esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results for the Research Questions
### RQ4: What Are the Methods That Have Been Used for Expert Finding Systems?
#### 3.4.2 Discriminative Probabilistic Models  
Discriminative models are an essential class of probabilistic models with a strong statistical base. There are theoretical results that indicate that, according to the increase in training set size, these models have a lower asymptotic error [110]. When some training data is available, discriminative models have been preferred over generative models in several information retrieval applications such as text classification and information extraction [111]. The success of generative models mainly relies on the validity of the model assumptions. These assumptions are occasionally too strong, such as the independence assumption of term distributions. Discriminative models usually make fewer model assumptions and prefer to let the data speak for itself.  
Initial work on applying discriminative models in information retrieval dates back to the early 1980s, when the maximum entropy method was examined to get around the term independence assumption in generative models [112]. Recently, discriminative models have been applied to retrieval problems such as homepage finding [113], e-mail retrieval [114], and question answering [115]. One of the most significant interests in the IR community in recent years is learning to rank for ad-hoc retrieval [116]. Maximum learning to rank models are discriminative in nature, and they have been presented to be superior to generative language models in ad-hoc retrieval. Benchmark data sets such as LETOR [116] are also available for the study of learning to rank. With respect to expert finding, when generative models determine the relations between people and documents, the relations tend to be ambiguous in the TREC Enterprise settings as well as in numerous realistic scenarios. In generative models, the number of relations signals is very limited, but the method of combining them is regularly heuristic, and shortages are clearly justified. An additional significant component in generative models is document evidence, which potentially contains many document relevance features. These features contain document authority information such as the PageRank, indegree, and URL length [117].
# T-Move: A Light-Weight Protocol for Improved QoS in Content-Centric Networks with Producer Mobility
## 4 T-Move  
T-Move is built on CCN [2]/NDN [4], but improves system performance by modifying two policies of CCN/NDN--the cache replacement strategy and the mobility support strategy. The idea is to use prefix-matching to measure the popularity of content and use this for cache replacement and efficient mobility support. We describe the two important modules of T-Move in the sections below. In this discussion, we use the term edge router to specifically denote edge routers connected to the producers of data. Other routers along the route from this edge router to the consumer (network routers and edge routers connected to the consumer) are called _upstream routers_.
# An Overview of Distributed Firewalls and Controllers Intended for Mobile Cloud Computing
## 3 SDN, NFV, and CASB Technologies
### Software-Defined Network (SDN)
#### 3.1.5 Current Solutions of SDN  
SDN utilises an abstraction model that decouples the control plane from the forwarding plane, employing open APIs to prevent data compromise. This separation enhances security by isolating control functions and providing greater control over network operations [43]. SDN deployment involves separating the control and data planes, allowing for centralised network control and programmability. This division enhances the network'sflexibility, scalability, and management efficiency. Additionally, SDN facilitates the virtual deployment of essential security functions, like Network Address Translation (NAT) and firewalls, at a reduced cost compared to conventional hardware-based approaches [43; 44].
# A Maximum Power Point Tracking Control Method Based on Rotor Speed PDF Shape for Wind Turbines
## 1 Introduction
In these studies of the proposed algorithm designs, the verification of control algorithm usually requires appropriate simulation and analysis on the established wind turbine model. The specific application environment should be fully considered in the establishment of wind turbine model. For example, the floating wind turbine, as a new concept wind turbine, is modeled by analytically calculating the forces operating on the system, such as buoyancy force, drag force, air thrust, drag force, and cable drag force [16]. To reduce the difficulty of modeling and focus on the comparison and analysis of control algorithms, a simplified mechanical dynamic model of wind turbine considering only stochastic wind is adopted in this paper. The improvement of conventional OT algorithm is usually achieved by adding additional torque. This paper, firstly, presents a PDF control with full consideration of wind speed distribution characteristics. This PDF control is achieved by using linear least-square (LLS) method to solve FPK equation and can accurately track the desired turbine rotor speed. Secondly, a nonlinear MPPT optimal control design, completed by creatively introducing PDF shape control into OT control, will improve the dynamic performance of the wind turbine while playing the strengths of PDF control. This design can control the wind turbine to capture more wind energy while reducing the fluctuation of system variables, which would be verified by simulating the operation of turbulent wind speed in a 1.5 MW wind turbine.
