esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix F Deep Network Model and Machine Learning Predictions  
Simulations of quantum algorithms with a large number of qudits by classical computers are very demanding to computational resources. Simulating a QRWS algorithm for \(m\)-dimensional hypercube requires constructing a tensor product of the three registers used in the algorithm (control register of size 2, coin register of size \(m\), and node register with dimension \(2^{m}\)), so the total state space is \(m2^{(m+1)}\). The number of the algorithm's iterations needed to find solution also grows with increasing the coin size. Raising the size of both operators and the number of iterations leads to much larger computational times.  
That requires an alternative approach to the task. In this paper, predictions of the studied quantum algorithm's parameters for bigger walk coin size, and, respectively, higher searchable space size, are made with ML methods.  
Here, by Monte Carlo simulations of QRWS algorithm with different size of the coin register, we are able to obtain larger set of independent data that is used to train a ML model.  
A feed-forward deep neural network (DNN) was built using the Monte Carlo generated datapoints for coin size \(2\leq m\leq 10\) to train it. Each datapoint consists of the walk coin parameters' values \(\phi,\zeta,\,m\), and the corresponding MC results for the probability \(p(\phi,\zeta,m)\) as labels. The neural network consists of L hidden layers, each with N neurons. The structure of the model used is shown in Figure 11. Next, the ML model is trained for a large number of epochs. Finally, it is used for predictions of the three-parameter function \(p(\phi,\zeta,m)\), where \(\phi\in[0,2\pi],\zeta\in[0,2\pi],m\geq 2\) (the results for coin size 11 and 16 are plotted in Figure 3). The values of parameters \(\alpha\) and \(\varepsilon\) defined in Section 4.1 are extracted by analyzing the above relations. Although our ML model gives relatively reliable predictions for several characteristics of the QRWS algorithm that are explained in the paper, others are not so good.  
It is a well known fact that using DNN for interpolation gives very good results. However, extrapolations away from the training region of the model result in inaccuracies in the predictions. In our case, they mainly manifest in small asymmetric widening of the central high probability strip of \(p(\phi,\zeta,m)\) (Figure 3--middle and right images) that leads to some overestimation of the width \(\varepsilon\), particularly for higher coin size m, seen in Figure 6. These deviations are more clearly visible when we focus on the highly nonlinear part of the function \(p(\phi,\zeta,m)\) shown in Figure 3 for \(m=11,16\).
# Improving the Functional Reliability of an Urban Public Transport Line
## 2 Materials and Methods
### Ways to Improve Functional Reliability  
Actions aimed at improving the functional reliability of urban public transportation are intended to increase the attractiveness of public transport and usually depend on tactical and operative decisions. This attractivity increase is mainly based on reducing service variability. That is obtained by operation according to the planned, publicly known schedule, as faithfully as possible. These actions correspond to a systemic treatment of the performance of physical resources that must be mobilized to satisfy the estimated travel demands addressed to the services' network. Also at the systemic level, the improvement of the reliability of urban public transportation aims at the actions of continuous monitoring of the actual circulation and, when needed, the interventions required in case of disruptive events on the planned circulation.  
Functional reliability also reflects the results of dynamic actions of the competent authority aimed at increasing the performance of the system components [28]. Some of these actions on the components whose positive impacts are found in the functional reliability of the urban public transport network are as follows:  
* Improving the physical performance of existing infrastructures or network developments to obtain additional capacities or improve the quality of existing ones. However, capacity increases are generally expensive and time-consuming, and their political acceptance is often uncertain and difficult.
* Better incident management by infrastructure managers and better organization of corrective and preventive maintenance operations through proactive monitoring of the status of network components accompanied by adequate communication with users.  
* Differentiated charging of infrastructure use for different areas of the network to increase the overall reliability of the network (difficult to achieve) or for categories of users and/or intervals of the day, week, or year (practiced with good results in reducing the variability of the demand on the network components)
* Informing users in advance and in real-time to mitigate the negative effects of the lack of reliability.  
All these actions and others like them (which, however, exceed the objectives of the study) through the positive effects on the system's components improve the performance of the physical network. As such, premises are created for the service network, which also has a more advanced physical network as its support, to become more attractive.  
This study aims to find the solutions for the functional reliability improvement of an urban tram line with an exclusive way. As already mentioned, we aim to increase the probability of respecting the planned schedule and respecting the planned headways between vehicles. The motivation for the choice resides in the fact that the degree of comfort of the travel is also reflected in the vehicle headways (in terms of loading degree of tram capacity). Moreover, the headways are important for the user in his/her daily activity planning.
