esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
### Ablation Study  
The new rating model has several components, and it is not immediately clear how each of them contributes to the rating system as a whole. To shed some light on this, an ablation study is conducted, where six different model variations are created by eliminating certain components from the full model. These model variations are then evaluated in the same framework as the full model, so that the importance of each eliminated component can be measured. The following variations of the model are considered:  
1. [label=(A)]
2. Removing segment weights, that is, setting \(w(m,s)=1\).
3. Removing the effect of player age, by setting \(u_{i}(t)=0\).
4. Ignoring home field advantages, by setting \(f^{H.MATCH}(m)=f^{A.MATCH}(m)=0\).
5. Not considering red cards, by ignoring segments with missing players, i.e., removing all \(s\) from \(S_{m}\) where \(r(m,s,n)\
eq 0\) for any \(n\).
6. Removing the regularization terms for the difference between offensive and defensive ratings, by setting \(f^{REG.O.D.PLAYER}(p)=0\).
7. Not skipping offensive ratings for goal keepers, by enforcing \(P^{O}_{mst}=P^{D}_{mst}\) and adjusting \(f^{AUX}(p,t,w)\) and \(f^{REG.O.D.PLAYER}(p)\) by removing the exceptions for \(V_{p}=\{GK\}\).  
The results of the ablation study are illustrated in Figure 4, based on the validity and reliability of the resulting ratings. The differences between the model variations are very small, and the figure is only showing a small portion of the values for prediction loss and correlation given in Figure 3. The overall conclusion is that none of the model variations outperform the full model, but that the relative degradation in performance is small when modifying only a single model component. When ignoring home field advantages, the prediction loss of the method improves, but with a reduction in the reliability of ratings. On the other hand, when removing the additional regularization term for the difference between the offensive and defensive ratings of each player, the reliability increases  
Figure 3: Evaluation of rating methods based on reliability, measured as the correlation of ratings calculated on a data set randomly split in two halves, and validity, measured as the prediction loss for rating-based match outcome predictions.  
at the expense of worse prediction loss values. However, in both of these cases, the deviations are very small.
# Towards Automatic License Plate Detection
## 3 Proposed Method
### Vehicle Detection
\(\triangleright\) to obtain \(H_{Image}\), \(S_{Image}\), and \(V_{Image}\)
28 for perform color segmentation for each of the LP color; do
29 define HSV threshold limits ( \(HT_{low},HT_{high},ST_{low},ST_{high},VT_{low},VT_{high}\) )
30 obtain mask images
31 if\(H_{Image}\geq HT_{low}\) & \(H_{Image}\leq HT_{high}\)then
32\(H_{mask}=1\)
33 end if
34 if\(S_{Image}\geq ST_{low}\) & \(S_{Image}\leq ST_{high}\)then
35\(S_{mask}=1\)
36 end if
37 if\(V_{Image}\geq VT_{low}\) & \(V_{Image}\leq VT_{high}\)then
38\(V_{mask}=1\)
39 end if
40 obtain the LP blob image (\(A\))
41 if\(H_{mask}=1\) & \(S_{mask}=1\) & \(V_{mask}=1\)then
42 fill all the pixels with \(1\) in (\(A\))
43 end if
44
45 end for
46
47 apply morphological operations using Eq. (4)-(5). 48 dimension analysis on the basis of aspect ratio and spatial area. 49
50 end for Output :(\(I^{\prime}\))=detected license plate with green rectangle around
```  
**Algorithm 1**Proposed license plate detection procedure. The RoI is viewed in this work as a rectangular window onto (\(\psi\)). Furthermore, each RoI is represented by a four-tuple \((r,c,h,w)\) that specifies its top-left corner \((r,c)\) and its bottom-right corner \((h,w)\) (\(h,w)\). Every feature vector \((\Gamma)\) is fed into a series of Fully Connected (FC) layers as an input. The RoI pooling layer, on the other hand, uses max-pooling to turn the features inside a binding zone into a compact feature map. Furthermore, only a few RPN concepts have a lot of overlap. As a result, we use Non-Maximum Suppression (NMS) [27] on the proposal regions to eliminate redundancy. For \(NMS\geq 0.5\), we set the IoU threshold to 2000 proposal regions per picture, resulting in a considerable reduction in the amount of suggestions. Following the NMS, the top-N rated proposal regions are predicted to detect the vehicle in line (20) of Algorithm 1 by drawing a red rectangle around the vehicle. Once the vehicle(s) are spotted in the input image, in next step, we use our own developed LPLM methodology to detect the license plate within the detected vehicle image. In the next section, we describe our developed LPLM that takes the vehicle detected image as input.
