esa-sceva/satcom-chunk-collection huggingface dataset:
### Motivation, Objectives, and Contributions  
The remarkable recent developments in IoT technology have provided the opportunity to broadly deploy tiny sensors for wireless communication for various applications such as smart homes, smart cities, smart healthcare, and the smart industry [13]. The motivation behind this contribution is that in critical situations, IoT devices can effectively deliver highly secure information, less power consumption, and low-cost solutions to the wireless network. Given these exciting prospects for IoT, several technical challenges need to be tackled and used effectively for each specific IoT application [14]. The principal contributions in this paper are:  
* To clarify IoT vision and definitions and to provide a comprehensive overview of IoT features
* To elaborate on the various IoT architectures, and protocols that result from the integration of IoT with different technologies
* Designing a technical taxonomy to classify the various IoT applications
* To provide a discussion about open IoT issues and challenges in IoT applications.  
The paper is structured as follows: Section 2 provides the required background and related work. Section 3 details IoT architecture such as electronic tools, cameras, actuators, radio frequency identification devices ZigBee, Bluetooth, CC, networking technologies, near-field communication, etc. IoT applications are discussed in Section 4. Section 5 describes recent IoT protocols like MQTT, CoAP, RPL, etc. Section 6 discusses the key challenges of IoT, and Section 7 describes future directions. Finally, the article is concluded in Section 8. Moreover, the outline of the paper is illustrated as shown in Figure 2.  
Figure 2: Outline of the paper.
## 2 Related Work  
Speech disfluency detection models are generally based on the concept of speech recognition systems, which involve extracting speech features and classifying speech samples as fluent or disfluent. Speech disfluency detection models typically have three stages: preprocessing, speech feature extraction, and feature classification [7]. The preprocessing stage involves preprocessing the raw speech signal to prepare for subsequent feature extraction and classification. The speech feature extraction stage involves converting the preprocessed speech signal into a set of feature vectors for subsequent classification. Speech signals are time-domain signals that can be analyzed in the time domain and frequency domain to extract features. Feature classification involves classifying the extracted speech feature vectors into two categories: fluent and disfluent.  
For speech disfluency detection, the focus is mainly on the extraction of disfluent features and the classification of disfluent speech, which involves several main categories of disfluent features, including repetitions, prolongations, interjections, and blocks. Different feature extraction and classification techniques for speech disfluency detection have been introduced in the literature [1; 8; 9; 10; 11], and the accuracy of different methods has been compared and analyzed. Many of these studies were conducted on non-public datasets or public datasets with non-public annotations [12; 13]. There are now two publicly available datasets with public annotations: the stuttering events in podcasts SEP-28k dataset [14] and the KSoF dataset [15]. In the following sections, we will provide a detailed introduction to the related work on feature extraction and disfluent speech classification tasks.
# Parallel Direct Solution of Flexible Multibody Systems Based on Block Gaussian Elimination
## 2 Flexible Multibody Formulation with the Craig-Bampton Method
### Time Integration Procedures of Flexible Multibody Systems
#### 2.1.3 Equation Linearization and Assembly
^{(n,j)}, \tag{14}\]  
wherein the Jacobian matrix and the right-hand terms are calculated at variables \(\xi^{(n,j)}\) and \(\lambda^{(n,j)}\). The terms related to the external forces \(\mathbf{\tau}\) are enclosed with parenthesis. After \(\Delta\xi^{(n,j)}\) and \(\Delta\lambda^{(n,j)}\) are solved by a linear solver, the iteration proceeds from \(j\) to \(j+1\) as  
\[\xi^{(n,j+1)}=\xi^{(n,j)}+\Delta\xi^{(n,j)}, \tag{15}\]  
until the following convergence errors are satisfied:  
\[\left|\left|\Delta\xi^{(n,j)}\right|\right|\leq e_{\xi^{\prime}},\left| \left|\Delta\lambda^{(n,j)}\right|\right|\leq e_{\lambda^{\prime}}\left| \left|\mathbf{b}^{(n,j)}\right|\right|\leq e_{\eta},\left|\left|\mathbf{c}^{(n,j)} \right|\right|\leq e_{\zeta}, \tag{16}\]  
