esa-sceva/satcom-chunk-collection huggingface dataset:
# Text Line Extraction in Historical Documents Using Mask R-CNN
## 7 Results and Discussion
### Experimental Results
#### 7.2.1 VML-AHTE and DIVA Datasets
The results of the MRCNN does contain such cases, where the MRCNN polygons of the text line lead into multiple disconnected polygons with the same label. Therefore, evaluating the MRCNN results in their raw form yields low results unfairly. This fact can be seen in the results shown in Table 4, since DIVA evaluator calculates an IU score for every possible pair of ground truth and prediction polygons and considers the pairs with the maximum IU score as matching pairs. Therefore, text lines represented by multiple polygons are considered only by the largest polygon. To solve this problem we combined the blobs of a single text line in the MRCNN results (see Figure 11). Two end points from two different blobs are combined if the distance between them is less than the average stroke width of blobs in that text line. Combination is performed by dilating the skeleton of two blobs. After combining the blobs, DIVA evaluator can measure the performance reasonably. Note that Table 1 shows the evaluation results of the MRCNN without combining the blobs. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Metric** & **CB55** & **CSG18** & **CSG863** & **AHTE** \\ \hline Line IU & 67.98 & 95.99 & 63.24 & 90.45 \\ Pixel IU & 38.26 & 92.80 & 31.85 & 86.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Diva evaluator results of MRCNN extraction with disconnected bounding polygons on DIVA and VML-AHTE datasets. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**CB55**} & \multicolumn{4}{c}{**CSG18**} & \multicolumn{4}{c}{**CSG863**} \\ \hline  & **DR** & **RA** & **FM** & **DR** & **RA** & **FM** & **DR** & **RA** & **FM** \\ \hline MRCNN & 96.4 & 57.8 & 72.2 & 90.76 & 64.53 & 75.26 & 98.03 & 67.58 & 79.77 \\ MRCNN+ & 97.70 & 98 & 97.85 & 95.11 & 97 & 96 & 98.63 & 98.26 & 98.44 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of the proposed method on the Diva dataset. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **Line IU** & **Pixel IU** & **DR** & **RA** & **FM** \\ \hline MRCNN & 93.08 & 86.97 & 84.43 & 58.89 & 68.77 \\ MRCNN+ & 97.83 & 89.61 & 88.14 & 87.78 & 87.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the proposed method on the VML-AHTE dataset. DR denotes detection rate, RA denotes recognition accuracy, and FM denotes F-measure. Figure 12 shows example prediction from both AHTE and DIVA datasets. We can see that in both cases the main body of the text lines are segmented correctly. However, in the AHTE dataset, the Mask R-CNN misses few punctuations and in some cases, part of the descender characters. The difference can be attributed to the more complex layout of the Arabic writing, compared to Latin writing in the DIVA dataset.
## 3 Results and Discussion
### PLSR Analysis  
The PLSR analysis was used in this study to build a quantitative prediction model to predict the biochemical properties of VBA, and its performance evaluation indexes included calibration correlation coefficient RC2, prediction correlation coefficient RP2, root mean square error of calibration (RMSEC), and root mean square error of prediction (RMSEP). RMSEC is used to measure the deviation between the predicted value and the measured value, and RMSEP can be used to evaluate the predictive ability of the model for unknown samples. Generally, the smaller the RMSEC and RMSEP are, the better the prediction ability of the model [38]. PLSR analysis of CSA against pH, TTA, and nitrite (Figure 7D-F) was performed. The correlation coefficients of Rc2 were 0.836, 0.837, and 0.873, and RMSEC were 0.247, 0.0897, and 0.273. Rp2 were 0.803, 0.802, and 0.823, and RMSEP were 0.276, 0.0994, and 0.327, indicating that the data acquired by the olfactory visual sensor had a certain correlation with the changes of VBA chemical properties and CSA results had good prediction ability for the chemical properties of VBA.
