esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Attack Taxonomy
### Attacks Based on Information Damage Level  
**Interruption:** An adversary limits the capabilities of device-to-device and device-to-gateway communications by interfering with radio signals. The adversary can use certain jamming devices to interfere with radio signals [97]. Interruption attacks are threats to quality and availability of services provided by an IoT device.  
**Man-in-the-Middle:** In the Man-in-the-Middle (MITM) attack, a malicious IoT node sits between two victim nodes and tricks them into thinking that they are communicating with each other. Figure 7 shows an example of the MITM attack in the RPL network. A malicious node M spoofs the identities of node A and B. Node M establishes a session with node A impersonating Node B and initiates another session with node B impersonating node A. Hence, Node M tricks Node A and Node B into providing sensor information.  
**Modification:** In this type of attack, a malicious node modifies messages that are routed through it. An adversary attempts to trick a victim node into performing unauthorized actions or revealing sensitive information. A scenario of the message modification attack is shown in Figure 8. A malicious Node M is located in the communication path between Node S and Node D. Node M modifies the content of a message sent by Node S before forwarding it to Node D. Node D may accept the altered message if it does not implement a security scheme that validates the integrity of a received message. As a result, the victim node may perform an action based on the modified information.  
**Fabrication:** In this attack, a malicious node does not modify a message; instead, it inserts counterfeit information into the original message. The goal of the adversary is to create confusion between communicating peers and trick a victim node into issuing malicious commands. As shown in Figure 9, a malicious node inserts a forged header (\(k_{n+1},v_{n+1}\)) into a request. The fabrication attacks threaten message integrity.  
Figure 7: Man-in-the-Middle attack.  
**Message Replay:** An adversary stores messages that are routed through, although the adversary is not authorized to do so. The adversary can also store a message if the victim node and adversary share the same radio signal. As shown in Figure 10, the adversary and source node share a radio signal although they are not located in the same subtree of an RPL network. The adversary eavesdrops on the communication link and stores the message exchanged between the source and destination nodes. Later, the adversary replays the message to impersonate the source node and get authenticated to the destination node. If an IoT node does not have a method to identify whether a message was delivered in the past, the node will consider a replayed message as a legitimate one and perform according to the instruction included in the message regardless of whether it is sent by an adversary.  
Figure 8: Message modification.  
Figure 9: Message fabrication.
## 4 Comparative Analysis of Theoretical and Experimental Results
### Modal Comparative Analysis  
By using the Lanczos method for modal analysis of two models, the natural frequencies and vibrational modes were obtained. Figure 25 shows the first-order mode shapes of two models: rigid and soft soil interlayer isolation models.  
Figure 24: Finite-element model of soil isolation structure.  
Figure 25: First-order vibration modes of the finite-element model (X-direction). (**a**) First-order vibration mode of rigid interlayer isolation model; (**b**) First-order vibration mode of weak soil interlayer isolation model.  
The comparative analysis shows that the deformation of the structure in both models is mainly reflected in the lateral displacement and rotation, and the lateral displacement is mainly reflected in the position of the isolation layer. The lateral displacement of the interlayer isolation structure under the rigid foundation conditions is more significant than that on the soft soil foundation. Table 9 provides a comparison between the natural and experimental vibration period values of the two models.  
Table 9 shows that the natural vibration period of interlayer isolation structures on the soft soil foundation when considering SSI effect is approximately 1.2 times longer than that in the case of a rigid foundation. The error between the natural and experimental vibration period values of the rigid and soft soil interlayer isolation models is within 3%, thus verifying the reliability of using two finite-element models for seismic-response analysis.
