esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Governing Equations and Boundary Conditions
\begin{cases}m_{y}(x,t)=\rho ly\frac{\partial^{2}\theta_{z}(x,t)}{\partial t^{ 2}}-Ely\frac{\partial^{2}\theta_{z}(x,t)}{\partial x^{2}}-Ely\frac{\partial^ {2}\theta_{y}(x,t)}{\partial x^{2}}+EAz_{c}\frac{\partial^{2}u_{x}(x,t)}{ \partial x^{2}}\\ \qquad\qquad\qquad\qquad\qquad\qquad\qquad-\mu z_{c}\frac{\partial^{2}u_{x}( x,t)}{\partial t^{2}}+\rho lzy\frac{\partial^{2}\theta_{y}(x,t)}{\partial t^{2}}-k_{z}AG \left(\frac{\partial u_{z}(x,t)}{\partial x}-\theta_{z}(x,t)\right)\\ \qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad-K_{z}\frac{\partial^{2}\psi (x,t)}{\partial x^{2}}+c_{3y}\frac{\partial\theta_{z}(x,t)}{\partial t}+c_{4} \left(-z_{c}\frac{\partial u_{x}(x,t)}{\partial t}+\frac{\partial\theta_{y}(x, t)}{\partial t}\right),\\ m_{z}(x,t)=\rho Iz\frac{\partial^{2}\theta_{y}(x,t)}{\partial t^{2}}-EI_{z} \frac{\partial^{2}\theta_{y}(x,t)}{\partial x^{2}}-EI_{y}\frac{\partial^{2} \theta_{z}(x,t)}{\partial x^{2}}+EAy_{c}\frac{\partial^{2}u_{x}(x,t)}{ \partial x^{2}}\\ \qquad\qquad\qquad\qquad\qquad\qquad\qquad-\mu y_{c}\frac{\partial^{2}u_{x}( x,t)}{\partial t^{2}}-\rho lzy\frac{\partial^{2}\theta_{z}(x,t)}{\partial t^{2}}-k_{y}AG \left(\frac{\partial u_{y}(x,t)}{\partial x}-\theta_{y}(x,t)\right)\\ \qquad\qquad\qquad\qquad\qquad\qquad\qquad-K_{y}\frac{\partial^{2}\psi(x,t)}{ \partial x^{2}}+c_{3z}\frac{\partial\theta_{y}(x,t)}{\partial t}-c_{4}\left(y_ {c}\frac{\partial u_{x}(x,t)}{\partial t}+\frac{\partial\theta_{z}(x,t)}{ \partial t}\right),\\ m(x,t)=GJ\frac{\partial^{2}\psi(x,t)}{\partial x^{2}}+\rho I\frac{\partial^{4} \psi(x,t)}{\partial x^{2}\partial t^{2}}-P\left(\frac{l_{s}}{\mu}\frac{ \partial^{2}\psi(x,t)}{\partial x^{2}}-z_{c}\frac{\partial^{2}u_{y}(x,t)}{ \partial x^{2}}+y_{c}\frac{\partial^{2}u_{z}(x,t)}{\partial x^{2}}\right)\\ \qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad-\mu y_{c}\frac{\partial^{2}u_{ z}(x,t)}{\partial t^{2}}+\mu z_{c}\frac{\partial^{2}u_{y}(x,t)}{\partial t^{2}}-l_{s} \frac{\partial^{2}\psi(x,t)}{\partial t^{2}}-EI\frac{\partial^{4}\psi(x,t)}{ \partial x^{4}}\\ \qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad+K_{y}\frac{\partial^{2} \theta_{y}(x,t)}{\partial x^{2}}+K_{z}\frac{\partial^{2}\theta_{z}(x,t)}{ \partial x^{2}}+c_{5}\frac{\partial^{3}\psi(x,t)}{\partial x^{2}\partial t}-c_ {2}\frac{\partial\psi(x,t)}{\partial t}\\ \qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad-c_{1y}y_{c} \frac{\partial u_{z}(x,t)}{\partial t}+c_{1z}z_{c}\frac{\partial u_{y}(x,t)}{ \partial t}.\end{cases}  \tag{20}\]  
where all the variables and symbols are defined in the _Abbreviations_. It can be seen that the various types of coupled vibration equations for beams proposed by previous researchers are all special cases of this set of equations, and under different conditions, they can degenerate into the existing coupled vibration equations of different beams [2; 3; 4; 5; 6; 7; 8; 9; 10].
