esa-sceva/satcom-chunk-collection huggingface dataset:
# Meta-Data-Guided Robust Deep Neural Network Classification with Noisy Label
## 3 The Proposed CoNet-MS Framework and Its Main Modules
### Cross Dual-Branch Classifier Network Architecture
\[\theta^{*}(\alpha,\beta)=\underset{\theta}{argmin}\sum_{i=1}^{N}L(f_{\theta}(x_{i} ),\alpha_{i}y_{i}+\beta_{i}y_{i}^{pseudo}) \tag{7}\]  
where \(\alpha\) and \(\beta\) are used to balance the weights between the observed sample labels and pseudo-labels output by the upper branch. When \(\alpha\) is small and \(\beta\) is large, \(\theta\) is designed to narrow the gap between the model prediction and the pseudo-label for label correction. When both \(\alpha\) and \(\beta\) are small, the weight of the samples in the loss accounts for a relatively small amount of weight, whose effect on the parameter update can be ignored, thus implicitly realizing the sample selection. Therefore, the assignment of \(\alpha\) and \(\beta\) needs to be very accurate to achieve the above goal. To this end, a method for generating meta-learning weights is introduced, which utilizes a small portion of the clean validation set \(D_{val}=\big{\{}\left(x_{i}^{v},y_{i}^{v}\right)\big{|}i=1,\cdots,M\big{\}}\) as the meta-data and generates the optimal loss weights via a meta-net, given as Equation (8). \[\alpha,\beta=\underset{\alpha,\beta\geq 0}{arg\,min}\frac{1}{M}\sum_{i=1}^{M}L \Big{(}f_{\theta^{*}(\alpha,\beta)}(x_{i}^{v},y_{i}^{v})\Big{)} \tag{8}\]  
where \(\theta^{*}(\alpha,\beta)\) is the result of updating the parameter \(\theta\) in round \(t\) after Equation (7). The solution of Equation (8) can be approximated by the gradient descent on each batch validation set, shown as Equation (9). \[(\alpha_{i},\beta_{i})\,=\,-\eta\
abla\left(\sum_{i=1}^{m}f_{\theta^{*}(\alpha,\beta)}(x_{i}^{v},y_{i}^{v})\right)\Bigg{|}_{\alpha_{i}=0,\beta_{i}=0} \tag{9}\]  
where \(\eta\) is the step size for updating. Then, the following Equations (10) and (11) are simply used to ensure that \(\alpha\) and \(\beta\) are non-negative and normalized. \[\alpha_{i}=max(\alpha_{i},0),\beta_{i}=max(\beta_{i},0) \tag{10}\]  
\[\alpha_{i}^{*}=\frac{\alpha_{i}}{\sum_{i=1}^{n}\alpha_{i}+\beta_{i}},\beta_{i} ^{*}=\frac{\beta_{i}}{\sum_{i=1}^{n}\alpha_{i}+\beta_{i}} \tag{11}\]  
Finally, as shown in Equations (12) and (13), the parameters of dual-branch classifier networks are updated based on the optimal weights obtained, where \(\{x_{\theta},y_{\theta}\}\) and \(\{x_{\varphi},y_{\varphi}\}\) are the high-confidence samples computed by Equations (4) and (5). \[\varphi^{(t+1)}=\varphi^{(t)}-\lambda\
abla\Bigg{(}\sum_{i=1}^{P(t)\cdot n}L \big{(}f_{\varphi}(x_{\theta,i}),y_{\theta,i}\big{)}\Bigg{)} \tag{12}\]  
\[\theta^{(t+1)}=\theta^{(t)}-\lambda\
abla\Bigg{(}\sum_{i=1}^{P(t)\cdot n} \alpha_{i}^{*}L\big{(}f_{\theta}(x_{\varphi,i}),y_{\varphi,i}\big{)}+\beta_{i} ^{*}L\Big{(}f_{\theta}\big{(}x_{\varphi,i}\big{)},y_{\varphi,i}^{pseudo}\Big{)} \Bigg{)} \tag{13}\]  
where \(\lambda\) is the step size for updating classifier parameters \(\theta\) and \(\varphi\).
