esa-sceva/satcom-chunk-collection huggingface dataset:
# Constrained Dynamic Mean-Variance Portfolio Selection in Continuous-Time
## Appendix A
### The Proof of Lemma 2  
Proof.: Firstly, we will prove the convexity of the value function \(V(t,x)\). Let \((X_{1}(t),u_{1}(t))\) and \((X_{2}(t),u_{2}(t))\) be any different admissible pairs. We define \((\tilde{X}(t),\tilde{u}(t))=(aX_{1}(t)+(1-a)X_{2}(t),au_{1}(t)+(1-a)u_{2}(t))\), for any \(0<a<1\). It is easily to verify that \((\tilde{X}(t),\tilde{u}(t))\) satisfies \(H(t)\tilde{u}(t)\geq 0\). That is to say, \((\tilde{X}(t),\tilde{u}(t))\) is an admissible pair. Therefore,  
\[V(t,\tilde{X}(t)) =V(t,aX_{1}(t)+(1-a)X_{2}(t))\] \[\leq\text{E}_{t}[(aX_{1}(T)+(1-a)X_{2}(T)-\lambda)^{2}]\] \[\leq a\text{E}_{t}[(X_{1}(T)-\lambda)^{2}]+(1-a)\text{E}_{t}[(X_ {2}(T)-\lambda)^{2}],\]  
where the last inequality holds true due to the convexity of square function. Since \((X_{1}(t),u_{1}(t))\) and \((X_{2}(t),u_{2}(t))\) were chosen arbitrarily, we can find that  
\[V(t,aX_{1}(t)+(1-a)X_{2}(t))\leq aV(t,X_{1}(t))+(1-a)V(t,X_{2}(t)).\]  
Therefore, the convexity of \(V(t,x)\) can be established.  
Secondly, before we show that \(V(t,x)\) is strictly decreasing on \((-\infty,\lambda\rho(t)]\) and is strictly increasing on \([\lambda\rho(t),+\infty)\), we need show that the value function \(V(t,x)=0\) while \(x=X(t)=\lambda\rho(t)\). Indeed, we assume that \(X(t)=\lambda\rho(t)\). After that, we could obtain \(X(T)=\lambda\) if taking \(u(t)=0\). Therefore, we have  
\[V(t,\lambda\rho(t))\leq\text{E}[X(T)-\lambda]^{2}=0.\]  
Based on the non-negative of square function, we could obtain the result that \(V(t,\lambda\rho(t))=0\).  
Finally, we will focus on proving \(V(t,x)\) is strictly increasing on \([\lambda\rho(t),+\infty)\), the other part is similar. For any \(X_{b}(t)>X_{a}(t)\geq X_{0}(t)=\lambda\rho(t)\), there exists \(k\) such that  
\[X_{a}(t)=kX_{b}(t)+(1-k)X_{0}(t)\]  
where \(k\in[0,1)\). Thus, based on the convexity of \(V(t,x)\), we have  
\[V(t,X_{a}(t))\leq kV(t,X_{b}(t))+(1-k)V(t,X_{0}(t))\leq kV(t,X_{b}(t))<V(t,X_{ b}(t)),\]  
where the second inequality is from the fact that \(V(t,\lambda\rho(t))=0\) and the last inequality is according to the assumption that \(k\in[0,1)\). This result implies that \(V(t,x)\) is strictly increasing in \([\lambda\rho(t),+\infty)\), which completes the proof.
# Assessing the Homogeneity of Forage Mixtures Using an RGB Camera as Exemplified by Cattle Ratios
## 5 Conclusions  
Based on theoretical and experimental studies, we found that different absorption/reflection light flux levels of the feed mix components and materials determine the homogeneity of produced mixes of natural-origin components (green mass of freshly mowed grass, haylarge, corn silage, etc.) and concentrated components (grain milling, sunflower and soybean meal).  
Unlike existing systems based on NIR analyzers and spectrometers, the proposed approach based on an RGB camera with filters and a diode lamp has a lower implementation cost and performs online measurements using IoT technology.  
