esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study on the Effects of Vertical Mass Irregularity on Seismic Behavior of BRBFs and CBFs
## 1 Introduction
### Objective of the Study  
The present study aims to illustrate a new study into the effect of far-field earthquake in selecting appropriate braces in regular and irregular structures by comprehensively comparing regular and irregular structures with concentric braces and buckling-restrained braces frames (BRBFs) in terms of their seismic behavior. In other words, this study examined the effect of this type of brace on the seismic responses of irregular and regular structures. Nonlinear analyses were performed on four 10-story and four 15-story structures, each group comprising two regular structures with concentric braces and BRBs and two mass-irregular structures with concentric braces and BRBs. A nonlinear time history analysis was conducted on all the structures to compare their seismic responses as well as drift, floor displacement, floor acceleration and base shear. Twelve far-field earthquake records were used to analyze the structures given that their majority were at least 10 km away from the closest active fault.
# An Energy-Efficient Mobile Sink-Based Unequal Clustering Mechanism for WSNs
## 1 Introduction
One important parameter in mobility-based WSNs is the velocity of mobile sinks. Recently, this parameter has been adjusted to reduce end-to-end packet delays [18] and to increase the packet delivery ratio. Adjusting the velocity of MSs in different layers of the network can solve the energy hole problem, which was previously not considered. The proposed algorithm in this paper is efficiently designed for circularly-symmetric WSNs since it is easy to abstract in a view of routing optimization and the energy consumption o can be easily controlled in this type of network [19]. In the first part of the proposed mechanism, the optimal cluster size interval is obtained. The lower bound interval is obtained according to the node density needed to achieve a coverage guarantee and the maximum value is calculated according to the energy consumption of sensor nodes before and after clustering to achieve energy efficiency. We use the two-stage Genetic Algorithm (GA) to find the upper bound interval and also to derive the exact value of the angle from the obtained interval. Thus, a coverage-guarantee and energy-efficiency-based unequal clustering technique is proposed is the first part of this paper. This is followed by the use of some mobile sinks to solve the energy hole problem. In the proposed method the velocity of mobile sinks is adjusted in different zones to balance the energy consumption ratio of CHs belonging to the different layers of the network. The remaining part of this paper is organized as follows: Section 1 develops the introduction. Section 2 provides a literature review of the research area. The system model is explained in Section 3. A mobility-based energy efficiency algorithm is introduced in Section 4. Numerical results and conclusions are finally given in Sections 5 and 6, respectively.
## 2 Eruption Forecasting Algorithm
### Node 1: Detection of Volcanic Unrest  
Anomalous geologic activity (e.g., volcanic unrest) is detected using the method described in [63]. Historic information collected by a variety of multidisciplinary remote and in-situ sensing techniques is used to identify activity that is markedly different (_i.e._, outliers) from past behavior. Discipline specific outlier thresholds are derived from  
\[Outlier\ Threshold=Q_{3}+c(Q_{3}-Q_{1}) \tag{15}\]  
where \(c\) is a constant, \(Q_{1}\) and \(Q_{3}\) are the first and third quartiles of the sample distribution, and \((Q_{3}-Q_{1})\) is the interquartile range [64]. Unique thresholds are derived empirically on a volcano-by-volcano and monitoring discipline basis using Equation 15, where \(c\) is set empirically for each monitoring discipline.  
The severity of the anomaly is estimated from the weighted summation of a collection of binary variables. The general form of the severity estimate is defined as  
\[\
u_{1}=\sum_{m=1}^{N}\beta_{m}X_{m} \tag{16}\]  
where \(\beta_{m}\) are weighting coefficients and \(X_{m}\) are binary variables that are toggled from 0 to 1 when measurements from their respective monitoring discipline exceeds its outlier detection threshold. Here the functional form of Equation 16 is expressed as  
\[\
u_{1}=0.25(X_{sr}+X_{df}+X_{lm}+X_{md}) \tag{17}\]  
where each binary variable is defined in Table 2. In this case, all variables are weighted equally (\(\beta_{m}\) = 0.25) to allow activation of the forecasting algorithm upon the detection of unrest by one or more monitoring techniques. Severity declarations are based on the number of simultaneous cross-disciplinary detections, where \(\
u_{1}\) values of 0.25, 0.50, 0.75, and 1.0 represent low, moderate, heightened, and extreme levels of unrest. This triggering mechanism differs from those described in previously published event tree implementations (e.g., [7]) through its use of outlier analysis for detection threshold estimation and the incorporation of source modeling information.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Explanatory Variable** & **Description** & **Value** \\ \hline \(X_{sr}\) & Seismicity Rate & 0/1 \\ \(X_{df}\) & Surface Deformation & 0/1 \\ \(X_{lm}\) & Large Magnitude & 0/1 \\ \(X_{md}\) & Model Indicates Intrusion & 0/1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Explanatory variable names, descriptions, and possible values for node 1.
