esa-sceva/satcom-chunk-collection huggingface dataset:
# Rapid Prototyping of Multi-Functional Battery Energy Storage System Applications
## 4 Application Development with the EMSOnto Approach
### Alignment with Smart Grid Domain Models  
The EMS-ontology is aligned with smart grid domain standards to provide a broadly accepted generic use case representation, covering requirement R1. In this context, appropriate domain approaches have been analyzed and used as basis to formulate the EMS-ontology. In the following, the concepts taken from those approaches are outlined.  
The structure of the EMS-ontology is motivated from concepts defined within the SGAM framework [24] and by elements introduced in the generic use case suggested by the Smart Grid Coordination Group (SGCG)--sustainable processes [40]. Both approaches are being used in various smart grid research and demonstration projects and are taken as a basis for modelling use cases [25], indicating a broad acceptance of them by the power system community. The study [40] suggests a classification of devices, actors and systems involved in a use case. Some of them are following outlined: the concept High Level Use Case (HLUC) gives a general idea of a function and is technology-neutral. An HLUC invokes Primary Use Cases (PUC) to detail its functionalities. In turn, the concept System is an arrangement of components and systems to accomplish a use case. The mentioned concepts are taken as a basis for modelling the function layer of the EMS-ontology.  
Previous studies carried out in Section 3.1 presented a list of smart grid data models. Among them, this article selects an IEC 61850 approach because of its maturity and the availability of data models for power system components and functions mainly concerned by UC1-UC5. The standard IEC 61850-7-3 [41] proposes data models for run-time signals in a communication structure. Hence, the definition of common data classes for status information (CDCSstatusInfo), measured information (AnalogueInfo), control signals (CDCSstatusCtl, CDCAnalogueCtl), status settings (CDCSstatusSet), etc. The attributes of that data are of type Boolean, TimeStamp, String, etc. The mentioned concepts motivate the design of the information layer within the EMS-ontology.
## 3 Results
### Antioxidant Capacity  
For the sequestration of the DPPH radical, the best antioxidant capacity values were found for the fungal extracts obtained via the use of the AcOEt solvent at both temperatures tested (Figure 3a,b). However, the results obtained for this solvent in the extraction times of 30 min and 2 h at 20 \({}^{\circ}\)C showed the highest antioxidant capacity values (0.45 and 0.47 mmol TEq/g, respectively), though, without statistical difference between them. In addition, extraction using US (Figure 3c) did not induce high antioxidant activity values (all the values were below 0.20 mmol TEq/g) when compared with the other assays.  
For the analysis of the antioxidant capacity of the extracts against the ABTS radical, the best results were obtained using AcOEt at 20 \({}^{\circ}\)C after 2 and 4 h of extraction (0.45 and 0.47 mmol TEq/g, respectively) and at 30 \({}^{\circ}\)C, with the same solvent, after 2 h (0.45 mmol TEq/g), without statistical differences (Figure 4a,b). For the extraction using  
Figure 2: Quantification of the total phenolic compounds in the fungal extracts obtained using the different extraction conditions (solvents/times). (**a**) Extraction at 20 \({}^{\circ}\)C. (**b**) Extraction at 30 \({}^{\circ}\)C. (**c**) Extraction using an ultrasound bath. The values that share the same lowercase letters for the same solvent do not present a statistically significant difference between them over time and the values that share the same uppercase letters between the same time and for the different solvents do not present a statistically significant difference between them according to the Tukey test (\(p<0.05\)).  
US (Figure 4c), the extracts obtained with the three solvents after 7.5 min achieved results without statistical difference from those mentioned above (0.43, 0.47, and 0.53 mmol TEq/g, respectively). It is, therefore, observed that for the sequestration of the ABTS radical, the use of US in the extraction of fungal metabolites was positive, since we obtained similar results in a much shorter extraction time when compared to the extraction without US.  
For antioxidant activity using the FRAP method, the values were significantly lower than those obtained for the sequestration of DPPH and ABTS radicals. The highest ferric reduction powers were observed for the extracts obtained at 20 \({}^{\circ}\)C via the use of AcOEt after 30 min, 2, and 4 h (0.28, 0.29, and 0.29 mmol/g, respectively) (Figure 5a,b). In addition, the application of the US (Figure 5c) did not yield favorable results regarding antioxidant activity when the FRAP method was employed. This is particularly evident when the results obtained with the extraction at 20 \({}^{\circ}\)C are considered.
