esa-sceva/satcom-chunk-collection huggingface dataset:
# Mask R-CNN-Based Stone Detection and Segmentation for Underground Pipeline Exploration Robots
## 3 Experimental Results and Analysis  
### Evaluation of the Stone Detection Model  
The loss function change of Mask R-CNN during training is shown in Figure 5. In the training process of our model, while the learning rate was set to 0.001, the loss value dropped quickly from 0.9 to 0.6 then gradually dropped to less than 0.2. The Precision-Recall curve shown in Figure 6 indicates that the trained model has achieved an adequate detection accuracy.  
An example of true positive results of the stone detection model is shown in Figure 7, where all the stones are detected and segmented correctly. Figure 7a shows the original image of a regular pipe with low light intensity. In Figure 7b, the detected stones are shown in the square box with the confidence score. And Figure 7c shows the binary image with all the segmented stones.  
Figure 5: Training loss of Mask R-CNN.  
Figure 6: Precision–Recall curve of Mask R-CNN.  
While our model successfully identified the stones in the majority of test photos, there were instances where it was unable to identify the objects. To demonstrate the shortcomings of our model, we ran the test for false negative and false positive scenarios. The details of these tests are shown in Figures 8 and 9, respectively.  
In Figure 8, two objects indicated by white square boxes are expected to be detected by the model but are not detected and thus create false negative cases. There were sufficient training images similar to the Figure 8 backgrounds that are partially filled with sand. The original image of the stones with a sand background in moderate light intensity is shown in Figure 8a. Figure 8b shows the detected stones with a color mask and the undetected stones with square boxes. Figure 8c demonstrates the binary image of the segmented stones.  
Figure 8: Examples of false detection. (**a**) Original image with sand-filled background and moderate light intensity. (**b**) Output of the stone detection model (_false negative_ case), and (**c**) Binary image of segmented stones.  
Figure 7: Example of accurate detection result. (**a**) Original image with normal background and low light intensity. (**b**) Output of the stone detection model (_true positive_ case), and (**c**) Binary image with all segmented stones.  
Figure 9: Examples of wrong detection. (**a**) Original image with wetted background and moderate light intensity. (**b**) Output of the stone detection model (_false positive_ case), and (**c**) Binary image with false positive segmented stones.  
Finally, an example of a false positive case is shown in Figure 9, where an object marked by a yellow squared box is wrongly detected as a stone by the stone detection model. Figure 9a shows the original picture of a wetted background with stones in moderate light intensity. Figure 9b shows the segmented stones with a false positive case. And Figure 9c shows the binary image of the segmented result.
# Inferential Statistics Is an Unfit Tool for Interpreting Data
## 1 Introduction  
The American Statistical Association recommends that researchers stop using null-hypothesis statistical significance testing (NHST) and move beyond \(p\)-values when analyzing and reporting data [1; 2]. The special volume on \(p\)-values in The American Statistician (volume 73, issue suppl. 1) is a recent interjection in a century-old debate [3], but has quickly had an impact on several journals' guidelines on statistical reporting [4; 5] and was itself influenced by other forerunner journals [6]. Some key arguments against NHST as _a tool for making inferences_ are that statistical significance testing does not give you the probability that your hypothesis is correct [7; 8], NHST obscures uncertainty and the quantitative nature of empirical data (it leads to \"dichotomania\") [9], NHST does not summarize the evidential value the data carries toward, or against, the researcher's hypothesis [10; 11; 12], the assumptions underlying NHST are often known to be wrong a priori [13], and NHST triggers questionable research practices [14; 15; 16]. Here I will advance another concern with the usage of NHST, namely that NHST, and other abstract statistical benchmarks, often are used as tools for _interpreting_ data.  
Researchers need to separate the acts of _interpreting data_ and _making statistical inferences from the data_. A researcher who has collected empirical data has two different considerations in front of them: (1) \"What is the result _in this study sample?_\"--they must _interpret_ how the data in their sample meaningfully relate to their research question, and (2) \"What can be _generalized_ from the result of this study?\"--they must describe what can be _inferred_ from the sample to a broader population. Even defenders of NHST _as a tool for making inferences_ will surely agree that NHST was never meant to be used as a tool for _interpreting_ data. This is because interpreting data requires domain knowledge--expertise in the scientific topic, clinical experience, practical insight--and an understanding of the study context, something that is not incorporated into NHST, which [PERSON] and other early developers of NHST, of course, were aware of [3; 8].