# Fault Diagnosis of HV Cable Metal Sheath Grounding System Based on LSTM
## 3 Analytical Calculations of Sheath Currents under Fault Conditions
### Sheath Grounding Fault  
In the actual operation of the cable, overvoltage caused by various reasons can cause the breakdown of the protector in the link box and trigger a sheath grounding fault. In addition, natural factors such as environmental humidity, soil acid and alkali corrosion, as well as external causes such as termites and rats gnawing lead to outer sheath breakage,  
Figure 7: Equivalent circuit diagram of flooding in link box J1 fault.  
which can also cause sheath grounding faults. Take the example of a sheath ground fault occurring at cable joint JA1, the equivalent circuit diagram is shown in Figure 8.  
Cable joint JA1 occurs sheath ground fault, the capacitive current component flowing into both ends of the sheath also changes, taking A1 minor section as an example, the capacitive current component is calculated as shown in Equation (21).  
\[\left\{\begin{array}{l}I_{\text{CLA1}}=\frac{I_{\text{Z=RA1}}+R_{t}}{Z_{ \text{LA1}}+I_{\text{Z=RA1}}+R_{\text{E}}+R_{t}}I_{\text{CA1}}\\ I_{\text{CRA1}}=\frac{Z_{\text{ZA1}}+R_{\text{E}}}{Z_{\text{mLA1}}+Z_{\text{MRA1 }}+R_{\text{E}}+R_{t}}I_{\text{CA1}}\end{array}\right. \tag{21}\]  
According to Figure 8, the sheath loop L1 sheath induced current is calculated as shown in Equation (22).  
\[\left\{\begin{array}{l}I_{\text{XL1}}=\frac{U_{\text{Z=AI}}}{Z_{\text{mLA1}}+ R_{\text{E}}+R_{t}}\\ I_{\text{XR1}}=\frac{U_{\text{Z=BI}}+U_{\text{mc3}}}{Z_{\text{mLB2}}+Z_{\text{ mc3}}+R_{\text{E}}+R_{t}}\end{array}\right. \tag{22}\]  
Therefore, when a sheath ground fault occurs at cable joint JA1, the sheath current of the first end direct grounding box is shown in Equation (23).  
\[\left\{\begin{array}{l}I_{\text{sa1}}=I_{\text{CLA1}}-I_{\text{XL1}}\\ I_{\text{sa2}}=-I_{\text{I2}}-\left(I_{\text{CRB1}}+I_{\text{CRC2}}+I_{\text{ CRA3}}\right)+\left(I_{\text{CLB1}}+I_{\text{CLC2}}+I_{\text{CLA3}}\right)\\ I_{\text{sa3}}=-I_{\text{I3}}-\left(I_{\text{CRC1}}+I_{\text{CRA2}}+I_{\text{ CRB3}}\right)+\left(I_{\text{CLC1}}+I_{\text{CLA2}}+I_{\text{CLB3}}\right)\\ I_{\text{sa4}}=I_{\text{I2}}+\left(I_{\text{CRB1}}+I_{\text{CRC2}}+I_{\text{CRA3 }}\right)-\left(I_{\text{CLB1}}+I_{\text{CLC2}}+I_{\text{CLA3}}\right)\\ I_{\text{sa5}}=I_{\text{I3}}+\left(I_{\text{CRC1}}+I_{\text{CRA2}}+I_{\text{ CRB3}}\right)-\left(I_{\text{CLC1}}+I_{\text{CLA2}}+I_{\text{CLB3}}\right)\\ I_{\text{sa6}}=I_{\text{XR1}}+\left(I_{\text{CRB2}}+I_{\text{CRC3}}\right)-\left(I _{\text{CLB2}}+I_{\text{CLC3}}\right)\end{array}\right. \tag{23}\]
## 2 Materials and Methods  
### Study Subjects and Procedure  
A total of 107 patients who underwent MDCT and atrial fibrillation catheter ablation in the Rehabilitation Hospital Cluj-Napoca, Romania, were included in our retrospective study. The research was conducted according to the guidelines of the Declaration of Helsinki. There was no need for informed consent as our study was retrospective. MDCT scans were performed in our radiology department using a 32-detector General Electric system. All images were integrated with the CARTO 3 Biosense Webster system using the image integration tool and 3-dimensional images were obtained for each left atrium. The mitral annulus was automatically traced by the system after manually defining 6 to 8 points of the mitral plane, with circumference and area of the annulus automatically calculated by the software.  
