esa-sceva/satcom-chunk-collection huggingface dataset:
# A Rotating Object Detector with Convolutional Dynamic Adaptive Matching
## 4 Experiment
### Ablation Studies  
#### 4.4.1 Evaluate Different Modules  
To verify the improvement in dynamic convolution and gradient adaptive equalization loss functions in RS object detection performance, we conducted ablation experiments on the HRSC-2016 and UCAS-ADO datasets. Table 1 lists the experimental results of this model on the HRSC-2016 dataset. The mAP of the baseline model is only 84.35%, which is because ordinary convolutions slide horizontally along the axis, making it difficult to model high-aspect-ratio and multi-directional targets. When DCM modules were added to the backbone network, mAP increased by 4.34%. This indicates that the dynamic rotation convolution proposed in this paper is more sensitive to the direction of aerial targets, improving the network's ability to extract directional features. After using the loss function GAE, mAP improved by 2.88%. This suggests inadequate training of high-quality samples in aerial targets. By optimizing the loss function and enhancing the gradients in this segment, the training performance of the model can be improved. When both DCM and GAE are added to the network model, mAP increases by 5.79% and reaches a maximum value of 90.14% This indicates that the dynamic convolution and gradient adaptive equalization loss functions could enhance the network's ability to model the target spatial direction while maintaining training stability, thereby significantly improving detection performance.  
We achieved similar experimental results on the UCAS-ADO dataset. Table 2 compares individual modules and proves that the superposition of two modules has the most significant improvement effect on the network. In feature extraction, DCM improves the model's ability to extract direction features. During training, GAE balanced the sample gradient and increased the contribution of high-quality samples to the loss. There is no conflict between the DCM and gradient adaptive equalization loss function, and when used simultaneously, the mAP reaches 90.52%.
# BTCAM: Attention-Based BiLSTM for Imbalanced Classification of Political Inquiry Messages
## 1 Introduction
Which category it belongs to requires more contextual information to determine. If this message is talking about road repair or expansion, it might be more inclined to _\"Transportation.\"_ If it is about construction of facilities within the community, it may fall under _\"Urban and rural construction\"_. * Another problem is data imbalance. Figure 2 shows the proportion of different categories of political inquiry messages, among which _\"Urban and rural construction\"_ is the most popular category, with a proportion of 22.7%, followed by _\"Labor and social security\"_. _\"Transportation\"_ comprises the smallest proportion, with 8.2%. This indicates the significant difference in the distribution of different categories in the dataset, which will negatively impact the performance of models. To address the above issues, we propose an attention-based BiLSTM for the classification of imbalanced political inquiry messages, named BTCAM. To address the data sparsity and data imbalance problems, we first propose a topic-based data augmentation algorithm to increase the number of high-quality messages. In addition, we design a dual-gated convolution mechanism that utilizes skip connections to preserve the semantics in the original input while extracting important features through a streamlined convolutional block attention module (SCBAM). The main contributions of this paper can be summarized as follows:  
* We propose a topic-based data augmentation algorithm (TDA). Experiments demonstrate that TDA not only enhances the linguistic quality of sentences but also markedly elevates the operational efficiency of data processing. * We design a streamlined convolutional block attention mechanism (SCBAM) and demonstrate its effectiveness in short-text classification. This plug-and-play module can be seamlessly integrated into diverse model architectures with minimal computational overhead. * We propose an attention-based BiLSTM for the imbalanced classification of political inquiry messages. Extensive experiments show that BTCAM outperforms the state-of-the-art baseline.
# Extraction of City Roads Using Luojia 1-01 Nighttime Light Data
## 3 Method
### Image Enhancement  
Due to the similarity of the DN values between facilities and roads, the edges of roads were difficult to extract from the nearby environment. In image processing, some image enhancement methods were often used to enhance the difference between pixels, thus emphasizing the characteristics of interest areas. As a representative image enhancing technology, image sharpening was often used in the image enhancement tasks, the principle of which is to compensate the contours of an image and enhance the edge gradation jump parts, thus enlarging the main characteristics of images. Compared with traditional histogram equalization, which often loses the image details and increases noise, image sharpening can effectively distinguish the small edges of objects and avoid noises. In image sharpening, the following three kinds of first order-based derivative operators [36,37]: Roberts, Prewitt and Sobel, are often used. However, it is difficult for the first order-based derivative operator to distinguish the steep edges and slow change edges. In this way, a second order-based derivative operator, Laplacian, which is more sensitive to distinguish the isolated points, was used in this study. For a discrete point \(f(m,n)\) in an image, the first order partial derivative is defined as [38]:  
\[\left\{\begin{array}{l}\frac{\partial f(m,n)}{\partial x}=f(m,n)-f(m-1,n)\\ \frac{\partial f(m,n)}{\partial y}=f(m,n)-f(m,n-1)\end{array}\right\} \tag{1}\]  
Figure 2: City roads extracted from OpenStreetMap.  
