esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis of Research Variables
### Coverage of Deveti Januar Motorway with Mobile LTE Technology  
This part of the section provides an overview of the mobile LTE technology coverage in the M:tel network, along the M9] road, from the aspect of the number of the base stations and cells. They are then textually and graphically processed providing a detailed insight into the 17 independent research variables marked as follows: (1) _DL PRB Usage Rate_, (2) _Average CQI_, (3) _RRC.ConnReq.Att_, (4) _RRC.ConnReq.Succ_, (5) _DL ReTrans Rate_, (6) _QPSK.TB.Retrans_, (7) _16 QAM.TB.Retrans_, (8) _64 QAM.TB.Retrans_, (9) _QPSK.TB_, (10) _16 QAM.TB_, (11) _64 QAM.TB_, (12) _Traffic.User.Avg_, (13) _DL iBLER_, (14) _DL Cell Traffic Volume_, (15) _QPSK.ErrTB.Ibler_, (16) _16 QAM.ErrTB.Ibler_, (17) _64 QAM.ErrTB.Ibler_.  
In the research process, the collected data, i.e., the values of the independent and dependent variables, as already mentioned, were given with a sampling frequency of 1 h, for a period of 30 days, for each cell that covers M9] with the LTE technology and observed parts of the roads, from the toll booths toward BL and Db. The signal coverage is provided by 28 base stations, and considering that some of them cover up to six cells, and some only one, a network structure of 98 cells was formed along and around the observed road. However, the specific number of the cells that cover the area above the road is smaller, so that in the results of the _Drive Test_ measurement of the M:tel operator from 16 December 2020 on the road, 34 cells can be identified which are shown in Figure 2 in different colors (different colors were used to show the coverage and distance between the adjacent cells).  
In the continuation of this section, eight research variables are analyzed under the points with subheadings of their titles. Integrated under these titles, the other nine independent variables are defined as follows: (1) within Section 4.5, the defined variables are _QPSK.TB.Retrans_, _16 QAM.TB.Retrans_, _64 QAM.TB.Retrans_, _QPSK.TB_, _16 QAM.TB_, and _64 QAM.TB_ due to the direct connection to the retransmission rate--_DL Retrans Rate_ which is also presented mathematically within this subtitle; (2) regarding that the variables _QPSK.ErrTB.Ibler_, _16 QAM.ErrTB.Ibler_ and _64 QAM.ErrTB.Ibler_ influence _Initial Block Error Rate_ (iBLER), they are analyzed within Section 4.7.
## 1 Introduction  
In wireless deployments of federated learning (FL), the network consists of numerous Internet of Things (IoT) devices, which exhibit significant differences in hardware performance, such as processor speed, memory capacity, and battery life [1]. These differences contribute to the heterogeneity of clients [2], a common characteristic of IoT environments. For example, smartphones typically have more storage space and greater processing capabilities compared to smartwatches and other IoT devices like sensors or wearables. In synchronous FL, IoT clients are responsible for updating local models and uploading these updates to a central server. However, when heterogeneous IoT clients attempt to synchronize model updates, devices with weaker computing capabilities require more time to complete local model updates, causing them to fall behind. This heterogeneity among IoT clients poses challenges to FL [3]: it may lead to inefficiencies in the system, as the varying computation speeds and resources of different clients can create bottlenecks in overall system performance; it also increases uncertainty and instability in the system,as the diverse states of IoT devices can lead to system failures, data loss, or communication disruptions. Therefore, to address the heterogeneity among different IoT clients in large-scale FL scenarios, aggregation strategies need to be adjusted [4].  
To address the challenges caused by heterogeneous computing capabilities among clients, this paper proposes an improved three-tier FL algorithm based on enforced synchronization. Specifically, a model dropout-based enforced synchronization strategy is designed to ensure that heterogeneous clients transmit their model parameters to the edge servers simultaneously, thereby mitigating issues related to client-side waiting and model staleness. Building on this, we further optimize the similarity-based client pairing by incorporating computational heterogeneity, introducing a Shapley-value-based reward and penalty mechanism, and jointly considering both contribution and computing capability for client selection and bandwidth resource allocation. Experimental results demonstrate the effectiveness of the proposed approach, achieving superior performance over baseline methods in terms of latency, energy consumption, and final model accuracy.  
