further elimination of X-outliers and/or Model-outliers would have been possible, allowing us to eventually further improve the performance of the models, but to the detriment of the loss of data and a further reduction in the size of the AD. In the end, it comes down to the user to select the most appropriate methods given the problem requirements and the available dataset characteristics.
# Deep Subspace Clustering with Block Diagonal Constraint
## Appendix A  
Proof of Updating the Variable \(C\)  
Proof.: The sub-problem of updating \(C\) is as follows  
\[\begin{array}{ll}\min_{C}&\frac{\alpha_{2}}{2}\|Z-C\odot D\|_{F}^{2}+\alpha _{4}\langle L_{c},W\rangle\\ \text{s. t.}&C\in\mathcal{C},\end{array} \tag{10}\]  
where  
\[\mathcal{C}=\{C\in\mathbb{R}^{N\times N}:0\leq C\leq C_{max},diag(C)=0,C=C^{ T}\}. \tag{11}\]  
We first modify formula (10) equivalently:  
\[\begin{array}{ll}&\frac{\alpha_{2}}{2}\|Z-C\odot D\|_{F}^{2}+\alpha_{4} \langle L_{c},W\rangle\\ =&\frac{\alpha_{2}}{2}\|Z-C\odot D\|_{F}^{2}+\alpha_{4}\langle diag(C1)-C,W \rangle\\ =&\frac{\alpha_{2}}{2}\|Z-C\odot D\|_{F}^{2}+\alpha_{4}\langle C,diag(W)1^{T }-W\rangle\end{array} \tag{12}\]  
The elements \(C_{ij}\) are independent of each other. We can update the \(C_{ij}\) separately. Thus, (12) can be expressed as  
\[\min_{C_{ij}}\quad\frac{\alpha_{2}}{2}(Z_{ij}-C_{ij}D_{ij})^{2}+\alpha_{4}C_{ ij}\overline{W}_{ij}, \tag{13}\]  
where  
\[\overline{W}=diag(W)1^{T}-W. \tag{14}\]  
Then, we have  
\[\begin{array}{ll}&\frac{\alpha_{2}}{2}(Z_{ij}-C_{ij}D_{ij})^{2}+C_{ij} \overline{W}_{ij}\\ =&\frac{\alpha_{2}}{2}(Z_{ij}^{2}-2Z_{ij}C_{ij}D_{ij}+C_{ij}^{2}D_{ij}^{2})+ \alpha_{4}C_{ij}\overline{W}_{ij}\\ =&\frac{\alpha_{2}}{2}C_{ij}^{2}D_{ij}^{2}-2C_{ij}(\frac{\alpha_{2}}{2}Z_{ij }D_{ij}-\frac{\alpha_{4}}{2}\overline{W}_{ij})+\frac{\alpha_{2}}{2}Z_{ij}^{2} \\ =&\frac{\alpha_{2}}{2}D_{ij}^{2}[C_{ij}^{2}-2C_{ij}\frac{\frac{\alpha_{2}}{2} Z_{ij}D_{ij}-\frac{\alpha_{4}}{2}\overline{W}_{ij}}{\frac{\alpha_{2}}{2}D_{ij}^{2}}]+ \frac{\alpha_{2}}{2}Z_{ij}^{2}.\end{array} \tag{15}\]  
Obviously, (10) is equivalent to the following optimization problem  
\[\min_{C_{ij}}\quad(C_{ij}-\frac{\alpha_{2}D_{ij}Z_{ij}-\alpha_{4}\overline{W} _{ij}}{\alpha_{2}D_{ij}^{2}})^{2}. \tag{16}\]  
To ensure that \(C\) is a symmetric matrix, we rewrite (16) as  
\[\begin{array}{ll}\min_{C_{ij}}&(C_{ij}-\frac{\alpha_{2}(D_{ij}Z_{ij}+D_{ji}Z_ {ji})-\alpha_{4}(\overline{W}_{ij}+\overline{W}_{ji})}{\alpha_{2}(D_{ij}^{2}+D _{ji})^{2}})^{2}\\ \text{s. t.}&C_{max}\geq C_{ij}\geq 0,\end{array} \tag{17}\]Then, we define  
\[\widetilde{C}_{ij}=\begin{cases}\frac{\alpha_{2}(D_{ij}Z_{ij}+D_{j}Z_{ji})-\alpha_ {4}(\overline{W}_{ij}+\overline{W}_{ji})}{\alpha_{2}(D_{ij}^{2}+D_{ji}^{2})},& \text{if }\ i\
eq j;\\ \mathbf{0},&\text{otherwise}.\end{cases} \tag{10}\]  
Solving the above, formula (10) can be obtained:  
\[C_{ij}=\begin{cases}\max\{0,\widetilde{C}_{ij}\},&\text{if }\ \widetilde{C}_{ij} \leq C_{max};\\ C_{max},&\text{otherwise},\end{cases} \tag{11}\]  
