esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 SAR Payload Performance Analysis
### PRF Diamond Diagram  
An appropriate PRF range of the SAR should be selected for a given orbit using the timing diagram--the PRF diamond diagram, in the case of a spaceborne SAR considering several aspects such as the nadir echo. Accordingly, the PRF of the SAR payload was selected such that it was larger than the Doppler bandwidth, and the pulse repetition interval (PRI; reciprocal of the PRF) was larger than the reception window length corresponding to the swath width. Moreover, the PRF was selected to avoid Tx eclipsing and off-nadir returns. The diamond diagrams of the S-STEP SAR payload in the high-resolution and wide-swath modes are illustrated in Figure 23a,b, respectively, wherein the black-solid line indicates the appropriate PRF range based on the incidence angle. Additionally, the green and blue zones indicate the off-nadir returns and Tx transmission eclipsing, respectively, which should be avoided. Furthermore, the swath width was selected using the PRF diamond diagram to produce an initial set of swaths covering the access region. We assumed a guard time of 0.2 us and a maximum transmit duty cycle of 30% for evaluating the off-nadir and transmit (Tx) pulse eclipsing regions. The antenna electronically steered the beam such that it pointed toward swaths between incidence angles of 15 and 35\({}^{\circ}\). Comprehensively, the designed operational parameters of the S-STEP SAR payload in the high-resolution and wide-swath modes are presented in Tables 4 and 5, respectively.  
Figure 23: PRF diamond diagrams of the S-STEP SAR payload in the (**a**) high-resolution and (**b**) wide-swath modes.  
Figure 22: (**a**) Collection geometry and (**b**) access region of the S-STEP SAR payload.
## 7 Discussion and Results of FE Simulation  
### Categories of the Damper Behavior  
To investigate the shearing of the main plate in dissipating the imposed energy, the yielding through the plates is shown in Figure 11. According to this figure, by adding the cross plate, the yielding spread through the plate. In other words, yielding from the bottom and top of plates moved to the middle of the plate. When the cross plate had no connection to the main plate, since the vertical edges of the plate were free, its edge did not yield but a tension field action was formed in the connected cross plate (making the plate yield). Moreover, in the main plate without a cross plate and in the cross plate not connected to the main plate, yielding started at the bottom and top of the plate, whereas it started in the middle of the plate when the cross plate was connected to the main plate. For slender plates (elastic buckling, \(\lambda>1.4\sqrt{K}\)) without a cross plate, the dampers did not experience adequate nonlinear zones. Thus, dampers with \(\lambda>1.4\sqrt{K}\) and without cross_flexural plates are not appropriate for use as seismic dampers.  
Since the boundary plates support the main plates in dissipating energy, the cover plates must remain elastic. If the boundary plates experience nonlinearity, the proposed damper will not be able to dissipate energy well. Figure 12 illustrates the yielding state of the proposed damper at the ultimate displacement, confirming that the boundary cover plates remained elastic.  
For dampers with cross_flexural plates, a flexural hinge was formed at the two ends of the plates. This finding validates the assumption that was used to predict the behavior of the damper.