## 4 Discussion
### Potential Extension and Outlook  
This research has the potential to extend to include further complexity. In the simulation, the tumor is generally considered as a structure in tissues with unique parameters for biological tissues containing tumors. Relying on the basic biological model provided in this work and the tumor model, the simulation of cancerous tissue could work. Parameters of tumor and tissues are selected according to existing public research [18; 37], such as electrical conductivity, thermal conductivity, dielectric constant, and perfusion rate. The difference is that the metabolic heat generation of the tumor itself is high. The metabolic heat generation is calculated according to the size and structure of the tumor, and it is put into the biological heat transfer model as a heat source item.  
Regarding the consideration of large vessels, the general biological tissue simulation describes the blood flow through blood perfusion, which is very vague for the analysis of vessels. Most models consider large vessels only as a heat transfer term in the heat transfer study, ignoring some key factors, including the distribution of large vessels and their essential branches, the direction and velocity of blood flow, and the relative positions of large vessels and tissues. Therefore, we suggest establishing the geometric structure of large vessels inside the tissue. Since vessels mainly transfer heat through blood flow, a physical field of fluid heat transfer is given to the large vessels to simulate the heat transfer. In addition, the small vessels in the tissue follow the traditional method, and the specific blood perfusion rate given to the tissue simulates the heat transfer of the small vessels. So, there is some potential to deal with more complex models, and that is the subject of a subsequent work.  
It is noted that the parameters selected are from some published research studies. The aim is to observe the difference in heat transfer between three universal biological tissues. However, the parameters are limited by the insufficiency of current research, only some representatives of which are considered. Thus, the details have not been fully considered. For example, the variation in thermo-physical parameters and electrical conductivity as a function of temperature, and the electrical characteristics change with the microwave frequency. There are also differences in boundary conditions, microwave antennas, etc. Therefore, the results of this work can only be used as a reference under specific conditions.
# Defending against FakeBob Adversarial Attacks in Speaker Verification Systems with Noise-Adding +
###### Abstract  
Speaker verification systems use human voices as an important biometric to identify legitimate users, thus adding a security layer to voice-controlled Internet-of-things smart homes against illegal access. Recent studies have demonstrated that speaker verification systems are vulnerable to adversarial attacks such as FakeBob. The goal of this work is to design and implement a simple and light-weight defense system that is effective against FakeBob. We specifically study two opposite pre-processing operations on input audios in speak verification systems: denoising that attempts to remove or reduce perturbations and noise-adding that adds small noise to an input audio. Through experiments, we demonstrate that both methods are able to weaken the ability of FakeBob attacks significantly, with noise-adding achieving even better performance than denoising. Specifically, with denoising, the targeted attack success rate of FakeBob attacks can be reduced from 100% to 56.05% in GMM speaker verification systems, and from 95% to only 38.63% in i-vector speaker verification systems, respectively. With noise adding, those numbers can be further lowered down to 5.20% and 0.50%, respectively. As a proactive measure, we study several possible adaptive FakeBob attacks against the noise-adding method. Experiment results demonstrate that noise-adding can still provide a considerable level of protection against these countermeasures.  