This theoretical and experimental base will further develop more reliable and accurate technological solutions, acting as decision-making tools or as feedback sensors for automated and robotic machines producing multicomponent mixes.  
The shortcomings of the existing methods based on Infrared spectroscopy include the measurement time. To obtain the result, it is necessary to accumulate the spectrum indicators at different frequencies.  
The proposed method has a number of advantages:  
* speed of information processing;
* simplicity of constructive implementation;
* the possibility of monitoring the homogeneity of mixtures with similar characteristics in another industry.  
However, the method based on the visible range does not allow to evaluate the nutritional value of feed, which can be attributed to disadvantages.  
The system will evaluate the homogeneity of the resulting mixes and make recommendations on the operating time of the mixing units, thereby improving energy efficiency by optimizing the operating time.  
Conceptualization and methodology, A.Y.I., E.A.N.; validation, D.Y.P.; formal analysis, A.G.A.; investigation and resources, E.A.N.; writing--original draft preparation, D.Y.P., E.A.N.; writing--review and editing, visualization, E.A.N.; supervision and funding acquisition, D.Y.P.; project administration, A.G.A. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a grant of the Ministry of Science and Higher Education of the Russian Federation for large scientific projects in priority areas of scientific and technological development (grant number 075-15-2020-774).  
Not applicable.  
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.
## 3 Initial Survey Results and Discussion  
A semi-structured questionnaire was developed based on gaps identified in the literature. To process the collected data, responses were categorised into simplified groups, making it easier to interpret the experiences and challenges faced by participants in the field. The results were quantified in percentage terms to identify trends and patterns.
# Energy-Efficient Hosting Rich Content from Mobile Platforms with Relative Proximity Sensing
## 3 Motivation
### Challenges  
We describe the challenges in the design of _T-Wing_ by outlining two simple solutions, and showing why they are not practical for hosting rich content from mobile devices.  
* _Host over Wi-Fi_: A simple solution is to host all the content over Wi-Fi. Mobile devices can keep the Wi-Fi interface turned on all the time, and beacon information about themselves. Nearby devices, which are in Wi-Fi range, can discover and access content over the Wi-Fi network. The advantage of this approach is that the Wi-Fi interface supports high throughput and large files can be downloaded very quickly. However, keeping the Wi-Fi interface in the on state all the time will quickly drain the battery of the device, thereby significantly impacting the user experience.  
* _Host over the Internet_: An alternative is for mobile devices to transfer their content to a server on the Internet. In most cases, since users will want their data to be available instantaneously, this content will be uploaded over the cellular data interface as this connection is mostly available. Mobile devices also upload their location to the server. A query from a mobile device would then return all the nearby mobile devices and their content would be delivered over the Internet. This approach is similar to the ones used by _Micro-Blogs_[26].  
Although this approach is promising, it suffers from three problems when serving large content. First, cellular uplink bandwidth is a severe constraint for mobile devices. This bandwidth is not expected to increase beyond 50 Mbps in 4G (LTE) networks [31]. If several mobile devices serve content using the same uplink connection, the experience when browsing mobile content will suffer significantly. Second, the battery power of mobile devices is limited, and the cellular network interface causes significant energy drain when it is active [32; 33]. It is because the communication over the Wi-Fi is more energy efficient than the communication over the cellular network from the energy efficiency (bits/Joules) perspective [11; 12; 13; 14; 15]. Finally, in several countries, the users still pay for cellular data usage on a per-packet basis. Therefore, users will get charged for uploading their content, as well as downloading content from other people, thereby reducing the incentive to host content.  
Finally, in several countries, the users still pay for cellular data usage on a per-packet basis. Therefore, users will get charged for uploading their content, as well as downloading content from other people, thereby reducing the incentive to host content. Our study can be applicable into next generation mobile networks such as 5G since gaps between cellular network and Wi-Fi may still exist from the network performance perspective and from the energy efficiency (bits/Joule) perspective [11; 12; 13; 14; 15].
