esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Background  
### Closed-Form Solutions to Sloshing Natural Period of a Tank with Vertical Baffles  
When a rectangular tank undergoes translational motion as described by Equation (1), the natural sloshing period of the liquid inside the tank, derived from linear theory, can be expressed as Equation (2). Here, \(A\) is the amplitude, and \(\omega\) is the angular frequency. \(g\) is the acceleration due to gravity, \(k\) is the wave number, where \(k=m/L\), with \(m\) representing the mode number. \(L\) is the length of the tank, and \(h_{w}\) is the water depth. Due to the nonlinear nature of the sloshing phenomenon, exact resonance may not always occur even when predicted by Equation (2). However, experimental studies have confirmed that for the lowest mode, the calculated values closely matched the observed results [10].  
\[x=A\sin(\omega t) \tag{1}\]  
\[T=\sqrt{\frac{4\pi^{2}}{gk\mathrm{tanh}(kh_{w})}} \tag{2}\]  
When a baffle is installed to suppress fluid motion inside the tank, the liquid flow characteristics within the tank change. Specifically, the presence of the baffle at the bottom of the tank results in an effect similar to a reduction in the water depth. [PERSON] et al. (2006) applied linear theory based on potential theory and introduced the concept of effective fill-depth to express the sloshing natural period, as shown in Equation (3) [16]. Instead of the water depth, the effective fill-depth, which accounts for the baffle's influence, is defined as \(h_{e}=h_{w}-\beta h_{b}\). Here, \(\beta\) is the depth reduction coefficient for a configuration where baffles of height \(h_{b}\) are arranged at equal intervals with \(n_{b}\) baffles on the tank floor.  
\[T=\sqrt{\frac{4\pi^{2}}{g\mathrm{tanh}(h_{b})}} \tag{3}\] \[\text{where }\beta=1-\gamma^{2}\frac{n_{b}+1}{2}\frac{\tanh(n_{b}+1) kh_{b}}{\tanh(h_{b})},\ \gamma=\frac{4}{\pi}\frac{n_{b}+1}{n_{b}^{2}+2n_{b}}cos\left\{\frac{\pi}{2(n_{b} +1)}\right\}\]  
[PERSON] et al. (2018) also developed a closed-form equation to calculate the natural sloshing period of a tank with vertical baffles based on potential theory [17]. Unlike [PERSON] et al. (2006) [16], they identified the ratio of water depth to tank length as a key parameter. Based on this parameter, they organized the analysis results, obtained using the boundary element method, for tanks with various geometries and developed fitting equations for these results. They proposed an empirical formula, as shown in Equation (4), based on these fitted results.  
\[\begin{gathered} T=\sqrt{\frac{h_{b}}{g\pi\left(\frac{h_{b}}{L} \right)_{eff}\tanh\left[\pi\left(\frac{h_{b}}{L}\right)_{eff}\right]}}\\ \text{where }\left(\frac{h_{b}}{L}\right)_{eff}=\frac{h_{b}}{L} \cdot\left[1-e^{-E(n,\frac{h_{b}}{L})(1-\frac{h_{b}}{Lw})}+e^{-E(n,\frac{h_{b} }{L})}\right]\\ E(x,y)=0.4138-0.4973x+3.386y+0.03317x^{2}+0.5107 xy+0.1182y^{2},\\ F(x,y)=0.1848+0.1329x+0.4365y-0.01212x^{2}+0.0002057 xy-0.1094y^{2} \end{gathered} \tag{4}\]
## 6 Numerical Examples
### Hyperbolic Evaluation  
The second numerical example is given by a one-dimensional transport equation. An input-output system is constructed by selecting the left boundary as the input and the right boundary as the output:  
\[\frac{\partial}{\partial t}z(x,t) =-\theta\frac{\partial}{\partial x},\quad x\in[0,1],\] \[z(0,t) =u(t),\] \[z(x,0) =0,\] \[y(t) =z(1,t),\]  
while the transport velocity \(\theta\in[1.0,1.5]\) is treated as a parameter. This partial differential equation system is spatially discretized by a first-order finite-difference upwind scheme, yielding a SISO ordinary differential equation system:  
\[\dot{x}(t) =A(\theta)x(t)+bu(t),\] \[y(t) =cx(t),\]  
with \(A(\theta)=\theta A\). For this example, a spatial resolution of \(\dim(x(t))=256\) is chosen, hence \(A\in\mathbb{R}^{256\times 256}\), \(b\in\mathbb{R}^{256}\) and \(c\in\mathbb{R}^{256}\). Since the system matrix is non-normal, using techniques such as POD may lead to unstable reduced order models. Thus, here a cross-Gramian-based balancing technique is used, guaranteeing stability of the reduced model. For training, impulse responses for the extremal velocities are used; for testing, a Gauss bell input is utilized over 10 uniformly random velocities, both utilizing the default integrator. The reduced order model quality is evaluated by the parametric norm [47]:  
\[\|y(\theta)-\tilde{y}(\theta)\|_{\mathcal{L}_{2}\otimes\mathcal{L}_{2}}=\sqrt {\int_{\Theta}\|y(\theta)-\tilde{y}(\theta_{r})\|_{\mathcal{L}_{2}}^{2}\,\mathrm{ d}\theta}.\]  
Figure 2: Model reduction error for the linear test system, see Section 6.1.  
