esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimating Interception Density in the BB84 Protocol: A Study with a Noisy Quantum Simulator
## 6 Performance Evaluation
### Simulators of Quantum Communication  
QS can be interfaced with a large amount of backend simulators, provided via IBM, and inspired by real quantum machines. These simulators mainly differ in terms of the following properties.  
* The number of qubits available for computations to the quantum computer.
* The maximum number of shots. A shot is a single execution of a quantum algorithm; for example, a shot is a single pass through each stage of a complete quantum circuit. The maximum number of shots represents how many times an algorithm can be run for a single task, resulting in a probability distribution of results [37].
* The noise model, used to simulate the noisy operations of a real quantum computer.  
After the analysis of all available backends (to take advantage of their inherent characteristics), our choice is to utilize the following two simulators: aer_simulator and fake_brooklin, both with the number of shots set to 1. Table 3 summarizes their main features.  
More specifically, the aer_simulator was selected for two main reasons:  
* The execution times are very low, enabling a high number of simulations in a short amount of time.
* It simulates an ideal, noise-free, quantum circuit, allowing for the isolation of the impact of [PERSON].  
On the other hand, the selection of fake_brooklin was due to its ability to model the system noise, giving more accurate and realistic results.
# Novel Internet of Things Platform for In-Building Power Quality Submetering
## 1 Introduction
### Motivation  
The decreasing costs of Information and Communication Technologies (ICT), combined with increased environmental and regulatory pressures towards energy efficiency and reduction of greenhouse gas emissions, means that many Smart Cities and Communities are now looking at the Internet of Things (IoT) as the proper way to address energy-saving within residential and commercial buildings. The concept of IoT comprises a plethora of applications and technologies and a good definition of IoT can be found in [10]. Inspired by the recent IoT model, and released by the NationalInstitute of Standards and Technology [11], four core primitives can be established and employed here: sensor, aggregator, analysis and decision trigger. The set of technologies that founded the IoT promised to swerve any device into a self-information source.  
At the heart of this ecosystem is data, remotely collected from IoT sensors and feed to cloud-based applications. Through IoT software platforms, the information can be analyzed, stored and visualized in a well-designed way, and what is most important is that it can be accessible from any part of the world. This data can be then analyzed through big data techniques, to enable deeper knowledge at a granular level of the localized supply and use of electricity. In this context, the power utilities Advanced Metering Infrastructure (AMI) play a main role, mostly because it is necessary to harmonize the integration of IoT PQ sensors in the existing Smart-Grid standards-based architecture of connected Intelligent Electronic Devices (IEDs).  
PQ is a concern not only because of the harm produced in equipment and the electrical distribution system itself but because it wastes energy and reduces electrical capacity. PQ problems in EU-25 countries create financial loss of more than \(\ell 150\) billion per year [12]. Detecting a problem from the beginning, before it worsens and when it is easy to tackle, minimizes the likelihood of costly equipment damage or costly business interruptions.  
If AMI and IoT continue overlapping trends--and they will--smart meters could be better supplemented with information from embedded IoT PQ submetering devices. This will enable appliances to self-monitor not only their electrical energy consumption but also quality, a hitherto unexplored approach. Subsequently, this would drive the development of more disruptive energy behavioral tools for making the energy data generated by IoT manageable by customers. As a conclusion, with an IoT PQ platform, it would be possible to offer advanced functionalities for energy management applications to final consumers, building managers, and municipalities on a single cost-effective and scalable multi-tenant platform.
# Electrophysical Properties of PZT-Type Ceramics Obtained by Two Sintering Methods
## 4 Conclusions  
The paper analyzed the influence of two sintering methods, pressureless sintering (PS) and hot-pressing sintering (HP), on the electrophysical properties of the Pb(Zr\({}_{0.49}\)Ti\({}_{0.51}\))\({}_{0.94}\) Mn\({}_{0.021}\)Sb\({}_{0.016}\)Gd\({}_{0.012}\)W\({}_{0.012}\)O\({}_{3}\) material. The X-ray analysis confirmed that the PZT-type material has a tetragonal structure with a P4\(mm\) space group at room temperature. The microstructure of the ceramic samples is characterized by grains with a high packing density, and the average grain size varies depending on the sintering technology used. Large grains are present in the sample obtained by using the pressureless sintering method, while the HP method allows for obtaining fine-grained ceramic material.  
