esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 A Modified HOSM Controller Using a PID-Sliding Surface for an ABS Laboratory Setup
### Design of the Modified HOSM Control Using a PID-Sliding Surface
Now, in order to determine the convergence of the sliding surface to zero in finite time, it is necessary to denote \(\lambda_{min}^{\mathbb{Q}_{1}},\lambda_{min}^{\mathbb{Q}_{2}}\) as the minimum eigenvalues of \(Q_{1},Q_{2}\), and one finally works out the following from (18) [42]:  
\[\hat{V}\leq-\frac{1}{|s_{v}|^{1/2}}\lambda_{min}^{\mathbb{Q}_{1}}||\chi||_{2}^{ 2}-\lambda_{min}^{\mathbb{Q}_{2}}||\chi||_{2}^{2} \tag{25}\]  
and  
\[|s_{v}|^{1/2}\leq||\chi||_{2}\leq\sqrt{\frac{2^{V}}{\lambda_{min}^{P}}}\]  
so that  
\[-\frac{1}{|s_{v}|^{1/2}}\leq-\frac{\sqrt{\lambda_{min}^{P}}}{\sqrt{2}}\frac{1 }{V^{1/2}}\]  
Then,  
\[\hat{V}\leq-\big{(}\kappa_{1}V^{1/2}+\kappa_{2}V\big{)}\]  
where  
\[\kappa_{1}=\sqrt{2\lambda_{min}^{P}}\frac{\lambda_{min}^{\mathbb{Q}_{1}}}{ \lambda_{max}^{P}};\qquad\kappa_{2}=2\frac{\lambda_{min}^{\mathbb{Q}_{2}}}{ \lambda_{max}^{P}}.\]  
Using the comparison lemma [42], the Lyapunov function \(\hat{V}\), and therefore \(||\chi||\), converge to zero in a finite-time less or equal to \(T_{f}=t_{0}+\ln\left(\frac{\kappa_{1}+\kappa_{2}V^{1/2}(0)}{\kappa_{1}} \right)^{2/\kappa_{2}}\) units of time. **Remark 1**. : _PID controllers are usually preferred in industrial applications due to their good performances in terms of asymptotic stability and precision in steady state. For this reason, it is acceptable to compare the performance of the modified HOSM control using PID-sliding surface (14) with that of the following PID-like controller [11; 26; 37]. The aim of this PID-like controller is to cancel the nonlinear dynamics of the system and to incorporate a desired dynamic._  
_For this purpose, one uses the Equations (6), (7) and (9), and the PID-like controller [43] is as follows:_  
\[ \begin{split}\dot{l}_{v}&=e_{v}\\ b(u)&=\frac{1}{k_{u}}\Big{(}-k_{p,c}e_{v}-k_{i,c}I _{v}-k_{d,c}e_{v}-f_{v}\Big{)}\end{split}  \tag{26}\]  
_so that from (8) and (26), one obtains the closed-loop dynamics_  
\[\dot{e}_{v}=-k_{p,c}e_{v}-k_{i,c}I_{v}-k_{d,c}\dot{e}_{v} \tag{27}\]  
_where the gains \(k_{p,c}\), \(k_{d,c}\), and \(k_{i,c}\) are \(>0\) ensure the global exponential closed-loop stability of the error dynamics \(\dot{e}_{v}+k_{p,c}e_{v}+k_{i,c}I_{v}+k_{d,c}\dot{e}_{v}=0\)._
## 1 Introduction
In another previous work [15], the concept has been extended to the case of unknown velocity of propagation, designing a cluster with three shaped sensors. It is worth noting that the relationship between DToA and DoA affects the uncertainty in DoA estimation. In this paper, we define a new cluster of three shaped sensors in order to further reduce the uncertainty with respect to [15]. The conducted numerical tests show that the DoA estimation performed by the new cluster is more accurate than the one achieved with the conventional cluster or with the cluster proposed in [15], and it is robust, even when the measurements are affected by noise or acquired at low sampling frequencies. It is also worth noting that the computational cost that is associated to the processing of the shaped cluster signals for DToA estimation is fully equivalent to the one of conventional clusters, while the DoA estimation is even simpler because it purely linear w.r.t the DToAs ratio, while the conventional approach is based on trigonometric functions. The experimental validation of the shaping concept is beyond the scope of this paper. Nevertheless, it is worth mentioning that the proposed cluster of shaped sensors can be realized by relying on different piezoelectric materials and manufacturing techniques. e.g., metallized PVDF (polyvinylidene fluoride) sheets can be used by shaping the electrodes on the upper surface with a laser cut as in [16]. Alternatively, the shaping strategy can be based on printing metallic electrodes on PVDF films in order to obtain the desired shape sensors, as proposed in [17], or by using litographic procedures as in [18]. In the practical adoption of these devices, a fundamental step is the definition of a well controlled bonding procedure, because it may heavily affect the sensor response [19]. The paper is organized, as follows. In Section 2, by exploiting the Propagation of Uncertainty theory [20], the functions