esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
Based on the inspiration of the above two methods, a plug-and-play plug-in called the location feature extraction module (LFE module, for short) is designed that can achieve the extraction of location features in deep layers. The LFE module uses the larger receptive field in the deeper network to establish the global spatial context association and then uses the attention mechanism to strengthen the attention to spatial information so as to implement the location feature extraction process. In order to enhance the generalization performance of neural networks, regularization methods are proposed to reduce overfitting. Knowledge distillation is one of the most effective regularization strategies, which is used to improve the generalization ability of networks. The existing knowledge distillation methods [16; 17; 18] require training a complex teacher network first and then passing it on to the student network, which is both time- and cost-consuming. Therefore, self-distillation learning--learning what you teach yourself--is proposed. Traditional self-distillation is realized under supervision, and supervised learning requires a lot of manually labeled label information [19]. The rise of self-supervised learning has solved this problem. Applying self-supervised contrastive learning to self-distillation may produce desirable results. Our contributions are:  
(1) We found that location feature extraction had a great impact on the improvement of small object detection, and improving location feature extraction can effectively improve detection accuracy. (2) We proposed a location extraction structure that can extract location features at a deep level, use the larger receptive field to establish a global spatial context mapping, and then use the attention mechanism to strengthen the attention to spatial information to extract location features. Combined with CSPNet [20], it is integrated into the above structure to reduce the use of repeated gradient information. (3) Based on traditional self-knowledge distillation learning, a self-knowledge distillation model was proposed that can be learned in a self-supervised environment. By using the linear combination of the change information generated after data augmentation and the current prediction results to distill knowledge and soften the target. This paper was organized as follows: In Section 2, the related work in self-supervised learning, self-knowledge distillation, feature extraction, etc. was generalized. In Section 3, a detailed description of the design and principle of the specific structure of the model was given. In Section 4, the experiment, including the experimental environment, the deployment of the experiment, and the analysis of the corresponding experimental results, were elaborated. In Section 5, conclusions were finally drawn.
# Energy-Efficient Industrial Internet of Things in Green 6G Networks
## 1 Introduction
### The Background of the Study
Cloud, edge, and fog computing technologies can bring about node energy consumption decrease by coherent wireless network resource allocation. Data mining and wireless communication technologies articulate energy resource-based network computing efficiency, resulting in traffic congestion mitigation in industrial production environments. Data fusion technology can assist network resource distribution for energy effectiveness optimization and network traffic congestion mitigation. IoT-sensor-driven mobile devices and deep-learning-driven swarm-based edge cloud computing technologies [17] can shape energy and battery lifecycle management and monitoring for a sustainable environment by spectrum sharing capabilities, data transmission energy efficient mechanisms, and big data clustering and coordination, reducing energy consumption. IoT-driven mobile networked embedded systems across remote industrial automation integrate machine and deep-learning-based 6G radio resource allocation techniques and intelligent industrial processes, leading to energy consumption mitigation. Sixth-generation Internet of Everything (IoE) mobile ubiquitous intelligence and energy-efficient sustainable edge networks [18] can optimize interrelated edge node coordination by multi-agent deep reinforcement learning decentralized and collaborative computation offloading and resource distribution, resulting in decreased computing complexity and energy consumption. Transmission resource scheduling, base station coordination, and edge computing nodes can be optimized by resource- and energy-efficient 6G wireless Industrial Internet of Things (IIoT) network operation automation [19] and integrated sensing techniques in Industry 4.0 cyber-physical production wirelessly connected multi-robotic systems. Deep convolutional neural networks, cloud and edge computing technologies, and signal processing algorithms can deploy knowledge and contextual data in wireless industrial environments. Digital twin AI sensing and imaging technologies for wireless environment modeling can enable integrated sensing and machine multiservice communication automation in smart factories. Wireless blockchain and mobile edge computing technologies can be leveraged in resource allocation development for sustainable communication and power effectiveness [20] for sensor node collaborative decision-making. Data transmission, power management, and network performance processes are pivotal in IoT reinforcement learning-based 6G wireless sensor networks for energy consumption efficiency and reliable connectivity. Fog computing and energy-aware routing techniques can improve energy use optimization in IoT heterogeneous device data transmission across automated manufacturing operations.
