esa-sceva/satcom-chunk-collection huggingface dataset:
## Part A On-Line Energy Optimization
### Local Optimization: The Min-two Uniform Targets Protocol (M2 TP)  
[8] further assumes that the search phase always returns _two points \(p^{\prime\prime}\), \(p^{\prime\prime\prime}\)_ each uniform in \((-\alpha,\,\alpha)\) and that a modified protocol M2 TP selects the best of the two points, with respect to the local (vertical) progress. This is in fact an optimized version of the Local Target Protocol.  
In a similar way as in the proof of the previous lemma, the authors prove the following result:  
**Lemma 2** ([8]): _The expected \"hops efficiency\" of the \"min two uniform targets\" protocol in the \(a\)-uniform case is_  
\[E(C_{h})\,\simeq\,\frac{\alpha^{2}}{2(1-\cos\alpha)}\]  
_for \(0\,\leq\,\alpha\,\leq\,\frac{\pi}{2}\) and for large \(h\)._Now remark that  
\[\lim_{\alpha\to 0}E(C_{h})\;=\;\lim_{\alpha\to 0}\frac{2\alpha}{2\sin a}=1\]  
and  
\[\lim_{\alpha\rightarrow\frac{\pi}{2}}E(C_{h})\;=\;\frac{(\pi/2)^{2}}{2(1-0)}\;= \;\frac{\pi^{2}}{8}\simeq 1.24\]  
Thus, [8] proves the following:  
**Lemma 3** ([8]): _The expected \"hops\" efficiency of the min-two uniform targets protocol is_  
\[1\leq E(C_{h})\leq\frac{\pi^{2}}{8}\simeq 1.24\]  
_for large \(h\) and for \(0\,\leq\,\alpha\,\leq\,\frac{\pi}{2}\)._  
Remark that, with respect to the expected hops efficiency of the local target protocol, the min-two uniform targets protocol achieves, because of the one additional search, a relative gain which is \((\pi/2-\pi^{2}/8)/(\pi/2)\simeq 21.5\%\). [8] also experimentally investigates the further gain of additional (i.e. \(m>2\)) searches.
# Decentralized Coordination of DERs for Dynamic Economic Emission Dispatch
## 4 Decentralized Algorithm
### Algorithm Development  
To clearly illustrate the difference between our work and [35], defining \(U_{ij}=\left\{\begin{array}{l}I,\;\;i<j\\ -I,\;i>j\end{array}\right.\), problem (25) is equivalent to the following form  
\[\min\sum_{i=1}^{N}\phi_{i}(\lambda_{i})\] \[\text{s.t.}\;\;U_{ij}\lambda_{i}+U_{ji}\lambda_{j}=0,\;\;(i,j)\in \mathcal{E} \tag{26}\]  
Thus, we convert the DEED problem into a simplified form that is applicable to using a modified TriPD algorithm. The detailed iterative steps are presented in Algorithm 1. Figure 1 provides an illustration of the algorithm's iterative process.  