# A Comparative Study of 3D UE Positioning in 5G New Radio with a Single Station
## 2 System Hypotheses and Signal Modeling
### Sounding Reference Signal in 5G NR
#### 2.2.1 Zadoff-Chu Sequence Generation  
Let us assume that a \(\mathbf{s_{sss}}\in\mathbb{C}^{\mathbf{W}\times 1}\) is expected for a single-antenna UE. We start with the generation of Zadoff-Chu sequence \(\mathbf{r_{zc}}\in\mathbb{C}^{\mathbf{M}_{sc,b}^{rs}\times 1}\) from Chapter 6.4.1.4.2 in the 3 GPP standard TS 38.211 [16]. The length of \(\mathbf{r_{zc}}\) is \(\mathbf{M}_{sc,b}^{rs}\), which is shorter than OFDM symbol \(W\). \(\mathbf{r_{sss}}\) is a variant of one of 30 base sequences \(\mathbf{r}\). We use \(\mathbf{u}\) to indicate the base sequence index and \(\mathbf{v}\) to denote different variants. The Zadoff-Chu sequence \(\mathbf{r_{sss}}\) used for the 5G SRS can be obtained by using Equation (1):  
\[\mathbf{r_{zc}}(\mathbf{n},\mathbf{i^{{}^{\prime}}})=\mathbf{r_{u,v}}(\mathbf{ n}),\ \mathbf{n}=0,1,...,\mathbf{M}_{sc,b}^{rs}-1;\ \mathbf{i^{{}^{\prime}}}=0,1,...,\mathbf{N_{symb}^{SRS}}-1 \tag{1}\]  
where \(\mathbf{i^{{}^{\prime}}}\) is the location index of the SRS OFDM symbol in a 5G subframe. \(\mathbf{i^{{}^{\prime}}}\) determines the variant index \(\mathbf{v}\) (the detailed relation can be found in Appendix A.1). \(\mathbf{i^{{}^{\prime}}}\) can be chosen from zero to \((\mathbf{N_{symb}^{SRS}}-1)\). \(\mathbf{N_{symb}^{SRS}}\) is received from the radio resource control (RRC) layer message, which indicates the maximum number of OFDM symbols that can be used for SRS transmissions in a 5G subframe. The length of \(\mathbf{r_{sss}}\) is defined by the equation below:  
\[\mathbf{M}_{sc,b}^{rs}=\mathbf{m_{SRS,b}}\mathbf{N}_{sc}^{RB}/\mathbf{K}_{TC} \tag{2}\]  
Figure 2: Simplified SRS generation: to map a Zadoff–Chu sequence (orange grids) to the specified subcarriers and time slots.  
where \(\mathbf{N}_{sc}^{RB}\) is the number of subcarriers allocated for the Zadoff-Chu sequence element in each resource block (RB) and equal to 12 in this work, according to [10; 16]. The allocated RB value of \(\mathbf{m}_{\mathbf{SRS},\mathbf{b}}\) is chosen from a \(64\times 4\) SRS bandwidth configuration table defined in 6.4.1.4.3-1 [16], which is indexed by bandwidth configuration parameter \(\mathbf{C}_{SRS}\) and SRS transmission bandwidth indicator \(\mathbf{B}_{SRS}\), respectively. The value of structure controller parameter \(\mathbf{K}_{TC}\) can be chosen among 1, 2, and 4, and \((\mathbf{K}_{TC}-1)\) indicates the number of empty subcarriers between two Zadoff-Chu elements in an SRS OFDM symbol.
