esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Methods
### Loss Function  
The total loss includes background loss \(\mathcal{L}_{B}\), glare loss \(\mathcal{L}_{F}\), and reconstruction loss \(\mathcal{L}_{rec}\), which can be expressed as follows:  
\[\mathcal{L}=w_{1}L_{B}+w_{2}L_{F}+w_{3}L_{rec} \tag{20}\]  
where \(w_{1}\), \(w_{2}\), \(w_{3}\) are, respectively, set to 0.5, 0.5, and 1.0 in our experiments.  
In the computation of the background loss, the glare removal network can be defined as \(\Phi\), which takes the glare-corrupted image \(I\) as input. The estimated output image \(\hat{I}_{0}\) and the estimated flare image \(\hat{F}\) can thus be expressed as follows:  
\[\big{(}\hat{I}_{0},\hat{F}\big{)}=\Phi(I) \tag{21}\]  
Furthermore, the background loss \(\mathcal{L}_{B}\) can be expressed as follows:  
\[L_{B}=L_{1}\big{(}\hat{I}_{0},I_{0}\big{)}+L_{ggg}\big{(}\hat{I}_{0},I_{0} \big{)} \tag{22}\]  
where \(L_{1}\) denotes the L1 loss, \(L_{ggg}\) denotes the VGG perceptual loss, and \(I_{0}\) is marked background image.  
The flare loss \(L_{F}\) shares the same formulation as the background loss, which can be denoted as follows:  
\[L_{F}=L_{1}\big{(}\hat{F},F\big{)}+L_{ggg}\big{(}\hat{F},F\big{)} \tag{23}\]  
where \(F\) denotes the marked glare image.  
Finally, the reconstruction loss \(L_{rec}\) is defined as follows:  
\[L_{rec}=\big{|}I-Clip\big{(}\hat{I}_{0}\oplus\hat{F}\big{)}\big{|} \tag{24}\]  
where \(\oplus\) denotes the element-wise addition performed in the linearized, gamma-decoded domain using the previously sampled \(\gamma\). The resulting values are subsequently clipped to the range [0, 1] to ensure valid intensity levels.
## 6 Simulation Results and Discussions  
In this section, numerical evaluations are provided to validate the results in Sections 3-5. As shown in Figure 2, assume the BS is located at \(P_{\text{BS}}=[0\text{ m},\,h_{\text{BS}}\text{ m},\,200\text{ m}]^{\text{T}}\) and the vehicle is located at \(P_{\text{V}}(t)=[x_{\text{start}}+v\cdot t\text{ m},\,h_{\text{V}}\text{ m},\,100\text{ m}]^{\text{T}}\) where \(t\in\mathbb{Z}_{0}^{+}\) and its range is from 1 s to 10 s. Without loss of generality, we can consider all the RISs with zero \(z\) coordinate, i.e., \(\Delta_{i}^{z}=0\). The simulations are averaged over \(10^{3}\) different realizations. Note that \(d_{x}=d_{y}=\lambda/(2\sqrt{\pi})\). This is because all the RISs are modeled as lossless diffuse reflectors with isotropic antenna gain, and we ignore the correlations between the elements of each RIS. Parameter settings are shown in Table 5. Unless otherwise specified, all parameters are set to the values in Table 5 by default. All results are obtained by using a personal computer with a 1.8 GHz i7-8565U CPU and 8 GB RAM. The software environment is MATLAB2021a.
## 3 Proposed System
### Attribute-Based Encryption  
The system proposed in this paper uses an attribute-based encryption algorithm. It operates more flexibly than the existing attribute-based encryption algorithm. In other words, the delegation and withdrawal of attributes are simultaneously performed. The delegate holding the secret key may delegate the decryption authority of the ciphertext to the secondary user. Here, the delegate and the secondary user must correspond to a set of attributes. In addition, the delegate can decide whether to delegate his or her authority and then withdraw the attribute. This is to revoke the authority to decrypt the delegated ciphertext so that it cannot be accessed.  
The encryption algorithm consists of seven steps in total. Each algorithm includes a preparation step, a key generation step, an encryption step, a delegation step, a secondary delegation step, a secondary decoding step, and a decryption step. The preparation step and the key generation step are performed by a central certification authority. The encryption and delegation steps are performed by the delegate. The secondary delegation step and the secondary decryption step are performed by the policy management agency. Finally, the decryption step is performed by the secondary user.
