esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Frame Sampling and Feature Alignment for Multi-Frame Infrared Small Target Detection
###### Abstract
To solve these issues, this paper proposes a frame sampling pre-processing method based on mutual information for multi-frame infrared small target detection. Additionally, a multi-frame infrared small target detection model is introduced, which performs alignment at the feature level. The designed frame sampling method based on mutual information quantifies the motion variation between consecutive frames by computing the mutual information of the input dense frames. By accumulating the motion changes from \(f_{t-k}\) to \(f_{t}\), frame sampling is adaptively performed. Moreover, the proposed multi-frame infrared small target detection model, which performs alignment at the feature map level, embeds the registration operation from the preprocessing stage into the detection model and the model is trained in an end-to-end manner, enhancing its robustness. The main contributions of this paper are summarized as follows:  
1. We propose an adaptive frame sampling method based on mutual information for multi-frame infrared small target detection, to overcome the shortcomings of the fixed strategy frame sampling method. 2. A multi-frame infrared small target detection model is proposed, which incorporates feature alignment by placing the registration operation at the feature map level. The  
Figure 1: Redundant continuous frame pictures. model is trained in an end-to-end fashion, overcoming the limitations of manual registration in the preprocessing stage in terms of robustness. 3. A comprehensive set of experiments were conducted on a mobile infrared small target dataset to evaluate the effectiveness of the proposed method. The results provide strong evidence supporting the efficacy of the proposed approach. The structure of the remaining sections in this paper is as follows: In Section 2, an overview of previous work on infrared small target detection is presented. Section 3 outlines our proposed method and details the algorithm training procedure. Experimental datasets are discussed in Section 4, where quantitative results of various methods are analyzed and the detection performance is visualized. The paper concludes with a discussion and summary in Section 5.
# Wireless Channel Models for Over-the-Sea Communication: A Comparative Study
## 4 Channel Models for Over-the-Sea Wave Propagation  
As discussed earlier, approaches for over-the-sea channel modeling differ mainly in frequency, technology used, and implementation scenario. In this section, some existing channel models will be extensively reviewed.
# Power Allocation and User Grouping for NOMA Downlink Systems
## 3 Resource Allocation
### Power Allocation
#### 3.2.2 Power Allocation between the Users  
Power is now allocated between paired users within each subcarrier. We assume that the channel condition of user 1 on the subcarrier \(n\) is better than that of user 2; that is, \(\left|h_{n,1}\right|^{2}\geq\left|h_{n,2}\right|^{2}\). According to the SIC detection order, the signal of user 2 is directly demodulated and the signal of user 1 is treated as interference noise. User 1 needs to perform SIC. First, the receiver demodulates the signal of user 2, then subtracts it from the received signal, and finally demodulates the signal of user 1. The achievable rate of users can be calculated by the formulas [26]. Therefore, the rate of user 1 can be expressed as:  
\[R_{n,1}=B\log_{2}\left(1+\frac{p_{n,1}\left|h_{n,1}\right|^{2}}{\sigma_{n}^{2}}\right) \tag{12}\]  
The rate of user 2 can be expressed as follows:  
\[R_{n,2}=B\log_{2}\left(1+\frac{p_{n,2}\left|h_{n,2}\right|^{2}}{p_{n,1}\left|h_ {n,2}\right|^{2}+\sigma_{n}^{2}}\right) \tag{13}\]  
We have obtained the power, \(P_{n}\), allocated to each subcarrier. Assuming that the power allocation coefficient of user 1 is \(\alpha\), then \(p_{n,1}=\alpha P_{n}\) and \(p_{n,2}=(1-\alpha)P_{n}\). According to the power allocation principle of NOMA systems, users with good channel conditions are allocated less power, while users with poor channel conditions are allocated more power. Hence, we obtain \(0<\alpha<\frac{1}{2}\). The total transmission rate of the subcarrier \(n\) is:  