## 4 Case Study: Sustainability Assessment
### Sustainability Assessment Methodology
#### 4.1.2 Life Cycle Costing (LCC) Method  
Life Cycle Costing assessment allowed the evaluation of the economic impact of each solution during its life cycle. In this case, the methodology followed ISO 15686-5:2017 [53]. To perform this assessment, the data collected corresponded to materials acquisition cost,  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**Unit**} & **Month 0** & \multicolumn{2}{c}{**Month 6**} & \multicolumn{3}{c}{**Month 12**} & \multicolumn{3}{c}{**Limits DL**} \\ \cline{3-10}  & & **Before** & **Installation** & **Ref** & **Dregs** & **Grits** & **Ref** & **Dregs** & **Grits** & **183/2009** \\  & & **Before** & **Installation** & **Ref** & **Dregs** & **Grits** & **Ref** & **Dregs** & **Grits** & **with L/s = 10 L/kg** \\ \hline Zn & mg (Zn)/L & 0.05 & 0.30 & 0.12 & 0.83 & 0.20 & 0.49 & 1.08 & 0.4 \\ \hline Pb & mg (Pb)/L & 0.01 & 0.01 & 0.01 & 0.01 & 0.01 & 0.003 & 0.006 & 0.02 & 0.05 \\ \hline Hg & mg (Hg)/L & +0.01 (LQ) & +0.01 (LQ) & +0.01 (LQ) & +0.01 (LQ) & +0.01 (LQ) & +0.01 (LQ) & +0.01 (LQ) & 0.001 \\ \hline Cd & mg (Cd)/L & +0.005 (LQ) & +0.005 (LQ) & +0.005 (LQ) & +0.005 (LQ) & +0.005 (LQ) & +0.005 (LQ) & +0.005 (LQ) & +0.005 (LQ) & 0.004 \\ \hline Ni & mg (Ni)/L & 0.01 & 0.01 & 0.01 & 0.01 & 0.01 & 0.005 & 0.004 & 0.01 & 0.04 \\ \hline Cr & mg (Cr)/L & 0.004 & 0.01 & 0.004 & 0.01 & +0.003 (LQ) & +0.003 (LQ) & 0.004 & 0.05 \\ \hline Ba & mg (Ba)/L & 0.01 & 0.10 & 0.08 & 0.20 & 0.08 & 0.06 & 0.02 & 2 \\ \hline As & mg (As)/L & 0.002 & 0.018 & 0.018 & 0.002 & 0.146 & 0.012 & 0.002 & 0.05 \\ \hline Sb & mg (Sb)/L & 0.001 & 0.002 & +0.001 (LQ) & 0.00 & +0.001 (LQ) & +0.001 (LQ) & 0.004 & 0.006 \\ \hline Se & mg (Se)/L & +0.003 (LQ) & +0.003 (LQ) & +0.003 (LQ) & +0.003 (LQ) & +0.003 (LQ) & +0.003 (LQ) & +0.003 (LQ) & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._rental of equipment used which included the manpower, cost of transport of materials, and cost of energy consumed, meaning the cost of consumed fuel by the equipment used in construction works.  
For LCC, an Excel file was used in the analysis, being all data collected from process owners, literature review, and online product providers. The impact assessment methodology was based on ISO 15686-5:2017.