wherein \(||\square||\) represents an arbitrary given norm and \(e_{\xi^{\prime}},e_{\lambda^{\prime}},e_{b},\) and \(e_{c}\) are the corresponding error thresholds for displacement variables, constraint forces, body equation residuals, and constraint equation residuals, respectively. Let the system consist of \(m\) bodies and \(k\) constraints. Then, the system vector of body DOFs is \(\xi=[\xi_{1};\cdots;\xi_{m}]\), and the system vector of constraint multipliers is \(\lambda=[\lambda_{1};\cdots;\lambda_{k}]\). To assemble Equation (14), we need to calculate  
\[K=\mathrm{diag}(K_{1},\cdots,K_{m}),\mathbf{C}=\mathrm{diag}(\mathbf{C}_{1},\cdots,\bm {C}_{m}),\mathbf{M}=\mathrm{diag}(\mathbf{M}_{1},\cdots,\mathbf{M}_{m}),\mathbf{\tau}=[\mathbf{ \tau}_{1};\cdots;\mathbf{\tau}_{m}], \tag{17}\]  
and  
\[\mathbf{c}=[c_{1};\cdots;c_{k}],\ \frac{\partial c}{\partial\xi}=\left[\frac{ \partial c_{1}}{\partial\xi};\cdots;\frac{\partial c_{k}}{\partial\xi}\right] \tag{18}\]wherein the subscript indicates which body or constraint the variable or matrix belongs to, while the semicolon \";\" separates the components of a \"column\" vector. Additionally, \(\text{diag}(\cdots)\) signifies that the matrix is block-diagonally composed of submatrices. Since the flexible bodies are modeled using the Craig-Bampton method in this paper, we will elaborate on how the general mass matrix \(M_{i}\) and stiffness matrix \(K_{i}\) (\(i=1,\cdots,m\)) are computed in the subsection. The damping matrix \(C_{i}\) may be omitted in an undamped case or be considered as a linear combination of \(M_{i}\) and \(K_{i}\) under a Rayleigh damping hypothesis.
## 5 Research Challenges and Future Work  
The applications of ML in wireless edge networks are transpiring consistently. Live video stream for distance learning calls for innovative and intelligent solutions for network resource management. However, the EI proposals are in the early stages with initial findings requiring further work. There are many challenges towards the proposed implementation of EI in multicast distance learning. These are listed as follows:  
* **Data Availability:** Foremost, the data availability for the application of ML techniques is currently not sufficient. Very few datasets exist that reveal information regarding user sessions and preferences where the application of supervised learning is desired to predict content popularity for a region covered by an edge network [13].
* **Mobility prediction:** User mobility prediction is essential for the user-BS association and consequent edge caching decisions. Google and Apple have made mobility datasets available for pandemic studies [1]. However, the mobility datasets need to be explored for EI-based multicast solutions.
* **Community detection:** Predictive multicast requires detection of user communities requesting live and non-live videos at the MNO core. Influential users can be marked to cache live video stream for future D2D propagation to offline users. As session data are encrypted, service-less multicast solutions require the application of pre-trained ML models to transfer knowledge from relevant domains such as online social networks [33].
* **Privacy protection:** Proactive edge caching necessitates content popularity, user mobility, and content access pattern prediction to achieve high cache hit ratios. The privacy requirements of end-user necessitate that the user location and preferences data are not collected and processed in a central repository. Therefore, the application of distributed federated learning models is imperative for EI. Federated learning divides the task of machine learning on each participant node (student) avoiding the collection of data in a central repository [12].
* **Dynamics of wireless network:** As the distance learning sessions within an edge network can be numerous with dynamic wireless channel characteristics, edge cache replacement decisions demand the application of reinforcement learning [13].  
In summary, the application of EI for predicting the dynamics of user and edge network is indispensable and an intricate problem with several challenges requiring further investigation.
## 4 Discussion
### Secondary Outcome Measures: Pain Intensity, Days with Pain and Days with Disability  
For the secondary outcome measures used in this study, the results vary for the three subgroups. The decrease in headache pain intensity over time was comparable to the decrease in TMD-pain intensity in patients with migraine, but no decrease in pain intensity was found for TMD-pain patients with TTH or headache attributed to TMD. This corroborates the findings from the primary outcome measure in this study. In all headache groups, there was a decrease of the number of headache days as well as days with TMD-pain. According to the guidelines of the International Headache Society, 50% of reduction on the number of days with pain is considered clinically relevant in patients with m migraine [25]. In the present study, the reduction in headache days reached this clinically relevant level in all three subgroups.  