The dielectric loss factor and permittivity tests showed that both sintering methods allow obtaining a PZT-type material characterized by a high permittivity value and low dielectric loss factor value (in the measuring interval). At room temperature, the permittivity values for ceramic samples are 959 and 1245 for S-PS and S-HP, respectively. The dielectric loss factor value (at room temperature) is 0.004 for S-PS and 0.011 for S-HP. It was found that hot pressing has the effect of (i) increasing the blur of the ferroelectric-paraelectric phase transition (compared to the ceramic sample obtained by using the PS method), (ii) increasing the dielectric loss factor, and (iii) shifting the temperature \(T_{\text{m}}\) (by 33 \({}^{\circ}\)C) towards higher temperatures. Both ceramic samples have a high resistivity \(\rho_{\text{DC}}\) at room temperature (values from 1.20 \(\times\) 10\({}^{9}\)\(\Omega\)m to 1.51 \(\times\) 10\({}^{9}\)\(\Omega\)m). The analyzed ceramic samples show middle ferroelectric hardness. Individual parameters describing the hysteresis loop (\(P_{\text{r}}\), \(P_{\text{m}}\), and \(E_{\text{c}}\)) assume similar values for both ceramic samples. However, the S-PS ceramic sample shows a better saturated hysteresis loop in the heating cycle.  
The obtained ceramic materials show favorable dielectric and ferroelectric parameters, adequate for micromechatronic and microelectronic applications.  
Conceptualization, P.N. and D.B.; methodology, P.N. and D.B.; investigation, P.N., D.B. and G.D.; writing--original draft, P.N.; writing--review and editing, P.N. and D.B.; visualization, P.N. and D.B. All authors have read and agreed to the published version of the manuscript.  
The present paper was financed in part by the Polish Ministry of Education and Science within statutory activity.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors would like to thank [PERSON] and [PERSON] (Institute of Fundamental Technological Research Polish Academy of Sciences in Warsaw) for assistance in planning and optimizing the technological process of functional materials.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Unmanned Arial Spraying in Paddy Fields  
The field trial was conducted in a paddy field located in Daeya-myeon, Gunsan City, Jeollabuk-do, where the rice variety Shindongjin was cultivated. The pesticide application commenced on 22 July 2022, adhering to the safety usage standards which dictated three treatments. The test agrochemical was Nonsarang (Kyungnong, Deagu, Republic of Korea), a commercial formulation containing 8% tricyclazole and 15% ferimzone (Suspension concentrate; SC), which was diluted at an 8-fold rate for spraying. Three treatment plots were established: Plot A received the pesticide product alone; Plot B was treated with agrochemicals combined with an adjuvant, Cares (Dongbanggro, Seoul, Republic of Korea), which made up 1% of the spray solution; Plot C involved agrochemical combined with a different adjuvant, Gondor (De Sangosse, Agen, France), which constituted 2% of the spray solution; and Plot D was a pesticide untreated control group. Each treatment plot was arranged with a spraying radius of 2 m and spaced 8 m apart for three replicates. To minimize temporal variability in the application of treatments, three multi-copters were deployed simultaneously for plots A-C.  
The pesticide was dispensed using a multi-copter model YMR-08 (Yamaha Motor Co., Ltd., Shizuoka, Japan) under the following conditions: wind speed at 0 m/s, ensuring applications were made in the calm of early morning between 7 and 10 A.M. The multi-copters were flown at an altitude of 3 m and a speed of 11 km/h and equipped with XR-11001 nozzles (Spraying Systems Co., Glendale Heights, IL, USA) that operated at a pressure of 40 psi and produced an output velocity of 13 m/s to ensure even distribution of the agrochemicals across the designated field areas.
# An Overview of Variants and Advancements of PSO Algorithm
## 3 Parameters of PSO
### Inertia Weight  
The notion of inertia weight '\(\omega\)' of a particle in the basic PSO algorithm was first introduced into the literature by [PERSON] and [PERSON] [14], to improve its convergence performance. This coefficient controls the global and local exploration and the exploitation capacity of the swarm, by determining the influence of the previous velocity on the particle's current movement. With the introduction of this new parameter \(\omega\), the velocity update Equation (1) is changed to velocity update Equation (3). The suitable value of \(\omega\) results in fewer iterations on average to find a sufficiently optimal solution. To balance the global and local search and to ensure the convergence to a sufficiently optimal solution in a lesser number of iterations, an adjustment in the value of the inertia weight \(\omega\), is extremely important. Larger values of \(\omega\) facilitate the global exploration and smaller values promote the local exploitation. As originally proposed, the value of the inertia weight \(\omega\) is often decreased linearly from about 0.9 (\(\omega_{\max}\)) to 0.4 (\(\omega_{\min}\)) during a run.  