Additionally, the second order partial derivative is calculated as follows:  
\[\left\{\begin{array}{l}\frac{\partial^{2}f(m,n)}{\partial x^{2}}=\left|f(m+1,n)- f(m,n)\right|+\left|f(m-1,n)-f(m,n)\right|\\ \frac{\partial^{2}f(m,n)}{\partial y^{2}}=\left|f(m,n+1)-f(m,n)\right|+\left|f( m,n-1)-f(m,n)\right|\end{array}\right\} \tag{2}\]  
In this way, the final expression of the Laplacian operator is defined as follows:  
\[\begin{array}{l}\
abla^{2}f(x,y)=\frac{\partial^{2}f}{\partial x^{2}}+\frac {\partial^{2}f}{\partial y^{2}}=\\ \left|f(m+1,n)+f(m-1,n)+f(m,n-1)+f(m,n+1)-4\times f(m,n)\right|\end{array} \tag{3}\]  
To eliminate the influence caused by diffusion, images operated by Laplacian operator can be enhanced using the following method:  
\[g(m,n)=f(m,n)-k\times\
abla^{2}f(x,y) \tag{4}\]  
where \(k\) denotes the correlation coefficient of diffusion, which is often set as 1. Additionally, the formula is as follows [38]:  
\[g(m,n)=5\times f(m,n)-f(m+1,n)-f(m-1,n)-f(m,n-1)-f(m,n+1) \tag{5}\]
## 4 Analysis of Reservation Frame Slotted-ALOHA  
In this section, we analyze the contention process in a data collection round using RFSA. We propose an absorbing Markov chain to model the evolution of the number of contending end-devices and the number of free slots within the contention process. The proposed Markov chain is inspired by existing works in data collection scenarios [14,27]. In order to build the transition matrix that characterizes the Markov chain, we derive the probability that one or more end-devices succeed in transmitting their first data packet and the probability that a reserved slot becomes free in a given frame. Finally, we derive the average number of frames in which the process remains in every state, which is used later in Section 5 to formulate the delay and energy models for RFSA.
## 4 Results and Analysis
### Rain Attenuation Based on Mie Theory  
Detailed simulation parameters for exploring rain attenuation are presented in Table 2. Figure 6 depicts rain attenuation of THz waves under diverse rainfall rates and distributions, i.e., Joss distribution, M-P distribution, and Weibull distribution. According to Figure 6, rain attenuation of THz waves is relatively low at 0.1-0.7 THz and high at 0.7-1 THz. The rain attenuation increases exponentially with the increase of both frequency and rainfall rate. For the Joss distribution, the attenuation values at 1 THz are 7757.33 dB, 532.83 dB, 16.59 dB and 2.02 dB. The attenuation values at 1 THz for the M-P distribution are 4718.04 dB, 608.95 dB, 94.05 dB and 16.93 dB. The attenuation values at 1 THz for the Weibull distribution are 4500.98 dB, 411.22 dB, 66.38 dB and 19.32 dB. Meanwhile these four values correspond to the rainfall rate of 2 mm/h, 5 mm/h, 15 mm/h, and 55 mm/h, respectively.  
For the Joss distribution, the rainstorms attenuation of terahertz waves is the largest. The drizzle, moderate rain, and downpour attenuations of terahertz waves are smaller than those under the M-P distribution. The terahertz wave attenuation under the Weibull distribution is smaller than that under the M-P distribution for the first three rainfall rates, but the difference is not significant. The downpour and rainstorm attenuation of terahertz waves under the Joss distribution is more significant than that under the Weibull distribution, while drizzle and moderate rain attenuations of terahertz waves are smaller than those under the Weibull distribution. Therefore, different distributions significantly impact the rain attenuation of terahertz waves.  