# Improved PR Control Strategy for an LCL Three-Phase Grid-Connected Inverter Based on Active Damping
## 5 Conclusions  
In this paper, an improved current control strategy (QPIR) for a three-phase LCL grid-connected inverter based on active damping is proposed, and the simulation and example analysis were carried out using MATLAB/Simulink software. The following conclusions were obtained:  
1. The LCL grid-connected inverter based on active damping can realize independent control of active power and reactive power without coupling between the \(\alpha\) axis and \(\beta\) axis in a two-phase static coordinate system.
2. Compared with the traditional PR and QPR controllers, the improved QPIR controller has higher grid-connected current control accuracy under the condition of stable and fluctuating grid frequency.
3. The improved QPIR controller can realize the fast follow-up of active power and reactive power by the inverter, and when the active power jumps, the reactive power is not affected. When the reactive power jumps, the active power is not affected.  
To sum up, the improved QPIR current controller proposed in this paper realizes the independent control of active power and reactive power of the three-phase grid-connected inverter in the two-phase static coordinate system. It also ensures the control accuracy and rapidity of the grid-connected current and has certain practical significance for high-power factor applications and improving the utilization rate of the DC side.  
[PERSON] contributed to the project idea and the discussion of the results. [PERSON] contributed to the specific strategy, theoretical analysis, simulation experiment design, data analysis, discussion of the results, and conclusions. [PERSON] reviewed the final manuscript. [PERSON] contributed to data analysis and the simulation experiment. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Guizhou Province Science and Technology Innovation Talent Team Project ([2018] 5615).
## 5 Results and Discussion
### Decision-Making Scheme Analysis  
According to the above decision-making schemes, the processes of monthly water diversion and power generation in different typical years were shown in Figure 8. It can be seen that the total water diversion was smallest in the extraordinarily dry year, due to the little water inflow from the upper reaches of the Han River. The water diversion of each month was below the water demand line, and no month could meet the water demand. In addition, the water diversion in October was the smallest, and the power generation also dropped to the lowest level. The water inflow from December to April in dry year was relatively low, and the water diversion of Huangjinxia Reservoir was below the water demand line. The maximum water shortage occurred in March, and the power generation in these months was relatively low. The water inflow from December to March next year  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Scheme No.** & \(\alpha\) & \(\gamma\) & \(\
u\) & _WSI_ & \(\{\alpha,\gamma,\
u\}\) & \(\{\alpha,\gamma,\textit{WSI}\}\) & \(\{\alpha,\
u,\textit{WSI}\}\) & \(\{\gamma,\
u,\textit{WSI}\}\) \\ \hline
13 & 0.583 & 0.2 & 0.718 & 15.873 & & \(\surd\)\({}^{1}\) & \(\surd\) & \(\surd\) \\
51 & 0.583 & 0.2 & 0.716 & 16.273 & \(\surd\) & & \(\surd\) & \(\surd\) \\
85 & 0.667 & 0.25 & 0.887 & 19.991 & \(\surd\) & \(\surd\) & \(\surd\) & \\
35 & 0.500 & 0.33 & 0.770 & 18.257 & & \(\surd\) & & \(\surd\) \\
49 & 0.500 & 0.33 & 0.760 & 18.678 & \(\surd\) & & & \(\surd\) \\
15 & 0.500 & 0.33 & 0.769 & 18.535 & & & & \(\surd\) \\ \hline \hline \end{tabular}  
* \({}^{1}\) “\(\surd\)” represents that the scheme is non-dominated in the corresponding 3-order subspace.  
