esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Improving the ARKit Data during Post-Processing  
The good correlation results opened up the question of whether it is possible to improve the ARKit motion capture data through post-processing to approximate the Vicon data. A systematic error concerning detecting the hip joints in a position too far anterior is a possible explanation and is subject to further investigation. If this is the case, both the baseline shift and the amplitude reduction could be corrected by applying a scale factor and shifting the data on the \(y\)-axis. Compensating the baseline shift would reduce the wMAE results by 7.61\({}^{\circ}\) and lead to more reliable and accurate results. However, no systematic error could be found when shifting the ARKit data along the \(y\)-axis by vertically shifting the ARKit data (Figure 9). The observed shift instead seems to be caused by other factors such as the incorrect detection of joints.  
Figure 12: Boxplots representing the ME and MAE in degrees across all tracked angles in the experiments. The boxplots for the ME show a significant difference in the means of the upper and lower body angles, which is not visible for the MAE.  
Figure 13: Exemplary screenshot of the frontal ARKit recording of one subject during the Single Leg Deadlift exercise, showing a bad detection of the hip joints and confusion of the knee joints.  
During the data analysis, we used a sliding window approach to maximizing the cross-correlation between the ARKit and Vicon data to compensate for possible time lags, as no synchronization of the iPads and the Vicon system was possible during the experiment. Possible reasons for lags are different hardware clocks and the delay of the body detection algorithm of the ARKit framework. The sliding window was set to a maximum of 120 frames, which equals approximately 2 s, only to allow reasonable shifts within the exercises and compensate for the lag caused by technical limitations. The approach was chosen to maximize the comparability between the results of the two systems. However, as the sliding window approach was applied individually to each angle, exercise, subject, and view, each configuration was shifted to its optimal result within the given time window. This approach does not consider potential lags within ARKit's motion capture, for example, a slower recognition of changes for some parts of the recognized body.
## Materials and Methods
### Synergy between Antifungals and Essential Oils  
Possible synergistic action of common antifungal agents in combination with the essential oils was determined by the checkerboard method. After overnight culture (16-18 h) on YPD agar plates, colonies of the reference strains (C. _albicans_ ATCC 10231, _C. glabrata_ DSM 11226) were suspended in sterile water. Optical density (OD\({}_{600}\)) was adjusted to about 0.1 (corresponding to 10\({}^{6}\) cells in 1 mL) and the inoculum was diluted 50-fold to obtain a cell density of 2 \(\times\) 10\({}^{4}\) cells per 1 mL of suspension. A gradient of antifungal chemotherapeutic was established along the horizontal axis and essential oil along the vertical axis. The first row contained only the gradient of chemotherapeutic and the tenth column gradient of the natural product. One hundred microliters of the prepared suspension were inoculated to each well of the plate. Plates were incubated for 24 h at 37 \({}^{\circ}\)C. The MIC values of the compound alone or in combination were read visually. \(\Sigma\)FIC (fractional inhibitory concentrations) was determined according to the equation \(\Sigma\)FIC = FIC A + FIC B, where FIC A is the MIC of a medicament in combination/MIC of the drug alone, FIC B is the MIC of the second compound in combination/MIC of the compound alone. The combination is considered synergistic when the \(\Sigma\)FIC is \(\leq\) 0.5, indifferent when the \(\Sigma\)FIC is \(>\) 0.5 to \(<\) 4, and antagonistic when the \(\Sigma\)FIC is \(\geq\) 4.