Even though the system is hyperbolic, a steep decay in error is obtained (Figure 3). Yet, due to the hyperbolicity and the parameter-dependence of the reduced order model, a lower overall numerical accuracy (\(\approx\)\(10^{-7}\)) is achieved.
# Autonomous Multiple Tramp Materials Detection in Raw Coal Using Single-Shot Feature Fusion Detector
###### Abstract  
In the coal mining process, various types of tramp materials will be mixed into the raw coal, which will affect the quality of the coal and endanger the normal operation of the equipment. Automatic detection of tramp materials objects is an important process and basis for efficient coal sorting. However, previous research has focused on the detection of gangue, ignoring the detection of other types of tramp materials, especially small targets. Because the initial Single Shot MultiBox Detector (SSD) lacks the efficient use of feature maps, it is difficult to obtain stable results when detecting tramp materials objects. In this article, an object detection algorithm based on feature fusion and dense convolutional network is proposed, which is called tramp materials in raw coal single-shot detector (TMRC-SSD), to detect five types of tramp materials such as gangue, bolt, stick, iron sheet, and iron chain. In this algorithm, a modified DenseNet is first designed and a four-stage feature extractor is used to down-sample the feature map stably. After that, we use the dilation convolution and multi-branch structure to enrich the receptive field. Finally, in the feature fusion module, we designed cross-layer feature fusion and attention fusion modules to realize the semantic interaction of feature maps. The experiments show that the module we designed is effective. This method is better than the existing model. When the input image is 300 \(\times\) 300 pixels, it can reach 96.12% MAP and 24 FPS. Especially in the detection of small objects, the detection accuracy has increased by 4.1 to 95.57%. The experimental results show that this method can be applied to the actual detection of tramp materials objects in raw coal.  
col; gangue; tramp materials; object detection; SSD; feature fusion +
Footnote †: journal: Applied Sciences
###### Abstract  
This paper proposes a hybrid dual path sub-sampling phase-locked loop (SSPLL), including a proportional path (P-path) and an integral path (I-path), with 0.8 V supply voltage. A differential master-slave sampling filter (MSSF), replacing the sub-sampling charge pump (SSCP), composed the P-path to avoid the degraded feature caused by the decreasing of the supply voltage. The I-path is built by a rail-to-rail SSCP to suppress the phase noise of the voltage-controlled oscillator (VCO) and avoid the trouble of locking at the non-zero phase offset (as in type-I PLL). The proposed design is implemented in a 40-nm CMOS process. The measured output frequency range is from 5.3 to 5.9 GHz with 196.5 fs root mean square (RMS) integrated jitter and \(-\)251.6 dB FoM.  
SSPLL; hybrid dual path loop; low jitter; low power consumption 2021
## 4 Experiments and Results
### Implementation Details  
This experiment was implemented on Lenovo Legion 5 (model 15 IAH7H) laptop, manufactured in China, with an I7-12700H processor, 16 GB of RAM, and an Nvidia RTX-3060 GPU with 6 GB of RAM. The environment for training and testing all models was based on Python 3.8.13 and Tensorflow 2.5.0. The learning rate and batch size in this work were set to 0.0001 and 1, respectively. For evaluating resource usage and training time, the experiment was conducted on Google Colab using cloud computing resources with an A100 GPU.
## 8 Case Study
### Segmentation Algorithms Used  
Consider T1, an aggregated representative brain, derived from MRI data. It is of dimensions \(91\times 109\times 91\). See Figure 4. In the work described here as image format for the 3D or 4D image volumes we used the FITS, Flexible Image Transport System, format.  