For the first time, the longitudinal course of TMD complaints and headache was followed in TMD-pain patients with different types of headaches during a period of usual care multidisciplinary treatment. Although exploring the effects of this treatment was not the aim of this study, some interesting aspects of the treatment can be observed in our results. The treatment focused on the TMD complaints included occlusal splints and physiotherapy techniques such as relaxation exercises to the jaw, myofeedback, self-massage of the masticatory muscles and advice for the management of daily parafunction. Previously, these techniques were reported to improve headache intensity and frequency [26]. Additionally, the treatment addressed more general aspects related to chronic pain such as pain education, focusing on the concepts of sensitization, pain modulation and self-management in pain [27; 28]. Since both TMD and headaches are highly associated with psychological factors like stress and pain catastrophizing [29; 30; 31; 32], receiving proper pain education is important to reduce pain, disability and stress in patients with musculoskeletal pain [33; 34]. This study suggests that there may be a different effect of pain education between different headache types in patients with TMD, that should be explored further.  
A strength of this study is that all patients were seen in the OPD clinic, where standardized diagnostic procedures are implemented as part of the usual care. These procedures adhere to the full DC/TMD protocol [14], as well as incorporate a validated instrument to screen for migraine and TTH according to the ICHD-3 criteria [15]. Often, studies regarding TMD and headaches only address self-reported headache, and not headache subtypes. Taking different subtypes into consideration increases the external validity of the results and provide relevant information on differences between TMD patients with various types of headaches.
# Intelligent Identification of Trend Components in Singular Spectrum Analysis
## 5 Justification of EOSSA
### EOSSA and Separability  
Proposition 1 can be interpreted as stating that in the sum \(s_{n}=\sum_{j=1}^{r}A_{j}\mu_{j}^{n}\) all summands are separable from each other by EOSSA. In the real-valued case, for complex-conjugate roots \(\mu_{\ell}=\rho\exp^{2\pi\omega i}\rho>0\), \(0<\omega<0.5\), and \(\mu_{j}=\mu_{\ell}^{*}\), the coefficients \(A_{\ell}\) and \(A_{j}\) are such that the sum of the corresponding terms is \(A_{\ell}u_{\ell}^{n}+A_{j}\mu_{j}^{n}=A\rho^{n}\cos(2\pi n\omega+\varphi)\).  
Thus, as a result of Proposition 2 we obtain the following theorem.  
Theorem 1: _Let \(\mathsf{S}=\mathsf{S}^{(1)}+\dots+\mathsf{S}^{(k)}\), \(k=r_{1}+r_{2}/2\), the terms of the time series \(\mathsf{S}^{(i)}\) have the form \(s_{j}^{(i)}=A_{i}a_{i}^{j}\left(\operatorname{rank}\mathsf{S}^{(j)}=1\right)\) for \(i=1,\dots,r_{1}\) and \(s_{j}^{(i)}=B_{2}b_{2}^{j}\cos(2\pi\omega_{2}j+\phi_{2})\left(\operatorname{ rank}\mathsf{S}^{(j)}=2\right)\) for \(i=r_{1}+1,\dots,r_{1}+r_{2}/2\), with \(a_{i}\
eq 0,b_{z}>0,\omega_{z}\in(0;\frac{1}{2})\). We have \(r=\operatorname{rank}\mathsf{S}=r_{1}+r_{2}\). Let \(\min(L,K)\geq r\) and step 4 of Algorithm 2 provides elementary clustering into \(k=r_{1}+\frac{r_{2}}{2}\) clusters. Then Algorithm 2 applied to the trajectory matrix \(\mathbf{Y}=\mathbf{S}=\mathcal{T}(\mathsf{S})\) results in the decomposition with \(\mathsf{Y}^{(i)}=\mathsf{S}^{(i)}\), after some reordering of time series components._  
Remark 3: Theorem 1 means that in the case of no noise, Algorithm 2 solves the problem of lack of strong separability, since the algorithm does not need the orthogonality of time series with the terms \(A_{j}\mu_{j}^{n}\), and the singular values they generate are also not important.
## 2 Analytical Modelling  
### Governing Equation  
The governing equations of the OC4-FOWT are modelled via [PERSON]'s second law.  
\[\overset{\rightharpoons}{F_{wave}}+\overset{\rightharpoons}{F_{wind}}+\overset{ \rightharpoons}{F_{m}}=\overset{\rightharpoons}{M\cdot\cdot\overset{ \rightharpoons}{a}} \tag{1}\]  
The external force includes the wave load \(\overset{\rightharpoons}{F_{wave}}\) on the OC4 platform, the wind load \(\overset{\rightharpoons}{F_{wind}}\) on the FOWT, and the mooring force \(\overset{\rightharpoons}{F_{m}}\) on the whole system.  
