esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
In this context, this paper focuses on the allocation of energy storage devices in the distribution system and attempts to contribute to the size and site planning of energy storage systems. A bi-level programming approach is applied to evaluate the influence of location and installed capacity of energy storage on the reliability of power system. The bi-level programming approach provides a theoretical method for the rational allocation of energy storage capacity and position. Specifically, the key contributions of this work can be summarized as follows:  
1. Presenting an optimization problem that represents the sizing and siting of an energy storage system allocation in distribution systems. The novel optimal method to allocate the energy storage system determines the capacity and location of the energy storage system, which maximizes the system reliability improvement and minimizes the energy storage installation capacity. The sizing and siting of an energy storage system cannot be considered separately from the energy storage system planner; thus, the bi-level programming model takes into account both energy storage system size and site allocation, and this approach provides a consistent method to calculate the available capacity of an energy storage system;
2. Developing an efficient algorithm to solve the bilevel optimization problem. The impact of minimizing the installation capacity of an energy storage system on distribution system reliability improvement is analyzed. As a result, the original linear bilevel program with bilinear terms is transformed as a single-level linear equivalent that overcomes the shortcomings of the available solutions in industry practice and in the technical literature. This paper also introduces a search tree algorithm for solving the siting selection optimal problem in energy storage system planning and for identifying the optimal installation position of an energy storage system. The algorithm effectively reduces the calculations. 3. Illustrating the impacts of the influencing factors of distribution networks integrating wind power and energy storage systems and providing a reasonable and effective basis and a practical way to improve the system reliability, aiming at the role of key influences for the energy storage system allocation. The key installation points are analyzed, and the available solutions can be used in industry practice. The remainder of this paper is organized as follows: Section 2 proposes the energy storage system configuration model. Section 3 establishes the solution algorithm. The case studies are analyzed in Section 4. Finally, the conclusion is presented in Section 5.
# SOBER-MCS: Sociability-Oriented and Battery Efficient Recruitment for Mobile Crowd-Sensing
## 5 Performance Evaluation
### Simulation Setup
During every new build procedure, the maximum hypothetical value that would still cause the user to be removed is found, and a worthy candidate is calculated. Furthermore, we ensure that every participant receives a minimum payment equal to his/her announced bid if he/she is marked as a winner in the first step of the recruitment. To evaluate the performance of the proposed MCS system, the following metrics are used. * Average residual energy denotes the remaining battery level of users at the end of the entire sensing event. * Average energy consumption denotes the average energy consumption of mobile user devices at the end of the sensing event. * Platform utility: The utility of the MCS platform is the difference between the total value of sensing tasks and the total amount of rewards given to the winners through a reverse auction procedure. * User utility is formulated as the difference between total rewards received by the winners and the total cost of sensing for the winners, which actually consists of both ordinary cost and energy cost, as reported in Section 4.4.2. * The number of participants is used to represent the number of recruited users in a sensing campaign. * The number of dead phones is one of the key metrics to emphasize motivating the user to participate in the MCS platform. * The number of breakout phones denotes the number of devices that are discarded by the platform due to having their remaining battery levels below the minimum allowed level.
# Integrated Optical Phased Arrays for Beam Forming and Steering
## 8 Applications & Potential Alternatives
### Applications
#### 8.1.2 OPAs for LiDAR  
LiDAR is an important application of OPAs. In [14], the following requirements were listed as potential goals of developing OPAs for LiDAR applications: a resolution of 0.1\({}^{\circ}\)-0.2\({}^{\circ}\) in horizontal and vertical directions, a horizontal FOV of \(>\)90\({}^{\circ}\), a power budget of 10-30 W or lower, and a system cost of 100-200 dollars or lower.  
