esa-sceva/satcom-chunk-collection huggingface dataset:
# MS3D: A Multi-Scale Feature Fusion 3D Object Detection Method for Autonomous Driving Applications
## 4 Experiments and Analysis
### Evaluation Metrics  
AP (average precision): This metric evaluates model accuracy by averaging precision at various recall levels. A higher AP indicates superior detection accuracy.  
F1 Score: The harmonic mean of precision and recall, reflecting the balance between avoiding false positives and false negatives. Higher F1 scores denote improved overall performance.  
IoU (Intersection over Union): This metric measures the overlap between predicted and ground truth bounding boxes. IoU values range from 0 to 1, with higher values signifying better localization accuracy. This study employs an IoU threshold of 0.5.  
Frame Rate: This metric quantifies the number of frames processed per second in video, image, or point cloud analysis. It is measured in Hertz (Hz), where 1 Hz equals one frame per second.
# FD-LTDA-MAC: Full-Duplex Unsynchronised Scheduling in Linear Underwater Acoustic Chain Networks
## 4 Results and Discussion
### Large Scale Scenarios: 200, 500, and 1000 km Pipelines  
Large scale network scenarios include pipelines that span from 200 km to 1000 km. The achieved frame durations are presented in Figure 13. The results show that monitoring intervals for LTDA-MAC and LTDA-MAC over FD are low as frame durations are very long (607 s to 4457 s and 404 s to 2326 s for LTDA-MAC and LTDA-MAC over FD, respectively). From Figure 10, we can see that FD-LTDA-MAC compresses the frame durations to about 276 s-1399 s, thus providing much higher monitoring rates. Here, LTDA-MAC for the 1000 km pipeline may require up to 8000 seconds which may be impractical for some pipeline monitoring applications. Providing more regular monitoring for these longer pipelines may require high power and longer range costly acoustic modems, however, FD-LTDA-MAC on scenarios configured with 2 km sensing range acoustic modems significantly reduce the monitoring rate to more acceptable values, such as 498 s for a 500-hop 1000 km pipeline scenario. It is thus important to state that there should be a need to monitor quite regularly along the pipeline, this is the main reason for more hops which is backed up by the low cost modem making a greater number of devices a reasonable prospect.  
Figure 12: Frame duration cdfs of LTDA-MAC with HD nodes, LTDA-MAC with FD nodes and FD-LTDA-MAC for medium scale scenarios.
# UAV Image Small Object Detection Based on RSAD Algorithm
## 2 Related Work
### Datasets and Evaluation Metrics  
The VisDrone UAV image dataset [34] is used for the experiments in this study; the training set has 6471 samples, the validation set contains 777 samples, and each sample is 1920 \(\times\) 1080 pixels in size. The dataset consists of 10 classes: pedestrian, people, bicycle, car, van, truck, tricycle, awning tricycle, bus, and motor. More than 90% of the dataset's objects are small, which is a predominance (Figure 2).  
The assessment of object detection properties often relies on two metrics: Average Precision (AP) and mean Average Precision (mAP) [35], computed based on the intersection over union (IOU) between ground truth and predicted bounding boxes. AP is obtained from the area surrounded by the precision-recall curves (PR curve) and the axes. Additionally, mAP is the averaged AP value for all classes. The precision can gauge how well the object detection algorithm can classify objects. Additionally, the recall can gauge how well the model can identify an object. The higher value of precision and recall, the object detection algorithm performs better in classifying and identifying. The following demonstrates how the _Precision_ and _Recall_ are calculated:  
\[Precision=\frac{TP}{TP+FP} \tag{1}\]  
Figure 1: The network structure of SSD.  
\[Recall=\frac{TP}{TP+FN} \tag{2}\]  
In Equations (1) and (2), \(TP\) is true positive, which denotes the number of positive samples whose prediction result is true; \(FP\) is false positive, which denotes the number of negative samples whose prediction result is true; and \(FN\), false negative, denotes that the number of positive samples whose prediction result is false. Apart from that, the number of model parameters and the FPS were picked as indicators of the model's complexity and detection speed, respectively.