```
1:Initialization: Each committed generating unit \(i\in\mathcal{M}\cup\mathcal{S}\) sets \(\lambda_{i}^{0}\in R^{\mathsf{T}}\), \(w_{(i,j),i}^{0}\in R^{\mathsf{T}}\), \(j\in N_{i}\), and \(p_{i}^{0}=\arg\min_{p_{i}\in I_{i}}(\big{(}\lambda_{i}^{0}\big{)}^{\prime}p_{ i}+\mathcal{C}_{i}(p_{i}))\)
2:For\(k=0,1,\ldots,K\)do
3:For\(\forall j\in N_{i}\), each unit \(i\in\mathcal{M}\cup\mathcal{S}\) excute \[\tilde{w}_{(i,j),i}^{k} =\frac{1}{2}(w_{(i,j),i}^{k}+w_{(i,j),i}^{k})+\frac{\kappa_{(i,j )}}{2}(U_{ij}\lambda_{i}^{k}+U_{ji}\lambda_{j}^{k})\] (27) \[\lambda_{i}^{k+1} =\lambda_{i}^{k}-\tau_{i}(-p_{i}^{k}+V_{i}+\sum_{j\in N_{i}}U_{ij }^{\mathsf{T}}\tilde{w}_{(i,j),i}^{k})\] (28) \[w_{(i,j),i}^{k+1} =\tilde{w}_{(i,j),i}^{k}+\kappa_{(i,j)}U_{ij}\Big{(}\lambda_{i}^{ k+1}-\lambda_{i}^{k}\Big{)}\] (29) \[p_{i}^{k+1} =\arg\min_{p_{i}\in I_{i}}(\big{(}\lambda_{i}^{k+1}\big{)}^{\prime }p_{i}+\mathcal{C}_{i}(p_{i}))\] (30)
4:Unit \(i\) sends \(U_{ij}\lambda_{i}^{k+1}\) and \(w_{(i,j),i}^{k}\) to its neighbours.
5:End
```  
**Algorithm 1** decentralized algorithm for DEED problem  
In Algorithm 1, for \(i=1,\ldots,N\), \(k>0\), and \(j\in N_{i}\), where \(N_{i}\) represents the neighbors of \(i\), \(\tilde{w}_{(i,j),i}\) is an auxiliary variable, \(w_{(i,j)}\) is the edge variable corresponding to coupling edge constraint (26), and \(\tau_{i}>0\) is the stepsize; the edge weight/stepsize \(\kappa_{(i,j)}\) can be alternatively interpreted as an inherent parameter of the underlying graph.  
Figure 1: Block diagram of agent \(i\)’s operation at iteration \(k\).
## 3 Proposed Method
### Learnable Positional Embedding Methods  
Instead of initially merging fixed positional information with structural information, we propose initializing the information separately and integrating it after learning. In this paper, we call this method IMGCN-LPE. The overall architecture of the IMGCN-LPE is depicted in Figure 2.  
The procedure of initializing the positional information before supplying it in the IMGCN-LPE is essential. Initializing the positional information is defined based on a random walk (\(RW\)), as presented in Equation (19), and the initial positional embedding at the input layer \(l=0\) in Equation (20):  
\[p_{i}^{RW}=\left\{RW_{ii}^{1},\ RW_{ii}^{2},\ \ldots,\ RW_{ii}^{k}\right\}, \tag{19}\]  
\[p_{i}^{l=0}=LL_{p}(p_{i}^{RW})=D^{0}p_{i}^{RW}+d^{0}, \tag{20}\]  
where the \(RW\) operator is defined in Equation (21):  
\[RW=\mathcal{AD}^{-1}. \tag{21}\]  
This paper considers only the probability of returning to itself through random steps on each node, which solves the direction problem of Laplacian-based positional embedding methods. If the embedding dimension \(k\) is sufficiently large, it is possible to express the unique graph topology by learning the broad positional information up to the node reached by \(k\) hops. Figure 3 illustrates an example of an \(RW\)-based initialization of positional embedding with \(k=4\).  
Figure 2: Main architecture of the graph convolutional network with learnable positional embedding applied on images (IMGCN-LPE).  