# High-Precision Roller Supported by Active Magnetic Bearings
###### Abstract  
Magnetic bearings support rotors in a non-contact way using magnetic force. Therefore, there is no friction and it is possible to measure and control the position of a rotor in the air gap. In this study, the rotational vibration of a roller was minimized using magnetic bearings, and a precision roller capable of automatic alignment using the position control function of magnetic bearings was proposed. A rotation accuracy of approximately 4.6 \(\upmu\)m (peak-to-peak) was observed, even under a rotation of 30 rev/min and a radial force load of 300 N. The rotor position control experiment for magnetic bearings showed that 1-\(\upmu\)m resolution position control is possible. To further improve the accuracy, the automatic alignment algorithm was proposed using magnetic bearings for the roller misalignment condition, and it was confirmed that alignment is possible at a level that the pressing force difference between both ends of the roller is within 0.3 N. Through this study, it was confirmed that rollers with magnetic bearings can be applied to precision equipment. It is expected that the implementation of the automatic alignment function will simplify the equipment configuration and maintenance compared to conventional rollers with ball bearings.  
magnetic bearings; automatic alignment; high precision roller; roller applications Article
# Deep Learning Methods to Analyze the Forces and Torques in Joints Motion
## 2 Data and Modeling Details
### A Machine Learning Approach for Critical Points Detection  
The Body postures identification model is a fine-tuned model based on Detectron2 [14], a high-quality implementation of state-of-the-art object detection algorithm tool collection. As a Region-based Convolutional Neural Network (RCNN) algorithm [15], each image can be segmented into various boxed regions that are sensitive to the attached classifiers. The boundary of each box is represented by a pair of \"anchor points\". With RCNN, each image can be marked by multiple boxes that correspond to regions of interest (ROI) [16].  
The critical component of RCNN or Detectron2 is the Region Proposal Network (RPN), which processes features from each image and gives suggestions of segmented regions represented by \"anchor points\" pairs. Assume that there is a trained Convolutional Neural Network (CNN) classifier, the idea of the RPN is to score each picked boxed region with the classification score of the classifier. This research uses MASK-RCNN [17], a framework built on FASTER-RCNN [18], which is an extension of RCNN. FASTER-RCNN further accelerates the procedure of RPN to score ROIs. Detectron2, a library developed by Facebook AI Research (FAIR), is an open-source implementation MASK-RCNN.  
Though Detectron2 is designed for image segmentation, the algorithm is further customized to fit the needs of joint detection. The idea for joint detection is to find the common \"anchor point\" of limbs. For instance, the knee is the common \"anchor point\" of ROIs of a thigh and a shank. Based on a customized model (from Detectron2 model zoo), which identifies 17 key spatial points of humans as the initial point, further fine-tuning is made. The loss function of the fine-tuning is the summation of three components, as shown in Equation (1).  
\[TotalLoss=clsLoss+boxLoss+dflLoss \tag{1}\]  
where \"_clsLoss_\" is the cross entropy loss of the classifier. The \"_boxLoss_\" is the loss of the real box segmentation and the predicted one. The \"_dflLoss_\" is the Distribution Focal Loss to improve the precision of the \"anchor points\". As shown in Figure 1c, as the fine-tuning progress, the total loss decreases. The final loss by component is represented in Figure 1d, indicating good behavior across training and validation sets. The precision after tuning (Figure 1e) proves the quality of the tuning for the mechanical modelings.  
With fine-tuning, the model is adjusted to perform very well for analyzing the movements and techniques of fencers in videos. The precise identification of joint points enables further implementation with classical mechanics. There are more details of the model tuning in Appendix B.