# Image Encryption Algorithms: A Survey of Design and Evaluation Metrics
## 3 Evaluation Metrics
### Encryption Quality
#### 3.5.1 Mean Square Error  
The MSE is a metric used to calculate the average square difference between the pixel values of a plaintext image and its cipher. In image encryption, it is used to evaluate the quality of an image encryption algorithm [55]. A lower MSE value indicates a greater similarity between the plaintext image and its cipher, whereas a higher MSE value means a lower similarity between the plaintext image and its cipher; therefore, a higher MSE value signifies higher-quality encryption. The MSE between a plaintext image and its cipher image can be calculated as follows:  
\[MSE=\frac{1}{H\times W}\sum_{i=1}^{H}\sum_{j=1}^{W}[P(i,j)-C(i,j)]^{2}, \tag{23}\]where \(H\) and \(W\) represent the height and width of the images, respectively. \(P(i,j)\) and \(C(i,j)\) denote the pixel values of the plaintext image and the cipher image at position \((i,j)\), respectively. From Equation (23), the minimum and maximum possible MSE values for an image, regardless of its size, can be calculated. For the minimum value of MSE, if we assume the two images to be identical, then \(P(i,j)-C(i,j)\) will be 0. Thus, the MSE minimum value is 0. For the maximum value, we assume that the two images are completely different from each other (one with 255-pixel values, and the other with 0-pixel values); the maximum possible MSE can be calculated as follows:  
\[MSE=\frac{1}{H\times W}\sum_{i=1}^{H}\sum_{j=1}^{W}[255-0]^{2}\]  
\[\begin{split}&=\frac{1}{H\times W}\times H\times W\times[255]^{2} \\ &=\frac{H\times W\times[255]^{2}}{H\times W}\\ \end{split} \tag{24}\]  
\[MSE=65,025\]
## 4 Simulation Experiment and Analysis
### Q-Learning Routing Algorithm Performance Evaluation
#### 4.2.2 Average Transmission Delay of Service  
Figure 15 describes the average transmission delay curves for delay-sensitive services under different algorithms. At this time, set the parameters in the Q-routing algorithm take \(a_{1}\) to 0, \(a_{2}\) to 1, and \(a_{3}\) to 0. As shown in the figure, with the increase in service flow, the average transmission delay continues to rise, and the delay of the Q-routing routing algorithm is lower than that of the Dijkstra algorithm. Because for services with high delay requirements, when designing the reward function, the proportion of the reward for the delay is increased. Therefore, in the training phase, the transmission path with the lowest delay is tended to be selected. Before the link load reaches 80%, the transmission delay of the Q-learning algorithm and the Dijkstra algorithm are the same. Although it always forwards the data packet with the shortest number of hops, the link delay of each hop may not be the lowest, so the cumulative transmission delay is slightly higher than that of the Q-routing algorithm.  
Through the above comparison, we found that by setting the reward function in Q-routing, we can approach the optimal solution very well. In the process of comparing with other algorithms, the Q-routing routing algorithm can effectively balance the load and improve the request acceptance rate to a certain extent. It also has better performance than other algorithms for delay-sensitive services.
