esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of a Configurable Spike-Encoding Circuit Based on Focal Plane Array
## 3 Circuit Implementation and Simulation Results
### Encoding Circuit B  
This scheme is similar to the multi-color charge packet counting scheme in that it utilizes independent integration capacitors for frequency and temporal encoding modes, which optimize the capacitor capacitance accordingly. A newly developed comparator allows the reference signal to be adjusted freely [30], especially the ramp reference signal. Figure 9 depicts circuit construction.  
Figure 8: (**a**) Frequency encoding working mode, where YP is the pulse output and vint0 is the voltage waveform of the integrating capacitor. (**b**) Temporal encoding working mode, where YP is the TTFS pulse width output and vint0 is the voltage waveform of the integrating capacitor.  
Figure 7: Realization of circuit design in scheme A.  
The circuit in scheme B was primarily adjusted as follows:  
* Extra integral capacitor  
Different integration capacitors are designed for frequency-encoding mode and temporal encoding mode, respectively (100 fF for frequency-encoding mode, 900 fF for temporal encoding mode), as shown in Figure 10. The integral capacitor working in frequency-encoding mode is small and can be quickly filled to generate pulses. The integration capacitor working in temporal encoding mode is relatively large. Under the minimum PD current, the capacitance integration time covers the entire technical cycle as much as possible. Only one pulse is generated to reduce circuit energy consumption.  
* Self-biased comparator  
A specially designed rail-to-rail comparator with self-biased voltage is used. The comparator extends the comparison range between the power supply and ground to reduce delay. It is more sensitive to small PD currents, as depicted in Figure 11.  
Figure 10: Realization of circuit design in scheme B.  
Figure 9: Circuit diagram of encoding circuit B.  
* Ramp wave reference voltage Scheme B uses the ramp wave as a reference voltage for the comparator. The ramp wave starts at the beginning of the counting cycle, ends at the end of the technological cycle, and ends at the level of the power supply voltage. It is also possible to generate pulses with a fixed DC level as the reference voltage. In temporal encoding mode, however, the intersection position of the reference voltage and current is nonlinear in time due to different PD inputs. Consequently, we used the ramp wave as the comparator reference voltage in temporal encoding mode to reduce spike nonlinearity and improve interpretability.  
The simulation of Scheme B is depicted in Figure 12, where YP is the temporal encoding mode output, \(V_{int1}\) is the voltage waveform of the temporal encoding mode's integrating capacitor, and \(V_{ref}\) is the ramp reference signal. When the input current is low, the TTFS output pulse width is extremely broad and may extend the entire counting period.
## 3 Proposed System
### Attribute-Based Encryption
#### 3.2.1 Bilinear Mapping  
A bilinear map is a function that combines two vector spaces to create another third vector space. Let \(G_{1},G_{2}\), and \(G_{T}\) be cyclic groups of the same order. Given that \(G_{1}\times G_{2}\) is a product group, the bilinear map to \(G_{T}\), the product of \(G_{1}\) and \(G_{2}\), can be expressed as follows: \(g\) is the generator of \(G_{X}\), \(e\) is a function of the bilinear mapping, and \(u\) and \(v\) are elements of \(G_{1}\) and \(G_{2}\). Bilinear mapping \(e:G_{1}\times G_{2}\to G_{T}\) (\(G_{T}\) being the output space of the bilinear mapping) with respect to the two cyclic groups \(G_{1},G_{2}\) has the following three characteristics [5]:  
* Bilinear: \(e(u^{a},v^{b})=e(u,v)^{ab}\) for all \(u\in G_{1},v\in G_{2}\) and all \(a,b\in Z\);
* Nondegenerate: \(e(g,g)\
eq 1\) for the generator \(g\in G_{X}\) of \(G_{X}(X=1,2)\);
* Computable: There is an efficient algorithm to calculate \(e(u,v)\) for all \(u\in G_{1},v\in G_{2}\).
