esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Mass Matrix of the DFM  
The mass matrix of the DFM is defined by lumping the mass at nodal points, considering each node's volume of influence. This volume is calculated as the median distance from each node to the other vertices within the same macro-elements. Two examples of the area of influence are colored yellow and purple in Figure 7a. The yellow area represents a node fully surrounded by DFM elements, resulting in a more typical, often rectangular, area of influence. In contrast, the purple area represents a node located at the periphery of an opening, leading to an irregular, non-rectangular region shaped by the boundaries of the opening.  
Equation (22) defines the relationship between the mass \(M_{n}\) at node n, its corresponding area of influence \(A_{n}\), and the masonry's density \(\rho_{m}\). This mass is assumed to be identical in all three spacial directions. This approach simplifies the representation of mass distribution by exclusively focusing on the translational mass effects and excluding rotational degrees of freedom from the DFM's mass matrix formulation.  
\[M_{n}=\rho_{m}A_{n}t_{m}. \tag{22}\]  
While Equation (22) provides the standard approach for calculating the node mass within the DFM using uniform areas of influence and masonry density, wall intersections introduce unique geometric configurations that require a tailored approach. In typical scenarios, the areas of influence are rectangular, as depicted in Figure 7a. However, at wall intersections, more complex shapes emerge, such as L-shapes, U-shapes, T-shapes, and cross-shapes, as highlighted in Figure 7b by a blue region.  
For each of these cases, the mass at the node is not defined by Equation (22) as this would lead to an overestimation. For the example in Figure 7b, the lengths of the area of influence in Direction 1 and 2 are denoted by \(L_{1}\) and \(L_{2}\), respectively, and the thickness of the corresponding walls \(t_{1}\) and \(t_{2}\), respectively. In this configuration, the area of influ  
Figure 7: Colored areas show the areas of influence for nodes: (**a**) case when the node is not part of two perpendicular walls; (**b**) case when the node is part of two perpendicular walls.  
ence associated with the node \(n\), represented by a blue circle in Figure 7b, is defined by Equation (23). This equation can be adapted to any intersection of walls.  
\[A_{corner}=\left\{\begin{array}{ll}(L_{1}+0.5t_{2})t_{1}+(L_{2}+0.5t_{1})t_{2 }&\text{ if }L_{1}>t_{1}\text{ and }L_{2}>t_{2}\\ L_{1}t_{1}+L_{2}L_{1}+(L_{2}-0.5t_{1})t_{2}&\text{ if }L_{1}<t_{1}\text{ and }L_{2}>t_{2}\\ L_{2}t_{2}+L_{2}L_{1}+(L_{1}-0.5t_{2})t_{1}&\text{ if }L_{1}>t_{1}\text{ and }L_{2}<t_{2}\\ L_{1}L_{2}&\text{ if }L_{1}<t_{1}\text{ and }L_{2}<t_{2}\end{array}\right.. \tag{23}\]  
The mass for the nodes at the intersection of perpendicular walls is defined by Equation (24), where \(h_{inf}\) is the height of influence, as defined for the horizontal struts in Figure 4.  
\[M_{n,i}=\rho_{m}A_{corner}h_{inf}. \tag{24}\]
# DenseNet-Based Classification of EEG Abnormalities Using Spectrograms
## 3 Methodology
### Model Prediction  
The pre-trained DenseNet121 model, denoted as \(f_{\text{DenseNet}}\), is used to perform the inference. Given the input image \(\mathbf{I_{batch}}\), the model outputs a probability vector \(\mathbf{p}=[p_{1},p_{2},\dots,p_{K}]\), where \(K\) is the number of classes. In our binary classification task, \(K=2\), corresponding to class 0 (normal) and class 1 (abnormal).  
The predicted class \(\hat{y}\) is defined as the class with the highest predicted probability:  
\[\hat{y}=\arg\max_{i\in\{1,\dots,K\}}p_{i} \tag{10}\]  
The corresponding predicted probability, or confidence score, is defined as:  
\[a=p_{\hat{y}}=\max(p_{i}),\quad i=1,\dots,K \tag{11}\]  
This confidence score \(a\) indicates how certain the model is in its prediction and is used later as a reference value when interpreting the impact of individual superpixels in the LIME explanation. Specifically, higher confidence allows more robust interpretation of which regions most strongly contributed to the final decision.