It is described as follows Equation (7) ([PERSON] and [PERSON] [19]):  
\[\omega\ =\ \omega_{\max}\ -\ \frac{\omega_{\max}\ -\ \omega_{\min}}{\text{ iter}_{\max}}\times\ \text{iter} \tag{7}\]  
where \(\text{iter}_{\max}\) is the maximum number of iterations, iter is the current iteration, \(\omega_{\max}\) and \(\omega_{\min}\) are initial and final values of the inertia weight respectively.  
[PERSON] and [PERSON] [20] stated a strong relationship ( see Equation (8)) between acceleration coefficients \(c_{1}\), \(c_{2}\) and inertia weight \(\omega\) as:  
\[\omega\ >\ \frac{1}{2}\ (\ c_{1}\ +\ c_{2})\ -\ 1 \tag{8}\]  
[PERSON] and [PERSON] [19] made a comparison between the constriction coefficient (\(\chi\)) and inertia weight (\(\omega\)). The authors considered that both of the approaches are equally effective. As both the constriction coefficient and inertia weight aim to create a balance between the exploration and exploitation capabilities, this results in an improvement in the convergence time and quality of solution obtained. Low values of the constriction coefficient and inertia weight result in exploitation, whereas higher values result in exploration. [PERSON] and [PERSON] [19] also proved that if velocity clamping and constriction coefficient are used together, even then convergence can be faster.
# A Bayesian Network-Based Information Fusion Combined with DNNs for Robust Video Fire Detection
## 4 Experimental Results and Discussions
### Training LSTM and Its Performance before and after Majority Voting  
For the training of LSTM following Faster R-CNN, we collected 1709 video clips from YouTube, comprising 872 clips of fire, and 837 of non-fire, where a huge number of positive and negative frames were included. Figure 9 shows sample still shots of the video dataset.  
For training the LSTM network the video clips were divided into 60 consecutive frames with 30 frames of overlap, which implies that the LSTMs captured every 2 s of dynamic behavior of successive frames, but the decision in LSTM network was made for every second, i.e., \(T_{LSTM}=1\) s, if 30 frames per a second were assumed. As the same way as the training of Faster R-CNN, 70% of the data were selected for training, 10% for validation, and 20% for testing. From each video clip, we obtained \(d\)-dimensional feature for every consecutive frame as stated in Section 3.3, and fed into LSTM. Note that Faster R-CNN may not properly detect fire objects in some frames even though the frames are from a fire video sequence. In such cases, instead of WGAP, the GAP was taken for the undetected frames and used to make the \(d\)-dimensional feature for LSTM input, just as it would have been in a \"non-fire\" scenario.  
Table 6 shows the parameters of LSTM training, and the performance of the test data is shown in Table 7 depending on the number of memory cells in LSTM.  
Figure 8: False positive results of Faster R-CNN fire detection. The red color box is flame prediction blue color box is smoke prediction [26].  
Figure 9: Sample still shots including flame, smoke, and non-fire objects take from video clips for LSTM training [26]. The (**a**) images are taken from videos of fire accident, while the (**b**) images are from non-fire video clips.  
To compare the performance with other methods, we evaluated the results on the public dataset in [12]. Unfortunately, however, the Foggia dataset includes a video clip that lasts only 4 s, which made it hard to completely validate the proposed method that works well for long video clips. So, there were only three times of fire decision in LSTM to take the majority voting due to the short clip, i.e., \(T_{\text{\emph{rot}}}=3T_{LSTM}\).  
Table 8 compares our model's performance against other methods. Our method provided the most accurate fire detection when there were 512 memory cells in the LSTM network.  
In Table 8, the proposed method after majority voting results in 97.68% of accuracy, 2.64% of the false-positive rate, and 1.45% of false-negative rate, is inferior to other CNN-based methods proposed by [PERSON] et al. [19], [PERSON] et al. [21], and [PERSON] et al. [24]. The result implies the majority voting after LSTM fire decisions in this Foggia dataset is not proper to improve the performance due to the short duration of video clip.
# Full Field Inversion in Photoacoustic Tomography with Variable Sound Speed
## 3 Final Time Wave Inversion Problem for Variable Sound Speed
### Continuous Adjoint Operator  
Iterative methods for solving (9) require knowledge of the adjoint operator of \(\mathbf{W}_{T}\). In this subsection, we compute the continuous adjoint operator \(\mathbf{W}_{T}^{*}\colon L^{2}(\mathbb{R}^{3})\to L^{2}(B(0,R))\) and prove that it is again given by the solution of a wave equation. More precisely, we have the following result.  