that bind the DoA to the Differences in Time of Arrival (DToAs) of the wavefront at piezo-patches P1 and P2, and at P1 and P3, are defined to reduce the uncertainty in DoA estimation, when the wave mode velocity is not known. Section 3 shows how to implement the desired functions by properly shaping the sensors in the context of guided waves, in particular of Lamb waves. To this aim, the Radon Transform and its inverse are introduced. Section 4 validates the theoretically expected performances with numerical simulations. The estimation of the DoA is simulated in challenging working conditions, i.e., with noisy measurements or very low sampling frequencies, to emulate real field acquisitions. Moreover, a numerical comparison between the shaped sensors cluster and the conventional cluster is provided. Finally, Section 5 depicts the conclusions.
## 2 Overall Design
### Fuzzy Control  
Since the lawn lamp dimming system in this study is determined by the ambient light level and the number of pedestrians, these random, unpredictable factors have nonlinear and time-varying characteristics on the dimming of the lamps [14]. Therefore, the judgment made by the traditional controller only by analyzing the sensor data has errors and cannot adapt to this nonlinear, multivariable natural system. In contrast, fuzzy control can translate human control experience, expertise and reasoning decisions into automatic control methods that are suitable for variable optimization. In order to achieve the most suitable illumination level for pedestrians and save energy, this paper uses fuzzy control to regulate the luminous intensity of the lawn lamp. The fuzzy control principle is shown in Figure 3.  
As shown in Figure 3, the fuzzy control is divided into three parts, including the input, the fuzzy control algorithm and the output. In this study, ambient illumination and pedestrian flow are used as the input of fuzzy control and are represented by x, y and the dimming coefficient K of the light source is represented by u as the output. The input variables x and y are fuzzified (D/F) to obtain the fuzzy quantities X and Y.
# A Physicist's View on Partial 3D Shape Matching
## 4 Experimental Results
### Full Shape Similarity
#### 4.1.1 Synthetic Data: TOSCA
There are a few observations we can make based on Figure 3 and Table 1. First, the WKS signatures outperform the LD-SIFT signatures for full shape comparison. Under both OT frameworks (balanced or partial), shape comparisons based on WKS capture the similarities of the shapes within their own classes (the blocks along the diagonals on the distance matrices in the bottom row of Figure 3), as well as similarities between those classes. From the WKS/balanced OT distance matrix, we observed that the four-legged shapes (cats, centaur, horse, dogs) and the human-like shapes ([PERSON], [PERSON], and [PERSON]) show similarities between themselves, but are still distinguishable from each other. The seahorses stand on their own. We assign the difference between LD-SIFT and WKS to the fact that, by design, the WKS signatures capture the geometry of the mesh representing a shape by analyzing the Laplace-Beltrami operator on that mesh, while the LD-SIFT signatures are more focused on point distributions. Second, the discriminative power of the LD-SIFT signatures is significantly improved when those signatures are used in association with the same classes. \begin{table}
\begin{tabular}{l c} \hline \hline  & **3D-SIFT** & **WKS** \\ \hline OT (balanced) & 83.3 a  & 100.0 \\ OT (Partial) & 97.3 & 98.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Success rates of shape classification experiments over the TOSCA8 dataset. Figure 3: Distance matrices for shape similarity within the TOSCA8 dataset using the optimal transport framework with LD-SIFT (**top**) and WKS signatures (**bottom**) to represent the vertices on the shapes, and with balanced, i.e., fixed masses (**left**), and unbalanced, i.e., variable masses (**right**), optimal transport optimization to find the correspondence between vertices of two different shapes. Blue colors represent small distances (high similarity), while yellow colors represent large distances (low similarity). partial OT for computing correspondence. This is indicative of the importance of partial matching when those signatures are used, and will be discussed in more detail below.