## Appendix A Linear Group of Bivectorial Components
#### 4.2.5 Outer product  
\[\widetilde{\Delta}^{N,N}{}_{Re\text{ act}}=\sum_{p\in N}\widetilde{U}_{p}\wedge\sum_{q\in N} \widetilde{I}_{q\perp}^{*}=\sum_{p<q}e^{j(\alpha_{p}-\alpha_{q})}\underbrace {(U_{p}I_{q}sin\phi_{q}-U_{q}I_{p}sin\phi_{p})\sigma_{pq}}_{\widetilde{D}_{pq, Re\text{ act}}} \tag{14}\]  
The total linear group of bivectorial components is found to be:  
\[\widetilde{\Gamma}_{Lin}=\widetilde{\Gamma}_{\parallel}+\widetilde{\Gamma}_{ \perp}=\sum_{p}U_{p}I_{p\parallel}\sigma_{0}+j\sum_{p}U_{p}I_{p\perp}\sigma_{ 0}+\widetilde{\Delta}^{N,N}=\sum_{p}U_{p}I_{p}\cos\phi_{p}\sigma_{0}+j\sum_{p} ^{\widetilde{\Gamma}_{p}^{-N,N}}U_{p}I_{p}sin \tag{15}\]  
where from Equations (11) and (14):  
\[\widetilde{\Delta}^{N,N}=\widetilde{\Delta}_{Act}^{N,N}+j\widetilde{\Delta}_{ Re\text{ act}}^{N,N} \tag{16}\]  
Note that \(\widetilde{\Delta}_{pq,Act}^{N,N}\) and \(\widetilde{\Delta}_{pq,Re\text{ act}}^{N,N}\) represent the pq-active and reactive, \(\widetilde{D}_{pq,Act},\widetilde{D}_{pq,Re\text{ act}}\) terms rotated by \(e^{j(\alpha_{p}-\alpha_{q})}\) respectively.
# Analyzing TCP Performance in High Bit Error Rate Using Simulation and Modeling
## 3 Results
### FTP Client: Average Throughput  
Figure 6 shows the average throughput for the five selected packet drop probabilities (\(p_{drop}=0\%\), \(p_{drop}=0.01\%\), \(p_{drop}=0.1\%\), and \(p_{drop}=1\%\), and \(p_{drop}=10\%\)) that we considered in this study. The transferred file (10 MB) is divided by _FTP_ download response time to get the average throughput. We observe the effect of packet drop probability on average throughputs. For instance, the lowest throughput is achieved at a packet drop probability of 10%. The main conclusion is that TCP does not perform well (in terms of throughputs) at high _BER_s.  
Figure 3: Effect of TCP Congestion Window Size on Packet Dropping (first 80 s).  
Figure 4: _TCP_ Sent Segment ACK Number (_FTP_ Client) over 2700 s of simulation time.  
Figure 5: Figure 4 is redrawn for the first 80 s of simulation time.
# Overview of Predictive Control Technology for Permanent Magnet Synchronous Motor Systems
## 3 An Overview of the Recent Development for Predictive Control Methods of PMSM
### Deadbeat Predictive Control of PMSM  
Based on the PMSM discretized drive system, sampling delay, inverter delay, and control system delay will all lead to a decrease in the prediction accuracy of the DPC strategy. In addition, the robustness of algorithms has been extensively studied due to over-reliance on mathematical models.
# Image Segmentation Approaches for Weld Pool Monitoring during Robotic Arc Welding
## 1 Introduction
It comprises several novel image processing algorithms: A difference operation, a two-dimensional spline fitting enhancement operation, a gradient feature detection filter and the slope difference distribution-based threshold selection. The major goal of [23] is to cluster and characterize the laser lines under extremely harsh welding conditions. As a result, it omits the image processing methods for the images captured under less harsh welding conditions with mild welding parameters. In addition, quantitative results and comparisons to validate the effectiveness of the proposed segmentation approach were also not given. Due to the page limit, the reasons why the segmentation approach should contain these image processing algorithms were not explained adequately. One goal of this paper is to complement the research work conducted in Reference [23] and conduct a more thorough experiment to find the most effective solution both qualitatively and quantitatively. Another goal is to come up with more efficient segmentation approaches for images with relatively high quality that are captured under mild welding parameters. In the past research, the visual inspection of the weld pool surface was mainly used to understand the complex arc welding processes. The obtained data were used to validate and improve the accuracy of the numerical models and to gain insight into the complex arc welding processes. Few are used for in-process welding parameter adjustment and on-line feedback control, due to the lack of a robust and efficient approach that is capable of extracting meaningful feedback information on-line from most of the captured images. The proposed approaches in this paper are promising to accomplish this challenging task in the future. This paper is organized as follows. Section 2 describes the established monitoring system. In Section 3, state of the art methods for laser line segmentation are evaluated and compared. In Section 4, the combination approach proposed previously is explained theoretically. In Section 5, we propose different segmentation approaches by combining different image processing algorithms. In Section 6, the experimental results and discussions are given. Section 7 concludes the paper.