# Open-Set Recognition of Pansori Rhythm Patterns Based on Audio Segmentation
## 3 Methodology
### Loss Function
#### 3.3.3 Supervised Contrastive Learning  
The supervised contrastive learning approach, as proposed by Ref. [43], enhances the self-supervised contrastive learning method by leveraging fully labeled datasets to learn representations. In the self-supervised methodology, positive samples are generated by creating augmented versions of an anchor I. This technique typically falls short in effectively exploring correlations among anchors that belong to the same category. For instance, consider two different dog images: one of a husky, which is anchor I, and anchor \(I^{\prime}\) is a corgi. In the self-supervised context, the model recognizes that the husky image is similar to its augmented counterpart (e.g., a flipped image of the husky) but fails to identify the intrinsic similarity between the husky and the corgi, despite both belonging to the dog category.  
The supervised contrastive learning model addresses this limitation by ensuring that embeddings of samples within the same category (e.g., dog and husky/corgi) are positioned as closely as possible in the feature space, while maintaining clear distinctions from samples of different categories (e.g., cat and Persian). This method ensures that while the husky and the corgi share visual similarities that categorize them both as dogs, they are distinctly different from visually dissimilar categories, such as cats, as exemplified by a Persian cat. This concept is visually represented in Figure 3.  
The contrastive loss for supervised learning is defined as follows:  
\[L_{cont}^{sup}=-\frac{1}{|P_{1}|}\sum_{x^{1+}\in P_{1}}log\frac{\exp\Bigl{(} sim\Bigl{(}\left(f^{e}\big{(}x^{1}\big{)},f^{e}\big{(}x^{1+}\big{)}\right) \Bigr{)}/\tau\Bigr{)}}{\exp\Bigl{(}sim\Big{(}fin\Bigl{(}f^{e}(x^{1}),f^{e}(x^{1 +}\Bigr{)}\Bigr{)}/\tau\Bigr{)}+\sum_{x^{1-}\in N_{1}}\ \exp\Bigl{(}sim\Big{(}\left(f^{e}(x^{1}),f^{e}(x^{1-}\right)\Bigr{)}\Bigr{)}/ \tau\Bigr{)}} \tag{5}\]  
The similarity function in Equation (5) is the cosine similarity: \(sim(m,n)\triangleq\frac{m^{n}n}{\|m\|\|n\|}\). \(P_{1}\) and \(N_{1}\) denote the sets of the positive and negative samples of anchor data (first data), \(x^{1}\), in a batch, respectively; and \(|P_{1}|\) is the cardinality of the positive set. The positive samples are selected from the whole data in a batch if the label of the sample is the same as the positive samples, and the negative samples are randomly taken from the same population based on the labels of \(x^{1}\) in a batch. Note that the contrast behavior is effective over any \(\hat{N}\)-dimensional data at any time instant, except the anchor in a batch. Minimizing the contrastive loss tries to pull the positive examples and push the negative examples in the feature space to make compact clusters for classes. The center and contrastive losses are helpful in the open-set recognition to allow a large room for unknown classes, lowering the chance of them being recognized as a known class.
## 5 Dynamic Response Characteristics and Safety Analysis
### Pipeline Dynamic Responses Induced by the Blasting of Pilot Tunnels  
Figure 12 shows the pipeline dynamic responses induced by blasting the 1 st, 2 nd, 3 rd, and 4 th pilot tunnels, respectively. As shown in Figure 8, the pipelines are directly above the blasting area. The vertical vibration velocities were larger than the horizontal ones. So, we chose the vertical vibration velocity to analyze the dynamic responses. The blasting source was at \(L=3\) m. The relative distance \(S\) is the distance from the monitored points along the pipelines to the blasting source.  
As shown in Figure 12, the maximum peak velocities decreased with the increase in the relative distance \(S\). When the 1 st pilot tunnel was blasted, the peak velocities of the pipelines were: \(V_{\text{max}}\)1\(>\)\(V_{\text{max}}\)2\(>\)\(V_{\text{max}}\)3\(>\)\(V_{\text{max}}\)4 (the superscript is the pipeline number). The dynamic response of pipeline 1 decreased remarkably, whereas that of pipeline 2334 did not. The distance between the 1 st pilot tunnel and other pipelines is so far that the effect  
Figure 12: Dynamic responses of existing pipelines induced by blasting the pilot tunnels: (**a**) 1 st pilot tunnel; (**b**) 2 nd pilot tunnel; (**c**) 3 rd pilot tunnel; (**d**) 4 th pilot tunnel.  
