esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Analysis of Results
### Ablation Experiments  
To validate the individual contributions of each model component in PCES-YOLO, a comprehensive ablation study with eight experimental configurations was performed. Initial baselines were established using the standard YOLOv11n architecture, followed by the sequential integration of four novel modules. The quantitative experimental results are presented in Table 2.  
Integration of the C3k2-PRFE module yields performance gains, with mAP50 increasing by 0.9% and mAP50-95 increasing by 2.8%. These enhancements stem from expanded receptive fields and strengthened feature extraction in the backbone network, which reduces background interference and false detections. Subsequent incorporation of the ConvNeXtBlock module raises mAP50 by 0.6% and mAP50-95 by 2.3%. Implementation of the EFAN feature fusion mechanism achieves measurable benefits, with mAP50 increasing by 0.8%, mAP50-95 by 2.6%, and recall by 1.9%, indicating its role in preserving shallow feature details and reducing missed detections. Adoption of the Shape-IoU loss function raises mAP50 by 1.2%, mAP50-95 by 0.4%, and recall by 2.5% through optimized boundary-aware loss computation, which helps to address the challenge of bounding box regression instability.  
Progressive integration results in the PCES-YOLO architecture, delivering comprehensive performance gains of 2.5% higher precision, 4.9% better recall, 3.6% greater mAP50, and 15.2% higher mAP50-95 than the baseline model.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multicolumn{8}{c}{**Added Modules**} \\ \hline
**C3k2-PRFE** & **ConvNeXtBlock** & **EFAN** & **Shape-IoU** & **P/\%** & **R/\%** & **mAP50/\%** & **mAP50–95/\%** \\ \hline \(\surd\) & & & & 96.1 & 89.7 & 93.7 & 62.0 \\  & \(\surd\) & & & 96.8 & 90.9 & 94.6 & 64.8 \\  & \(\surd\) & & & 97.2 & 89.9 & 94.3 & 64.3 \\  & & \(\surd\) & & 96.2 & 91.6 & 94.5 & 64.6 \\  & & & \(\surd\) & & 97.0 & 92.2 & 94.9 & 62.4 \\ \(\surd\) & \(\surd\) & & & 97.6 & 91.2 & 95.1 & 67.0 \\ \(\surd\) & \(\surd\) & \(\surd\) & & 98.5 & 93.0 & 96.3 & 72.5 \\ \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 98.6 & 94.6 & 97.3 & 77.2 \\ \hline \end{tabular}
\end{table}
Table 2: Results of ablation experiments.
# Condition Monitoring of Railway Tracks from Car-Body Vibration Using a Machine Learning Technique
## 3 Development of Track Condition Monitoring System
### On-Board Sensing Device  
Figure 2 shows a photograph of the compact on-board sensing device used in the track condition monitoring system [1; 11]. The device consists of an accelerometer and rate gyroscope for detecting track errors, a global navigation satellite system (GNSS) receiver for detecting the train location and speed, and a sensor interface for feeding the signals from the sensors to the computer.  
The system is battery-powered and conducts continuous measurements for up to six hours, and thus measurements are simply obtained by placing it in a car. Measurements are constantly obtained if it is powered by a source on the car. Measurement data are stored into an on-board storage device. Furthermore, the device is equipped with a function to automatically transmit measured data from the on-board storage device to a server via a mobile phone network. The device is further equipped with a microphone, and thus, it is also possible to \"listen\" for corrugation and diagnose the condition.  
The measured data obtained from the measurement device are transmitted to the analysis centre either via a mobile phone network or via writing to external media. The diagnostic results produced by the track monitoring software are used to provide feedback to railway operators through online channels via smartphones or tablet computers. Railway operators use the information to establish the track maintenance priorities, and thereby facilitate the maintenance planning and work.  
