esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Analysis and Optimization
### Outage Probability  
The OP is an important performance metric of wireless communications systems operating over fading channels. It is defined as the probability that the instantaneous SNR at the receiver output, \(\gamma\) falls below a predefined outage threshold, \(\gamma_{th}\). Based on this definition, the outage probability of SSC over Fisher-Snedecor \(\mathcal{F}\) fading channels can be mathematically expressed as [1]:  
\[P_{out}=\Pr(0<\gamma_{SSC}<\gamma_{th})=\int_{0}^{\gamma_{th}}f_{\gamma_{SSC}}( \gamma)d\gamma. \tag{59}\]  
Therefore, the corresponding OP is readily deduced as follows:  
\[P_{out}=F_{\gamma_{SSC}}(\gamma_{th}), \tag{60}\]  
where \(F_{\gamma_{SSC}}(\gamma_{th})\) can be obtained by using (25)-(28) and replacing \(\gamma\) with \(\gamma_{th}\) over different fading conditions. Similarly, we can also obtain the OP of SEC and SECps by using (46) and (51) over i.i.d. fading channels.  
In order to find the optimal switching threshold (\(\gamma_{\tau}^{*}\)) with the minimum outage probability, the outage threshold (\(\gamma_{th}\)) is usually chosen as \(\gamma_{\tau}^{*}\)[1], namely \(\gamma_{\tau}^{*}=\gamma_{th}\). Thus, the optimal outage performance of SCC receivers in any fading channels is reduced to the performance of the PSC receivers.
# Novel Sparse-Coded Ambient Backscatter Communication for Massive IoT Connectivity
## 6 Conclusions  
In this paper, we have proposed an AmBC modulator and detector based on sparse code to address low connectivity and channel fading issues inherent in AmBC. The sparse code was employed at RF tags relying on ambient energy harvesting and supports non-orthogonal concurrent backscatter communications providing massive connectivity to Wi-Fi AP. The sparse code using SC-BMA can be implemented to small-form factor RF devices and enables NOMA with \(M\)-ary modulation, achieving modulation diversity by extending the dimension of signal space. For signal detection, the iterative D-MPA and the channel estimation based on D-CEA were proposed to overcome the DBC. By exploiting MAI and ISI in modulation and detection, energy efficiency of AmBC can be enhanced, realizing massive connectivity for passive RF devices relying on energy harvesting. Therefore, SC-AmBC can be applied to batteryless IoT including biosensors, wearables and smart homes where a massive number of these RF devices are seamlessly integrated into existing wireless infrastructure.  
T.Y.K. has contributed to the theoretical approaches, simulation and preparing the paper; D.I.K. carried out the revisions.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF-2014R1A5A1011478).  
The authors declare no conflict of interest.
# Tuning of a Viscous Inerter Damper: How to Achieve Resonant Damping Without a Damper Resonance
## 3 Viscous Inerter Damper
### Optimal Mass Ratio  
As seen from the sequence of subplots in Figure 6, the optimal front-end inertance \(m\) is associated with the two branches meeting at a bifurcation point, similar to the tuning concept for a classic tuned mass or inerter damper [9; 18]. This bifurcation point condition requires the two clamped roots \(\rho=\hat{\rho}_{1}\) and \(\hat{\rho}_{2}\) (squares) to be inverse points to the free root at \(\rho=1\) (intermediate circle), which mathematically can be expressed by the simple relation  
\[\hat{\rho}_{1}\hat{\rho}_{2}=1 \tag{36}\]  
When this relation is squared, the product \(\hat{\rho}_{1}^{2}\hat{\rho}_{2}^{2}\) is actually the product of the two roots to the clamped characteristic Equation (32), whereby \(\hat{\rho}_{1}^{2}\hat{\rho}_{2}^{2}\) must equal the constant term in (32). This provides the optimality condition  
\[\hat{\rho}_{1}^{2}\hat{\rho}_{2}^{2}=\frac{\kappa_{1}^{\prime}}{\hat{\mu}_{1} }=1\qquad\Rightarrow\qquad\hat{\mu}_{1}=\kappa_{1}^{\prime} \tag{37}\]  
which secures that the two complex roots meet at a bifurcation point.  