# Unbiased 3D Semantic Scene Graph Prediction in Point Cloud Using Deep Learning
## 4 Experimental Section
### Experimental Results and Analysis
The effect of the feature extraction by MP-DGCNN is weakened in this task. However, the relational inference network, ENA-GNN, used in our method, enhances the adaptive capability of message-passing in graph neural network inference by taking into account the cross-attention of the nodes and edges; at the same time, the loss function is weighted by group re-weighting (GW) and incorporates the UME a priori category knowledge coding. The experimental results show that the method proposed in this paper still significantly improved when compared with other baseline models, including the latest methods. The prediction results improved by about 3.4% and 5.9% under the R@50 and ngR@50 metrics, respectively, and about 1.5% under the mR@50 metric by about 1.9%. The results improved by about 7.6% and 10.3% under the R@20 and ngR@20 metrics, respectively, and by about 7.6% under mR@20. In addition, our proposed model takes more time in each epoch of training time; however, the convergence speed is improved, taking about 16-18 h to train and about 13-15 h for the baseline model, KSGPN [14]. Because of the use of a more robust feature extraction network, there is an increase in network complexity. To summarize the experimental process and results, our approach starts with point cloud feature extraction and initial coding using MP-DGCNN. During the training process, different categories in the loss function are re-weighted, and the meta-embedding vector is similarly grouped and weighted to obtain unbiased meta-embedding during the knowledge fusion process. Our model is compared with the baseline model for experiments, and various quantitative metrics showed significant performance improvements in the scene graph tasks. The experiments demonstrate that our model can better extract point cloud features. For the relational inference process, a cross-attention mechanism of nodes and edges is added to improve the robustness and inference capabilities of the network. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Classification Tasks** & **Model** & **R@1** & **R@5** & **R@10** & **mAcc** \\ \hline \multirow{2}{*}{Node/obj} & Obj-PointNet [14] & 51.7 & 78.4 & 86.4 & 17.2 \\  & MP-DGCNN & **60.1** & **83.6** & **90.2** & **20.1** \\ \hline
**Classification Tasks** & **Model** & **R@1** & **R@3** & **R@5** & **mAcc** \\ \hline \multirow{2}{*}{Edge/pred} & Pred-PointNet [14] & 38.9 & 68.3 & 85.6 & 23.9 \\  & MP-DGCNN & **42.1** & **69.5** & **86.2** & **29.9** \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison results of object coding modules (in % and bolded for best performance). Training the point cloud feature extraction and encoding network alone and using MLP directly as a classifier with other training parameters unchanged, such as the MP-DGCNN. Our point feature encoding network for scene graphs is compared with the KSGPN [14].
# Framework for Behavioral Analysis of Mobile Networks
## 4 Experiments and Results
### Cell Patterns Classification  
The patterns described in the previous section are used to train the Random Forest algorithm and build a pattern classifier. The results of accuracy for different values of Cross-Validation are shown in Table 7. It can be seen how the accuracy achieved by the classifier is very high with five splits. Despite the number of divisions being increased, the accuracy is similar given that it is already high enough.  
This information is shown in more detail in Table 8, where results per each pattern are shown. In addition to the _accuracy_ of each group, a couple of other statistics are calculated. _Recall_ refers to the rate of positives with respect to the total, while _F1-Score_ refers to the weighted average of _accuracy_ and _Recall_. In most cases, the obtained results are quite high, since values are close to 1, except for Pattern 3, which has a _Recall_ value below the average. The reason may be the similarity of this pattern with Pattern 5, which could lead to more misclassifications than expected.  