## 2 Materials and Methods
### OPVs Assembly and Evaluation  
OPV cells were fabricated on G/PEN substrates in an Ar-purged glovebox (0.1 ppm O\({}_{2}\) and H\({}_{2}\)O). First, the ZnO nanoparticle solution was spin-coated onto the G/PEN substrates at 3000 r/min for 50 s. This procedure was repeated twice to obtain a continuous smooth film and then baked at 120 \({}^{\circ}\)C for 10 min. After drying, 20 mg of poly(3-hexyl)thiophene (P3 HT, regioregular, Sigma-Aldrich, St. Louis, MO, USA) and 16 mg of phenyl-C61-butyricacid methyl ester (PCBM, Sigma-Aldrich, St. Louis, MO, USA) were dissolved in 1 mL of chlorobenzene to obtain a P3 HT:PCBM mixed solution, which was spin-coated on the top of ZnO/G/PEN at 3000 r/min for 1 min as the active layer and subsequently dried at 120 \({}^{\circ}\)C for 10 min. Then the following steps were processed in the ambient conditions. The poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) (1.3 wt % dispersion in H\({}_{2}\)O with conductive grade, Sigma-Aldrich, Samoa, USA) solution (mixed with 5 wt % isopropyl alcohol and 1 wt % surfactant (Zonyl-FSH, Dupont, Wilmington, DE, USA) and filtered through a 0.45-\(\upmu\)m filter) was spin-coated onto the active layer at 3000 r/min for 50 s. Finally, the PMMA/G or CNT films were transferred onto the devices as the top contact. In a natural atmosphere, the photovoltaic properties were measured under Air Mass 1.5 (AM 1.5) illumination (100 mW/cm\({}^{2}\)) and the current-voltage data were recorded by a Keithley 4200 SourceMeter (Tektronix, Beaverton, OR, USA). The efficient area of all devices was controlled by a circular mask with a diameter of 3 mm.
# Flexible Antennas: A Review
## 3 Fabrication Techniques for Flexible Antennas
### Special Fabrication Techniques for Flexible Wearable Antennas  
The special fabrication techniques for fabricating flexible antenna can be divided into the following categories: (1) SIW based technology; (2) stitching and embroidery; (3) the use of conductive textile yarns to embroider the conductive patterns of the antenna on a non-conductive textile substrate; and (4) inkjet and screen printed printing on non-conductive textile materials. This article focuses on the first two techniques.
## 5 Results and Discussion
### Synchronous Counter  
As a first example, an ordinary 4-bit counter was tried. For this purpose, Table 1, which describes the operation of the system, was created, and it consisted of 16 lines.  
\begin{table}
\begin{tabular}{c c c} \hline
**Clk** & **Inputs** & **Outputs** \\ \hline  
\ \hline \end{tabular}
\end{table}
Table 1: Description of the synchronous counter response.  
The first line specifies the initial state of the inputs and outputs that should appear immediately after starting the system. The state obtained after the first activation signal given to clk appears in line 2. The value 1 in the first column denoting the clk signal means that an activation signal (rising edge) appears on all clk inputs, causing a change in the value of the flip-flops. A value of 0 in this column means there is no edge, so there is no change in the state of all flip-flops. Since the circuit does not have access to constants, one input representing the constant value 0 was created, so the circuit could use this constant. In this way, it was intended to obtain a synchronous counter counting the signals appearing on the clk inputs. The assumed \(number\_columnsCGP=8\) (maximum number of CGP nodes) and the set of functions: JK flip-flop (function type 0), AND gate (function type 1), OR (function type 2), NOT (function type 3). The result was the genotype: *([1]3 0 0), *([2]0 1 1), *([3]0 2 2), *([4]1 3 2), ([5]1 1 2), *([6]0 4 4), *([7]1 6 4), *([8]0  
The parameters \(number\_columnsCGP\) and the set of functions have been left unchanged. The result was the genotype:  
* ([6]0 5 5), *([7]1 6 5), *([8]0 7 7), 8 6 4 3 This genotype represents the system shown in Figure 3.  
As can be seen, the resulting system is a classic asynchronous counter. It should be noted, however, that as a result of the research, even with the same parameters determining the operation of genetic programming, many different systems were obtained that met the requirements specified in the same table. For example, the genotype was obtained:  
* ([6]0 5 5), *([7]1 6 5), *([8]0 7 7), 8 6 3 2 representing the system shown in Figure 4.  
It should be noted that instead of using an AND gate, the JK 05 and JK 06 flip-flops were used in this circuit. Despite this, the outputs of this circuit behave exactly like the outputs of the circuit in Figure 3.  
\begin{table}
\begin{tabular}{c c c} \hline
**Clk** & **Inputs** & **Outputs** \\ \hline
1 & 0 & 0 0 0 \\ \hline \end{tabular}
\end{table}
Table 2: Revised description of synchronous counter response.  
Figure 3: Correct synchronous counter system obtained based on Table 2.
## 4 Experimental and Real-World Implementation  
In this testbed implementation, numerous metrics, including SINR, BLER, and data rate, are taken into account when evaluating system performance. Our LoRa algorithm is built using open-source software. Two USRP N321 devices, one used as a transmitter and the other as a receiver.  