The optimality criterion (37) is used to find the optimal front-end mass ratio \(\mu\) by the relation  
\[\frac{1}{\mu}=\frac{1}{\kappa_{1}^{\prime}}-\frac{1}{\mu_{1}^{\prime}} \tag{38}\]  
where \(\hat{\mu}_{1}\) has been eliminated by its definition in (23). If the residual mode ratios \(\kappa_{1}^{\prime}\) and \(\mu_{1}^{\prime}\) are determined with sufficient accuracy in (34) and (35), the expression (38) can be used directly to determine the front-end inertance \(m\) that secures optimal resonant damper behavior. However, in general, the inertance \(m=\mu m_{1}\) from (38) will differ from the \(m\) used initially to solve the clamped eigenvalue problem in (9). Thus, the tuning of \(m\) is an inherently iterative process, conveniently solved by the stepwise procedure in Table 1.
# Motor On-Line Fault Diagnosis Method Research Based on 1D-CNN and Multi-Sensor Information
## 2 Motor Multi-Source Information Fusion Processing
### Multi-Source Sensor Information Fusion Framework  
The multi-sensor fusion framework of the motor is shown in Figure 2, which consists of three parts.  
1. Multi-source sensor information data collection and preprocessing. The multi-source sensor information collected by each sensor is denoised, sliced, normalized, and divided into multiple fragments.
2. Multi-source homogeneous information data layer fusion. The method combining correlation and adaptive weighting is used to realize the data layer fusion of multi-source homogeneous sensor information of the motor, and the multi-source heterogeneous sensor information is formed.
3. Multi-source heterogeneous information feature layer fusion. The multi-source heterogeneous sensor information group of the motor is taken as the multi-channel inputs of 1D-CNN, and the data of each channel is subjected to multiple convolution and pooling operations to obtain its feature mappings [15]. Then the multi-source heterogeneous information features are flattened and fused by the Flatten layer.
## 2 Related Work
### Perception of Dynamic Obstacles  
Effective environmental exploration requires environment mapping and dynamic/static obstacle distinction [4]. For static obstacles, pixel-based occupancy methods are prevalent [1] but computationally intensive for large scales. Octree-based representations (e.g., OctoMap [2], UFOMap [3]) offer superior memory efficiency and effectively model occupied/unknown space. Hybrid approaches such as Post-D-Map [4] combine sensor data to improve static mapping accuracy. For dynamic obstacles, vision/depth methods prevail: enabling perception through depth images [5], asynchronous fusion for speed [6], or combined RGB depth for improved accuracy [7]. Point-cloud methods employ ICP for identification [8] or stereo camera clustering [9], with particle maps used for refined modeling [10]. However, a significant limitation is that these methods are predominantly specialized: focusing either on static mapping or dynamic detection. Consequently, they are often insufficient for complex environments containing multi-state obstacles, lacking integrated handling of both types simultaneously.
# Performance Evaluation Model of Landport and Seaport Collaboration Using the Support Vector Machine
## 3 Collaboration between Seaports and Dry Ports
### Selection of Kernel Function and Kernel Parameters  
The selection of kernel function and related parameters is an important process in designing the SVM evaluation model. On the premise of the research on the \"two-port\" cooperation performance, the kernel function and related parameters need to be determined. In this study, RBF is used as the inner product function in cooperative performance rating. For the RBF kernel function \(K(x,y)=\exp(-\big{|}\big{|}x-y\big{|}\big{|}/2\sigma^{2})\), the kernel width \(\sigma\) has the following properties:  
**Theorem 1**.: _In the RBF kernel function \(K(x,y)\), when \(\sigma\) tends to 0, all sample points are support vectors._  
**Theorem 2**.: _When \(\sigma\) tends to \(\infty\), the discriminant function of the SVM of the RBF kernel function is a constant; namely, all samples are classified into the same one category._  
It can be inferred from the above two theorems that, for a given training sample set, as long as \(\sigma>0\) is sufficiently small, the SVM of the RBF kernel function can correctly classify the samples; the larger the value of \(\sigma\), the poorer the classification ability of the SVM.  
