esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Hence, the mathematical relationship between the tracking performances of the ultra-tightly integrated GNSS/INS receiver and the quality of the selected IMU is built in this paper to make up for this insufficiency. To verify this relationship, some simulations are performed to compare the loop performances of four ultra-tightly integrated GNSS/INS receivers aided by different grade IMUs. This investigation is very valuable for the selection of the IMU in an ultra-tightly integrated GNSS/INS receiver. There are two loops in receivers: delay lock loop (DLL) and phase lock loop (PLL). Compared to the DLL, the PLL is more sensitive to dynamic stress and it loses lock much easier since the carrier wavelength is much shorter than the code chip length. Therefore, the tracking performances of the PLL get more attention than that of the DLL. In this paper, PLL loop noises are analyzed to evaluate the improvement of the tracking performances.
## 3 Results and Discussion
### Complex Conductivity
In the case of Figure 8b, the fit based on the Drude-tunneling model (Equation (12)) is not as good, especially in the case of \(\sigma_{1}\), and we need to stress that it was obtained for \(f=0.98\), i.e., the tunneling process is almost negligible in our case. The latter, combined with the fact that for both the thin and thick nanocomposite samples, \(\sigma_{2}\) is negative in the entire frequency range, points to that in our nanocomposites (based on either PET-DLA 4060 or PET-DLA 6040 copolymers) the graphene nanofiller is well-distributed, and consists of individual conducting nanoflakes that are well isolated from each other inside a polymer matrix. Using the Drude-Smith best-fit \(\omega_{\rm p}\) parameters, and by experimentally-determining the graphene layer-to-layer separation distance (see Figure 2b), we estimated the free-carrier concentration in our nanoflakes to be equal to \(\sim\)1.1 \(\times\)\(10^{9}\) cm\({}^{-2}\) and \(\sim\)3.2 \(\times\)\(10^{9}\) cm\({}^{-2}\) for the thick, and thin sample, respectively. The ultrashort carrier collision time \(\tau\) obtained from the Drude-Smith model indicates, in turn, that our graphene nanoflakes are, on average, much larger than the carrier mean-free path within the flake, estimated to be on the order of 10 nm. The latter confirms the applicability of the Drude-Smith approach to model the carrier transport in our graphene-polymer nanocomposites. Finally, we point to very similar THz conductivity studies, very recently reported by [PERSON] et al. [22]. The authors used samples consisting of FLG flakes embedded in polytetrafluoroethylene (PTFE) pellets, and measured them using the THZ-TDS method. They fitted only the \(\sigma_{1}\) spectrum, but also obtained \(c_{1}\) very close to \(-1\) and very similar free-carrier concentration, as well as came to very similar conclusions as ours.
## 3 Antenna Array Design
Overall, the matching of the antenna array is great when the S11 graph indicates three poles in the desired frequency range. The radiation pattern results reveal an HPBW of roughly 43\({}^{\circ}\) in the E-plane (y-z plane) and 50\({}^{\circ}\) in the H-plane (x-z plane), and the gain enhanced by 4 dB on average since the broadband antenna is arranged to 2 \(\times\) 2 will be shown in detail and compared to the measurement in the following section.
# Coupled Dynamic Modeling and Control of Aerial Continuum Manipulation Systems
###### Abstract  
Aerial continuum manipulation systems (ACMSs) were newly introduced by integrating a continuum robot (CR) into an aerial vehicle to address a few issues of conventional aerial manipulation systems such as safety, dexterity, flexibility and compatibility with objects. Despite the earlier work on decoupled dynamic modeling of ACMSs, their coupled dynamic modeling still remains intact. Nonlinearity and complexity of CR modeling make it difficult to design a coupled ACMS model suitable for practical applications. This paper presents a coupled dynamic modeling for ACMSs based on the Euler-Lagrange formulation to deal with CR and the aerial vehicle as a unified system. For this purpose, a general vertical take-off and landing vehicle equipped with a tendon-driven continuum arm is considered to increase the dexterity and compliance of interactions with the environment. The presented model is independent of the motor's configuration and tilt angles and can be applied to model any under/fully actuated ACMS. The modeling approach is complemented with a Lyapunov-wise stable adaptive sliding mode control technique to demonstrate the validity of the proposed method for such a complex system. Simulation results in free flight motion scenarios are reported to verify the effectiveness of the proposed modeling and control techniques.  
