esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiment and Results
### Building Extraction Results and Analysis  
For the three different PolSAR images, we extracted the buildings based on our method and five other methods separately. The five other methods include: using feature extraction and an eigenvalue-based method [12] and using the support vector machine (SVM) classifier [40] by using the feature set we propose for building detection, real vector representation tailored for Convolutional Neural Network (RVCNN) [20], Polarimetric-Feature-Driven Convolutional Neural Network (PFDCNN) [21], the proposed CNN of PauliRGB and G\({}^{0}\), and the method of adding superpixel constraints on this basis. The SVM classifier kernel is a radial basis function, the gamma coefficient is 0.2, determined by the number of channels, and the penalty parameter is 200. The RVCNN method inputs the normalized six-dimensional real feature vector, which contains the coherence or covariance matrix of multi-looked PolSAR data, into the four-layer convolutional neural network for PolSAR classification. The PFDCNN uses classical roll-invariant polarimetric features and hidden polarimetric features in the rotation domain to drive the proposed deep CNN model. The samples used for the experiments are consistent, whether it is a different CNN, SVM, or eigenvalue-based method.  
For the evaluation of accuracy, we use the accuracy rate (AR), the false alarm rate (FAR), and the F1-score [41] for the harmonic evaluation of the detection rate and false alarm rate. Among them, the accuracy rate is the correct proportion of the number of  
Figure 3: The convolutional neural network structure for building extraction.  
building pixels in the real building, and the false alarm rate is the number of non-building pixels in the extracted building. The F1-score is a way of combining the precision and recall of the model, and it is defined as the harmonic mean of the model's precision and recall. What is more, the CNN training samples are not excluded for the calculation of AR, FAR and F1-score.
# FDA Approvals of Biologics in 2022
## 4 Therapeutic Indications and Mechanisms of Action
### Aesthetic  
DaxifyTM (daxibotulinumtoxin A) was the Biologic for aesthetic purposes approved by the FDA in 2022 (Table 5) and it is administered by intramuscular injection. It is found in the literature as an advancement, considering past decades of the hegemony of BotoxTM (onabotulinumtoxin A) to treat glabellar lines. Daxibotulinumtoxin A shows promising results and greater internalization of the neurotoxin, and clinical trials have demonstrated significant differences in response rate and also a longer period of effect for this Biologic. Patients in this trial also showed a better response to this Biologic than to the placebo [78; 79; 80]. The mean duration of the effects of daxibotulinumtoxin A in clinical trials is around 24 weeks, while for onabotulinumtoxin A it is around 19 weeks [79].  
The IGA-FWS (Investigator Global Assessment-Facial Wrinkle Severity) and Global Aesthetic Improvement Scale (GAIS) were used to assess the results. Participants in the trial using 40 U of daxibotulinumtoxin A obtained between a 1 and 2 point improvement in glabellar lines, on both scales, over those using 20 U of onabotulinumtoxin A [79; 82].  
Before 2022, JeuveauTM (prabotulinumtoxin A) was the last Biologic authorized for aesthetic purposes (2019) [1]. Prabotulinumtoxin A and onabotulinumtoxin A presented similar outcomes in a 3-month study evaluating their effect on crow's feet. The main measure of efficacy for prabotulinumtoxin A vs. onabotulinumtoxin A was mean onset of action (3.81 days for prabotulinumtoxin A vs. 3.47 days for onabotulinumtoxin A) and time to peak effect (9.58 days for prabotulinumtoxin A vs. 11.11 days for onabotulinumtoxin A). The secondary measure was the duration of action (11.11 weeks for prabotulinumtoxin A vs. 11.22 weeks onabotulinumtoxin A) [83]. The literature is still lacking data comparing the novel daxibotulinumtoxin A with prabotulinumtoxin A for the treatment of glabellar lines.
# Energy-Efficient Distributed Edge Computing to Assist Dense Internet of Things
###### Abstract  
The Internet of Things (IoT) represents a rapidly growing field, where billions of intelligent devices are interconnected through the Internet, enabling the seamless sharing of data and resources. These smart devices are typically employed to sense various environmental characteristics, including temperature, motion of objects, and occupancy, and transfer their values to the nearest access points for further analysis. The exponential growth in sensor availability and deployment, powered by recent advances in sensor fabrication, has greatly increased the complexity of IoT network architecture. As the market for these sensors grows, so does the problem of ensuring that IoT networks meet high requirements for network availability, dependability, flexibility, and scalability. Unlike traditional networks, IoT systems must be able to handle massive amounts of data generated by various and frequently-used resource-constrained devices, while ensuring efficient and dependable communication. This puts high constraints on the design of IoT, mainly in terms of the required network availability, reliability, flexibility, and scalability. To this end, this work considers deploying a recent technology of distributed edge computing to enable IoT applications over dense networks with the announced requirements. The proposed network depends on distributed edge computing at two levels: multiple access edge computing and fog computing. The proposed structure increases network scalability, availability, reliability, and scalability. The network model and the energy model of the distributed nodes are introduced. An energy-offloading method is considered to manage IoT data over the network energy, efficiently. The developed network was evaluated using a developed IoT testbed. Heterogeneous evaluation scenarios and metrics were considered. The proposed model achieved a higher energy efficiency by 19%, resource utilization by 54%, latency efficiency by 86%, and reduced network congestion by 92% compared to traditional IoT networks.  