The remainder of this paper is organized as follows: Section 2 reviews related works; Section 3 formulates the problem; Sections 4-7 present the proposed methodology in detail; Section 8 provides simulation results; and Section 9 concludes the paper.
## 3 Results  
### Ion Irradiation  
Zircaloy-2 was irradiated at 573 K and 773 K to monitor the formation of prismatic dislocation loops. These loops are visible when imaged with g \(\sim\)01T0 close to beam direction [0001].  
Figure 3 presents a series of dark field micrographs showing the formation of prismatic defects in Zircaloy-2 at 773 K. The pre-irradiation microstructure shows a fairly clean sample surface, except for small hydrides in the contrast of white dots. However, at the first observed dose level 0.007 dpa, small defect clusters appear in very weak contrast. The mean size of these clusters was recorded as \(\sim\)2 nm. As the irradiation dose increased to 0.016 dpa, the mean size of these defect clusters grew slightly to 2.3 nm, and these loops showed much better contrast as compared to those observed at 0.007 dpa. As the ion dose further increased to 0.8 dpa, the loop size and DY increased with dose, as plotted in Figure 4.  
The DY calculated from Equation (1) at each dose level, plotted as a function of dose, at each irradiation temperature has been provided in Figure 4. From Figure 4 it is clear that the DY decreases with dose. The comparison of Zircaloy-2 and pure Zr data shows that the presence of alloying elements results in a higher DY at 773 K.  
In situ TEM analysis on the prismatic defect formation at 573 K is presented in Figure 5. Dark field TEM micrographs were taken under similar diffraction conditions and at the same dose levels as at 773 K. Irradiated microstructures at different dose levels show somewhat similar microstructure to those observed at 773 K, except for the finer loop sizes and slightly higher DY, as shown in Figure 4.  
To study the effect of alloying elements, the DY and mean size of dislocation loops were compared to pure Zr data reported in our previous paper [15]. Comparison between pure Zr and Zircaloy-2 shows that a higher number of loops nucleate in Zircaloy-2, as compared to pure Zr at 773 K, whereas loop size was found to be much smaller in Zircaloy-2 as compared to pure Zr. However, this trend does not repeat in the irradiation of two materials at 573 K.  
Figure 3: Series of dark field micrographs, taken during in situ irradiation experiment on Zircaloy-2 at 773 K, showing the formation of \(<\)a\(>\)-type loops. The bright dots are recognized as dislocation loops, among which a representative loop was circled in each dose.  
Figure 4: Mean loop size and defect yield in Zircaloy-2 plotted as a function of dose at both irradiation temperatures, compared with pure Zr.  
Figure 5: Series of dark field micrographs, taken during in situ irradiation experiment on Zircaloy-2 at 573 K, showing the formation of <a>-type loops, which present as bright dots in dark field. The orange circles in each micrograph enclose one or more dot-like loops.
## 1 Introduction  
A three-component magnetic survey system is composed of a triaxial magnetometer and an inertial navigation system (INS) and is widely used in resource exploration [1], magnetic anomaly detection [2], and geomagnetic aid for navigation [3, 4]. An INS is used to provide attitude information to restore three-component magnetic data to a geographic coordinate system. The accuracy of the restoration process is affected by the accuracy of the INS and the misalignment error of the system. There have been many research achievements made in error correction of a magnetometer [5, 6, 7] and improving the accuracy of an INS [8, 9, 10]. In 1997, [PERSON] proposed a method to align the magnetometer and star sensor [11]. However, research on misalignment error is still relatively weak. Theoretically, 1\({}^{\circ}\) misalignment error results in thousands of nT errors in the measurement results [12]. According to the above studies, calibration of misalignment error is necessary, and it has been classified into a physical method and an algorithm calibration method. Since the physical method is difficult to implementation and has low precision, we need to develop an algorithm method to calibrate misalignment error. In recent years, some achievements for calibrating misalignment error have appeared in the research.  
A class of method called \"dot product invariance\" (DPI) was proposed in 2012, based on the fact that the vector dot product of geomagnetic field and gravitational field is constant [13]. The method calibrates the misalignment error between gravimeter and magnetometer by using an auxiliary vector to simulate the gravitational field. The calibration accuracy of this method is easily affected by the selection of auxiliary vectors, which is reduced when there is a difference between the auxiliary vector and the gravitational field.  