## 3 Algorithm Based on Limited Search Space
### Local Search  
In this section, we propose a local search algorithm, named the limited search space (LSS) algorithm, to avoid the fluctuation of the solution performance. Candidate solutions are generated through the exchange of operations between different machines. First, after randomly selecting an operation, a list of exchangeable operations is generated by checking the machine compatibility and available time buckets. Machine compatibility can be easily checked by using the given data on the designated machines. The available time buckets in which an item can be processed is determined based on the EPST and LPST. EPST refers to the earliest time when all the sub items or raw materials are prepared so that the item is ready for the operation. LPST refers to the latest time that an item must be processed to avoid the delay of finished goods when compared to the previous solution. This process of checking the machine compatibility and available time buckets limit the search space; therefore, solution feasibility is always guaranteed even if there are many other constraints.  
Next, the randomly selected operation is exchanged with an operation in the list of exchangeable operations to generate a candidate solution. The processing times of the exchanged operations can be changed depending on the worker skill level assigned to the machine. When the completion time of the operation is delayed when compared to the previous solution, it affects the schedules of the following  
Figure 4: Pseudocode for initial solution generation.  
Figure 3: Workload.  
operations. To maintain the efficiency of the algorithm, the same amount of delay is applied to all the operations that begin after the operation that causes the delay. Then, the candidate is generated by modifying the operation's start time to be as early as possible based on the updated EPST and availability of worker and machine. Figure 5 depicts the pseudocode for the candidate generation.  
Finally, the solution is updated based on the performance improvement in terms of the objective functions. Two approaches are employed for the solution update: limited space full search (LSFS) and limited space interactive search (LSIS). LSFS generates all possible candidate solutions using the list of exchangeable operations and determines the best solution. When a better solution is obtained, the solution is updated and moves to the first step of the algorithm. Otherwise, the algorithm is ended. In contrast, LSIS generates only one candidate solution and calculates the objective function value. When the objective function is improved, the solution is updated and immediately moves to the first step of the algorithm and searches a different part of the solution space. Otherwise, the next candidate solution is generated. After generating the candidate solutions one by one, the algorithm stops when the solution is not improved.
# Small Infrared Target Detection via a Mexican-Hat Distribution
## 4 Experimental Results and Analysis
### Comparison with Other Methods
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Method** & **Our Method** & **RLCM** & **PCM** & **Var-Diff** & **AAGD** & **LoG** & **LIG** & **Max-Median** & **Max-Mean** & **Top-Hat** \\ \hline \(P_{d}\) & 0.9968 & 0.3438 & 0 & 0.0313 & 0 & 0.9063 & 0.5313 & 0.8750 & 0.9063 & 0.9063 \\ \(P_{f}\) & **0** & 0.5626 & 0 & 0.8750 & 0 & 0.0625 & 0.3125 & 0.1250 & 0.0625 & 0.0625 \\ SCRG & **4920.8611** & 0.0029 & 0.0252 & 0.0632 & 0.0030 & 0.0172 & 0.1482 & 0.2515 & 0.0901 & 0.1482 \\ BSF & **612.0357** & 0.0005 & 7.8553 & 2.1969 & 0.1317 & 0.0019 & 0.0009 & 0.0002 & 0.0004 & 0.0009 \\ Time (second) & 0.8690 & 14.6853 & 0.1861 & 0.1057 & 0.1102 & **0.1040** & 2.1391 & 2.0189 & 4.6056 & 0.1294 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative assessment of Seq.2. Tables 1 and 2 show that for both synthetic and real-world targets, our method gets the highest SCRG and BSF, indicating it performs better than other compared methods in terms of both clutter suppression and target enhancement. Though for a synthetic target, \(P_{d}\) and \(P_{f}\) of our method are slightly worse than the Max-median and the Max-median, they are much better than the other seven methods. Moreover, among all of the compared methods, our method gets the highest \(P_{d}\) and the lowest and \(P_{f}\) on Seq.2 that has a real-world target. In terms of time cost, our method has a medium performance. The average processing time of our method is about 1.86 s for an image of 468 by 584 pixels, and 0.87 s for an image of 240 by 320 pixels. Our method is slower than PCM, Var-diff, AAGD, LoG, and Top-Hat, but faster than LIG and Max-median, and much faster than RLCM.