The penalty coefficient C can find a compromise between the model's complexity and the training sample errors of the kernel function, which endows the model with good generalization ability.  
The insensitivity coefficient \(\varepsilon\) can ignore the error within a certain range of the true value and simultaneously ensure the existence of the global minimum solution.
# Deep Learning-Based Methods for Prostate Segmentation in Magnetic Resonance Imaging
## 5 Conclusions  
Our study demonstrates that faster and less computationally expensive DL networks can perform accurate prostate delineation and could facilitate the adoption of novel imaging parameters, through radiomics analyses, for prostatic oncologic diseases. Specifically, we assessed the performance of three DL networks using data augmentation, standardization, and normalization, and the five-fold cross-validation strategies, and the Tversky loss function in a small dataset of 85 studies. All DL networks achieved accurate prostate segmentations with a DSC > 87%. Nevertheless, differences related to training time and data requirements were highlighted. ENet and ERFNet, developed for self-driving cars, were much faster than UNet. In addition, ENet had better performance (DSC = 90.89%) than ERFNet (DSC = 87.18%). Future studies with more patients could improve the results.  
Conceptualization, A.C. and G.S.; Data curation, F.V. and G.S.; Formal analysis, A.C.; Funding acquisition, A.S. and A.C.; Investigation; A.S.; Methodology, A.S. and A.C.; Project administration, A.Y.; Resources, F.V., M.P., G.C., A.B., G.S.; Software, A.C. and N.D.; Supervision, A.Y.; Validation, F.V.; Visualization, A.C. and A.S. Writing--original draft, A.C., N.D., A.S., and F.V.; Writing--review and editing, A.C. and A.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This retrospective study was approved by the Institutional Review Board of our Hospital. Informed consent was obtained from all subjects involved in the study.  
This work was partially supported by grant W911 NF-18-1-0281, funded by the USA Army Research Office (ARO): \"Extending Accelerated Optimization into the PDE Framework\", and by grant R01-HL-143350 funded by the National Institute of Health (NIH) \"Quantification of myocardial blood flow using Dynamic PET/CTA fused imagery to determine physiological significance of specific coronary lesions\".  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 4 Methods and Heuristic Algorithm
### Nonlinear Models
#### 4.1.1 Back-Propagation Neural Network (BPNN) Model  
BPNN is a type of multilayer feed-forward neural network with a wide variety of applications. It is based on a gradient-descent method that minimizes the sum of the squared errors between actual and desired output values. The transfer function is of the neuron type. The output function is between 0 and 1, and can transform input to output for continuous nonlinear mapping [36].  
The topology of the BPNN is as follows:  
\[X^{\prime}=\left\{X^{\prime}i\right\}=2\times\frac{X_{i}-X_{\text{min}}}{X_{ \text{max}}-X_{\text{min}}}-1,(i=1,2,\ldots,n),X^{\prime}\subset[-1,1] \tag{3}\]  
where \(X_{\text{min}}\) and \(X_{\text{max}}\) are the minimum and maximum value of the input array or output vectors, and \(X^{\prime}i\) denotes the real value of each vector.  
Step 1. Calculate outputs of all hidden layer nodes:  
\[y_{j} =f(\sum_{i}w_{ji}x_{i}+b_{j})=f(net_{j})\ (i\ =1,\ldots,n\ ;\ j\ =1,\ldots,2n) \tag{4}\] \[net_{j} =\sum_{i}w_{ji}x_{i}+b_{j},\ (j\ =\ 1,\ldots,2n) \tag{5}\]  
where the activation value of node \(j\) is \(net_{j}\), \(w_{ji}\) representing the connection weight from input node \(i\) to hidden node \(j\), \(b_{j}\) represents the bias of neuron \(j\), \(y_{j}\) represents the output of hidden layer node \(j\), and \(f\) is the activation function of a node, which is usually a sigmoid function.  