[PERSON] proposed a method, in 2017, that combined the ellipsoid fitting method with the DPI method [14; 15]. This method could calibrate the misalignment error between an accelerometer and a magnetometer effectively, but it had the same defects as the DPI method.  
[PERSON] proposed a new method, in 2019, which was based on the fact that the trajectories of magnetic data obtained by a magnetometer which rotated in the same attitude under different background magnetic field were parallel to each other [16]. This method solved the problem of the DPI method that was sensitive to the selection of auxiliary vector. However, it needed at least two sets of data rotated about different axes to complete parameter optimization. When replacing the rotating plane, the misalignment error could change due to improper operation.  
Consider the deficiencies of the above methods, we propose an improved method to calibrate the misalignment error. The misalignment error of magnetometer and INS is analyzed and modeled firstly. Then the principle of the calibration method is introduced. Finally, the feasibility and accuracy of the proposed method are verified by simulation and field experiment.
## 2 Theoretical Analysis  
### Pressure Drop in the Pipe Network  
As the fluid flows through the pipe network, the pressure changes because a pressure drop occurs in the pipe system. This may be caused by friction between the fluid in the pipe and the pipe wall. Moreover, when the fluid flows through any pipe fittings, bends, valves, or components, there is a minor loss in pressure at these fittings. For the above reasons, the pressure drop in the pipe generally includes major losses and minor losses.  
To calculate friction loss in a pipe which is known as the Darcy-Weisbach equation or the Darcy-Weisbach formula is issued:  
\[h_{l}=f\frac{L}{D}\frac{V^{2}}{2}\rho \tag{1}\]  
where \(h_{l}\) is a head loss, \(f\) is a friction factor, \(L\) is the length of the pipework (m), \(D\) is the inner diameter of the pipework (m), \(V\) is the velocity (m/s), and \(\rho\) is the density of the fluid.  
When the Reynolds number is smaller than 2280, the following equation is applied to calculate the friction coefficient:  
\[f=\frac{64}{Re}\text{ if }Re<\text{ }2280 \tag{2}\]  
Moreover, [PERSON] [26] determined the relationship between the friction factor and Reynolds number when \(Re>2280\). It depends on the regimes of the flow as shown by the following equations:  
\[f=0.028\text{ if }2280\text{ }<Re<\text{ }2400 \tag{3}\]  
\[f=0.028+0.007(Re-2400)/600\text{ if }2400\text{ }<Re<\text{ }3000 \tag{4}\]  
\[f=\text{ }0.035+0.004(Re-3000)/800\text{ if }3000\text{ }<Re<\text{ }4400 \tag{5}\]  
To determine the pipe friction coefficient when the flow is turbulent, the Colebrook Equation [27] was used as shown in the following equation:  
\[\frac{1}{f^{0.5}}=-2.0\log\left(\frac{e/D}{3.7}+\frac{2.51}{Re\times f^{0.5}}\right) \tag{6}\]  
where \(f\) is the pipe friction coefficient, \(e\) is the roughness, \(D\) is the diameter of the pipe, and \(Re\) is the Reynolds number  
In the pipe, the above friction factor can be applied where there are no jet disturbances. When the disturbances occurred because of the jet flows from the sampling holes, the correction friction factor from Singh RK [6] was applied.  
In addition to the major loss, the minor loss is critical to evaluate the pressure drop in the pipe network. The pipe fittings, valves, and bends are considered when calculating the minor loss. There are some associated \(K\) factors or local loss coefficients that are used to calculate the minor loss. These coefficients allow the calculation of the pressure loss through the fitting for a particular fluid flowing at a specified velocity. Manufacturers of pipe fittings and valves often publish a fitting's associated \"\(K\)\" factor. The following equation can be used to calculate the minor loss:  
\[h_{lm}=K\frac{V^{2}}{2}\rho \tag{7}\]  
where \(K\) is generally the manufacturer's published factor, \(V\) is the velocity, and \(\rho\) is the density of the fluid.