**Theorem 2**.: _Let \(g\in C_{0}^{\infty}(\mathbb{R}^{3})\), consider the time reversed final state problem for the wave equation,_  
\[\begin{split} q_{tt}(\mathbf{x},t)-c^{2}(\mathbf{x})\Delta q( \mathbf{x},t)&=0,\qquad(\mathbf{x},t)\in\mathbb{R}^{3}\times(- \infty,T)\\ q(\mathbf{x},T)&=g(\mathbf{x}),\quad\mathbf{x}\in \mathbb{R}^{3}\\ q_{t}(\mathbf{x},T)&=0\qquad\mathbf{x}\in\mathbb{R }^{3}\,,\end{split} \tag{15}\]  
_and let \(\chi_{B(0,R)}\) denote the indicator function of \(B(0,R)\). Then, \(\mathbf{W}_{T}^{*}g=\chi_{B(0,R)}\,q(\,\cdot\,,0)\)._  
Proof.: It is clearly sufficient to show \(\mathbf{W}_{T}^{*}g=\chi_{B(0,R)}u_{t}(\,\cdot\,,0)\), where \(u\) solves the wave equation \(u_{tt}-c^{2}\Delta c=0\) on \(\mathbb{R}^{3}\times(-\infty,T)\), with the final state conditions \((u(\,\cdot\,,T),u_{t}(\,\cdot\,,T))=(0,f)\). Using the weak formulation (similar to [38]) for the wave equation, shows that \(\int_{0}^{T}\int_{\mathbb{R}^{3}}c^{-2}(\mathbf{x})u_{tt}(\mathbf{x},t)v( \mathbf{x},t)\,d\mathbf{x}dt+\int_{0}^{T}\int_{\mathbb{R}^{3}}\
abla u( \mathbf{x},t)\cdot\
abla v\,(\mathbf{x},t)\,\,d\mathbf{x}dt=0\) for \(v\in C_{0}^{\infty}(\mathbb{R}^{3})\). Two times integration by parts, rearranging terms and using the final state conditions for \(u\) yields \(\int_{\mathbb{R}^{3}}c^{-2}(\mathbf{x})[f(\mathbf{x})v(\mathbf{x},T)-u_{t}( \mathbf{x},0)v(\mathbf{x},0)+u(\mathbf{x},0)v_{t}(\mathbf{x},0)]\,d\mathbf{x} =\int_{0}^{T}\int_{\mathbb{R}^{3}}u(\mathbf{x},t)\left[c^{-2}(\mathbf{x})v_{tt }(\mathbf{x},t)-\Delta v(\mathbf{x},t)\right]d\mathbf{x}dt\). By taking \(v\) as the solution of (1)-(3) this yields \(\int_{\mathbb{R}^{3}}c^{-2}(\mathbf{x})g(\mathbf{x})\,\mathbf{W}_{T}(f)( \mathbf{x})\,d\mathbf{x}=\int_{\mathbb{R}^{3}}c^{-2}(\mathbf{x})u_{t}( \mathbf{x},0)f(\mathbf{x})\,d\mathbf{x}\), which implies \(\mathbf{W}_{T}^{*}g=\chi_{B(0,R)}u_{t}(\,\cdot\,,0)=\chi_{B(0,R)}q(\,\cdot\,,0)\) and completes the proof.  
We can reformulate the adjoint operator as follows.  
**Corollary 1**.: _For \(g\in C_{0}^{\infty}(\mathbb{R}^{3})\), let \(q\) be the solution of_  
\[q_{tt}(\mathbf{x},t)-c^{2}(\mathbf{x})\Delta q(\mathbf{x},t)=0\quad\text{for }( \mathbf{x},t)\in\mathbb{R}^{3}\times(0,\infty) \tag{16}\]  
_with initial conditions \((q(\,\cdot\,,0),q_{t}(\,\cdot\,,0)=(g,0)\). Then, \(\mathbf{W}_{T}^{*}g=\chi_{B(0,R)}\,q(\,\cdot\,,T)\)._  
Proof.: Clearly \(q\) solves (16) with initial data \((q(\,\cdot\,,0),q_{t}(\,\cdot\,,0)=(g,0)\) if and only if \((x,t)\mapsto q(x,T-t)\) solves (15). Therefore, the claim follows from Theorem 2.