Figure 5: (**a**) Advection fog attenuation under various visibilities; (**b**) radiation fog attenuation under various visibilities.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Parameter Values in Fog** & **Parameter Values in Rain** \\ \hline Particle Radius & 1\(\sim\)60 μm & 0.01\(\sim\)40 mm \\ \hline Frequency Band & 0.1\(\sim\)1 THz & 0.1\(\sim\)1 THz \\ \hline Refractive Index & 1.5 + 0.01\(i\) & 1.5 + 0.01\(i\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters based on Mie theory.
# Hamming Distance Optimized Underwater Acoustic OTFS-IM Systems
## 2 OTFS-IM for Underwater Acoustic Communication System
### Detection Algorithm
#### 2.3.2 GD Detection Algorithm  
The GD detection algorithm is divided into two steps. Again, using group \(\beta\) as an example: the \(k\) active sub-grid positions in the sub-grid group are first detected, and the \(k\) most energetic sub-grids corresponding to index \(\hat{I}_{\beta}=\left\{\hat{i}_{\beta,1},\hat{i}_{\beta,2},\cdots,\hat{i}_{ \beta,k}\right\}\) are detected by solving for the energy of each sub-grid.  
Next, ML detection is performed on the symbol information at each of the \(k\) active sub-grid locations detected in the sub-grid group.  
\[\hat{S}_{\beta}=\underset{S_{\beta}}{\mathrm{argmin}}\sum_{\alpha=1}^{k}\left| \tilde{y}_{\beta}^{DD}\big{(}\hat{i}_{\beta,\alpha}\big{)}-s_{\beta}(\alpha) \right|^{2} \tag{15}\]  
The GD detection algorithm changes the detection method of active sub-grid index positions compared to ML detection and uses the ML detection algorithm only for the detection of constellation symbols. The GD detection algorithm is therefore a major improvement over ML detection in terms of complexity, but at the cost of a certain loss of reliability of the detection algorithm.
## 2 Problem Description
### Optimization Constraints
#### 2.3.1 Task-Dependency Constraint
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(\mathcal{D}\) & \(\mathcal{T}\)**No.** & \(\mathcal{T}\) & \(\mathcal{L}_{s}\) & \(\mathcal{L}_{e}\) & \(\mathcal{A}\) & \(c\) \\ \hline \multirow{10}{*}{1} & 1 & Add 400 ml of milk to the pancake mix bottle & 10 & 11 & 6 & 1 & 0 \\  & 2 & Put the milk back & 10 & 6 & 11 & 1 & 0 \\  & 3 & Close the cap of the pancake mix bottle & 10 & 6 & 6 & 2 & 0 \\  & 4 & Shake the bottle with the head facing down & 60 & 6 & 6 & 1 & 0 \\  & 5 & Put the pancake mix bottle down & 10 & 6 & 6 & 1 & 1 \\  & 6 & Sit the pancake mix & 120 & 6 & 6 & 0 & 1 \\  & 7 & Shake the pancake mix again & 30 & 6 & 6 & 1 & 0 \\  & 8 & Open the cap of the pancake mix bottle & 10 & 6 & 6 & 2 & 0 \\  & 9 & Pour the mix into the frying pan & 10 & 6 & 3 & 1 & 1 \\  & 10 & Put the pancake mix bottle back & 10 & 3 & 6 & 1 & 1 \\  & 11 & Wait for 3 min & 180 & 3 & 3 & 0 & 1 \\  & 12 & Flip the pancake & 20 & 3 & 3 & 2 & 1 \\  & 13 & Wait until all pancakes are baked & 180 & 3 & 3 & 0 & 0 \\  & 14 & Plate the food & 15 & 3 & 10 & 2 & 0 \\  & 15 & Bring the pan to the wash station & 10 & 10 & 15 & 1 & 0 \\ \hline \multirow{10}{*}{2} & 16 & Put the coffee bean powder in the coffee dipper & 10 & 6 & 1 & 1 & 0 \\  & 17 & Wait until the water boils & 90 & 2 & 2 & 0 & 1 \\  & 18 & Pour the water slowly & 60 & 2 & 1 & 1 & 0 \\  & 19 & Put the pot back & 10 & 1 & 2 & 1 & 0 \\  & 20 & Wait until the drip finishes & 60 & 1 & 1 & 0 & 1 \\  & 21 & Pour the coffee & 15 & 1 & 10 & 1 & 0 \\  & 22 & Bring the dipper to the wash station & 10 & 10 & 15 & 1 & 0 \\ \hline \multirow{10}{*}{3} & 23 & Place all of the ingredients into a mixing bowl & 10 & 8 & 9 & 1 & 0 \\  & 24 & Put the ingredient bowl back & 10 & 9 & 8 & 1 & 0 \\  & 25 & Toss the ingredients together until evenly combined & 45 & 9 & 9 & 2 & 0 \\  & 26 & Mound the ingredients into a large serving bowls & 10 & 9 & 10 & 1 & 0 \\  & 27 & Put the mixing bowl back & 10 & 10 & 9 & 1 & 0 \\  & 28 & Place the mandarin orange segments around the salad & 45 & 8 & 10 & 1 & 0 \\  & 29 & Put the ingredient bowl back & 10 & 10 & 8 & 1 & 0 \\  & 30 & Sprinkle the almonds and sesame seeds over the salad & 45 & 8 & 10 & 1 & 0 \\  & 31 & Put the ingredient bowl back & 10 & 10 & 8 & 1 & 0 \\  & 32 & Garnish the salad with some thinly sliced snow peas & 10 & 8 & 10 & 1 & 0 \\ \hline \end{tabular}