For convenience, the solution of (10) can be re-expressed as:  
\[\Pi_{\mathcal{C}}(\widetilde{C}), \tag{12}\]  
where  
\[\Pi_{\mathcal{C}}(S)_{ij}=\begin{cases}\max\{0,S_{ij}\},&\text{if }\ S_{ij} \leq C_{max};\\ C_{max},&\text{otherwise}.\end{cases} \tag{13}\]  
The proof is completed.
# A Data-Driven Deep Learning Framework for Prediction of Traffic Crashes at Road Intersections
## 5 Performance Evaluation
### Sequential phase ablation test of RoadInTCP model.  
To validate the influence of diverse historical time-step features in Phase III of _RoadInTCP_ on traffic crashes, by capturing multi-scale pattern characteristics within traffic crashes, we conduct an analysis and comparison of the variation of input data length in Phase III using the New York City traffic dataset from the first half of 2016. The lengths of immediate feature, short-term feature, and long-term feature are set to \(\omega_{hour}=1,\ 2,\ 3\), \(\omega_{day}=1,\ 2,\ 3\), \(\omega_{week}=1,\ 2,\ 3\), respectively. As shown in Table 4, immediate feature, short-term feature, and long-term feature all have a significant impact on traffic crash prediction, and the results from these features collectively outperform the experimental results of existing works that only consider hourly features. Consequently, in _RoadInTCP_ the selection of \(\omega_{hour}=3\), \(\omega_{day}=3\), \(\omega_{week}=3\) as the optimal combination has been determined.  
\begin{table}
\begin{tabular}{c c c c} \hline  & \(\omega_{hour}=1\) & \(\omega_{hour}=2\) & \(\omega_{hour}=3\) \\ \hline \(\omega_{day}=1,\omega_{week}=1\) & 0.8246 & 0.8311 & 0.8414 \\ \(\omega_{day}=1,\omega_{week}=2\) & 0.8224 & 0.8320 & 0.8429 \\ \(\omega_{day}=1,\omega_{week}=3\) & 0.8247 & 0.8322 & 0.8419 \\ \(\omega_{day}=2,\omega_{week}=1\) & 0.8239 & 0.8342 & 0.8436 \\ \(\omega_{day}=2,\omega_{week}=2\) & 0.8268 & 0.8360 & 0.8457 \\ \(\omega_{day}=2,\omega_{work}=3\) & 0.8281 & 0.8325 & 0.8469 \\ \(\omega_{day}=3,\omega_{work}=1\) & 0.8247 & 0.8351 & 0.8455 \\ \(\omega_{day}=3,\omega_{week}=2\) & 0.8239 & 0.8362 & 0.8471 \\ \(\omega_{day}=3,\omega_{week}=3\) & 0.8290 & 0.8366 & 0.8493 \({}^{\star}\) \\ \hline \end{tabular}
\end{table}
Table 4: Performance of Input Lengths in the Process of RoadInTCP Model in Phase III.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **2014** & **2014** & **2015** & **2015** & **2016** \\  & **(First Half)** & **(Latter Half)** & **(First Half)** & **(Latter Half)** & **(First Half)** \\ \hline RoadInTCP\({}_{PI}\) & 0.8202 & 0.8111 & 0.7864 & 0.8392 & 0.7889 \\ RoadInTCP\({}_{PII}\) & 0.8224 & 0.8001 & 0.8019 & 0.8318 & 0.7982 \\ RoadInTCP\({}_{PI+PII}\) & 0.8230 & 0.8129 & 0.7999 & 0.8307 & 0.8093 \\ RoadInTCP\({}_{PI+PII}\) & 0.8291 & 0.8205 & 0.8042 & 0.8414 & 0.8286 \\ RoadInTCP\({}_{PI+PIII}\) & 0.8344 & 0.8197 & 0.8472 & 0.8472 & 0.8139 \\ RoadInTCP\({}_{PI+PIII}\) & 0.8236 & 0.8135 & 0.8132 & 0.8328 & 0.8101 \\ RoadInTCP & 0.8499 & 0.8568 & 0.8366 & 0.8694 & 0.8493 \\ \hline \end{tabular}