## 4 Experiment and Explanation
### Comparison with the Sota  
In this part, we compare results against some state-of-the-art methods. As shown in Tables 3-5, our method is also significantly superior compared to other methods. The performance results convincingly validate the effectiveness of our approach and further show that the individual modules in the framework are capable of incremental performance improvements. It is worth mentioning that, on VeRi-776, it exceeds the second-best method in MAP accuracy by 2.2%, and it also performs well on the other datasets. Our methods are also very advantageous compared to the best methods currently available.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Small**} & \multicolumn{2}{c}{**Medium**} & \multicolumn{2}{c}{**Large**} \\ \cline{2-7}  & **R1** & **R5** & **R1** & **R5** & **R1** & **R5** \\ \hline VAMI [58] & 63.1 & 83.3 & 52.9 & 75.1 & 47.3 & 70.3 \\ FDA-Net [12] & - & - & 59.8 & 77.1 & 55.5 & 74.7 \\ AGNET [55] & 71.2 & 83.8 & 69.2 & 81.4 & 65.7 & 78.3 \\ AAVER [52] & 74.7 & 93.8 & 68.6 & 90.0 & 63.5 & 85.6 \\ OIFE [17] & - & - & - & - & 67.0 & 82.9 \\ CCA [54] & 75.5 & 91.1 & 73.6 & 86.5 & 70.1 & 83.2 \\ PRN [20] & 78.4 & 92.3 & 75.0 & 88.3 & 74.2 & 86.4 \\ BS [53] & 78.8 & 96.2 & 73.4 & 92.6 & 69.3 & 89.5 \\ VehicleX [57] & 79.8 & 93.2 & 76.7 & 90.3 & 73.9 & 88.2 \\ \hline Our & **84.3** & **93.5** & **83.5** & **90.6** & **79.9** & **86.5** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the Sota on VehicleID (%).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **MAP** & **R1** & **R5** \\ \hline Siamese-CNN [50] & 54.2 & 79.3 & 88.9 \\ FDA-Net [12] & 55.5 & 84.3 & 92.4 \\ Siamese-CNN+ST [50] & 58.3 & 83.5 & 90.0 \\ PROVID [51] & 53.4 & 81.6 & 95.1 \\ AAVER [52] & 66.4 & 90.2 & 94.3 \\ BS [53] & 67.6 & 90.2 & 96.4 \\ CCA [54] & 68.0 & 91.7 & 94.3 \\ PRN [20] & 70.2 & 92.2 & 97.9 \\ AGNET [55] & 71.6 & 95.6 & 96.6 \\ PAMTRI [56] & 71.8 & 92.9 & 97.0 \\ VehicleX [57] & 73.3 & 95.0 & 98.0 \\ APAN [57] & 73.5 & 93.3 & - \\ MDL [44] & 79.4 & 90.7 & - \\ \hline Our & **81.6** & **96.9** & **99.0** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison with the Sota on VeRi-776 (%).
## 4 Experimental Results and Discussions
### Performance of Sensing System Calibration  
Firstly, the sensing system, including the combined _T_-type PM and magnetic sensor array, was calibrated in advance based on the proposed calibration procedure in Section 2.3. To evaluate the error brought by the magnetic moment **M** bias, \(e_{\mid\textbf{B}\mid}\) (%) is defined by \((\mid\textbf{B}_{m}\mid-\mid\textbf{B}_{c}\mid)/\mid\textbf{B}_{c}\mid\), where \(\mid\textbf{B}_{c}\mid\) is the norm of the magnetic field with no magnetic moment bias. Tables 2 and 3 present the calibration results of the _T_-type PM and eight three-axis magnetic sensors, respectively.  
The combined _T_-type PM connects the TCI system and the tongue motion manipulated by human intentions, and the calibration of the PM is one of the prior issues in TCI system evaluation. In Table 2, the standard deviation of the calibrated magnetic moment magnitude \(m\) for PM\({}_{v}\) and PM\({}_{h}\) is 0.0001 and 0.0005, respectively, verifying the accuracy of the calibration results. The alignment angle \(\varphi_{m}\) of the magnetic moment deviation for each PM is smaller than 2\({}^{\circ}\), and the error \(e_{\mid\textbf{B}\mid}\) brought by the magnetic moment deviation for each PM is smaller than 0.5%, implying that the error brought by the magnetic moment bias can be ignored here. As presented in Table 3, the calibrated rotational matrices \(\textbf{R}_{i}\) (\(i=1,\,\ldots\,8\)) between the local sensor frame \(\{S_{i}\}\) and Cartesian coordinate for all of the AMR sensors are orthogonal, verifying the calibration accuracy of the sensing axis.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(m\) (**A**\(\cdot\)**m\({}^{2}\))** & _(\(\varphi_{m}\), \(\theta_{m}\))_ (\({}^{\circ}\))** & _e\({}_{\mid\textbf{B}\mid}\)_ **(\%) at P(0, 0, 0.025) (m)** \\ \hline PM\({}_{v}\) & 0.0125 \(\pm\) 0.0001 & (1.71, 74.18) & 0.33 \\ PM\({}_{h}\) & 0.0123 \(\pm\) 0.0005 & (0.97, 137.53) & 0.21 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Calibration results for the PM marker.  
