esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Phased Array Radar (PAR)
### Rocket Tracking System (RTS)
#### 2.1.2 RTS Operation
Before being transferred between different radar sensors, the information must be processed, in order to correct the delay time and differences in the origin of coordinates systems. On the other hand, if no radar is tracking, it is possible to opt for the designation of NT, which is far more accurate than manual scanning. However, NT is not completely reliable since climatic conditions (such as wind direction and speed) and changes in the target behavior during propulsion might significantly change the real trajectory in relation to the NT. In Figure 3, there is an RTS with two radar sensors in network and a flowchart of Radar No. 1 operation.
## 3 Results
### Evaluation of the Significant Region in Age Estimation  
Table 4 shows the results of the Shapiro-Wilk test for the data on AEs obtained in this study. In Table 4, since there is no normality in the data on AEs obtained in this study, it was decided that the statistical significance be examined by using the Wilcoxon rank sum test. Table 5 shows the results of the Wilcoxon rank sum test. This table shows that although there is one case each for Male_Model and Female_Model in which there is no statistically significant difference. In all other cases, there is a statistically significant difference in the accuracy of age estimation before and after the deletion of the corpus callosum region. In sum, the overall results show that the accuracy of age estimation significantly decreases before and after the removal of the corpus callosum region. Table 6 shows the specific values of MAE and other parameters.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**ALL\_Model**} & \multicolumn{2}{c}{**Male\_Model**} & \multicolumn{2}{c}{**Female\_Model**} \\ \cline{2-7}  & **With CC** & **Without CC** & **With CC** & **Without CC** & **With CC** & **Without CC** \\ \hline Model\_A & \(p<0.0001\) & \(p<0.0001\) & \(p<0.0001\) & \(p=0.0024\) & \(p<0.0001\) & \(p=0.0010\) \\ Model\_B & \(p<0.0001\) & \(p=0.0003\) & \(p<0.0001\) & \(p=0.0013\) & \(p=0.0145\) & \(p=0.0644\) \\ Model\_C & \(p<0.0001\) & \(p=0.0003\) & \(p<0.0001\) & \(p=0.0289\) & \(p<0.0001\) & \(p<0.0001\) \\ Model\_D & \(p<0.0001\) & \(p=0.0001\) & \(p<0.0001\) & \(p=0.0090\) & \(p=0.0003\) & \(p=0.0002\) \\ Model\_E & \(p<0.0001\) & \(p=0.0005\) & \(p<0.0001\) & \(p=0.0004\) & \(p<0.0001\) & \(p<0.0001\) \\ \hline \hline \end{tabular}  
* CC: corpus callosum.  
\end{table}
Table 4: Results of the Shapiro–Wilk test.  
Figure 9: Typical example of this age estimation method. (CA, chronological age; EA, estimated age).  
Results of Statistical Analysis on the Accuracy of Age Estimation between Male and Female Age Estimation Models  
As shown in Table 4, the Wilcoxon rank sum test should be used in statistical analysis using AE. Therefore, Table 7 shows the results of the Wilcoxon rank sum test to examine whether there is a statistically significant difference in the accuracy of age estimation between men and women. Table 7 shows that in 17 of 25 cases, the \(p\) value is higher than the significance level of 0.05, indicating that there is no statistically significant difference between the two cases. In the remaining eight cases, the \(p\) values are below the significance level of 0.05, indicating that there is a statistically significant difference. These results indicate that there is no statistically significant difference in the age-estimated sex between men and women when evaluated as a whole.
## 4 Parameterization and Analysis of the Proposed TDL Model
### Fading Statistics and K-Factor Values of Individual Tap
The obtained shape parameters are \(\beta_{w1}\) are 1.026, 1.026 and 1.020 at altitude 300 m, 500 m and 800 m, respectively. The scale parameters \(\Omega_{w1}\) are 11.52, 13.62 and 14.07 at altitude 300 m, 500 m and 800 m, respectively. The result reveals that the scale parameter of the Weibull distribution increases with the UAV altitude. As the altitude of the UAV increases, the probability of presence of LoS path also increases. Under LoS conditions, the direct propagation path between transmitter and receiver is the dominant component in the first tap and signal fading tends to be more stable, with less depth of fading.