# Research on Localization Algorithms Based on Acoustic Communication for Underwater Sensor Networks
## 3 Localization Algorithm
### Communication between Nodes
#### 3.5.2 Multi-Stage Scheme  
Similarly, we introduce several papers to start the multi-stage communication between nodes. In [64], the authors propose a hierarchical approach. First, all anchor nodes label themselves as references and confidence values are set to 1. The non-localized nodes are regarded as ordinary nodes. All nodes periodically broadcast the beacon message that contains its id. Hence, all neighboring nodes can receive the beacon message that can be used to measure their distances to this node. In this localization process, the ordinary node is localized and becomes the reference node. Then, the authors extend the Euclidean distance propagation method to adopt the 3D environment.  
A framework that can establish localization and routing is presented in [65], and is suitable for mobile UWSNs. In this scheme, the localization and routing are done in two consecutive rounds, which means that the localization messages are used in the routing decision. In addition, the authors take the problem of decreasing the number of Mobile Beacon and Sink (MBS) nodes into account and then employ the iterative localization--that is to say that the localized node is active and broadcasts its coordinates. The \"catch up or pass\" forwarding algorithm that belongs to the greedy geo-forwarding scheme is proposed.  
In this subsection, we have discussed the communication between nodes and the difference between single-stage schemes and multi-stage schemes. We use some papers to show the different characteristics of the two schemes. Furthermore, we summarize these papers that use communication between nodes to achieve the positioning in Table 6. We mainly compare the coverage, localization time, accuracy, computational complexity, and energy consumption. According to the following table, the reader can choose the suitable algorithm for doing some application or doing some improvement. The comparisons between the localization algorithm referred to in this subsection and other localization algorithms that are introduced in other subsections will be shown in Section 4.
# Service-Oriented Cooperation Policies for Intelligent Ground Vehicles Approaching Intersections
## 2 Related Work and Literature
### Communication Based Cooperation at Intersections  
V2X communication-based inter-vehicle coordination at intersections is a hot topic in the domain of C-ITS. Typically, several cooperative communication protocols, such as Ballroom Intersection Protocol (BRIP), CAM/DENM(Cooperative Awareness Message/Decentralized Environmental Notification Message)-based cooperation messages etc. [40; 41], and further novel cooperation mechanisms have contributed [12; 42; 43; 44]. In order to solve the problem of BRIP protocol resulting in potential accidents at intersections for GPS errors or control system failures, [PERSON] et al. [40] designed a new safe and practical intersection protocol named the Configurable Synchronous Intersection Protocol (CSIP). [PERSON] et al. [45] proposed a paradigm shift based upon a self-organizing and cooperative control framework, in which one vehicle will calculate its velocity and trajectory with others coordinately via V2V communication. Then, two controllers are used to separately manage vehicular approaching maneuvers and regulate vehicle-following procedures. After designing a discrete model of an intersection and V2I communication protocols, [PERSON] et al. [13] implemented a safe trajectory generation mechanism to avoid possible collision when autonomous vehicles pass through intersections. [PERSON] et al. [46] proposed a decentralized cooperative adaptive cruise control algorithm for vehicles in the vicinity of intersections (CACC-VI), to improve the throughput of intersections by reorganizing vehicle platoons. Similarly, [PERSON] et al. [47] focused on the traffic coordination problem at traffic intersections and presented a decentralized coordination approach, combining optimal control with model-based heuristics that are suitable for a fast-online implementation. [PERSON] et al. [48] proposed a novel distributed conflict-free cooperation method for multiple connected automated vehicles at unsignaled intersections and eventually designed a distributed controller to stabilize the virtual platoon for conflict-free cooperation at intersections. [PERSON] et al. [49] proposed a new system called Cooperative Intersection Collision Avoidance Persistent (CICAP) system, and introduced a new intersection collision avoidance system by means of V2V communication. [PERSON] et al. [50] proposed a novel traffic control method based on a genetic algorithm aimed at reducing the time in an isolated intersection without traffic lights. Experiments show that this method can greatly shorten the time delay. The problem of autonomous PTI has increased. All these contributions will establish a good foundation for our study of complicated PTI problems that depend on precise positioning and good V2V and V2I communication.