Figure 5: The custom-made graphical user interface (GUI) constructed based on Labview (NI, Austin, TX, USA) and MATLAB (Mathworks, Natick, MA, USA).
## 2 Research Background Overview
### Spatial Pattern Types and Attributes
A feature object may contain six components of a feature-based object [25]: (1) unique identifier; (2) positional or geometric information (depending on the type of the feature and application); (3) non-spatial attribute data (such as names or values of attributes); (4) topological relations among geometric objects (such as boundaries, neighbors, and interior); (5) non-topological relations (non-geometric links between features); and (6) methods embedded in each object. According to the other classification [26], the main objectives of the spatial information formalisms are to provide specific concepts and notations used in conceptual models to facilitate the following goals: (1) representation of types of basic spatial objects (points, lines, etc. ); (2) enumeration of spatial relationships between objects (such as an embankment always being adjacent to a river); (3) description of the evolution of spatial objects over time (birth and death, object shape changes); (4) modeling of multi-representation of objects; (5) description of objects with uncertain boundaries or positions (such as areas of pollution); (6) representation of continuous spatial data (temperature, vegetation cover density); and (7) modeling of the structure of networks (such as water systems). The features of the spatial data model of geologic map database presented in Reference [27] divide all data into four categories: (1) occurrence (spatial geometry for each geologic feature); (2) descriptor (descriptive information for each feature); (3) concept (essential concepts and definitions essential for querying the database); and (4) symbol (cartographic entities for symbolizing the map on-screen and in print form). The spatial database dedicated to morphological research on architectural heritage [28] divides all data into two categories: (1) spatial data (topographic map, which shows the locations and the shapes of historic architectures, and the relation between them); and (2) non-spatial data (including attribute data and architectural thematic data). These different, but at the same time similar, structures for the storage of spatial information must be reflected in the design of the general SP and meta-pattern model.
## 3 IEEE 802.15.6: The Key-Enabling Standard for WBANs
### Coexistence and Interoperability
#### 3.5.3 Interleaving  
A WBAN may share the same operating channel with one or more other WBANs with or without interleaving their active super frames. A coordinator that supports active super frame interleaving and operates in non-beacon mode with super frames sends a B2 frame in every active super frame to avoid channel interference. More details on Interleaving are provided in Section 6.13.3 of the standard.
## 2 Materials and Methods
### GPR Data Preprocess and Alignment
#### 2.1.1 Preprocess  
Usually, the different acquired frequency GPR data contain some disturbing noise and random errors. To eliminate the disturbing information and obtain better fusion results, the data preprocess sequences are mainly dealt with using a band-pass filter and zero offset elimination [29; 30].