## 4 Results and Discussion
### Risk Assessment Results of Toxic Gas Leak  
This section combines the methods mentioned in Section 2 to calculate the weight vector based on both the degree of dispersion of the indices as well as the importance order. Thus, we need to obtain the appraisal and importance relationship of each secondary index through the Delphi and clustering methods. We invited ten iron- and steel-producing experts and integrated their scores. The fuzzy membership matrix for the toxic gas leak risk is shown in Table 2.  
The value of \(r_{ij}\) represents the proportion of judgment for a second-level index, and the whole \(r_{ij}\) of a first-level index form a matrix \(R\). Therefore, the matrix \(R\) for each first-level index is different. The matrix \(R_{1}\) is shown below, and \(R_{2}\), \(R_{3}\), and \(R_{4}\) can be obtained similarly. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**First-Level Indices** & **Number** & **Secondary Indices** & **Number** \\ \hline \multirow{4}{*}{Blast furnace safety performance} & \multirow{4}{*}{\(V_{1}\)} & Three-way valve effectiveness & \(u_{11}\) \\  & & Gas water seal height & \(u_{12}\) \\  & & Corrosion degree of gas tank & \(u_{13}\) \\  & & Aging degree of gas tank & \(u_{14}\) \\ \hline \multirow{4}{*}{Protective facility} & \multirow{4}{*}{\(V_{2}\)} & CO monitoring alarm device & \(u_{21}\) \\  & & Mechanical ventilation device & \(u_{22}\) \\  & & CO concentration detector & \(u_{23}\) \\  & & Low pressure alarm means & \(u_{24}\) \\ \hline \multirow{4}{*}{Evacuation and dilution facilities} & \multirow{4}{*}{\(V_{3}\)} & Emergency evacuation route & \(u_{31}\) \\  & & Evacuation channel settings & \(u_{32}\) \\ \cline{1-1}  & & DCS system & \(u_{33}\) \\ \cline{1-1}  & & Nitrogen sealing device & \(u_{34}\) \\ \hline \multirow{4}{*}{Poisonous gas management} & \multirow{4}{*}{\(V_{4}\)} & Management System & \(u_{41}\) \\ \cline{1-1}  & & Monitoring duty & \(u_{42}\) \\ \cline{1-1}  & & Emergency plan & \(u_{14}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Risk assessment index system. \[R_{1}= \begin{bmatrix}0.8&0.1&0.1&0&0\\ 0.7&0.2&0.1&0&0\\ 1&0&0&0&0\\ 0.9&0&0.1&0&0\\ \end{bmatrix} .\]  
The importance gradation was used in the calculation of ORM, and the specific values are shown in Table 3. The importance order in Table 3 shows the rank of the secondary indices in the first-level index, where the importance ratio \(x_{ij}\) represents the importance degree of an indicator to the next important indicator. The ranks and the importance ratios are obtained through experienced experts in the steel plant. Based on the original data of Tables 2 and 3, the comprehensive weights of the first-level indices were obtained using Equations (1)-(9). Based on the evaluation results, the weight vectors of the first-level indicators are presented in Table 4. The weight set \(w_{i}\) was calculated for every second-level index, where the weights of the second-level indices in the same first-level index form a weight vector \(W_{i}\)
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} MDPI & Multidisciplinary Digital Publishing Institute \\ DOAJ & Directory of Open Access Journals \\ ASE & Amplified Spontaneous Emission \\ AWGN & Additive White Gaussian Noise \\ BPS & Blind Phase Search \\ BPSK & Binary Phase Shift Keying \\ BER & Bit Error Rate \\ CPR & Carrier Phase Recovery \\ DSP & Digital Signal Processing \\ DWDM & Dense Wavelength Division Multiplexing \\ ECOC & European Conference on Optical Communication \\ FEC & Forward Error Correction \\ FSO & Free-Space Optical (Communications) \\ HCF & Hollow Core Fiber \\ LO & Local Oscillator \\ NANF & Nested Antiresonant Nodeless Fiber \\ OSNR & Optical Signal-to-Noise Ratio \\ PM-QPSK & Polarization-Multiplexed Quadrature Phase Shift Keying \\ QAM & Quadrature Amplitude Modulation \\ QPSK & Quadrature Phase Shift Keying \\ ROADMs & Reconfigurable Optical Add-Drop Multiplexers \\ SNR & Signal-to-Noise Ratio \\ VV Algorithm & Viterbi-Viterbi Algorithm \\ \end{tabular}
# Corrected Tilt Calculation for Atmospheric Pressure-Induced Seismic Noise
## 5 Conclusions  
The use of the complete expression for the tilt angle in the ground has a large effect on the predicted horizontal displacements below the surface. The effect is important when \(\omega z/c\) is moderately large and \(g/(\omega c)>1\). For depths of a few hundred meters, this limits the significance of the correction to frequencies between 0.1 Hz and 0.01 Hz.  