As shown in Figure 5, the USRP oscillator uses the GPS antenna as a timing reference, enabling precise time synchronization for signal identification. The USRP oscillators use the GPS antenna as a timing reference, enabling precise time synchronization for signal identification. Vehicle-mounted unidirectional antennas are employed, with the center frequency set at 169.7 MHz. During the mobile trials, the USRP and PC are both powered by an uninterruptible power source, and the PC is connected to the USRP through a 10 Gigabit Ethernet connection. There were two phases to this implementation: stationary and mobile. To determine a specific SINR value for LoRa, the stationary USRP N321, including the Key Performance Indicator (KPI) outcomes, is mostly used as a reference.  
The selection of USRP N321 devices of LoRa in IIoT under NLoS conditions is driven by their superior configurability and capabilities. These devices offer precise control over radio parameters, allowing detailed analysis of communication protocols. With a wide frequency range, they simulate diverse industrial environments accurately. While Arduino and STM32 are cost-effective, they lack the processing power for in-depth signal analysis. Raspberry Pi provides more processing power, but may have higher power consumption. The USRP N321, with its real-time capabilities and specialized focus on software-defined radio research, aligns with our need for accurate and detailed data collection in exploring the nuances of LoRa communication in industrial settings. If the received signal power is less than \(-93\) dBm, the LoRa receiver will not be able to synchronize with the transmitted signal. Buildings with glass higher than 20 m and surrounding the 300 m NLoS communication area of the mobile can result in high reflection coefficients, shadowing, and multipath. The LoRa parameters are shown in Table 3. For optimum performance, CR = 4/6 and SF = 10 are used. BW = 200 kHz and a sample rate of 240 kHz are also considered to improve SINR and signal coverage.  
Figure 5: Testbed scenario.
## 7 A Repetition-Aware CST
### Analysis  
For the analysis we will assume that we hierarchically group the terminals and nonterminals of consecutive symbols in \(C\) (by extending the grammar) using a grouping factor of \(g\), although, as explained, it turned out to be faster in practice not to do so. We will call \(h\) the height of the grammar, which can be made \(O(\log n)\) by generating balanced grammars [48] (we experiment with balanced and unbalanced grammars in the next section). We also use sampling steps \(c\) to access \(C\) and \(s\) for the _PLCP_ representation. We also assume \(s\) is the sampling step of the RLCSA, thus accessing one _PLCP_ cell takes \(O(s\log n)\) time.  
Let us first consider operation _NSV_ (and _PSV_). We spend \(O(\log n+c)\) time to binary search _Pos_ and move from \(C[cj^{\prime}]\) to \(C[j]\). If we have to expand \(C[j]\), the cost increases by \(O(h)\): Note that if we had entered \(a\), and obtained no answer inside it, then we can directly use \(min_{b}\) and thus do not enter \(b\), so we only enter one of the two. At the leaf of the grammar tree of \(C[j]\), we may scan \(O(t)\) symbols of _LCP_ using _PLCP_, which costs \(O(ts\log n)\). Further, the scanning of \(C[j+1,n]\) takes time \(O(g\log n)\) assuming the hierarchical grouping, and the final expansion of \(C[k]\) takes again \(O(h+ts\log n)\) time.  
Now consider operation _RMQ_. Again, we spend \(O(c+\log n)\) time to find \(C[i]\) and \(C[j]\), and then expand them as before: Only one of the two branches is expanded in each case, so the time is \(O(h+ts\log n)\). Finally, we need time \(O(g\log n)\) to traverse the area \(C[i+1,j-1]\).  
Thus, for all the operations, the cost is \(O(c+h+(ts+g)\log n)\).  
Let \(r^{\prime}=|R|+|C|\), and recall that \(r\) is the number of runs, so \(n^{\prime}=O(r\log\frac{n}{r})\) if using Re-Pair. The sampling parameters involve \(O((n^{\prime}/c)\log n^{\prime})\) bits for the sampling of \(C\), plus \(O((r/s)\log n)\) for the sampling of _PLCP_, plus \(O((n/s)\log n)\) for the sampling of the RLCSA, plus \(O((n^{\prime}/g)\log n^{\prime})\) for the new rules created in the hierarchy for \(C\). Parameter \(t\) involves in practice an important reduction in \(|R|\), the set of rules, but this cannot be upper bounded in the worst case unless the grammar is balanced, in which case it is reduced to \(O(|R|/t)\) rules.  