aerial manipulation; continuum robot; Euler-Lagrange; modeling and control
# GPSR: Gradient-Prior-Based Network for Image Super-Resolution
## 2 Related Work  
### Gradient Prior  
Extensive studies have confirmed that using common priors, which describe natural image properties, such as sparsity [16; 17], spatial smoothness [18; 19], non-local similarity [13; 14; 15; 20], and gradient prior [21; 22; 23], can effectively strengthen the high-frequency details of the reconstructed images. Among them, the gradient prior, as one of the most critical priors, is widely used in SR tasks since it is easy to extract and can provide more advantageous information to generate sharp edges and textures. [PERSON] et al. [22] proposed the first recurrent network model with residual learning for SR, which introduced the gradient prior between the LR images and HR images into the SR process to enhance the detail-preserving ability. To address the problem of structural distortion in GAN-based SR methods, [PERSON] et al. [21] proposed a structure-preserving Super-Resolution (SPSR), which introduces additional gradient prior to guide the generator to restore straight and sharp edges while suppressing undesired spatial distortions as much as possible. Considering that recovering diverse low-level image elements at a single stage is not an optimal strategy due to the different characteristics of various low-level components in natural images, [PERSON] et al. [23] proposed the Component Divide-and-Conquer (CDC) model, which produces attention masks corresponding to smooth, edge, and corner regions by utilizing gradient prior, to acquire the corresponding structural components, and then merges these three structural components as the final output. The above studies [21; 22; 23] have revealed that gradient prior is able to effectively improve the high-frequency details of reconstructed images. However, for SR tasks with large scaling factors (i.e., \(\times 4\) and \(\times 8\)), these methods are unable to predict sharp edge and texture details, and even produce unacceptable geometric distortions due to the inability to supplement multi-scale structural information.
## 3 Results and Discussion
However, it is also possible that hydrophobic interactions between apigenin and the phospholipid membrane are involved [33]; the location of AP in the hydrophobic region of the membrane was confirmed by DPH fluorescence measurements in our experiment. The decrease of the liposomes' hydrodynamic diameter in the presence of AP in our study is probably due to the effect of OA on the structure of the bilayer, thus modifying the action of apigenin. As with apigenin, the presence of rutin also caused a reduction of phosphatidylcholine-choline liposome size from about 220 nm to about 155 nm at a flavonoid concentration of 100 \(\upmu\)mol (Figure 6c). According to [PERSON] and co-workers [36], the addition of rutin to liposomes composed of egg phosphatidylcholine/cholesterol, 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC)/1,2-di-(9Z-octadecenyl)-sn-glycero-3-phosphoethanolamine (DOPE) and 1,2-dioleoyl-sn-glycero-3-phosphate (DOPA)/DOPE caused an increase of hydrodynamic diameter from 107 nm, 114 nm and 112 nm to 147.2 nm, 129 nm and 139 nm, respectively. On the contrary, liposomes containing egg yolk L-\(\alpha\)-phosphatidyl/cholesterol/ceramide-3/oleic acid have sizes between 136.55-517.49 nm, and after the addition of rutin this decreases to between 131.48-152.76 nm. Thus, the effect of rutin on liposomes size depends on membrane composition. In the case of rutin (as for apigenin), the effect of the flavonoid on the hydrodynamic diameter of the liposome is probably determined by the presence of OA in its structure, which can affect the hydrophilic-lipophilic balance of the system. Similar to flavonoids, loading liposomes with resveratrol led to a reduction in hydrodynamic diameter from about 205 nm to about 150 nm (Figure 6d). A slight reduction in the hydrodynamic diameter of membranes composed from soybean phosphatidylcholine/choline after the addition of resveratrol was found by [PERSON] and co-workers [37]. This held true for multilamellar vesicles and liposomes obtained by extrusion, but not for the sonication technique. This observation indicates a different effect of RS on the liposome's size compared to flavonoids. It seems that the decrease in the diameter of phospholipid membranes is not determined by the presence of OA. Ferulic acid added to membranes containing 20 umol of OA caused liposome size reduction from about 250 nm (empty liposomes) to about 170 nm (liposomes loaded with FA; Figure 6e). [PERSON] and co-authors [38] observed a similar phenomenon in liposomes composed of egg phosphatidylcholine. Empty membranes (20 umol) had a hydrodynamic diameter of 112.5 nm, and after adding ferulic acid (0.2 umol), this value decreased slightly to 109.3 nm. It can be concluded from the above that the reduction of liposome size by FA is independent of the presence of OA. Figure 6: Distribution of PC liposomes hydrodynamic diameter in the presence of oleanolic acid (**a**), and oleanolic acid mixed with apigenin (**b**), rutin (**c**), resveratrol (**d**) and ferulic acid (**e**).