of blasting on these pipelines was insensitive. When the 2 nd pilot tunnel was blasted, the peak velocities were: \(V_{\text{max}}\)4 \(>V_{\text{max}}\)3 \(>V_{\text{max}}\)1 \(>V_{\text{max}}\)2. The dynamic response of pipeline 1 changed little because it is far away from the 2 nd pilot tunnel. When the 3 rd pilot tunnel was blasted, the peak velocities were: \(V_{\text{max}}\)2 \(>V_{\text{max}}\)1 \(>V_{\text{max}}\)3 \(>V_{\text{max}}\)4. The changes in the dynamic responses of the pipelines were obvious. Notably, the peak velocity of pipeline 1 was much larger than that of pipeline 3, but the pipeline 3 is closer to the blasting source rather than the pipeline 1. It did not conform to the abovementioned characteristics. The reason is that the hollow effect amplifies the pipeline vibration [39]. The 1 st pilot tunnel below the pipeline 1 had been excavated when the 3 rd pilot tunnel blasted. When the 4 th pilot tunnel was blasted, the peak velocities were: \(V_{\text{max}}\)2 \(>V_{\text{max}}\)3 \(>V_{\text{max}}\)4 \(>V_{\text{max}}\)1. The peak velocity of pipeline 2 was much larger than that of pipeline 3. The hollow effect of the 3 rd pilot tunnel below the pipeline 2 also amplified the pipeline vibration.
# Real-Time Fall Monitoring for Seniors via YOLO and Voice Interaction
## 3 Materials and Methods
### Pose Estimation and Fall Detection Algorithm  
This section presents the complete methodology for real-time human pose estimation and fall detection using the YOLOv11-Pose model and a set of rule-based computations. The system processes video feeds from two cameras to improve detection accuracy and spatial coverage, making it suitable for indoor elderly monitoring scenarios. The system achieves fall detection by analyzing spatial relationships between anatomical keypoints in each frame.
## 2 Related Works
### The Topology Control Algorithms of Underwater WSNs  
The topology control algorithms of underwater WSNs are not as extensively investigated as those of terrestrial WSNs. In [22], considering the signal irregularity phenomenon can affect network performance, especially in underwater environments, the authors constructed an authentic signal irregularity model which can easily be degenerated into a variety of special cases. Based on this model, three representative topology control objectives are concluded in this work. In [23], two topology control algorithms are proposed for underwater WSNs: improved Distributed Topology Control (iDTC) and Power Adjustment Distributed Topology Control (PADTC). These two algorithms can increase network throughput while conserving energy at the same time. The algorithms guarantee the delivery of data by dealing with the communication void problem in geographic opportunistic routing. In [24], the authors investigate scale-free underwater WSNs. The algorithm begins with a scale-free network model for calculating the edge probability, which is used to generate an initial topology randomly.  
Subsequently, a topology control strategy based on complex network theory is put forward to construct a double clustering structure, where there are two kinds of cluster-heads to ensure connectivity and coverage. Considering that using the Global Positioning System (GPS) may not be feasible in adverse underwater environments and the anchored sensor nodes towed by wires are prone to offset around their static positions which causes each node to move within a spherical crown surface, in [25], the authors proposes a mobility model for underwater WSNs and three representative topology control objectives are attained. Based on these objectives, the authors design a distributed radius determination algorithm for the mobility-based topology control problem. Due to the fact the coverage requirements in different regions are probably different in underwater environments, in [26], the authors proposed two algorithms for different coverage problems in underwater WSNs: a Traversal Algorithm for Different Coverage (TADC) and a Radius Increment Algorithm for Different Coverage (RIADC). The TADC adjusts the sensing radii at each round and the RIADC sets the sensing radii of nodes incrementally at each round. In [27], the authors illuminated network topology modeling from a routing viewpoint. The probabilistic multipath routing behavior which is driven by opportunistic routing protocols in underwater WSNs are modeled in this paper. Based on these models, the authors proposed the PCen centrality metric to measure the importance of underwater sensor nodes to the data transmission through opportunistic routing, which is aimed at identifying critical nodes that can be used to guide topology control solutions.