# Cutting Performance and Wear/Damage Characteristics of PCBN Tool in Hard Milling
## 1 Introduction
It was found that tribochemical reactions, adhesive wear, and mild abrasive wear controlled the flank and crater wear. [PERSON] et al. [26] investigated the failure mechanism of PCBN tools in milling hardened steel based on damage equivalent stress. By analyzing the above literature, it was found that chip morphology and cutting temperature were rarely used to evaluate the cutting performance of PCBN tools, whether in turning or milling of hardened steel. Two or three evaluation indicators were often selected to evaluate the cutting performance of PCBN tools in the existing literature. Cutting is a very complex process, and comprehensive evaluation indicators can more accurately reflect the cutting performance of PCBN tools. In the machining of hardened steel, the wear mechanism of PCBN tools varies with the material and hardness of the workpiece. At present, there is no research on the wear mechanism of PCBN tools in the intermittent machining of hardened steel (Cr12 MoV, 59 HRC). Meanwhile, the research on the damage characteristics of PCBN tools in the intermittent machining of hardened steel is still blank, which limits the application of PCBN tools. In this study, the cutting performances (cutting force, chip morphology, surface quality, tool life, cutting temperature) and the wear/damage characteristics of PCBN tools were investigated for the intermittent machining of hardened steel.
# Fine-Grained Detection of Pavement Distress Based on Integrated Data Using Digital Twin
## 3 Methodology
### Fine-Grained Pavement Distress Detection Based on Deep-Object Detection Networks  
This paper first trains six typical deep object detection networks using a dataset with a virtual-to-real ratio of 0:1 to reveal the most discriminative network structures  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Integrated Dataset**} & \multicolumn{6}{c}{**Virtual-to-Real Ratio**} \\ \cline{2-7}  & **0:1** & **1:1** & **2:1** & **3:1** & **4:1** & **5:1** \\ \hline \multirow{2}{*}{Training set/image} & 0 & 100 & 200 & 300 & 400 & 500 \\  & 100 & 100 & 100 & 100 & 100 & 100 \\ \multirow{2}{*}{Test set/image} & 100 & 100 & 100 & 100 & 100 & 100 \\ \multirow{2}{*}{Total set/image} & 200 & 300 & 400 & 500 & 600 & 700 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Integrated datasets with different virtual-to-real ratios.  
Figure 4: Schematic diagram of 5 types of virtual pavement distress. (**a**) Transverse crack. (**b**) Longitudinal crack. (**c**) Cross crack. (**d**) Alligator crack. (**e**) Pothole.  
Figure 5: Schematic diagram of 3 types of virtual pavement distress under complex scenarios. (**a**) Shadow. (**b**) Occlusion. (**c**) Blur.  
for the five different types of pavement distress. The most discriminative deep object detection network is then fully trained using each of the six integrated datasets with virtual-to-real ratios from 0:1 to 5:1, respectively, and evaluated on the same test set to reveal the optimal virtual-to-real ratio of the integrated dataset, which can achieve refined detection of pavement distress with the highest detection accuracy. Finally, the fully trained deep object detection network with two virtual-to-real ratios (0:1 and the optimal ratio) is used to test pavement inspection images under complex deployment scenarios (shadows, occlusions and blurs), respectively, to evaluate their versatility and adaptability to different application conditions.
## 2 Results and Discussion  
### Results  
#### 2.1.1 Sequencing Output and Transcriptome Assembly  
We obtained a total of 55,166,532 and 54,784,414 raw reads from Pg-Con and Pg-MeJA respectively. By filtering all the raw reads, we obtained 52,821,238 (95.75%) clean reads from Pg-Con and 52,661,812 (96.13%) from Pg-MeJA with an average length of 90 bp. High-quality reads from two samples were combined and provided to the transcriptome assembly program \"Trinity\" [26]. A total of 136,776 and 132,474 contigs were assembled with an average length of 316 and 307 bp and N50 of 525 and 448 bp from Pg-Con and Pg-MeJA respectively. Later, contigs were further assembled into 75,956 unigenes with a mean size of 698 bp from Pg-Con and 73,439 with a mean size of 690 bp from Pg-MeJA. There were 33,672 (\(\geq\)500 bp) and 17,543 (\(\geq\)1000 bp) unigenes in Pg-Con, while 32,393 (\(\geq\)500 bp) and 16,598 (\(\geq\)1000 bp) unigenes in Pg-MeJA. The majority of unigenes were smaller than 500 bp in both cases that accounted for approximately 56% of all unigenes. Finally, the unigenes from both samples were assembled into 71,095 all-unigenes in total, with a mean length of 871 bp and N50 of 1307. An overview of assembled sequences has been summarized in Table 1.