# MDIFL: Robust Federated Learning Based on Malicious Detection and Incentives
## 4 Experiment Results and Discussion
### Reputation Setting and Malicious Node Identification Analysis of MDIFL  
In this section, we analyze the proposed MDIFL's reputation setting method and malicious node identification method. We set up three types of participants: (1) malicious nodes (M_P), such as: free-riders and label-flipping, etc.; (2) participants who do not intend to do evil, but have low-quality data or other factors that lead to low performance of the trained model (S_P); (3) high-quality nodes (G_P).  
As shown in Figure 2, M_P is directly assigned a reputation of 0 after being identified in the first round, and will not be able to continue to participate in federated learning training tasks in the future. After the S_P class participants are identified three times, they also lose the opportunity to continue to participate in the training and assign the reputation value to 0. At the beginning of the next training, we will provide priority to selecting participants with a high reputation from the remaining G_P to continue to participate in model training.  
The method of MDIFL judging malicious nodes is described in detail in Figure 3, and we select some representative nodes among all nodes as a display. Among them, it can be clearly observed that M_P cannot participate in training after the malicious node is identified in the first round. S_P was identified three times; thus, the gradient similarity is in the range of \((\lambda_{1},\lambda_{2})\), and it was also excluded.
# Assessment of CYGNSS Wind Speed Retrievals in Tropical Cyclones
## 2 Datasets and Methodology
### CYGNSS Wind Products
The \"track-wise\" bias correction is then determined by comparing the observed average \(\sigma_{0}\) along each track to the average \(\sigma_{0}\) predicted by the GMF, calculated using the ECMWF model winds and Wavewatch model SWH, interpolated to the time and location of the CYGNSS observations. These winds are processed at NOAA and are subsequently delivered to the CYGNSS SOC and distributed to the public via PO.DAAC. A new version of SDR winds (V3.1), which will include SWH as a parameter, is under development at the SOC [15], but is not considered here as it is not yet public. A similar track-wise correction is applied to the CYGNSS Climate Data Record produced by the SOC. A preliminary version (V1.0) includes both FDS and YSLF wind products, while the most recent version (V1.1) includes only the FDS winds. The latest CYGNSS wind product is a storm-centric gridded (L3) dataset developed at the University of Michigan [26; 34], specifically designed for storm analyses and released to the public in early 2021 (data available at [[https://podaac.jpl.nasa.gov/dataset/CYGNSS_L3_S1.0](https://podaac.jpl.nasa.gov/dataset/CYGNSS_L3_S1.0)]([https://podaac.jpl.nasa.gov/dataset/CYGNSS_L3_S1.0](https://podaac.jpl.nasa.gov/dataset/CYGNSS_L3_S1.0)), accessed on 14 August 2021). This dataset provides storm-centric regional wind speed maps created by aggregating the CYGNSS SDR V3.0 L2 data in a \(\pm\)6 h temporal and \(\pm\)0.3\({}^{\circ}\) spatial window. The data are reported within a 7.2\({}^{\circ}\)\(\times\) 7.2\({}^{\circ}\) box at a regular 0.1\({}^{\circ}\) latitude-longitude grid. The box center is provided by the National Hurricane Center's Best-Track data estimates for the storm center. This preliminary dataset has been produced for most storms that occurred in the 2018-2020 Atlantic and Eastern Pacific hurricane seasons (where the Best-Track data are available). The data are organized into a single file for each storm, with a variable number of 6-hourly samples, depending on the length of the storm and the availability of CYGNSS observations in each 6-hourly timeframe. Table 1 summarizes the various CYGNSS wind datasets utilized in the analyses described here. Notice that we did not consider the older version SDR V2.1, and instead focus on the newer SDR V3.0 as a GPS calibration monitoring was implemented to reduce biases [10; 19].