# Research on a Support System for Automatic Ship Navigation in Fairway
## 4 Automatic Navigation System of a ship in a Fairway
### Navigation Algorithm and Decision Making Block  
As shown in Figure 5, the task here is to calculate the desired variables, such as course (\(\psi_{\mathrm{d}}\)), distance between the ship and the relevant waypoint (d), and the distance from the ship to the current segment of the planned route (\(\Delta\)). The ship moves ahead on each segment from waypoint WP1-(\(x_{d\_1}\), \(y_{d\_1}\)) to waypoint WP1(\(x_{d\_}\), \(y_{d\_1}\)),. It is assumed that ship position at current time (\(t\)) is equal to \(OS(x_{t}\), \(y_{t})\). In this segment, the desired course angle (\(\psi_{\mathrm{d}}\)) between the two waypoints WP1-(\(x_{d\_1}\), \(y_{d\_1}\)) and WP1(\(x_{d}\), \(y_{d}\)) is:  
\[\psi_{\mathrm{d}}=\text{ arctan }\bigg{(}\frac{y_{d}-y_{d\_1}}{x_{d}-x_{d\_1}} \bigg{)}. \tag{11}\]  
The cross track error from the ship to the current segment of the planned route (\(\Delta\)) is given by:  
\[\Delta = \frac{ax(t)+by(t)+c}{\sqrt{a^{2}+b^{2}}}, \tag{12}\]  
where \(x(t)\), \(y(t)\) are the ship position; \(a\), \(b\), \(c\) are coefficients of the line constructed by waypoints (WP1-(\(x_{d\_1}\), \(y_{d\_1}\)) and WP1(\(x_{d\_}\), \(y_{d}\))). The coefficients are defined as:  
\[a=-1.\]  
Figure 4: The designed route of ship in fairway.  
\[b=\ \frac{y_{d-1}-y_{d}}{x_{d}-x_{d-1}} \tag{13}\]  
\[c=\ x_{d}+\ y_{d-1}\bigg{(}\frac{y_{d}-y_{d-1}}{x_{d}-x_{d-1}}\bigg{)}.\]  
The distance between the ship and waypoint WP\({}_{\text{i}}\)(\(x_{d}\), \(y_{d}\)) is calculated as follows:  
\[\text{d}= \sqrt{\left(x_{d}-x(t)\right)^{2}\ +\ (y_{d}-y(t))^{2}}. \tag{14}\]  
The ship reaches the current waypoint (WP\({}_{\text{i}}\)) with a certain remaining distance (d). The distance (d\({}_{0}\)) determines the margin for the course change to the next waypoint. If the distance (d) is less than the fixed distance (d\({}_{0}\)), then the controller will alter the ship rudder angle to change the ship heading toward the next waypoint (WP\({}_{\text{i+1}}\)). By applying this rule, the ship course controller will change the ship course to the new waypoint.  
To change the operating modes of the proposed system, the decision-making block is used. According to the correlation of ship position and ship track, this block chooses the corresponding controllers. For example, in (Figure 4), from waypoint No.1 to waypoint No. 6, only the track-keeping controller is used to control the ship with the rudder. From way-point No.6 to waypoint No. 9, the track-keeping controller and the speed controller are operated simultaneously. On the segment between way-point No.9 to waypoint No. 10, the heading stabilizing controller is activated to bring the ship heading to the desired angle for the berthing process.
###### Abstract  
This paper advocates for spatial simultaneous functioning (SSF) over time division multiple access (TDMA) in joint communication and sensing (JCAS) scenarios for improved resource utilization and reduced interference. SSF enables the concurrent operation of communication and sensing systems, enhancing flexibility and efficiency, especially in dynamic environments. The study introduces joint design communication and sensing scenarios for single input single output (SISO) and multiple input multiple output (MIMO) JCAS receivers. An MIMO-JCAS base station (BS) is proposed to process downlink communication signals and echo signals from targets simultaneously using interference cancellation techniques. We evaluate the communication performance and sensing estimation across both Rayleigh and measured realistic channels. Additionally, a deep neural network (DNN)-based approach for channel estimation and signal detection in JCAS systems is presented. The DNN outperforms the traditional methods in the bit error rate (BER) versus signal-to-noise ratio (SNR) curves, leveraging its ability to learn complex patterns autonomously. The DNN's training process fine-tunes the performance based on specific problem characteristics, capturing the nuanced relationships within data and adapting to varying SNR conditions for consistently superior performance compared to the traditional approaches.  
J +
Footnote †: journal: Applied Sciences  
1
## 4 Results
### Collision Interaction with RoomMesh  
#### 4.2.1 SubMesh Extraction and Collision Interaction Process  
In this study, to enable precise physical interaction between virtual cloth and real-world surfaces, we utilize the RoomMesh scanning capabilities of Meta Quest 3. The user selects four points using the controller, based on which an Axis-Aligned Bounding Box (AABB) is generated. Only the RoomMesh triangles contained within the AABB are filtered to create a SubMesh, which is used as a collision surface for cloth simulation experiments.  