# A Predefined-Time Control for the Laser Acquisition in Space Gravitational Wave Detection Mission
## 3 Mathematical Model
### Mathematic Model for the Satellite Platform
According to [PERSON]'s equation, finally we can obtain the dynamic model of the satellite platform:  
\[\mathbf{H}(\mathbf{q})\ddot{\mathbf{q}}+\mathbf{C}\big{(}\mathbf{q},\dot{\mathbf{q}}\big{)}\dot{\mathbf{q }}=\mathbf{Q} \tag{10}\]  
where \(\mathbf{H}(\mathbf{q})\) denotes the inertia matrix, \(\mathbf{C}\big{(}\mathbf{q},\dot{\mathbf{q}}\big{)}\) denotes centrifugal and Coriolis effects, \(\mathbf{Q}\) denotes generalized force. Consider disturbance, we have:  
\[\mathbf{H}(\mathbf{q})\ddot{\mathbf{q}}+\mathbf{C}\big{(}\mathbf{q},\dot{\mathbf{q}}\big{)}\dot{\mathbf{q }}=\mathbf{\tau}+\mathbf{d} \tag{11}\]  
where \(\mathbf{d}\) is a vector of unknown disturbances. There are some properties for this equation:  
**Property 1. **\(\dot{\mathbf{H}}(\mathbf{q})\) _is symmetric and a positive definite, \(\mathbf{H}(\mathbf{q})=\mathbf{H}(\mathbf{q})^{T}\) and \(\mathbf{x}^{T}\mathbf{H}(\mathbf{q})x>0\)._  
**Property 2. **\(\ddot{\mathbf{H}}(\mathbf{q})-2\mathbf{C}\big{(}\mathbf{q},\dot{\mathbf{q}}\big{)}\) _is a skew-symmetric matrix, namely \(\mathbf{x}^{T}\Big{(}\dot{\mathbf{H}}(\mathbf{q})-2\mathbf{C}\big{(}\mathbf{q},\dot{\mathbf{q}}\big{)} \Big{)}\mathbf{x}=0\)._  
Property 1 is obvious because the kinetic energy \(T=\frac{1}{2}\ddot{\mathbf{q}}^{T}\mathbf{H}\dot{\mathbf{q}}\) is positive for any \(\dot{\mathbf{q}}\
eq 0\) and Property 2 can be proved by the kinetic energy theorem with the identity which is expressed as \(\frac{1}{2}\frac{d}{dt}\Big{[}\dot{\mathbf{q}}^{T}\dot{\mathbf{H}}\dot{\mathbf{q}}\Big{]}= \dot{\mathbf{q}}^{T}\mathbf{\tau}\).
## 5 Performance Evaluation of BSNCare+
That is, the nurse/doctor can utilize his/her mobile gateway for real-time data collection and provide better-quality healthcare services to the patient with further data analysis and the mining of the patient's needs. It is obvious that the BSNCare+ system locates in a very-reasonable computation cost level because at most 9.634 ms is required in executing a \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Underlying** & **Phase** & **Computation Cost** \\ \cline{2-4}  & **Architecture** & & \
\begin{tabular}{c} Registration Phase \\ Authentication Phase \\ \end{tabular} &
\begin{tabular}{c} \((1+k)\) RN + \((1+k)\) H \\ 2 RN + 8 XOR + 12H \\ (\(3+k\)) RN + 8 XOR + (\(13+k\)) H \\ \end{tabular} \\ \hline \multirow{2}{*}{USM-IoT [10]} & \multirow{2}{*}{Client-Client-Server} & Registration Phase & \((1+k)\) RN + \((1+k)\) H \\  & & Authentication Phase & \(3\)RN + \(6\)XOR + \(21\)H \\  & & Total & \((4+k)\) RN + \(6\)XOR + \((13+k)\) H \\ \hline \multirow{2}{*}{Scheme in [14]} & \multirow{2}{*}{Client-Client-Server} & Registration Phase & \((2k+2)\) RN + \((3k+7)\) XOR + \((2k+8)\) H \\  & & Authentication Phase & \(4\)RN + \(12\)XOR + \(24\)H \\  & & Total & \((6+2k)\) RN + \((3k+19)\) XOR + \((32+2k)\) H \\ \hline \multirow{2}{*}{BSNCare+} & \multirow{2}{*}{Client-Client-Server} & Registration Phase & \((2+k)\) RN + \((2+k)\) H \\  & & Authentication Phase & \(4\)RN + \(8\)XOR + \(18\)H \\  & & Total & \((6+k)\) RN + \(8\)XOR + \((20+k)\) H \\ \hline \hline \end{tabular}  
* \(k\) is the size of \(\mathit{SID}\), which contains \(\mathit{sid}_{k}\)values, in the implementation, \(k=100\). RN means a random number not including the user identity and chosen password. XOR means a bitwise exclusive-or operation. H means the one-way hash function, i.e., SHA-3 (512 bits). Note that the generation of the secret key, i.e., \(K_{ch}\), shared by the cluster head and the H IoTS is not explained in [10]. We skip the corresponding generation cost here.  