Additionally, the real-time measurement of vehicle vibrations during in-service operation allows for rapid response, such as emergency track inspection and maintenance, in situations in which vehicle vibration observations detect irregularly large deviations from standard control values. Thus, the use of the monitoring system to perform continuous monitoring of vehicle vibrations allows early detection of deterioration or other track irregularities, and thereby enables infrastructure providers to conduct effective maintenance work.
# Frequency-Dependent Schroeder Allpass Filters
## 3 Proposed Frequency-Dependent Schroeder Allpass
### Example  
Figure 7 shows an example for the proposed frequency-dependent Schroeder allpass filter. The gain filter is  
\[g(z)=\frac{0.4119-1.0844z^{-1}-0.8101z^{-2}}{1.0000-1.3931z^{-1}+0.5384z^{-2}} \tag{23}\]  
and \(m=100\). Figure 6(a) is depicting the poles and zeros of \(g(z)\) alongside the resulting magnitude response \(\left|g(e^{\mu\omega})\right|\). In Figure 6(b), the magnitude response of \(\left|g(z)\right|^{1/m}\) is plotted along the poles and zeros of the proposed Schroeder allpass in (20). The small deviation of the pole location from the gain curve is due to the neglected phase component. Figure 6(c) shows the resulting impulse response. While the overall structure resembles the classic Schroeder allpass, the pulses are smeared in time iteratively by passing through the gain filter \(g(z)\). The last subplot, Figure 6(d) shows the group delay of the proposed Schroeder allpass. Comparing Figure 6(d) and Figure 6(b), it is easy to recognize the group delay as a superposition of the individual first-order allpass filters (see (12)).  
Figure 6: Proposed frequency-dependent allpass for FIR gain \(g(z)=b(z)\).
## 5 Conclusions  
This paper proposes a new inter-satellite balanced partition routing algorithm. Firstly, the total computation complexity and single node's pressure of the algorithm and other traditional algorithms are analyzed from the point of view of mathematics. The simulation model is built to simulate the performance of the four inter-satellite routing algorithms, including average path propagation delay, the variance of the path propagation delay, and the calculated pressure of a single node. Simulation results reveal that compared with other algorithms, the proposed balanced partition routing algorithm achieves better performance. In the latter part of this paper, two further optimization directions of a balanced partition routing algorithm are analyzed. Firstly, experimental results show that these two optimization methods can further reduce the complexity of the algorithm. Moreover, the hardware cost of nodes is greatly reduced. Finally, the further optimization of the balanced partition routing algorithm can adapt to various scenarios and has good mobility. This algorithm could not only be used as an alternative scheme in the routing planning of low-orbit micro-satellite constellations and star links but also be used as a general  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sequence**Types of Algorithms** & **Method 1 \((10^{6})\)** & **Method 2 \((10^{5})\)** & **Method 3 \((10^{5})\)** \\ \hline
1 & 1.96 & 5.37 & 6.37 \\ \hline  
scheme for decentralized data fusion in cooperative systems such as unmanned aerial vehicle formations. In our following work, we intend to study the grouping of balanced partition routing algorithms. In our branch of experimental study, we found that the average path delay calculated by different grouping methods is huge for the balanced partition routing algorithm. The good grouping method will greatly improve the efficiency of the algorithm. On the other hand, the processing delay of data within the nodes will also be considered in our future research [1]. Conceptualization, Z.X., Z.J. and X.J. ; Formal analysis, Z.X. and Z.J. ; Methodology, H.C. and X.G. ; Resources, J.Y. and K.X. ; Software, H.C. and X.G. ; Validation, H.C. and S.L. ; Writing--original draft, H.C.; Writing--review and editing, H.C. and Z.X. All authors have read and agreed to the published version of the manuscript. This research was funded by National Natural Science Foundation of China grant number 62073289. Not applicable. The authors declare no conflict of interest. The following abbreviations are used in this manuscript:
## 3 Results and Discussion
### Surface-Initiated Anionic Polymerization of DMSB on Cross-Linked PS Nanoparticles  
As a solid model substrate for the intended surface-initiated anionic polymerization of DMSB and to study the crystallization behavior of surface-attached PDMSB chains, PS nanoparticles were used, which were cross-linked with 10 wt% divinylbenzene. Those particles were used because of their simple synthesis via seeded emulsion polymerization, similar to a previously described procedure, leading to almost monodisperse particles [57]. For further purification, the particles were dried by three-fold azeotropic distillation with dry toluene under high vacuum conditions. To ensure the absence of protic impurities, which would immediately cause a termination of the anionic polymerization, the particle dispersion in toluene was additionally titrated, as described in the following. Therefore, _sec_-BuLi was slowly added with a \(\upmu\)L-syringe, until a slightly yellowish color appeared, which remained for at least 15 min, due to the nucleophilic addition at the accessible vinyl groups in the swollen particles' shell. In the next step, the calculated amount of initiator for the intended molar masses of PDMSB was added, followed by DMSB monomer addition (Figure 6). The polymerization was terminated with degassed methanol, and surface-grafted particles were purified by repeated centrifugation and redispersing in neat THF for at least five times.  