## 2 Related Work  
### Attention Mechanism  
The attention mechanism originates from the study of human vision. When humans observe the surrounding environment, they will selectively pay attention to the salient part of the information, while ignoring other information that is not obvious. The introduction of the attention mechanism into the convolutional neural network enables the model to learn where and what to pay attention to, so that it can automatically select information that is more valuable to the current task from a large amount of information. The residual attention network [22] performs down-sampling and up-sampling through a soft mask branch, generates a spatial attention map, and weights it to the trunk branch. SENet [23] first compresses the global spatial information into channel descriptors through the Sequeeze operation, and then captures the channel dependency through the Excitation operation of two fully connected layers, similar to the bottleneck structure [14], to generate channel attention weights. CBAM [24] generates attention maps by concatenating channel and spatial dimensions, and then multiplies the channel and spatial attention maps by the input feature map, respectively, for adaptive feature refinement. In contrast, BAM [25] computes the attention maps along the channel and spatial dimensions in parallel, and then aggregates the channel and spatial attention maps into the final attention recalibration weights. SKNet [26] applies convolution kernels of different sizes to the feature map to obtain receptive fields of different scales, and finally calculates the gate vector to control the aggregation of feature maps of all branches to obtain the output feature map.  
Google first proposed applying self-attention to explore global dependencies in the transformer architecture for machine translation [17]. In the field of computer vision, for global context information, NLNet [18], ANN [19], GCNet [27], and DANet [28] introduced non-local attention mechanisms to construct dense spatial feature maps to capture long-range dependencies in non-local operations, and use non-local mechanisms to capture different types of spatial information. However, since non-local modules need to calculate a large relationship matrix for each spatial position, it brings expensive computational costs and storage space occupancy, and is usually used in large models. SPNet [29] replaces global spatial pooling with horizontal and vertical strip pooling layers to more efficiently capture long-range dependencies and large-scale contexts. Since channel attention represented by SE ignores position information, CA [30] merges and captures feature maps in two directions and then decomposes them into two 1D feature encoding processes to aggregate features along two spatial directions, effectively embedding position information. ELA [31] processes feature vectors independently for each direction, avoiding dimensionality reduction in the intermediate process and achieving better results.
# Adaptive Downward/Upward Routing Protocol for Mobile-Sensor Networks
## 4 Simulation Environment and Performance Evaluation
### Simulation Results  
In order to assess the efficiency of ADUP in dealing with mobility, we compared it to two other existing protocols that cope with mobility: AODV and Flooding. In addition, AODV and Flooding are generic protocols that are designed for any application scenario containing upstream and downstream traffic. As we introduced in Section 2, AODV is a typical unicast-based routing protocol. It uses route discovery and route maintenance to support dynamic topologies. Furthermore, it is based on periodic advertisements and distance vector routing, which is more adaptive to mobile scenarios compared  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Parameters** \\ \hline Network simulator & Cooja under contiki OS (3.0) \\ Radio propagation model & MRM with random behavior \\ Medium access control & CSMA/CA \\ Simulation time & 5 min \\ Emulated platform & Sky starter platform \\ Sensor Nodes Deployment & Random Deployment \\ Data size & 30 Bytes \\ Packet queue size & 16 \\ Upward transmission rate & 1 pkt/s \\ Downward transmission rate & 1 pkt/s \\ Transmission power & \(-\)20 dBm \\ Transmission range & [30 m, 50 m] \\ Number of nodes & 20, 40, 60 \\ Area of deployment & 200 m \(\times\) 200 m \\ Frequency range & 2.4 GHz \\ Mobility model & Random Waypoint \\ Minimum speed & 1 m/s \\ Maximum speed & 3 m/s \\ Speed-changing interval & 5 s \\ New location pause time & 5 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation setup.  
to DSR for example. Flooding is a broadcast-based routing protocol that is not sensitive to mobility. It may support mobile scenarios well with few transmission events. It is also interesting to estimate the performance of Flooding in mobile scenarios with different traffic patterns. In order to limit the number of transmissions using Flooding routing protocol, nodes only route the same packet once. We used unique identifiers for packets in order to manage this issue.  
Note that in this paper we did not compare ADUP to RPL because in previous papers we showed that RPL does not cope well with mobility, and that RRD+ outperforms RPL.  