This SubMesh extraction method reduces computational overhead by focusing collision processing only on necessary areas, thus ensuring real-time performance. The overall process of SubMesh extraction and collision interaction is illustrated in Figure 6.  
The overall SubMesh extraction and collision process consists of the following four steps:  
* **Step 1: RoomMesh Acquisition** The environment is scanned using Meta Quest 3 to acquire a full RoomMesh.
* **Step 2: Region Selection (White Pointer)** The user defines the target region by selecting six points with the controller, where a white pointer visually indicates the selected points.
* **Step 3: SubMesh Generation and Collision Position Display** Based on the selected points, an AABB is generated, and RoomMesh triangles within the AABB are extracted to form a SubMesh. After dropping the virtual cloth onto the SubMesh, the final collision position is displayed and held on screen for approximately 1 s.
* **Step 4: Cloth Collision Experiment** The SubMesh is instantiated as the collision surface, and the cloth simulation experiment is performed.
## 4 III-V-on-Silicon Devices for Optical Communication Applications
### III-V-on-Silicon Laser Sources for Optical Communication Applications
#### 4.1.5 1550 nm Mode-Locked Lasers
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Self-Colliding [41]** & **Ring [41]** & **Anti-Colliding [42]** \\ \hline
10 dB optical bandwidth (nm) & \(>\)10 & 7 & 3.5 \\ Pulse width (ps) & 1.5 & - & 3 \\
3 dB electrical line width (kHz) & 12 & 16 & 1.7 \\ Integrated timing jitter (50 kHz–10 MHz) (ps) & 2.65 & 1.65 & 2.8 \\ Threshold current (mA) & 40 & 50 & 30 \\ Output power at thermal roll-over (mW) & 0.2–0.3 & 0.04–0.08 & 8–10 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the performance of the different mode-locked laser (MLL) designs. Figure 11: Layout of the different III-V-on-silicon mode-locked laser cavity designs: (**a**) A linear cavity colliding pulse geometry; (**b**) A ring cavity geometry; (**c**) The linear cavity anti-colliding pulse geometry. Figure 12: (**a**) High resolution optical spectrum (bandwidth: 20 MHz) of the linear cavity colliding pulse mode-locked laser. (**b**) Wide span electrical spectrum of the generated pulse train of the anti-colliding pulse MLL. (**c**) Linewidth of the fundamental RF tone of the anti-colliding pulse MLL. Reproduced from [41, 42]. Furthermore, the anti-colliding pulse MLL clearly outperforms the other two geometries in terms of output power and RF line width. This is in accordance with the theoretical analysis of such a cavity under passive mode-locking [43]. Figure 12b shows a wide span electrical spectrum of the anti-colliding pulse MLL, showing both the fundamental RF tone and its harmonics. The electrical line width of the fundamental RF tone of this laser (1.7 kHz 3 dB linewidth and 5 kHz 10 dB linewidth) is plotted in Figure 12c.
## 3 The Motion State Estimation Method
### Motion State Estimation
#### 3.4.1 Spatio-Temporal Correlation and Clustering  
The Lidar acquisition frame rate is generally 10 Hz, that is, 100 ms per frame. By overlapping the motion object point clouds at different times, the motion object point cloud containing spatial and temporal dimensional information can be obtained, and the same object across different times can be associated by clustering the motion-segmented objects in the temporal and spatial dimensions. Although the point cloud data from different time frames are overlaid, the moving objects occupy only a small proportion of the total points in each scanning scene. Consequently, the overall number of moving points remains relatively low, leading to highly efficient processing.  