# Biosynthesis of Edible Terpenoids: Hosts and Applications
## 3 Application of Terpenoid Microbial Cell Factories in the Food Industry
### Acting as Medicinal Health Food (MHF)
#### 3.4.2 Valencene and Nootkatone  
Valencene and nootkatone are sesquiterpenes derived from plants. Valencene provides an orange-like odor, while nootkatone has a grapefruit-like flavor. They are GRAS and widely used as food additives in the fragrances and flavors industry [129; 130]. In addition, both valencene and nootkatone possess interesting biological activities, such as anti-cancer, anti-oxidant, and anti-inflammatory. Among them, nootkatone has been proven to be a major active component in traditional Chinese medicine _Aipinia oxyphylla_ ([PERSON]). Because of their great market value, they were primarily produced by chemical synthesis. Recently, in the exploration of enzyme functions, researchers focused on biotransformation or heterologous synthesis to produce valencene and nootkatone [131].  
For de novo valencene biosynthesis, [PERSON] et al. introduced a valencene synthase (_Ct_VS) into an _S. cerevisiae_ platform BN-91A, which was obtained through laboratory adaptive evolution and capable of utilizing mannitol as a sole carbon source. By comprehensively optimizing the metabolic network, a titer of 5.6 g/L of valencene was achieved in high-density cultivation with mannitol feeding [101]. In another research study, [PERSON] and co-workers identified a more efficient valencene synthase (_Eg_VS) and performed site-directed mutation to increase its activity. By optimizing the biosynthetic pathway, they increased the titer of valencene. Furthermore, they adopted a GAL promoter to control URA3 expression in order to couple cell growth and biochemical pathways, and the titer of valencene reached 16.6 g/L in a high-density fermentation. Accompanying the following chemical conversion, the yield of noottadone reached 80% [102]. The combination of biosynthesis and chemical transformation makes the industrial production of noottadone possible. Recently, researchers have elucidated the natural nootkatone biosynthetic pathway in _A. oxyphylla_ and achieved its total biosynthesis in an engineered _S. cerevisiae_ strain [129]. This provides new resources and strategies for the subsequent production of nootkatone using green cell factories.
## 4 Experimental Results
### Method Validation and Performance Analysis
The values of different combinations  
Figure 13: Azimuth estimation value calculated based on CNR and CPSD. The unit of ordinate is radians. of detection variables are above the detection threshold, indicating that the method has good immediacy. By calculating the average response time of the detection variable SSE to first exceed the detection threshold after spoofing occurs, it is found that the average response time is approximately 3.2 s, indicating that the method has good immediacy. Figure 14: The variation curves of detector values under different satellite combinations. The colors of the lines and patterns of the points represent different satellites. Blue indicates that the combination contains PRN10, yellow and round indicates that the combination contains PRN12, red and square indicates that the combination contains PRN15, green and diamond indicates that the combination contains PRN18, purple and pentagram indicates that the combination contains PRN23, and a hexagon indicates that the combination contains PRN24. The black dashed line represents the optimal detection threshold calculated based on the Neyman–Pearson criterion, and the red dashed line represents the time of the spoofing start. Figure 15: ROC curves under different detection variables. In summary, for navigation signals in pairs, using MLE for parameter estimation and comparing the detection variable with the threshold can determine whether the combination contains spoofing signals. The above experimental results demonstrate the effectiveness and immediacy of this method.