Figure 6: Surface-initiated anionic ROP of DMSB on the surface of PS nanoparticles.
## 3 The Hysteresis Current Controller
### Validation of the Novel Expression
The trapezoidal reference signal was considered as it closely represented the speed of change in the MC harmonic currents. Figure 4 shows the simulation for the trapezoidal reference signal. For a harmonic frequency of 6 kHz, the minimum coupling inductance size required for the shunt active filter is calculated from equations based on [39] considering a 1 kW 6 kHz converter coupled to a 400 V 50 Hz supply. The maximum current change rate of the VSC and the minimum coupling inductance, \(L\), can be calculated based on Figure 5 for a 6 kHz harmonic frequency at t = 90/\(\omega\) as Equation (29) using Equation (28). \[ \begin{array}{l}max\left|\frac{d_{IL}}{dt}\right|=\frac{1.44}{0.1\times 1.67\times 10^{-4}}=86.23\times 10^{3}\;\mathrm{A/s}\\ L\geq\frac{2\frac{V_{dc}-V_{\mathrm{s}}}{\mathrm{max}}\left|\frac{d_{IL}}{dt} \right|}{\frac{d_{IL}}{dt}}=\frac{466.667-V_{\mathrm{S}}}{86.23\times 10^{3}}=0.77\; \mathrm{mH}\end{array}  \tag{29}\]  
Figure 4: Open-loop schematic diagram of HCC-HAPF for MC input current harmonics elimination. Source: [39]. Figure 5: Maximum rate of change in the harmonic load current. Source: [37]. T: total period in seconds; I\({}_{z}\): current in amperes. With Equation (28), the rate of change in the VSC output current can be calculated using Equations (14) and (15) for the rising and falling currents and the RMS value of the supply voltage. \[\frac{d_{f}^{+}(t)}{dt}=3.89\times 10^{5}\text{A/s}\qquad\qquad\qquad\frac{d_{f}^{ -}(t)}{dt}=-1.43\times 10^{6}\text{ A/s} \tag{30}\]  
For a trapezoidal wave reference current of Figure 6, the rate of change in the reference current, considering the first four terms of a trapezoidal wave equation, can be expressed as Equation (31). The rate of change in the converter current at harmonic frequency of 6 kHz is calculated as Equation (32). \[ \begin{array}{c}\dot{i}_{f}^{*}=\frac{8\surd^{2}}{\pi^{2}}I_{f}^{*}(\sin( \omega t)+\frac{\sin(3\omega t)}{9}-\frac{\sin(5\omega t)}{25}-\frac{\sin(7 \omega t)}{49})\\ \frac{d\dot{i}_{f}^{*}}{dt}=\frac{8\surd^{2}}{\pi^{2}}I_{f}^{*}\omega\left(1+ \frac{1}{3}-\frac{1}{5}-\frac{1}{7}\right)\end{array}  \tag{31}\]  
\[\frac{d\dot{i}_{f}^{*}}{dt}=6.16\times 10^{4}\text{ A/s} \tag{32}\]  
From Equations (30) and (31), the maximum switching frequency for a trapezoidal wave shape reference current can then be obtained as Equation (33). \[ \begin{array}{c}f_{inst}=\frac{1}{2\varepsilon}\left(\frac{1}{\frac{3.89 \times 10^{9}-6.15\times 10^{6}}{\varepsilon}+\frac{1}{6.16\times 10^{9}+1. 43\times 10^{6}}}\right)\\ f_{inst}=\frac{15.80}{\varepsilon}\text{kHz}\end{array}  \tag{33}\]  
The trapezoidal reference signal by nature closely represents the matrix converter load current shape as seen in Figure 7 and by the above deductions; an instantaneous switching frequency, based on the parameters used, is 131.9 kHz considering a hysteresis band of 1 A.  