By choosing \(c=g=\log^{3}n\), \(s=\log^{2}n\), \(t=\log n\), and assuming a balanced grammar, the cost of the operations simplifies to \(O(\log^{4}n)\) and the extra space incurred by the samplings is \(o(r)+O(n/\log n)\) bits. This can be reduced to any \(o(r)+O(n/\log^{k}n)\), for any constant \(k\), by raising the time complexity to \(O(\log^{k+3}n)\).
# Fuzzy-PID-Based Atmosphere Packaging Gas Distribution System for Fresh Food
## 2 Materials and Methods
### System Data Transmission and Architecture  
The complete automatic gas distribution line consists of four sub-systems: the operation terminal system, the data transmission and management system, the feedback control system, and the gas distribution execution system. Based on the analysis of the demand for automatic gas distribution for fresh food preservation, this paper designs a 4G network-based automatic gas distribution data transmission, monitoring, and storage system. Its main functions are shown below. Figure 3 illustrates the data transmission architecture of the gas distribution system.  
1. Real-time data transmission: the gas concentration and flow rate are monitored in real time by the gas conditioning and preservation gas distribution system, and the data are transmitted to the working terminal and return control system.
2. Data recording and storage: the historical data are stored so that it would be convenient to check them.
3. Data maintenance management: the on-site gas concentration and flow rate data could be transmitted to the cloud server.
4. Completion of on-site equipment correlation, adjustment, corresponding data storage, etc.  
Figure 2: Workflow chart of the gas distribution system.  
The remote data transmission and monitoring of the gas distribution system is realized by using a 4G DTU with the recommended standard 485 (RS485) communication. The sensor data could then be stored in a cloud platform or at the work terminal, and the set control data or command could be sent to the programmable logic controller (PLC) in turn. This enables the remote control of the gas distribution system.  
With the data transmission and conversion protocols, a conventional 4G network can be used to enable the gas distribution system to send commands from the working terminal to the PLC to complete the acquisition and distribution of various types of data. In this paper, 4G is chosen as the communication network for two reasons: (1) the communication modules supporting the 5 th-generation mobile communication technology (5G) are not mature yet, while 4G-based products are inexpensive and widely available, and (2) a 4G network can already achieve data transmission in millisecond, which can fully meet the requirements for reliability and efficiency. Therefore, it is more economical and reliable to choose communication modules based on 4G technology, and the system is designed with future communication network upgrades in mind, leaving space for 5G and more efficient communication technologies in the future.
## 4 Implementation of ROA\({}_{3}\)
### Optimum Design of the Bidirectional EDFA
#### 4.2.1 Backup Optical Amplifier for ROA\({}_{1}\)  
For ROA\({}_{3}\) to serve as a backup optical amplifier for ROA\({}_{1}\), its pumping configuration should be backward pumping in order to provide the function of a high output power similar to that of a post-amplifier (post-amp).  
In the simulation setup, shown in Figure 8, a single-channel signal is amplified by an EDFA, which is then analyzed by a signal analyzer. The single-channel signal is generated by a transmitter that comprises a continuous wave (CW) light from a laser diode at 193.1 THz, which is modulated via a Mach-Zehnder modulator (MZM). The parameters of the transmitter are shown in Figure 8b. Before launching into the EDFA, the signal from the transmitter will suffer some IL due to passive components. As shown in Figure 8a, the total IL from A to B is 7.8 dB; hence, the input optical power of the EDFA is \(-10.8\) dBm.  
The NF and the output power (P\({}_{\text{out}}\)) of the backward-pumping EDFA vary along with the EDF length at different pumping powers of 50, 100, 150, and 200 mW, as shown in Figure 9. To achieve the output power requirement of 20 dBm, the EDF length and pumping power must be greater than 7.5 m and 150 mW, respectively. It is found that the NF decreases gradually as the pumping power increases. This trend is similar to the simulation result in [20].  
Figure 8: Simulation setup of a backup optical amplifier for ROA\({}_{1}\): (**a**) detailed setup and (**b**) simplified setup (ATT: attenuator, RBW: resolution bandwidth, OSA: optical spectrum analyzer).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model Parameter** & **Value** \\ \hline Er\({}^{3+}\) Core Radius of EDF & 3 \(\upmu\)m \\ Length of EDF & 2.5 to 20 m \\ Pumping Power & 50, 100, 150, \& 200 mW \\ Pumping Wavelength & 1480 nm \\ Er\({}^{3+}\) Concentration of EDF & \(1.25\times 10^{25}\) m\({}^{-3}\) \\ Pumping Configuration & Forward and Backward \\ \hline \hline \end{tabular}
\end{table}
Table 3: Typical parameters of the rate equation model EDFA in VPI.