# Optimization Scheme for Modulation of Data Transmission Module in Endoscopic Capsule
## 2 Principle of the MIP-MDCSK Scheme
### IP-MDCSK and General MIP-MDCSK Sharing One Reference Signal over In-Body Channel 2.2.1. IP-MDCSK
#### 2.2.2 A General MIP-MDCSK with Shared Reference Signal  
The IP-MDCSK can improve the BER performance at the expense of bandwidth efficiency [38]. Considering the bandwidth utilization, MIP-MDCSK is proposed. The block of the scheme is shown in Figure 2, which has \(t\) information-bearing signals mapped to \(t\) different indexes that are sharing the same reference signal. The total transmitted bits are \(m_{c}+N_{c}\), where \(N_{c}=tn_{c}\). Similarly, the output of the transmitter for MIP-MDCSK is  
\[\mathbf{s_{x}}=[\underbrace{\mathbf{c_{x}}}_{reference\,\
## 3 Numerical Results
(**b**) Corresponding evolution of the modulus of the complex degree of first-order coherence (black dashed lines indicate the position of ZDWs). (**c**) Corresponding soliton number calculated at pump wavelength as a function of input pulse and fiber parameters. (**d–f**) Corresponding profiles of 100 simulated power spectra and calculated degree of coherence obtained for three distinct core diameter 3, 3.5, and 4 \(\upmu\)m, respectively. Figure 6: (**a**) Evolution of the average SC spectrum. The fiber length was fixed to 12 cm, and the input peak power of 200-fs pulses at 2300 nm was set to 5 kW (equivalent to 1-nJ pulse energy). (**b**) Corresponding evolution of the modulus of the complex degree of first-order coherence. (**c**) Corresponding soliton number calculated at pump wavelength. Another set of simulations was performed by changing the pumping wavelength, namely 2100 nm (see Figure 8) and 2500 nm (see Figure 9), while keeping the same input peak power as Figure 5 (i.e., equal to 3 kW). Again, this variation of pumping wavelength induces the corresponding increase (Figure 8) or decrease (Figure 9) of the input soliton number above or beyond the \"threshold value\" of 10 since group velocity dispersion changes. Thus, consequences of mechanism of decoherence are observed when pumping at 2100 nm in the anomalous dispersion regime, whereas an excellent degree of coherence is obtained regardless of the fiber dispersion when pumping at 2500 nm. The latter case also allows for SC generation further in the mid-IR on a larger range of fiber core diameter. To conclude these numerical studies, we show that coherent mid-IR supercontinuum generation is obtained in our step-index tellurite fibers with distinct dispersion regimes in the femtosecond regime provided that the moderate input power satisfies an injected soliton number \(N<10\). In particular, the largest SC spectrum into the mid-IR extending from 1.5 to 4.5 \(\upmu\)m is obtained for fiber core diameters around 3.5 \(\upmu\)m, thus corresponding to dispersion profiles with two ZDWs, and requiring the pumping into the anomalous dispersion regime (i.e., between the ZDWs). Figure 8: **(a)** Evolution of the average SC spectrum. The pump wavelength is now fixed at 2100 nm with 200-fs pulses and 3 kW peak power. The fiber length is 12 cm. **(b)** Corresponding evolution of the modulus of the complex degree of first-order coherence. **(c)** Corresponding soliton number calculated at pump wavelength. Figure 7: **(a)** Evolution of the average SC spectrum. The fiber length was fixed to 12 cm, and the input peak power of 200-fs pulses at 2300 nm was set to 1 kW (equivalent to 200-pJ pulse energy). **(b)** Corresponding evolution of the modulus of the complex degree of first-order coherence. **(c)** Corresponding soliton number calculated at pump wavelength.