## 4 Pose Decoupling of Axis TF
### Plucker Coordinates of Axis TF  
Axis TF is determined by two end points on two end faces of the tolerance zone. Under this circumstance, Plucker coordinates of the straight line are introduced in order to describe its pose [22]. In Figure 2, the points of intersection that the axis passes through the two end faces of the cylinder tolerance zone are \(S_{1}\Big{(}x_{1},y_{1},-\frac{d}{2}\Big{)}\) and \(S_{2}\Big{(}x_{2},y_{2},\frac{d}{2}\Big{)}\), thus getting the Grassmann determinant of axis TF:  
\[\left|\begin{array}{ccc}1&x_{1}&y_{1}&-\frac{d}{2}\\ 1&x_{2}&y_{2}&\frac{d}{2}\end{array}\right| \tag{10}\]  
According to [PERSON] [22], coordinate values (\(L\), \(M\), \(N\); \(P\), \(Q\), \(R\)) of the axis correspond to 6 of 2 \(\times\) 2 determinants:  
\[L=\left|\begin{array}{cc}1&x_{1}\\ 1&x_{2}\end{array}\right|\text{, }M=\left|\begin{array}{cc}1&y_{1}\\ 1&y_{2}\end{array}\right|\text{, }N=\left|\begin{array}{cc}1&-\frac{d}{2}\\ 1&\frac{d}{2}\end{array}\right|\text{, }P=\left|\begin{array}{cc}y_{1}&- \frac{d}{2}\\ y_{2}&\frac{d}{2}\end{array}\right|\text{, }Q=\left|\begin{array}{cc}- \frac{d}{2}&x_{1}\\ \frac{d}{2}&x_{2}\end{array}\right|\text{, }R=\left|\begin{array}{cc}x_{1}&y_{1} \\ x_{2}&y_{2}\end{array}\right| \tag{11}\]  
(\(L\), \(M\), \(N\); \(P\), \(Q\), \(R\)) is the pose of the axis in the tolerance domain, where \(L\), \(M\) and \(N\) are direction ratios of the axis, \(P\), \(Q\) and \(R\) are moments of line of unit force on the axis, relative to the origin \(O\). It can be seen from Equation (11) that \(N=d\) is a zero-order constant. \(L\), \(M\), \(P\) and \(Q\) are first-order small quantities with the same magnitude of tolerance [\(t\)], while \(R\) is the high-order small quantity of [\(t\)]\({}^{2}\) level. Therefore, \(R\) can be omitted [19].  
Base lines \(\$_{1}\), \(\$_{2}\), \(\$_{3}\), \(\$_{3}\), \(\$_{4}\) and \(\$_{5}\) in Figure 4 are ultimate locations on DOF when the axis is in the tolerance zone. Therefore, Plucker coordinates of each base line in the tolerance zone can be expressed as forms of matrix [**X**]. It can be seen from Equation (12), that the \(i\)th column in [**X**] is the Plucker coordinate of \(\$_{i}\) (\(i=1\) to \(\sim\)5).  
\[[\textbf{X}]=\left[\begin{array}{ccccc}0&0&0&t&0\\ 0&0&0&0&t\\ d&d&d&d&d\\ 0&0&\frac{t}{2}d&0&0\\ 0&-\frac{t}{2}d&0&0&0\end{array}\right] \tag{12}\]
## 4 Equalizer Design Techniques
### LPC Analysis  
In LPC analysis, the room response is modeled with a minimum-phase all-pole filter and the equalizer is a finite impulse response (FIR) filter. The all-pole model can be obtained by different techniques, including the efficient Levison-Durbin algorithm [62]. LPC analysis has been one of the most successful approaches for minimum-phase equalization, and has been successfully used by many researchers [63; 64; 61; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75]. An all-pole filter can adequately model the spectral peaks of the room response, while it provides a less accurate model of the notches. We should remember that in the human auditory system the spectral peaks are more audible than the notches [20]. Moreover, the room response varies significantly with respect to the position in correspondence to notches [49]. An all-pole equalizer can compensate the most audible parts of the room response (the spectral peaks), without boosting the notches, which is another desirable property of the equalizer.  
The main limitation of LPC analysis is the fact that it can be used only for minimum-phase equalization, and it must be complemented with other techniques to equalize the excess-phase.
# HTSA-LSTM: Leveraging Driving Habits for Enhanced Long-Term Urban Traffic Trajectory Prediction
## 3 Experiments and Evaluation
### Analysis of Temporal and Spatial Attention Allocation
#### 3.5.2 Spatial Attention Allocation
The target vehicle 1585 executed its first lane change from lane 4 to lane 5 at the 447 th time step and its second lane change from lane 5 to lane 6 at the 518 th time step, as shown in Figure 9. Where blue and purple means the car is changing lanes, and green means the car is unchanged in the current lane. Additionally, Figure 9 displays the road grid cells that received the highest spatial attention weight at each time step during this process. It can be  
Figure 8: Spatial attention allocation. observed that before the 446 th time step, the target vehicle (Vehicle 1585) primarily focused on the neighboring vehicles ahead in the current lane, with the highest spatial attention weights concentrated on grid cells (C, 2), (C, 3), and (C, 4). From the 447 th to the 469 th time step, the maximum attention gradually shifted from the current lane to (R, 1), and then to (R, 2) as the vehicle prepared to change lanes to the right. At the 518 th time step, the maximum attention weight again shifted from the current lane to the right lane, remaining on grid cells (R, 1) and (R, 2) until the 554 th time step, marking the completion of the lane change from lane 5 to lane 6. The duration of the maximum spatial attention weight shift indicates that the first lane change took 2.2 s (22 \(\times\) 0.1), while the second lane change lasted 3.6 s (36 \(\times\) 0.1).