## 3 Results and Discussion
As shown by these results, the proposed method performs experiments with regards to three types of DBs and demonstrates high results even in DBs containing abnormal signals. Consequently, the non-fiducial \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Methods** & **Subject Number** & **DB Type** & **Subject Identification Rate (\%)** & **Window/Heart Beat Identification Rate (\%)** \\ \hline \multirow{2}{*}{Proposed method} & Non-fiducial /NN, & NER:18 & NER:10 & NER:98.67 \\  & SVM, LDA & PTE:50 & NER, PTB, QT & PTE:100 & PTE:98.65 \\  & QT:36 & QT:100 & QT:92.3 \\ \hline \multirow{2}{*}{[PERSON] et al. [10]} & Fractal/LDA & 29 & Acquisition from lab. & 100 & S2 \\ \hline \multirow{2}{*}{[PERSON] et al. [11]} & \multirow{2}{*}{Fiducial/SIMCA model} & \multirow{2}{*}{20} & STEMENS equipment & 100 & - \\ \hline \multirow{2}{*}{[PERSON] et al. [12]} & \multirow{2}{*}{Fiducial/NN, LDA} & NER:13 & NER, PTB & NER:100 & NER:99.43 \\  & & PTE:13 & & PTE:100 & PTE:98.90 \\ \hline \multirow{2}{*}{[PERSON] et al. [17]} & \multirow{2}{*}{Fiducial / non} & \multirow{2}{*}{51} & \multirow{2}{*}{PTB} & Fiducial: 99.85 & \multirow{2}{*}{-} \\  & & matching, NN & & & \\ \hline [PERSON] et al. [18] & Non-fiducial/NN & 56 & NER, PTB, MIT & 96.42 & 96.20 \\ \hline \multirow{2}{*}{[PERSON] et al. [19]} & \multirow{2}{*}{Non-fiducial/wavelet} & \multirow{2}{*}{50} & Acquisition from lab. & 89 & - \\ \hline \multirow{2}{*}{[PERSON] et al. [20]} & \multirow{2}{*}{Non-fiducial/NN} & \multirow{2}{*}{35} & QT & 100 & - \\ \hline \multirow{2}{*}{[PERSON] et al. [21]} & \multirow{2}{*}{Non-fiducial/ neural net} & \multirow{2}{*}{15} & Acquisition from lab. & 100 & 99.52 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of results with other studies. Figure 7: Random selection results: (**a**) NSR random selection result; (**b**) PTB random selection result; and (**c**) QT random selection result. technique-based features proposed in this study are not influenced by obvious fiducial point detection, unlike the fiducial technique-based method. In addition, the proposed algorithm can be executed simply and the window removal method can be applied to remove windows that are difficult to recognize. Thus, our method can enhance the overall identification rate. The threshold value settings for the window removal method demonstrate excellent results with regards to the various DBs, but this may not have been appropriate for all situations of ECG signals with non-stationary characteristics. In addition, data including other diseases, such as commonly occurring supraventricular tachycardia or atrial fibrillation, accompanied by irregularities other than the diseases included in the PTB DB, may differ from the signals that are used in this experiment, thus, degrading the performance of the proposed algorithm.
# Aerodynamics Analysis of Speed Skating Helmets: Investigation by CFD Simulations
## 6 Results and Discussions
### Experimental Comparison  
As can be seen from the smoke flow visualization illustrated in Figure 14, in the absence of any helmet the bare head of the skater produces a wide wake; instead, when the Omega helmet is employed, the flow separation is postponed and the wake behind the head becomes narrower.  
The results from the infrared thermography measurements, compared with the skin friction coefficient evaluated from the numerical simulations, are presented in Figure 15.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Helmet** & \(A[m^{2}]\) & \(C_{D}[-]\) & \(AC_{D}[m^{2}]\) & \(\Delta C_{D}[\%]\) & \(\Delta AC_{D}[\%]\) \\ \hline Bare Head & 0.24949 & 0.529 \(\pm\) 0.002 & 0.13198 & - & - \\ Omega & 0.25398 & 0.506 \(\pm\) 0.002 & 0.12851 & \(-\)4.3 & \(-\)2.6 \\ Design 1 & 0.25406 & 0.506 \(\pm\) 0.002 & 0.12855 & \(-\)4.3 & \(-\)2.6 \\ Design 2 & 0.25406 & 0.504 \(\pm\) 0.002 & 0.12804 & \(-\)4.7 & \(-\)2.9 \\ Design 3 & 0.25406 & 0.506 \(\pm\) 0.002 & 0.12855 & \(-\)4.4 & \(-\)2.6 \\ Design 4 & 0.25406 & 0.519 \(\pm\) 0.002 & 0.13185 & \(-\)1.8 & \(-\)0.1 \\ Design 5 & 0.25411 & 0.499 \(\pm\) 0.002 & 0.12680 & \(-\)5.7 & \(-\)3.9 \\ Design 6 & 0.25410 & 0.498 \(\pm\) 0.002 & 0.12654 & \(-\)5.9 & \(-\)4.1 \\ \hline \end{tabular}
\end{table}
Table 2: Total drag and area-drag coefficients comparison between bare head and every helmet design evaluated.  
