esa-sceva/satcom-chunk-collection huggingface dataset:
## Article  
SFINet: Shuffle-and-Fusion Interaction Networks for Wind Power Forecasting  
[PERSON], [PERSON] and [PERSON],*(c)  
1 Department of Technical Development, AI Forward Technology Co., Ltd., Beijing 100801, China; [EMAIL_ADDRESS]  
2 School of Electronic and Control Engineering, North China Institute of Aerospace Engineering,  
Langfang 065000, China; [EMAIL_ADDRESS]  
3 Faculty of Engineering and Information Sciences, University of Wollongong,  
Wollongong, NSW 2522, Australia  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +61-2-4221-4821  
**Abstract:** Wind energy is one of the most important renewable energy sources in the world. Accurate wind power prediction is of great significance for achieving reliable and economical power system operation and control. For this purpose, this paper is focused on wind power prediction based on a newly proposed shuffle-and-fusion interaction network (SFINet). First, a channel shuffle is employed to promote the interaction between timing features. Second, an attention block is proposed to fuse the original features and shuffled features to further increase the model's sequential modeling capability. Finally, the developed shuffle-and-fusion interaction network model is tested using real-world wind power production data. Based on the results verified, it was proven that the proposed SFINet model can achieve better performance than other baseline methods, and it can be easily implemented in the field without requiring additional hardware and software.  
wind power forecasting; attention mechanism; shuffle operation; interactive learning; shuffle-and-fusion interaction network
# Emerging Biomedical Applications of Carbon Dot and Polymer Composite Materials
## 2 General Aspects of Carbon Dot/Polymer Composites
### Carbon Dots and Polymer Hybrid Composite Materials
#### 2.4.3 Integrative Feature of Carbon Dot and Polymer Composites  
The selection of the polymer and conjugation strategy can retain the required functional groups, such as -COOH and -OH groups, for the secondary loading of bioactive molecules [28]. Integrated CD/polymer composites with bioactive molecules have been demonstrated to possess reliable biocompatibility, bioimaging functions, anticancer effects, and combined drug tracing in vivo. Diverse applications can be achieved with abundant reactive groups, such as drug tracing, optical temperature sensing, magnetic/near-infrared (NIR)-thermally responsive drug delivery, enhanced photosensitivity, and other biomedical sciences, by integrating fluorescent CD/polymer composites with functional compounds. As a result, the major polymeric characteristics of CD/polymer composites result in more changeable properties, promoting their applications in several emerging fields [50].
## 2 Materials and Methods
### Data Sources
#### 2.2.2 LAI Products  
The GLASS (Global LAnd Surface Satellite) LAI products were obtained from NESSDC. Their spatial resolution was 1 km with a sinusoidal projection, and the temporal resolution was 8 days. The 2001\(-\)2015 GLASS LAI data were used for correlational analysis. The GLASS LAI algorithm can be briefly described as follows: first, the GRNNs (general regression neural networks) were trained using preprocessed MODIS/AVHRR reflectance data; subsequently, GLASS LAI products were generated through a rolling processing approach [43; 44]. Among the various global LAI products, GLASS LAI is much more continuous in time and complete in space [43]. In this study, we used MRT (MODIS Reprojection Tools) to transform the format of GLASS LAI from HDF to GeoTiff and reproject it. Then, we clipped the GLASS LAI using Arcpy. Finally, the mean composition method was used to calculate the temporal LAI dataset for the 2001\(-\)2015 growing season mean LAI (LAI\({}_{\text{GS}}\)).