###### Abstract  
This study investigates the thermal response and surface modification of low-carbon manganese-alloyed 20 GL steel during electrolytic plasma hardening. The objective was to evaluate the feasibility of surface hardening 20 GL steel--traditionally considered difficult to quench--by combining high-rate surface heating with rapid cooling in an electrolyte medium. To achieve this, a transient two-dimensional heat conduction model was developed to simulate temperature evolution in the steel sample under three voltage regimes. The model accounted for dynamic thermal properties and non-linear boundary conditions, focusing on temperature gradients across the thickness. Experimental temperature measurements were obtained using a K-type thermocouple embedded at a depth of 2 mm, with corrections for sensor inertia based on exponential response behavior. A comparison between simulation and experiment was conducted, focusing on peak temperatures, heating and cooling rates, and the effective thermal penetration depth. Microhardness profiling and metallographic examination confirmed surface strengthening and structural refinement, which intensified with increasing voltage. Importantly, the study identified a critical cooling rate threshold of approximately 50 \({}^{\circ}\)C/s required to initiate martensitic transformation in 20 GL steel. These findings provide a foundation for future optimization of quenching strategies for low-carbon steels by offering insight into the interplay between thermal fluxes, surface kinetics, and process parameters.  
Electrolytic plasma hardening; heat flux density; voltage; numerical solution; boundary conditions; thermal conductivity; martensite; microhardness
Materials_**2013**, \(6\), 5613-5624; doi:10.3390/ma6125613  
_Materials_  
**ISSN 1996-1944**  
www.mdpi.com/journal/materials  
_Article_  
**A Compact Band-Pass Filter with High Selectivity and Second Harmonic Suppression**  
**[PERSON] *, [PERSON] and [PERSON]**  
Signal Theory and Communications Area, Department of Electrical Engineering, University of  
Oviedo, Multi-Purpose Building, Module 8, Gij on 33203 (Asturias), Spain;  
E-Mails: [EMAIL_ADDRESS] (M.E.C.G.); [EMAIL_ADDRESS] (F.L.-H.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS].  
_Received: 3 October 2013; in revised form: 6 November 2013 / Accepted: 19 November 2013 / Published: 3 December 2013_  
**Abstract:** The design of a novel band-pass filter with narrow-band features based on an electromagnetic resonator at 6.4 GHz is presented. A prototype is manufactured and characterized in terms of transmission and reflection coefficient. The selective passband and suppression of the second harmonic make the filter suitable to be used in a C band frequency range for radar systems and satellite/terrestrial applications. To avoid substantial interference for this kind of applications, passive components with narrow band features and small dimensions are required. Between 3.6 GHz and 4.2 GHz the band-pass filter with harmonic suppression should have an attenuation of at least 35 dB, whereas for a passband, less than 10% is sufficient.  