# Defining Blood Plasma and Serum Metabolome by GC-MS
## 5 Workflow of GC-MS Analysis of Blood Metabolome
### Sample Preparation
#### 5.1.1 Quenching  
Biological samples can be divided into metabolically active, whose metabolic composition can change over time (cells, tissues), and metabolically inactive, whose metabolic profile is \"fixed\" (saliva, urine). For metabolically inactive samples, minimal sample preparation is usually applied. Highly active samples require metabolic processes to be quenched [69; 70]. Plasma and serum are biological samples of moderate metabolic activity, occupying an intermediate position between inactive urine and active cells, so they still may need a quenching stage (Figure 3, stage 1) [68].  
To quench metabolic processes in plasma or serum, it is necessary to abruptly stop enzymatic activity by changing the medium's polarity, temperature, or pH. Generally, quenching of enzymatic processes in plasma and serum coincides with the extraction of metabolites with an organic solvent.
## 2 Geometry and Signal Model  
The geometry configuration of BFL-SAR with a stationary transmitter and a maneuvering receiver is shown in Figure 1. The receiver works in forward-looking mode and travels in a descending curvilinear path along curve AB in plane yoz as the antenna points in the same direction. The fixed transmitter is mounted on a high tower where it can illuminate the imaging area easily. \(t_{\text{a}}\) and \(t_{\text{r}}\) denote the slow time (azimuth time) and the fast time (range time). The positions of the transmitter and receiver are \(T_{0}(X_{\text{r}},Y_{\text{r}},H_{\text{t}})\) and \(R_{0}(0,0,H_{\text{r}})\) at \(t_{\text{a}}=0\). The initial velocity and acceleration of the receiver are \(V_{0}=\left(0,V_{\text{r}\text{y}\text{o}},V_{\text{r  
\[\begin{array}{l}R(t_{\rm a})=R_{\rm t}+R_{\rm r}(t_{\rm a})=\sqrt{\left(X_{\rm t }-x_{\rm p}\right)^{2}+\left(Y_{\rm t}-y_{\rm p}\right)^{2}+\left(H_{\rm t} \right)^{2}}\\ +\sqrt{x_{\rm P}^{2}+\left(V_{\rm ry0}t_{\rm a}+\frac{1}{2}a_{\rm ry}t_{\rm a}^ {2}-y_{\rm p}\right)^{2}+\left(H_{\rm r}+V_{\rm rz0}t_{\rm a}+\frac{1}{2}a_{ \rm rz}t_{\rm a}^{2}\right)^{2}}\end{array} \tag{1}\]  
where \(R_{\rm t}\) and \(R_{\rm r}(t_{\rm a})\) are the transmitter range and receiver range, respectively.  
The signal model of the BFL-SAR is similar to the monostatic SAR, except for the range history. The transmitter range is only dependent on the position of \(P\) and independent of slow time \(t_{\rm a}\). Thus, the azimuth resolution depends on the motion of the receiver alone.
## 4 Experiments and Analysis
### Experimental Results and Analysis
#### 4.2.2 Validity Analysis for the SSE Feature Set  
The emissivity imagery and the raw thermal infrared hyperspectral imagery data were both upsampled to the same high resolution as the visible data. The first five components of these two images were used for the integration with the spatial-spectral feature set from the visible imagery, respectively, and they can be represented as the SSE feature set and the SSR feature set (integrating the raw radiance image with the spatial-spectral feature set). The SSE feature set was constructed based on the retrieved emissivity information from the raw thermal infrared hyperspectral imagery data. The constructed feature sets were input into the SVM classifier separately, and the confusion matrices were obtained using the test samples. As shown in Tables 3 and 4, by the use of the SSE feature set, the OA is increased from 91.7864 to 91.8101, and the kappa is increased from 0.8775 to 0.8779. It can be revealed that the classification results obtained using the SSE feature set improve the identification of the bare soil compared with the results obtained using the SSR feature set, and the confusion between bare soil and red roof is improved. In addition, the accuracies of the roof classes are improved slightly when using the SSE feature set. Overall, the results show that the construction of the SSE feature set leads to a better classification performance.  