\end{table} 
Table 7: Performance comparison among BSN-Care [9], USM-IoT [10], scheme in [14] and BSNCare+ 1.  
normal protocol session of the BSNCare+ system on a common IoT-based testbed. We thus believe the practicality of the BSNCare+ system as it guarantees better security density with a user-acceptable computation cost.
# Applications for Microwave Kinetic Induction Detectors in Advanced Instrumentation
## 7 Further MKID Applications  
MKIDs are still a relatively new technology whose capabilities have not yet been exploited to the fullest, partly because the optimization of these innovative detectors is still ongoing. As described through the length of this review paper, Microwave Kinetic Inductance Detectors are being deployed intensively in astronomical applications. Further scientific applications already discussed include particle and astro-particle experiments, and material science, in particular as synchrotron radiation detectors. MKIDs also showed promising results for practical applications such as security scanners for passengers at airports and freight at harbors. In this last chapter we want to present further applications of Microwave Kinetic Inductance Detectors which have been proposed or are at the first stages of investigation but did not fit well within the areas already discussed.  
A preliminary study investigating the possibility of using polarization-sensitive MKIDs for plasma diagnostics has been published by [PERSON] et al. [153]. Polarimetric measurements allow for the determination of various plasma parameters. Usually, polarimetric systems are based on gas lasers emitting in the THz range which pose many issues with reliability, safety, and stability. A possible alternative approach involves a Quantum Cascade Laser, but due to its low power this new source comes with a need for an extremely sensitive detector, such as MKIDs [153].  
Another promising application for Microwave Kinetic Inductance Detectors is the development of a Quantum-Limited Atomic Force Microscopy (QAFM) [154]. Such an ambitious project requires the development of electro-mechanical sensors which are capable of performing measurements at the quantum limit. The designed sensor would exploit the mechanical strain in the superconductor to create quasi-particles and vary the kinetic inductance of the resonator. By monitoring the superconducting resonator, it is then possible to identify the resonant frequency of the cantilever and therefore produce an image of the scanned surface. Further studies on the behavior of the resonator under strain are ongoing; in order to produce vibrations a piezoelectric shaker is being included in the experimental setup [154].  
Sections 4 and 7, sketches of Figure 1: M.D.L.; Sections 5 and 6: E.B.; conceptualization, supervision, Sections 1-3, review and editing: G.U. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Science Foundation Ireland under Grant number 15/IA/2880.  
Not applicable.  
The authors would like to thank [PERSON], Dublin Institute of Advanced Studies, for making this work possible and many helpful discussions.  
The authors declare no conflict of interest.