## 5 Bubble-Particle Interaction
### Bubble-Particle Interaction Energy
[37] discovered that the energy barriers associated with van der Waals (\(\mathrm{E_{V-RA}}\)), electrical double-layer (\(\mathrm{E_{W-RA}}\)), and hydrophobic (\(\mathrm{E_{H-RA}}\)) interactions decreased as the size of the asperities on magnesite particle surfaces increased. Specifically, when the asperity size grew from 2 nm to 12 nm, the energy barrier for the attachment to a flat bubble surface dropped from approximately 2.2 \(\times\) 10\({}^{-16}\) J to0.6 \(\times\) 10\({}^{-16}\) J. Moreover, the location of this barrier shifted to a larger separation distance (2 nm at 4.8 nm, compared to 12 nm at 13.9 nm) (Figure 16d) [37]. They also noted that both the E\({}_{\text{V-RA}}\) and E\({}_{\text{H-RA}}\) significantly decreased, while the E\({}_{\text{W-RA}}\) experienced a slight increase as the size of the asperities on magnetite particle surfaces increased. The energy barrier required for a particle to attach to a bubble surface diminished from 7.03 \(\times\) 10\({}^{-17}\) J to 1.75 \(\times\) 10\({}^{-17}\) J when the asperity size increased from 9.47 nm to 28.03 nm. Additionally, the location of this barrier was pushed to a larger separation distance (9.47 nm at 11.1 nm, while 28.03 nm at 30.3 nm) (Figure 16e). This suggests that larger asperities may facilitate an earlier attachment of the particle to a gas bubble, thereby contributing to a larger flotation rate constant [40]. [PERSON] et al. [175; 176; 177] found that hydrophobic asperities with a radius of just a few nanometers significantly reduce the energy required for the particle attachment to a hydrophobic surface. Notably, this energy barrier can be reduced by up to two orders of magnitude when the radius of the nano-sized asperities increases to several tens of nanometers. Figure 15: Interaction energy profiles between simulated spherical ((**a**) 10 nm radius and (**b**) 100 nm radius) latex particle and rough polyamide membranes with varying average roughness [173]; contour plots of XDLVO energy barrier for particle–bubble interaction as function of h\({}_{\text{SR}}\) and its fraction for particle diameter of (**c**) 20 and (**d**) 120 μm [174]. The DLVO theory and extended DLVO theory can predict the interactions between smooth and rough surfaces, or between spherical particles and bubbles. The results show that surface roughness effectively reduces the energy barrier for hydrophobic particles and facilitates the attachment between rough particles and bubbles. However, since mineral surfaces are typically heterogeneous, this complicates the prediction model of bubble-particle interactions and reduces the accuracy of the predictions.
# A Cross-Layer, Anomaly-Based IDS for WSN and MANET
## 2 AODV Routing and Blackhole Attack  
The routing protocol used in this work is the adhoc on-demand distance vector (AODV), which is a reactive routing protocol that performs the discovery process between two nodes only when needed. There are three main control packets used in this protocol: route request (RREQ), route reply (RREP), and route error (RERR).  
The black hole attack is a network layer-oriented attack that affects the routing pattern in the MANET by forging a fake RREP with a high-sequence number, thereby deceiving the network into believing that the attacking node has the shortest path to the destination [15]. Once the sender receives the forged RREP, the traffic behavior in the network changes by directing the traffic to a malicious node that drops the packets.
## 6 Experiments and Results  
The validation of AtomGID is then achieved by implementing each of the contributing modules in the smart home infrastructure described in Section 2. Since collecting a large number of naturalistic RFID data for that purpose is challenging, we also considered simulation results. Moreover, our validation leverages several years of development and would not be possible without our previously developed and validated approaches. First, we leverage an RFID localization system improved incrementally over several publications by our team [29; 30; 31]. Then, we also reuse the clustering method published in [45] to evaluate it in the context of activity recognition in a smart home. Section 6 describes the various experiments and results. In order to manage the noise coming from different objects (for instance, metal objects, large quantity of water, etc.) in passive RFID technology readings before data preprocessing, we deployed several strategies to improve the quality and reliability of the collected data. First, we optimized the antennas' placement to ensure that RFID readers are strategically positioned to maximize signal strength and minimize interference. We performed several prior experiments with different reader configurations and orientations to identify optimal placement for reliable data capture. Secondly, we used only high-quality antennas and calibrated them properly to optimize signal reception and minimize signal degradation. We adjusted antenna parameters such as gain, polarization, and beam width to enhance performance in our specific environments. Third, we selected the appropriate RFID frequencies (e.g., low-frequency, high-frequency, ultra-high-frequency) based on the specific application and environmental conditions. Different frequencies may exhibit varying levels of susceptibility to noise and interference, so we chose the most suitable frequency band for reliable data collection. Finally, we ensured that RFID tags are positioned correctly and oriented optimally for consistent and reliable reading. We experimented with tag placement and orientation to minimize shadowing, reflections, and other factors that may affect signal reception.
# Multi-Vehicle Tracking Based on Monocular Camera in Driver View
## 2 Related Work  
System latency is a major concern in autonomous vehicles. However, existing feature extractors are usually time-consuming. In addition, existing MOT frameworks usually deal with detection and association separately, resulting in the accumulation of delays and errors. To avoid such problems, the ability to use a shared neural network for detection and tracking may reduce latency and enhance accuracy.  