Figure 4: Accuracy of the classification results for each class when fusing the visible imagery with different combinations of extracted features.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Road** & **Trees** & **Red Roof** & **Grey Roof** & **Concrete Roof** & **V Vegetation** & **Bare Soil** \\ \hline Road & 92.83 & 0.18 & 0.04 & 1.84 & 0.79 & 1.33 & 0.63 \\ Trees & 0.05 & 89.32 & 0.13 & 0 & 0.03 & 20.65 & 0 \\ Red roof & 0.01 & 0.02 & 96.57 & 4.31 & 0.01 & 0.01 & 7.39 \\ Grey roof & 5.24 & 0.31 & 2.77 & 91.02 & 3.55 & 0.06 & 0.25 \\ Concrete roof & 1.84 & 0 & 0.22 & 2.84 & 95.62 & 0.01 & 0.13 \\ Vegetation & 0 & 10.03 & 0.21 & 0 & 0 & 77.93 & 2.37 \\ Bare soil & 0.03 & 0.14 & 0.07 & 0 & 0 & 0.01 & 89.22 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy assessment of the classification results obtained with the SSR feature set.
# Conversational AI over Military Scenarios Using Intent Detection and Response Generation
## 4 Experiments
### Performance in Response Generation  
Next, it evaluates the performance of retrieval-based answers. First, a question-and-answer dataset of 180 military joint tactical action lists is manually collected as standard answers. Three sentences are then randomly selected for each query, resulting in four candidates. Responses in the training dataset are ranked from 1 to 4, where 4 represents the highest score for the questions-answer pairing. We use the NDCG indicator as to the evaluation indicator for the LTR model. The training and testing datasets are divided into three different scales, and the experimental results for each scale are shown in Figure 7. Since the 9:1 ratio gives the highest NDCG score, this model is used as the LTR model for our conversational system.  
Finally, eight military scenarios are randomly selected from 40 question-and-answer test data for performance evaluation. The Table 9 shows questions and answers for eight military scenarios. The intent is first identified for each question, and then responses are generated based on the extracted intents and entities and intent-entity relationships. Each intent may generate multiple sets of candidate sentences, and the first set of responses is the candidate sentence with the highest relevance to the query. The results of the dialogue were qualitatively assessed by 47 military-related personnel. The proportion of responses  
Figure 5: Performance of four types of intent classification.  
Figure 6: Performance of five types of named-entity recognition (NER).  
that meet user needs is shown in the Figure 8. If the user is not satisfied with the first response, the dialogue system generates a new query and then provides the second answer. Experimental results show that, on average, 66% or more of users are satisfied after the second round of conversations. Therefore, new queries generated by the system in the second round of dialogue (when the first answer is not satisfactory) significantly improved user satisfaction.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Session** & **Examples of Question Answering** \\ \hline \multirow{3}{*}{S1} & Q:
## 2 Materials and Sample Preparation
### PAUT Guided Wave Method  
Two different types of materials were used in this experiment, Aluminum (Al) and CFRP plates, as introduced in Table 1. The reason for selecting these materials for guided wave evaluation was that they were available in plate shape and desired thicknesses (in the range of 1 to 2 mm) for guided wave generation. For both Aluminum and CFRP samples, artificial defects, in the form of drilled holes, were made into the samples. Figure 2 shows a schematic for the location and depth of the artificial holes in both inspected samples.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample Name** & **Material** & **Thickness (mm)** \\ \hline Al-1 & Aluminum 6063 (Plate) & 2.2 \\ Al-2 & Aluminum 6063 (Plate) & 0.635 \\ CFRP & Unidirectional Carbon Fiber Composite (5 layers of carbon fiber fabric) & 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test samples description used for the phased array ultrasonic (PAUT) guided wave method.  