## 3 Results
### PLSR Models for Estimating Soil pH and EC with HSI-Resampled Spectra  
#### 3.3.1 Prediction Performances Using HSI-Resampled Spectra  
All spectra of soil samples in the range of 505-956 nm were resampled to the HSI image bandwidths (bands 21-115). The HSI-resampled spectra were used to construct the PLSR models to estimate the soil pH and EC. All the soil samples in dataset 1 were divided into different pH-EC levels and selected randomly for calibration (two-thirds) and validation (one-third). The calibration set was used to build the model and the validation set was used to evaluate the accuracy of predicted results. The PLSR models exhibit good performance when the number of components is three. The models are described as:  
\[pH=3.60+0.034b21+0.033b22+0.032b23+\cdots+0.0073b114+0.0047b115 \tag{6}\]  
\[EC=-38.39+0.454b21+0.439b22+0.423b23+\cdots+0.147b114+0.149b115 \tag{7}\]  
where \(b21\), \(b22\), \(b23\),..., \(b113\), \(b114\), and \(b115\) are the spectral reflectances resampled in accordance with wavelengths of the HSI bands 21, 22, 23,..., 113, 114 and 115, respectively. The R\({}^{2}\) values of PLSR models to estimate the soil pH and EC were tested at the 0.05 probability level. For pH, the PLSR model exhibits good performance with R\({}^{2}\) = 0.77, RMSE = 0.95 and RPIQ = 3.84 (Table 4), suggesting a satisfactory relationship between the predicted and measured pH values. For EC, the R\({}^{2}\) of the PLSR model is low (R\({}^{2}\) = 0.48), the RMSE value reaches 17.92 dS/m, and RPIQ is 0.14 (Table 4), indicating the non-reliable model. When the model validation was performed, the RMSE values for pH and EC reached 1.06 and 18.92 dS/m, respectively. Nevertheless, the statistics of the PLSR models in Table 4 indicates the feasibility of the PLSR model in estimating the soil alkalinity and salinity on the basis of the resampled HSI-like spectra in the VIS-NIR region.  
Figure 2: Average soil spectra curves from 505 nm to 956 nm in different pH-EC levels.
## 4 Discussion  
This study addressed the optimization of the plastic injection molding process through an approach based on the integration of a classification model, an intelligent agent, and a graphical user interface. Critical process variables have been analyzed, identifying the cycle time (CT) as the primary control variable for optimization.  
The developed classification model has demonstrated high accuracy in predicting CT behavior, enabling the anticipation of potential process anomalies and the proactive implementation of corrective measures. The intelligent agent, utilizing the classification model and expert knowledge, generates specific recommendations for CT optimization, considering factors such as material temperature, injection pressure, and cooling time. The GUI facilitates interaction with the system, enabling data visualization, graph generation, and recommendation export.  
The results obtained in this study demonstrate the feasibility of the proposed approach for optimizing the plastic injection molding process. The integration of a classification model, an intelligent agent, and a GUI provides a powerful tool to improve process performance in terms of productivity, product quality, and energy efficiency.  
This study presents some limitations that should be considered for future research. Firstly, the classification model has been trained with a specific dataset from a particular plastic injection molding process. To generalize the results, it is necessary to validate the model with data from different processes and operating conditions.  
Secondly, the intelligent agent uses a set of predefined rules to generate recommendations. The effectiveness of the agent could be improved by implementing Machine Learning techniques that allow the agent to learn from historical data and adapt to changing process conditions.  
Figure 12: Historical data (**a**) average backpressure (purple), (**b**) hold transfer pressure (red), and (c) max injection pressure (green).  
Finally, the GUI was designed for a specific process and may require modifications to adapt it to other industrial processes.
## 3 Geographical Adaptive Fidelity (GAF)
### Determining node equivalence  
Even with location information, it is not trivial to find equivalent nodes in a network. Nodes that are \"equivalent\" between some nodes may not be equivalent for communication between others.  