###### Abstract  
An intelligent reflecting surface (IRS) is a programmable device that can be used to control electromagnetic waves propagation by changing the electric and magnetic properties of its surface. Therefore, IRS is considered a smart technology for the sixth generation (6G) of communication networks. In addition, machine learning (ML) techniques are now widely adopted in wireless communication as the computation power of devices has increased. As it is an emerging topic, we provide a comprehensive overview of the state-of-the-art on ML, especially on deep learning (DL)-based IRS-enhanced communication. We focus on their operating principles, channel estimation (CE), and the applications of machine learning to IRS-enhanced wireless networks. In addition, we systematically survey existing designs for IRS-enhanced wireless networks. Furthermore, we identify major issues and research opportunities associated with the integration of IRS and other emerging technologies for applications to next-generation wireless communication.  
intelligent reflecting surfaces (IRSs); machine learning; multiple input multiple output; wireless networks +
Footnote †: journal: Sensors  
**Citation**: [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Machine Learning for Intelligent-Reflecting Surface-Based Wireless Communication towards 6G: A Review. _Sensors_**2022**, 22, 5405. [[https://doi.org/10.3390/s21245405](https://doi.org/10.3390/s21245405)]([https://doi.org/10.3390/s21245405](https://doi.org/10.3390/s21245405))
## 4 EMTARP: System Model and Protocol Description
### Proposed Routing Metrics  
The detailed description of routing metrics at node \(i\) is as follows.  
* \(Id_{i}\): Unique identifier of node \(i\).
* \(H_{i}\): Number of hop count of node \(i\) to the sink node. This value is updated by exchanging the \(HELLO\) packet with neighbor node.
* \(Temp_{i}\): Normalized temperature of node \(i\). This value is computed by Equation (4) where \(T_{th}\) denotes the allowed threshold of the temperature [20] while \(T_{i}\) does current temperature of node \(i\). Note that we use the pennes bio heat equation [3] used in TARA [2] in order to estimate the current temperature \(T_{i}\). The main reason for the temperature rise is radio signals generated via wireless communication [21]. Since the radio signals generate electric and magnetic fields, which lead to radiation absorption, Specific Absorption Rate (SAR) is used in the pennes bio heat equation. Therefore, it is important to limit the number of wireless communications of a node which will reach the temperature threshold. \[Temp_{i}=\frac{T_{th}-T_{i}}{T_{th}}\] (4)
* \(Link_{i,j}\): Normalized link quality between node \(i\) and \(j\) which was carried by \(HELLO\) packet. This value is computed by Equation (5) where \(RSSI_{min}\) indicates the minimum signal strength for packet transmission. \[Link_{i,j}=\frac{RSSI_{i,j}-RSSI_{min}}{RSSI_{i,j}}\] (5)
## 3 System Model
### Spectral Efficiency  
In this paper, the spectral efficiency estimated by using Truncated Shannon Bound (TSB). The TBS has been used in 3 GPP to optimally approximate the actual throughput by treating it as a function of SINR experienced by the user [32; 33; 34]. The spectral efficiency can be estimated as:  
\[Thr_{TBS}=\begin{cases}Thr=0&For\quad SINR<SINR_{min}\\Thr=B_{w}\alpha log_{2}(1+SINR)&For\quad SINR<SINR_{max}\\Thr=Thr_{max}&For\quad SINR>SINR_{max}\end{cases} \tag{3}\]  
In (3), bandwidth is denoted by \(B_{w}\), the lower limit of SINR denoted by \(SINR_{min}\) (throughput is zero below \(SINR_{min}\)) and upper limit denoted by \(SINR_{max}\) ( equal to the throughput of highest coding rate/modulation), whereas \(\alpha\) is used to match the link level performance.
## 5 Discussion
### Future Work  
The area for future work for both use cases is discussed in the following points:  
* The algorithmic results for the RSs in both use cases can be enhanced with the incorporation of a cost feature. In the first use case (thermal envelope RS), it is possible to incorporate the cost of insulating materials as a factor. This would enable users to specify their budgetary constraints, hence allowing for personalized suggestions that align with their financial preferences. Similarly, the second use case (HVAC Systems RS) involves including the expenses associated with each building service.  
This enables the user to make informed decisions on their desired level of automation for a certain building service based on their allocated budget.
* For the first use case, a safety element can be added. Insulation materials are not just selected for the lower U value but also for their resistance to fire in certain climates. This is not considered in this work. Adding this feature would make the RS more practical.