Figure 6: Trapezoidal wave shape reference current. t: rising and falling time in seconds; I\({}_{f}\): current in amperes.
# Magnetic Solid-Phase Extraction of Organic Compounds Based on Graphene Oxide Nanocomposites
## 2 Preparation and Applications of GO for the MSPE of Organic Compounds
### Functionalized Nanocomposites of Magnetic GO with MOPs
developed a mixed hemimicelles MSPE method for the extraction of cephalosporions from biological samples based on ionic liquid-coated magnetic graphene oxide nanoparticles prior to their determination by HPLC-UV [124]. For the fabrication of the modified sorbent, GO/Fe\({}_{3}\)O\({}_{4}\) nanoparticles were treated with 1-hexadecyl-3-methylimidazoliumbromide in a phosphate buffer solution (pH 7.0) and the mixture was subjected to ultrasonic mixing 5 min. Due to the \(\pi\)-\(\pi\) stacking, the hydrophobic and the electrostatic interactions between the mixed hemimicelles and analytes, the modified material exhibited high surface area and excellent adsorption capacity. Moreover, after the extraction and desorption cycle, it is possible to regenerate the GO/Fe\({}_{3}\)O\({}_{4}\) with water and methanol. A decrease in the sorbent amount was observed after six cycles, however no significant loss of the sorption capacity occurred. A three-dimensional ionic liquid functionalized magnetic GO/Fe\({}_{3}\)O\({}_{4}\) nanocomposite was prepared and used for the MSPE of PAHs from vegetable oil prior to their determination by GC-MS [125]. The ionic-liquid modified sorbent was prepared with a one-pot solvothermal method with 1-(3-aminopropyl)-3-methylimidazolium bromide. Subsequently, free-radical copolymerization took place to form a porous and 3D structure. After modification, the modified material exhibited high adsorption toward PAHs due to strong \(\pi\)-\(\pi\) bonds between the sorbent and the analytes, as well as good mechanical stability and excellent reusability (for at least five times). [PERSON] et al. evaluated different DESs, based on choline chloride (ChCl) for the modification of GO/Fe\({}_{3}\)O\({}_{4}\), in order to develop a MSPE sorbent for the preconcentration of methadone in biological and water samples followed by GC-FID and GC-MS [126]. For this purpose, choline chloride was used as hydrogen bond acceptor and phenol, glycerol, and 5,6,7,8-tetrahydro-5,5,8,8-tetramethyllnaphthalen-2-ol (TNO) as hydrogen-bond donors. The modified nanocomposites were prepared by mixing the DES and the GO/Fe\({}_{3}\)O\({}_{4}\) in a round-bottomed flask under stirring at room temperature for 1 h. The highest extraction recovery for methadone was observed with the TNO hydrogen-bond donors at a molar ratio of 1:2. of ChCl:TNO. The developed methods exhibited high preconcentration factor and satisfactory extraction recovery, however no reusability data were reported.