Step 2. Calculate the output data of the neural network:  
\[O_{1}=\ f_{0}(\sum_{j}w_{0j}y_{i}+b_{0}),\ (i\ =\ 1,\ldots,2n) \tag{6}\]  
where \(w_{0j}\) represents the connection threshold from hidden node \(j\) to the output node, \(b_{0}\) represents the bias of the neuron, \(O_{1}\) represents the output data of the network, and \(f_{0}\) is the activation function of the output layer node.  
Step 3. Minimize the global error via the training algorithm:  
\[\text{Mean Square Error }=\frac{1}{m}\sum\left(O_{1}-Z\right)^{2} \tag{7}\]  
where \(Z\) represents the real data vector of the output, m represents the number of output.
## 2 Introduction
The perceptive impact of large-magnitude earthquakes and the intervening destructive episodes of historical and antique cultural heritage sites located in earthquake-prone areas near active faults (especially in the Zagros) are of scientific, architectural, engineering, cultural, and social significance. The Main Recent Fault (MRF), located on the north-western side of the Zagros Mountains (Figure 1b), is the most active fault system in the region where the convergence of the Arabian and Eurasian plates is oblique to the range's trend [27; 28; 29]. The oblique convergence is consummulated by the spatial separation of the strike-slip and reverse components on parallel faults [30; 31]. It seems that a significant portion of the strike-slip mechanism is spent along the MRF [32]. The trend of the MRF along its length changes from \(\sim\)330\({}^{\circ}\) in the northwest, between Nahavand and Borujerd, to \(\sim\)300\({}^{\circ}\) in the central part and to \(\sim\)315\({}^{\circ}\) in the Doroud-Borujerd basin, where it ends in frequent splay faults. Based on the geomorphology along with the northwestern and southeastern general segments of the MRF, as well as the focal mechanism of earthquakes, some researchers (e.g., [30]) suggest that the normal slip vector is approximately parallel to the middle segment (Doroud Fault), which has experienced destructive earthquakes (Figure 2a). The Doroud Fault, throughout its length, has cut through Quaternary sedimentary units, the Bakhtyari formation (Pliocene-Pleistocene), Khanehkat-Neyriz formations (Triassic-Jurassic), Gerey and limestone rock units (Triassic-Jurassic), and Dalan (Permanian) and Mila formations (Cambrian-Late Ordovician) (Figure 2b). Within and on the border of the rock units, the Doroud Fault trace with a deformation of the geomorphic features can be clearly seen. In this paper, the geomorphology and paleoseismology of the Doroud Fault, as well as its geometry and kinematics, are investigated. The primary goal of this research is to improve seismic hazard assessments, thereby providing the means to mitigate the eventual devastation caused by large earthquakes resulting from the activity of the Doroud Fault. A crucial factor in determining the seismic potential of active faults is the slip rate. The lateral slip rate of strike-slip faults is estimated by reconstructing the offset of linear features with known ages. Geomorphological analysis of surface features, such as offset channels [33; 34; 35; 36; 37; 38], can be used to figure out the offsets, or paleoseismological trenching can be used to measure the offsets from linear features that are buried, such as paleochannels [39; 40; 41; 42; 43; 44]. In this study, paleoseismology and geomorphology were used appropriately to determine the slip rate of the Doroud Fault in order to understand its seismic behavior and accurately assess the earthquake risk in SW Iran.