Integrated OPA-based LiDAR has been implemented [11; 17; 144]. A coherent solid-state LiDAR was presented by an integrated transmitting and receiving frequency- modulated continuous-wave circuit [17]. A triangular frequency modulation method was performed to measure distances and velocities simultaneously. The OPA using a grouped cascaded phase shifter architecture was demonstrated for an FOV of 46\({}^{\circ}\)\(\times\) 36\({}^{\circ}\), a beamwidth of 0.85\({}^{\circ}\)\(\times\) 0.18\({}^{\circ}\), and a noise-limited resolution of 20 mm with a 2 m range [17]. A 1D 512-element OPA consuming \(<\)1 mW total power exhibited high-speed (\(<\)30 \(\upmu\)s) 2D beam steering within an FOV of 56\({}^{\circ}\)\(\times\) 15\({}^{\circ}\)[11]. Simultaneous velocity extraction of diffusive targets at a range of \(\sim\)200 m was shown by a coherent solid-state LiDAR, and OPA-based 3D coherent LiDAR was presented with raster-scanning arrays [11]. Owing to difficulties in providing electrical and photonic connections to every pixel, previous LiDAR systems were typically restricted to fewer than 20 pixels [144]. By leveraging achievement in monolithically integrated photonic and electronic circuits, a large-scale coherent detector array with 512 pixels was demonstrated recently [144]. The LiDAR system contains a dense optical heterodyne detector array combined with an integrated electronic readout architecture. An accuracy of 3.1 mm at 75 m was achieved using only 4 mW of light, which was an order of magnitude more accurate than existing solid-state systems at such ranges. The system enabled straightforward scaling ability and eliminated trade-off between FOVs and ranges [144].
###### Abstract  
The optical and geometrical properties of transparent conductive oxide (TCO) are crucial factors influencing the efficiency of a \(-\) Si : H/c - Si heterojunction (HIT) solar cells. Graphene is a potential candidate to be used as TCO due to its optical and electrical properties. Here, the effect of graphene as TCO is numerically analyzed by varying the number of graphene layers from one to ten. First, the optical properties are calculated based on the transmittance data, and then the HJT cell's performance is simulated under the AM1.5 standard spectrum and the mean Atacama Desert solar spectral irradiance in Chile. In the modeling, the most relevant properties are calculated with the spectrum of the Atacama Desert. The most relevant values were obtained as follows: open circuit voltage \(V_{oc}=721.4\) mV, short circuit current \(I_{sc}=39.6\) mA/cm\({}^{2}\), fill factor \(FF=76.5\%\), and energy conversion efficiency \(E_{ff}=21.6\%\). The maximum power of solar panels irradiated with the Atacama Desert spectrum exceeds the results obtained with the AM1.5 standard spectrum by 10%. When graphene is the transparent conducting oxide, quantum efficiency has a higher value in the ultraviolet range, which shows that it may be convenient to use graphene-based solar cells in places where ultraviolet intensity is high.  
H +
Footnote †: journal: Physics Letters B  
HIT solar cell; Atacama Desert; solar spectral irradiance; graphene; AFORS-HET
# An Anonymous Offline RFID Grouping-Proof Protocol
## 7 Security Analysis and Comparison
### Security Analysis
#### 7.1.7 Resist to Replay Attack  
The replay attack denotes when the adversary uses a tag's response to a rogue reader's challenge to impersonate the tag. Suppose the adversary collected the message of \(Tag_{i}\): \(\{m_{i}^{1}\), \(r_{i}^{1}\), \(r_{s}^{1}\), \(s^{1}\), \(T_{i}^{1}\}\) inEAGP session \(p1\), trying to replay these messages in session \(p2\) in order to forge a valid grouping-proof including \(Tag_{i}\) while it is absent. The adversary begins the attack as follows:  
1. The adversary sends \(m_{1}^{1}\) to the reader.
2. The adversary sends \(T_{i}^{1}\) as \(T_{i}^{2}\) to \(Tag_{i+1}\) via the reader.
3. \(Tag_{i+1}\) calculates \(T_{i+1}^{2}=(m_{i+1}^{2}+T_{i}^{1}\cdot K_{i+1}^{2})Y\) Due to the different session \(p1,p2\), we know \(r_{s}^{1}\
eq r_{s}^{2}\), so \(T_{i}^{1}\
eq T_{i}^{2}\), we get: \[k_{i+1}^{t}P\
eq(y^{-1}\times T_{i+1}^{2}-m_{i+1}^{2}P)\times x(T_{i}^{1})^{-1}\] (23) The grouping proof cannot pass the validation of the reader. EAGP can resist the replay attack.