The \(\overset{\rightharpoons}{F_{wave}}\) is calculated via the Potential flow theory, including the wave radiation force \(F_{r}\) and the exciting force \(F_{e}\). The cases in this study use extreme wave conditions with shallow water; therefore, the second-order wave force (difference frequency) is considered. The governing equation is then presented as:  
\[\{m+A_{\infty}\}\tilde{\zeta(t)}=F_{wind}+F^{1 st}(t)+F^{2 nd}(t)+F_{M}(t)-c_{ \zeta}^{z}(t)-B\Big{|}\zeta(t)\Big{|}\zeta(t)-K_{\zeta}^{\zeta}(t) \tag{2}\]  
where \(m\) is the mass of the FOWT including the platform, the wind turbine, and the tower; \(F_{wind}\) is the aerodynamic load on wind turbine and tower; \(F^{1 st}(t)\) is the first order wave excitation force; \(F^{2 nd}(t)\) is the second order wave excitation force, called wave drift force in this study; \(F_{M}(t)\) is the mooring force; \(c\) is the linear damping matrix, including drift damping effects; \(B\) is the quadratic damping matrix; \(K\) is the total stiffness matrix; \(A_{\infty}\) is the added mass matrix at the infinite frequency; and \(\zeta(t)\), \(\zeta(t)\) and \(\zeta(t)\) represent the displacement, velocity, and acceleration time-series vectors in translational or rotational motions, respectively. Damping and added mass matrix are frequency-dependent.  
The second-order wave excitation force is simulated by the QTF data to all pairs of the wave components in the sea state, which is a superposition of a number, \(n\), of the regular wave components present in all the wave trains specified. The wave drift load can be present as:  
\[F^{2 nd}(t)=\sum_{j=1}^{n}\sum_{k=1}^{n}Re\big{\{}Qd\big{(}\beta_{j},\beta_{k},\tau_{j},\tau_{k}\big{)}\ a_{j}a_{k}\ exp\big{[}j(\omega_{j}-\omega_{k})t- \big{(}\phi_{j}-\phi_{k}\big{)}\big{]}\big{\}} \tag{3}\]where \(Re\). denotes taking the real part of a complex number; \(Qd\left(\beta_{j},\beta_{k},\tau_{j},\tau_{k}\right)\) is wave drift (QTF) in complex-valued form for the interaction of wave components \(j\) and \(k\); \(\beta_{j}\) and \(\beta_{k}\) are direction relative to the heading of the platform; \(a_{j}\) and \(a_{k}\) are the wave amplitudes; \(\phi_{j}\) and \(\phi_{k}\) are the phase lags relative to the simulation time origin at the position of the platform's QTF origin; \(\tau_{j}\) and \(\tau_{k}\) are the wave period of different components; and \(\omega_{j}=2\pi\tau_{j}\) and \(\omega_{k}=2\pi\tau_{k}\) are angular frequencies of different components.
## 4 Optimization Mathematical Model
### Topology Optimization Methodology
\begin{array}{c}\min_{x}:c(x)=U^{T}KU=\sum\limits_{\varepsilon=1}^{N}(x_{i})^{ p}u_{\varepsilon}^{T}k_{0}u_{\varepsilon}\\ \text{subject to : }\frac{V(x)}{V_{0}}=f\\ \text{: }KU=F\\ \text{: }0<x_{min}\leq x_{i}\leq 1\end{array}  \tag{8}\]  
in which the design variable vector, \(x\), in the formulation \(c(x)\) presents the volume fraction \(\frac{V(x)}{V_{0}}=f\), where \(f\) is the load vector, \(V_{0}\) is the initial volume, and \(V\) is the final volume. \(K\) is the global stiffness matrix and \(U\) is the global displacement, such that \(KU\) equals \(F\), the force vector. In addition, \(u_{\varepsilon}\) represents the displacement vector, and \(k_{0}\) is the elemental stiffness matrix. The shape of the density filter is selected as defined by [PERSON] [38; 39]. The filtration is defined for the physical relative density by \(\widetilde{x_{i}}\) and can be demonstrated using the following equation:  
\[\widetilde{x_{i}}=\frac{\sum_{j\in N_{e}}w(r_{i})v_{j}\widetilde{x_{j}}}{ \sum_{j\in N_{e}}w(r_{i})v_{j}} \tag{9}\]  
where \(N_{e}=\{i\|r_{i}-r_{e}\|\leq R\}\) denotes a neighborhood set defined by the filter radius, \(R\). The filter radii around the centers of elements \(i\) and \(e\) are denoted by \(r_{i}\) and \(r_{e}\), respectively. The weighting function is \(w(r_{i},\ r_{e})=R-\|r_{i}-r_{e}\|\), and the volume of the \(ith\) element is \(v_{i}\)[38]. In this study, the major objective is to minimize each component's weight within the optimized span morphing wing. TO ensures the lightest possible wing weight by maximizing its static stiffness. Therefore, minimizing compliance is a means to increase the structural stiffness subjected to the set load. The compliance optimization can be expressed as follows [37]:  
\[C=\int\limits_{V}fudV+\int\limits_{S}tudS+\sum_{i}^{n}F_{i}u_{i} \tag{10}\]  
where \(V\) denotes the volume of the continuum, \(f\) is the distributed body force, \(u\) is the displacement area, \(t\) is the traction force, \(Fi\) represents the point load on the \(ith\) node, \(u_{i}\) is the \(ith\) displacement degree of freedom, and \(S\) is the surface area of the continuum.