* For the first use case, the EU building stock observatory [40] can also be used as a source of data, as it contains most of the variables included in the dataset prepared for this work, and it does not require any manual form of data entry, as the data are available in CSV files.
* For the first use case, external factors like local regulations with regard to the insulation can also be added to the existing RS.
* The SRI data are quite difficult to collect. Therefore, a central database at the national level would be very helpful for future researchers in the SRI domain.
* With larger datasets for both SRI and BIM, the scalability of the RS can be better tested.
* The subjectivity of the SRI framework needs to be addressed by adding quantitative elements to it.  
Finally, given the collective potential of the algorithms developed in both use cases, by using larger datasets, it is possible to integrate this tool with existing BIM software packages. BIM software packages like Revit [41] offer certain options and suggestions for the improvement of energy efficiency in the building model developed by the user; however, the level of personalization of these suggestions is still not very high. By integrating the tool proposed in this paper, this can be significantly improved.
# Vision-Based Concrete-Crack Detection on Railway Sleepers Using Dense U-Net Model
## 3 Methodology
### Crack Severity Analysis
#### 3.4.1 Counting the Cracks  
In the initial phase of our crack severity analysis, we determined the number of individual cracks within the railway sleeper images. This count was achieved by employing contour detection, a method that identifies closed curves with consistent pixel intensities, outlining the boundaries, and connecting continuous points within the images. Let an image as 2D function \(f(x,y)\) then,  
\[f(x,y)=c \tag{8}\]  
where \(c\) is the constant pixel value. So, through the contour detection process, we are identifying the connected regions of predicted crack pixels from the Dense U-net model, outlining the crack boundaries. This method enables the calculation of the number of detected contours, essentially representing the count of individual crack objects within the images.
## 3 IVE-KF Algorithm
### KF and EKF Algorithm  
When the metal target's initial state \(\mathbf{\hat{\alpha}}_{0}\) has been estimated, the filter algorithm can be used to accurately estimate the metal target's parameters.  
Applying DSSM to the underground metal detection field produces unusual results because the state of the target (position, posture, and polarizability) is constant. Therefore, the changes in the state can be omitted:  
\[\mathbf{\alpha}\left(k\right)= \mathbf{\alpha}\left(k-1\right)+\mathbf{\sigma}\left(k-1\right), \tag{27}\]  
where \(\mathbf{\alpha}\left(k\right)\) is the state of the target so that when the \(k\)th set of data are collected, \(\mathbf{\upsilon}\left(k-1\right)\) consists of independent and identically distributed Gaussian noise. Formula (27) is called the state transition equation. The collected data at the \(k\)th sampling point are:  
\[\mathbf{C}\left(k\right)= \mathbf{F}\left(\mathbf{\alpha}\left(k\right)\right)+\mathbf{n}\left(k \right), \tag{28}\]  
where \(\mathbf{F}\left(\cdot\right)\) is a nonlinear function for \(\mathbf{\alpha}\). Formula (28) is called the observation equation.  
In IVE-KF, the magnetic polarizability tensor \(\mathbf{M}\) is estimated by the KF algorithm, and the target's position \(\mathbf{r}\) is estimated by the EKF algorithm. After the IVE-KF algorithm converges, the estimation of the target's state \(\mathbf{\hat{\alpha}}\left(k\right)\) is obtained.  
Figure 4: Secondary field spatial response of spherical and cylindrical objects. (**a**) shows detection scenes, and the red lines and small circles denote the track of the detector; (**b**) shows the secondary field strength map of (**a**).  