# LSOS: An FG Position Method Based on Group Phase Ranging Ambiguity Estimation of BeiDou Pseudolite
## 4 Implementations and Evaluation
### Simulation Analysis
#### 4.2.2 Positioning Performance Analysis  
According to the above results, the measurement errors will be generated randomly in the simulation process from the range errors of 30 cm, 15 cm and 10 cm. The impact on the positioning performance is further verified under different combination of range errors.  
The location result is shown in Figure 12 when the range error is \(\pm\)30 cm. The maximum location error in x direction is 1 m, the maximum location error in y direction is 1 m, and the maximum location error in z direction is about 0.5 m. Under the influence of space environment, the measurement error of pseudo-satellite signal is not 30 cm in any time epoch. In this case, the positioning position may deviate from the actual position, but the positioning accuracy will be better than 1 m.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**SN**} & **Frequency Point Combination Mode** & **ME (m)** & **RMSE** & **True Value (m)** \\ \hline
1 & (B1I, B1C) & 3.7814 & 0.0312 & \\
2 & (B1I, B2a) & 3.3086 & 0.0018 & \\
3 & (B1I, B2I/B2b) & 3.2045 & 0.0971 & \\
4 & (B1I, B3I) & 3.3127 & 0.0018 & \\
5 & (B1I, B1C, B2a) & 3.5048 & 0.0018 & \\
6 & (B1C, B2a, B3I) & 3.4186 & 0.0018 & \\
7 & (B1I, B1C, B2I/B2b) & 3.5285 & 0.0018 & \\
8 & (B1I, B1C, B3I, B2I/B2b) & 3.3628 & 0.0292 & \\
9 & (B1I, B1C, B2a, B3I, B2I/B2b) & 3.3670 & 0.0147 & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ranging performance under different frequency combination.  
Figure 12: Positioning accuracy analysis when ranging accuracy is within 30 cm.  
When the ranging precision is reduced to 15 cm, the location errors in three directions are obviously improved. In Figure 13, the maximum location error in x direction is 0.8 m, and the maximum location error in y direction is 1 m, the positioning error in z direction is 0.3 m. The BeiDou pseudo-satellite can achieve 0.63 m positioning accuracy in 1 confidence interval under the three-frequency combination mode.  
Further, it can be found that the positioning error is further significantly improved, and statistical analysis can be obtained. The position accuracy, which under 1 confidence interval, is 0.37 m. In this case, this method can well solve the problem of high-precision absolute positioning of BeiDou pseudo-satellite in indoor space (Figure 14).
## 3 Numerical Study
### Numerical Model  
A finite element (FE) model considering the soil, tunnel, and void on the soil-structure interface was built in Midas GTS, shown as Figure 13. The material parameters are shown in Table 2. The coordinate system based on the right-hand rule was shown, and the dimensions of the model were 84 \(\times\) 84 \(\times\) 40 m\({}^{3}\). The external diameter of the tunnel was 6 m, and the thickness of the tunnel lining was 0.3 m. In order to simulate the wave shape, the size of the mesh needs to be divided smaller than 1/8 of the smallest wavelength, which is expressed as:  
\[\Delta l\leq\frac{V_{\mathrm{s}}}{8f_{\mathrm{max}}}, \tag{10}\]  
where \(V_{\mathrm{s}}\) is the shear wave velocity and \(f_{\mathrm{max}}\) is the highest analysis frequency, which is 150 Hz in this model. According to the equation, the element of soil should be smaller than 0.25 m and the element of tunnel lining should be smaller than 1.88 m. In this model, the tunnel elements were set at 0.3 m. The soil elements near to the top of the tunnel were set as 0.2 m, and the size was getting bigger as the elements spread from the centre to the boundary of the model. The viscoelastic artificial boundaries were applied to the model to simulate the infinite soil. The viscoelastic artificial boundary is a continuous distributed parallel spring-damping system with calculated stiffness and damping factors. Once these coefficients are input into the Midas GTS software, the equivalent spring-damping elements will be applied on the boundary nodes.  
In the experimental study, the effect of void size on the response was not considered. In contrast, for the numerical study, four conditions with varying void sizes were designed as listed in Table 3. The cross-sections of the voids were fan-shaped with different radius, and their longitudinal length was kept the same as 4 m. An impact force signal was applied on the inner surface of the tunnel lining in the model. The duration of the force was 0.0032 s, and the peak value was 88.26 kN. The frequency range influencing the impact force was between 1 and 150 Hz (Figure 14). The vibration detecting point was 0.1 m close to the impact point. To analyse the effect of the impact-void position, the impact and response
## 4 Experiments and Discussion
### Experimental Results
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **15 min** & \multicolumn{5}{c}{**30 min**} & \multicolumn{5}{c}{**60 min**} \\  & **MAE** & **RMSE** & **MAPE** & **MAE** & **RMSE** & **MAPE** & **MAE** & **RMSE** & **MAPE** \\ \hline HA [19] & 2.88 & 5.59 & 6.80\% & 2.88 & 5.59 & 6.80\% & 2.88 & 5.59 & 6.80\% \\ VAR [20] & 1.74 & 3.16 & 3.60\% & 2.32 & 4.25 & 5.00\% & 2.93 & 5.44 & 6.50\% \\ SVR [21] & 1.85 & 3.59 & 3.80\% & 2.48 & 5.18 & 5.50\% & 3.28 & 7.08 & 8.00\% \\ DCRNN [8] & 1.38 & 2.95 & 2.90\% & 1.74 & 3.97 & 3.90\% & 2.07 & 4.74 & 4.90\% \\ STGCN [9] & 1.36 & 2.96 & 2.90\% & 1.81 & 4.27 & 4.17\% & 2.49 & 5.69 & 5.79\% \\ STSGCN [11] & 1.44 & 3.01 & 3.04\% & 1.83 & 4.18 & 4.17\% & 2.26 & 5.21 & 5.40\% \\ GWnet [10] & 1.30 & 2.74 & 2.73\% & 1.63 & 3.70 & 3.67\% & 1.95 & 4.52 & 4.63\% \\ GMAN [12] & 1.34 & 2.82 & 2.81\% & 1.62 & 3.72 & 3.63\% & 1.86 & 4.32 & 4.31\% \\ \hline MFDGCN & 1.30 & 2.75 & 2.75\% & 1.61 & 3.66 & 3.64\% & 1.88 & 4.33 & 4.45\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of 15, 30, and 60 min traffic prediction performance between MFDGCN and baseline models on the PEMS_BAY data set. compared with GMAN and the MAE of GMAN at 60 min for a long-term prediction is 2% higher. In general, the comprehensive performance of MFDGCN is better. The comparison of the prediction performance of each model at each time step on the PeMS_BAY data set is shown in Figure 8. Figure 9 shows the comparison of the truth and predicted values of MFDGCN for a certain day on the PeMS_BAY data set.
## 3 Results
### Caffe Acid Derivatives with Known Sensitizing Properties  
#### 3.1.1 First Group: Strong Sensitizing Agents  
A percentage of 0.01% of CAPE, a typical biomarker present in poplar propolis, produced strong reactions as well as 1% of propolis [11]. Indeed, CAPE (**s1**) is absent in both _Z. punctata_ and related propolis. Other isomers having the same [M-H]\({}^{-}\) at _m/z_ 283.0976 and with different sensitizing character, like benzyl isoferulate (**s5**), coniferyl benzoate (**m1**), and benzyl ferulate (**w1**) are absent as well. Another important allergenic component described in the literature is 3-methyl-2-butenyl (phenyl) caffeine (**s2**) ([M-H]\({}^{-}\) at _m/z_ 247.0976). This is one of the three isomeric forms of pentenyl caffe acid esters, being 3-methyl-2-butenyl the strongest sensitizer followed by 3-methyl-3-buthenylcaffeate and 2-methyl-2-buthenyl-caffeate. In the reported experimental conditions, a single chromatographic peak, probably containing the three isomeric forms, was found only in propolis samples, as shown in Figure 1. This precursor ion at _m/z_ 247.0976 exhibited the daughter ion at _m/z_ 134 as base peak and negligible signals at _m/z_ 161 and 178. According to bibliographic data [29], when a fragment ion at _m/z_ 134 is the base peak, as shown in Figure S2 in the supplementary material, the identification of 3-methyl-2-butenyl caffeine (**s2**) or 2-methyl-2-butenyl sulfate can be tentatively given. Another compound identified in propolis was the benzyl caffeine (**s4**) (already described in NAPs [20]). These two compounds accounted for a maximum of about 9% each one in AV, whilst they were not detected in ZP.  
Differently, a compound with _m/z_ 315.1602 (Figure S3), already assigned to geranyl caffeine (**s7**) [21], was detected in both _Z. punctata_ and propolis at various relative ratios. Geranyl caffeine was described as a strong sensitizer in experiments with guinea pigs [10] and, if confirmed, so far it is the unique relevant allergenic substance determined in _Z. punctata_ (9.4%).
###### Abstract  
A broadband receiver front-end with low noise figure and flat conversion gain response is presented in this paper. The receiver front-end is a part of the broadband spectrum sensing receiver and processes 30-40 GHz of broad input spectrum followed by down-conversion to DC-10 GHz of IF signal. The proposed work is comprised of a low noise amplifier (LNA), on-chip passive Balun, down conversion mixer, and output buffer. To achieve front-end target specification over 10 GHz input bandwidth, the stagger-tuned LNA is employed and the down conversion mixer is loaded with a 3 rd-order LC ladder low pass filter. The prototype chip was implemented in 45 nm CMOS technology. The chip achieves 10.3-16.5 dB conversion gain, 5.9 dB integrated NF, and \(-\)11 dBm IIP3 from 30 to 40 GHz. The chip is realized within 0.42 mm\({}^{2}\) and consumes 96 mW from a 1.2 V supply.  
Chebyshev; current-reuse; Ka-band; ladder filter; push-pull; receiver; spectrum-sensing; stagger-tuned +
Footnote †: journal: Electronics Letters
## 3 Results and Discussion
### The New LTE Network
Figure 7 shows the LTE radioelectric coverage with 39 sites, in which the dark zones have been reduced with respect to the case with 34 sites (see Figure 6b). We can also analyze other situations of interest for the PPDR agencies, such as the radioelectric coverage in natural parks (represented as polygons in Figure 7). Table 5 shows the percentage of radioelectric coverage for each natural park. The percentage of LTE radioelectric coverage is less than 85% for 6 of 20 natural parks (assuming either 46 or 39 sites), although the percentage of radioelectric  
Figure 7: LTE optimized radioelectric coverage (39 sites). coverage for municipal areas is equal or above 85% (see Table 4). Therefore, we need, in this case, to add several sites to improve the radioelectric coverage in these six natural parks.
## 3 Results
### Verification of Manufacturing Accuracy after Precision Bonding  
The primary evaluation parameter for manufacturing precision was the total runout of \(\mathcal{O}40\) relative to base B (Figure 1). Sample inspection was conducted using a Zeiss Contura G2 3D CMM and a Formline Roundscan 555 (Figure 4). The measured values are presented in the graph (Figure 5). Out of the 15 samples, seven (46.6%) achieved the required  
Table 2 illustrates that none of the adhesives resulted in a 100% success rate for the samples. Notably, adhesive DP 125 did not produce a single sample with the required precision, which could initially suggest unsatisfactory performance when compared to the other adhesive types. However, it is important to consider the small statistical sample size, which may introduce deviations due to factors such as incorrect assembly (particularly evident in the high deviation for DP 125 samples) or suboptimal adhesive applications resulting in the presence of bubbles/voids in the bonded joint. When examining the average deviations, DP 125 and DP 190 adhesives exhibit lower performance, while DP 110, Eprosin, and Spabond achieve comparable results.  