\end{table}
Table 4: Optimization processes of 3-order effective schemes in dry year.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Typical Years** & **Scheme No.** & \(\alpha\) & \(\gamma\) & \(\
u\) & _WSI_ \\ \hline Extraordinarily dry year & 32 & 0 & 0.090 & 0.931 & 48.038 \\ Dry year & 85 & 0.583 & 0.20 & 0.718 & 15.879 \\ Normal year & 63 & 0.667 & 0.25 & 0.689 & 9.130 \\ Wet year & 52 & 0.833 & 0.50 & 0.6224 & 3.976 \\ Extraordinarily wet year & 44 & 0.916 & 1 & 0.577 & 2.770 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Decision-making schemes in different typical years.  
in normal year was relatively low, and the water diversion struggled to meet the water demand of the Guanzhong plain. In other months, the water diversion was above the water demand line, and the power generation head was higher, so the power generation was larger. The water demand could be met in the wet year and the extraordinarily wet year except for January and February, and the reservoir could maintain a high head for power generation.  
Figure 8: Water diversion and power generation processes in different typical years. (**a**) Extraordinarily dry year (\(p\) = 95%), (**b**) Dry year (\(p\) = 75%), (**c**) Normal year (\(p\) = 50%), (**d**) Wet year (\(p\) = 25%), (**e**) Extraordinarily wet year (\(p\) = 5%).
# Magnetic Nanoparticles: Synthesis, Characterization, and Their Use in Biomedical Field
## 4 Biomedical Applications
### Magnetic Hyperthermia Therapy (MHT)  
MHT takes advantage of MNPs' capacity to interact with an alternating external magnetic field in order to induce heat generation for cancer treatment applications by means of hyperthermia. This non-invasive technique elevates the temperature of tumors to  
Figure 2: Biomedical applications of MNPs. Created with BioRender.com, accessed on 6 February 2024.  
a range of 41-47 \({}^{\circ}\)C, causing cancer cell destruction through either apoptosis or necrosis. Magnetic nanoparticles (MNPs) are administered into the body, gather at the location of the tumor where they specifically aim, and raise the temperature of cancer cells without causing damage to nearby healthy tissues. This approach offers distinct advantages compared to chemotherapy, as it exhibits precise targeting and minimizes harm to healthy cells [123; 124], being implemented in cancer treatment since the 1950s [125].  
Many studies have been reported in the literature, but in the next rows we would like to highlight some of the recent and interesting findings in the field of MHT applications.  
It has been shown that the effectiveness of various molecules (chemotherapeutics, nucleic acids, proteins) has been enhanced when they were used in conjunction with MHT [126; 127; 128]. In other cases, MNPs harmed the cells even in the absence of MHT [91].  
[PERSON] et al. have proposed a combination between superparamagnetic and magnetic immuno-conjugated nanoparticles. The strategy was to treat the cancer cells with immuno-conjugated magnetic nanoparticles and then with superparamagnetic iron oxide nanoparticles (SPIONs) and, by applying an external alternating magnetic field, to enhance the efficacy of the treatment [129]. The study showed promising results offering new perspectives for the development of new nanostructures for cancer applications.  
Developing inhalable composites is a very challenging aspect when it comes to lung cancer treatment. Magnetic nanocomposite microparticles via MHT can serve as an effective thermal therapy platform inducing targeted damage at the tumor site [130].  
Exosome-based research has been influenced by the advances of hybrid nanostructures in tumor targeting by means of MHT. For example, by attaching magnetic nanoparticles at the surface of the exosomes previously loaded with targeting and therapeutic molecules, and by applying an external magnetic field, it has been developed an anti-cancer hybrid drug delivery system [131].  
Many drug delivery nanostructures take advantage of MHT in delivering their cargo at the tumor site. Such applications together with other valuable examples of MHT applications can be found in Table 1.