The patients we excluded from this study were those with the following: abnormal LV systolic function or dilated LV, organic valvular disease, such as rheumatic mitral stenosis or regurgitation, MV prolapse, moderate or severe calcifications of the mitral valve, and patients who underwent surgery for mitral valvaluplplasty or replacement of the mitral valve.  
Transverse diameter of the mitral annulus was defined as the longest septal to lateral distance, perpendicular to the contact line between LVOT and mitral annulus. The longitudinal diameter of the mitral annulus was defined as the longest distance measured between intercommissural points, perpendicular to the transverse diameter (Figures 1 and 2). Supero-inferior diameter of the left atrium was defined as the distance between the point of intersection between longitudinal and transversal diameters of the mitral annulus and the highest left atrial point. Latero-lateral diameter of the left atrium was defined as the longest distance between the lateral and septal walls of the left atrium, parallel to the mitral annulus. AP diameter of the left atrium was defined as the distance between the anterior and posterior walls of the LA, perpendicular to the LL diameter.  
All catheter ablation procedures were guided by the 3D CARTO 3 Biosense Webster mapping system. Electrogram mapping at the level of the pulmonary vein (PV)'s sodium guided pulmonary vein isolation. To map the pulmonary veins and the LA, a PentaRay or lasso catheter was used, and RF ablation lesions were performed using an open-irrigated 7 French 3.5 mm ablation catheter (SmartTouch, Biosense Webster). The procedure ended with the dissociation of PV potentials (PVP) or the full disappearance of PVP. In the case of persistent AF, we used lines between the left and right PV, mitral isthmus line, cavotricuspid isthmus line, or vein of Marshall ethanol ablation following PV isolation. A second ablation procedure was performed in 3 of the 107 patients.
# Enabling Multi-Mission Interoperable UAS Using Data-Centric Communications
## 3 The Data Distribution Service Open Standard in UAS Environments
### Built-In (\"Application-Level\") QoS  
A specific, rich, and very articulated feature of DDS is the possibility of associating QoS capabilities and requirements to each publisher/subscriber and to each exchanged dataset, thus enabling to flexibly address many differentiated use cases and application-specific challenges in a completely interoperable way. In particular, DDS allows the negotiation of a large set of QoS characteristics among the data producers/consumers sharing one partition at the beginning of their interaction session. In short, QoS policies to be supported according to the standard specification and to be negotiated/set at session initiation include the middleware-automated management of the following:  
* real-time delivery (for example, DEADLINE forces a publisher to send data with a minimum frequency, RELIABILITY sets the possibility or not to send duplicated data to counteract data losses, while TIME_BASED_FILTER can reduce the traffic of generated samples);
* bandwidth (for example, LIVELINESS forces publishers to notify their availability when they do not generate data to be distributed and DEADLINE informs the DDS middleware about time delivery constraints);
* redundancy (for example, BY_RECEPTION_TIMESTAMP can result in two subscribers having data in different orders, differently from BY_SOURCE_TIMESTAMP);
* persistence (for example, DURABILITY = TRANSIENT or DURABILITY = PERSISTENT ).  