# Numerical and Statistical Analyses of Tsunami Heights with the L-Moments Method
## 3 Best-Fit Distribution  
### The L-Moments Method  
We used the method of L-moments to calculate the parameters of the best-fit distribution of the tsunami heights. [PERSON] (1990) [7] introduced L-moments to estimate parameters of distribution through a linear combination of probability-weighted moments. It is similar to the existing method of moments in terms of application, but it has little bias because the estimates of the samples by L-moment are always linear combinations. As L-moments are a linear combination of probability-weighted moments, a probability-weighted moment must be defined prior to the explanation of the L-moment [8]. A probability-weighted moment is defined as follows:  
\[\beta_{r}=E\left(X[F_{Xn}(x)]^{r}\right) \tag{8}\]where \(F_{X}(x)\) is the cumulative probability distribution function of \(X\). An unbiased estimator of the probability-weighted moment must be used in order to apply it to the data of \(n\) samples, which is calculated as follows [9].  
\[\hat{\beta}_{r}=n^{-1}\sum_{j=1}^{n}\frac{(j-1)(j-2)\cdots(j-r)}{(n-1)(n-2) \cdots(n-r)}x_{j} \tag{9}\]  
The sample data \(x_{j}\) are rearranged in ascending order \((x_{1}\leq x_{2}\leq\cdots\leq x_{n})\). Using the unbiased estimator, the L-moment is calculated using the following equations:  
\[\lambda_{1}=\beta_{0} \tag{10}\]  
\[\lambda_{2}=2\beta_{1}+\beta_{0} \tag{11}\]  
\[\lambda_{3}=6\beta_{2}-6\beta_{1}+\beta_{0} \tag{12}\]  
\[\lambda_{4}=20\beta_{3}-30\beta_{2}+12\beta_{1}-\beta_{0} \tag{13}\]
## 4 Our Hybrid Quantum-Classical Optimization Algorithm  
Combinatorial optimization problems are solved by encoding them to minimize a binary quadratic form, which can be translated to finding the minimal eigenvalue of a Hamiltonian. The number of qubits needed is the dimension of the Hamiltonian. To solve an \(n\)-node TSP problem, the qubits needed become \((n-1)^{2}\). Clearly, the QAOA cannot effectively handle a large-scale combinatorial optimization problem with the current qubit size, and therefore, the QAOA is far from achieving quantum advantage at this time.  
Our approach to solving the TSP is based on a hybrid parallel ACO algorithm and possibly a quantum computer to optimize the sub-tours present in a large tour. After an initial parallel ACO of the entire tour, we partitioned the tour into disjoint sub-tours. Each sub-tour could then potentially be optimized on a quantum computer, and the optimized sub-tours could be assembled and further optimized to provide the complete tour. Figure 2 depicts this scenario.  
We repeated the partitioning and optimization process until the solution converged to an optimal or near-optimal value. Note that the optimization of the partitioned sub-graphs lost the global perspective, and thus the resulting assembled solution should be globally optimized. The steps of our optimization algorithm are as follows:  
1. Partition the TSP graph in sub-graphs. The number of nodes in the sub-graphs is chosen according to the available number of qubits in the quantum computer.
2. Optimize each sub-graph on a quantum computer using the QAOA algorithm.
3. Assemble the sub-graphs and optimize the complete graph using the parallel ACO algorithm for a few iterations. Repeat steps 1-3 until the solution converges. In the next sub-section, we describe the details of our new parallel ACO algorithm for achieving a global optimization of the solution to the TSP.