## 2 Methodology
### Clustering
#### 2.2.3 SSA-KHM Algorithm Flow  
Since the KHM algorithm has a more straightforward structure than the SSA algorithm, it converges faster; however, it typically falls into local optimum solutions. To overcome the local optimum issue, this study uses a hybrid model comprising the SSA and KHM algorithms to determine the clustering centers. The model can maintain the advantages of the KHM and SSA algorithms to better cluster the data and capture the characteristics of the data in the time series, enabling a more accurate multi-step prediction of the dam deformation data. The specific flow of the SSA-KHM model is shown below:  
1. Divide the dam deformation data into a 60% training set and a 40% test set and normalize. The training set is clustered, and its cluster centers are calculated, and the test set is clustered using the cluster centers of the training set.
2. Initialize the cluster center and set the maximum number of iterations _Iter_max.
3. Set \(G_{1}=0\).
4. Set \(G_{2}=G_{3}=0\).
5. The SSA method 5.1 Use the SSA algorithm to update the clustering center.
6. \(G_{2}=G_{2}+1\). If \(G_{2}<3\), then return to 5.1.
7. The KHM method 6.1 The current location of the individual is used as the initial cluster center and updated by the KHM algorithm.
8. \(G_{3}=G_{3}+1\). If \(G_{3}<7\), then return to 6.1.  
\(G_{1}=G_{1}+1\). If \(G_{1}<\textit{Itermax}\), then return to 4.
* Classify sample \(x_{i}\) into the largest group \(k\) of \(m(c_{k}|x_{i})\).
## 2 State of the Art in Optical Wireless Communications
### The Contribution of Optical Camera Communications in the Processes of Information Transmission
At the moment, the image sensors of cameras demonstrate a limited capacity in detecting clear answers that have frequencies up to 6-7 kHz [44]. The tests so far have been based on real-time processing using smartphones, which is also quite difficult due to camera limitations and low processing power. However, the mixed use of cameras and photoreceptors in the front area of mobile phones can substantially adjust the way data are processed and transmitted. Even if the technological advance in this field is extremely difficult, this technology can have an enormous contribution to future IoT paradigms, especially in terms of security and high connectivity of users. Numerous experimental works [45; 46] in the direction of OCC have been highlighted with their objectives and achievements in the direction of individual implementations. The usefulness of the solution in which a mobile application communicates directly with the infrastructure and vehicles can be declared viable only by the way it meets certain characteristics and provides satisfactory results. Most of the experiments performed by the research groups were limited to external scenarios and used single-lens digital reflex cameras (DSLRs) or portable devices for MATLAB processing and analysis [47]. Table 1 shows the OCC reference implementations with notable results, using smartphones, but also DSLR cameras, highlighting the problems or limitations they faced. The most relevant systems tested in the OCC direction based on OCC used combined modulation schemes and ON/OFF coding in the form of phase shift subsampling (UPSOOK), and the processing used multiplexing with a division into wavelengths (WDM) [48; 49]. These aspects have reached quite decent distances near the communication area of 1.5-2 m, but the transfer rate is between 378 bps and 480 bps [50]. These limitations can also be found in the external environment, in some places being threshold problems and even saturation of the communication channel in the conditions of parasitic environments. The processing with the help of smartphones is a delicate problem due to the limitations that the hardware part of a phone has and what the software imposes.
## 3 Problem Formulation
### Throughput of Training DL Model over GPU  
In DL, the training process notably relies on the Stochastic Gradient Descent (SGD) optimisation algorithm. Its main objective is to optimise (or train) the parameters of a model in order to minimise prediction error by performing the following steps:  
* predict results based on the current state of the model and the training dataset;
* calculate the prediction error;
* update the model parameter.  
The number of samples used in the aforementioned steps is referred to as batch size, i.e., the number of samples of the training dataset which are run through the model before updating its parameters based on the quality of the prediction.  
One epoch means that each sample in the training dataset was used to update the model parameters. An epoch is usually composed of several batches, and the number of batches that composes one epoch can be referred to as the number of iterations; see Figure 1. For example, the CIFAR-10 dataset [21] is composed of over 60,000 images, and thus, a batch size of 32 would require 1875 iterations to complete one epoch. Finally, the training of a DL model usually requires multiple epochs to reach a desired level of quality.  
There are multiple definitions of throughput; we consider the one defined in [8]. It refers to the number of iterations a model can train per second, which is optimised in the problem defined in Section 3. Normally, the framework for DL logs the training time per epoch while training. The throughput can be calculated from the training time per epoch, as seen below:  
\[iterations=\frac{total\_images}{batch\_size} \tag{1}\]  
\[throughput=\frac{iterations}{time\_per\_epoch} \tag{2}\]  
where \(total\_images\) is the total of images in the dataset. \(batch\_size\) is the batch size configured for each job. \(time\_per\_epoch\) is the training time per epoch recorded when training a model in a different GPU architecture.