# Multi-Criteria Optimization in Operations Scheduling Applying Selected Priority Rules
## 3 Multi-Criteria Optimization in Scheduling
### Multi-Criteria Optimization Problem
Additionally, [PERSON] [40] provided a comprehensive introduction and survey of research results in multicriteria scheduling. In the context of scheduling problems with multiple objectives, the concept of Pareto-optimality acts as commonly accepted. [PERSON] [1] states that \"a schedule is Pareto-optimal if it is impossible to improve on one of the objectives without making at least one other objective worse\". The idea of Pareto-optimality in MOP solving implies the existence of the set of trade-off solutions instead of one single solution, therefore an expert in a position of decision maker (DM) is needed to support the identification of the most preferred solution of all available. If a priori method of MOP solving is used, DM expresses the preferences at the start of a solving process. In the case of the a posteriori method, the selection of MOP solution is generated after finding a set of optimal solutions. An interactive method is employing DM preferences directly into the running process of solving. Within the classification of the a priori methods, several approaches are developed to deal with multi-objective scheduling problems in a priori arrangement. To mention some of them, in a hierarchical approach, the objectives are ranked in priority order and optimized in this order. In goal programming, all the objectives are considered as constraints expressing satisficing levels, and the aim is to obtain a solution that offers a value as close as possible to the desired goal for each objective. One objective can also be selected as the main one and then an optimized subject to other objectives [14]. The utility approach is based on an aggregation of multiple optimization objectives into one single utility or weighting function. The utility function very frequently employs a weighted linear combination of the objectives, and it represents the composite objective function in the most popular Weighted Sum Method (WSM). [PERSON] and [PERSON] [24] present other methods from the utility approach category, such as the more general weighted global criterion method, or weighted min-max method, weighted product method, and exponential weighted criterion method.
# Method of Calculating Desynchronization of DVB-T Transmitters Working in SFN for PCL Applications
## 6 TDoA in SFN  
In the case of multiple emitters working in an SFN, the results of cross-correlation are different. Considering two SFN fully-synchronized transmitters as sources of emission, more than one peak will  
Figure 3: TDoA (Time Difference of Arrival) geometry: (**a**) for single hyperbola pair of receivers (Rx1 and Rx2), Tx are hyperbola foci and the difference of R\({}_{1}\) and R\({}_{2}\) distances is constant; (**b**) by adding more hyperbolae one can find the localization of the source of emission.  
occur. Since each receiver acquires the same signal from two transmitters simultaneously (see Figure 4), there are four possible peaks on cross-correlation:  
1. Correlation between Signal 1-1 and Signal 1-2. This peak is the result of the TDoA for the signal emitted by Tx1.
2. Correlation between Signal 2-1 and Signal 2-2. This peak is the result of the TDoA for the signal emitted by Tx2.
3. Correlation between Signal 1-1 and Signal 2-2. This peak is a ghost detection. No physical interpretation.
4. Correlation between Signal 1-2 and Signal 2-1. This peak is a ghost detection. No physical interpretation.  
Assuming that the precise position of transmitters (\(x_{Ti},y_{Ti}\)) is well known, the calculation of the exact time of occurrence for all four peaks is possible. Writing distance between point \(A(x_{A},y_{A})\) and point \(B(x_{B},y_{B})\) as  
\[R_{A-B}=\sqrt{\left(x_{A}-x_{B}\right)^{2}+\left(y_{A}-y_{B}\right)^{2}}, \tag{4}\]  
and the times of appearance for all described peaks are  
\[\begin{array}{l}t_{1}=\frac{R_{\text{Rx1-T3-T4}}-R_{\text{Rx2-T4}}}{t_{2}}\\ t_{3}=\frac{R_{\text{Rx1-T2}}-R_{\text{Rx2-T2}}}{t_{3}}\\ t_{4}=\frac{R_{\text{Rx2-T1}}-R_{\text{Rx1-T2}}}{c}\end{array} \tag{5}\]  
Through the comparison of the calculated values and the real results of cross-correlation, it is possible to compute:  
* The TDoA measurement error in the case of \(t_{1}\) and \(t_{2}\)
* The sum of the TDoA measurement error and the transmitters' synchronization accuracy in the case of \(t_{3}\) and \(t_{4}\).  