The interested readers can find an exhaustive description of the rich and articulated possibilities of DDS QoS negotiation described in [16; 20]. In particular, for constrained networks, such as the ones usually of UAS interest, the DDS QoS policy support can help in addressing the technical challenges associated with highly error-prone communication channels, variable bandwidth (varying noise levels generate the dynamic adjustment of waveforms and forward error correction techniques, with consequent effects on bandwidth), receive-only situations (e.g., emission control in defense applications where receivers do not wish to broadcast, usually to avoid location detection), etc.  
In addition, about adaptability, DDS QoS policies can be re-configured at runtime through re-negotiation and modification of the associated DDS session state; this can be specifically suitable for dynamically encountered lossy networks in open interoperable deployment environments, which is non-negligibly frequent when using/changing wireless connectivity options between UAVs and GCSs.  
Let us also rapidly note that additional QoS-related features are often available as proprietary extensions of DDS middleware implementations, such as data compression, optimization of DDS, or enhanced discovery to reduce the launch latency of distributed applications with specific startup constraints, as for example in the RTI DDS implementation [21], but they are less relevant here given the centrality of DDS named data for maximum interoperability of UAS sub-systems.
## 4 Experiment
### Evaluation Metrics  
The semantic quality of generated responses was evaluated using widely adopted text generation metrics. Bilingual evaluation understudy (BLEU) [27] measured n-gram overlap between generated and reference text, with BLEU-4 used for assessment. Recall-oriented understudy for gisting evaluation (ROUGE) [28] evaluated recall-based text similarity, applying the longest common subsequence measure. Bidirectional Encoder Representations from Transformers Score (BERTScore) [29] calculated semantic similarity using contextual embeddings from a pretrained transformer model, with F1 scores reported.  
For SER performance evaluation, the weighted F1 score was used to account for class imbalance in emotion distribution. ASR performance was assessed using word error rate (WER) and character error rate (CER).  
To evaluate response emotion diversity and consistency with input emotion, the output emotion distribution given an input emotion was computed for the StyleTalk, DailyTalk, and EmoSC datasets. This analysis enabled comparison of the quality and diversity of emotional responses across different datasets.
## 3 Analyzing the Voting Process
\tag{16}\]  
Finally, we compute the trace distance between the basis kets \(|0\rangle\), \(|1\rangle\) and the pure state \(|\psi\rangle\),  
\[d_{\text{tr}}(\rho_{0},\rho_{\psi})=\frac{1}{2}\operatorname{tr}\big{|}\rho_ {0}-\rho_{\psi}\big{|}\stackrel{{\eqref{eq:U_0}}}{{=}}|b|, \tag{17}\]  
and  
\[d_{\text{tr}}(\rho_{1},\rho_{\psi})=\frac{1}{2}\operatorname{tr}\big{|}\rho_ {1}-\rho_{\psi}\big{|}\stackrel{{\eqref{eq:U_0}}}{{=}}|a|. \tag{18}\]  
A typical way to represent any pure state \(|\psi\rangle\) as a point on the Bloch sphere is to write:  