# Review on the Recent Numerical Studies of Liquid Atomization
## 5 Hybrid of Interface Capturing and Particle Tracking
### Application Cases
A numerical study of atomization fully covering from inside the nozzle until far away was present in [PERSON] et al. [67]. Inside the nozzle, a length of 24.2 mm of single-phase flow was solved by \(k\)-\(\varepsilon\) model. For the region of primary atomization corresponding to a length of 12 mm, VOF and LES were adopted. For the secondary atomization region with a length of 2988 mm, the DPM and \(k\)-\(\varepsilon\) model were adopted. Two artificial planes were used to divide the three regions with different grid densities created to improve simulation efficiency, and the data including the particles' size and velocity were transferred across the plane by user-defined functions. One of the complete simulations of primary and secondary atomization, evaporation, and combustion of acetone in air were performed by [PERSON] et al. [68]. The Eulerian phase was assumed to transit into a Lagrangian phase in a predefined plane so as to form a hybrid interface capturing and particle tracking method. The combustion was accounted for through an LES-based flamelet progress variable model. Despite advancements in multiscale modeling, critical gaps persist in current atomization simulations. First, phase transition criteria--such as sphericity thresholds [63] and volume-based rules [64]--rely heavily on empirical parameters rather than physics-driven metrics, causing significant errors in high-viscosity droplet predictions (27% for alloy steels [65]). Second, multiphysics couplings, particularly cavitation-turbulence interactions, remain underexolved, leading to 15% underestimation of primary breakup lengths in nozzle flows [67]. Third, validation frameworks lack cross-scale rigor, often focusing narrowly on particle size distributions while ignoring ligament dynamics [66]. These issues stem from fragmented modeling approaches that decouple interfacial, turbulent, and phase-change phenomena. To address these limitations, three transformative directions are proposed. First, intelligent adaptive frameworks should integrate VOF, DPM, and SPH methods with reinforcement learning to dynamically allocate computational resources across scales--resolving primary breakup via CLSVOF-LES method, tracking secondary droplets with KH-RT-optimized DPM, and modeling microdroplets using ML-corrected SPH, achieving 5\(\times\) faster simulations with <10% errors. Second, quantum annealing algorithms should optimize high-dimensional parameters (e.g., cavitation coefficients in ACT models), enabling 1000\(\times\) faster calibration for aerospace injector designs. Third, industrial digital twins must unify embedded sensor networks, GPU-accelerated solvers, and PINN-based optimization to reduce defects in additive manufacturing. Supported by multimodal validation databases and initiatives, these strategies will bridge the gap between empirical models and predictive simulation.
# Physicochemical and Microbiological Changes Associated with Processing in Dry-Cured Tuna
## 3 Results and Discussion
### Microbiological Parameters  
The microbiological quality characteristics of dry-cured tuna are crucial in determining the safety of the manufacturing process. All the microbiological results demonstrate the significant impact of the salting step (Table 4).  
The main antimicrobial mode of action of salt is creating a low \(\mathrm{a_{w}}\) environment (<0.9), which forces microorganisms to experience a prolonged lag phase and eventually enter the death phase. The aerobic mesophilic counts in every step were far below the maximum limit established by the reference regulation (\(1\times 10^{5}\) cfu/g) in the dry-cured fish product [74]. Hence, the salting and salt-washing stages did not promote the growth of mesophilic bacteria. After drying, the _mojama_ samples showed the lowest values (4.9 \(\times\) 10\({}^{2}\) cfu/g), which could be explained by the lowest \(\mathrm{a_{w}}\) in the final product. These analyses (mesophilic and psychrophiles flora) are used as an indicator of hygiene and good manufacturing practices. Hence, the manufacturing process can be viewed as acceptable, in accordance with good microbiological standards.  
Mold counts in the samples of raw tuna (1.2 \(\times\) 10\({}^{3}\) cfu/g) were significantly higher than those obtained after the salting and salt-washing steps (3.8 \(\times\) 10\({}^{2}\) and 4.2 \(\times\) 10\({}^{2}\) cfu/g, respectively). However, the values for the samples obtained after the drying step were \(1.1\times 10^{3}\) cfu/g on average, with a slight increase compared to those obtained after the salting process. These results are in accordance with those reported by [PERSON] et al. [71] for fresh tuna and _mojama_ samples from _Thunnuus obesus_, where the mold counts decreased after the salting stage (4 \(\times\) 10 cfu/g) and increased after the drying step (3 \(\times\) 10\({}^{4}\) cfu/g).  
All the samples analyzed during the different steps of the elaboration of _mojama_ were free from _Salmonella_ and _S. aureus_. _Salmonella_ does not occur naturally in marine waters and its occurrence is usually due to unhygienic handling, infected water, or fresh fish itself containing it [75]. Hence, _Salmonella_ is frequently cited in food safety problems. In the same way, _Staphylococcus aureus_ is found in the skin of animals and its presence could be an indicator of contamination since it can survive for prolonged periods in extreme environments such as salt, dry-cured products, and low \(\mathrm{a_{w}}\) products [76]. This result agrees with those reported by [PERSON] et al. [77], who reported that halophilic bacteria, such as _S. aureus_, could not grow at \(\mathrm{a_{w}}\) below 0.9. Besides, _Listeria monocytogenes_ and _Enterobacteriaceae_ were not detected during the entire process.  