The first model-based segmentation was carried out as follows.  
* We use \"Marginal Range\" in the \"Segmentation\" pull-down menu to decide, from the plot produced, that the BIC criterion suggests that a 6 cluster solution is best.
* Then we use \"Marginal\" with 6 clusters requested, again in the \"Segmentation\" pull-down menu. Save the output as: T1_segm_marg6.fits.  
Figure 4: Frame number 15 from an MRI brain image.  
Next an alternative model-based segmentation is carried out in wavelet space.  
* Investigate segmentation in wavelet space. First carry out a wavelet transform. The B\({}_{3}\) spline a trous wavelet transform is used with 4 levels (i.e. 3 wavelet resolution scales). The output produced is in files: T1_1.fits, T1_2.fits, T1_3.fits, T1_4.fits.
* Use the wavelet resolution scales as input to \"K-Means\", in the \"Segmentation\" pull-down menu. Specify 6 clusters. We used 6 clusters because of the evidence suggested by BIC in the former modeling, and hence for comparability between the two modelings. Save the output as: T1_segm_kmean6.fits.
# Comparative Study of Different Approaches for Islanding Detection of Distributed Generation Systems
## 2 Literature Review
### Islanding Detection Methods  
#### 2.2.1 Passive Method  
Passive method is based on continuous monitoring of the local site system parameters like change in voltage, frequency, and harmonic distortion as these parameters vary during the islanding condition initiation [49]. These variations are directly influenced by the power mismatch between the supply and the loads. Due to passive methods of islanding detection being dependent on the changes of the aforementioned parameters, the efficiency is thus dependent on the threshold set [3; 50]. Setting a lower threshold results in false islanding detection as non-islanding faulty conditions of the power system  
Figure 1: An example of a DGS.  
may cause system parameter changes too. The higher setting of the threshold results in the occurrence of islanding condition being undetected as system parameter changes during islanding will not cross the set thresholds [51]. Though cheap on implementation, inability of the method to detect islanding during balanced islanding and large non-detectable zone (NDZ) renders this method unreliable to be used alone [52]. The rate of change of voltage/frequency relies on the fact that power imbalance causes transients in an islanded system and causes the voltage and frequency to vary. The over/under voltage and frequency, however, deal with the thresholds of the voltage and frequency, respectively, to detect an islanded system. The Harmonics Detection Method monitors the total harmonic distortion in the grid, while the phase monitoring method constantly detects any sudden changes in the output current of the inverter and its terminal voltage to detect the islanding condition [53]. Explanations to some of the passive methods are:
## 2 Materials and Methods
### Monitoring Setup  
The high-speed camera (Photron, Fastcam Mini AX50, Tokyo, Japan) contains a CMOS chip with a square pixel size of 20 \(\times\) 20 \(\upmu\)m\({}^{2}\) and a relative spectral sensitivity between 400 and 700 nm. The recordings were saved on an onboard ring buffer on the camera and must be downloaded after capturing. The maximum image size depends on the variable sampling rate and decreases for higher sample rates. To visualize the thermal radiation for stainless steel and zinc-coated steel, the kerf is recorded with a frame rate of 20\(\cdot\)10\({}^{3}\) fps and an image size of 256 \(\times\) 256 pixels. The exposure time is set to 20 \(\upmu\)s to prevent overexposure.  
As the melting temperature for aluminum is significantly lower than for steel, the thermal emission intensity decreases considerably for aluminum. In general, the exposure time depends on the sampling rate and cannot be set longer than the inverse of the sampling rate. For too short exposure times (i.e., high sampling rates), the camera cannot accumulate sufficient process radiation from the melt in the kerf. Therefore, to visualize the thermal radiation for cuts in aluminum, the exposure time had to be set to 1.0 ms and the sampling rate to 1\(\cdot\)10\({}^{3}\) fps accordingly.  
The process radiation from the cutting kerf was recorded without additional illumination to measure the thermal radiation solely.