## 2 Metal-Organic Frameworks
For several years, publications have been made on the possibility of catalytic degradation and hydrolysis of chemical warfare agents (CWAs), using specially adapted MOFs [17]. _Durability of MOFs in the Aquatic Environment_  
When using MOFs as sorbents in the preparation of water-based samples for analysis, their durability, especially their water resistance, is extremely important. This determines whether the selected MOF, firstly, can be used as a sorbent in the process of preparing water-based samples and, secondly, whether its structure will remain undamaged or will gradually degrade. And if it degrades, how will it affect the sorption properties, and how long can the selected MOF serve as a sorbent? Factors affecting the stability of the MOF in water are divided into thermodynamic and kinetic. In the case of thermodynamic agents, it is said to be the stability or instability of the MOF that matters. In the case of kinetic factors, it is the indifference or lability of the structure that counts. Thermodynamic factors include, for example, the pKa of an organic ligand, the oxidation state, and the atomic radius of the metal; kinetic factors include, for example, steric obstacle or hydrophobicity. However, it is possible to design an MOF to increase its resistance to water [12; 27]. The stability of the MOF depends, among other things, on the strength of the coordination bond between the metal ion (or cluster) and the organic ligand. The strength of this bond depends on the charge density of the metal cation. The bond strength is proportional to the charge of the metal cation (its value) and inversely proportional to the radius of the ion. Metal ions with a higher valence (with a higher charge), e.g., Ti\({}^{4+}\), Zr\({}^{4+}\), Fe\({}^{3+}\), Al\({}^{3+}\), and Cr\({}^{3+}\), form a coordination bond with organic ligands containing functional groups such as carboxyl group (hard base), and metal cations with lower valences (less charge), e.g., Ni\({}^{2+}\), Cu\({}^{2+}\), Mn\({}^{2+}\), Zn\({}^{2+}\), and Ag\({}^{+}\), form coordination bonds, for example, with a free electron pair of nitrogen in imidazole (soft base), forming stable MOFs. This is consistent with [PERSON]'s theory of hard and soft acids and bases (HSAB). MOFs whose structure is consistent with this theory are characterized by higher mechanical, chemical, and thermal stability [28], making them suitable for use as sorbents in the preparation of liquid samples for analysis (Figure 4). In practice, one of the most important parameters proving the durability of the MOF is the strength of the coordination bond formed between the linker and the node. The strength of this bond can be controlled by increasing the pKa of the organic ligand, which is why ZIF materials show high durability for hydrolysis and thermal degradation in practice.
# Research on the Correlation Filter Tracking Model Based on the Deep-Prunned Feature Network
## 3 Methods
### Correlation Filter Tracking Model Based on the Deep-Pruned Feature Network  
A deep-pruned convolutional neural network was obtained after a secondary pruning operation based on alternative kernels to maximize the tracking speed (at least the minimum requirement for practical applications, i.e., 25 FPS). The correlation filter tracking model based on the deep-pruned feature network is formed by cascading the deep-pruned feature network and the correlation filter. It mainly includes feature extraction, correlation filter initialization, target location prediction, correlation filter updating, and deep-pruned feature network updating, as shown in Figure 6. The details are described as follows.  
Figure 6: Correlation filter tracking model based on the deep-pruned feature network and online updating process of the deep-pruned feature network (including alternative kernels). (#\(i\) indicates the _ith_ picture in the video sequence).
## 6 Conclusions  
This study explored the challenges of concurrent space-time block coding (STBC) and modulation identification for multi-user upstream broadcasts in single-carrier frequency division multiple access (SC-FDMA) systems. The theoretical study exhibited that a space-alternating expectation-maximization (SAGE) strategy was utilized to put into practice the maximum-likelihood (ML) option of the parameters under discussion. The estimation of multi-user channels was undertaken as a secondary operation. The deliverable offered by the channel decoder were utilized in a sequential model so that the accuracy of identifying STBC and modulation kinds and estimating multi-user channels could be improved. The simulation results that were achieved under a variety of different scenarios, providing evidence that the offered architecture is a viable option for implementation in practical applications.  