We used four performance metrics to evaluate the efficiency of these protocols: (i) packet-delivery ratio, (ii) average end-to-end delay, (iii) dropped packet ratio, and (iv) number of control packets. For each network size, we generated 10 different random mobility scenarios. Each performance metric was averaged over 10 iterations for each network size.
# Reducing Model Complexity in Neural Networks by Using Pyramid Training Approaches
## 3 Methodology
### Experimental Fields
#### 3.5.2 Semantic Segmentation  
Semantic segmentation involves detailed pixel-wise classification, crucial for tasks such as medical image analysis. We employ the U-Net architecture [35], known for its efficacy in detailed segmentation tasks, to implement feature size reduction. The U-shaped architecture of U-Net, which includes skip connections that help preserve spatial hierarchies, poses unique challenges when integrating feature size reduction. These complexities necessitate adaptations in our pyramid training approach to ensure that crucial spatial information is not lost during feature reduction. The tailored approach and its architectural nuances are detailed in Figure 3.
## 5 Simulation and Performance Analysis
### Performance Evaluation
As a digital satellite-borne base station system uses a unified software installation method, compared with the independent installation tasks associated with each component of a traditional ground station system, its upgrade time will also be greatly improved. Because of the flexible architecture of a digital satellite-borne base station system, it is possible to reconstruct a single functional unit in time as needed. That possibility is not available with a traditional ground station system. As can be seen, the system mentioned in this paper outperformed the first-generation high-orbit satellite signaling gateway station system in terms of single-function unit deployment time, minimum system deployment time, and system software upgrade time by at least 30-fold, 120-fold, and 240-fold, respectively, and could achieve a breakthrough in reconfiguration of unit functions from scratch. The digitized satellite base station system proposed in this research increases the adaptability of the satellite network service and enables rapid network deployment, flexible scheduling, and dynamic function loading.
## 3 Case Study
### Training the Network by Using Frequency-Domain Data
#### 3.2.2 Analysis of Training Results  
By using another set of 12.8 s vibration signals in Section 3.1.2 at the same location, a Fourier transform was performed on the vibration signals. The LSTM network as trained above was used to predict the incomplete sequences that lacked data from Channels 7 and 8, with the prediction results shown in Figure 20. Compared with prediction using raw time domain data, where a Fourier transform was performed after the original data were used to make a prediction, Figure 20 shows that the predicted signal curves fit more closely to the vibration curves.  
After the inverse Fourier transform, the vibration acceleration signal is further wavelet-transformed to obtain a more intuitive time-frequency image of Channel 7's vibration acceleration frequency over time. Figure 21 shows the wavelet-transformed time-frequency image of the measured value, and Figure 22 shows the wavelet-transformed time-frequency image of the predicted value. It can be seen from the figures that the height of the signal frequency of the missing data's predicted value and measured value over time is approximately the same, and that the peak frequency appears at approximately the same height.  
Figure 21: Wavelet-transformed time-frequency image of the measured value from Channel 7 using frequency-domain data.  
Figure 20: Comparison of acceleration frequency spectrum from Channel 7 using processed data.  
In summary, in the scenarios of collecting multi-channel data, this model can use the data from adjacent channels to restore the data of the lost channels and ensure that the restored data have high accuracy. This method can reduce the cases of discarding whole data groups due to the loss of individual channels' data. Primarily, if the original data are applied to the LSTM network after performing a Fourier transform, the model can deliver higher accuracy because the processed data have higher correlations than the original data.
## 4 Discussion
### Limitations and Future Work  
Despite the significant achievements of this study, there are still some limitations. Firstly, the model's performance is largely dependent on the pre-trained BERT model, which may limit its generalization ability on some specific marine texts [36]. Secondly, the current training dataset is relatively limited in size, which may affect the model's robustness [37]. Future work can focus on exploring more advanced pre-trained models to further improve the model's performance and generalization ability. Additionally, expanding the training dataset, especially by increasing the samples of different types of  
Figure 9: Comparison of overall recognition performance with mainstream models (%).  
marine texts, is an important direction for enhancing the model's robustness [38; 39]. These limitations suggest that while our model performs well within the scope of the current study, broader applications across diverse marine texts require further enhancement and validation. Addressing these limitations will help in refining the model and expanding its applicability in the marine domain.