Synchronization accuracy information can be extracted due to the fact that the peaks at \(t_{3}\) and \(t_{4}\) are results of the correlation of two signals each from two different transmitters. Based on this fact, the authors have proposed an algorithm to determine the desynchronization time between DVB-T transmitters, which is described in the next section of this paper.
# A Robust Intelligent Controller for Autonomous Ground Vehicle Longitudinal Dynamics
## 1 Introduction
The combination has proven its superiority, with an average tracking error of 0.718 kph in various driving conditions. Moreover, the RBNN was deployed by [27] in order to design an intelligent braking system while contributing to the brake assist system (BA) as an ADAS feature. A self-adaptive PID using the RBNN was proposed by [28] for improving the ACC and stop-and-go features. The vehicle dynamic model utilized in this study included an engine model, a transmission system model, and a brake model. This approach has proven to be very reliable, with low-speed tracking errors of less than 0.203 (m/s), which leads to better driving comfort. In addition, a hybrid solution involving the mentioned NN algorithm and a fuzzy system was investigated in [29] by designing an ANFIS strategy to diagnose centrifugal pumps' fault types. Famous for its ability to approximate and identify nonlinear quantities in modeling and control, the RBNN is widely involved in different aspects of AGV control. The vast majority of these studies focused on contributing to lateral stability issues [1; 18; 30; 31]. Notwithstanding its utility for longitudinal control purposes, it remains underutilized, with a tiny portion of the research addressing the longitudinal control of the AGVs. Consequently, this study was intended to bridge this gap by suggesting a novel robust intelligent control technique for addressing AGV longitudinal dynamics. This study proposes an adaptive SMC law taking advantage of the RBNN for the robust stability of AGV longitudinal dynamics, considering uncertainties and external disturbances. The salient distinguishing points of this research are as follows:  
* The proposed design contributes to the ADASs in general and ACC with ISA in particular. * An adaptive control design provides disturbance rejection and uncertainty compensation. * The novel robust law is a function of the upper bounds of lumped uncertainties and does not require any prior knowledge of the latter. * An immediate adaptation identifies the upper bounds of perturbations and uncertainties via estimating the neural architecture's parameters. The current study is arranged as follows: The vehicle modeling is displayed in Section 2. The proposed control strategy, with the necessary derivations and stability proof, is specified in Section 3. Section 4 exemplifies the performance of the suggested method through a simulation study. A conclusion is then reached in Section 5.
# Feedback Approach for the Relay Channel with Noisy Feedback and Its Security Analysis
## Appendix B Proof of Theorem 2
### Channel Transformation Strategy
\begin{split}& Y_{f,n}=\mathbf{h}_{\text{ds}}X_{f,n}+\eta_{f,n},\\ \Rightarrow&\frac{\mathbf{h}_{\text{ds}}^{\mathcal{H}} }{\|\mathbf{h}_{\text{ds}}\|^{2}}Y_{f,n}=X_{f,n}+\frac{\mathbf{h}_{\text{ds}}^{ \mathcal{H}}}{\|\mathbf{h}_{\text{ds}}\|^{2}}\eta_{f,n},\\ \Rightarrow& Y_{f,n}^{{}^{\prime}}=X_{f,n}+\eta_{f,n}^{{}^{ \prime}},\end{split}  \tag{27}\]where \(\mathbf{h}_{\text{ds}}\in\mathbb{C}^{B\times 1},X_{f,\mu}\in\mathbb{C}^{1\times 1},Y^{ \prime}_{f,n}=\frac{\mathbf{h}_{\text{ds}}^{2}Y_{f,n}}{\|\mathbf{h}_{\text{ds}} \|^{2}}\in\mathbb{C}^{1\times 1},\eta^{\prime}_{f,n}=\frac{\mathbf{h}_{\text{ds}}^{M} \eta_{f,n}}{\|\mathbf{h}_{\text{ds}}\|^{2}}\in\mathbb{C}^{1\times 1}\sim \mathcal{CN}(0,\frac{\sigma_{f}^{2}}{\|\mathbf{h}_{\text{ds}}\|^{2}})\). This indicates that the complex-valued feedback SIMO channel can be transformed into complex-valued SISO channels. Therefore, according to (A24), (A25) and (A27), we complete the transformation of MISO FRC with noisy feedback into the SISO FRC with noisy feedback.