# Design and Application Space Exploration of a Domain-Specific Accelerator System
## 2 System Design and Workflow
### System Architecture Overview  
The proposed system is a heterogeneous digital signal processor built around two primary entities, a low-power core, and an accelerator engine. We use the low-power core to coordinate the system and the accelerator while retaining programmability for the serial part in an application that is not suited for acceleration. The low-power cores act as system supervisors, running operating systems and managing the initialization and configuration of the accelerators. The acceleration engine of the system is a configurable domain-specific accelerator that aimed at high precision and high throughput radar digital signal processing.  
Like most of the standalone accelerators [15; 16; 17], the accelerator in the proposed system-called reconfigurable application specified processor (RASP), is a loosely coupled configurable engine attached to the AXI bus. Data communication between on-chip memory and external storage, i.e., DDR3 is provided through direct memory access (DMA) in the RASP. Configuration bit streams are generated by compiling customized application programming interface (API) functions and specific address space in external DDR is allocated to store them. With the help of API functions, programmers only need to define a set of parameters to build a task, without too much worry about hardware implementation and restriction. Figure 1 shows the high-level design; each engine consists of a PE array and scratchpad, connected with the memory via DMA. It is initialized by a RISC core, which compiles and sends commands to the main controller in RASP. Inside each accelerator, we use main controller for processing scheduling and algorithm controller for kernels execution.
# The Power of Machine Learning Methods and PSO in Air Quality Prediction
## 2 Materials and Methods
### Machine Learning Regressors
#### 2.2.4 XGBoost (eXtreme Gradient Boosting)  
The gradient boosting technique has been modified with several changes to produce XGBoost, a high-performance variant [21]. In 2016, [PERSON] and [PERSON] presented the algorithm. Its most crucial characteristic is the algorithm's capacity to manage empty data, prevent overlearning, attain high prediction power, and execute quickly. Ensemble learning blends multiple weak techniques to build a stronger model. XGBoost uses decision trees as its base learners, combining them sequentially to enhance the method's performance. Each new tree is trained to correct mistakes made by the previous tree, a process called boosting. XGBoost also supports customizations that allow users to adjust model parameters to optimize performance according to the specific problem [16].
# Theory of Electrical Breakdown in a Nanocomposite Capacitor
## 2 Methods
### Electrical Breakdown in Conventional Capacitors  
We assume that the main dependencies which govern the electrical breakdown in capacitors remain valid when a small volume fraction of NPs is embedded into the host dielectric. Namely, the avalanching effect in the dielectric in a strong electric field leads to an exponential increase in the electron current density with the dielectric film thickness as follows [17]  
\[j_{e}(d)=j_{e}(0)\exp(\gamma Ed), \tag{1}\]  
where \(j_{e}(0)\) is the injected current density at the electrode with a negative bias (at \(z=0\)), the constant \(\gamma\) can be determined in terms of the electron energy sufficient for impact ionization and the recombination rate, \(E\) is the electric field strength inside the dielectric and \(d\) is the dielectric film thickness. The breakdown occurs when the electron current density reaches the critical value \(j_{B}\) which is sufficient for the dielectric destruction. For a given thickness, this happens at a certain value of the field strength, \(E_{B}\), which determines the breakdown voltage \(V_{B}=E_{B}d\).  
The quantity \(j_{e}(0)\) is dictated, in its turn, by the work function of the electrode and the potential barrier for electrons formed by both the electric field potential and the image potential for an electron originating from the polarization of the electrode. Two processes  
Figure 1: **(a)** The model of a nanocomposite capacitor. **(b)** The model of an NP.  
contribute to this current: the quantum-mechanical tunneling through the barrier and the thermionic current above the barrier.  