**Keywords:** band-pass filter; artificial magnetic conductor; dispersion diagram; second harmonic suppression; transmission coefficient
## 3 Hyaluronic Acid in Treating the IVD: Structure, Synthesis and Turnover  
HA, which was first isolated in the 1930s, is a distinctive glycosaminoglycan, which, unlike other glycosaminoglycans such as heparan sulphate, chondroi sulphate, and keratan sulphate, is non-sulphated [52]. It is widely used in treating osteoarthritis [36; 53; 54; 55; 56; 57; 58] and in tissue-engineering approaches for disc repair [59; 60]. HA (Figure 3) is an abundant polymer of the ECM with a molecular weight of approximately \(10^{6}\)-\(10^{7}\) Da in vivo [61]. HA molecule comprises of chains of constantly repeating disaccharides, glucuronic acid and \(N\)-acetylglucosamine, which are symbolized by Glc-\(\beta\) (1\(\rightarrow\)3) and GlcNAc-\(\beta\) (1\(\rightarrow\)4) respectively (Figure 3) [62]. On the intracellular aspect of the cell membrane, synthesis of HA occurs through the hyaluron synthases (HAS) family, comprised of three types of synthases, activated via different GFs to produce diverse forms of polymerized HA [63]. HAS1 and HAS2 produce the high molecular weight (HMW) of HA of 2-\(4\times 10^{6}\) Da, while the low molecular weight of HA (LMW) of \(1\times 10^{5}\)-\(1\times 10^{6}\) Da is produced by HAS3 [63]. These enzymes are regulated by miRNAs [64] and GFs such as TGF-\(\beta\)[65]. Once synthesized, HA is secreted either on the cell surface from the plasma membrane or into the ECM because of its large size [63; 66]. The body composition ofHA is 15 g [67]. Daily, 33% of HA is turned over through several roots: (i). via the lymphatic system or (ii). via the circulation through the liver or (iii). lysosomal digestion after HA binds to its receptor and is internalized [68]. Furthermore, HA turnover can take place as a result of pathological conditions such as tissue injury and remodelling as a result of oxidative stress and free radicals, HA internalization and degradation, and clearance of HA from tissues via the lymphatics and vascular system [69]. Regarding the receptors through which HA binds to be cleared by the liver and lymph nodes, there are particular receptors such as the hyaluron receptor for endocytosis 1 (HARE1), lymphatic vessel endothelial hyaluron receptor 1 (LYVE1) and CD44. From the previously mentioned three receptors, HARE1 is considered the major role player through the lymph and vasculature [68]. HA is internalized and degraded when it cannot be removed by the vasculature and lymph [69; 70]. In this process, first HA binds to one of its receptors such as receptor for HA-mediated motility (RHAMM), layilin or CD44 after which it is degraded by hyaluronidase-2 [65; 69; 70], then it is digested into fragments through \(N\)-glucuronidases and \(N\)-acetylglucosaminidasedases to completely break down the remaining glycans into monosaccharides [70].
# BioShare: An Open Framework for Trusted Biometric Authentication under User Control
## 3 BioShare Design
### Enclave Module (EM)  
The Enclave Module is a module running on the server side that conducts biometric recognition and comparison using the standard interfaces listed in Table 1. During the user-authorizing process, EM exports private biometric data from Terminal TEEs and stores it temporarily in the server enclave, as described in Section 3.2. Based on the data, EM can conduct biometric authentication without the intervention of user terminals. Because all actions are performed in the server enclave, biometric services provided by EM are secure and trusted.  
**Biometric recognition:** Biometric recognition transmutes biometric raw data into feature data. EM conducts biometric recognition with the same algorithm and the same steps as TA on the terminal side: data processing, and liveness detection and feature extraction, as described in Section 3.4.  
**Biometric comparison:** EM on the server side conducts a biometric comparison with the same algorithm as TA on the terminal side, which is described in Section 3.4.  
**Privacy Protection:** Within BioShare, the security mechanisms for server enclaves are used to protect biometric data during both storage and processing. Specifically, the EPC (Enclave Page Cache) protects all EM code and data in encrypted format during runtime state, the Sealing/Unsealing mechanism secures EM data in encrypted format during persistent storage, and the Attestation technology ensures that genuine code runs in secure enclave environments to avoid data interception and code tampering.  
**Constraints:** Server enclaves are protected execution environments with restricted capabilities, and the restrictions include available CPU cores, maximum RAM, and memory access latency, etc. Meanwhile, in EM, we have to choose the same algorithm as that of TA on the terminal side due to the incompatibility of different algorithms.  
\begin{table}
\begin{tabular}{l l l l l} \hline
**CPU Model** & **TEE** & **CPU Cores** & **Max RAM** & **Max Secure Storage** \\ \hline SamSung Exynos 1080 & ARM Trustronic & 8 & 100 MB & 5 MB \\ \hline Qualcomm Snapdrago 888 & QSEE & 8 & 100 MB & 16 MB \\ \hline Hisilicon Kirin 9000 & iTrustee & 8 & 48 MB & 12 MB \\ \hline \end{tabular}
\end{table}
Table 2: TEE Capabilities of Typical CPUs.
# Capacity Analysis of Incentive Schemes in Opportunistic Networks
## 2 Mathematical Model for Incentive Schemes  
### Typical Incentive Schemes  
Before detailing the mathematical model, we briefly touch on typical incentive schemes to see what can be taken into account. Most existing incentive schemes are reputation-based or credit-based [10]. They use currency-like metrics to reveal the participation degree for nodes forwarding messages [12].  