## 3 Future Perspectives  
Despite the mentioned advantages, OCT-A is still infrequently used in children. Only few ophthalmic pediatric conditions have been studied by OCT-A and normal values for patients younger than 18 years are not available yet. Although there have been some initial reports for Chinese children [84], normative databases for OCT-A parameters in healthy pediatric populations for other ethnicities is still not accessible. Moreover, in all likelihood a specific database should be built for each of the commercially available OCT-A devices since they use different segmentation algorithms.  
A non-invasive technique such as OCT-A would be ideal for pediatric disease screening purposes in patients at risk or as an aid in assessing the stage of a disease. For example, studies by OCT-A in diabetic adults have found a correlation between microvascular abnormalities and different stages of the retinopathy. Even in diabetic patients, with the absence of diabetic retinopathy, some alterations have been found [39]. Until now, OCT-A measurements between healthy and diabetic children without  
Figure 2: Optic nerve OCT (DRI OCT Triton system, Topcon) corresponding to the right eye of a patient with chronic bilateral papilledema secondary to intracranial hypertension (**A–E**). (**A,B**) Vascular tortuosity in different segmentations and enlargement of the vessels and capillaries can be seen. (**C–E**) The corresponding vessel density map, fundus photograph and B scan are provided in (**C–E**) respectively.  
retinopathy have not shown differences and longitudinal studies will be needed to stablish if these measurements have any predictive value [107].  
OCT-A could also be useful in a different spectrum of pediatric conditions in which anatomic ocular changes are not a hallmark of the disease. In this regard, a recent study has found that OCT-A reveals diminished superficial and deep retinal capillary density in the macula of patients with amblyopia [104]. In the future, pediatric neurologic diseases could also be analyzed by OCT-A in search for new biomarkers.
## 2 Design and Fabrication
### Design and Circuit Analysis
#### 2.1.2 Models outside the Segment Box  
The capacitance effect should be considered, owing to the compact structure of the outer spiral inductor. \(C_{i,j}\) is the coupling capacitance between the adjacent metal tracks, where \(i\) and \(j\) are the number of the segments marked in Figure 2a. The capacitor is calculated using the following Equation [21]:  
\[C_{i,j}=\ \varepsilon_{0}\frac{Q}{V}\, \tag{7}\]  
where \(\varepsilon_{0}\) is the vacuum permittivity, and \(Q\) and \(V\) are the potentials between the normalized charge and the center position of the two ends, respectively. In addition, the capacitive effect of the four air bridge structures as a result of the air gap between the leads and bond layers cannot be neglected and is expressed as follows [21]:  
\[C_{ABi}=\frac{\varepsilon_{0}(\textit{overlap area})}{t_{text}}. \tag{8}\]  
The two types of capacitors can be evaluated simultaneously using the length calculated in Section 2.1.1 as given in Equations (7) and (8).
# Influence of Ingestion of Lactulose on \(\gamma\)-Lactones Enanating from Human Skin Surface
## 2 Methods
### Skin Gas Sampling and Analysis  
The amount of dermal emission of human skin gases was determined in healthy participants using a passive flux sampler (PFS) in conjunction with GC-MS [15; 16; 19]. The target analytes were six kinds of \(\gamma\)-lactones, namely, \(\gamma\)-hexalactone (C6), \(\gamma\)-heptalactone (C7), \(\gamma\)-octalactone (C8), \(\gamma\)-nonalactone (C9), \(\gamma\)-decalactone (C10), and \(\gamma\)-undecalactone (C11), and SCFAs, such as acetic acid, propionic acid, and butyric acid.  
Figure 2 shows a schematic drawing of the PFS (MonoTrap(r) SG DCC18) commercially available from GL Sciences, Tokyo, Japan. The tiny sampling device was composed of a glass vial, a polypropylene (PP) screw cap, a disk-type adsorbent material as trapping media, and a PTFE O-ring as a stopper. The PFS was gently fixed on the surface of the participant's forearm using a piece of medical tape for one hour.  