# Positioning Systems for Unmanned Underwater Vehicles: A Comprehensive Review
## 1 Introduction
### Positioning Systems of UUVs  
Positioning systems in Unmanned Underwater Vehicles (UUVs) are crucial for accurately determining and maintaining the vehicle's location and trajectory underwater. Given the challenges of operating in the underwater environment, these systems use a combination of technologies to ensure precise navigation and positioning [4; 5; 13; 40; 41; 42; 43].
# Outlier Detection and Prediction in Evolving Communities
## 6 Predicting Outlying Behavior of Nodes  
In this section, we present the results of predictive techniques for nodes' future behavior based on their outlier score. We model the problem as a classification problem,  
Figure 4: Outlier scores of community members: StackExchange (**left**) and MovieLens (**right**).  
Figure 5: Outlier scores of nodes not participating in any community: StackExchange (**left**) and MovieLens (**right**).  
with each node labeled as either an outlier or an inlier. Our goal is to study the performance of the outlier score as a predictive feature for this classification problem. To this end, we compared its behavior against other commonly used node features based on nodes' structural characteristics. In particular, we utilized the node degree, betweenness centrality, and PageRank to experimentally verify that using our proposed outlier score provides a better understanding of a node's outlying behavior and its possible outcome in later time slices.
## 2 Materials and Methods
### Finite Element Method Simulations  
Abaqus Explicit was used for wave propagation simulations [39], referred to as FEM modeling. Two models were considered: the first was a 2-D plane strain model to investigate \(A_{0}\) mode interaction with simple-shaped defects, using a plate meshed with four-node plane strain elements (CPE4R) with reduced integration and hourglass control, each node having two degrees of freedom. The second configuration was a 3-D plate meshed with eight-node brick elements (C3D8R), with three degrees of freedom at each node. An element size of 1 mm was used which provided a converged solution for elastic wave propagation. Smooth defects were modeled by deforming the plate mesh surface, while sharp-edged defects were simulated by removing mesh elements. Absorbing boundary conditions at the edges of the plate models were used to reduce the size of the simulation to the region of interest only [40]. The \(A_{0}\) mode was excited by applying an out-of-plane force similar to that in FDM simulations to a node on the plate surface, and vertical displacements were measured at the monitoring nodes. The time step increment of 0.01 us was established as sufficient for accurate modeling.
# Sigfox and LoRaWAN Datasets for Fingerprint Localization in Large Urban and Rural Areas
## 2 Low Power Wide Area Networks  
In recent years, numerous operators have been investing in the IoT by rolling out nation-wide LPWAN networks such as Sigfox [9], LoRaWAN [10] and Narrowband-IoT (NB-IoT) [11]. This section discusses these technologies and their constraints. Although our current datasets only include Sigfox and LoRaWAN data, this section also describes the NB-IoT standard to provide the reader with a complete overview of the state of the art. In the near future, we will conduct measurement campaigns to create NB-IoT datasets as well. Table 1 displays a summary of the aforementioned LPWAN standards.
# A GPS Phase-Locked Loop Performance Metric Based on the Phase Discriminator Output
## 10 Conclusions  
In this work, we derived an augmented GPS phase-lock loop (PLL) linear model which includes the effect of coherent averaging. It was shown that the augmented model allows for accurate calculation of tracking error standard deviation in the presence of additive white Gaussian noise (AWGN) and clock phase-noise, given an accurate input clock phase-noise PSD model.  
The standard deviation of tracking error was shown to be a useful PLL performance/design metric. Using both PLL simulation and experiment, a threshold of 45 degrees on the tracking error standard deviation was shown to be a more reliable/robust indicator of PLL performance under interference conditions than the widely used phase jitter metric. In addition, the augmented linear model is shown to be valid up until this threshold, which facilitates efficient performance prediction, without the need for time-consuming direct PLL simulation.  
This new performance metric can be used to evaluate the performance of stationary GPS receivers that are vulnerable to wideband radio frequency interference (RFI). The method is directly applicable to ground-based reference receivers for differential GPS systems such as the Ground Based and Space Based Augmentation Systems (GBAS and SBAS), as well as other ground-based receivers that require high continuity of service. It is also relevant to roving receivers, if the additional dynamic stresses on the PLL are also taken into account.  
Recommendations based on the aforementioned results would be to maximize coherent averaging time, which has the most significant effect in reducing the PLL tracking error standard deviation. This should be the priority when the goal is maintaining lock (i.e., preventing cycle slips). Using an oscillator with low phase-noise is also important because it will allow the receiver to handle more AWGN. In addition, it will allow the PLL bandwidth to the tightened, improving PLL stability and carrier measurement quality.  