## 3 Results
### Powered Stapling System
In particular, SIG was tested for video-assisted thoracic surgery by [PERSON] et al. [93], which also introduced a new procedure, named \"sliding technique\", to optimize safe surgical margins with minimal stapler movement. The advantages of PVS in comparison to manual endoscopy are highlighted in [PERSON] et al. [94] for laparoscopic bariatric surgery, in [PERSON] et al. [95] for thoracoscopic lobectomy lung cancer, and video-assisted thoracic surgery lobectomy in [PERSON] et al. [59]. In comparison to the non-powered alternatives, these studies showed, respectively, a decrease of 47% (3.05% to 1.61%) in terms of hemostasis-related complications, a decrease of 56% in terms of intraoperative blood loss, and a decrease of 47% in terms of bleeding complications. The PVS device has also been applied for scientific investigations on a novel physiologic lung model by [PERSON] et al. [96]. Some direct comparisons of PVS and SIG have been performed by [PERSON] et al. [97] in laparoscopic sleeve gastrectomy. The authors report a lower observed incidence of hemostasis-related complications during the surgical admission in the ECHELON FLEX(tm) group in comparison with the SIG group (3 events vs. 11 events). Regarding the ECHELON CIRULAR(tm) Powered Stapler (ECPS), this has been proven to reduce the risk of anastomotic leakage rates in left-sided colorectal anastomosis from 11.8% to 1.7% in a single institution clinical study [98]. Postoperative anastomotic leaks were experienced in 2.4% of subjects in a cohort study of left-sided colorectal surgery performed by [PERSON] et al. [99]. Similarly, it has been successfully benchmarked against manual operation for left-sided colorectal resections by [PERSON] et al. [100] and [PERSON] et al. [101].
# IMM Filtering Algorithms for a Highly Maneuvering Fighter Aircraft: An Overview
## 10 Numerical Simulations and Results
### Comparison of IMM-CKF MoP with Varying Azimuth SD with \(T=0.5\) s  
Since all IMM-based filters have nearly the same state estimation accuracy, we use the IMM-CKF for this comparison. In Figures 11 and 12, the RMS position and velocity errors for three different values of azimuth error SDs using the IMM-CKF and corresponding PCRLBs are plotted. It is observed that as the azimuth error SD increases, the RMS position and velocity errors and PCRLBs also increase. The same trend can be observed in Table 7, which presents the RTAMS position and velocity errors and corresponding PCRLBs for all three cases of azimuth error SDs. The velocity RMS error and corresponding PCRLB are nearly the same for all three azimuth error SDs. As the azimuth error SD increases, the difference between the RTAMS position error and corresponding PCRLB increases slightly.
# Systematic Review: Anti-Forensic Computer Techniques
## 4 Results
### Weaknesses and Anti-Forensic Use Cases
#### 4.1.3 Weaknesses and Anti-Forensic Use Cases in Databases and the Cloud  
The works whose main topic focuses on local or distributed DBs, as well as cloud functionalities, are collected in this section. Authors such as [PERSON] et al. [152; 153] expose anti-forensics techniques and detection in the cloud, and others, like [PERSON] [154], discuss weaknesses in SQLite Corpora.  
[PERSON] et al. [155] propose a system that monitors anti-forensic attacks on SQL, identifying them using a pattern-matching algorithm and generating corresponding reports.  
[PERSON] et al. [156] present a unique methodology by which to mitigate anti-forensics in a cloud with the effective usage of the relevant graphs.  
[PERSON] et al. [157] deploy a mechanism for identifying suspicious packets in a cloud environment. To categorize the type of attack that affected the packet, both signature analysis and anomaly detection at the cloud layers are applied.  
The analyzed articles explore the challenges of cloud forensics due to anti-forensic techniques used to hide digital evidence [152; 153; 156]. Various technical solutions are proposed to detect and mitigate these attacks, including detection frameworks [153], monitoring systems [155], the use of graphs for mitigation [156], and the identification of suspicious packets [157]. Article [154] differentiates itself by focusing on the vulnerability of SQLite databases on mobile devices and the need for improved forensic tools to analyze them.  
As novel contributions, article [154] presents a collection of manipulated SQLite databases to evaluate the effectiveness of forensic tools. In addition, papers [156; 157] propose novel methods with which to mitigate and detect anti-forensic attacks in the cloud using graph and packet analysis, respectively.
## 4 Results and Discussion
### Effects of Base Motions
#### 4.1.2 Base Pitching Motion
Figure 13: Frequency–response curves of harmonic components under base pitching motion (\(C/R=0.485\%\), \(U=144.02\) g \(\cdot\)mm, \(\omega_{by}=6\) rad/s) at nodal points of: (**a**) SFD; (**b**) disk. the nonlinear characteristics of the oil film stiffness, subsequently altering the system's intrinsic modal properties.