The trapped human skin gases were eluted into 500 \(\upmu\)L dichloromethane with 15 min of ultrasonic extraction. The chromatographic analysis was performed for the extracts using a gas chromatograph model 7890B (Agilent Technologies, Santa Clara, CA, USA) and a mass selective detector, JMS-Q1050 GC MkII (JEOL, Tokyo, Japan). One microlitre of sample extracts, blank extracts, and quantitation standards was injected with a split ratio of 35:1 into a DB-IMS capillary column (Agilent Technologies, Santa Clara, CA, USA), 30 m \(\times\) 0.25 mm I.D. \(\times\) 0.25 \(\upmu\)m film thickness. The carrier gas was G1 grade helium (Taiyo [PERSON], Tokyo, Japan) at a flow rate of 1.0 mL min\({}^{-1}\). The injector port was maintained at 280 \({}^{\circ}\)C. The oven temperature was programmed as follows: held at 50 \({}^{\circ}\)C for 8 min, ramped at 6 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\) to 120 \({}^{\circ}\)C, and ramped at 20 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\) to 280 \({}^{\circ}\)C and held for 2 min. Signals for sample extracts were acquired using a real-time Selected Ion Monitoring mode. The emission flux of the target human skin gas, \(E\) (ng cm\({}^{-2}\) h\({}^{-1}\)) was calculated using the following formula:  
\[E=\frac{W}{S\cdot t} \tag{1}\]  
where \(W\) is the amount (ng) of collected human skin gases, \(S\) is the effective cross-section of the adsorbent (0.594 cm\({}^{2}\)), and \(t\) is the sampling duration (1.0 h). The emission fluxes of SCFAs were calculated as their free acid forms, which are the typical volatile species of SCFAs.  
Figure 2: Schematic drawing of the PFS for the collection of \(\gamma\)-lactones and SCFAs. PFS was put on the surface of the non-dominant forearm of a healthy participant.
# Comparative Analysis of Measurement Tools in the Cognex D900 Vision System
## 3 Applications of Cognex Vision Systems
### Inspection--Defect Identification  
Machine vision systems are critical for detecting defects, contaminants, functional faults, and other irregularities in manufactured products. These systems can inspect pharmaceutical tablets for defects, verify icons or the presence of pixels on displays, and measure backlight contrast levels on touchscreens.  
Machine vision systems also ensure product completeness. For instance, in the food and pharmaceutical industries, they verify that a product matches its packaging and check safety seals, caps, and rings on bottles [29].  
Designed for inspection, machine vision systems monitor the visual appearance of the material under observation. By using statistical analysis, the system automatically identifies potential surface defects and classifies them into groups based on similarities in contrast, texture, and/or geometry [29].
# TAB Series-Resonant DC-DC Converter and Multi-Phase-Shift Based Global Optimization Modulation
## 2 Analysis of Topology and Characteristics
### Power Characteristics Analysis  
The power flow analysis in this section adopts the fundamental harmonics approximation (FHA) approach. The variables are normalized to port 3 and represented by \" \" \". \(v_{\text{ABf}}\)\({}^{\prime}\)(\(t\)), \(v_{\text{CDf}}\)\({}^{\prime}\)(\(t\)), and \(v_{\text{EFF}}\) are calculated as  
\[v_{\text{ABf}}^{\prime}(t)=\frac{4v_{3}}{k_{1}\pi}\cos(\frac{D_{1}\pi}{2})\sin( \omega_{\text{s}}t+\varphi_{13}\pi) \tag{1}\]  
Figure 1: Schematic of the proposed three-port converter.  
Figure 2: Waveforms of multi-phase-shift modulation for the proposed converter.  
\[v^{\prime}_{\text{CDf}}(t)=\frac{4v_{3}}{k_{2}\pi}\cos(\frac{D_{2}\pi}{2})\sin( \omega_{\text{s}}t+\varphi_{23}\pi) \tag{2}\]  
\[v_{\text{EFf}}(t)=\frac{4v_{3}}{\pi}\cos(\frac{D_{3}\pi}{2})\sin(\omega_{\text{s }}t) \tag{3}\]  
where \(k_{1}=n_{1}v_{3}/n_{3}v_{1}\) is the voltage gain between port 1 and port 3 and \(k_{2}=n_{2}v_{3}/n_{3}v_{2}\) is the voltage gain between port 2 and port 3.  