# Non-Volatile Memory Forensic Analysis in Windows 10 IoT Core
## 2 Windows 10 IoT Core  
Windows 10 IoT Core is the free version of the IoT-based operating system developed by Microsoft, namely Windows 10 IoT. It was launched in 2015 and it is a combination of the desktop and the mobile versions of the Windows 10 family, optimized for ARM and x64/86 devices such as Raspberry Pi, Dragon Board or Minnow Board [6]. The multi-language Universal Windows Platform (UWP) is the common app used to develop applications for the system, supporting C++, C#, JavaScript and Visual Basic. To remotely interact with the system, manage it and set it up, Microsoft provides the Windows 10 IoT Core Dashboard application, which can be installed on Windows 10 computers.  
Some of the main features of this system are:  
* Startup application, namely Windows 10 IoT Core Default App, to graphically interact with the system.
* Secure Boot: UEFI located security feature to only allow the execution of trusted applications signed by known authorities.
* Bitlocker encryption.
* Device Guard: allows the execution of only trusted code, identifying the firmware, drivers and applications that should run on the device [7].
* Cortana (no longer available since version 1809).
* PowerShell.
* Windows Update.
* Bluetooth.
* Web, SSH, and FTP Server.
* Compatibility with Arduino boards.
* Miracast.
* WiFi Direct.
* Other hardware compatibility such as WiFi Adapters, Ethernet Adapters, Cameras, NFC, RFID, and multiple sensors.
# Rendezvous Based Adaptive Path Construction for Mobile Sink in WSNs Using Fuzzy Logic
## 5 Performance Evaluation
### Network Lifetime  
In this section, we compare the performance of the proposed approach with the DBRkM approach in terms of network lifetime. Three metrics are used for this purpose: the number of alive nodes, total remaining energy, and standard deviation of the remaining energy. Figure 6 shows the number of active nodes per round in both algorithms. The figure shows a significant improvement of the proposed Fuzzy_RPs approach compared to the DBRkM approach. This improvement is achieved by updating the locations of the RPs based on the energy of the nodes and therefore avoiding the energy holes around the RP.  
Figure 7 shows the overall amount of energy consumed throughout the first 3000 rounds. As can be seen in the figure, the proposed approach consumes less energy than the DBRkM approach. This improvement is achieved by keeping the RPs' points updated with the fewest hops and the shortest transmission distance possible. Figure 8 shows the standard deviation of the total remaining energy for live nodes per round for the proposed approach and the DBRkM approach. As shown in Figure 8 the standard derivation in the case of DBRkM approach increases compared to the Fuzzy_RPs approach. This increase comes from the fact that the RPs in DBRkM approach are fixed and therefore the nodes that are far away from these RPs will drain their energy fast compared to the nodes close to the RPs. On the contrary, the Fuzzy_RPs approach always achieves a balance in energy consumption by changing the RPs' locations in each round.  
Figure 9 shows the network lifetime when the number of sensor nodes is changed from 150 through to 300. The network lifetime in this figure represents the number of rounds until the first node in the network dies. As shown in the figure, the proposed Fuzzy_RPs approach always outperforms the DBRkM approach. Table 4 shows the percentage of improvement that has been achieved by using the Fuzzy_RPs approach compared to DBEkM approach.  
Figure 6: Number of alive nodes vs. rounds.  
Figure 8: Standard deviation of the remaining energy vs. rounds.  
Figure 7: Total remaining energy vs. rounds.