\[\begin{split}& R_{n}(\alpha)=R_{n,1}+R_{n,2}\\ &=B\log_{2}\left(1+\frac{\alpha P_{n}\left|h_{n,1}\right|^{2}}{ \sigma_{n}  
We simplify (15) to get  
\[f^{\prime}(\alpha)=\frac{\Big{(}|h_{n,1}|^{2}-|h_{n,2}|^{2}\Big{)}P_{n}\sigma_{n} ^{2}}{\Big{(}\alpha P_{n}|h_{n,2}|^{2}+\sigma_{n}^{2}\Big{)}^{2}} \tag{16}\]  
As a result of \(|h_{n,1}|^{2}\geq|h_{n,2}|^{2}\), we can obtain that \(f^{\prime}(\alpha)>0\). \(f(\alpha)\) is a monotone increasing function in the domain, so \(R_{n}(\alpha)\) is also a monotone increasing function in the domain. According to the minimum transmission rate, \(R_{\text{min}}\); that is, \(R_{n,1}\geq R_{\text{min}}\) and \(R_{n,2}\geq R_{\text{min}}\), we obtain  
\[\frac{\Big{(}2^{\frac{R_{\text{min}}}{\beta}}-1\Big{)}\sigma_{n}^{2}}{P_{n}|h _{n,1}|^{2}}\leq\alpha\leq\frac{1-\Big{(}2^{\frac{R_{\text{min}}}{\beta}}-1 \Big{)}\sigma_{n}^{2}}{2^{\frac{R_{\text{min}}}{\beta}}}. \tag{17}\]  
From the above analysis, the sum rate is at a maximum when  
\[\alpha=\frac{1-\frac{\Big{(}2^{\frac{R_{\text{min}}}{\beta}}-1\Big{)}\sigma_{ n}^{2}}{P_{n}|h_{n,2}|^{2}}}{2^{\frac{R_{\text{min}}}{\beta}}}. \tag{18}\]  
Hence, we obtain the power of multiplexed users, \(p_{n,1}=\alpha P_{n}\), \(p_{n,2}=(1-\alpha)P_{n}\). Above, we have obtained the closed-form expressions (CFE) method that maximizes the sum rate under the constraints. All power allocation steps have been completed.
## 5 Experimental Section
### Experiments about L1 Optimization with MATLAB
#### 5.1.4 The Time for L1 Optimization  
Our algorithm includes a sub-algorithm, which is a concise optimization algorithm. There are many available L1 optimization sub-algorithms. These kernel algorithms will be compared on aspect of the time consuming. We will choose the fastest L1 optimization sub-algorithm as the kernel of our algorithm. They are compared as shown in Figure 13. This mainly refers to [36]. The concise meaning of the abbreviations in the legend are also shown in this webpage [36]. The abbreviations in the legend refer to different L1 optimization. It is easy to see that Homotopy algorithm is the fastest algorithm and it is adopted in our algorithm.  
Generally speaking, when L1 optimization is applied to communications, the primary concern is that the computation consumes too much. However, we will show, compared with the decoding algorithm of traditional codes, the L1 optimization is also very efficient in time. Our scheme is compared with a (256,128) Low Density Parity Check Code (LDPC) in time consuming. LDPC is fast and generally adopted in a real industrial environment. It is a traditional block code and has efficient decoding algorithm in time. (256,128) means the normalized information rate is \(1/2\), the minimum distance is 64. Its most tolerant error density is 0.25, so we assume the error fraction is less than 0.25. Given a network with max-flow-min-cut 6 (<7), we perform network coding in the finite field with size 7. A 128-symbols long message is coded with LDPC, and then gets through the network performing network coding in relay nodes. Finally, we decode the 128 messages in the sink. This simulation is performed on a common PC, rather than GPU or FPGA. From Table 2, we can see our algorithm is also efficient in terms of the time consumed.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Definition** \\ \hline \(m\) & The number of rows of coding matrix \(A\in R^{m\times n}\) \\ \(n\) & The number of columns of coding matrix \(A\in R^{m\times n}\) \\ \(\|x_{0}\|\) & 0 norm \\ \(\hat{\theta}\) & The fraction of messages sent by the secret channel \\ \hline \hline \end{tabular}
\end{table}
Table 1: Terms used in the experiment.  
Figure 12: Error correction in L1 optimization at different \(\hat{\theta}\) when \(m/n\) decrease. (**a**) \(\hat{\theta}=0.1\); (**b**) \(\hat{\theta}=0.3\).
## 3 Results and Discussion of CNN Models Classification
### Framework and Implementation Details
#### 3.2.3 Explainable AI (XAI) Techniques  
To interpret the decisions of the CNN models, two XAI techniques were integrated: Gradient-weighted Class Activation Mapping (Grad-CAM) and Local Interpretable Model-agnostic Explanations (LIME).  