For relatively weak electric fields, the latter one prevails over the first one and is given by the Richardson-Schottky equation [18]  
\[j_{e}(0)=AT^{2}\exp\left(-\frac{W}{kT}\right)\exp\left(\frac{BE^{1/2}}{kT} \right), \tag{2}\]  
where \(A\) and \(B\) are constants, \(A\) being known as the Richardson constant, \(W\) is the work function of the electrode, \(E\) is the electric field strength inside the dielectric film, \(k\) is the Boltzmann constant and \(T\) is the temperature.  
In strong electric fields, the tunneling mechanism is the dominant one and the injection current is given by the Fowler-Nordheim formula [19]  
\[j_{e}(0)=CE^{2}\exp\left(-\frac{D}{E}\right), \tag{3}\]  
where the constants \(C\) and \(D\) are determined by the work function of the electrode and do not depend on the temperature.
## 1 Introduction
Our main focus is providing a user a reliable VWSN network, and we show one solution providing a user IoT resources by dividing providers into infrastructural and VWSN providers. Then, we show how we connect modules to construct a reliable VWSN network. Our contributions are as follows. * Our main idea for constructing a robust VWSN topology is shown in our previous work [13]. In this paper, we show a series of procedures for running services on the VWSN topology and demonstrate the feasibility of our proposal by simulation, including from user-level requests for a new virtualized WSN to packet-level behaviors. * We evaluate the features of our proposed method, especially adaptivity. Here, we define the adaptivity of the constructed VWSN as the ability to quickly recover its function, where various types of traffic exist. Supposing a node failure scenario, we show that our proposal can be adaptive by proactively setting up routers and reactively recovering them. The hierarchical modular structure of VWSNs allows a memory-efficient routing method with low overhead by restricting the flooding area used during route recovery. We also show that adaptivity can be low even when the VWSN network has robust connectivity. Note that our focus is not the proposal of a routing or route recovery method itself. The routing method and the route recovery method shown in this paper are used for clarifying the structural problem of our VWSN network that harms the reliability of the VWSN topology. Therefore, another routing method can do. Moreover, we assume that each application can use different routing strategies, and other protocols can also be configured by users according to their demands. Clearly, it should be in the virtualization scenario. The rest of this paper is organized as follows. Related work is shown in Section 2. In Section 3, we present a use-case scenario and give an overview of the virtualization of WSNs, which is the focus of this paper. The description of the method that we propose for constructing a VWSN topology inspired by brain networks is shown in Section 4. In Section 5, we show a routing algorithm that combines a hierarchical modular structure with a simple method for the discovery of an alternative route after node failure. We evaluate our proposal against the use-case scenario of Section 6. In Section 7, we conclude this paper and describe future work.
# Stream-DBSCAN: A Streaming Distributed Clustering Model for Water Quality Monitoring
## 3 Stream-DBSCAN Water Quality Detection Clustering Model
### Build the Model
After analyzing the relationship between the water quality situation represented by each cluster and the time attribute distribution of each element in the cluster, we discovered the pattern of water quality changes in Yantai Menlou Reservoir.
# From Security Frameworks to Sustainable Municipal Cybersecurity Capabilities
## 4 Results
### People
#### 4.4.2 Adaptation and Practical Realities: Role Combination and Dispersion  
The contributors indicate that the number of roles and specializations in the ECSF framework is deemed unachievable given the current organization of municipal ICT services, including cybersecurity. This results in individuals experiencing too many tasks and, especially in smaller organizations, a lack of professional support for discussing complex cybersecurity issues. This lack of resources to fill the roles also contributes to a less proactive stance, with cybersecurity struggling to keep pace. This leads to risks being identified late in the process, ultimately affecting efforts to implement digital solutions.  
Facing a situation of both a general lack of personnel with sufficient knowledge and skills (in relation to the ECSF role definitions) and understaffing/under-resourcing, the municipalities utilized two main strategies: role combination and role dispersion. Contributors described that roles are frequently combined--for example, Cybersecurity Architect, Implementer, and Incident Responder for a technical role or CISO, Risk Manager, and Auditor for a governance-focused role. In smaller municipalities, where no dedicated technical security role is established, the technical role is also combined with other technical tasks outside cybersecurity. On the other hand, several persons could collectively serve a role (role dispersion); an example was a cross-functional group serving as a joined CISO function.  