###### Abstract  
This study examines boundary layer control and pressure recovery in low Reynolds number supersonic flow with chemical reactions in a chemical laser system. Our work prescribes a novel boundary layer control method for the optical cavity of a chemical laser system, and a design of a supersonic diffuser is compared and proposed to make a stable flow for the system. The flow characteristics of a low Reynolds number and internal reaction heat release were analyzed. Three types of experimental pieces were designed to passively control the boundary layer in the optical cavity. An active booster-type supersonic diffuser is proposed to study the pressure recovery problem of a low Reynolds number and chemical reaction supersonic flow generated by an optical cavity. A supersonic chemical reaction platform (SCRP) was established to conduct experimental research on boundary layer control and docking the active booster supersonic diffuser with the SCRP. The experimental results indicate that increasing the boundary layer pumping capacity within a certain range can reduce both the boundary layer thickness and the pressure on the optical cavity while simultaneously enhancing the SCRP energy power. The supersonic diffuser based on active gas pressurization can create the necessary conditions for the normal chemical reaction and improve the ability of the SCRP to resist high back pressure and airflow disturbance. Moreover, the chemical reaction energy release was full and stable with the docking of supersonic diffuser test pieces, resulting in energy power increases, which could be a significant improvement for the design of chemical laser systems.  
boundary layer control; chemical reaction flow; cavity; supersonic diffuser; energy power Article
## 2 Materials and Methods
### Summary of Feature Generation Techniques  
We consolidate three distinct approaches to feature generation: time domain, frequency domain, and time-frequency domain analyses, which are applied to monitoring the milling process. The cumulative exploration led to the identification of 308 potential features, offering a comprehensive toolkit for enhancing recognition accuracy.  
In traditional machine learning approaches such as support vector machines (SVMs) or boosting ensemble decision trees, the feature generation process typically involves extracting relevant information from the dataset without considering sequential dependencies. Each training sample is represented as a single point in a high-dimensional feature space, with a size of \(1\times 308\). This method is effective for models that do not take into account the temporal sequence of the data.  
However, when dealing with long short-term memory (LSTM) networks, the scenario changes significantly. LSTMs are designed to recognize patterns in sequences of data, making them particularly suitable for time-series analysis. In those applications, the order and context of the data points are crucial. For LSTMs, the feature generation process must incorporate sequential information, leading to a transformation in the data representation.  
In the context of the presented application, while traditional machine learning models treat the entire dataset as a single block (frame size equal to the length of the signal, resulting in a sample size of \(1\times 308\)), LSTM requires the data to be divided into smaller sequences. This division results in the creation of 100 frames from the original signal, with each frame being a sequence of data points. The frame size for LSTM is determined by dividing the length of the signal by 100 and rounding down to the nearest integer (floor function). This ensures that each sequence captures a portion of the temporal pattern present in the data. Consequently, for LSTM, the dimensionality of each training sample transforms to \(100\times 308\), with each sequence representing a step in the temporal pattern.  
This fundamental difference in feature generation highlights the versatility of LSTM in handling time-series data, enabling the model to capture the dynamic changes over time. This factor is particularly advantageous in applications such as tool state recognition. The ability of LSTM to process data in sequences allows it to learn from the temporal dependencies and variations in the signal, providing a more nuanced understanding of the changes in the tool's condition over time.
## 5 Conclusions  
Experimental data on the actual EIRP levels of the massive MIMO BSs (based on single-user MIMO codebook-based beamforming with 8 CSI-RS ports) operating in a live 5G network (Telstra, Australia) were obtained by monitoring the dynamics of the antenna radiation pattern and the time-averaged transmit power. About 13 million samples of the actual EIRP were collected and processed for 25 BSs for 24 h. The results show that assuming peak power transmission in a fixed beam for a long-time interval leads to unrealistic exposure levels. This translates into a compliance distance that is less than half than what is obtained for the theoretical maximum EIRP. These findings are consistent with the assessments made by the International Electrotechnical Commission in [15] and provide useful insights for the development of RF EMF exposure assessment procedures of 5G BSs.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON], [PERSON] and [PERSON] contributed with analysis tools. All authors contributed with writing the paper. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors acknowledge the support received from the Ericsson colleagues in Australia [PERSON], [PERSON], [PERSON] and [PERSON] with collecting network data, and [PERSON], [PERSON] and [PERSON] from Ericsson in Ottawa for their help with the processing tools. The authors are also grateful to [PERSON], [PERSON] and [PERSON] at Telstra for providing fruitful comments and suggestions on the manuscript.  
The authors declare no conflict of interest.  
Figure 7: Compliance boundary (CB) or exclusion zone for the BS determined according to \(EIRP_{\text{act}}\) (red solid line) and the theoretical maximum EIRP (green dashed line).