Grad-CAM was implemented using TensorFlow and Keras. This technique generates class-discriminative localization maps by computing gradients of the target class concerning the feature maps in the final convolutional layer. The resulting heatmaps highlight the regions of the input image that contribute the most to the model's prediction. The implementation followed standard practices, using the tf.GradientTape() for computing gradients and overlaying the heatmaps on the original images for visualization.  
LIME was implemented using the ILIME package in Python. This technique approximates the model's behavior by generating perturbed versions of the input image and fitting an interpretable model (such as a linear model) to the local predictions. The LIME explanations provide a feature importance map, indicating which parts of the image were critical for the classification decision. The ImageExplainer class was used for generating explanations specific to image data.
# Full-Abstract Biomedical Relation Extraction with Keyword-Attentive Domain Knowledge Infusion
## 4 Evaluation
### Ablation Study  
Table 5 shows the result of an ablation study, in which four models are evaluated, including BERT, Kw-BERT, BioBERT, and Kw-BioBERT. There are two primary observations. First, adding a keyword attention layer to the base models brought a performance gain of about two points, with a 2.1-point gain (in F1) on BERT and a 1.9-point gain on BioBERT. Second, switching BERT to BioBERT brought a gain of around eight points, by looking at BERT vs. BioBERT, and Kw-BERT vs. Kw-BioBERT. This gain is surprising but explainable since BioBERT is pretrained on corpora in the biomedical domain at a large scale; thus, BioBERT can better encode and represent the semantic meaning of PubMed abstracts.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **\# epochs** & **1** & **2** & **3** & **4** & **5** \\ \hline \multirow{4}{*}{Training} & Pre & 87.6 & 94.4 & 97.4 & 98.5 & **98.8** \\  & Rec & 89.1 & 94.2 & 97.2 & 98.5 & **99.0** \\  & F1 & 88.3 & 94.3 & 97.3 & 98.5 & **98.9** \\ \hline \multirow{4}{*}{Test} & Pre & 72.2 & **75.6** & 75.5 & 75.1 & 74.6 \\  & Rec & 75.6 & 76.6 & **77.5** & 77.5 & 77.0 \\ \cline{1-1}  & F1 & 73.6 & 76.1 & **76.4** & 76.2 & 75.8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Training epochs vs. performance. The highest value of each metric is marked in bold.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**\# transformers** & **Pre** & **Rec** & **F1** \\ \hline Kw-BioBERT-2 & 55.3 & 58.2 & 56.7 \\ Kw-BioBERT-4 & 68.3 & 69.3 & 68.8 \\ Kw-BioBERT-6 & 73.4 & 75.7 & 74.5 \\ Kw-BioBERT-8 & 74.5 & 75.2 & 74.9 \\ Kw-BioBERT-10 & 74.3 & 75.2 & 74.7 \\ Kw-BioBERT-12 & **74.6** & **77.0** & **75.8** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Number of transformer layers vs. performance. The highest value of each metric is marked in bold.  
This experiment also validates the efficacy of the proposed method of keyword-attentive knowledge infusion, which nicely complements the pretrained language models. Essentially, the utilization of BioBERT and keywords can be both regarded as knowledge infusion, but at two levels. BioBERT receives domain knowledge by pretraining, which is self-supervised; the keyword-attentive layer, on the other hand, injects task-specific knowledge (i.e., relation-suggestive tokens and semantic interaction between bio-entities) during training, which is supervised.
# Adaptive 3D Imaging for Moving Targets Based on a SIMO InISAR Imaging System in 0.2 THz Band
## 4 Three-dimensional Imaging of Moving Targets with InISAR Technology
### Image Registration  
As mentioned in the above section, a shift occurs among the ISAR images obtained by different receiving channels. Before performing the interferometric processing, image registration is required to align the images, which ensures that the same scatterer will appear in the same pixel in the ISAR images of different channels. In order to accomplish this goal, it's essential to acquire the target moving velocities in the azimuth and vertical directions, with which the target motion can be compensated in the received echoes directly.  