The learning process of IMGCN-LPE is defined in Equation (22):  
\[IMGCN-LPE:\begin{cases}H_{i}^{l+1}=\mathcal{F}_{h}\left(\begin{bmatrix}h_{i}^{l} \\ p_{i}^{l}\end{bmatrix}\end{bmatrix},\,\left\{\begin{bmatrix}h_{i}^{l}\\ p_{j}^{l}\end{bmatrix}\right\}_{j\in\mathcal{N}_{i}^{l}},\,e_{ij}^{l}\right) \\ \begin{array}{c}p_{i}^{l+1}=\mathcal{F}_{p}(p_{i}^{l},\,\left\{p_{j}^{l} \right\}_{j\in\mathcal{N}_{i}^{l}},\,e_{ij}^{l})\\ e_{ij}^{l+1}=\mathcal{F}_{c}(h_{i}^{l},\,h_{j}^{l},\,e_{ij}^{l})\end{array} \end{cases}.\end{cases} \tag{22}\]  
The difference between _IMGCN-LPE_ and the standard _MP_ defined in Equation (18) is that it continues updating the feature and positional embedding using learned positional embedding \(p_{i}^{l}\), initialized based on the _RW_ in \(l=0\). In addition, using an activation function, such as rectified linear unit (ReLU), for selecting the message-passing function is the same, but in the case of \(\mathcal{F}_{p}\), _tanh_ is used to allow both positive and negative values for position coordinates.
## 2 Materials and Methods  
### Selection Equation of Deep Coalbed Directional Hydraulic Splitting Angle  
1.1 Technology of Directional Hydraulic Splitting and Permeability Enhancement for Deep Coalbed Methane Mining  
As shown in Figure 1, the new hydraulic directional drilling splitting device consists of two parts: the directional drilling system and the hydraulic splitting system. The directional drilling system consists of a directional drilling rig and pipe. The directional drilling system can carry the hydraulic splitting system into deep coalbeds. When the designated position is reached, the splitting pistons in the hydraulic splitting device protrude outward to create a directional seam. The hydraulic splitting system consists of a hydraulic control valve, a hydraulic splitting device, and a splitting piston. The hydraulic splitting device was modified and installed at the end of the directional drilling device, as shown in the yellow part in Figure 1.  
An analysis of Figure 1 shows that the splitting pistons protrude outward to cause fracturing of the coal, but the damage effect of the coal is different under different splitting angle selection equations.
## 1 Introduction
The idea was to employ a data compression algorithm in order to:  
* preserve the information needed for single neuron identification;
* reduce the throughput and thus the power consumption, since the power needed to the antenna is directly proportional to the bit rate [8];
* keep the bandwidth limited to few MHz, thus reducing the probability of RF interference and verifying the possibility to make in the future the system compliant to Medical Implanted and Communication Service (MICS) or Industrial Scientific and Medical (ISM) bands, in the \(402\)-\(405\) MHz and \(902\)-\(928\) MHz frequency range, respectively. In particular, having reduced the data rate, it was possible to extend the transmission distance well beyond the \(1\) m value reported in the above-mentioned works to make possible realistic _in vivo_ experiments with a small penalty in terms of power consumption. The proposed integrated circuit, already presented in [9], has been included in a complete system, made of a transmission and a receiving unit, that is in use in a neuroscience laboratory on freely-behaving animals in place of commercial wired systems to avoid tethering effects and to provide electrical isolation. In this paper we describes in detail the architecture and circuit design choices, the electrical performance of the IC and the _in vivo_ tests on small-laboratory animals. The paper is organized as follows. Section 2 describes the overall system made of a signal conditioning and transmitting unit and a receiver built with off-the-shelf components. Section 3 discusses in detail the implementation of the system-on-chip (SoC) building blocks and validates the data compression algorithm by means of ad-hoc simulations, while Section 4 shows the electrical characterization of the integrated circuit and reports the results of _in vivo_ tests on freely-behaving laboratory animals. Finally, a comparison of the implemented SoC with the state-of-the-art is presented in Section 5.
## 1 Introduction  
### RFQ Concept  
RFQ accelerators can simultaneously focus, bunch, and accelerate low-energy and high-intensity beams; high-intensity RFQ accelerators have a number of applications such as a common front-end injector for high-energy accelerators [1; 2], accelerator-based diagnostics in magnetic fusion devices [3; 4], and a compact neutron source for radiography and cancer therapy [5; 6].  