# Research on the Compound Jamming Method against FDA-MIMO Beamforming
## 4 Jamming Methods and Models
\begin{array}{ll}y_{n,m}&=\xi_{j1}e^{-j4\pi f_{m}f_{j}/c}e^{j\pi(m\sin\theta _{j}+n\sin\theta_{j})}\\ &=\xi_{j1}e^{-j4\pi mA\Delta ft_{j}/c}e^{j\pi(m\sin\theta_{j}+n\sin\theta_{j}) }\end{array}  \tag{39}\]  
where \(\xi_{j1}\) represents the complex coefficient after signal matching filtering, and \(\xi_{j1}{}^{\prime}=\xi_{j1}e^{-j4\pi f_{0}f_{j}/c}\). Considering a self-defense jammer at the position \(r_{j}=r_{0},\theta_{j}=\theta_{0}\), the weight vector of the target can be set as  
\[\mathbf{w}_{0}=\mathbf{S}(r,\theta)\big{|}_{r=r_{0},\theta=\theta_{0}}=\mathbf{a}(r_{0}, \theta_{0})\otimes\mathbf{b}(\theta_{0}) \tag{40}\]  
So, its normalized antenna pattern can be expressed as  
\[ \begin{array}{ll}B(r,\theta)&=\left|\frac{\mathbf{w}_{0}{}^{\mathrm{H}}.\mathbf{S} (r,\theta)}{MN}\right|^{2}\\ &=\left|\frac{1}{M}\sum\limits_{m=0}^{M-1}{e^{2\pi[-m\Delta f\frac{2(r-r_{0} )}{c}+m\frac{(\sin\theta-\sin\theta_{0})}{2}]}}\right|^{2}\times\left|\frac{1} {N}\sum\limits_{n=0}^{N-1}{e^{j2\pi m\frac{(\sin\theta-\sin\theta_{0})}{2}}} \right|^{2}\end{array}  \tag{41}\]  
After jamming forwarding, the resulting jamming signal weight vector can be expressed as  
\[\mathbf{w}_{j}=\mathbf{S}(r,\theta)\big{|}_{r=r_{0},\theta=\theta_{0}}=\mathbf{a}(r_{0}, \theta_{0})\otimes\mathbf{b}(\theta_{0}) \tag{42}\]  
Then its normalized beam gain can be expressed as  
\[ \begin{array}{ll}G=B(r,\theta)\Bigg{|}_{r=r_{0},\theta=\theta_{0}}=\left| \frac{1}{M}\sum\limits_{m=1}^{M}{e^{0}}\right|^{2}\times\left|\frac{1}{N}\sum \limits_{m=1}^{M}{e^{0}}\right|^{2}=1\end{array}  \tag{43}\]  
From Equation (43), it can be seen that the jamming signal can obtain the same maximum beam gain as the desired target, and its power was not significantly reduced at the beamforming level, thus it can form an effective jamming effect against FDA-MIMO. By adjusting the time delay, the jamming signal can appear at different range bins, thus forming an effective compound jamming effect, which can affect the normal detection and positioning of the radar.
###### Abstract
The resulting \(\alpha\)-ketoacids are then degraded to aldehydes, alcohols, carboxylic acids, esters, methanethiol and other sulfur compounds. The most of these compounds are produced by enzymatic degradation, but some of them are the result of chemical degradation in oxidation [15; 16]. The characteristics of cheese (flavor, texture, and color) are influenced by the free fatty acids (FFAs) of the milk, the main products of enzymatic hydrolysis of triacylglycerides by esterases and lipases [17]. The goat and ewe's milk contain high levels of short-and medium-chain fatty acids, in comparison with cow's milk. Specifically, short-chain fatty acids are very important because of their low perception thresholds. Some studies point out that milk fatty acid content could be a distinguishing clue between breeds [18]. Partial lipolysis occurs in \"bryndza\" cheese during its ripening. Additionally, FFAs may participate in catabolic reactions and cause an increase in the amount of aroma compounds such as methyl ketones, esters, alkanes, lactones, aldehydes, and secondary alcohols [19]. VOCs are usually analyzed by gas chromatography (GC) after the extraction or pre-concentration of the volatile fraction. The most exhaustive methods for this purpose are high vacuum distillation (HVT), solvent-assisted aroma evaporation (SAFE) or solid phase microextraction (SPME) [20; 21] combined with headspace. [PERSON] et al. [20] used SPME with gas chromatography-olfactometry (GC-O) for the determination of volatile odorants in May \"bryndza\" cheese. There were 25 olfactometric responses from the groups of alcohols, aldehydes, esters, ketones, fatty acids, and hydrocarbons recorded, depending on the degree of cheese maturation and from a GC-O point of view. There was an electronic nose (e-nose) based on a gas chromatography used for aroma profile determination of soft, steamed cheese called \"parenica\" from Slovakia in a previous study [22]. The aim of this study was to obtain microbiological parameters and parallel information of principal VOCs in \"bryndza\" cheese produced by 8 Slovak producers with the use of e-nose and a GC-MS. The other goal was to prove a significant impact of the dairies and the type of milk on the content of VOCs in \"bryndza\" cheese.