Since most of the literature which cites [PERSON] [3] is concerned with surface measurements or is restricted to vertical displacements, the correction of the tilt angle has little or no effect. We have identified one borehole measurement [91] which may reflect the enhanced attenuation of the tilt contribution, and a few theoretical plots which should be modified. We speculate that the additional attenuation of the tilt correction may affect the inversions of Ref. [4].  
The prediction of large contributions of horizontal noise may have discouraged measurements of the horizontal displacement at depths. Many papers limit measurements to the vertical displacements based on the prediction of [PERSON] [3] that the horizontal will suffer much larger noise contributions from the wind.  
Finally, the predicted complex nature of the total horizontal displacement offers the possibility of sensitive determination of ground properties through combined horizontal and vertical measurements at different burial depths in the ground. At an appropriate fixed depth, the sign reversal displayed in Figure 4 will also occur in plots of horizontal displacement versus frequency, so that Equation (34) can also be used to evaluate ground properties.  
[PERSON] developed the theoretical formalism, performed the analytic calculations, and provided the original draft. Both [PERSON] and [PERSON] performed the numerical simulations. [PERSON], [PERSON], and [PERSON] contributed to the interpretation and writing--review and editing. [PERSON] was responsible for project administration and funding acquisition. All authors have read and agreed to the published version of the manuscript.  
[PERSON] was supported by the BASS summer research program at the University of Mississippi, National Center for Physical Acoustics. This work was supported in part by the U.S. Department of Agriculture under Non-Assistance Cooperative Agreement 58-6060-6-009 and the Office of Naval Research under Award No. N00014-18-1-2489. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the respective Federal agencies.  
We would like to acknowledge the assistance in researching the literature base of [PERSON], former Lead STEM Librarian at the University of Mississippi.  
The authors declare no conflict of interest.
## 4 Electrical Tomography Applications
### Examples of Applications of Electrical Tomography to Chemical Engineering Processes
#### 4.1.3 General Applications of ET  
There are many application areas to which electrical tomography can be applied, including phase distribution analysis and mixing quality analysis. When several sensing electrode planes are distributed along the pipe (and where their distance from one another is known), images of the flow behaviour throughout a time period can be obtained for each plane; the cross-section correlation of the images permits the estimation of information related to, for instance, velocity distribution flow rate, multi-dimensional flow monitoring, flow regime identification, and jet mixing investigation.  
Additionally, through the cross-correlation of images obtained from consecutive sensing electrode planes for the passage of a conductivity modification (for instance, a second phase in a two-phase system), estimation of flow velocity distribution can be achieved. Several approaches have been used for cross-correlation; the most commonly used have been the best-correlated pixel, in the case of flow rate measurements [117], and pixel-pixel correlation, in the case of velocity distribution measurement [56].  
When the measurements needed for electrical tomography application are performed in mixing containers, in a similar way to flow processes, the obtained multidimensional conductivity data can be used in the investigation of various aspects of the mixing system, such as mixing time and mixer performance. These aspects have typically been the ones to which more attention has been devoted, because they are critical for the analysis of diverse processes and reactions in the Chemical Engineering field; in fact, information about mixing time and performance makes it possible to evaluate, for example, the degree of homogeneity [75; 118].  