speaker verification; FakeBob adversarial attacks; defense system; denoising; noise-adding; adaptive attacks +
Footnote †: Copyright © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 2 Description and Numerical Model of the Foil under Waves
### Function of Hydrodynamic Performance  
In numerical simulations based on a CFD model, the thrust force \(F_{x}\) and the lateral force \(F_{y}\) in the direction of the \(x_{1}\) and \(y_{1}\) axes, respectively, as well as the moment \(M\) around the pitching center of the foil, can be obtained by integrating the pressure and viscous force of each element along the surface of the foil in different axis directions. Non-dimensional coefficients corresponding to the above parameters can be expressed as:  
\[C_{t}=\frac{F_{x}}{\rho V_{0}{}^{2}c/2},\ C_{y}=\frac{F_{y}}{\rho V_{0}{}^{2}c /2},C_{m}=\frac{M}{\rho V_{0}{}^{2}c^{2}/2} \tag{6}\]  
where \(C_{t}\), \(C_{l}\), \(C_{m}\) are the non-dimensional thrust force coefficient, the lateral force coefficient and the moment coefficient, respectively, and \(\rho\) is the water density. The instantaneous input power \(P_{i}\) and the output power \(P_{o}\) can be expressed as [33]:  
\[P_{i}=-F_{y}\frac{\mathrm{d}y}{\mathrm{d}t}-M\frac{\mathrm{d}\theta}{\mathrm{ d}t}P_{o}=F_{x}V_{0} \tag{7}\]  
As in Equation (6), the input power coefficient \(C_{pi}\) and the output power coefficient \(C_{po}\) can be defined as:  
\[C_{pi}=\frac{P_{i}}{\rho V_{0}{}^{3}c/2}C_{po}=\frac{P_{o}}{\rho V_{0}{}^{3}c /2} \tag{8}\]  
Based on Equations (3) and (4) and Equations (6) and (7), the propulsive efficiency of the flapping foil \(\eta\), can be obtained as:  
\[\eta=\frac{C_{T}\times|V_{0}|}{\left(\int\limits_{0}^{T}C_{ly}\
u_{1}{}^{\prime }(t)dt+\int\limits_{0}^{T}C_{m}\theta^{\prime}(t)dt\right)\times f} \tag{9}\]  
where \(y_{1}{}^{\prime}(t)\) and \(\theta^{\prime}(t)\) are the time derivatives of \(y_{1}(t)\) and \(\theta(t)\) in Equations (3) and (4), that is, the linear and angular velocity of foil, respectively. \(C_{T}\) is the average thrust force coefficient, \(C_{T}=\frac{1}{T}\int_{0}^{T}C_{l}(t)dt\)In order to analyze the wave energy utilization ability of the foil, the average power of a regular wave experienced by the foil, with unit width \(P_{w}\) and its corresponding coefficient \(C_{pw}\), can be deduced by the water wave theory:  
\[P_{w}=\frac{1}{2}\rho ga^{2}(c_{g}+V_{0}),C_{pw}=\frac{P_{w}}{\rho V_{0}{}^{3}c /2} \tag{10}\]  
where \(c_{g}\) is the group celerity of the wave (i.e., the transferring velocity of wave energy passing through a certain vertical plane in space and an integrated water volume), and \(c_{g}\) equals half the wave celerity \(c_{w}\) in deep water, (i.e., \(c_{g}=c_{w}/2=(g/k)^{0.5}/2\)). Based on Equation (10), we can continually define the wave power recovering coefficient \(C_{pr}\)[30], i.e., \(C_{pr}=C_{pow}-C_{pn}\), in which \(C_{pow}\) is the output power coefficient when the foil is with the presence of waves, while \(C_{pn}\) is that when the foil is under calm water surface with the same submergence as in waves. Finally, the percentage of wave energy recovery [30] for a flapping foil \(\eta_{r}\) can be expressed as \(\eta_{r}=C_{pr}/C_{pw}\).
## Preliminaries of MPC and Framework Overview  
### Preliminaries of MPC  
MPC is realized through an iterative on-line optimization over a moving finite prediction horizon, as illustrated in Figure 1. At time step \(k\), starting from the state \(\xi_{k}\), an optimal control sequence \(u^{*}(k+i)\), \(i\in[0,N_{p}-1]\) is computed by solving an open-loop, constrained, finite-time optimal control problem over the prediction horizon \(N_{p}\). The optimal control sequence \(u^{*}\) is bounded by the input saturations \(u_{max}\) and \(u_{min}\), and the resulting predicted states \(\xi^{*}\) satisfy the state constraints \(\xi_{max}\) and \(\xi_{min}\). Although the optimal control sequence is calculated over the prediction horizon \(N_{p}\), only the first step of that, \(u^{*}(0)\), is executed.  
Due to unmodeled dynamics, parameter uncertainties and/or external disturbances, the measured control action \(u_{m}\) and vehicle state \(\xi_{k}\) are highly likely to be different from the predicted value of those obtained from the previous loop, \(u_{p}\) and \(\xi_{p}\). The differences between them are denoted as \(u_{c}\) and \(\xi_{c}\). In the next step, the optimal control problem is resolved over a shifted horizon based on new measurements. This process is repeated at each step until the terminal requirements are satisfied.