# Implementation of an IoT Based Radar Sensor Network for Wastewater Management
## 6 Background
### LoRaWAN Background and Implementation
#### 6.2.1 LoRaWAN Communications Protocol  
The LoRa modulation techniques were investigated further, as well as both the LoRaWAN MAC, security and a specific free-to-use network backend.  
1. [label=()]
2. _LoRa Modulation_  
LoRa is a recent physical layer modulation technology developed by Semtech Corporation [28]. Utilising a proprietary Chirp Spread Spectrum (CSS) technique, LoRa spreads a narrow-band signal over a wide bandwidth channel. From the resulting noisy characteristics, LoRa allows for bidirectional communication with the ability to detect severely attenuated LoRa packets up to 20 dB below the noise floor. Once a transmitter broadcasts a LoRa packet, a receiver utilises a Fractional-N Phase-Lock-Loop to lock onto the preamble and begin reception of the packet stream.  
Figure 26: The effect of radar beam angle on the beam width diameter.  
A combination of the CSS and 4/5 FEC coding rate provides a receiver sensitivity of up to \(-137\) dBm, enabling a strong resilience to channel noise and interference.  
LoRa Alliance specifications [29] outlines the provision of numerous spreading factors (SF7-SF12) supported by LoRa. These spreading factors (orthogonal codes) offer a trade-off between data/bit rates and transmit range. Table 4 below shows that the larger the spreading factor, the lower the bitrate and receive sensitivity, and the longer the time-on-air.
## 2 Methodology
### Background-Noise Correlation Factor in the PQA Score  
To compute the background-noise correlation factor in the \(PQA\) score definition, we sample the indexes of the VP and the swapping of the corresponding items. This background correction is aimed to remove inherent noise in the data, even though the score may still be subjected to noise from the chosen clustering algorithm or discrepancies in the posterior classification.
## 3 Path-Planning Design and Dynamic-Obstacle Avoidance Strategy  
For drones performing near-ground search missions, feasible path planning should integrate path quality and real-time obstacle avoidance capabilities. A safe and reliable autonomous-obstacle-avoidance path is planned under the condition of ensuring the shortest path and the best quality. The proposed path optimization method and the dynamic-obstacle-avoidance strategy are based on the global set of path nodes derived from the 3D JPS algorithm, as shown in Figure 3.  
Figure 3: The working framework of this article.  
Figure 2: Search-angle limitation of JPS algorithm. (**a**) Limitation of extension angle of JPS algorithm in 2D structure, (**b**) Limitation of extension angle of JPS algorithm in 3D structure.
## 2 Mathematical Modeling and Analysis of the Proposed Detector Algorithm
### Clutter Map Cfar Mixed with the Gabor Transform Algorithm
#### 2.2.2 Detection with the Gabor Transform  
The Gabor transform, which is a linear time-frequency analysis technique, has long been recognized as a very useful tool in signal stationarity over time. It uses a Gaussian window to create a time sample from which the spectrum of local signal values is calculated [19]. Any finite sequence X(k) that is periodized can be decomposed based on a shifted Gaussian distribution and amplitude modulation. The following equation defines its discrete Gabor expansion [18]:  
\[X(k)=\sum_{n=0}^{N-1}C_{mn}h(k-mN)e^{2j\pi\frac{km}{N}} \tag{8}\]  
where C\({}_{mn}\) (m, n \(\in\mathbb{Z}\) ) are the Gabor coefficients, m (m = 1...M) and n (n = 1...N) are the time and frequency samples, respectively, and k (k = 0...NM\(-\)1) represents the samples of the finite sequence X(k). The coefficients of the Gaussian window chosen with the Gabor transform are calculated from the following equation:  
\[h(n)=e^{-\frac{1}{2}(\alpha\frac{2n}{h_{n}})^{2}} \tag{9}\]  
Hence, N\({}_{h}\) is the width of window h, and \(\alpha\) is proportional to the inverse of the standard deviation. The analysis window h, which is both well localized in time (if h is short) and in frequency (if h is long), must also be at finite energy, as shown in the following equation:  
\[\sum_{k=0}^{L-1}|h(n)|^{2}\langle\infty \tag{10}\]  
Figure 6: CMAP-CFAR image mixed with the Gabor transform block diagram.  