## 4 Conclusions  
From this work, the following conclusions can be drawn:  
* This study highlights that is possible to detect and quantify HCQ in real water matrix by DPV technique using electrochemical cork-graphite device. The DPV method using the composite sensor showed a satisfactory current-response and higher sensitivity to determine HCQ in a polluted river water matrix. It is important to emphasize that in previous work [14], an electroanalytical approach was compared with the spectrophotometric method achieving good performances, confirming the analytical confidence of the measurements using cork-graphite sensor.
* The HCQ degradation (26.8 mg L\({}^{-1}\)) was studied under different \(j\) (15, 30 and 45 mA cm\({}^{-2}\)), demonstrating that higher current densities accelerated the organic matter elimination from solution because of the efficient electrogeneration of oxidants with BDD anode. Increasing of \(j\), favor the electrogeneration of different oxidants at the BDD surface or via the participation of \({}^{\bullet}\)OH radicals, favoring the elimination of HCQ from real water matrix.
* Detection of NO\({}_{3}\)- and free chorine after the degradation of HCQ, evidenced by electrochemical SDG6 technology being effectively applied, promoting clean water and sanitization outcomes.  
Figure 5: (**a**) Total current efficiency (%TCE) over time and (**b**) energy consumption (EC) as a function of COD removal, during the treatment of a solution containing HCQ (26.8 mg L\({}^{-1}\)) at different current densities (\(\bullet\)) 15, (\(\bullet\)) 30 and (\(\bullet\)) 45 mA cm\({}^{-2}\) on the abatement of 26.8 mg L\({}^{-1}\) of HCQ in a 0.1 M H\({}_{2}\)SO\({}_{4}\) solution.  
**Author Contributions:** Conceptualization, E.V.D.S. and C.A.M.-H.; methodology, D.M.d.A.; formal analysis, E.V.D.S. and C.A.M.-H.; resources, C.A.M.-H.; data curation, D.M.d.A., E.V.D.S., C.A.M.-H. and A.D.B.; writing--original draft preparation, D.M.d.A. and E.V.D.S., writing--review and editing, D.M.d.A., E.V.D.S., C.A.M.-H. and A.D.B.; funding acquisition, E.V.D.S. and C.A.M.-H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (Brazil), grant number 306323/2018-4, and Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (Brazil), grant numbers 2014/50945-4 and 2019/13113-4.  
**Acknowledgments:** Financial supports from the Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq-306323/2018-4) and from Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (Brazil) (FAPESP 2014/50945-4 and 2019/13113-4) are gratefully acknowledged.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results
### Effect of Hfq-CTR on RNA I-RNA II Annealing
#### 3.2.3 Hfq-CTR Strongly Stabilizes the RNA I-RNA II Duplex, in Particular at High Temperature  
Next, the effect of Hfq-CTR on the stability of the RNA I-RNA II complex formed was evaluated using meting curves. SRCD analysis was performed on the RNA I-RNA II complex in the absence and presence of Hfq-CTR. As described under Methods, SRCD spectra were acquired every 3 \({}^{\circ}\)C between 15 \({}^{\circ}\)C and 96 \({}^{\circ}\)C. As seen in Figure 8A, from the increase in CD signal at 190 nm, the RNA I-RNA II complex was considerably more stable in the presence of Hfq-CTR, especially at high temperatures (>80 \({}^{\circ}\)C). This result was unexpected as usually high temperatures (>80 \({}^{\circ}\)C) melt NA structures and do not stabilize it, as is the case for the RNA I-RNA II complex without CTR (Figure 8B). Three independent repetitions of this experiment clearly demonstrate that the protein stabilizes  
Figure 4: ColE1 origin temperature melting using SRCD (from blue 15 \({}^{\circ}\)C to red 95 \({}^{\circ}\)C). (**A**) Melting in the presence of the CTR. (**B**) Melting without the CTR. At 15 \({}^{\circ}\)C, ColE1 origin forms a B helix. This can be concluded from the comparison of the whole spectra with the literature [44; 45] and our in-house database. The correlation between spectra in 4 and that of B-form helix was investigated using Pearson correlation tests. We found a correlation of 0.94611 for a \(p\)-value of 9.09136 \(\times\) 10\({}^{-76}\) for colE1 dsDNA at 15 \({}^{\circ}\)C and a correlation of 0.9604 for a \(p\)-value of 3.8568 \(\times\) 10\({}^{-88}\) for colE1 dsDNA in the presence of the CTR. (**C**) The corresponding T\({}_{\rm m}\) were 76.0 \(\pm\) 0.5 \({}^{\circ}\)C, 67.5 \(\pm\) 0.3 \({}^{\circ}\)C, 78.6 \(\pm\) 0.6 \({}^{\circ}\)C and 69.7 \(\pm\) 0.8 \({}^{\circ}\)C at 187 (blue) and 274 (red) nm with (full circle) and without the CTR (empty circle), respectively. Note that SRCD values in Figures 3 and 4A cannot be compared due to molecular alignment and the influence of SRLD signal that depends on cell orientation (see Section 3.1.3).  