# A Method Based on an Autoencoder for Anomaly Detection in DC Motor Body Temperature
## 1 Introduction
Simulation results show that an autoencoder with an attention detector provides an improvement in detection and false alarm rates. In [12], a stack autoencoder architecture is proposed for detecting the anomalies in the signals of rotary machine motors. The results show that the anomalies can be detected before machine failure. In [13], a long-short-term-memory-based autoencoder approach is developed to find anomalies in multivariate time series data, and the performance of the method is evaluated on datasets from three data sources. It is demonstrated that the approach can detect previously known and injected anomalies in the datasets. In [14], deep recurrent and convolutional neural networks with residual connections are used to monitor the important component temperatures in a permanent magnet synchronous motor. It is reported that the performances of both methods match the performance of classic thermodynamics-based approaches. Some components of industrial systems are temperature sensitive, such as motors, and they are prone to failure under high-temperature conditions [14]. Correct and timely detection of anomalies in the temperatures of these components is critical for predictive maintenance. In this study, we aim to analyze the performance of an autoencoder (AE) for anomaly detection problems arising from components of industrial systems. We focus on anomaly detection in DC motor body temperature as a real-life problem since it is one of the most used components in industrial systems. The performance of the AE is examined on a dataset that was created specifically for the study. In the experiments, the three-sigma outlier method is also applied to the same dataset for the same purpose and its performance results are used for comparison. The performance results of both methods are represented in terms of accuracy, precision, and recall measures. This paper is organized into seven sections. In Section 1, we define the problem and review the related works on anomaly detection. Section 2 introduces the anomaly dataset and the detection methods used in the study. A system overview for autoencoder-based anomaly detection is given in Section 3. The measures used for the performance evaluation are explained in Section 4. Section 5 presents the experiments and the results. In Section 6, we discuss the experimental results. Finally, Section 7 presents the conclusions.
## 3 Numerical Methodology and Procedure
### Governing Equations for Flow  
The behavior of compressible flows can be characterized using the equations for the conservation of mass and momentum.  
Mass conservation:  
\[\frac{\partial\rho}{\partial t}+\
abla\cdot(\rho\overset{\rightarrow}{v})=0 \tag{1}\]  
Momentum conservation:  
\[\frac{\partial}{\partial t}(\rho\overset{\rightarrow}{v})+\
abla\cdot(\rho \overset{\rightarrow}{v}\overset{\rightarrow}{v})=-\
abla p+\
abla\cdot( \overline{\overline{\pi}})+\rho\overset{\rightarrow}{\
ot{g}}+\overset{ \rightarrow}{F} \tag{2}\]  
where \(\rho\) is the density, \(\overset{\rightarrow}{v}\) is the mean velocity, \(p\) is the static pressure, \(\rho\overset{\rightarrow}{g}\) is the gravitational body force, and \(\overset{\rightarrow}{F}\) is the external body forces. Stress tensor \(\overline{\overline{\pi}}\) is given as follows:  
\[\overline{\overline{\tau}}=\mu\bigg{[}(\
abla\overset{\rightarrow}{v}+\
abla \overset{\rightarrow}{v})-\frac{2}{3}\
abla\cdot\overset{\rightarrow}{v}I \bigg{]} \tag{3}\]  
where \(\mu\) is the molecular viscosity, \(I\) is the unit tensor, and the second term on the right-hand side is the effect of volume dilation.  
Additionally, taking into account the conservation of energy, the comprehensive form of the governing equations is expressed as follows:  
\[\frac{\partial(\rho\phi)}{\partial t}+div(\rho\mu\phi)=div(\Gamma grad\phi)+S \tag{4}\]  
where \(\phi\) denotes the dependent variable, \(\mu\) denotes the velocity vector, \(\Gamma\) denotes the diffusion coefficient, and \(S\) is the general source term.  
Figure 4: Structure of the type B RC-DTH air hammer.  
In the Spalart-Allmaras model, the transported variable, \(\widetilde{v}\), is the turbulent kinematic viscosity, with the exception of the near-wall region. The transport equation form \(\widetilde{v}\) is  
\[\frac{\partial\widetilde{v}}{\partial t}+\frac{\partial}{\partial x_{\rm i}}( \rho\widetilde{v}{\rm u}_{\rm i})=G_{v}+\frac{1}{\sigma\widetilde{v}}\left[ \frac{\partial}{\partial x_{\rm j}}\left\{(\mu+\rho\widetilde{v})\frac{ \partial\widetilde{v}}{\partial x_{\rm j}}\right\}+C_{\rm b2}\rho\left(\frac{ \partial\widetilde{v}}{\partial x_{\rm j}}\right)^{2}\right]-Y_{v}+S_{ \widetilde{v}} \tag{5}\]  
where \(G_{v}\) is the production of turbulent viscosity, \(Y_{v}\) is the destruction of turbulent viscosity occurring in the near-wall region, \(\sigma\widetilde{v}\) and \(C_{\rm b2}\) are constants, \(v\) is the molecular kinematic viscosity, and \(S_{\widetilde{v}}\) is a user-defined source term.