In Table 9, the importance of each KPI in the classification of the cells is shown. For each KPI, the aggregate importance of the four statistics (Mean, standard deviation and 5 th and 95 th percentiles) used for the classification is calculated. Although there is a certain difference, it cannot be concluded that a certain KPI is greatly influencing the final classification given by the algorithm. Regarding the importance in the classification, some KPIs stand out from the rest; for instance, KPIs related to throughput both in uplink and downlink directions. However, there are others whose influence is very small due to the similarity of these in most groups. _Restablishments per connected user_ is a good example because it is a KPI that is always 0, except in peaks, where the values reached are much higher. Another example is _Restablishment success rate_, since most of the time it is kept at values close to 100%, and it only suffers changes in certain moments.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Cross-Validation** & **Accuracy** \\ \hline
5 & 97\% (\(\pm\)2\%) \\
10 & 97\% (\(\pm\)2\%) \\
15 & 97\% (\(\pm\)3\%) \\
20 & 97\% (\(\pm\)3\%) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Accuracy in the classification phase.  
Finally, the accumulative and average importances of the general categories are shown in Table 10. Some categories have a greater accumulative incidence (Traffic and Throughput, 43.79%) than others (Quality, 10.11% or Users, 11.55%). However, these categories include more KPIs than others, achieving higher values of percentage of importance. On the other hand, the average importance shows that no single KPI category is too influential in the classification of the behavior pattern. Thus, the highest average incidence is 7.29% (Traffic and Throughput), while the lowest is 2.37% (Other) with just 5% difference between them.
## 4 Application Analysis and Results
### Application Results  
As the input for the SAA approach, the solving time limit was set to 3600 s. The number of scenarios was set to 100. The SAA approach was coded in Python 3.9.13 and the deterministic model was solved by Gurobi 11.1.2, with the optimal gap for the mixed integer programming problem set to 0.01. The experiments were performed on a computer with an Intel(R) Xeon(R) Gold 6254 CPU 3.10 Ghz RAM 128 GB.
# A Next-Generation Core Network Architecture for Mobile Networks
## 5 Basic Operations
### Internet Access  
As mentioned earlier, an SN handles basic tasks for multiple UEs. However, when one of these UEs starts some data intensive activities (e.g., photo uploads, audio and video streaming), its SN deploys a VM running a dedicated VCN for that UE and redirects the UE SIP signaling and routing (see Section 6) to such VCN. From that moment on, the UE communicates with the outside world through its VCN. In particular, if a legacy eNodeB is used, the VCN behaves as a dedicated EPC; if, on the other hand, an eeNodeB is used, the VCN behaves as a dedicated Internet gateway. The respective standard procedures apply to both scenarios.  
Once these data intensive tasks end, the VCN notifies the SN, which performs three main actions: it triggers a routing redirect in order to redirect all UE traffic to itself; it points the UE's AoR back to itself by updating the P2 PSIP DHT; it destroys the VM running the UE's VCN as it is no longer needed.
## 4 Simulation Experiments and Results
### Effect of Back-Rake Angle
Within the 15\({}^{\circ}\) to 25\({}^{\circ}\) range, the average cutting force increases slower, even though the axial and frictional forces continue to grow with the angle. However, due to the elastic nature of the rock and the mechanical properties of rock damage, the standard deviation of the cutting force exhibits a fluctuating pattern. This indicates that using a back-rake angle within this range under the same rock-breaking conditions will lower output torque and reduce disturbances caused by drilling cuts, thereby improving core stability [31]. After the back-rake angle exceeds 25\({}^{\circ}\), both the standard deviation of cutting forces and the cutting forces increase rapidly. This is likely due to the increased contact area and a shift in the rock-crushing mode from shear crushing to impact crushing. It should be noted that the shear strength of the rock is significantly lower than its compressive strength. As a result, the cutting force required to crush the rock gradually increases. Therefore, choosing the optimal back tilt angle within the 15\({}^{\circ}\) to 25\({}^{\circ}\) is recommended to achieve better core fidelity, as it balances cutting force, output torque, and drilling stability.