## 4 Discussion
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **KB26.5** & **KB26.5-BHRF1** \\ \hline \(\mu\) (h\({}^{-1}\)) & 0.036 \(\pm\) 0.006 & 0.048 \(\pm\) 0.007 \\ t\({}_{\text{d}}\) (h) & 19.823 \(\pm\) 3.588 & 14.593 \(\pm\) 2.001 \\ q\({}_{\text{Glu}}\) (nmol/(mg:h)) & \(-\)933.447 \(\pm\) 134.567 & \(-\)542.206 \(\pm\) 177.457 \\ q\({}_{\text{Lac}}\) (nmol/(mg:h)) & 1435.500 \(\pm\) 348.745 & 654.556 \(\pm\) 34.663 \\ Y\({}_{\text{Glu/X}}\) (mmol/L)/(10\({}^{6}\) cell/mL) & 8.805 \(\pm\) 0.361 & 3.735 \(\pm\) 0.346 \\ Y\({}_{\text{Lac/X}}\) (mmol/L)/(10\({}^{6}\) cell/mL) & 14.010 \(\pm\) 0.665 & 4.730 \(\pm\) 1.160 \\ VCD\({}_{\text{max}}\) (10\({}^{6}\) cell/mL) & 2.34 \(\pm\) 0.39 & 3.93 \(\pm\) 0.01 \\ IgG\({}_{\text{3}}\),max (mg/L) & 12.90 \(\pm\) 3.82 & 47.70 \(\pm\) 2.69 \\ q\({}_{\text{P}}\) (tg/(10\({}^{6}\) cell/h)) & 0.24 \(\pm\) 0.08 & 0.48 \(\pm\) 0.02 \\ V\({}_{\text{P}}\) (tg/L)/h) & 252.12 \(\pm\) 74.63 & 750.22 \(\pm\) 0.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Main physiological parameters determined for KB26.5 and KB26.5-BHRF1 cultures in 2 L bioreactors. Values were calculated as the means of data obtained from duplicate cultures. for the production of lactate is based on the regeneration of the reducing power (NADH) in the cytoplasm due to the high glycolytic fluxes [46; 48]. In this regard, there are two ways to regenerate NADH into the cytoplasm: (1) lactate generation and (2) malate-aspartate shuttle [48]. It should be noted that the malate-aspartate shuttle facilitates the regeneration of NADH and increases the TCA cycle flux (importing malate), thereby generating energy in the form of ATP. However, the generation of lactate provokes a total loss of both carbon sources and ATP generation. Therefore, the mechanism of lactate generation may lie in the flux limit of the Malate-Aspartate Shuttle, leading cells to generate lactate and display this wasteful metabolism. In the breakdown of glucose into two pyruvate molecules, two molecules of ATP and two NADH are generated. Since the inner mitochondrial membrane is impermeable to NADH, the malate-aspartate shuttle works as an indirect transport system. It has been reported that the flux through this transport occurs at lower rates than glycolysis [49], and that the increased LDH activity is due to the inability to transport NADH through the shuttle at the same rates at which it is generated. Under conditions of increased cellular energy demand, higher glycolytic fluxes are observed, and consequently, NADH production rates increase proportionally. Such an increase in the need for NAD\({}^{+}\) regeneration is compensated for by higher LDH activity in the cytosol, as not much increase is observed in the malate-aspartate shuttle fluxes [50].