\end{table}
Table 3: Impacts of Different Phase of Model Structures on Experiment Performance.
## 2 Background and Review of Related Works
### Securing the ICV with XAI: Background Information
#### 2.4.3 Practical Implementation of ML and XAI-Based Models  
The authors of [71] employed layer-wise relevance propagation (LRP) to decompose input significance ratings in gait classification using convolutional neural networks (CNNs). LRP is a later technique for generating interpretations for machine learning model predictions [71,72]. It operates in the input space, where clinical practitioners typically analyze signals. LRP divides a function's prediction \(f(y)\) for an input data variable \(y\) into element and time input significance values \(S_{i}\) for each input data \(y_{i}\). It allows the interpretation of the prediction of an ML model as the individual roles of each input data. LRP enables the comprehension of the data a model employs for prediction. It verifies the significance of the ground reaction force characteristics correlating to the areas with the highest input relevance scores. A highly skilled diagnostic expert examined the experimental results of the derived relevance scores of LRP from a medical point of view. Another study [73] demonstrates how the combination of deep neural networks and SHapley Additive exPlanations (SHAP)  
Figure 3: The concept of XAI.  
or \"DeepSHAP\" can interpret the impacts on ML for spoofing identification. According to the experimental results, SHAP analysis highlights the consideration adapted by a given classifier at reduced spectro-temporal ranges. In [74], the authors investigate side-channel website fingerprinting attacks by employing XAI methods. Because the dataset includes both side-channel measurements and captured network traces, the XAI technique was to determine the most prevalent network request types that significantly impact website detection. The ROAR metric was used in the study, which demonstrated that LIME and saliency maps accurately detect the most dominant features in side-channel measurements. A study that interprets the performance of mobile traffic classification using DL methods [75] gives specific insights for performance enhancement. It involves the analysis and evolution of a multimodal-DL approach called mimetic-enhanced using explainable artificial intelligence tools. The strategy was evaluated based on the predictability of trust and confidence using calibration and explainability using SHAP-based methods. Meanwhile, adjustment by focal loss attains a 6X reduction over the uncalibrated case, resulting in a substantial increase in trustworthiness. Considering interpretability, a global interpretation for each modality was obtained, measuring the relevance of each and emphasizing the importance of retaining high payload, despite the enormous majority of encrypted traffic.