In credit-based schemes, relay nodes can obtain credits by forwarding messages, then send their own messages with these additional credits. The better design and calculation of reward and price remains a challenge. Payoff according to the time-to-live of a message has been proposed in [12], which is direct to price difference. A Markov chain can be used to calculate price according to the society degree, selfish degree, and cooperation degree with buffer, energy, and interaction to stimulate nodes with high possibility [13]. The clearance credit center rewards relay nodes with extra credits to avoid nodes being unable to obtain enough credits to send messages due to selfish nodes who hold most credits and are not willing to forward messages. Therefore, attackers are not considered in our model or in other similar proposals, such as [8; 10].  
Other incentive schemes, such as reputation-based ones, usually replace credits with internal bounds for the same purpose. Punishment is used to restrain free-riding nodesin peer-to-peer (P2P) networks via game theory [7]. Grouping nodes by bilinear pairing and cooperation probability prediction has been proposed in [14]. Evolutionary game theory replaces the classical theory for more precise cooperation evaluation [15]. With the exception of node behaviour under different policies, these bounds, such as cooperation probability, are similar to credits both in usage and principle. Thanks to the extended strategy space in quantum game theory, it is possible to simply consider increasing bounds as rewards and decreasing bounds as costs.  
An environment involving movement path, link quality, and others can affect communications as a main domain. However, these do not affect incentive schemes directly, which is shown simply by a scenario argument without detailed considerations in the following analysis.  
To sum up, for an analytical model, the cost and reward function can represent an incentive scheme. Other parameters such as buffer and energy can be considered as a part of cost, as they may reduce the cooperation degree. A simple argument briefly summarizes the impact of the environment. Furthermore, interactions among nodes must be considered, which can be solved by a quantum game model and Nash equilibrium.
# Automatic Literature Mapping Selection: Classification of Papers on Industry Productivity
## 2 Methodology
### Categories of Productivity in Industry  
In our investigation, we undertook a Literature Mapping using the conventional methodology involving manual keyword selection and filtering. Subsequently, we employed both Category Classification of Scientific Articles and Relevance Classification of Scientific Articles as equitable indicators of workload to make a comparison and exhibit its efficacy.  
We are poised to adhere to a stringent approach to conducting an extensive bibliometric examination centered on productivity in the industry. This entails applying specific filters to ensure the pertinence and contemporaneity of the encompassed studies. To encompass a substantial temporal scope, we intend to scrutinize articles published from 2014 to 2023, drawing upon various reputable sources.  
In industrial productivity, six primary dimensions (Table 1) exist that play pivotal roles in driving progress and reshaping the sector. These dimensions encompass innovation, sustainability, Industry 4.0, Industry 5.0, efficiency, and artificial intelligence. Each of these will be examined below:  
Innovation catalyzes augmenting industrial productivity by introducing novel concepts, technologies, and processes that streamline operations and amplify efficiency. It also fosters adaptability to market shifts and inventive solutions to industrial challenges. Sustainability is critical in the industry, entailing practices that curtail resource consumption, minimize environmental impact, and advocate for energy efficiency. Companies adopt sustainable strategies to enhance productivity while diminishing their ecological footprint.  
Industry 4.0 Industry 4.0 integrates cutting-edge digital technologies, including IoT, big data, and artificial intelligence, to establish intelligent factories. This leads to automation, heightened quality control, and augmented productivity.  
Industry 5.0 represents a fusion of advanced technologies, such as collaborative robotics and artificial intelligence, with human labor to bolster productivity. Humans and machines collaborate, harnessing their distinct capabilities to amalgamate efficiency and creativity.  
Efficiency is a cornerstone of industrial productivity, entailing the optimization of processes, waste reduction, and quality enhancement. This is achieved through a perpetual process analysis and refinement cycle for more efficient operational performance.  
Artificial Intelligence drives industrial productivity by ushering in automation, refining supply chain logistics, and steering data-powered decision-making. These advancements result in heightened efficiency and enhanced quality.  
These six categories interact with each other, forming a set of approaches and technologies that contribute to boosting productivity in the industry. Adopting and integratingthese elements into industrial strategies and processes can result in significant efficiency, competitiveness, and sustainability gains.