The basic types of RFQ include the four-vane type and the four-rod type, with the former being more suitable for high-frequency ranges and acceleration of light ions [7]. Consequently, we chose the four-vane structure for this 162.5 MHz proton RFQ. The electrode configuration of the four-vane RFQ is illustrated in Figure 1, operating in a TE210 mode. As we can see from Figure 1a, the four electrodes symmetrically encircle the axis and are excited with \(\pm V/2\) RF voltages, generating an electric quadrupole field for transverse beam focusing. As the electric force is independent of velocity, the RFQ is especially effective in the focusing and bunching of low-velocity ions compared with the conventionally used magnetic force [8]. To establish a longitudinal accelerating field, the electrodes need to be modulated, i.e., periodic transverse displacements relative to the axis. Figure 1b illustrates the varying distances between the electrode and the axis, denoted as \(a\) at the closest point and \(ma\) at the farthest, where \(m\) represents the modulation parameter. The length of a unit cell is \(\beta\lambda/2\), which is half of the varying period. When \(m=1\), the electric field has no longitudinal component and cannot accelerate the beam, while the focusing effect is the strongest. As \(m\) increases, the acceleration effect of the accelerator becomes better, but the focusing effect becomes weaker. The modulation phase difference between the horizontal and vertical electrodes is \(\pi\)[9].  
Figure 1c provides a visualization of the electromagnetic (EM) field distribution within the cross-section of the RFQ, showing the transverse distribution of the electric field and the longitudinal distribution of the magnetic field. Since the electric field of a four-vane RFQ is mainly distributed in the region close to the axis, and the magnetic field is mainly distributed in the region close to the cavity wall, we can effectively characterize these fields using a simplified lumped circuit model. Each quadrant can be analyzed as an individual resonant cavity, which is represented by capacitance \(C^{\prime}\) and inductance \(L^{\prime}\) as illustrated in Figure 1d. The resonant frequency of the four-vane RFQ can be expressed mathematically as  
\[f=\frac{1}{2\pi\sqrt{L^{\prime}C^{\prime}}}. \tag{1}\]  
Within the cavity, \(C^{\prime}\) (F/m) is associated with the electric field, while \(L^{\prime}\) (H-m) corresponds to the magnetic field. Before convenient simulation software was available, semi-empirical formulas were used to estimate the values of \(C^{\prime}\) and \(L^{\prime}\) of cavities, so as to obtain the resonant frequency of them [10].
## 3 Verification of the Strength of Age-Hardened Maraging Steel Samples Produced with SLM Technology  
### Tensile Testing  
The static tensile test was performed using an Instron 3382 machine. The tests were carried out on 10 samples of circular cross section with a nominal initial diameter of d\({}_{0}\) = 6 mm, a nominal length of the measuring section of l\({}_{0}\) = 25 mm, and an as-built surface finish of Ra 5-12 \(\upmu\)m. Each sample had a unique name, for example, P1/128/01, etched on by a laser machine. The samples were printed by an SLM 250 machine with a 50 \(\upmu\)m layer thickness setup. The tests were carried out at room temperature according to the standard PN-EN ISO 6892-1: 2016-09. The test results are presented in Table 2. When analyzing the obtained results, it was observed that the elongation under fracture (parameter A) varied from 1.2% to 6.9%, while diameter reduction (parameter Z) varied between 5% and 21%. The parameter R\({}_{0.2}\) is the 0.2% offset yield strength. Additionally, for some samples, the curves of the change in internal stress in the sample as a function of strain were recorded (Figure 1).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample No.