# Design of Micro-Phasor Measurement System to Identify Phasor of Distribution Transformers
## 1 Introduction  
Taiwan Power Company (Taipower) has installed more than 1.4 M distribution transformers to serve 12 M residential and commercial customers in Taiwan. Those single-phase transformers cause to unbalance and increase line loss of distribution systems. To improve system unbalancing, Taipower has developed a standard procedure to rephase the transformer before measuring feeder and lateral current from the feeder terminal unit (FTU) of distribution automation system (DAS). To improve the accuracy of the DMMS database, this paper developed a micro-phasor measurement system to measure the phase of a distribution transformer and update the attributes of distribution transformers. This system consists of a master station and portable devices. The master station is installed in the substation to measure voltage angle of each phase based on the 1 PPS. After the measurement, the mobile communication network is used to transmit data to the server and stored in the database. The portable devices are designed to measure the distribution transformers or test points of medium voltage (MV) plugs. The portable devices also use GPS 1 PPS to synchronize sampling signal between substation and field measurement sides [1, 2]. In the urban areas, most of the transformers are installed in the underground distribution systems. To maintain the accuracy of the sampling signal, the TCXO is used to generate highly accurate timing for the consecutive measurement. The field measurementresults are transmitted via Bluetooth to the mobile device. The mobile communication network is also used to retrieve data from the server and display phasor real-time in the screen. All the measurement results are also stored in the mobile device to update the attributes of distribution transformers in DMMS and provide ancillary services of distribution system such as phasor balancing.
## 2 Results and Discussion
### HPLC-DAD-MS\({}^{n}\) Analysis of LE, DM and UM  
The HPLC-DAD-MS\({}^{n}\) analysis of flavones in the nine commercial cultivars allowed us to identify 11 compounds, such as luteolin, apigenin, quercetin glycosides, and the hep-specific flavonoids cannflavin A and B. Furthermore, the lignanamides cannabis A and B were also identified and quantified. These compounds have been previously identified mainly in the seeds and are considered markers for hepp [55]. UM presented large variations in the amounts of these 11 phenolic derivatives. Among them, luteolin glucuronide was observed in a larger amount (more than 7.0 mg/g in Pablito UM and 4.2 mg/g in Lemon Conti Kush UM). Considering the sum of all the phenolic and lignanamide derivatives, Pablito UM presented these compounds in larger amounts, accounting for a total of more than 12.0 mg/g. Considering DM, a general decrease in the contents of the 11 detected compounds was observed, probably due to the washing away caused by water during the hydrodistillation process. Nevertheless, DM still contained a significant amount of certain phenolics. Notably, not all the compounds were washed away during EO distillation; these include cannflavins, which present low water solubility, and were quantified in a comparable amount in UM and DM of most varieties. On the other hand, glycosidic flavonoids, such as luteolin glycosides or rutin, which are in general more soluble in water at high temperatures, were less present in DM than in UM (Table 7).  
LE presented significant amounts of phenolic compounds (Table 8). Very high levels of rutin have been observed in Venom OG, White Shark, 24 K, and Lemon Conti Kush LE. Other varieties, for example Fresh Mountain, contained only a limited concentration (0.3 mg/g) of rutin. Vitexin-2\"-O-glucose was observed in significant amounts in Venom OG (5.0 mg/g), Lemon Conti Kush (3.5 mg/g), and 24 K (3.2 mg/g). As expected, cannabinoids were present in negligible amounts in LE, due to their poor water solubility.  
UM and DM were also studied for their cannabinoid composition, and the results are summarized in Table 9. As previously indicated by the preliminary investigation by \({}^{1}\)H-NMR all the varieties were rich in acidic forms of cannabinoids, mostly CBDA. The cultivar Pablito presented CBGA as the most abundant cannabi  
Figure 3: \({}^{1}\)H-NMR spectra showing the cannabinoids profiles of UM and DM chloroform extract.  
of 8-9-THC have been detected, and the maximum observed levels were all below 0.2 mg/g. Comparing the levels of cannabinoids in UM and DM, most of the CBDA was converted to CBD in DM. In fact, DM possessed a larger amount of CBD, compared to the corresponding UM, due to decarboxylation reaction and conversion of CBDA in CBD. The cannabinoid levels were remarkably higher with respect to some data published on Finola and Futura varieties [56] and were comparable with those of other studies reporting on the amount of CBD in several hemp varieties cultivated in Slovenia [57].