\end{table} 
Table 1: An example _taskset_\(\mathbb{T}\). As an example of dependency, to prepare dish #3 (\(\mathcal{D}_{3}\)), tasks \(\mathcal{T}_{23}^{3}\) to \(\mathcal{T}_{32}^{3}\) must be executed in this order. The dependency constraint can be written as \(t_{j+1}^{i}\geq t_{j}^{i}+\tau_{j}^{i}\) in the task set of the \(i\)th dish. Considering continuity, when \(c_{j}^{i}=1\), the formula becomes \(t_{j+1}^{i}=t_{j}^{i}+\tau_{j}^{i}\) because \(\mathcal{T}_{j+1}^{j}\) must be started right after \(\mathcal{T}_{j}^{i}\) finishes by the definition of \(c_{j}^{i}\).
# An Energy-Efficient Evolutionary Clustering Technique for Disaster Management in IoT Networks
## 5 Conclusions and Future Work  
In this work, energy-efficient cluster head selection and routing methods based on evolutionary algorithms were proposed for safety-critical WSN applications, focusing on forest fire. We have targeted energy efficiency as a major issue in such IoT networks. To overcome this challenge, an improved hybrid PSO-HSA-based CH selection technique with a novel fitness function including energy-efficiency, cluster closeness, and network coverage criteria were proposed. We employed PSO to select RNs with a fitness function including energy-efficiency and communication link quality, which resulted in the development of a modified tree encoding method based on a new data packet format. We validated the efficiency of the proposed method for a forest fire scenario compared with  
Figure 14: The average residual energy without forest fire scenarios for various sink locations.  
Figure 15: PDR without forest fire scenarios for various sink locations.  
the related works, considering different sink node placements. By considering different performance metrics, such as the overall residual energy, the number of live nodes, network coverage, and the packet delivery ratio in simulation evaluations, we could make the following conclusions: (1) the proposed algorithm in a disaster area is much better than the state-of-the-art methods [27; 29]; (2) the placement of the sink node affects the performance of the algorithm; (3) since the variation of HMCR and PAR parameters affect the fast convergence of the algorithms, it appears that these parameters may change dynamically; (4) probabilistic algorithms such as LEACH [22] cannot perform properly in a disaster application; and (5) in scenarios where events occur uniformly, our algorithm improves the network lifetime compared with other methods.  
In future work, we are planning to design efficient MAC protocols based on this application to prevent long delays and extend simulation scenarios, especially for heterogeneous networks.  
Conceptualization, Software, Methodology and Writing--original draft: M.B.; Validation: M.B., H.F. and N.Y.; Visualization: M.B. and H.F.; Supervision: H.F. and N.Y.; Writing--review and editing: H.F. and N.Y.; All authors have read and agreed to the published version of the manuscript. This work was supported by the Swedish Foundation for Strategic Research via the FiC project, and by the Swedish Research Council (Vetenskapsradet) through the MobiFog starting grant, and by the Swedish Knowledge Foundation (KKS) through the FlexiHealth Prospekt, and the EU Celtic_Next/Vinnova project, Health5G (Future eHealth powered by 5G). The authors declare no conflict of interest.