Particularly in smaller organizations, the lack of dedicated colleagues to discuss complex cybersecurity issues and share the workload was acutely felt. This resource scarcity contributes to a more reactive cybersecurity posture, where efforts struggle to keep pace with emerging threats and proactively address vulnerabilities. Consequently, risks are often identified late in the process, at times hindering the effective implementation of secure digital solutions. In some instances, contributors conceded that some roles exist as \"paper roles\"--formally recognized but lacking sufficient resources, dedicated skills, and role-specific knowledge for effective fulfillment. Conversely, some cybersecurity roles have evolved organically, with personnel in these positions described as having \"created their own role\" based on necessity and individual initiative.  
Most contributors reported challenges in recruiting personnel with documented cybersecurity education or experience, such as market competition for the same resources, limitations on salaries compared with the private sector, or geographical localization.
# High-Frequency and Spectrum-Clean Shear-Horizontal Acoustic Wave Resonators with AlN Overlay
## 4 Propagation Characteristics of SH Wave in AlN/LiNbO\({}_{3}\)/Si
### Slowness Curve and Propagation Direction  
Although the propagation direction can be lithographically controlled in most cases along the \(X\) direction (all the previous analysis assumes the \(X\) propagation direction), the polar plots of propagating characteristics versus propagation direction can be good indicators for understanding the wave properties as well as fostering the device design [20]. Figure 11a,b show the slowness (\(S\)) curve and the \(k_{\rm eff}\)\({}^{2}\) of the SH wave versus different propagation directions on the LiNbO\({}_{3}\) substrate, LiNbO\({}_{3}\)/Si (\(h_{\rm LiNbO3}\)/\(\lambda\) = 0.25), and AlN/LiNbO\({}_{3}\)/Si (\(h_{\rm LiNbO3}\)/\(\lambda\) = 0.25, \(h_{\rm AlN}\)/\(\lambda\) = 0.2). The slowness curve for the LiNbO\({}_{3}\) substrate shows a concave feature, whereas for the LiNbO\({}_{3}\)/Si and AlN/ LiNbO\({}_{3}\)/Si stacks it is nearly straight near the \(x\)-axis, indicating minimal diffraction of the nonleaky SH wave. Usually, for a well-guided wave with convex slowness curves, a faster region is required at the lateral ends to guide the wave, and for the concave case there might be lateral leakage to the fast regions. For the \"straight\" type [3], the IDT gap region design would be different and less sensitive to the concave or convex cases. In addition, from Figure 11a, the phase \(v_{\rm p}\)'s is largely enhanced in all propagation directions after adding the AlN overlay.  
Figure 10: Calculated \(TCF_{\rm s,lst}\) and \(TCF_{\rm p,lst}\) by varying AlN thickness for the SH wave propagating in AlN/LiNbO\({}_{3}\)/Si. The inset depicts the short-circuited and open-circuited grating conditions, corresponding to resonance and parallel resonance, respectively.  
In Figure 11b, the \(k_{\text{eff}}^{2}\) decreases drastically when the propagation direction deviates from the \(X\)-axis. In propagation directions close to \(X\), the \(k_{\text{eff}}^{2}\) is slightly reduced from LiNbO\({}_{3}\) substrate to the LiNbO\({}_{3}\)/Si nonleaky stack (agreeing with Figure 2b in the X axis case); in propagation directions close to \(Z\), the \(k_{\text{eff}}^{2}\) is very slightly improved from LiNbO\({}_{3}\) substrate to the LiNbO\({}_{3}\)/Si bonded structure. After adding the AlN overlay on top of the transducer, the \(k_{\text{eff}}^{2}\) reduces drastically due to the mechanical loading effect, and in the \(X\) direction the \(k_{\text{eff}}^{2}\) falls to between 10% and 15%, which is still more than enough for the advanced LTE bandwidth specification and most of the 5G NR bands.  
In summary, the optimized propagation direction for the AlN/LiNbO\({}_{3}\)/Si resonator is the material \(X\) direction of LiNbO\({}_{3}\) thanks for the fast wave-travelling velocity and the ultralarge \(k_{\text{eff}}^{2}\). The wave is also better-guided in the transversal direction compared to the traditional leaky-SH resonator based on LiNbO\({}_{3}\) substrate.