Once the Gabor coefficients are calculated, again consider the squared modulus of the Gabor coefficients from Equation (12), and by projection on the time axis, as shown in Figure 10, we calculate the maximum instantaneous frequency of the frequency sample (n) for each temporal sample (m). Finally, we preserve these instantaneous powers (m) in an energy vector Y as follows:  
\[\text{Y(m)}=\text{MAX}(\,|\,\text{C}_{\text{mn}}\,|^{2}) \tag{14}\]  
To better clarify the vector Y(m), Figure 10 presents the envelope detector based on the Gabor transform.  
Finally, we obtain a spectrogram based on the Gabor transform, which represents an estimate of the signal's power and its instantaneous frequency content [21].
# Sensorimotor Control Using Adaptive Neuro-Fuzzy Inference for Human-Like Arm Movement
## 2 Musculoskeletal Modeling
### Relation Between Task and Joint Space  
The proposed musculoskeletal model has three biaarticular and six monoarticular muscles in a curved arrangement and a structure of three links, as shown in Figure 1. We define the end-point arm position \(p=\left[p_{x},p_{y}\right]^{\mathsf{T}}\in\mathbb{R}^{2}\) in terms of the joint variables of the arm \(\theta=[\theta_{1},\theta_{2},\theta_{3}]^{\mathsf{T}}\in\mathbb{R}^{3}\). The coordinates of the end-point \(p\) without any orientation are described in Cartesian space as follows:  
\[p=\left[\begin{matrix}L_{1}\cos(\theta_{1})+L_{2}\cos(\theta_{1}+\theta_{2})+ L_{3}\cos(\theta_{1}+\theta_{2}+\theta_{3})\\ L_{1}\sin(\theta_{1})+L_{2}\sin(\theta_{1}+\theta_{2})+L_{3}\sin(\theta_{1}+ \theta_{2}+\theta_{3})\end{matrix}\right] \tag{1}\]  
where \(L_{1},L_{2}\), and \(L_{3}\) denote the lengths of the shoulder, elbow, and wrist links, respectively, \(\theta_{1}\) is the shoulder angle relative to the \(x\)-axis coordinate, \(\theta_{2}\) is the elbow angle in reference to the shoulder, and \(\theta_{3}\) is the wrist angle with regard to the elbow. By differentiating Equation (1), we establish a relationship between the end-point velocity and the angular velocities of the links as  
\[\dot{p}=\mathbf{J}\dot{\theta} \tag{2}\]  
where \(\mathbf{J}\in\mathbb{R}^{2\times 3}\) represents the Jacobian matrix that maps the joint space to the task space, \(\dot{p}=[\dot{p}_{x},\dot{p}_{y}]^{\mathsf{T}}\) is the end-point linear velocity, and \(\dot{\theta}=[\theta_{1},\dot{\theta}_{2},\dot{\theta}_{3}]^{\mathsf{T}}\) is the angular velocity.  
The Jacobian matrix \(\mathbf{J}\) is also employed to find the relationship between the joint torques \(\tau\in\mathbb{R}^{3}\) and the end-point forces in task space \(F\in\mathbb{R}^{2}\) as  
\[\tau=\mathbf{I}^{\mathsf{T}}F. \tag{3}\]
## 4 Experiments and Analysis
### Performance Analysis for the Galileo System
Lastly, based on the broadcast ephemeris of the Galileo navigation system, it is obvious that the eccentricities of these two satellites are higher than other satellites. This may also lead to larger standard deviations for E14 and E18. However, since these two satellites are in the incorrect orbit, this phenomenon is also not universal for the Galileo system.