After iteration, \(\hat{\mathbf{M}}\) and \(\hat{\mathbf{r}}\) ultimately converge. When the algorithm is completed, the estimation of the target parameters is obtained:  
\[\hat{\mathbf{\kappa}}=\left[\hat{\mathbf{r}}^{T},\hat{\mathbf{M}}^{T}\right]^{T}. \tag{38}\]  
\(\hat{\mathbf{\alpha}}\) contains the estimation information of the detected target's position and the magnetic polarizability tensor. Formulas (9)-(13) imply that, once the \(\hat{\mathbf{M}}\) is obtained, the polarizability \(\mathbf{\beta}=\left[\beta_{x},\beta_{y},\beta_{z}\right]^{T}\), and the posture angle \((\psi,\theta,\phi)\) of the detected target can be easily obtained by eigendecomposition of the magnetic polarizability tensor \(\hat{\mathbf{M}}\).
# A Non-Smooth Numerical Optimization Approach to the Three-Point Dubins Problem (3 PDP)
## 3 Derivation of Dubins Path with Trigonometry
### Case CSC
#### 3.1.1 The Subcase \(Csc^{+}\): LSL and RSR  
As stated in the previous section, this solution type falls into the CSC\({}^{+}\)case, where \(s_{2}=0\) and \(s_{1}=s_{3}=s\). Hence, we can rewrite equations (10) in terms of the parameters \(\mathcal{P}_{\alpha,\beta}\) using notation (6) as follows:  
\[\begin{split}(d+s\,S^{-})\sin\vartheta_{S}+s\,C^{-}\cos \vartheta_{S}&=\,0\\ (d+s\,S^{-})\cos\vartheta_{S}-s\,C^{-}\sin\vartheta_{S}& =\,\ell_{2}.\end{split} \tag{11}\]  
From the first equation, we can solve for \(\vartheta_{S}\), and the second is already solved for \(\ell_{2}\), as a function of \(\vartheta_{S}\). Thus, we have  
\[\begin{split}\vartheta_{S}^{\text{CSC}^{+}}=\text{atan2}\big{(} -C^{-},\,d+s\,S^{-}\big{)}\mod\pi.\end{split} \tag{12}\]  
We choose the only \(\vartheta_{S}\) producing a non-negative \(\ell_{2}\). Any \(\vartheta_{S}\) not respecting this constraint is discarded. The length \(\ell_{2}\) is obtained from (11), knowing the solution in (12), and from the condition \(\vartheta_{1}=\vartheta_{2}=\vartheta_{S}=\alpha+s\ell_{1}=\beta-s\ell_{3}\), we can derive the other lengths \(\ell_{1}\) and \(\ell_{3}\), which are as follows:  
\[\begin{split}\ell_{1}^{\text{CSC}^{+}}&=\,s(\vartheta _{S}^{\text{CSC}^{+}}-\alpha)\mod 2\pi\\ \ell_{2}^{\text{CSC}^{+}}&=\,(d+s\,S^{-})\cos \vartheta_{S}^{\text{CSC}^{+}}-s\,C^{-}\sin\vartheta_{S}^{\text{CSC}^{+}}\\ \ell_{3}^{\text{CSC}^{+}}&=\,s(\beta-\vartheta_{S}^{ \text{CSC}^{+}})\mod 2\pi.\end{split} \tag{13}\]  
where \(t=\frac{\theta^{2}}{2}-1\). The real roots of \(P_{\text{CSC}^{+}}(Z;\beta)\) represent the border of the range interval where \(\alpha\) can run, to obtain a solution for the \(CSC^{+}\) problem for a fixed \(\beta\). The same holds for \(P_{\text{CSC}^{+}}(W;\alpha)\) for the interval range of \(\beta\).  
The range detection is fundamental in the solution to the 3 PDP. In general, the length of the 3 PDP is a discontinuous function of the middle angle (see Figure 2). Standard direct search [33; 34] and non-smooth minimization algorithms [35; 36] can fail to find the solution if evaluation points are located in a small discontinuity region (see Figure 3). Hence, our proposed approach, discussed in the later sections, employs the solution of polynomial (21) to isolate the discontinuity points and treat them separately in the minimization algorithm.  