Conceptualization, M.M. and H.M.; methodology, M.M. and H.M.; software, M.M. and H.M.; validation, M.M. and H.M.; formal analysis, M.M. and H.M.; investigation, M.M. and H.M.; resources, M.M. and H.M.; data creation, M.M. and H.M.; writing--original draft preparation, M.M. and H.M.; writing--review and editing, M.M. and H.M.; visualization, M.M. and H.M.; supervision, M.M. and H.M.; project administration, M.M. and H.M.; funding acquisition, M.M. and H.M. All authors have read and agreed to the published version of the manuscript.  
Princess Nourah bint Abdulrahman University Researchers Supporting Project number PNURSP2023R137, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.  
Not applicable.  
Figure 7: The impact of the number of the training symbols on the proposed identification algorithm. The system’s parameters are \(U=4\), \(\mathcal{N}=2048\), \(\langle\Phi(u)\rangle=512\), \(u=0,1,2,3,v=11\), \(\chi=60\), 10 channel paths; convolutional codes of rate 0.5; interleaved sub-carrier mapping; an STBC set of S1, S2, S3, S4, and S5; and a modulation set of 4-QAM, 16-QAM, 64-QAM, 128-QAM, 256-QAM, and 512-QAM.
## 5 Conclusions  
Our newly proposed method for calculating diatom concentrations has some advantages over other approaches:  
* A lower cost.
* It is relatively simple and requires only basic laboratory equipment, whereas for the microsphere method, more specialized equipment and materials, that can be more expensive, may be necessary.
* Wider applicability: counts based on FOV can also be used for the analysis of the different diatom life forms in a variety of aquatic systems, including lakes, rivers, and marine environments. This means that researchers can easily adapt it, potentially increasing its applicability.
* Higher resolution: our proposed method allows for the counting of individual diatoms, whereas the other methods rely on the counting of external markers. This means that our method may provide higher-resolution data, particularly for smaller diatom taxa.
* Compatibility with existing datasets: there are many existing slides already available that could potentially be re-analyzed for diatom abundance using our proposed method. This compatibility with existing datasets could save time and resources.  
It is important to note, however, that there may also be potential drawbacks to using our proposed method for diatom analysis, such as the potential for sample distortion during the concentration process. Therefore, it is important to carefully evaluate the strengths and limitations of the different methods before adopting one for analysis.  
**Author Contributions:** Conceptualization, M.C.; methodology, M.C.-R. and F.R.; software, M.L.; validation, M.L.; formal analysis, M.L.; resources, M.C.-R. and F.R.; data curation, M.L.; writing--original draft preparation, M.C.-R. and F.R.; writing--review and editing, M.C. and M.L.; visualization, M.L.; supervision, M.C.; project administration, M.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** We are grateful to [PERSON] (MUSE--Museo delle Scienze, Trento, Italy) for useful practical suggestions during the development of our FOV method.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results
### Adsorption Performance of MIPs and NIPs
#### 3.3.2 Kinetics Adsorption of MIPs  
The kinetic adsorption curves of the MIPs and NIPs were all evaluated to explore the adsorption rate (see Figure 5a). A high adsorption rate was performed, and the binding amount linearly increased with increasing contact time before reaching an adsorption equilibrium. At bacteria concentrations of \(1.0\times 10^{5}\) CFU mL\({}^{-1}\), the equilibrium time of the polymer beads was almost 2 h, which could be shortened upon reducing the bacterial concentration. The fast adsorption rate could have occurred because the bacterial-NAC-QD complex in the water phase was used as the surfactant to construct the stable O/W Pickering emulsion during the imprinting process; thus, the template bacteria were located on the surface of the polymer beads. After removing the template, several effective and tailor-made imprinted sites remained on the MIP surface. These were easily accessible for the rebinding of template bacteria. In addition, a small reduction of adsorption amounts on the polymer beads occurred over an extended time. One possible reason for this observation is that the bacteria died through a nutrient deficiency in the PBS, resulting in a gradual decrease in \(L\). _monocytogenes_ viability.