\[|\psi\rangle=\cos\frac{\theta}{2}\left|0\right\rangle+e^{i\varphi}\sin\frac{ \theta}{2}\left|1\right\rangle,\;0\leq\theta\leq\pi,0\leq\varphi\leq 2\pi. \tag{19}\]  
In the above representation (19), angles \(\theta\) and \(\varphi\) are the elevation and azimuth angles respectively of the point \(|\psi\rangle\) of the Bloch sphere. In view of (19), (17) and (18) become:  
\[d_{\text{tr}}(\rho_{0},\rho_{\psi})=\sin\frac{\theta}{2} \tag{20}\]  
\[d_{\text{tr}}(\rho_{1},\rho_{\psi})=\cos\frac{\theta}{2}. \tag{21}\]  
If \(\rho_{\psi_{j}^{A}}\) is the density matrix corresponding to the vote \(|\psi_{j}^{A}\rangle=\cos\frac{\theta_{j}^{A}}{2}\left|0\right\rangle+e^{i \varphi_{j}^{A}}\sin\frac{\theta_{j}^{A}}{2}\left|1\right\rangle\) of representative \(j,1\leq j\leq n_{A}\), of [PERSON]'s party, then, according to (20), the requirement that \(|\psi_{j}^{A}\rangle\) lies inside the _free will_ sphere, with center \(|0\rangle\) and radius \(r_{A}\), is captured by the next constraint  
\[d_{\text{tr}}(\rho_{0},\rho_{\psi_{j}^{A}})=\sin\frac{\theta_{j}^{A}}{2}\leq r _{A}\Rightarrow\theta_{j}^{A}\leq 2\arcsin r_{A},\;0\leq\theta_{j}^{A} \leq\pi. \tag{22}\]  
Symmetrically, if \(\rho_{\psi_{k}^{B}}\) is the density matrix corresponding to the vote \(|\psi_{k}^{B}\rangle=\cos\frac{\theta_{k}^{B}}{2}\left|0\right\rangle+e^{i \varphi_{k}^{B}}\sin\frac{\theta_{k}^{B}}{2}\left|1\right\rangle\) of representative \(k,1\leq j\leq n_{B}\), of [PERSON]'s party, then, according to (21), the requirement that \(|\psi_{k}^{B}\rangle\) lies inside the _free will_ sphere, with center \(|1\rangle\) and radius \(r_{B}\), is equivalent to  
\[d_{\text{tr}}(\rho_{1},\rho_{\psi_{k}^{B}})=\cos\frac{\theta_{k}^{B}}{2}\leq r _{B}\Rightarrow\theta_{k}^{B}\leq 2\arccos r_{B},\;0\leq\theta_{k}^{B}\leq\pi. \tag{23}\]  
Thus, every legislator from [PERSON]'s party has to adhere to the party rules and vote so that requirement (22) is satisfied. Analogously, every representative from [PERSON]'s party must vote in such a way that (23) is fulfilled. Independent legislator, when they exist, have complete freedom to do as they deem fit, and their vote is abstracted by the most general Formula (19). This situation is summarized in Table 1.
# Dynamic Optimization of Buckling Problems for Panel Structures with Stiffening Characteristics
## 3 Optimization of the Panel with Ribs and Frames
### Method of the MMCs
#### 3.2.2 Sensitivity Analysis  
The sensitivity of the objective function to design variables can be expressed as  
\[\frac{\partial\frac{1}{\omega_{1}}}{\alpha}=-\frac{1}{\omega_{1}2}\frac{\partial \omega_{1}}{\partial\alpha} \tag{23}\]  
Substitute \(\lambda_{j}=\omega_{j}^{2}\) into the first constraint function [25].  
\[\mathbf{K\phi}_{j}-\omega_{j}^{2}\mathbf{M\phi}_{j}=0 \tag{24}\]  
Derive the above equation from the design variables.  
\[\left(\frac{\partial\mathbf{K}}{\partial\alpha}-2\omega_{j}\frac{\partial\omega_{ 1}}{\partial\alpha}\mathbf{M}-\omega_{j}^{2}\frac{\partial\mathbf{M}}{\partial\alpha} \right)\mathbf{\phi}_{j}+\left(\mathbf{K}-\omega_{j}^{2}\mathbf{M}\right)\frac{\partial \mathbf{\phi}_{j}}{\partial\alpha}=0 \tag{25}\]  
Normalize the eigenvector with respect to \(\mathbf{M}\).  
\[\mathbf{\phi}_{j}^{T}\mathbf{M\phi}_{k}=\delta_{jk}=\begin{cases}0,&j\
eq k\\ 1,&j=k\end{cases} \tag{26}\]  
The left side of equation (26) multiplies Equation (26) by \(\mathbf{\phi}_{j}^{T}\).  