# Integrated Precise Orbit Determination of Multi-Gnss and Large LEO Constellations
###### Abstract  
Global navigation satellite system (GNSS) orbits are traditionally determined by observation data of ground stations, which usually need even global distribution to ensure adequate observation geometry strength. However, good tracking geometry cannot be achieved for all GNSS satellites due to many factors, such as limited ground stations and special stationary characteristics for the geostationary Earth orbit (GEO) satellites in the BeiDou constellation. Fortunately, the onboard observations from low earth orbiters (LEO) can be an important supplement to overcome the weakness in tracking geometry. In this contribution, we perform large LEO constellation-augmented multi-GNSS precise orbit determination (POD) based on simulated GNSS observations. Six LEO constellations with different satellites numbers, orbit types, and altitudes, as well as global and regional ground networks, are designed to assess the influence of different tracking configurations on the integrated POD. Then, onboard and ground-based GNSS observations are simulated, without regard to the observations between LEO satellites and ground stations. The results show that compared with ground-based POD, a remarkable accuracy improvement of over 70% can be observed for all GNSS satellites when the entire LEO constellation is introduced. Particularly, BDS GEO satellites can obtain centimeter-level orbits, with the largest accuracy improvement being 98%. Compared with the 60-LEO and 66-LEO schemes, the 96-LEO scheme yields an improvement in orbit accuracy of about 1 cm for GEO satellites and 1 mm for other satellites because of the increase of LEO satellites, but leading to a steep rise in the computational time. In terms of the orbital types, the sun-synchronous-orbiting constellation can yield a better tracking geometry for GNSS satellites and a stronger augmentation than the polar-orbiting constellation. As for the LEO altitude, there are almost no large-orbit accuracy differences among the 600, 1000, and 1400 km schemes except for BDS GEO satellites. Furthermore, the GNSS orbit is found to have less dependence on ground stations when incorporating a large number of LEO. The orbit accuracy of the integrated POD with 8 global stations is almost comparable to the result of integrated POD with a denser global network of 65 stations. In addition, we also present an analysis concerning the integrated POD with a partial LEO constellation. The result demonstrates that introducing part of a LEO constellation can be an effective way to balance the conflict between the orbit accuracy and computational efficiency.  
Integrated precise orbit determination; LEO constellations; multi-GNSS; onboard observations 2019 11 2514 10.3390/rs11212514
## 3 Results and Discussion
### Rheological Properties
#### Viscosity  
The \(t_{0}\) concentrates displayed shear-thinning behavior (Figure 5a,c). In other words, the viscosity of the \(t_{0}\) concentrate suspensions decreased with increasing shear rate, in line with previous observations for _Porphyridium_ concentrates [33]. Furthermore, concentrates of other algae species exhibited shear-thinning behavior [34] once their concentration exceeded a critical value that depends on the algae species [31]. Figure 5a,c indicate that this critical value was exceeded in this study. The high viscosity values of _Porphyridium_ sp. suspensions are generally ascribed to the presence of EPS that entangle once the biomass concentration is sufficiently high. EPS are believed to disentangle at higher shear rates, resulting in shear-thinning behavior [32; 33; 35; 36].  
The discussion on the storage effect will focus on the viscosity at a shear rate that is relevant for field applications. A shear rate of \(10\) s\({}^{-1}\) was chosen as it is within the expected shear rate range for algae harvest and pumping [31] and food processing [37]. During concentrate storage (Figure 5b), storage time, temperature, and their interaction affected the viscosity (\(p<0.01\)). Already after 1 day of storage at 20 \({}^{\circ}\)C, the viscosity increased strongly, i.e., about 10 times compared to the \(t_{0}\) situation. The viscosity increased at a slower pace during 4 \({}^{\circ}\)C and 8 \({}^{\circ}\)C concentrate storage (Figure 5b). This finding has important implications for the organization of a _P. purpureum_ processing facility. Once the culture is centrifuged, the concentrate should either be immediately processed or immediately cooled in order to minimize energy requirements for pumping and mixing. In the case of dilute culture storage, only storage time had an impact on the viscosity of the concentrate obtained after dilute culture storage (\(p<0.001\)), and this effect interacted with storage temperature (\(p=0.013\), Figure 5d). No large viscosity increase was observed compared to the \(t_{0}\) concentrate.
## 3 Proposed Calibration Process
### Step 3: Image Analysis  
The third step is the image analysis. This process can be done using both the statistical and visual results, or just one of them. The statistical data has been especially necessary during our test process to check the correspondence between the visual perceived information and the analytical data behind it.  