## 2 Materials and Methods
### Background
#### 2.1.3 Scalability and Energy Efficacy Issues Caused from Bidirectional Traffic
In addition, the blocking of GW opportunities to transmit due to duty cycle limitation causes an increase to the number of retransmissions, leading to a huge increase in the energy consumption on EDs, which is about 500% for the 100% packet acknowledgement scenario. This happens because, when EDs do not hear back from a GW, they assume that their uplink has lost due to the lack of confirmation. Furthermore, apart from the battery drain, retransmissions introduce a large drop in network goodput, especially on larger network sizes. Additionally, the introduction of downlinks in addition to the ACKs leads to competition between them, thus retransmissions increase, and end up reducing the uplink goodput even further [3]. In summary, deployment parameters are crucial for the overall energy efficiency of a network [26].
## Materials and Methods
### Raman and MIR Setup  
Figure 1 shows the measurement setup for the simultaneous process analysis of yeast fermentation using Raman and MIR spectroscopy.  
The Raman measuring system consisted of a Raman immersion probe (RamanProbeTM II, InPhotonics Inc., Norwood, USA) with a focal length of 7.5 mm and an adjusted working distance of 2.7 mm, quartz fiber optics, and a Raman spectrometer (MultiSpec(r) Raman, tec5 AG, Oberursel, Germany). The Raman spectrometer had an integrated 785-nm diode laser with an output power of 500 mW (230 mW inside the bioreactor at the sample area) and a Peltier-cooled back-thinned charge-coupled device (CCD) array detector (\(1024\times 58\) pixel, pixel size \(24\times 24\) um). Data acquisition was performed using the software MultiSpec(r) Pro II (tec5 AG). Spectra were collected every minute in the range from 300 cm\({}^{-1}\)-3200 cm\({}^{-1}\) with an integration time of 30 s and a resolution of 7 cm\({}^{-1}\)  
Figure 1: Measurement setup for simultaneous monitoring of yeast fermentations using Raman and mid-infrared spectroscopy.  
After the medium without glucose and inoculum was filled into the fermenter, the dark current was measured and used as a background spectrum. Subsequently, the cyclic recording of the fermentation process was started. In contrast to the Raman measurements, which were performed in-line, the MIR measurements were carried out on-line in a bypass. For this purpose, an ALPHA FT-MIR spectrometer with an ATR flow cell (Bruker Optik GmbH, Ettlingen, Germany) was part of the MIR measurement setup. The FT-MIR spectrometer included an ATR crystal of diamond with single reflection at the interface crystal/sample. The incidence angle of radiation was 45\({}^{\circ}\), and the realized optical path lengths were between 1 and 10 \(\upmu\)m depending on the wavelength [10]. The spectrometer was a RockSolidTM interferometer equipped with a permanently-adjustable gold mirror and a DTGS detector. A globar was used as a light source. The sample to be measured was pumped with a peristaltic pump (Ismatec(r), Cole-Parmer GmbH, Wertheim, Germany) through a flow cell via a bypass at a constant volume flow of 2.4 mL min\({}^{-1}\). It took 20 s to pump the sample from the reactor to the ATR cell. Within this period, the concentration changes of glucose, ethanol, and biomass were less than one tenth of the calibration and prediction errors at any time of the fermentation process. Therefore, the resulting time gap between the Raman and the MIR measurement could be neglected. MIR data acquisition was performed by the software OPUS(r) (Version 7.0, Bruker Optik GmbH). The spectra were recorded every minute in a spectral range from 600 cm\({}^{-1}\)-4000 cm\({}^{-1}\) using a resolution of 4 cm\({}^{-1}\) with 32 scans (corresponding to a measuring time of 30 s). A spectrum against ambient air was recorded shortly before the beginning of the fermentation and used as a background spectrum.
## 5 Processing of GNSS Data  
### Data Source and Processing  
Our area of interest is located within the seismically active regions of Greece, where medium-strong earthquakes are relatively frequent. In Greece, in addition to the seismic network, a dense network of permanent GNSS stations has been built to detect earthquakes (and subsequent tectonic movements). Raw data from GNSS stations are usually not freely available. However, access to the raw GNSS data was gained through cooperation with the Research Institute of Geodesy, Topography and Cartography at Zdiby, the Department of Geophysics at Charles University in Prague, and the University of Patras in Greece.  
Data from permanent GNSS stations, located near the epicentre of the earthquake, were available to analyse the surface displacement caused by an earthquake. Data produced by the GNSS receiver in Compact RINEX format were available for the analysis. In all cases, full-day files with a 30 s data sampling rate were collected. Satellites of the U.S. GPS-NAVSTAR system and the Russian GLONASS system were used during the observations. Datasets were available several days (between 5 and 30) before and after the earthquake.  