Assuming that the object moves slightly between consecutive frames, the Tracktor [12] uses an object detector and converts it into a tracker. It adapts to the Faster R-CNN detector [8] by adding a regression head, which regresses the position of the bounding box of the object in the new frame from the previous frame. However, there are two disadvantages. First of all, it is suitable for high frame-rate videos with less inter-frame motion. Secondly, it fails to capture the appearance of tracked objects, and when several objects are close together and under temporary occlusion, the model becomes unreliable. To solve the second problem, Tractor++ came into being. It integrates appearance featuresgenerated by a separate Siamese network, and it achieves better tracking accuracy at the cost of efficiency. However, this part offsets the advantage of Tracktor.  
To obtain better long-term correlation, [PERSON] first proposed a synchronous detection and tracking method based on appearance features called JDE [7]. It uses a unified network to combine output detections with their corresponding appearance embedding. JDE extends YOLOv3 [9] with a re-identification branch that is trained using the triplet loss and jointly learned with the detection loss. For each new frame, JDE uses the combination of all embedding of detections belonging to the trajectory wave to update the appearance embedding of the trajectory wave. The similarity score is a combination of the cosine distance between the appearance embedding and the Mahalanobis distance between the current detection and the predicted bounding box of the trajectory wave computed using the Kalman filter. Then, the Hungarian algorithm is used to find the optimal allocation. JDE speeds up the reasoning time. However, it is only evaluated on the pedestrian tracking dataset. Meanwhile, the detection module and Kalman filter can be further improved.  
These multi-object tracking algorithms always focus on pedestrian tracking in monitor view. Few such works pay attention to multi-vehicle tracking from the driver's point of view. JDE methods show better results and take less running time than the non-joint learning method. Therefore, we propose a new detection structure based on the idea of JDE and improve the update strategy of the noise matrix. The proposed method can be used as the basis of autonomous driving or ADAS.
# Post-Occupancy Evaluation in High-Rise Apartent Buildings in Vietnam
## 1 Introduction  
High temperatures and humidity directly impact cooling energy consumption, a significant issue in hot and humid regions [1]. With rapid economic development and urbanization in Vietnam, traditional buildings (e.g., folk houses, tubular houses) are increasingly being replaced by high-rise apartment buildings, while resident lifestyles and cooling needs are evolving [2]. Over the past decade, the proportion of households with air-conditioning (AC) units has quintupled to approximately one-third [3], leading to a sharp increase in AC-related energy consumption, particularly in urban areas affected by the heat island effect [4]. This rising cooling demand significantly burdens local electricity systems, emphasizing the urgent need to improve building energy efficiency.  
While energy-saving strategies in design, construction, operation, and maintenance have been extensively explored, occupant behavior (OB) remains crucial, but it is challenging to predict factors influencing building energy use and indoor environmental conditions [5, 6, 7]. OB is diverse, dynamic, and stochastic, making it challenging to incorporate accurately into the building design process. An effective way to gain insight into thecharacteristics of OBs and their impact on building performance is through Post-Occupancy Evaluation (POE) [8]. POE is essential in assessing building performance, studying the relationship between occupant behavior and energy use, optimizing indoor environments, and supporting decision-making for future building designs [9].
# Multiple Observations for Secret-Key Binding with SRAM PUFs
## 1 Introduction
### Contributions and Outline  
In Section 3, we define secret-key capacity for multiple enrollment observations and calculate its value for the SRAM-PUF statistical model. We show that secret-key capacity can increase significantly when more enrollment observations are considered. This observation is the main motivation for the results presented in this paper, which are listed as follows.  
* We introduce the MO helper data scheme in Section 4. We prove that the helper data does not reveal any information about the key when the SRAM-PUF statistical model meets the symmetry assumption (Section 2). Then, we prove that secret-key capacity can be achieved with the MO helper data scheme for any number of enrollment observations.
* In Section 5, we present a code construction and evaluate performance of the scheme through Monte Carlo simulations.
* We propose a new variation on the Soft-Decision (SD) helper data scheme from [7] in Section 6. In contrast to the original scheme, this scheme considers binary SRAM-PUF observations as an input. We prove that the new SD strategy is optimal (achieves secret-key capacity) and that it can achieve the same reconstruction performance as the MO helper data scheme.
* In Section 7, we present a variation on the MO helper data scheme that can update the helper data sequentially. The error-correcting performance of the scheme improves after each successful key reconstruction, and therefore the performance improves over the lifetime of the device. This enables usage of less observations during the enrollment phase, when allowing worse initial reconstruction performance that is improved over time.  