A correctly illuminated cyclorama will have as a visual result an image divided in regions of a very similar area and with the centroids in their geometric centers (Figure 7a). When an area is over-lighted the regions in this area will be smaller than the ones in the ideal reference image (Figure 7b, area 1). When it is in shadow, the regions will be bigger (Figure 7b, area 2). Therefore, the user must look for areas that differ from the average size in order to detect light artifacts. Due to these differences between the region's areas, the distribution of the centroids is also nonhomogeneous. In order to check the variations on the position of the centroids, we match the results in the real image with those in the ideal configuration. A correspondence is made between both sets of centroids so that all the centroids in the real image are associated to the closest centroid in the ideal image. As a result, when an area is over lighted several real centroids correspond to a single ideal one (Figure 7c, area 1). In the same way, when an area of the image is shadowed, some centroids in the ideal image will have no correspondence in the real one (Figure 7c, area 2), as they will not be the closest to any of them. Therefore, the user should detect ideal centroids with more than one real centroid correspondence and those with no correspondence at all, in order to find light artifacts.  
Besides this visual analysis, the statistical data computed by the CYIA can be used to detect smaller artifacts or illumination problems that are not obvious in a visual interpretation. The use of the statistical data is explained in the next test.
## 3 Short-Reach Systems Classification and Enabling Technologies
### Data Center Network Requirements and Challenges  
A data center is a physical facility employed by different organizations to accommodate vital data and applications for the advancement in the information technology industry and economic transformation [72]. Moreover, the design of data centers is mainly contingent on several factors such as the storage resources and network of computing that facilitate the effective provision of the shared data and applications. Typically, DCNs are designed to support different applications and activities such as productivity applications, email, file sharing, artificial intelligence, big data, machine learning, customer relationship management, enterprise resource planning, communications, and collaboration services [73]. In this subsection, we present a comprehensive discussion of the DCN requirements and the associated challenges. Moreover, we proffer potential solutions to address the related challenges.
## 3 Results
### Computational Results  
For a thorough investigation, the threshold \(\tau\) has been varied from 0.1 to 0.9 with step size 0.1. For the sake of shorthand, only \(\ell_{1}\)-SVM has been used for classification since it has been proved successful both in terms of efficiency and effectiveness for the previous PCN experiment.  
Figures 5 and 6 show the classification results on test set averaged across five splits for \(\alpha=1\) and \(\alpha=0.5\), respectively. By matching the top plots from Figures 5 and 6, the best threshold values are in range \(\tau\in[0.5,0.7]\) for \(\alpha=1\) and \(\tau\in[0.5,0.6]\) for \(\alpha=0.5\): in the latter case, as \(\tau\to 0.7\), precision (PPV) starts deteriorating. Indeed, for very low threshold values (i.e., \(\tau\to 0.1\)) there will be a lot of'soluble' proteins with respect to the 'non-soluble' ones (Many positive instances with respect to the negative ones). Trivially, this is reflected in very high positive-related performance indices (circa 100%) such as SNS and PPV and rather low negative-related performance indices (circa 80-90%) such as NPV and SPC. The opposite is true for very high thresholds (i.e., \(\tau\to 0.9\)). In the aforementioned ranges, all performance indices are rather balanced: in Figure 5, for \(\tau\in[0.5,0.7]\), all performance indices are in range 89-94%; in Figure 6, for \(\tau\in[0.5,0.6]\), all performance indices are in range 89-92%. This (minor) shift in performances is counterbalanced by the number of selected symbols: for \(\alpha=1\) approximately 20% of the alphabet symbols have been selected, whereas for \(\alpha=0.5\) the percentage of selected symbols is always below 5%. Interestingly, see Figure 6, the range \(\tau\in[0.5,0.7]\) is also  
Figure 4: Solubility distribution within the final 4781 proteins set.  
featurered by the largest alphabet: a slightly more complex embedding space is needed for maximising the overall performances.