Figure 14: Smoke flow visualization on the head of the skater mannequin for the bare head case (**left**) and the Omega helmet case (**right**).  
\begin{table}
\begin{tabular}{c c c} \hline
**Helmet** & \(t[min\text{:}s]\) & \(t_{gain}[s]\) \\ \hline Bare Head & 1:40.17 & - \\ Omega & 1:39.34 & \(-\)0.83 \\ Design 1 & 1:39.35 & \(-\)0.82 \\ Design 2 & 1:39.23 & \(-\)0.94 \\ Design 3 & 1:39.35 & \(-\)0.82 \\ Design 4 & 1:40.14 & \(-\)0.03 \\ Design 5 & 1:38.93 & \(-\)1.24 \\ Design 6 & 1:38.86 & \(-\)1.30 \\ \hline \end{tabular}
\end{table}
Table 3: Estimated long track 1500 m racing timings of each helmet configuration. Bare head case was assigned as the reference 1500 m world record by the Dutch [PERSON] [32].  
It can be seen that the experimental and numerical results present clear similarities. In both cases, the separation line moves downstream in presence of the helmet, reducing the region of separated flow and thus yielding lower drag on the head.  
Finally, the results from the force balance measurements are presented. While the numerical simulations returned a drag area reduction by 2.6% (see Table 2 in the manuscript), the experimental results yielded an even higher drag area decrease, namely by 4.7%. Therefore, the above results confirm that the use of the Omega helmet provides an aerodynamic benefit to the skater with respect to the bare-head configuration.
## 4 Numerical Modelling for Structural Analysis of TEL-Gridshells
### Joints Model  
All the screws used in the joints _Type 1_, _Type 2_ and _Type 3_ were modelled as a sequence of aligned couplings. Each of the couplings had a length equal to half the height of the timber lath it passed through, so that one of its ends was located on the longitudinal axis of the lath, and the other on the shear plane. At the end of one of the couplings reaching the shear plane, there were two springs perpendicular to each other that allowed only movements perpendicular to the axis of the connector, i.e., movements in the shear plane. The stiffness of both springs, \(k_{i}\), was assumed to be identical, so \(k_{iy}=k_{iz}\). In all shear planes of joints _Type 1_ and _Type 2_, rotation around the axis of the coupling was also allowed.  
The proposed connector concept by coupling is shown in Figure 7, while Figure 8 presents a detail of the joint _Type 1_, as well as the scheme of the complete model of this joint consisting of a total of eight aligned couplings.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Unloaded State**} & \multicolumn{2}{c}{**Loaded State**} \\ \cline{2-5}  & **Error (mm)** & **Error (\%\({}_{\infty}\))** & **Error (mm)** & **Error (\%\({}_{\infty}\))** \\ \hline min & 0.93 & 0.11 & 0.99 & 0.11 \\ max & 1.94 & 0.23 & 2.43 & 0.23 \\ average & 1.41 & & 1.63 & \\ SD & 0.45 & & 0.48 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Minimum, maximum, average, and standard deviation (SD) values of the errors in the distances between five pairs of checking points for the unloaded and loaded states.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Control Points** & **A** & **B** & **C** & **D** & **E** \\ \hline Photogrammetry & – & – & 14.60 & 38.97 & 32.71 \\ LVDTs & 0.18 & 0.45 & 13.80 & – & – \\ \hline \hline \end{tabular}
\end{table}
Table 2: Displacement of the deformation control points A, B, C, D and E (in mm).
## 2 A Quantum Conferencing Scheme with Entanglement in the Middle
### Collective Attacks on Quantum Conferencing Scheme with Entanglement in the Middle  
The best attack strategy of the eavesdropper [PERSON] on the quantum star network is to perform attacks on all the quantum links and the entangled source in the middle. Since the Gaussian state can maximize the information of both the users and [PERSON], we consider the worst case that [PERSON] fakes the source and prepares the multipartite Gaussian GHZ state herself [16]. Moreover, the Gaussian attacks of the links and the entangled source can be reduced to an attack of the links only [21].  