Combining the equivalent circuit of the three-winding transformer, the equivalent circuit of the proposed converter is shown in Figure 3a. The impedance of each branch is calculated as  
\[\left\{\begin{array}{l}X_{12}=\frac{X^{\prime}_{12}X^{\prime}_{22}}{X^{ \prime}_{13}}+X^{\prime}_{r1}+X^{\prime}_{r2}\\ X_{13}=\frac{X^{\prime}_{12}X^{\prime}_{22}}{X^{\prime}_{11}}+X^{\prime}_{r1}+ X^{\prime}_{r3}\\ X_{23}=\frac{X^{\prime}_{22}X^{\prime}_{23}}{X_{r1}}+X^{\prime}_{r2}+X^{\prime} _{r3}\end{array}\right. \tag{4}\]  
where \(X_{\text{r1}}\), \(X_{\text{r2}}\) and \(X_{\text{r3}}\) are the impedance of the resonant tank on port 1, port 2, and port 3, respectively.  
Then, the transformer current of each branch of the equivalent circuit is calculated as  
\[i_{\text{r13f}}(t)=\frac{4v_{3}}{\pi X_{13}}\bigg{[}\cos(\frac{D_{3}\pi}{2}) \cos(\omega_{\text{s}}t)\,-\,\frac{1}{k_{1}}\cos(\frac{D_{1}\pi}{2})\cos( \omega_{\text{s}}t+\varphi_{13}\pi)\bigg{]} \tag{5}\]  
\[i_{\text{r23f}}(t)=\frac{4v_{3}}{\pi X_{23}}\bigg{[}\cos(\frac{D_{3}\pi}{2}) \cos(\omega_{\text{s}}t)\,-\,\frac{1}{k_{2}}\cos(\frac{D_{2}\pi}{2})\cos( \omega_{\text{s}}t+\varphi_{23}\pi)\bigg{]} \tag{6}\]  
\[i_{\text{r12f}}(t)=\frac{4v_{3}}{\pi X_{12}}\bigg{[}\frac{1}{k_{2}}\cos( \frac{D_{2}\pi}{2})\cos(\omega_{\text{s}}t+\varphi_{23}\pi)-\frac{1}{k_{1}} \cos(\frac{D_{1}\pi}{2})\cos(\omega_{\text{s}}t+\varphi_{13}\pi)\bigg{]} \tag{7}\]  
Next, the average transfer power in a switching period is expressed as  
\[P_{13}=\frac{8v_{3}{}^{2}}{\pi^{2}k_{1}X_{13}}\cos(\frac{D_{1}\pi}{2})\cos( \frac{D_{3}
## 3 Formal Description of the Problem  
Let us consider a warehouse layout where each product \(i\in I\) has to be processed by different workstations, \(j\in J\). A typical working day has a final time \(t_{f}>0\). There is a depot station, 0, where all products are located initially. An AGV has to move each product \(i\in I\) (one at the time) from depot 0 to each workstation \(j\in J\) and then, once the product \(i\) has been processed in \(j\), bring it back to depot 0. Given a product \(i\in I\) and a workstation \(j\in J\), the actual time required by \(i\) to complete a circuit \(0\to j\to 0\), \(T_{ij}\geq 0\), is given by the sum of the travel and processing times in ideal conditions, \(t_{ij}^{*}>0\), plus the sum of the actual delays throughout the circuit, \(D_{ij}\geq 0\). In other words: \(T_{ij}=t_{ij}^{*}+D_{ij}\), where \(D_{ij}\) is a function of time \(t\in[0,t_{f})\), i.e., \(D_{ij}=D_{ij}(t)\). Notice that delay times are dynamic, i.e., the delays are a function of the system conditions at each moment in time \(t\in[0,t_{f})\). It is precisely this dynamic behavior that makes the optimization problem a non-trivial one. In addition, completing any single circuit \(0\to j\to 0\) should take no more than a user-defined time threshold \(t_{0}>0\). Under these circumstances, the objective is to minimize the total time required for the AGV to complete all the assigned tasks. The problem can mathematically be defined as follows: