esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Attributes in Quality Monitoring of Manufacturing Processes: The Welding Case
## 3 Case Studies
### Case Study I
(4) Feature extraction:  
For every frame of each video, a single feature, the standard deviation, was calculated, converting practically every video into a time-series signal. Consequently, in a sense, a pooling operation was performed to summarize the information in each frame into one point. However, due to the high framerate of the camera, the number of points is still quite large for creating a model that would not most certainly overfit [46]. For that, the resulting signals were split into equal parts, and after first removing the background-only frames at the start and the end of these signals, the mean, maximum, and standard deviation were calculated for each part. Finally, the best class separation was achieved by constructing a 1D signal from the standard deviation of each frame, splitting it into three parts, and calculating their mean value. (5) Model training:  
Figure 2: Laser welding of batteries (configuration on the **left**) and quality monitoring (data processing on the **right**). To reduce the complexity of the subsequent data-investigation actions, increase the overall expandability of the decision-making mechanism, and harmonize with the distribution of the data into the feature space, a simple perceptron was selected for the classification. This level of simplicity is preferred in order to minimize the model capacity for overfitting and bias that are inevitable when a large number of parameters is tuned using a small dataset. In this case, the number of tunable parameters is defined by the number of inputs. Additionally, keeping the overall model complexity low as well as its training process enables reproducibility among the different cases of varying the Vs, thus making it easier to track the cause of corresponding performance variation. The perceptron was equipped with a sigmoid activation in order to be differentiable during training by utilizing gradient-based algorithms. Weights were initialized to zero, and training took place in batches using 2/3 of the available 270 data entries. An adaptive moment estimation (ADAM) algorithm [47] was used as an optimizer along with a cross-entropy loss function, as typically performed in classification problems. As mentioned, the number of inputs was defined by the number of features, which, along with the bias element, formed the available tunable parameters. Finally, the following table (Table 3) summarizes the potential effect of the various stages on the veracity of the dataset. In the context of this study, the major goal is to investigate and confirm otherwise theoretical statements about the link between selected Vs and the performance of data-driven applications. All the training algorithms were implemented using Python 3.7 (NumPy 1.21 and TensorFlow 2.2/Keras 2.3.1).
## 2 The Design of the Control System
### Fuzzy Logic
#### 2.4.2 Fuzzification  
Fuzzification is the process of converting explicit inputs into fuzzy variables or fuzzy sets. In fuzzy control, the aim of fuzzification is to map explicit inputs into linguistic variables that can be used for fuzzy rule bases and inference.  
Combined with the input fuzzy control in this paper, \(DS\) represents driving style whose discrete domain is \([-1,\,0,\,1]\), \(SL\) represents slope whose continuous domain is \([-8,\,8]\), \(Jerk\) represents rate of change in acceleration whose continuous domain is \([0,\,10]\), \(Vel\) represents vehicle speed whose continuous domain is \([0,\,100]\) and the continuous domain of the output variable is \([10,\,20]\).  
Based on the changes in the input and output variables, the corresponding fuzzy linguistic variables are defined so that the corresponding knowledge base can be constructed, as in Equation (8).  
\[DS=\{NB,Z,PB\}\]  
\[SL=\{NB,NS,Z,PS,PB\}\]  
\[Jerk=\{NB,Z,PB\}\]  
\[Vel=\{NB,Z,PB\}\]  
\[Q=\{NB,NS,Z,PS,PB\} \tag{8}\]  
where, \(NB\) is negative big, \(NS\) is negative small, \(Z\) is positive medium, \(PS\) is positive small, and \(PB\) is positive big.  
The affiliation function selected for driving style is a triangular distribution, while the remaining input and output variables use a combination of triangular and trapezoidal distributions. The affiliation function for each input and the affiliation function for the output are shown in Figure 6.
# Development and Validation of Universal 3D Blast Fragmentation Model
## 3 Three-Dimensional Fragmentation Model
### Blasting Patterns and Charge Initiation Sequence  
Mining production in hard rock, where blasting is the primary excavation technique, assumes the application of different blasting patterns suitable for given conditions. Open-pit and most underground mining methods rely on blasting patterns where boreholes are parallel, which is simple and efficient in design and execution. However, there are methods where ring blasting with non-parallel boreholes is applied in underground mining conditions.  
The problem with non-parallel boreholes is that the spacing of explosive charges is not even along the borehole's axis, so rock fragmentation is influenced. This is especially emphasized in underground mining conditions since areas where explosive charges are close to each other may experience excessive generation of fines or overbreak of the rock mass. Fines usually do not provide additional operational problems except for larger explosive consumption or mineral losses, while overbreak may influence difficulties in subsequent blasts. In cases where ring blasts are not adequately designed, oversized rock blocks are encountered, raising the need for downsizing and increasing time consumption. Non-uniform spatial size distribution of blasted fragments has the most influence in sublevel saving operations, directly impacting gravity flow and economic outcome. In summary, proper estimation of fragment size distribution and even their spatial positions has a crucial impact on mining and economic efficiency.  
The model presented herein can handle parallel, non-parallel, or even arbitrarily placed boreholes. Each explosive charge is represented by a line or polyline using Rhino3D built-in capabilities. Therefore, every charge has its properties, such as spatial position, length, and direction, that determine its interaction with primary rock blocks and other charges. The initiation sequence is a critical parameter for successful blasting, and each charge in the model is assigned a unique number representing its position in the firing sequence (Figure 10). This is carried out during the model preparation phase and is a mandatory parameter.  
Besides mentioned, the influence of previously fired explosive charges is also considered. Previous charges induce additional fractures in the rock mass, changing initial conditions for fragmentation analysis. Only radial fractures are considered within previous explosive charges since pressure wave propagation will not reach the free surface, and its energy will be spent without forming other fracture sets.  
Figure 9: Initial model geometry.
# Monitoring of Industrial Machine Using a Novel Blind Feature Extraction Approach
## 3 Proposed Signal Processing Methodology
### Pre-Processing Algorithms  
Spikes from data that may have been caused due to random events and fluctuations do not represent any defects; this event may occur due to electrical noise, it is recommended to remove the noise components that mask the defect patterns in the signal. Two pre-processing functions are developed to ensure the data is clean before features extraction. The description of these operations is presented below.
## 2 Statement of the Problem  
Assume there are two alternatives, \(A_{i}\) and \(A_{j}\), and their means and spreads are \(\mu_{i}\), \(\mu_{j}\), and \(\sigma_{i}\), \(\sigma_{j}\), respectively. According to [10], the rules for ranking fuzzy numbers is \(A_{i}\succ A_{j}\) if \(\mu_{i}>\mu_{j}\) or \(A_{i}\succ A_{j}\) if \(\mu_{i}=\mu_{j}\) and \(\sigma_{i}<\sigma_{j}\). However, if we assume there are two imprecise numbers with uniform distributions, then the rules seem incorrect when there is a smaller mean and a smaller spread, as in Figure 1.  
It is intuitive for a decision-maker to prefer \(A_{1}\) over \(A_{2}\), even though \(A_{2}\) has a higher mean than \(A_{1}\). That being said, the higher mean does not necessarily have the higher ranking order if the spread is too large, i.e., the mean and the spread comprise the trade-off relation. This situation is also called risk premium (RP), which represents the additional compensation required for assuming a higher risk, and is usually discussed in economic and financial management.  
In addition, [12] proposed another criterion, called the \(CV\) index, to improve [PERSON] and [PERSON]'s method [11] according to the following equation:  
\[CV=\frac{\sigma}{|\mu|} \tag{1}\]  
Figure 1: The conflicting situation in precise data.  
where \(\sigma\) denotes the spread and \(\mu\) denotes the mean.  
However, it is clear that the \(CV\) index fails when there are crisp values or the mean is zero. In addition, another shortcoming is that the result is difficult to interpret because the unit has changed. Hence, many researchers provided different methods to efficiently rank fuzzy numbers, e.g., [13; 14; 15; 16; 17], or interval numbers, e.g., [16; 17].  
Another practical issue is that ranking imprecise numbers is not enough for further applications. A decision-maker might like to derive the crisp values of alternatives [18]. Surely, plenty of defuzzification methods have been proposed to efficiently derive the crisp values from imprecise numbers, e.g., the center of sums method, the CoA method, the BoA method, the weighted average method, and different kinds of maxima methods. However, none of them can handle all the considered situations here, and we will compare our method with CoA and BoA to justify the proposed method. Here, we incorporate the concept of risk premium into our method and derive the trade-off coefficient between the mean and spread to rank and defuzzify imprecise numbers.
# Research on Vehicle Re-Identification Algorithm Based on Fusion Attention Method
## 3 Experiments and Discussion
### Experimental Results and Analysis
#### 3.4.2 Experimental Results and Analysis of VehicleID Dataset
The comparison results with the current Re-ID methods on the VehicleID dataset are shown in the table. Table 4 shows the performance comparison between ResNet-SDLAU and the most current methods on the vehicle re-identification dataset. The comparative analysis of the experimental results shows that the model proposed in this paper consistently achieves very high recognition rates of Rank-1 and Rank-5 on the three test sets. The results show that ResNet-SDLAU outperforms most methods in recent years on the VehicleID dataset, demonstrating the model's effectiveness. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Small (800)**} & \multicolumn{2}{c}{**Medium (1600)**} & \multicolumn{2}{c}{**Large (2400)**} \\ \cline{2-7}  & **Rank-1** & **Rank-5** & **Rank-1** & **Rank-5** & **Rank-1** & **Rank-5** \\ \hline GooLeNet [24] & 47.9\% & 67.4\% & 43.4\% & 63.5\% & 38.2\% & 59.5\% \\ FACT [1] & 49.5\% & 67.9\% & 44.6\% & 64.1\% & 39.9\% & 60.4\% \\ NuFACT [27] & 48.9\% & 69.5\% & 43.6\% & 65.3\% & 38.6\% & 60.7\% \\ SAN [30] & 79.7\% & 94.3\% & 78.4\% & 91.3\% & 75.6\% & 88.3\% \\ VAMI [9] & 63.1\% & 83.2\% & 52.8\% & 75.1\% & 47.3\% & 70.2\% \\ RAM [20] & 75.2\% & 91.5\% & 72.3\% & 87.0\% & 67.7\% & 84.5\% \\ AAVER [9] & 74.7\% & 93.8\% & 68.6\% & 90.0\% & 63.5\% & 85.6\% \\ PRN [24] & 78.4\% & 92.3\% & 75.0\% & 88.3\% & 74.2\% & 86.4\% \\ VANet [23] & 83.2\% & 95.9\% & 81.1\% & 94.7\% & 77.2\% & 92.9\% \\ VehicleNet [31] & 83.6\% & 96.8\% & 81.3\% & 93.6\% & 79.1\% & 91.8\% \\ MTC [32] & 88.1\% & 97.1\% & 82.1\% & 93.7\% & 81.7\% & 91.7\% \\ \hline ResNet-Baseline & 88.5\% & 93.3\% & 87.5\% & 91.6\% & 84.6\% & 90.6\% \\ Our(SDLAU) & **95.8\%** & **97.8\%** & **95.5\%** & **96.9\%** & **94.4\%** & **96.6\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison results with the most advanced methods on the VehicleID dataset. The vehicle data matching analysis is shown in Figure 11, which is an example of ten vehicle re-identification results (Rank-1 to Rank10) on the VehicleID dataset. For each probe, green boxes indicate correct matches, and red boxes indicate false matches. The matching results on VehicleID dataset can be observed from the figure, where the first image represents the vehicle image to be queried, and the rest of the images are retrieved from the gallery and sorted by similarity score. The correct matches are marked with green borders. The first image in each original image is the query result, and the remaining 10 images are the first 10 retrieved results. It can be seen that the model proposed in this chapter has a higher accuracy on the VehicleID dataset, it can identify vehicle nuances and correctly match vehicle images with high inter-class similarity, and the correctly matched rankings are relatively high.
## 6 Conclusions  
In this paper, taking the Baochang section of the Jinan Metro Line R2 as the engineering background, in the process of shield construction crossing the abrupt geological interface, three factors, i.e., the dip angle of the geological interface, the distance between the excavated surface, and the geological interface and the excavation footage, were considered, respectively, which affected the surrounding rock deformation, the ground settlement, and the deformation of the buildings. The deformation laws of the surrounding rock and surface buildings during shield construction are summarized. According to the actual engineering situation, the original model is improved, and then the actual engineering situation is predicted and analyzed. The following conclusions are summarized in detail in the process of the numerical simulation of shield construction:  
1. When excavating from hard rock to soft rock and from soft rock to hard rock, surface settlement increases with the increase of excavation footage. The maximum settlement occurs at the longitudinal center of the tunnel, which is 1.66 mm and 4.82 mm. It can be seen that the direction of tunnel excavation has a significant impact on surface deformation. To reduce the impact of tunnel excavation on surface buildings, the excavation direction should prioritize excavation from hard rock to soft rock.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Number of**} & \multicolumn{4}{c}{**Deformation Value of Building Column/mm**} \\ \cline{2-5}  & **D-01** & **D-02** & **D-03** & **D-04** \\ \hline
11 & 0.08 & 0.15 & 0.22 & 0.29 \\
20 & 0.15 & 0.28 & 0.41 & 0.54 \\
28 & 0.21 & 0.39 & 0.57 & 0.75 \\
37 & 0.26 & 0.48 & 0.70 & 0.84 \\
46 & 0.30 & 0.55 & 0.80 & 0.96 \\
54 & 0.34 & 0.58 & 0.82 & 1.02 \\
63 & 0.37 & 0.60 & 0.83 & 1.03 \\
77 & 0.39 & 0.62 & 0.85 & 1.07 \\
91 & 0.41 & 0.64 & 0.87 & 1.09 \\
110 & 0.43 & 0.66 & 0.89 & 1.11 \\
120 & 0.45 & 0.67 & 0.89 & 1.11 \\
130 & 0.47 & 0.68 & 0.89 & 1.12 \\
139 & 0.49 & 0.70 & 0.90 & 1.12 \\
180 & 0.53 & 0.72 & 0.92 & 1.14 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Monitoring table for deformation of building columns.  
Figure 13: Layout of measuring points of building columns.  
2. When there are buildings above the tunnel, shield tunneling will have an impact on surface buildings. Taking the excavation of the tunnel from hard rock to soft rock as an example, the maximum deformation values in the vertical and horizontal directions of the buildings are 2.04 mm and 1.92 mm, respectively.
3. In response to the impact of shield tunneling on building deformation, two control measures, synchronous grouting and correction of shield tunneling posture, were adopted. The comparison between the monitoring results and numerical simulation results shows that the deformation of the surface and buildings is reduced.
# Automatic Brix Measurement for Watermelon Breeding
## 4 Discussion  
The development of breeding technology plays a vital role in improving agricultural crops. Currently, research on breeding improvement techniques is a hot topic, and has been widely applied in the breeding of various grain crops and fruits. However, whether it is haplotype-based speed breeding, transgenic breeding, or gene editing breeding, they all face the same problem, namely high cost, due to factors such as the high equipment and labor costs required when conducting large-scale cross-processing experiments [29]. The technologies for phenotyping crops have significantly improved the efficiency of information acquisition of crop traits during the breeding process.  
In this study, we proposed a solution for automatic large-scale Brix measurement of watermelon for breeding purposes. We also conducted a set of experiments to obtain the important parameters of the manipulation, providing certain technical support for the watermelon breeding research field. However, some deficiencies that appeared in this study need to be further investigated by researchers. First, when squeezing the pulp of a watermelon with a pen-type sensor, which, in this study, was PEN-PRO, there may be juice overflow or tipping moment, possibly causing errors in the measurement. The robustness and efficiency of this kind of method can be improved by optimizing the structure of the sensor's probe, reducing the squeezing force, and avoiding the outflow of juice. Moreover, the method of cutting the watermelons in half in this study was achieved by the squeezing force of the blade on the watermelon. This means when the rind is tough, the cutting resistance will change abruptly, which is not conducive to forming a complete cutting surface. The cutting device can be enhanced by changing the angle of the blade appropriately, so that there is a shear force between it and the watermelon during the cutting process. Third, the sampling speed of this machine is around 1 sample per minute, which is close to the speed of manual sampling. The sampling speed can be increased by applying high-speed step motors, decreasing the inertia of movable parts, and using sharper blades. Since the process of this machine is automated, the labor cost of using it would simply include placing the sample onto it and recording the results, but this process will be replaced by an additional automatic feeder and recorder.
## 4 Discussion and Conclusions
### Arrhenius Analysis of CSP for KDP  
As summarized above, CSP is clearly a multi-component process, likely to involve various competing phenomena, e.g., densification versus grain growth. In such situations, caution must be used in applying an over-simplified Arrhenius analysis to make predictions of the kinetics [14; 27; 28]. Nevertheless, Figure 4 does suggest that a linear densification regime may exist over part of the CSP time regime. This prompts us to attempt an Arrhenius analysis of the densification process over the relevant time regimes, using data at the 3 temperatures obtained for both p = 100 MPa and p = 125 MPa. Figure 13 presents linear fits to the loss of porosity in the linear densification regimes, and then Arrhenius plots and fits based on absolute temperature, T, densification rate, and normalization with respect to moles of KDP that filling the pore volume as a result of densification. In accordance with accepted sintering theory [28; 29], note that the Arrhenius plots fitted are for In(T x densification rate) versus 1/T. The activation energies obtained for the two compressive stresses, p = 100 MPa and 125 MPa, are: (88.3 \(\pm\) 8.1) kJ/mol and (91.9 \(\pm\) 7.2) kJ/mol, respectively. These values are acceptably close to each other, are significantly less than quoted in the literature for ceramic sintering processes at more conventional temperatures [27; 28; 29] and are within the range of activation energies found for CSP, elsewhere [6].
# Precoded Generalized Spatial Modulation for Downlink MIMO Transmissions in Beyond 5G Networks
## 4 Numerical Results  
In this section, we present numerical simulations, both link level and system level. Link performance results, namely, block error rate (BLER), are used as input by the system level simulator. The system is illustrated in Figure 2, where the C-RAN is comprised of 19 radio remote units (RRUs) connected through fiber to a central unit (CU), each RRU with \(N=60\) active pedestrian users. Each RRU consists of three transmission and reception points (TRP), each one equipped with \(N_{tx_{total}}=256\) antennas while users have \(N_{rx}\) antennas (i.e., each RRU corresponds to a BS according to the system model presented in Section 2). The RRUs array configuration corresponds to cylindrical arrays: \(16\times 16\times 3\), where the separation between antennas of the array is half wavelength [35].  
The system level block diagram can be found in references [36; 37]. This simulator is based on the one described in [37]. In the system level simulator, there are general parameters that must be defined, such as network layout and antenna parameters. The setup used considers several of the same parameters adopted in the case study presented in Section 7.7 of [38] for the deployment of a Massive MIMO based outdoor network. Our system level simulator considers the 3D urban macro 3D-Uma scenario [36], where the BSs are mounted above rooftop levels of surrounding buildings with antenna height: 25 m and pedestrians height: 1.5-2.5 m. To each pedestrian is assigned line-of-sight (LOS) or non-line-of-sight (NLOS) propagation conditions, depending on the distance to RRU. It is generated correlated large-scale and small-scale parameters to create channel coefficients and pathloss and shadowing are applied with \(\sigma_{SF}=7.8\) dB. For the NLOS pathloss distance, we have \(PL=32.4+20\log(f_{c})+30\log(d_{3\text{D}})\) dB, where \(d_{3\text{D}}\) is the distance in meters [36]. Other simulator parameters are: carrier frequency \(f_{c}=3.5\) GHz, maximum TRP transmit power 46 dBm, receiver spectral noise power density -174 dBm/Hz, cyclic prefix overhead 5%, pilots/TRP = 15 and arrays with uni-polarized antennas. We choose the 5G NR numerology 1 and slot configuration parameters taken from [39]: the bandwidth is \(B_{t}=20\) MHz with normal CP where the subcarrier spacing is 30 kHz and 28 OFDM symbols are transmitted in every subframe of 1 ms. Each user feedbacks all CSI and signal-to-interference-plus-noise ratio (SINR) to TRPs. The static clustering technique partitions the network into three adjacent RRUs sets where each user is served by at least one RRU, while the  
Figure 2: Cloud radio access network (C-RAN) considered for the system level simulations. RRU = radio remote unit.  
others perform inter-user interference. The RRU inter-site distance is 433 m corresponding to a radius of 250 m.