## 4 Discussions and Conclusions
An example of modelling the damage on a recoverable structural element is proposed by [PERSON] et al. [32]. Although interesting and a harbinger of suggestions, their experimental campaign is tailored for the single tested steel element, rather than to a complete frame structure. Hence, the present approach is a tentative implementation of existing techniques for wider use of artificial intelligence for structural health monitoring. As highlighted by [PERSON] and [PERSON] [20], the majority of the studies focusing on the use of pattern recognition for SHM serve to detect damage. The localization of the damage and its extent are not possible since information on the evolution of the damage is not known a priori on the structure. This is the result of the fact that the structural behavior is directly dependent on damage location. In this sense, artificial intelligence can play an integrated role with structural numerical modeling. The novelty of the proposed approach consists in the fact that the dataset that servers for training the learning algorithm comes from numerical analyses, rather than from observations on the structure. A set of damage configurations (location and extent) are modelled and the resulting structural displacements and dynamical properties are used for supervised training, in the present case, of a shallow neural network. In this sense, the use of machine learning can be enlarged to damage location and damage extent. The proposed approach was aimed at overcoming the limitations that emerged in the previous studies. Although theoretical, the approach can be applied for studying structures that are very similar (in construction engineering two structures cannot be identical since there is always human intervention in the construction process). This is the case, for example, of the overpasses of Highway A21 \"Torino-Piacenza-Brescia\" in the Northwest of Italy. Here, there are several overpasses that are coeval and have the same structural scheme, details, and techniques (Figure 7). The design of such infrastructures was performed by Dott. Ing. [PERSON] [33]. The hybrid approach herein proposed fits well with monitoring such types of structures, for which an in-depth preliminary study and a detailed numerical modeling can be performed. The simulated structural dataset can be considered for a large number of similar real structures. Future studies on a real monitored structure are planned, as well as tests on more complex structural types, for example, portal frames. Conceptualization, B.C. and V.D.B. ; methodology, software, validation, formal analysis, writing--original draft preparation, V.D.B. ; writing--review and editing, B.C. and V.D.B. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest.
# High-Throughput Cross-Technology Communication via Chip-Level Side Channel
## 3 Framework Design
### Framework Overview  
As illustrated in Figure 8, our intuitive idea is that we make the ZigBee sender modify some chips within one symbol to modulate CTC messages and these modification can be detected in WiFi preamble detection. Theoretically, ZigBee devices transmit 4 bits with a 32-bit chips sequence that consists of a high redundancy. Based on the results in [17], if we do not modify too many chips, ZigBee receiver could still decode ZigBee data correctly. Then, the question is how to modify ZigBee chips to generate a detectable pattern in WiFi preamble detection.  
According to our analysis in Section 2.2, if the consecutive chips on I-phase and Q-Phase form a continuous sine wave, the phase offset value at WiFi preamble detection is stable at \(-\frac{2\pi}{5}\). Then, if we could modify some of the ZigBee chips, we would be able to extend the duration of the stable phase pattern at WiFi preamble detection. Hence, we control ZigBee sender to generate a longer sinusoidal waveform, which induces longer stable phase offset values at WiFi preamble detection. The modified ZigBee chips would be tolerated at the ZigBee receiver; hence, the communication between ZigBee devices would not be affected severely. Meanwhile, the sinusoidal waveform (generated by modified chips) can be detected in WiFi preamble detection and the carried CTC data would be demodulated by the WiFi device.
## 2 The Signal Energy Decreases with Distance from Satellites and Major GNSS  
### Interference Sources  
In this article we look at positioning systems in which a navigation receiver \"listens\" to buoys, receives their messages, and determines a position based on the geographic coordinates of the buoys. Such systems are called Local Navigation Satellite Systems (LNSS). An acoustic signal generator transmits simulated signals from multiple satellites. The receiver of the underwater object \"picks up\" the false signal and calculates the false XYZ. All receivers in the spoofed area compute the same XYZ.  
Unwanted RF signals affecting the USV receiver can be natural, unintentional, or intentional using jammers. GNSS radio signals are hundreds of times weaker than the background noise of radio signals such as VHF, microwave, 3G, Wi-Fi, and Bluetooth (Figure 1), but the introduction of new protocols such as 5G is pushing out GNSS radio signals even more.  
The radiated energy of the satellite transmitting antenna decreases when approaching the USV receiving antenna (Figure 2).