** & **R\({}_{\textbf{0.2}}\) (MPa)** & **R\({}_{\textbf{m}}\) (MPa)** & **A (\%)** & **Z (\%)** \\ \hline P1/128/01 & 1317 & 1432 & 2.9 & 18 \\ P1/128/02 & 1359 & 1450 & 5.1 & 21 \\ P1/128/03 & 1445 & 1530 & 4.4 & 21 \\ P1/128/04 & 1309 & 1397 & 1.2 & 5 \\ P1/128/05 & 1530 & 1603 & 2.8 & 13 \\ P1/128/06 & 1338 & 1417 & 6.8 & 10 \\ P1/128/07 & 1268 & 1336 & 2.8 & 5 \\ P1/128/08 & 1259 & 1347 & 6.0 & 6 \\ P1/128/09 & 1204 & 1287 & 4.8 & 8 \\ P1/128/10 & 1442 & 1514 & 6.9 & 13 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Tensile testing results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Grade** & \multicolumn{2}{c}{**Wrought**} & **Cast** & \multicolumn{2}{c}{**AM Powder**} \\ \cline{2-6}  & **18 Ni1400** & **18 Ni1700** & **18 Ni1900** & **18 Ni2400** & **17 Ni1600** & **M300/1.2709** \\ \hline
**Nominal 0.2\% Proof** & **1400** & **1700** & **1900** & **2400** & **1600** & **1800** \\ \hline Ni & 17–19 & 17–19 & 18–19 & 17–18 & 16–17.5 & 17–19 \\ Co & 8.0–9.0 & 7.0–8.5 & 8.0–9.5 & 12–13 & 9.5–11.0 & 8.5–9.5 \\ Mo & 3.0–3.5 & 4.6–5.1 & 4.6–5.2 & 3.5–4.0 & 4.4–4.8 & 4.5–5.2 \\ Ti & 0.15–0.25 & 0.3–0.5 & 0.5–0.8 & 1.6–2.0 & 0.15–0.45 & 0.6–0.8 \\ Al & 0.05–0.15 & 0.05–0.15 & 0.05–0.15 & 0.1–0.2 & 0.02–0.1 & 0.05–0.15 \\ Si max. & 0.12 & 0.12 & 0.12 & 0.1 & 0.1 & 0.1 \\ Mn max. & 012 & 0.12 & 0.12 & 0.1 & 0.1 & 0.1 \\ C max. & 0.03 & 0.03 & 0.03 & 0.01 & 0.03 & 0.03 \\ Fe & Balance & Balance & Balance & Balance & Balance & Balance \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of the most popular maraging steels [33; 34; 35].  
Comparing the obtained results with the technical specifications of maraging steels produced in a conventional manner, the tensile strength of the printed samples in this case was comparable to that of 18 Ni1400 and approximately 16% lower than that of 18 Ni1700.
## 2 Instrument Design
### Control of the Instrument  
As discussed in Section 2 and illustrated in Figure 2, the instrument has two controllers, the Instrument Controller (IC) acting as master, and the Temperature Power Controller (TPC) acting as slave. The controllers communicate through two serial connections in master (IC)-slave (TPC) mode. The TPC is described in Section 2.1.4. In this section we will focus on the IC. The IC is based on a MSI GSE board with a low power Atom N270 processor running a stripped version of Ubuntu 9.04. Access to the IC is through an Ethernet (TCP/IP) connection. The selection of TCP/IP allows remote access to the instrument and has the advantage that various items that are available as shelf hardware can be built on, e.g., wireless links to the instrument. Two services for user interactions are running on the IC, a secure web server (lighttpd) and secure shell (openssh). The web server hosts an AJAX-enabled (PHP and Javascript) graphical user interface to operate the instrument, and can be accessed by any current web browser. The web interface enforces user authentication and communication is SSL encrypted. The following actions can be performed _via_ the web interface: (i) accessing status information of the radiometer and of the elevation tracker; (ii) steering the elevation tracker; (iii) initiating ad-hoc measurements; (vi) managing files and maintaining the operating system (the full system is available through secure shell access); and (v) programming data acquisitions.  
The selection of Free Open Source Software (FOSS) for the operating system, graphical user interface and instrument control (Python) has the advantage that additional functionality can easily be added to the instrument if necessary. For example a camera or additional sensors, such as an infrared radiometer, may be added. In addition to the instrument access through Ethernet, a hand-control interface can be used to start/stop the instrument, to show status information and to set some system parameters.