Data processing, to acquire observation station coordinates, was performed using the PPP method, detailed in Section 3. Freely accessible software [27] from the Canadian geological service NRCan (Geodetic Survey Division, Natural Resources Canada) was used. GNSS satellite precise ephemeris and onboard clock corrections, published with a 14-day delay by IGS (International GNSS Service), were used to determine station coordinates. Satellite ephemeris accuracy is currently at 3 cm in position and 1 ns in satellite clock correction. The necessary calibration and atmospheric corrections for individual observations were determined from the models. The method continued to operate through the iteration process. For each day-long data set, results characterised by internal accuracy (with 95% probability) in the NS (north-south direction): 2-3 mm, in the WE (west-east direction): 4-6 mm and in the Up (vertical direction): 8-12 mm, were obtained.  
One result was available for each day's file. Therefore, it was possible to analyse the time series in the interval before and after the earthquake. Anharmonic analysis, detailed in Section 3, was used. Based on the time series analysis before and after the earthquake, the differences that express the co-seismic displacement of the stations caused by the earthquake were determined. A prerequisite is that during the earthquake, the GNSS antenna on the station was not moved with respect to the background, and results of the analysis represent a displacement of the surface.
# FusionTrack: Multiple Object Tracking with Enhanced Information Utilization
## 5 Analysis and Discussion
### Choosing Parameters for Matching  
Since the association is made implicitly in the in-frame query fusion step, using only IoU for detection filtering can achieve excellent performance. The advantage of using such an end-to-end-like method is that the number of parameters that need to be adjusted is minimal compared with the two-stage matching method used in traditional MOT models like ByteTrack [4]. The two most important parameters are the IoU threshold \(\theta_{iou}\) and the confidence threshold \(\theta_{conf}\) responsible for deleting duplicate detection objects and low-quality tracking boxes, respectively. Experiments on different combinations of these parameters on part of the DanceTrack validation set are conducted to find optimal parameters; the results are shown in Figure 9.  
Figure 8: Visualization of token weights in the score-based multi-level query fuser. First row: weights of the same instance in a clip. Second row: two example frames with different weights generated by different occlusion states, the fluctuation level is visualized in the rightmost plot.  
We draw two conclusions from the experiments: Firstly, the performance of this model is greatly affected by the IoU threshold for filtering. If the threshold for IoU between the detection box and the existing tracking box is too high, a large number of false positives will be generated, influencing the performance of the model. This indicates that detecting objects that reappear shortly from behind foreground objects is still a challenging problem. Secondly, when the IoU threshold is reasonable, the confidence threshold does not have a significant impact on model performance, indicating that the results may still be accurate, even when the track confidence score is low and the tracking branch can be further improved. Based on the experimental results, we empirically choose \(\theta_{iou}=0.3\) and \(\theta_{conf}=0.4\) as the final parameters.
# Reviews Based on the Reconfigurable Intelligent Surface Technical Issues
## 2 Technical Considerations for RIS
### Reflective/Transmissive RIS and Hybrid RIS
Reflective RISs and STAR-RISs differ in terms of the components and substrates that make up the hardware; for example, in the case of reflective RISs, the substrate of reflective RISs uses conductors to prevent radio signals from penetrating the RIS at the operating frequency. On the other hand, the substrate of a STAR-RIS has a complex design structure because it must simultaneously transmit and reflect wireless signals at the operating frequency. However, when it comes to improving the efficiency of wireless networks, the STAR-RIS can provide more versatility for smart wireless environments, as shown in Figure 31, thanks to its ability to adjust the transmission and reflection coefficients [125]. Figure 30: Types of transmissive RISs. (**a**) Receiver–transmitter structures. Adapted with permission from [120]. Copyright 2023 IEEE (**b**) Stacked FSS structures. Adapted with permission from [121]. Copyright 2017 IEEE.
## 3 Probabilistic Forecasting of ALSC
### The Procedure of Probabilistic Forecasting of ALSC  
The calculation steps of probabilistic ALSC forecasting can be summarized as: First, the stacking ensemble learning model is used to forecast wind power, photovoltaic power and load power. Secondly, the forecasting distribution is obtained based on the point forecasting results. Then, the LHS method is used to sample the forecasting distribution, and the sampling values are input into the RPF considering multi-slack buses to calculate the ALSC, and finally the probabilistic prediction results of the ALSC are obtained. The whole calculation process is shown in Figure 4.