All in all, the microbiological evaluation of the fresh tuna joins during the manufacturing process of _mojama_ indicates that they were of high quality and safe for consumption as fresh fish and for further processing and use, such as _mojama_.
# A New Orthogonal Least Squares Identification Method for a Class of Fractional Hammerstein Models
## 3 Identification Algorithm
### Construction of the Permutation Matrix  
Assuming that the sub-information matrix \(\mathbf{\Phi}_{\Xi_{k-1}}\) has already been constructed based on the active set \(\Xi_{k-1}\), the next step involves selecting a candidate column from the remaining columns of \(\mathbf{\Phi}\). Let \(\mathbf{\chi}_{\xi_{k}}\) represent the candidate column selected at the \(k\)-th iteration, and define  
\[\mathbf{\kappa}_{\xi_{k}}=(\mathbf{L}_{k}\mathbf{\chi}_{\xi_{k}})(1:k-1)\in \mathbb{R}^{k-1},\] \[\mathbf{\
u}_{\xi_{k}}=(\mathbf{L}_{k}\mathbf{\chi}_{\xi_{k}})(k:m)\in\mathbb{R }^{m-k+1}. \tag{28}\]According to Equations (24)-(26), we have  
\[e_{m-k+1}^{\rm T}\hat{\mathbf{H}}_{k} =\frac{\mathbf{v}_{ck}^{\rm T}}{\|\mathbf{v}_{\mathbf{v}_{k}}\|}, \tag{29}\] \[\mathbf{L}_{k}\mathbf{Y} =\mathbf{H}_{k}\mathbf{L}_{k-1}\mathbf{Y}=\left[\begin{array}{cc}\mathbf{I}_{k-1 }&\mathbf{0}\\ \mathbf{0}&\hat{\mathbf{H}}_{k}\end{array}\right]\left[\begin{array}{c}\mathbf{g}_{k-1} \\ \mathbf{h}_{k-1}\end{array}\right]=\left[\begin{array}{c}\mathbf{g}_{k-1}\\ \hat{\mathbf{H}}_{k}\mathbf{h}_{k-1}\end{array}\right]=\left[\begin{array}{c}\mathbf{g}_ {k-1}\\ \hat{\mathbf{h}}_{1}\\ \hat{\mathbf{h}}_{k-1}\end{array}\right], \tag{30}\]  
where \(\mathbf{g}_{k-1}=(\mathbf{L}_{k-1}\mathbf{Y})(1:k-1)\), \(\mathbf{h}_{k-1}=(\mathbf{L}_{k-1}\mathbf{Y})(k:m)\), and \(\tilde{h}_{1}\) denotes the first element of \(\tilde{\mathbf{H}}_{k}\mathbf{h}_{k-1}\). From Equations (21) and (29), we have  
\[J(\hat{\mathbf{\theta}}_{k}) =\|\mathbf{h}_{k}\|^{2}=\|\tilde{\mathbf{h}}_{k-1}\|^{2}=\|\mathbf{Y}\|^{2}- \|\mathbf{g}_{k-1}\|^{2}-\|\tilde{h}_{1}\|^{2}, \tag{31}\] \[\tilde{h}_{1} =\mathbf{e}_{m-k+1}^{\rm T}\tilde{\mathbf{H}}_{k}\mathbf{h}_{k-1}=\frac{\mathbf{ v}_{\xi_{k}}^{\rm T}\mathbf{h}_{k-1}}{\|\mathbf{v}_{\xi_{k}}\|}. \tag{32}\]  
By substituting Equation (32) into (31), we obtain  
\[J(\hat{\mathbf{\theta}}_{k})=\|\mathbf{Y}\|^{2}-\|\mathbf{g}_{k-1}\|^{2}-\frac{(\mathbf{v}_{ \xi_{k}}^{\rm T}\mathbf{h}_{k-1})^{2}}{\|\mathbf{v}_{\xi_{k}}\|^{2}}, \tag{33}\]  
and then we can obtain that  
\[\xi_{k}=\arg\max_{i\in\Omega\setminus\Xi_{k-1}}\frac{|\mathbf{\chi}_{i}^{\rm T}(k :m)\mathbf{h}_{k-1}|}{\|\mathbf{\chi}_{i}(k:m)\|}, \tag{34}\]  
where \(\Omega=\{1,2,\cdots,n\}\). We employ the indices \(\xi_{k}\), \(k=1,2,\cdots,K\), where \(K=n_{a}+\sum\limits_{i=1}^{r}n_{b_{i}}m_{i}\) represents the level of sparsity. The permutation matrix \(\mathbf{P}_{\Xi_{k}}\) can be formed using Equation (17).