# First-Principles Study of the Contact Resistance at 2D Metal/2D Semiconductor Heterojunctions
## 5 T/2H MoS\({}_{2}\) Edge Contacts
Interestingly, the formation of the more stable distorted 1T'-phase of MoS\({}_{2}\)[56] was promoted by the adsorption of H and Li, as indicated by the distorted Mo atoms forming \"one-dimensional (1D) zigzag chains\", which are connected by red dashed lines in Figure 11a,b. We also studied the interaction between a sulfur vacancy and an H\({}_{2}\)O molecule. An O-H group tended to fill the vacant site, with the O atom bonding to neighbor Mo atoms (2.22 A average Mo-O bond length); this resulted in the local distortion of the 1T-MoS\({}_{2}\) lattice, as indicated in Figure 11c. A neighbor S-H bond was also formed, with a bond length of 1.38 A. The adsorption energies \(E_{ads}\) of these atoms or molecules on 1T-MoS\({}_{2}\) are computed using the following equation:  
\[E_{ads}=E_{tot}(MoS_{2}+adsorbate)-E_{tot}(MoS_{2})-E_{tot}(adsorbate), \tag{4}\]  
where the last term, representing the total energy of the adsorbate, was fixed to \(\frac{1}{2}E_{tot}(\text{H}_{2})\) for H, \(\frac{1}{2}E_{tot}(\text{bulk-Li})\) for Li, and \(E_{tot}(\text{H}_{2}\text{O})\) for H\({}_{2}\)O [62]. All the calculated adsorption energies were negative (see Table 2), indicating that these atoms/molecules were favorably adsorbed onto 1T-MoS\({}_{2}\). A partial electron transfer, from the adsorbate to 1T-MoS\({}_{2}\), also occurred, which led to its \"n-type doping\". Consequently, the adsorption of all these chemical species led to the reduction of the 1T-MoS\({}_{2}\) work function; an almost linear decrease of \(\Phi_{\rm m}\) was indeed computed for all the adsorbates considered here [62]. In fact, as shown in Figure 12, the work function shift \(\Delta\Phi_{\rm m}\) depended almost linearly on the partial charge transferred between the adsorbate and 1T-MoS\({}_{2}\). Figure 10: Schematic energy band diagram of a 1T-MoS\({}_{2}\)/2H-MoS\({}_{2}\) lateral heterojunction [62]. The impact of these adsorbates on the contact resistance of 1T/2H-MoS\({}_{2}\) heterojunctions is highlighted in Table 2. The adsorbate coverages were fixed to 8% Li, 12% H\({}_{2}\)O, and 22% H; such coverages are consistent with experimental values obtained during the functionalization of transition-metal dichalcogenide monolayers [65]. The corresponding 1T-MoS\({}_{2}\) work function was about 4.3 eV, resulting in an energy barrier at the 1T/2H-MoS\({}_{2}\) interface of about 0.1 eV. The functionalization of 1T-MoS\({}_{2}\) by the adsorbates subsequently resulted in the decrease of the contact resistance by more than two orders of magnitude (see Table 2), being comparable to the experimental values (100-200 \(\Omega\)-\(\upmu\)m) reported in References [16] and [20].
## 4 Discussion
### Coverage of Data Quality Aspects
#### 4.2.1 Missing Related Implementations  
Ideally, any missing value in a dataset has been assigned a code that indicates the reason for why it is missing (i.e., \"missing by design\", \"technical error\", and \"refusal\"). This is the only way to correctly assess key figures, such as response rates or refusal rates. In contrast, representing missing values only with _NAs_, which is frequently the case, can be misleading. Consider, for example, observing _NAs_ in an item on the number of cigarettessmoked per day for a person who previously stated to be a non-smoker. The value is in fact not missing but zero. Therefore, the DQ framework provides a distinction between \"crude missingness\", where DQA cannot rely on missing codes, as opposed to \"qualified missingness\", which computes indicators under the precondition of knowing missing reasons [6]. According to our review, almost all packages handle only the crude missingness approach. Consequently, more common standards are needed to code missing values and make them available in packages [70]. Note that qualified missing codes are not the same as value codes of categorical variables, since the former must be identified as such by a package. Thus, a package supporting value labels for a bar chart does not qualify for the qualified missingness indicator, as it does not interpret these values differently from \"normal\" categories. Another way to improve missing assessments may be to incorporate statistical methods which assess missing data structures, such as clustering methods [71; 72].