## 3 Geocasting Based on Local Coverage Areas
### Geocasting Using Local Coverage Areas  
With the above described algorithms, the sinks are informed of a \"crude\" description of their coverage area. Next, this information can be used to optimize handling of position dependent messages e.g., sinks can use the information whether a certain query is relevant for their coverage area. If not, the sink discards the query without inserting it in the WSN, which in the end saves energy and prolongs the lifetime of the wireless sensor network. In this section, the geographical routing of location dependent queries is discussed.  
Queries are always forwarded from parent nodes to child nodes to get delivered to an area that is specified in the query. Let \(\mathbf{R}=\{r_{0},r_{1},\ldots,r_{n}\}\) be the coordinate set describing the region of interest extracted from the query, \(\mathbf{H}_{\text{i}}\) the local coverage area of node \(i\) and \(p_{i}\) the position of node \(i\).  
Upon receiving a query, a node analyses \(\mathbf{R}\) and takes two decisions: (1) _execute decision_ when the node is within the region of interest and (2) _forward decision_ when the node has child nodes or further descendants in the region of interest. Both decisions use \(\mathbf{R}\) as input together with \(p_{i}\) and \(\mathbf{H}_{\text{i}}\), respectively (Figure 2).
# Force-Based Self-Organizing MANET/FANET with a UAV Swarm
## 4 A Novel Self-Organizing Approach to Drone-Based MANETs
### Drone Movement
#### 4.1.3 Maintaining Connectivity and Routing  
Finally, we consider the impact of the one node that serves as a parent node (in the path to the root node) to our node (recall that the parent node was explicitly excluded inEquation (6)). Please note that we (a) reverse the direction again (\(dx_{j,i}\) as opposed to \(dx_{i,j}\)) and that the second term (in brackets) causes the resulting force to switch signs around \(r_{j,i}=1\), with it being zero at that specific point. This enables \(f^{\prime\prime\prime}\) to be either, _attractive_ or _repulsive_.  
Mathematical Modeling of the Force Ensuring Connectivity  
\[\begin{split} f^{\prime\prime\prime}_{x_{i}}&= \frac{dx_{j,i}}{r_{j,i}^{2}}\times\left(r_{j,i}-\frac{1}{r_{j,i}^{2}}\right) \\ f^{\prime\prime\prime}_{y_{i}}&=\frac{dy_{j,i}}{r_ {j,i}^{2}}\times\left(r_{j,i}-\frac{1}{r_{j,i}^{2}}\right)\end{split} \tag{7}\]
## 4 Sensor Signal Analysis  
### Morphology of Sensor Signals  
The thrust force and torque signals acquired during CFRP/CFRP stack drilling are greatly scattered with presence of high-frequency oscillations (Figure 6): this behaviour is strongly related to the anisotropic nature of CFRP laminates [27]. Previous studies on cutting of CFRP materials disclosed that the fiber orientation with respect to the cutting direction determines the mechanism of chip formation and the cut surface quality [28; 29]. Thus, different cutting modes can be identified based on the angle formed between cutting edge and reinforcing fibers, also known as fiber cutting angle (Figure 7) [28]. During unidirectional CFRP drilling, the fiber cutting angle varies continuously with drill rotation, determining different mechanical loading and surface quality conditions (Figure 8) [29]. The case of multidirectional CFRP laminate drilling is even more complex: not only the fiber cutting angle varies during drill rotation, but also different cutting modes occur at the same time along the cutting edge, according to the diverse fiber orientations of the multiple plies simultaneously cut by the cutting edge (Figure 9). High-amplitude oscillations in the force and torque signals during drilling of multidirectional CFRP laminates are the sum of multiple waves with a phase difference depending on the different fiber orientations (\(0^{\circ},45^{\circ},90^{\circ},-45^{\circ}\)) and an amplitude related to the number of plies with same fiber orientation simultaneously cut by the cutting edge.  
Figure 8: Variation of fiber cutting angle during drilling [29].  
Figure 6: Trust force high frequency signal oscillations (6000 rpm–0.15 mm/rev) [27].