## 3 Results and Discussion
The most prominent effect caused by the texturing of the cell layers is the decrease of the cell's total reflection, similarly to the texturing of TCO layer to minimize the reflection [28] or introduction of antireflection layer on top of the cell stack [29]. The significant decrease of reflection is observable over the entire simulated spectrum. It can be also seen that all the interference fringes get lowered and almost extinct. The effect of reflection decrease is more visible for the texture with period of 200 nm. Texturing causes a rise of the absorption within the ITO layer in the range of 400-930 nm. The rise is more prominent in the case of the 400 nm texture period. For the wavelengths above 930 nm there is an equal decrease of absorption within ITO layer for the both presented cases of the texturing. Figure 4: Simulation results of the triangular structuring with texture height and period of \(h=300\) nm and \(d=400\) nm, for both TE and TM polarizations. The increase of the absorption within Mo back electrode layer is also observable in the textured layers stack. This increase is observable at 700 nm and gradually rising as the wavelength increases. This shows that the light trapping mechanism is not achieved in the back of the absorber layer in the case of the presented configuration. Based on these results it can be concluded that the light management introduced by the back texturing of the solar cell stack has led to considerable decrease of reflection, which lead to the overall enhancement of the absorption in the absorber layer. Although the parasitic absorption in the Mo layer is not reduced, the enhancement achieved in the short wavelengths is much more prominent considering the distribution of the AM1.5 spectrum. In order to directly compare the effects of texturing on the absorption increase within the absorber layer for all simulated cases (with both triangular and convex textures), the absorption was integrated over the whole range of wavelengths and divided with the absorption in the flat reference cell. The increase of the absorption within the absorber (CIGS) layer is presented in Figure 6. The results show that the best enhancement was achieved using triangular features with 100-200 nm texture periods with high aspect ratios. The enhancement in the small wavelengths range can be attributed to the reduction of reflectance of the solar cell's surface. The enhancement of the absorption is more prominent with the further reduction of absorber's thickness. Figure 5: Absorption in individual solar cell layers (ITO, CIGS and Mo) as well as total reflection from the surface of the cell. The results are shown for flat cell, and two textured cells with different texture size (\(d=400\) nm and \(h=200\) nm; \(d=200\) nm and \(h=100\) nm). Absorption within the buffer layer (CdS) is not displayed since its change with the variation of texturing dimensions is very small.
## 3 Results and Discussion
### Water Holding Capacity, Cooling Loss, and Cooking Loss  
The WHC expresses the ability of the muscle to retain its water content. In the case of the used method, a greater value indicates greater water loss, i.e., poorer WHC. If WHC is poor then weight loss may occur leading to financial loss; moreover, it influences the sensory quality of the meat, e.g., juiciness and toughness [44]. Meat with poor WHC called PSE is found more frequently in the case of pigs stunned electrically compared to CO\({}_{2}\)-stunned [10]. Some authors have shown that pigs exposed to electrical stunning generally had higher drip loss, i.e., decreased WHC, than pigs stunned by CO\({}_{2}\)[8; 9; 18; 19]. Results showed a noticeable effect of the stunning method on WHC. The CO\({}_{2}\) stunning resulted in significantly poorer WHC compared to electrical stunning (Table 2). To the best of our knowledge, the influence of stunning methods on muscle WHC is less known but may be related to post mortem metabolism and pH changes. Due to muscle fiber contraction during rigor mortis, the water placed between myofibrils could enter the space formed between the fibers and cell membranes due to the calpain decomposition effect. From these channels, water could release to the exterior [45]. Most of the water in muscle is held by capillary forces between actin and myosin filaments. The WHC of myofibrils is determined by interfilament spacing which is mainly affected by electrostatic forces through pH [37]. Close to the meat isoelectric point that can be found in the range pH 5.0-5.4, the WHC is at a minimum due to the equalization of myofibrils' negative and positive charges which results in increasing interfilament distance [46]. Furthermore, decreasing pH reduces the ability to bind water to myofibrils' hydrophilic side chains as well. As can be seen in Figure 1, post mortem pH values of CO\({}_{2}\)-stunned pigs were significantly lower (\(p<0.05\)) compared to electrically stunned pigs which resulted in decreased WHC. However, in the context of this, the cooling loss during post mortem cooling was 3.01% (electrical) and 3.06% (CO\({}_{2}\)) of the initial weight which did not prove to be a significant difference between stunning methods (Table 2). The CO\({}_{2}\)-stunned pigs means showed a significantly higher cooking loss compared to electrically stunned, which is linked to poorer WHC in CO\({}_{2}\)-stunned meats.