Figure 1: Samples used to evaluate capability and sensitivity of defect detection in single element ultrasonic (SEUT) versus phased array ultrasonic (PAUT) methods: (**a**) glass fiber reinforced polymer (GFRP) samples from a wind turbine blade and (**b**) schematic for the artificial hole locations in GFRP sample with 25 mm thickness (Thk.). Sample size is \(250\times 100\times 25\) (L \(\times\) W \(\times\) Thk.) mm. Width of the sample is 100 mm and holes drilled in the middle of the width.
# Linear Computation Coding: A Framework for Joint Quantization and Computing
## 4 Proposed Scheme for Linear Computation Coding
### Codebook Design
#### 4.4.4 Codebook Evolution  
If multiple wiring matrices are used, the codebook evolves towards the target matrix. For multiple wiring matrices, the previous approximation of the target matrix serves as a codebook. Thus, with an increasing number of wiring matrices, the codebook gets closer and closer to the target matrix, no matter what initial codebook was used.  
Codebook evolution can become a problem if the target matrix is not a suitable codebook, e.g., it contains collinear columns or is rank deficient. In such cases, multiple wiring matrices should be avoided or reduced to a small number.  
Codebook evolution can also be helpful. This is the case if the original codebook is worse than the target matrix, e.g., because it shall be computationally very cheap as for the self-designing codebook.
# Scene Adaptive Segmentation for Crowd Counting in Population Heterogeneous Distribution
## 3 Methodology
### Density Estimation Map  
Due to existing perspective distortion, utilizing straightaway on the annotation with the Gaussian function for generating the density map is not excellent. In this research, we adopt the labeled image that inputs the central point of a head, and outputs the density estimation map. The normalization employs a geometry adaptive kernel. The specific calculation can be presented.  
\[H(x)=\sum_{i=1}^{N}\delta(x-x_{i}) \tag{1}\]  
\[F(x)=\sum_{i=1}^{N}\delta(x-x_{i})*G_{\sigma_{i}}\left(x\right),\ \sigma_{i}=\beta\overline{d_{i}} \tag{2}\]  
\[\overline{d_{i}}=\frac{1}{m}\sum_{j=1}^{m}d_{j}^{i} \tag{3}\]  
where \(x_{i}\) represents the pixel of a head, \(N\) is representing the people counting in a given region, and \(\overline{d_{i}}\) represents the mean distance between a head and these heads of its nearest \(m\) neighbors. \(\delta\) represents ground truth, and \(G_{\sigma_{i}}\) is the performed Gaussian blur processing on each head. In the experiment, the parameter setting of CSRNet is adopted as the standard deviation, \(\beta=0.3\), \(m=3\).
## 3 Proposed Design
### Ground-to-Underwater Channel Model  
In the proposed design, a line-of-sight (LOS) UWOC link is incorporated from a ground-to-underwater link. The received power for the LOS UWOC is presented as [18; 23]:  
\[P_{r\_LOS}=P_{t}\alpha_{t}\alpha_{r}exp\left[-z(\lambda)\frac{d}{\cos(\delta )}\right]\frac{A_{R}\cos(\delta)}{2\pi d^{2}[1-\cos(\delta_{0})]} \tag{7}\]  
where \(P_{t}\) is the average power at Tx, \(\alpha_{t}\) is the optical efficiency at Tx, \(\alpha_{r}\) is the optical efficiency at Rx, \(d\) is the vertical distance between the Tx and Rx planes, \(\delta\) is the angle between the Tx and Rx trajectories as well as that from the normal to the Rx plane, \(A_{R}\) is Rx aperture area, and \(\delta_{0}\) is the laser beam divergence angle. Table 2 indicates the various optical properties of different water types used in the proposed design.  