# Reliability Analysis of Response-Controlled Buildings Using Fragility Curves
## 3 Fragility Curve Based on Incremental Dynamic Analysis
### Fragility Curves  
Analyzing the models with 20 GMRs provides a bunch of discrete points on an IM_DM plot. The IM values corresponding to 0.5, 1.0, and 2.0% story drift were obtained using interpolation.  
By using the estimated mean and standard deviation values of IM, the fragility curve was obtained as the lognormal cumulative distribution function expressed by Equation (1), as follows:  
\[P(DM\geq DMco)=\varnothing\bigg{(}\frac{lnX-\mu lnX}{\sigma lnX}\bigg{)} \tag{1}\]  
where \(\Phi\) is the standard cumulative distribution function, \(lnX\) is the natural logarithm of the variable \(X\)(Sa(T\({}_{1}\))), and \(\mu lnX\) and \(\sigma lnX\) are the mean and the standard deviation of the natural logarithm of \(X\), respectively.  
The lognormal distribution of IM (Sa(T\({}_{1}\))) corresponding to 0.5, 1.0, and 2.0% story drift are presented in Figure 6a for the TS building and in Figure 6b for the CS building. The mean (\(\mu\)) and standard deviation (\(\sigma\)) of Sa(T\({}_{1}\)) for the required levels of damage are given in Table 7.  
Figure 5: IDA curves for models: (**a**) 10F_TS_FM, (**b**) 10F_TS_HM, (**c**) 10F_TS_OM, (**d**) 10F_CS_FM.  
The fragility curves for the TS building are presented in Figure 7. The lower the fragility curve, the less the probability of damage. For instance, considering 0.5% story drift, the probability of damage is higher for the hysteresis damper model (10F_TS_HM (0.5%0)) than for the oil damper model (10F_TS_OM (0.5%0)) for any value of Sa(T\({}_{1}\)). On the other hand, for 2% story drift, the probability of damage seems to be higher for the model equipped with oil dampers (10F_TS_OM (2.0%0)) compared to that equipped with hysteresis dampers (10F_TS_HM (2.0%0)).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(\theta\)**max** & \multicolumn{2}{c}{**10F\_TS\_FM**} & \multicolumn{2}{c}{**10F\_TS\_HM**} & \multicolumn{2}{c}{**10F\_TS\_OM**} & \multicolumn{2}{c}{**10F\_CS\_FM**} \\ \hline  & \(\mu\) & \(\sigma\) & \(\mu\) & \(\sigma\) & \(\mu\) & \(\sigma\) & \(\mu\) & \(\sigma\) \\ \hline
0.5\% & \(-2.645\) & 0.421 & \(-2.572\) & 0.541 & \(-2.242\) & 0.466 & \(-1.606\) & 0.290 \\
1.0\% & \(-1.961\) & 0.416 & \(-1.517\) & 0.434 & \(-1.584\) & 0.408 & \(-0.965\) & 0.257 \\
2.0\% & \(-1.348\) & 0.346 & \(-1.000\) & 0.383 & \(-1.056\) & 0.332 & \(-0.358\) & 0.343 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Mean (\(\mu\)) and standard deviation (\(\sigma\)) values.  
Figure 6: Lognormal distribution of Sa(T\({}_{1}\)): (a) trimmed section (b) conventional section.  
Figure 7: Fragility curves of trimmed section models.