# A Flexible and Simple Lossless DWT Filter Bank Using a MAXFLAT FIR Half-Band Filter
## 1 Introduction
It was shown that the class of QMFs that satisfies these conditions is quite limited. One of the most common design methods for designing two-band lossless DWT filter banks is by starting from a half-band product filter \(P(z)\) and then factorizing it to obtain the filters in the lossless filter bank, i.e., \(P(z)=H_{L}(z)H_{U}(-z)\), followed by its factorization to obtain \(H_{L}(z)\) and \(H_{U}(-z)\) that satisfy \(T(z)=cz^{-n_{d}}\). Regularity is imposed in the design of \(P(z)\) by forcing \(P(z)\) to have zeros at \(z=-1\), i.e., terms of the form \(\left(1+z^{-1}\right)\)[26; 27]. However, the number of possible spectral factors grows exponentially with respect to the order of the filters and the resulting filters are not guaranteed to be optimal. Moreover, the design of lossless DWT filters with integer coefficients for computational-complexity minimization and calculation-error free is more difficult. However, aside from the Le Gall 5/3 filters (of JPEG2000) among lossless DWT filters announced so far, integer coefficient filters are rare [11; 24; 28]. Hence, a new lossless filter bank structure is needed to obtain integer coefficient filters satisfying the perfect reconstruction conditions. The objective of this paper is to present a novel two-band lossless single (s-) filter bank which allows more computationally efficient and flexible design strategies than existing lossless filter banks [11; 24; 28]. From PR conditions a generalized filter polynomial is derived which allows a unity frequency response and gives a computationally effective and efficient filter (half of coefficients are zeros). A single, arbitrary FIR filter (with integer coefficients) is demonstrated able to be easily obtained for the proposed s-filter bank. In that sense, the design of a computationally superior and error-free filter bank through  
Figure 1: Conventional two-band filter bank. this new approach gives an additional insight into the physical significance of the design flexibility. Design examples are shown which demonstrate the power of the new technique.
# Priority-Based Machine-To-Machine Overlay Network over LTE for a Smart City
## 6 Performance Evaluation
### Discussion  
It may seem that 1320 and 1560 M2M calls per second, obtained through the use of the PM2M overlay, are not that impressive a performance limit. However, it is known that there are \"major difficulties in carrying the M2M traffic of more than 2000 UEs with an acceptable success ratio, even when all the 64 available preambles are dedicated TO M2M contention-based access\" [29]. Improvements are reported when more preambles are used, up to 500 in [29], even though this is highly unrealistic. Namely, ZC sequences are mutually orthogonal, but they nonetheless cause interference with each other and decrease the achievable SINR. An increased number of preambles thus leads to increased error probability and degraded performance.  
On the contrary, the PM2M overlay described here achieves this level of performance by using only eight out of 54 available ZC preambles, as explained in Section 3.3, which leaves the remaining 46 free for use with H2H traffic (10 preambles are set aside for handoff H2H calls); when 16 preambles are used, our CSMA overlay scheme can accommodate up to 2000 devices [12], which clearly shows its superiority over plain LTE random access for M2M calls.  
A different scheme has been recently proposed that tries to resolve collisions and, presumably, improve performance by reassigning a subset of available preambles to calls that failed an access attempt [9]. However, they use a simplified error calculation based on slotted ALOHA, which does not account for interference caused by simultaneous random access attempts and/or other channels in the neighbouring cells. Moreover, their scheme requires modification of PRACH handling for M2M traffic, as all M2M nodes are required to listen to eNodeB announcements through which the subset of preambles used for repeated access is publicized, as it changes from one PRACH resource to the next one.  
Unlike those schemes, our analysis uses the SINR calculation given in [2], which explicitly models the interference from other random access attempts both in the given and surrounding cells, which allows for more accurate and more realistic results. Furthermore, we model the impact of errors made during the medium sensing process, which no other scheme takes into account.  
Finally, we note that all other schemes use random access just to initiate connection and obtain resources, as is common for H2H calls, and the actual data are sent later. In contrast, our scheme allows short messages typical for M2M devices to be actually transmitted during random access, which would lead to reduced latency and improved performance.