In order to realize the fast spatio-temporal clustering of objects, we use DBSCAN (Density-Based Spatial Clustering of Applications with Noise) [26] to cluster the object points, and fuse the temporal information in the clustering. Specifically, the description of a point \(p(x,y,z)\) is added with time \(t\) to indicate the acquisition sequence of the point cloud in addition to the 3D spatial coordinates \((x,y,z)\) to obtain \(p=(x,y,z,t)\). The moving objects in consecutive frames are related by accumulating the spatial dimensions during clustering and utilizing the temporal information saved in the \(t\)-channel. Since the density is shifted on different time frames, we cannot know the exact offset vector. In order to capture the position after the density offset, we incorporate the density core of the initial frame into current frame to compensate for the density offset in the temporal dimension. By accumulating the points over time, we can obtain the individual moving objects and correlate the objects over time after the clustering operation. In this way, the point cloud clusters for each moving object in multiple frames can be identified as Equation (10).  
\[C_{N}=\sum_{i=1}^{n}\Big{\{}P_{i}^{t}\|\|P_{i}^{t}-P_{center}\|^{2}<\epsilon,P _{i}^{t}\in R^{3\times n}\Big{\}}_{t=1}^{T} \tag{10}\]  
where \(P_{center}\) is the core point of DBSCAN and \(\epsilon\) is a hyper-parameter.  
Figure 7: The structure of the motion state estimation.
## 3 Results and Discussions
### Comparing Our Proposed Methods to Existing Methods  
As a final experiment, we compare the performance of our proposed methods with LSTM and CNN-LSTM [16; 23]. This experiment proposed a fall-detection study using competing deep learning methods that use multi-windowing and competing methods of balancing the class data samples. We implemented the existing methods to compare them with our method and evaluate them using our dataset. Table 5 and and the confusion matrix in Figure 4 compare the best results of the LSTM encoder and transformer encoder with those of [16; 23]. Compared to [16; 23], which reported mean F1 scores of 0.89 and 0.86, respectively, the proposed LSTM encoder performs better with a mean F1 score of 0.97 for shank datasets. Moreover, the proposed transformer encoder method with multi-window segmentation identified falls against non-fall activities with an average F1 score of 0.96 for the shank datasets. These performances show that our proposed approach improves the results by using multi-windowing segmentation and a customized loss function for handling imbalanced datasets while using six features (three acceleration and three angular velocity features).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Balancing Techniques** & **F1 Score** & **Balanced Accuracy** \\ \hline Accelerometer & 0.90 & 0.92 \\ Accelerometer + Gyroscope & 0.97 & 0.98 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results for the different sensor types.  
Figure 4: Confusion matrix for the different methods: LSTM [23], CNN-LSTM [16], transformer encoder (our method), and LSTM encoder (our method), from left to right.  
Lastly, we visualized the actual and predicted classes of the LSTM-based model for the sample data points. As shown in Figure 5, the visualization shows the data points and their corresponding predicted and actual classes. In the third column of the first row, the ADL, a near-fall activity, is correctly classified as a non-fall activity. This shows that our model is stable even for near-fall events.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Balancing Technique** & **F1 Score** & **Balanced Accuracy** \\ \hline LSTM [23] & 0.86 & 0.92 \\ CNN-LSTM [16] & 0.89 & 0.94 \\ Our proposed (transformer-based) & 0.96 & 0.97 \\ Our proposed (LSTM-based) & 0.97 & 0.98 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of our proposed methods against existing methods.  
Figure 5: Sample predicted classes using the LSTM encoder-based model. Non-fall activity (class 0), fall activity (class 1), and True, Pred are the actual and predicted classes, respectively.
## 1 Introduction
Their results conclusively demonstrated the superior effectiveness of RF classification. Currently, great progress has been made in the application of hyperspectral data for vegetation information extraction and classification, but it is essential to recognize that satellite hyperspectral images often have a limited spatial resolution. Moreover, the mixed distribution and high similarity in spectral signatures among different vegetation types in wetland areas underscore the critical importance of feature extraction and selection [17]. This study employs Zhuhai-1 satellite images, which integrate a high spatial resolution with hyperspectral resolution, for the innovative assessment of recent changes in five prevalent wetland vegetation types within the Yellow River Delta. A separability lookup table for the range of differences in the five vegetation bands is established, combining the random forest algorithm, support vector machine, and maximum likelihood estimation to classify the study area. Subsequently, based on the classification results obtained from the superior algorithms, statistical analyses are conducted to scrutinize changes in the area of different vegetation types. This study offers guidance for the safeguarding and surveillance of the wetland flora in the Yellow River Delta.