To address this issue, the GAF uses location information and virtual grids to determine node equivalent. Two nodes are equivalent if they locate in the same virtual cells. The size of each virtual cell is determined based on the nominal radio range \(R\). Assume that the virtual grid is a square with \(r\) units on a side as show in Figure 1. In order to meet the definition of virtual cell, the distance between two possible farthest nodes in any two adjacent cells, must not be larger than \(R\). For example, \(node_{I}\) and \(node_{S}\) in Figure 1 are at the end of the long diagonal connecting two adjacent cells. Therefore, we get \(r^{2}+(2r)^{2}\leq R^{2}\) or \(r\leq R/\sqrt{5}\).
## 4 Experiments by Simulations
### Evaluation with a Large Room  
We conducted an evaluation with a large room shown in Figure 4 which has four air conditioners under the ceiling. In the same way as the evaluation with small room, we used ResNet-50 with pretraining with ImageNet. We trained the model for 600 epoch, and the other training parameters were the same as the evaluation with the small room. We conducted 5100 simulations for generating the training dataset.  
We validated the performance of the trained models with 90 sets of images of temperature and airflow distributions in the room. The examples of input images, regenerated images, and difference images are shown in Figure 8. We also conducted a comparison between two room sizes as shown in Table 4. Moreover, the result of a Welch T-test between the two sizes of the room are shown in Table 5. Along with this test, there was no significant difference in the temperature but there was a significant difference in the airflow. As shown in the Table 4, the MAEs of temperature and airflow are 1.5 \({}^{\circ}\)C and 0.1 m\({}^{3}\)/min, respectively. The results indicate that there is less of an effect of room size on the performance of air distribution control by our method. Although, the number of training epochs should be increased along with the room size.  
\begin{table}
\begin{tabular}{l l c c} \hline \hline  & **Room Size** & **MAE** & **Std. Dev.** \\ \hline Temperature & Large & 1.45 & 0.820 \\ \({}^{\circ}\)C & Small & 1.56 & 0.987 \\ \hline Flow Vol. & Large & 0.104 & 0.0218 \\ m\({}^{3}\)/min & Small & 0.0962 & 0.0284 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison between small and large rooms.  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **t-val** & **p-val** \\ \hline Temperature & \(-0.86910\) & 0.38600 \\ Airflow volume & \(2.01258\) & 0.04481 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Test between small and large room.  
Figure 7: Variation of the loss with pretraining and without pretraining.
# RS-DETR: An Improved Remote Sensing Object Detection Model Based on RT-DETR
## 4 Results and Discussion
### Experiments on the TGRS-HRRSD Dataset
#### 4.5.2 Comparison Experiments with Other Models  
Table 4 presents the comparison results of RS-DETR with other mainstream models on the TGRS-HRRSD dataset. Compared to the baseline model, our proposed algorithm improves mAP50 by 1.5% and mAP by 0.8%. For the mAP50 evaluation metric, DINO achieved the highest result. However, our proposed method surpassed DINO on the more stringent mAP metric, indicating superior detection performance across various IoU thresholds. Additionally, our model requires fewer parameters and has a lower  
Figure 10: The visual results of RS-DETR on the TGRS-HRRSD dataset.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Class** & **P (\%)** & **R (\%)** & **mAP50 (\%)** & **mAP (\%)** \\ \hline all & **90.4** & **86.3** & **91.6** & **65.4** \\ ship & 93.2 & 89.6 & 94.5 & 76.5 \\ bridge & 94.2 & 88.2 & 92.3 & 50.9 \\ ground track field & 92.8 & 97.1 & 98.4 & 83.2 \\ storage tank & 93.1 & 92.4 & 96.7 & 76.7 \\ basketball court & 90 & 67.1 & 79.9 & 54.4 \\ tennis court & 94.4 & 92.5 & 96.6 & 83.1 \\ airplane & 98.7 & 96.3 & 98.4 & 72.9 \\ baseball diamond & 89 & 87.6 & 91.8 & 66.8 \\ harbor & 91 & 88.9 & 93.2 & 70.9 \\ vehicle & 94.5 & 82.3 & 94.2 & 67.1 \\ crossroad & 88.3 & 93.5 & 92.4 & 53.6 \\ T junction & 82.6 & 80.3 & 84.9 & 44.4 \\ parking lot & 73.5 & 66.5 & 71.7 & 46.1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The detection results of RS-DETR for all categories on the TGRS-HRRSD dataset.  
computational load than other models, indicating that RS-DETR achieves an excellent balance between accuracy and model size.  