The technological suitability of the adhesives is an important factor as well, determined by parameters such as processing time, viscosity, mixability, and the tendency to leak out of the bonded joint. These parameters are detailed in Table 3.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Adhesive Type** & **Accuracy Rate** & **The Average Value of Total Runout [mm]** \\ \hline DP 110 & 2/3 & 0.041 \\ DP 125 & 0/3 & 0.159 \\ DP 190 & 1/3 & 0.074 \\ Spabond & 2/3 & 0.051 \\ Eprosin & 2/3 & 0.041 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Post-bonding accuracy rate.  
Figure 5: Values of total runout.  
Based on the conducted experiments and measurements, the following conclusions can be drawn:  
* A total of 46.6% of the samples exhibited a total runout deviation below 0.05 mm, meeting the specified requirements and thus were considered satisfactory.
* The noncompliant samples likely experienced stress generation during the curing process, leading to a radial shift after removal from the assembly fixture. Potential stress sources include excessive adhesive shrinkage and uneven filling of the adhesive joint, which may be further verified through a CT scan.
* Among the tested adhesives, DP 110, Eprosin, and Spabond yielded the most favorable results and appear to be suitable choices for these bonding applications.
* Enhancing the accuracy of the assembly fixture and the precision of metal counterparts could lead to increased sample accuracy, consequently improving the guaranteed geometric precision.
# Tree Based Approaches for Predicting Concrete Carbonation Coefficient
Therefore, new models to describe the complex behaviour of the concrete carbonation coefficient still have to be defined, aiming at improving the reliability concerning corrosion prevention. The recent DDTs of Model Tree (MT) and Random Forest (RF) can be suitable vehicles for this task owing to their learning ability from the available data. Carbonation depth has been predicted by adopting decision trees such as the regression tree, bagged ensemble and reduced bagged ensemble regression tree, with excellent performance and understanding of the influential variables [19]. The carbonation depth of concrete using Gene Expression Programming (GEP) was modelled and validated by [PERSON] et al. [20] using the six parameters that predominantly control the carbonation depth of concrete. Currently, there is a trend to carry out concrete mix design and concrete strength prediction through RF and Multi-Gene Genetic Programming (MGGP) [21,22]. In the studies presented so far, no agreement on the way that the carbonation coefficient should be determined has been found. As concrete carbonation depends on many factors and the interactions between them, the modelling of carbonation depth evolution is indeed a complex task. Multiple Linear Regression (MLR) models developed earlier (by some of the authors) displayed a satisfactory performance that can still be improved. Due to the linear nature of MLR, this technique was not capable of modelling the nonlinear relationship between \(RH\) and carbonation, and thus the sample analysed was then divided into two subsamples, and two different models were developed: one for an \(RH\) of 70% or less and one for an \(RH\) higher than 70%. This calls for the use of DDTs such as MT (with M5 Algorithm as splitting criterion), RF and MGGP. The major advantage of these techniques lies in their method of presenting the output and understanding the influential variables. Model Tree displays the output in the form of series of equations, RF in the form of trees and MGGP as equations. The respective outputs are the main innovation of the current work, along with the understanding of the influential parameters in all the techniques. This understanding of the influential parameters gives the researcher more insight into the problem of selecting the most suitable variables and subsequently the models. The current study is a horizontal extension of the studies performed using MLR [23], ANNs and GP [11]. Models are developed to predict the carbonation coefficient of concrete with relevant input parameters, and the performance of the models is compared with that of the existing models [11; 23]. The techniques used for this study are Model Tree with M5 Algorithm (MT), Random Forest (RF) and Multi-Gene Genetic Programming (MGGP). The next section presents a brief introduction of these DDTs, followed by details of the data used in the model development. The methodology adopted for creating the prediction models is discussed further. The results along with the discussion are presented in the subsequent section. This article ends with concluding remarks.