Void fraction is also a very important characteristic in multi-phase systems, because it makes it possible to better understand the observed flow patterns. Several investigations have been carried out using electrical tomography for the measurement of gas phase hold-up or void fraction in multiphase mixtures [119; 120]. A few researchers, taking advantage of the capacity possessed by electrical tomography to detect conductivity changes in the flow, have used it with the aim of monitoring diverse precipitation processes [101; 121]. Electrical tomography has also been used in other domains, such as in the identification of faults or damage, and stresses and pathologies; examples of the latter have been reported by [PERSON] et al. [116], who applied ERT for the detection of milk mixing process pathologies, and by [PERSON] et al. [122], who used ET to detect and filter cake pathologies in solid-liquid filtration.
###### Abstract  
Currently, many problems such as variable signal resources, complex execution environments, and low efficiency of scheduling algorithms are faced by heterogeneous signal processing platforms. The task scheduling algorithm is one of the key factors that directly affect the performance of the processing platform. In order to solve the problems of low efficiency of task scheduling algorithms and high computational cost of processors, a heterogeneous platform scheduling algorithm based on the quantum genetic algorithm is proposed in this paper. The algorithm constructs a task scheduling model by using a directed acyclic graph. This paper quantifies the mapping relationship between the quantum genetic algorithm and task scheduling. It corresponds qubits to binary, chromosomes to processor numbers, and individuals to processor scheduling strategies. In this paper, a new way of coding chromosomes using quantum coherence properties is designed to reduce the population size and increase population diversity. Crossover operations are performed on all individuals using full-interference crossover to avoid the results falling into local optimal solutions. The population of slow convergence is solved by implementing mutation operations on populations through quantum rotation gates. In addition, a task pre-ordering stage is designed based on the table scheduling algorithm. The task scheduling priority developed at this stage is used as the reference value for the initial encoding of the population, so that the search space for solutions is reduced. Finally, experiments are conducted using randomly generated task graphs. The algorithm is compared with improved genetic algorithms and existing intelligent scheduling algorithms. The results show that the algorithm can still obtain better results when the number of populations and iterations is small. It is more appropriate for heterogeneous platforms and computation-intensive tasks.  
heterogeneous platform; qubit; full-interference crossover; quantum rotation gate; quantum genetic algorithm; computation-intensive +
Footnote †: journal: Applied sciences
## 3 The mmWave Band
### The mmWave Path Loss  
The mmWave channel measurements conducted in [12; 48] illustrate the highly sensitive nature of mmWave to blockages, resulting in distinct Line-Of-Sight (LOS) and Non-Line-Of-Sight (NLOS) path loss characteristics. During transmission, mmWave signals suffer from substantial penetration loss when passing through solid materials. Furthermore, heavy rain can lead to significant attenuation as a result of signal scattering because the size of raindrops is of a similar size to mmWave wavelengths [47]. Consequently, the LOS link is limited due to the presence of obstructions. On the other hand, NLOS transmission occurs due to reflected waves from blockage surfaces, typically resulting in weaker signals but still contributing to providing coverage for users in shadowed areas [48].  
The path loss exponent for LOS transmission is similar to that of free space and is represented by a value of 2 [49]. However, for the NLOS link, the path loss exponent has been measured to be larger than the LOS exponent, with values of 3.86 at the University of Texas, Austin campus, and 5.76 at NYC downtown [12; 49]. Additionally, the 3 rd Generation Partnership Project (3 GPP) released a path loss model for frequencies ranging from 0.5 to 100 GHz [50]. Consequently, the path loss between a 6G-transmitting tower and a receiving device, as described in this context, is defined for LOS and NLOS by the model proposed in [50] as follows:  
\[L_{\text{LOS}}=32.4+20 log_{10}(f)+21 log_{10}(d) \tag{1}\]  
\[L_{\text{NLOS}}=32.4+20 log_{10}(f)+31.9 log_{10}(d) \tag{2}\]  
where \(f\) is the link frequency in GHz, and \(d\) is the distance between a transmit antenna and a mobile antenna in meters, as shown in Figure 5. A path loss map is shown in Figure 6 for Co-located Base Station (CBS) and DBS networks, in terms of the Signal-to-Interference-plus-Noise Ratio (SINR) [51].  
Figure 5: The path loss model.