At the beginning of eavesdropping, [PERSON] replaces each of the original lossy channels with a noiseless channel and a beam splitter, of which the transmissivity is equal to the channel loss of the original channel \(\eta\), and the thermal noise is \(\hat{n}\). [PERSON] prepares entangled ancillary modes \(\hat{\mathbb{E}}_{1,\cdots,N}\) and injects them onto the entangled modes \(\hat{\mathfrak{a}}^{\prime}_{1,\cdots,N}\) by her beam splitters of the channels, respectively. The output ancillary modes \(\hat{\mathbb{E}}^{\prime}_{1,\cdots,N}\) are stored in [PERSON]'s quantum memory, and the injected modes \(\hat{\mathfrak{a}}^{\prime\prime}_{1,\cdots,N}\) are sent to the users, respectively. For each ancillary mode of the beam splitters, [PERSON] performs a collective Gaussian attack [22; 23]. Although the entangled cloner collective attacks are simpler for analysing the security, coherent attacks are the most general and powerful attacks for the eavesdropper  
Figure 1: Schematic of the multipartite CV quantum conferencing scheme with entanglement in the middle. The source prepares CV squeezed states \(\hat{\mathfrak{a}}_{1,\cdots,N}\), and combines them by beam splitters \(BS_{1,\cdots,N}\). The output entangled modes \(\hat{\mathfrak{a}}^{\prime}_{1,\cdots,N}\) are distributed to legitimate users \(Bob_{1,\cdots,N}\) through noisy quantum links, which are controlled by the eavesdropper [PERSON]. The users receive the modes \(\hat{\mathfrak{a}}^{\prime}_{1,\cdots,N}\) and perform homodyne detection on them to distill mutual information.  
Eve [22]. Since the performance of coherent attacks can be calculated as in Ref. [20] by applying a Gaussian de Finetti reduction [24], in our work, we focus on the security analysis of the quantum conferencing scheme against collective Gaussian attacks.
## 4 Discussion
### Strengths and limitations  
This study's strength lies in its rigorous methodology, including strict inclusion criteria, decision-making, a standardized follow-up protocol, and systematic evaluation criteria to ensure objective and reliable outcome assessments. Unlike previous studies focusing on single diagnostic systems, multiple classification frameworks were analyzed, allowing for a broader comparison of clinical relevance and prognostic accuracy. Additionally, by including all stages of pulpal inflammation, this study provides a comprehensive understanding of pulp pathology and treatment outcomes, enhancing its applicability in VPT. Moreover, one experienced operator performed all the treatments, which excludes differences due to the operator. Another strength of this study is that it includes only permanent teeth of adults without limitations regarding age.  
This study had limitations, including a small sample size, limited follow-up, and the use of cold spray as the sole method for sensibility testing. Full pulpotomy rarely responds to cold tests; the success criterion of a \"positive response to cold\" cannot be applied to this treatment modality. Case selection posed another challenge, as full pulpotomy was only performed in cases of irreversible pulpitis. In contrast, for teeth with normal pulp, exposed pulp, or reversible pulpitis, direct pulp capping, partial pulpotomy, and full pulpotomy were all performed.  
Additionally, the lack of randomization in the treatment modality level could have introduced bias; however, proper randomization was not feasible due to the nature of the treatment.
# Road Crash Analysis and Modeling: A Systematic Review of Methods, Data, and Emerging Technologies
## 6 Applications and Policy Implications
### Economic Analysis, Crash Costs, and Resource Allocation
[122] demonstrated the application of zonal crash prediction models to evaluate travel demand management strategies, specifically examining fuel cost increases as a safety intervention. Their analysis of a 20% fuel price increase scenario in Flanders, Belgium, predicted an 11.57% reduction in vehicle kilometers traveled and a corresponding 2.83% decrease in crash frequency, illustrating how economic policies can yield measurable safety benefits through reduced exposure.
## 2 Related Work
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Related Work** & **Attack Type** & **Detection Method** & **Response Method** & **Real-Time Capability** \\ \hline
[23; 24] & RTP flooding & Hellinger distance & Dynamic rate limiting & Yes \\ \hline
[30] & DDoS & Network-wide anomaly analysis & Monitoring scheme & No \\ \hline
[32] & Various & Testbed simulation & Security policy and configuration & No \\ \hline
[33] & Softphone vuln. exploitation & Context-aware filtering & NAT implementation SIP authentication & No \\ \hline
[44] & DDoS in IoT devices & Blockchain-based detection & Blockchain-based mitigation & No \\ \hline
[43] & Cloud-based SIP flooding & Temporal and structural fingerprints & Fingerprint whitelist DB & Yes \\ \hline
[46] & SIP flooding & Multidimensional sketch w/Hellinger distance & Selectively discard SIP messages & Yes \\ \hline
[47] & DDoS & Recurrent neural networks w/token & Discard SIP messages & Yes \\ \hline
[48] & SIP-based DDoS & Deep packet inspection (DPI) & New attack detection rules & Yes \\ \hline
[49] & DRDoS & Deep forest model & Filtering w/differentiated service procedure & No \\ \hline
[15] & SIP-based DDoS & Statistical analysis, packet inspection & Drop attack packets & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the related works on SIP-based DDoS defense mechanisms.