We would like to thank our sponsors at the Federal Aviation Administration (FAA) for supporting this research. The views and opinions expressed in this paper are those of the authors and do not necessarily reflect those of any other organization or person.  
[PERSON] and [PERSON] conceived and derived the theoretical material of the paper. [PERSON] performed the experiments and simulations; [PERSON] and [PERSON] analyzed the results. [PERSON] wrote the initial version of the paper which was further reviewed and edited by [PERSON].  
The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
# Quality of Experience (QoE)-Aware Fast Coding Unit Size Selection for HEVC Intra-Prediction
## 5 Experimental Results and Discussion  
The following section presents the experimental results of the proposed fast CU size selection and encoding algorithm for HEVC video encoding. The proposed algorithm is implemented in HM16.1 [15], and the optimised SVM library _libSVM_[46] is used to implement the SVM models described in Section 4. The RD and encoding time performance of the proposed algorithm are compared with several state-of-the-art algorithms in the literature. These include HM16.1 [15], SVM-based CU size selection algorithm proposed by [PERSON] et al. [10], and fast CU selection algorithms proposed by [PERSON] et al. [34].
# Protection of Digital Elevation Model--One Approach
## 2 Materials and Methods
### Security Technologies Applied in the Model  
The model is based on cryptographic technologies and PKI [40]. Encryption is the process of changing data so that the content is unreadable to anyone other than the entity performing the encryption. We used the following cryptographic technologies: symmetric and asymmetric cryptography. The model uses a combination of symmetric and asymmetric cryptography to provide encryption and signing of messages, as well as generation and encryption of the secret key.  
PKI is a complex structure that includes a hardware-software-application platform and is completed with rules to manage public keys and digital certificates. The PKI uses the principles of asymmetric and symmetric cryptography to establish a secure data exchange between end entities (users, devices, software) by ensuring the authenticity, confidentiality, integrity, and non-repudiation of transactions. The PKI provides a framework that allows the effective implementation of digital certificates and digital signatures in systems with my users.  
PKI gives an electronic identity to resources so that they can reliably represent themselves to other resources, communicate securely, and be responsible for transaction performing. Military applications are increasingly using PKI to guarantee high data security.  
A digital certificate represents the electronic identity of an entity by establishing a connection between the entity and the public key. The entity's private key corresponds to the public key. The electronic identity is established by the certification authority, which establishes the entity-public key connection by digitally signing the entity's certificate with its private key.
# Innovative Approaches for Minimizing Disinfection
## 3 DBPs
### Health Effects of DBPs
Moreover, a hypothesis has been formulated based on mechanistic studies conducted in vitro, in rodents and humans, seeking to elucidate how drinking water could contribute to cancer risk in a specific population subset with adequate exposure and a predisposing genotype. Further investigations, ranging from fundamental research to molecular epidemiology, are crucial to validate this hypothesis and explore additional models that could shed light on cancers associated with drinking water exposure. In addition to volatile THMs, the discovery of HAAs represented another milestone in the 1980s, identifying a second class of DBPs with low volatility. Dichloroacetic acid and trichloroacetic acid, commonly found HAAs, exhibit elevated concentrations in chlorinated water. The adverse health effects of THMs and HAAs prompted regulatory agencies worldwide to establish guidelines regulating their presence in treated drinking water [17]. Subsequently, attention shifted from aliphatic THMs and HAAs to aromatic DBPs. In 1987, the heterocyclic compound 3-chloro-4-(dichloromethyl)-5-hydroxy-2(5H)-furanone was the first aromatic DBP to be identified. Typically, heterocyclic aromatic DBPs like halopyroles and halopyridines have a lower stability in treated drinking water due to their heteroatomic rings' reactivity, rendering them less stable. In contrast, aromatic DBPs with phenyl structures demonstrate excellent stability. Consequently, identifying phenyl DBPs has become a prominent area of research due to their relatively high toxicity [18]. NAs, likely human carcinogens, pose a significant risk to the safety of drinking water consumers [19]. DCAN has been detected numerous times during the disinfection process, drawing considerable attention. Dissolved organic nitrogen (DON) serves as the primary precursor substance for N-DBPs, and the effective control of N-DBP sources predominantly involves the removal of DON. Relative to natural organic substances, DON typically manifests characteristics such as low molecular weight and low electrostatic charge [15].