We conclude with a summary of our results. In the following, we first introduce the notation and the statistical model that we use for SRAM PUFs.
# Low Power Multi-Hop Networking Analysis in Intelligent Environments
## 7 Conclusions  
The electronic systems for intelligent environment applications are usually embedded, adaptive, as well as sensitive and responsive to the presence of living beings in such a way that the technology integrates into environments smoothly. Given that Bluetooth standard initially focused on replacing the cables of electronic equipment, Bluetooth provides intelligent environment systems with low latency and low power wireless connectivity. It is a wireless technology extended all over the world that saves the license costs of radio emission. Bluetooth provides the establishment of low power  
Figure 8: Current consumption vs. inactivity interval in sniff mode, \(T_{\text{sniff}}\).  
multi-hop networks. An important QoS metric of any communication network is the latency in the transmission of data. In fact, this metric is a major problem for multi-hop networks.  
Even though there is a vast literature about Bluetooth, it features a gap relative to the analysis of the latency in multi-hop communications. Latency depends on several factors. So, in this paper the dependences of latency with the major factors, namely the hop count, the power mode of the nodes (sniff and park) and the period of inactivity are studied.  
Furthermore, the results of the tests derived in the set of equations to calculate the upper limits of the latency in communications over ACL links in scatterers when links are in _park_ or _sniff_ modes. The equations are the first approximation in terms of latency to model the performance of low power modes in Bluetooth multi-hop wireless networks. They set the foundation for future approximations where the processing stage of the nodes or the topology of the network would be more complex. Therefore, the set of equations benefits the designers of devices for intelligent environments and wireless networks. It gives them the estimation of the upper limits of the latency that each hop of the network would add when setting, for instance, an intelligent environment with a wireless network of access points.  
None of the low power modes under test offered always the lowest latency results in the tests. So the modes were characterized to make a good choice. As the systems for intelligent environments can be divided into long and short monitoring period systems, the most suitable low power mode depending on the specifications of the application is set in this paper.  
Finally, the future work includes the use of devices of the very last release of Bluetooth standard to provide a model that extends the topology, and the network application scenario.  
**Acknowledgments:** The research described in this paper was included in AIRHEM IV project and financially supported by the Basque Government Research Program called Elkartek 2015 (code KK_2015/0000085).  
**Author Contributions:** [PERSON] designed the experiments, implemented the test bed, performed the experiments, analyzed the data and wrote the paper; [PERSON] designed and implemented the nodes, the test bed and the experiments.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Soft-Voting Ensemble Classifier for Detecting Patients Affected by COVID-19
## 1 Introduction
The diagnostic network was able to process 2D images and to classify input slides into five categories, i.e., non-pneumonia, community-acquired pneumonia (CAP), influenza-A, influenza-B, and COVID-19. [PERSON] and [PERSON] [17] proposed a deep learning voting approach combining multiple CNNs to detect COVID-19 by analyzing chest X-ray images. Transfer learning was used to build 17 models. Then, the outcome obtained by these models is combined using a majority voting rule. The built model is able to distinguish patients affected by COVID-19 from those with bacterial pneumonia or without pneumonia. Transfer learning has been also used in [18]. In this work, the authors used transfer learning on residual networks (i.e., ResNet-50) to detect COVID-19 positive patients from 2D chest CT images. A novel model called deep contrastive mutual learning (DCML) for COVID-19 recognition was proposed in [19]. In this work, to avoid the overfitting, the training set was enriched using a multi-way data augmentation strategy based on the Fast AutoAugment algorithm [20]. Then, the contrastive learning was combined with the conventional deep mutual learning (DML) framework to obtain more discriminative image features through a new adaptive model fusion method. The interested reader can find an outline of the current state-of-the-art of COVID-19 image classification in [21]. This work addresses the issue of detecting COVID-19 infections by analyzing chest CT scans with 3D Inception-based models, with the goal of identifying patients with no infections, affected by common pneumonia, and affected by COVID-19 pneumonia. To this end, first, individual models have been trained starting from 3D Inception-V1 and Inception-V3 CNNs; then, the voting ensemble method has been experimented with to improve their performance. The comparative setting in which experiments have been carried out includes the individual models and the ensembles. Experimental results have also been compared with those obtained by other works proposed in the literature. The remainder of this article is organized as follows. First, materials and methods are described; then, experiments and the corresponding results are presented. A discussion on the results and on the challenges that arise from this classification task follows. Finally, conclusions are drawn.