The range detection is fundamental to finding the points that are candidate to be points of discontinuity of the length of the Dubins path with respect to initial or final angles \(\alpha\) and \(\beta\).  
Figure 2: Example of the total length of a three-point Dubins path. Spatial coordinates are in meters, and angles are in radiant. (**Top**: path result with alternating colors for each arc; **Middle**: total length \(\ell\) as a function of middle angle \(\theta_{m}\); **Bottom**: derivative of total length \(\ell\) as a function of middle angle \(\theta_{m}\)).
## 4 Problem Formulation  
Streaming media consumes a lot of bandwidth; moreover, with the increased request of high-quality videos, their size becomes too heavy. Even if new compression standards have been introduced [31], that alone is not enough. DASH can help in delivering the most suitable quality to the user with reduced bandwidth wastage [32]. Fog computing would leverage computation and storage by distributing computing to various nodes, therefore reducing latency by having wider geographical distribution. The combination allows to break the video into segments and distribute them to fog nodes, which in turn makes it reach the user faster and with better quality.  
By assuming that the \(q\)th quality level is mapped on an average video bit rate equal to \(VBR(q)\), the video segments delivery is constrained to:  
\[\sum_{p_{m}=1_{m}}^{p_{m}}t^{k,n}(p_{m})\leq\sum_{p_{m}=1_{m}}^{p_{m}}\frac{d^ {\tilde{q}p_{m}}}{VBR(\tilde{q})}\quad\forall\tilde{p}_{m}=\{1_{m},\ldots,P_{m}\} \tag{5}\]  
in order to avoid video latency disruption during the streaming. Equation (5) models that the delivery time for the first segment should be lower than the average playout time, the delivery time of the first two segments should be lower than the playout time of both, and so on.  
At the same time, the video quality, in terms of video bit rate, is a qualitative feature to be considered during the video streaming; thus, a reduced quality is sometimes tolerated, while a video stuck represented by underbuffering is undesired. To this aim, a relaxing assumption could be included, allowing to retrieve a given segment with a lower quality.  
Given a certain initial video segment placement, the goal of the problem is to optimize the placement of the segments in order to minimize the delivery latency, defined as:  
\[\mathbf{P1}:\;\min\sum_{n}T(n) \tag{6}\]  
such that  
\[\mathbf{C1}:\;\sum_{p_{m}=1_{m}}^{p_{m}}t^{k,n}(p_{m})\leq\sum_{p_ {m}=1_{m}}^{p_{m}}\frac{d^{\tilde{q}p_{m}}}{VBR(\tilde{q})}\qquad\forall\tilde {p}_{m}=\{1_{m},\ldots,P_{m}\} \tag{7a}\] \[\mathbf{C2}:A(k,p_{m})=\begin{cases}q&\text{if $C_{1}$}\\ 0&\text{otherwise}\end{cases}\] (7b) \[\mathbf{C3}:\;\mathcal{Q}\Bigg{(}\sum_{m,p_{m}}A(k,p_{m})\Bigg{)} \leq B_{k}\forall k\] (7c) \[\mathbf{C4}:\;\sum_{k}A(k,p_{m})<\sum_{m,p_{m}}A(k,p_{m})\] (7d) \[\mathbf{C5}:A^{0}(k,p_{m})=rand\{0,q\} \tag{7e}\]  
where \(\mathcal{Q}(\cdot)\) is a function able to map the quality level into a given amount of Bytes. Constraint (7d) posits that the amount of segments for all the videos that can be stored in the fog computing environments is smaller than all the segments composing all the videos. Indeed, the fog computing environment is a resource-limited scenario and is able to store only a portion of the videos. The remaining are supposed to be stored on the remote cloud. Constraint (7e) posits that the starting condition is that the segments are randomly scattered on the fog nodes with a random quality. It is worth noticing that despite (6) minimizing the overall delay, its value depends on the delay components, as defined in (2), which, in turn, depend on the allocation, following (3).