As shown in Figure 11, the RS-DETR model outperforms the baseline model in detection performance on the TGRS-HRSRD dataset. In the top comparison, RT-DETR incorrectly classified buildings as ships, while our model effectively reduced this misclassification. In the middle comparison, RS-DETR successfully detected the basketball court and corrected the ground track field instance that the baseline model incorrectly detected. In the bottom comparison, the baseline model incorrectly detected and classified the airport runway as a bridge, while our model avoided this misclassification, thereby enhancing detection accuracy. Overall, our proposed model significantly improves remote sensing image object detection performance.
## 3 Security Implications and Fundamental Approaches of Blockchain-Based Data Storage Methods
### Blockchain-Based Data Integrity and Traceability Solutions
#### 3.5.2 Balancing Efficiency and Security in Off-Chain Approaches  
Off-chain solutions reduce costs and processing overhead by storing only data references on the blockchain. This method offers a cost-effective and efficient alternative for sectors such as education, healthcare, and logistics [35]. However, in applications requiring high security, off-chain solutions are not as reliable as on-chain methods. Particularly in scenarios where critical data protection is necessary, off-chain storage provides only a limited level of security [36].
# Study on Active Tracking of Underwater Acoustic Target Based on Deep Convolution Neural Network
## 3 Simulation and Verification of Real World Signal
### Verification of Real World Signal  
We further tested the performance of the DCNN-KFAT method using some prerecorded real-world active sonar signals, with data derived from experimental data from a sea trial in May 2020. We intercepted part of the data containing the two targets whose azimuths overlapped during the motion for processing. The active sonar is set to transmit LFM signals at a frequency of \(f=320\)-400 Hz, and the duration of the transmitted signal is 10 s. The echo signal is received by a vertical array of 20 array elements. The time domain waveform and frequency domain waveform of the received target echo signal are shown in Figure 20. As can be seen from Figure 20, in practical applications, it is difficult to discover the target quickly from the time domain or frequency domain only due to the high energy of the low-frequency components in the ambient noise, which can cover the target echo signal.  
We used the method proposed in Section 2.1 to generate the target echo signal dataset. Some of the data in the dataset are shown in Figure 21.  
We processed the data using the DCNN-KFAT method to obtain tracking results and compared them with the KFAT method. The results are shown in Figure 22. The actual motion trajectories of the two moving targets recorded by GPS are shown as black lines.  
In Figure 22, for two moving targets with overlapping azimuths, the KFAT method lost one of the targets after the overlap, while the DCNN-KFAT method was able to continue tracking for two different targets after the two targets overlapped in azimuth without the problem of target loss.  
In Figure 23, the DCNN-KFAT method is more accurate than the KFAT method, with deviations in the range of 100-200 m, which is within the expected range of the simulation.  
Figure 20: Time domain data and frequency domain data of the target echo signal.  
The DCNN-KFAT method proposed in this paper is further validated by processing and analyzing the prerecorded real-world signals. The new method is able to continue accurate tracking after two moving targets overlap in azimuth, and has less deviation in tracking accuracy than the KFAT method.  
Figure 21: Data set of target echo signals, obtained after processing according to the method in Section 2.1, containing the time-frequency spectrograms of the echo signals of two different targets.  
Figure 22: Results of processing the actual sea trial data using both methods. The KFAT method lost the orientation of one target.  
Figure 23: Comparison of the deviation of tracking results for target 1 between the two methods. The DCNN-KFAT method is more accurate than the KFAT method.