the complex between tested RNAs at high temperatures. Without Hfq-CTR, this effect was not observed. Indeed, this observation is unique and needs further analysis leading to the understanding of the thermodynamics involved.
## 3 Test Equipment and Analysis Method  
### Test Equipment  
The pulsation test and analysis of the straight outlet conduit of the axial-flow pump device with or without a division pier were carried out. The pulsation test of the straight outlet conduit was tested on the \(\Phi\)120 hydraulic mechanical closed-loop test bench, as shown in Figure 7. The physical model of the axial-flow pump device includes four parts: an elbow inlet conduit, an impeller, a guide vane and a straight outlet conduit. The impeller model is ZM55, the nominal diameter of the impeller is 120 mm, the hub ratio is 0.40, the number of blades is 4, the blade placement angle is 0\({}^{\circ}\), the average blade tip clearance is 0.2 mm, the number of blades of the guide vane is 5 and the rated speed is 2200 r/min. The unilateral equivalent diffusion angle of the straight outlet conduit is 2.41\({}^{\circ}\), the inlet diameter is 1.17 times the nominal diameter of the impeller, the outlet area is 2.06 times that of the inlet area and the horizontal distance between the inlet face of the test section and the pump shaft is 1.87 times the nominal diameter of the impeller. The impeller is made of brass material, the guide vane is made of stainless-steel metal material and the elbow inlet conduit and the straight outlet conduit are made of transparent organic glass. The flow measurement used the LDG-125S-92 electromagnetic flowmeter produced by Shanghai Smooth Instrument Co., Ltd. (Shanghai, China), the head measurement used the EJA intelligent differential pressure transmitter and the torque and speed were measured by the JCO type 0.1 speed torque sensor. The CY302 high precision digital pressure sensor produced by Chengdu ShengYing measurement and Control Co., Ltd. (Chengdu, China) was used in the pressure fluctuation characteristic test, the pressure sensor probe size was \(\Phi\)5 mm, the sampling rate was 1000 times/s, the accuracy was 0.1% and the test range was 0-30 kPa. The output terminals of each sensor were connected to the Powerlink JW-3 display. The synchronous collection of the data of the four pressure sensors was realized through the 485 hub and the data collection software, and the water and air temperature during the laboratory experiment were kept unchanged, respectively.
## 2 Theoretical Background
### Preliminary Design and Conceptual Cost Estimation in Architectural Planning
However, as the project information that can be utilized in the planning stage is limited--given that specific project-related data such as design drawings or specifications have not been finalized--cost estimation is often performed by relying on the experience and judgment of the estimator based on the data obtained from previous similar construction projects [13]. In addition, the construction cost estimation methods for calculating the optimal construction cost in South Korea are mainly employed in the construction phase rather than the planning phase, and there is a lack of standards or references for construction cost prediction in the planning stage [14]. In South Korea, the commonly applied construction cost estimation methods are (1) construction cost calculation through statistical and empirical approaches and analysis of execution data, and (2) quantity-based construction cost estimation based on construction cost per unit area with reference to the building plan [2]. However, these approaches produce estimation results that do not appropriately reflect the importance of construction cost estimation in the planning stage or the available information, presenting limitations such as a high error rate due to a lack of reference data and association with construction in practice. Therefore, in this study, the scope of conceptual cost estimation was defined as the cost during the planning stage before the design development drawings were created. The main objective of this study was to develop a construction cost estimation model that reflects the information generated in the planning stage as parameters in addition to the area information.