\[\frac{\partial\omega_{1}}{\partial\alpha}=\frac{1}{2\omega_{1}}\bigg{(}\mathbf{ \phi}_{j}^{T}\frac{\partial\mathbf{K}}{\partial\alpha}\mathbf{\phi}_{j}-\omega_{j}^{2 }\mathbf{\phi}_{j}^{T}\frac{\partial\mathbf{M}}{\partial\alpha}\mathbf{\phi}_{j}\bigg{)} \tag{27}\]  
In the above equation,  
\[\frac{\partial\mathbf{K}}{\partial\alpha}=\prod_{j=1}^{NE}\frac{\partial\mathbf{K}^{ \epsilon}}{\partial\alpha_{k}}=\frac{E}{n}\prod_{j=1}^{NE}\left(\sum_{i=1}^{n }q\left(H\left(\mathcal{K}_{i}^{j}\right)\right)^{q-1}\frac{\partial H( \mathcal{K}_{i}^{\epsilon})}{\partial\alpha}\right)\mathbf{K}^{\epsilon} \tag{28}\]  
\[\frac{\partial\mathbf{M}}{\partial\alpha}=\frac{\rho}{n}\prod_{j=1}^{NE}\left( \sum_{i=1}^{NE}\frac{\partial\mathbf{H}(\mathcal{K}_{i}^{\epsilon})}{\partial \alpha}\right)\mathbf{M}^{\epsilon} \tag{29}\]  
Here, \(E\) is the elastic modulus of the solid material; \(\rho\) is the density of the solid material; \(NE\) is the total number of design domain elements; and \(n\) is the number of element nodes. \(q\) is a constant, usually taken as \(q=2\). \(\mathbf{K}^{\epsilon}\) represents the element stiffness matrix corresponding to the elastic modulus \(E=1\) of the element. \(\mathbf{M}^{\epsilon}\) represents the element mass matrix corresponding to the density \(\rho=1\) of the element.  
Figure 11: The 2 nd-order variable-thickness component.  
\(H(x)\) represents the regularized Heaviside function:  
\[H(x)=\begin{cases}1&if\ x>\varepsilon\\ \frac{3(1-\tau)}{4}\left(\frac{\tau}{\varepsilon}-\frac{x^{3}}{3\varepsilon^{3} }\right)+\frac{(1+\tau)}{2}&if\ -\varepsilon\leq x\leq\varepsilon\\ \tau&otherwise\end{cases} \tag{30}\]  
In this equation, \(\varepsilon\) controls the size of regularization, and \(\tau\) is a parameter that controls the background mesh material. Usually, \(\tau=10^{-3}\) is selected.
# Design of an Autonomous Cooperative Drone Swarm for Inspections of Safety Critical Infrastructure
## 7 Algorithms and Protocols
### Swarm Membership Management  
Collected inspection and telemetry data must be securely communicated between the drones and the cloud service systems. To prevent adversaries from overhearing this potentially sensitive data, all communication remains encrypted. To facilitate this, the drones self-organize in groups in which a single drone acts as a _controller_, acting both as an interface for drones to manage their membership of the group and as a router for all traffic to flow through. The controller is also responsible for routing information between different groups, allowing the entire swarm to segment itself into multiple groups [52]. Our proposed protocol introduces different cryptographic key types, each of which provides a specific functionality:  
* Root key, a symmetric key that is known to each drone and distributed out-of-band before the mission execution. This key acts as proof that a drone is allowed to join a swarm.
* Group key, a symmetric key unique to a swarm is used for broadcasting messages throughout the swarm. This key is updated whenever the composition of the swarm changes, thereby proving forward and backward secrecy [53].
* Member-specific asymmetric key pairs, used by pairs of drones to prevent spoofing attacks or to send unicast messages between them.  
Figure 13 shows a state diagram in which drones change their states by exchanging messages with other drones in a swarm. Initially, each drone is _unjoined_ and isolated from other drones. Any drone in possession of the root key can call the controller by advertising its group through broadcast messages. To join a swarm, a candidate member initiates a joining process, in which it presents the root key and its public key to identify itself to the controller of the group and obtains the swarm's group key in return. When a new drone is joining, the swarm's controller renews the group key and redistributes it to its members. Similarly, a drone can announce its departure from the group, which also triggers a group key update. Member drones can miss these group key updates (e.g., through packet loss), which would make them unable to understand future communication. The protocol allows  
Figure 12: Illustration of AI algorithm development for fault detection. Real-time onboard (OB) inspection provides fault extraction from classified objects. Detected faults are communicated to the Cloud Services to be stored and analyzed by the mission operation function.  
these members to _synchronize_ with the controller, obtaining the most recent group key. The protocol handles general node failures by removing members from the group after a timeout when no messages were received from a member for a given time window.