## 4 Conversion Calculation between Monitoring Data and Risk Probability  
The calculation formula of the coupling coefficient of the two risk factors has been given in Section 2.2, in which the risk factors need to be used \(\Gamma_{1}\) (\(c\)) and \(\Gamma_{2}\) (\(c\)), and what we get through numerical calculation or model test is the extrusion deformation of the tunnel face. Therefore, it is necessary to give the conversion relationship between the deformation and the risk occurrence probability. [PERSON] [23] gave the conversion relationship between the deformation of risk event monitoring and the probability of loss event in the safety review study of subway station construction scheme, as shown in Table 2.  
Professor [PERSON] of Italy proposed in the ADECO-RS method that the strength and deformation characteristics of the core soil in front of the tunnel are the fundamental reasons for the tunnel deformation. Pre-convergence deformation and face extrusion deformation are the main mechanical indexes to measure the stability of the face. Because the extrusion deformation of the face is easy to monitor, this paper takes it as the evaluation index of the stability of the face.  
According to the definition in Table 2 and referring to the provisions on risk occurrence probability in code for risk management of underground engineering construction of urban rail transit GB50652-2011, we can define the corresponding relationship between tunnel face collapse and risk event probability. For the limit value of the extrusion deformation of the tunnel face, 80% is the alarm value and 70% is the early warning value, as shown in Table 3. In the table, when the extrusion deformation of the tunnel face is within the range, the corresponding risk probability is calculated according to the linear difference method. In addition, the higher the risk level, the greater the risk probability and loss.
## 6 Evaluation
### Performance Study
#### 6.4.1 Hybrid Consensus Parameter Analysis: Block Period  
In this section, we vary the block period in the slot-based block proposition consensus algorithm of either AuRa or BABE. The block authoring or proposition happens at fixed customizable intervals termed the block period. The results of AuRA and BABE block authoring are represented in Figure 11 and Figure 12, respectively. The inference is based on the following explanations:  
* Higher interval of block period directly increases the time associated with block production
* Lower or minimal block period decreases the turn-around time for block creation time. Still, it results in a race condition between transaction verification and consensus operation which overflows consensus slot time.  
Figure 11: AuRa consensus block period influence.  
AuRA consensus, as represented in the results, is tested across 3, 6 and 12 s, exhibiting an ideal throughput of 380 transactions per second for a 6 s block period time. The input transactions are around 1000 transactions per second against the output of 380 transactions per second as the transaction processing and consensus factor is to be considered. Meanwhile, the throughput ideal for BABE is around 277 transactions per second. BABE has a high block period time of 60, 90 and 120 s due to the consensus constraints. Smaller block time periods of 3, 6 and 12 s applied for BABE result in a higher amount of forks as a consistency drift is observed in the network. So even though the BABE offers privacy and better security due to its choice of verifiable random computation function-based selection of the proposer, it has a lesser throughput than AuRa due to its computation. Another issue noted here is that there is a GRANDPA-level stalling in the network due to the missing pre-commit votes, which occurs in the case of lesser block periods, which signifies the inconsistency of validators assuring the earlier results experienced in works [31,32].
# An Active Indoor Noise Control System Based on CS Algorithm
###### Abstract  
Environmental noise causes enormous harm to human health. This research suggests an active noise control (ANC) system based on the cuckoo search (CS) algorithm to reduce indoor noise pollution. The indoor environment's ANC is more complicated than the conventional linear ANC system used in cars, aircraft, and other confined spaces. The suggested system architecture considers noise from many directions in order to reduce the impact of external noise on persons within a given environment. The effective search strategy of the CS algorithm enhances the approach of updating filter coefficients based on the LMS/NLMS algorithm in the conventional ANC systems. The results from the simulations demonstrate that the suggested strategy successfully validates the hypothesis and provides significant noise reduction. Additionally, we developed the system's hardware, which is based on a digital signal processor (DSP). The experimental results show that the proposed technology could perform well with respect to ANC.