Fortunately, the target locations are measured and tracked during movement, and the values of \(V_{x}\) and \(V_{z}\) can be estimated by analyzing the recorded trajectory. Then the phase component to be compensated can be formed as follows:  
\[\phi_{match}=\frac{2\pi}{\lambda R_{p}}(x_{Lci}V_{x}+z_{Lci}V_{z})t_{m} \tag{23}\]  
For each receiving channel, a phase history is synthesized by substituting the coordinates of its antenna phase center to the above formula and then compensated in the corresponding raw signal.  
After the image registration is performed, the ISAR images of the receiving channels will be shown in the range-Doppler domain with the following expressions:  
\[\begin{array}{rl}I_{ci}(f_{a},r)=&\text{sinc}[\frac{2B}{c}(r-R_{p})]e^{-j \frac{4\pi}{\lambda}R_{p}}e^{-j\phi_{Lci}}\\ &\text{sinc}[T(f_{a}-f_{0})]\end{array} \tag{24}\]  
It can be seen that the shift of ISAR images of different channels have been mitigated.
## 4 Discussion
### Time Series Forecast Implications  
The findings presented in Table 5 provide a comprehensive view of the performance of selected forecasting methods, taking into account both classical academic and managerial KPIs. The SES model, from an academic perspective, performs better than most of the machine learning models in all instances, making it more desirable from an academic viewpoint. Looking at SES from a managerial perspective, it does not outperform any of the other forecasting methods. Looking at the industrial KPIs on Table 5, it consistently under-forecasts and experiences stockouts in every period for all three items. In contrast, the W-LSTM model performs well from a managerial standpoint, despite having higher error scores than the SES. W-LSTM boasts a higher FR in all instances, although experiencing stockouts in some periods, it is still more desirable than SES from an managerial perspective. This performance balance illustrates the importance of considering various KPIs from multiple perspectives when evaluating forecasting models. Looking at it from a managerial perspective, there has to be a trade-off between high inventory levels and holding costs or 100% FR. In conclusion, there is no single approach to selecting a forecast model. The  
Figure 4: Results for non-deteriorating items with time-varying demand.  
choice heavily depends on the business context and the trade-offs a company is willing to make between various KPIs. For instance, a company that values customer satisfaction and wants to avoid stockouts at all costs might prefer the W-LSTM model, while a company that wants to minimize costs might choose the W-ANN model because of the lower AIL level and lower holding cost. However, this comes at the cost of having more frequent stockouts and lower customer satisfaction.
# Blade Shape Optimization and Analysis of a Propeller for VTOL Based on an Inverse Method
## 1 Introduction
However, there are few similar works on tilt-wing or tilt-rotor aircraft, which is of importance to the safety of VTOL aircraft [22]. In addition, VTOL aircraft designed for a fast response have corresponding requirements for the maximum climb rate of vertical flight, which should also be considered in the design framework [23]. Regarding general propeller design methods, [PERSON] [24] proposed an inverse design method for a minimum energy loss propeller based on the Betz theory [25] and the work of [PERSON] [26], which were suitable for the case with a prescribed chord distribution. Applying this inverse method to a VTOL propeller design toward the cruising stage provides a rapid approach to expedite the optimization procedure. This article develops an optimization framework for the shape design of a VTOL propeller based on the inverse method. An FPP and a VPP were both optimized using this framework and then compared. The rest of this paper is organized as follows. The Section 2 introduces the BEMT, the inverse method for general propeller design and a low-order model of a brushless DC (BLDC) motor, which was used in the subsequent optimization framework. A three-blade NACA 5868-9 propeller with wind tunnel test data was used to validate the BEMT and the computational fluid dynamics (CFD) method. The Section 3 presents a new parameterization method for the chord distribution and the optimization framework for the VTOL propeller, including the design variables, constraints and objectives. The Section 4 focuses on the application of the proposed framework on the optimization of a propeller for the Vahana A3 tilt-wing aircraft. The optimization results of the VPP and FPP are given and discussed here. In the Section 5, the impacts of parameters such as hover disk loading, cruising flight speed and the level flight energy ratio on the optimization are studied. The general rules are given in this section. The results provide a universal reference to design a propeller for VTOL aircraft.