## 1 Introduction
To cope with these issues, ACDC uses bilateral filtering [38] applied on the original image to smooth the input cell image while preserving edge sharpness. This is followed by watershed transform [39; 40] and morphological filtering [41; 42], applied in a fully automatic manner. Thus, ACDC efficiently yields reliable results by only requiring the settings of a few parameters, which may be conveniently adjusted by the user. Therefore, unlike sophisticated solutions that do not provide any interactive control, ACDC makes the end-user sufficiently aware of the underlying automated analysis, thanks to the resulting interpretability of the segmentation model. We demonstrate applications of ACDC on two different cell imaging datasets in order to show its reliability in different experimental conditions. The main contributions of this work are summarized hereafter:  
* ACDC is a fully automatic pipeline for cell detection and counting that exploits watershed transform [39; 40], morphological filtering operations [41; 42], and bilateral filtering [38];* ACDC is designed and developed to cope with the analysis of stacks of time-lapse microscopy images in real-time;
* ACDC does not require any training phase, and represents a reliable solution even without the availability of large-scale annotated datasets. The manuscript is structured as follows. Section 2 describes the analyzed fluorescence imaging datasets, as well as the proposed method. Section 3 presents the results achieved by ACDC. Discussions and final remarks are provided in Section 4.
# TCNformer Model for Photovoltaic Power Prediction
###### Abstract  
Despite the growing capabilities of the short-term prediction of photovoltaic power, we still face two challenges to longer time-range predictions: error accumulation and long-term time series feature extraction. In order to improve the longer time range prediction accuracy of photovoltaic power, this paper proposes a seq2 seq prediction model TCNformer, which outperforms other state-of-the-art (SOTA) algorithms by introducing variable selection (VS), long- and short-term time series feature extraction (LSTFE), and one-step temporal convolutional network (TCN) decoding. A VS module employs correlation analysis and periodic analysis to separate the time series correlation information, LSTFE extracts multiple time series features from time series data, and one-step TCN decoding realizes generative predictions. We demonstrate here that TCNformer has the lowest mean squared error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) in contrast to the other algorithms in the field of the short-term prediction of photovoltaic power, and furthermore, the effectiveness of each module has been verified through ablation experiments.  
transformer; SkipGRU; TCN; photovoltaic power prediction; time series data prediction 2023
# Highly Compact Through-Wire Microstrip to Empty Substrate Integrated Coaxial Line Transition
## 2 Design of the Transition
The rest of the parameters are set following [10], with the exception of \(l_{ms}\) and \(l_{wall}\), whose values do not affect the structure and are arbitrarily set to ease the manufacturing. A performance study (transmission and reflection coefficients of the transition) was carried out to analyze which parameters have the biggest influence apart from those controlling the pad-gap structure and the isolation of the coaxial. As the cable diameter is smaller, the response is better; then \(d_{cable}\) = 0.5 mm is chosen, given that it is the smallest commercial filament that can be easily manipulated and soldered. The position of the filament in the inner coaxial is also sensitive, the response being improved as it is closer to the beginning of the coaxial line; then, just a tiny guard margin of 0.05 mm is included in \(l_{hole}\) to ensure the soldering of the filament within the coaxial. After that, just four dimensions are critical to adjust the transition (\(pad_{T}\), \(pad_{B}\), \(gap\), \(l_{inc}\)), which are optimized with a Nelder-Mead simplex algorithm in CST Studio Suite 2019 to maximize the return losses in the band of interest.