## 2 Related Works
### Hyperspectral Image Classification  
Most existing methods deal with the classification of hyperspectral images according to the conventional paradigm of pattern recognition, which is built on complex hand crafted features and shallow trainable classifiers, such as support vector machines (SVM) [38] and neural networks (NN) [39]. However, due to the high diversity of depicted materials, they are highly reliant on domain knowledge to determine which features are important for the classification task. A large number of deep learning models, capable of automatically discovering and learning semantic features, have been developed to tackle HSI classification problems [24; 40; 41; 42; 43]. [PERSON] et al. [40] introduced the concept of deep learning into hyperspectral data classification for the first time. [PERSON] et al. [41] employed several convolutional and pooling layers to extract deep features from HSIs, which are nonlinear, discriminant, and invariant. [PERSON] et al. [42] proposed a spatial pixel pair feature that better exploits both the spatial/contextual information and spectral information for HSI classification. In [24], the image was firstly segmented into different homogeneous parts, called superpixels. Then a superpixel-based multitask learning framework was proposed for hyperspectral image classification. [PERSON] et al. [43] proposed a novel recurrent neural network(RNN) model that can effectively analyze hyperspectral pixels as sequential data and then determine information categories via network reasoning. These approaches normally require large scale datasets whose size should be proportional to the number of parameters used by the network to avoid overfitting.  
Unlike these deep learning-based approaches, we first reduce the computation by decreasing the dimensions of HSI with PCA. Then, a multi-scale network is proposed to expand the receptive filed and automatically capture spectral and spatial feature. Finally, we fuse the features and feed into the CNN model.
## 2 Piezoceramic Transducer and Monitoring Principle
### Time Reversal (TR) Method  
The time reversal (TR) method enabled by the piezoceramic transducer is a relative new approach, and has found use in some applications, including therapy, electromagnetic, SHM, underwater acoustics, and ultrasound medical imaging. For guided waves in cylinders, the propagation of the stress waves is complex, and numerous vibrating modes propagate simultaneously at one single excitation frequency [32]. For steel bars, the propagation of the guided waves in them will be more complex, and non-uniform  
Figure 1: The flow chart of steel bar corrosion monitoring. PZT—lead zirconate titanate.  
distributed points or pits on the bar surface as a result of the corrosion can cause serve noise. As an inherent property, the time-reversal method has the capability of self-adaptive focusing and robust anti-noise [67], which can make up for the limitation of multi-path transmission [68], enhance the received pulse energy [69; 70], and improve the detection capability. [PERSON] [71] applied the TR methods to the acoustic field, and carried out an in-depth research on the theory, experiment, and application of the adaptive focusing principle. The TR method has the above advantages, and has been applied to steel bar corrosion monitoring in this study. Taking the advantages of having the ability to generate and detect stress waves in a wide frequency range, two PZT disks, which are bonded to the end-surfaces of the steel bar, are used as transducers to enable the TR method, as illustrated in Figure 2.  
First, in Figure 3a, PZT1 emits a Gauss signal, then, PZT2 records the propagated signal, which shown in Figure 3b. Second, we reversed the recorded signal in the time domain. After being amplified by the power amplifier, the reversed signal was re-emitted to PZT1. Figure 3c shows the reversed signal. Figure 3d is the focused signal generated by PZT1. When the corrosion becomes more severe, more masses will be lost and the surface of the steel bar will be full of corrosion pits, which will negatively impact the stress wave propagation and reduce the peak of the focused signal. As a result, the corrosion status of the steel bar was monitored by observing the peak value of the focus signal through the time reversal process.  
Figure 3: Illustration of time reversal process.  
Figure 2: Time reversal process of steel bar corrosion monitoring.
# Evolution towards Smart and Software-Defined Internet of Things
## 6 State-of-the-Art IoT Simulators
### Cooja/Contiki  
The practicality of Contiki is high and it is one of the very few simulators, such as OMNET++, to provide security with a custom extension. If we talk about the built-in IoT standard, it supports a large range of protocols that are all supported by the Contiki operating system [90]. It is especially generic and focuses on low power IoT devices or sensors. It provides dynamic loading and unloading of the applications which is open source, lightweight, and highly portable. It is also highly memory efficient and uses 2 KB of RAM and 40 Kb of ROM. It also provides security with a custom extension.