# KeyNet: An Asymmetric Key-Style Framework for Watermarking Deep Learning Models
## 3 Attack Model  
To ensure the robustness of a watermarking methodology, it should effectively overcome (at least) three potential attacks:  
* **Model fine-tuning**. In this attack, an attacker who has a small amount of the original data retrains the WM model with the aim of removing the WM while preserving the accuracy in the original task.
* **Model compression**. The compression of a DL model's weights minimizes its size and speeds up its performance. Model compression may compromise the WM within the marked model, thereby affecting its detection and extraction.
* **Watermark overwriting**. This type of attack is a major threat to the WM because it might result in the attacker being able to overwrite the owner's WM, or also to embed another WM of his own and thus seize ownership of the WM model. We make the following assumptions about the attacker. First, the attacker is assumed to have a small amount of training data when compared to the owner. Otherwise, he can use his data to train a new model from scratch, or use the predictions of the WM model to create an unmarked copy of it by predictive model-theft techniques [7]. Second, the attacker is assumed to be aware of the methodology used to embed the WM but to be unaware of the carrier set distribution, the owner's signature, or the topology of the owner's private model. An attack is considered to be successful if the attacker manages to overwrite the WM without losing much accuracy in the original task. The goal is to prevent the attacker from overwriting the WM without significantly impairing the original task accuracy, proportional to the amount of data he knows. To make a realistic trade-off, the relationship between the size of the data known by the attacker and the loss in original task accuracy should be inversely proportional: the smaller the attacker data, the greater the accuracy loss is.
## 4 Simulation Result and Discussion
### Analysis of Case Studies
#### 4.3.4 Case IV: Impact of Assessment Time  
In this scenario, the performance of the proposed model is evaluated under different assessment times. The input data set is generated for different OTW lengths (0.15 s, 0.25 s and 0.35 s). Then, the performance of the proposed model is compared with other deep learning-based time series classification models (TempCNN and LSTM) and machine learning models (SVM and DT). The result of both the test systems in Figure 14 demonstrate that as the OTW duration is increased, all the models' performance accuracy improves,  
Figure 13: Performance with missing PMUs data on test systems.  
with TempCNN-LSTM having greater accuracy than the TempCNN, LSTM, SVM and DT models. Increased assessment time results in more accurate STVS evaluations but less time for remedial response and vice versa. Therefore, there is always a trade-off between the assessment time and model performance accuracy. However, for an online application, a reliable assessment time would be with acceptable accuracy. Specifically, if the acceptable accuracy is assumed to be more than 98%, then for the IEEE 9 Bus system, the proposed model can deliver the STVS status within the OTW of 0.25 s. However, for the New England 39 Bus system, the proposed model can deliver the STVS status reliably only for the OTW of 0.35 s. Similarly, TempCNN and LSTM can provide STVS assessment only for the IEEE 9 Bus system with an OTW of 0.35 s, while for the New England 39 Bus test system, it requires an OTW greater than 0.35 s.  
Figure 14: Performance with different OTW lengths on test systems.  
In particular, if reliable accuracy is desired within a short assessment time, then the trade-off solution for an appropriate setting of OTW can be obtained using optimization techniques. However, similar approaches have been discussed in the literature [10], and it is out of scope of our research.