# Software Defined Networking Architecture for Energy Transaction in Smart Microgrid Systems
## 6 Discussion and Limitations
### Reliability Improvements and Considerations in the SDN Architecture  
Software-defined networking improves the reliability of a network by design. However, specific design parameters in the SDN architecture can be improved. The design considerations to improve the SDN architecture's reliability are decentralized controller placement and redundant link configuration. These design considerations are not found in a typical SDN architecture design.  
A decentralized controller placement reduces the number of network elements between the consumer and prosumer in the transactive energy process. The data path availability improves because of the reduction. The P2P trading strategy sequence experience an improved information flow, resulting in accurate and updated trade parameters.  
A decentralized controller placement reduces the number of network elements in the data path and the resource requirement of the controller. Reducing the combined failure rate of the data path improves the reliability of the SDN architecture. A decentralized controller placement reduces the downtime of the data path and resource requirement of the controller by the factor of the reduction in the number of elements.  
The negative impact of the decentralized controller placement is that another level of control is required to manage all the controllers through an east\(-\)west interface as per Figure 3. Another impact to consider is the technology availability in certain areas that will not be able to provide a stable environment for the controller. Certain areas and environments still operate on legacy and outdated technologies. An unstable controller environment reduces the data path availability.  
The redundant configuration improves the data path availability by providing an alternative communication link for the SDN node. A link failure during a transaction between the consumer and prosumer can continue if there is a redundant link configuration.  
The negative impact of the redundant link configuration is the commercial aspect of the second link. A second agreement with a second ISP and vendor is required, which can complicate the management of the redundant link configuration. Another impact to consider is the technology availability for redundant link configuration. Certain remote areas only have one communication technology available, which makes a redundant link configuration not possible.  
The improvement of the decentralized controller placement and redundant link configuration in the data path availability confirms the improvement in the reliability of the SDN architecture.
# Enhanced IoT-Based End-To-End Emergency and Disaster Relief System
## 3 The Crow\({}^{2}\) System
### History of the Crow\({}^{2}\) System  
The CROW\({}^{2}\) system is realized under the CROW\({}^{2}\) project. Among the contributions of the project, notably, we proposed realistic channel models and simulation environment for Body Area Networks (BAN) and Body-to-Body Networks (BBN or B2B) [24]. We evaluated the IEEE 802.15.6 WBAN standard under the realistic channel, radio and mobility models; in particular, the proposed MAC protocols were compared for application-specific design; additionally, new dynamic MAC protocols were proposed in [25; 26]. Furthermore, at the MAC layer, the IEEE 802.15.6 standard's proposed coexistence schemes for co-channel were evaluated in order to investigate the impact of interference from co-located BANs [24].  
For that, we studied and compared the effectiveness of distributed and cluster-based architectures for Body-to-Body communications (BBNs or B2B). Then, various routing protocols among different classes including proactive, reactive, geographic-based and gradient-based were simulated and evaluated in [2]. Finally, we proposed a new optimized routing protocol specifically designed for the emergency and disaster relief communication networks. The routing protocol was implemented and evaluated on the WSNet [27] simulator within a realistic disaster mobility pattern. Finally, we implement the entire system on real mobile devices (smart phones and Raspberry Pi devices) for performance assessment within this paper.
# Sustainable Cold Chain Management: An Evaluation of Predictive Waste Management Models
## 5 Discussion
### Practical and Theoretical Implications for Cold Chain Logistics  
The findings underscore the practical benefits of integrating IoT with machine learning models in cold chain logistics. According to the Resource-Based View (RBV), unique resources--such as real-time data and predictive analytics--offer a competitive advantage by enabling more efficient resource utilization [16]. Company A's case demonstrates how machine learning models, even with limited data, can improve demand forecasting and waste management, aligning with RBV principles of operational efficiency. Integrating Industry 5.0 principles within sustainable supply chain management highlights the balance of technological innovation and environmental goals, aligning with the objectives of IoT and predictive models in promoting sustainability and efficiency in cold chains [17].  
This study also contributes to Supply Chain Resilience Theory by demonstrating how adaptable, predictive models bolster flexibility in supply chains, enabling cold chain operators to respond efficiently to demand changes. Integrating IoT data would further enhance this adaptability, as real-time environmental metrics could improve responsiveness to sudden demand shifts and spoilage risks. This approach aligns with sustainable logistics objectives, providing a pathway for cold chain companies to achieve a balance between environmental responsibility and operational efficiency.