# Algorithm Development for the Non-Destructive Testing of Structural Damage
## 3 Damage Visualization  
### Developed Image Processing Algorithms  
The proposed algorithms are used to visualize and detect the suspected critical cracks, using a spot measurement on the structures. The basis of this section relies on the fact that the structural components provide pixels that are visible to the high-resolution sensors, which can demonstrate the topographical appearance characteristics of structural integrity. The changes in structural integrity can be recognized by the variation of image intensities at different intervals. To begin with, upon the antenna measurements of the specimens, the information of the returned signals from the target was gathered as the raw data. The recorded data included the magnitude and phase at different frequencies. This data was processed to achieve a two-dimensional image, through Fourier Transform (FT). The phase and magnitude at each frequency were multiplied by a 'Kaiser' window function to minimize the FFT affect over non-integer numbers. The windowed frequency data was transformed to the time-domain, by employing an inverse Fourier transform. Therefore, the antenna time-domain measurement result was subsampled as an image in Matlab.
# Noise-Tolerant Data Reconstruction Based on Convolutional Autoencoder for Wireless Sensor Network +
###### Abstract  
Maintaining data dependability within wireless sensor network (WSN) systems has significant importance. Nevertheless, the deployment of systems in unattended and hostile areas poses a major challenge in dealing with noise. Consequently, several investigations have been conducted to address the issue of noise-affected data recovery. Nevertheless, previous research has primarily focused on the internal noise of the system. Neglecting to include external factors that impact the WSN system in the study might lead to findings that are not true to reality. Hence, this research takes into account both internal and external noise factors, such as rain, fog, or snow conditions. Moreover, in order to maintain the temporal characteristics and intersensor relationships, the data from multiple sensor nodes are consolidated into a two-dimensional matrix format. The stacked convolutional autoencoder (SCAE) model is proposed, which has the capability to extract data features. The stack design of the SCAE enables it to effectively mitigate the issue of vanishing gradients. Moreover, the weight sharing approach used between the two subnetworks also enhances the efficiency of the weight initialization procedure. Thorough experiments are conducted using both simulated WSN systems and real-world sensing data. Experimental results demonstrate that the SCAE outperforms existing methods for reconstructing noisy data.  
Keywords:wireless sensor network; noise reduction; weather effect; data reconstruction; convolutional autoencoder +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 3 Materials and Methods  
Our approach, whose functional scheme is depicted in Figure 1, aims to present the operator with the 'best' technical assets available for the context. A technical asset is an atomic piece of technical information conveyed in a specific modality. Technical assets are usually visual cues such as CAD models, video, text, images, icons, graphs, and so on. The technical assets can be static but also associated to dynamic and time-dependent information. It is worth noting that, in our definition, the same technical information can be mapped to different assets sharing the same content but with different media and interface presentation style (e.g., short text, long text, animated text, video, audio). Thus, the choice of the optimal technical asset for the same technical information is paramount.  
For CATIM, we assume that the context in which to perform a task can be known. For example, the system could infer the activity to be performed by automatically using state sensing, IoT or by user selection. CATIM could know the operator technical, physical and cognitive skills, as well as interface preferences by, for example, user profiling and wearable sensors. User profiling is currently carried out through a preliminary questionnaire provided to the operator. Lastly, the system could sense the environment using sensor suites and computer vision to identify available tools, lighting condition, available AR devices, and so forth.  
Using our proposed approach, upon operator initiation of a task procedure, CATIM browses the technical information database to find the technical assets which best match the context and presents them to the operator via an AR user interface. The information search is based on the context description rather than having the user selecting the information needed. The information associated with each step of the procedure can be conveyed alternatively using different technical assets (e.g., text, icon, video, audio). Each asset (as used in each step of a procedure) can be linked to a particular state of the activity, of a process, of a product. Therefore, a procedure can be presented at runtime to the operator as a sequence of best context matching technical assets. Consequently, the presentation can be dynamically adapted to an operator's learning curve, as the operator can be continuously profiled by the acquired data (e.g., number of presentations of the same asset and task time for a repetitive procedure).  
In the following sub-sections, we provide further details on each component of CATIM.  
Figure 1: CATIM Functional scheme: the Context-Aware Technical Information Manager dynamically maps the context and provide user with technical information through the best available technical assets into an active AR output.