# Building Equi-Width Histograms on Homomorphically Encrypted Data
## 1 Introduction
Additional challenges related to secure computations such as comparisons, divisions or exponentiation over the encrypted domain still need to be addressed [28; 29]. Overall, encryption/decryption mechanisms and HE data processing generate very large computational overheads that may negatively influence the system's responsiveness, making it difficult to address the trade-off between privacy and system operation [30]. Finally, noise induced by the encryption scheme can affect the data analysis accuracy, especially when dealing with small deviations from the IoT device's normal raw data values [31; 32]. Therefore, in this paper we propose a solution for building histograms on homomorphic encrypted data generated by IoT devices without relying on partial or total decryption. To leverage the flexibility of the Fast Fully Homomorphic Encryption over the Torus (TFHE) scheme implemented in Concrete [33; 34], we redesign the traditional histogram construction algorithm and address its limitations in handling nested loops and conditional statements. To assess the effectiveness of the private histogram, we implement an outlier detection use case, where outliers are identified using a thresholding rule applied to histogram counts, with all computations performed on encrypted data. The proposed privacy-preserving outlier detection mechanism has consistent results with the corresponding mechanism that operates on plain data, without needing to decrypt the results for decision-making. Moreover, the computational overheads are kept within reasonable limits. The key contributions of this paper are as follows:  
* A privacy-preserving method for constructing histograms on homomorphically encrypted data, enabling outlier detection without requiring decryption. * Adaptation of traditional histogram construction and histogram-based outlier detection techniques for the Fast Fully Homomorphic Encryption over the Torus (TFHE) scheme in Concrete. We address challenges related to input size constraints and computational depth by vectorizing key operations, including addition, value placement in buckets, labeling abnormal buckets based on threshold frequency, and identifying outliers by their range and assigned bucket labels. * Performance evaluation and analysis of computational overhead trade-offs across different configuration setups, assessing their impact on the effectiveness of privacy-preserving histograms in IoT environments.
# Zero-Shot Detection of Visual Food Safety Hazards via Knowledge-Enhanced Feature Synthesis
## 4 Experimental Evaluation
### Analysis of Visual Attribute Influence on Detection Performance
Hazards cluster into distinct groups that align with their visual characteristics rather than their hazard categories, suggesting that visual appearance rather than hazard type should guide the design of detection systems. Interestingly, visually similar hazards across different food categories (e.g., mold on bread and mold on cheese) show a comparable detection performance despite their different contexts. To validate the statistical significance of the improvements achieved by our method, we performed paired t-tests comparing ZSFDet against each baseline across all attribute categories. The results confirm that our performance improvements are statistically significant (\(p<0.01\)) for all comparisons, with the most significant difference observed for texture-based attributes (\(p=0.003\)). This comprehensive analysis demonstrates that our knowledge-enhanced approach effectively captures the complex visual patterns associated with different types of food safety hazards, resulting in a superior detection performance across diverse food categories and attribute types.
## 8 Performance Comparison among TMD, TMDI, and Pendulum PTMD
### Prevention of Local Damage and Impact Noise Reduction  
Figure 24 presents the bounding force under El-Centro excitation. Initially, the bounding force was smaller because the magnitude of excitation was small. Later, with increased excitation magnitude, the bounding force also increased and peaked at 106 N. During seismic events, the magnitude of the bounding force can be excessively high, causing local damage to the bounding area. Therefore, it is essential to implement a protection technique to prevent local damage at the bounding surface. There has been an extensive study to minimize the damage caused by structural bounding, especially in adjacent tall buildings during seismic excitations [94]. Local damage brought by the bounding force can be minimized by dispensing the concentrated bounding force over a large surface area. A secondary structural member having a trapezoidal shape can be introduced between the viscoelastic material and the building so that the bounding force per unit area on the building is reduced. The rectangular plate attached to viscoelastic material, as shown in Figure 14, transfers the bounding force to the building floor. The concentration of bounding force can also be reduced by connecting the rectangular plate to the core of the building so that local damage can be prevented.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline
**China** & \multicolumn{3}{c}{**Unventional**} & \multicolumn{3}{c}{**Pondulum PTMD**} & \multicolumn{3}{c}{**TMDI**} \\ \hline  & 4\% & **-15\%** & **-15\%** & **-4\%** & **-15\%** & **-4\%** & **-15\%** & **-4\%** & **-15\%** & **  
Despite its potential to control vibrations under natural hazards, implementing the pendulum PTMD in real life may have issues with noise from the impact mass. The pendulum PTMD under wind loads could produce noise that may result in human discomfort or even problems with hearing loss when exposed for an extended period [95]. There have been several studies on impact noise control in mechanical devices and during the construction of civil engineering structures [96; 97]. In the case of PTMD impact noise, a simple enclosure can be installed on the impact area with acoustic materials such as perforated plywood or fibrous plaster.