The BER of the proposed design under atmospheric conditions is presented as [18; 23]:  
\[BER=0.5\int_{0}^{\infty}f_{h_{a}}(h_{a})\ erfc\left(\frac{\langle SNR\rangle s }{2\sqrt{2}\langle i_{s}\rangle}\right)ds \tag{8}\]  
\begin{table}
\begin{tabular}{c c c c} \hline
**Water Type** & **x(\(\lambda\)) m\({}^{-1}\)** & **y(\(\lambda\)) m\({}^{-1}\)** & **z(\(\lambda\)) m\({}^{-1}\)** \\ \hline Pure sea & 0.0405 & 0.0025 & 0.043 \\ Clear ocean & 0.037 & 0.114 & 0.151 \\ Coastal ocean & 0.219 & 0.179 & 0.398 \\ Harbor & 0.913 & 0.187 & 1.1 \\ \hline \end{tabular}
\end{table}
Table 2: Optical properties of different water types [18; 23].  
where \(\langle SNR\rangle\) is the mean signal-to-noise ratio (SNR), \(i_{\rm s}\) is mean signal current value, and \(erfc\) is a complementary error function.
# Radiation-Balanced Lasers: History, Status, Potential
## 3 Theory of Radiation-Balanced Lasing
### Athermal Lasing  
As already mentioned, in 1999, [PERSON] [14] suggested to use cooling with anti-Stokes fluorescence to suppress heat generated in RE-doped solid-state lasers. He developed a new solid-state bulk laser design without internal heat generation called a radiation-balanced or athermal laser. The processes of lasing and anti-Stokes cooling in athermal lasers occur in the same system of RE ions. In 2001, [PERSON] and [PERSON] proposed a scheme in which lasing occurs in one system of ions (Nd\({}^{3+}\)), while anti-Stokes cooling takes place in another system of ions (Yb\({}^{3+}\)) doped in the KY\({}_{3}\)F\({}_{10}\) laser host [25].
# An Improved Hybrid Transfer Learning-Based Deep Learning Model for PM2.5 Concentration Prediction
## 4 Discussion
### Test of Generalization Ability for Different Regional Sites  
The previous experiment was to transfer data from Beijing to Tianjin. Because Tianjin and Beijing are very close, the effect of transfer learning is not universal. Cities farther away from Beijing are now selected as the target domains to verify the generality of the proposed method. In this study, the PM2.5 concentration in Guangzhou was predicted by transfer learning from the Beijing sites data. The details of the dataset in Guangzhou are as follows: the time range and the number of the samples are the same as those of Tianjin site. The standard deviation and mean value of the dataset in Guangzhou are 22.48 and 37.02, respectively. To facilitate comparison, we intercepted the Guangzhou site at the same point in time as the previous target domain. Seventy-five percent of the target domain data are used for training, and the remaining 25% of the samples are used for testing. The interpolation of missing values is the same as that in Section 3.1. The experimental results of using different models to predict PM2.5 concentrations in Guangzhou are shown in Table 4.  
The results in Table 4 show that the indicators of TL-DSTP-DANN are the best, indicating that the proposed method has good generalization ability. The true and predicted values of PM2.5 concentrations at the Guangzhou site predicted using different models are shown in Figure 6. It can be seen from the figure that compared with the TL-LSTM and TL-DSTP-DANN models, the predicted and true values of the TL-BiLSTM and TL-DSTP models in 24-72 h are quite different. However, the predicted results of the TL-LSTM model in 120-168 h are not ideal, while TL-DSTP-DANN is relatively ideal for predicting PM2.5 concentration in Guangzhou. Therefore, the comprehensive performance of the TL-DSTP-DANN model is the best.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **MAE** & **RMSE** & **MAPE** \\ \hline TL-LSTM & 18.68 & 24.72 & 0.38 \\ TL-BiLSTM & 16.13 & 20.99 & 0.30 \\ TL-DSTP & 13.45 & 19.89 & 0.21 \\ TL-DSTP-DANN & 12.20 & 17.84 & 0.20 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance of different models in predicting PM2.5 concentrations in Guangzhou.  
Figure 5: Prediction of PM2.5 concentrations at Tianjin site using models with different feature extractors.