distributed computing; IoT; fog computing; resource utilization; multiple access edge computing; reliability; scalability; dense networks +
Footnote †: journal:  
_Article_
# Hybrid Model-Based Traffic Network Control Using Population Games
## 2 Notations  
In this section, we describe the main notations used in the mathematical model description of Section 3.  
* \(\mathcal{G}=(\mathcal{V},\mathcal{E},\psi)\): Directed graph representing a vehicular traffic network.
* \(\mathcal{V}=\{1,2,3,\ldots,m\}\): Set of network nodes. They represent the traffic network intersections.
* \(\mathcal{E}=\{1,2,\ldots,n\}\): Set of network links. They represent the links between traffic network intersections (streets).
* \((p,r)\): Input and output nodes to link \(i\in\mathcal{E}\), respectively.
* \(\psi(i)\): Incidence function that matches each edge \(i\in\mathcal{E}\) to an ordered pair \((p,r)\in\mathcal{V}\).
* \(\mathcal{I}_{p}\), \(\mathcal{O}_{p}\): Sets of input and output links from node \(p\), respectively.
* \(L_{p}\): Idle time for node \(p\). Includes red-yellow transition and yellow light time.
* \(g_{i}\): Green time for link \(i\in\mathcal{E}\).
* \(\mathcal{C}_{p}\): Cycle time for node \(p\). Includes the idle and green times.
* \(o_{i}\): Output flow capacity of link \(i\in\mathcal{E}\), given in \([veh/h]\).
* \(w_{i}\): Storing capacity of vehicles in link \(i\in\mathcal{E}\).
* \(\tau(i,j)\): Flow rate of vehicles from link \(i\) to \(j\), \(\tau(i,j)\in\{0,1\}\).
* \(s_{i}\): Traffic light state of link \(i\in\mathcal{E}\), \(s_{i}\in\{0,1\}\).
* \(\sigma\): Network mode given by the traffic light states.
* \(s_{i}(\sigma)\): Traffic light state in a link \(i\) and mode \(\sigma,s_{i}\in\{0,1\}\).
* \(l_{l}\): Queue length. Number of vehicles in a given link.
* \(q_{i}\): Queue input flow, given in \([veh/h]\).
* \(h_{i}\): Queue output flow, given in \([veh/h]\).
* \(\mathcal{F}_{p}\): Set of stages for node \(p\). One stage is the time at which an input link to a node \(p\) has the right of way (ROW).
## 4 Experimental Evaluation and Discussion
### Experiment and Discussion on the Automatic Coordination of Resource Planning
The DDPG algorithm has a response time that is 8.4% shorter than that of the gray search algorithm. The DDPG algorithm has a response time reduced by 19.7% compared to that of the random search algorithm. The deep deterministic policy gradient algorithm is an optimization of the DQN that combines the idea of the deterministic policy gradient algorithm and innovatively adopts a model-free deep reinforcement learning algorithm. The dual neural network architecture is used in the DDPG algorithm architecture. Both the strategy function and value function use a dual neural network model architecture (i.e., an online network and a target network). This dual structure makes the learning process of the algorithm more stable and accelerates the convergence speed. Moreover, the DDPG algorithm introduces an experience replay mechanism; the experience data samples generated by the interaction between the actor and the environment are stored in the experience pool, and batch data samples are extracted for training. This training method is similar to the experience replay mechanism of the DQN. This mechanism can eliminate the correlation and dependency of samples, facilitating algorithmic convergence. This is also the reason why the DDPG algorithm can achieve good results in comparative analysis.
# Low-Cost Data-Driven Robot Collision Localization Using a Sparse Modular Point Matrix
###### Abstract  
In the context of automatic charging for electric vehicles, collision localization for the end-effector of robots not only serves as a crucial visual complement but also provides essential foundations for subsequent response design. In this scenario, data-driven collision localization methods are considered an ideal choice. However, due to the typically high demands on the data scale associated with such methods, they may significantly increase the construction cost of models. To mitigate this issue to some extent, in this paper, we propose a novel approach for robot collision localization based on a sparse modular point matrix (SMPM) in the context of automatic charging for electric vehicles. This method, building upon the use of collision point matrix templates, strategically introduces sparsity to the sub-regions of the templates, aiming to reduce the scale of data collection. Additionally, we delve into the exploration of data-driven models adapted to SMPMs. We design a feature extractor that combines a convolutional neural network (CNN) with an echo state network (ESN) to perform adaptive feature extraction on collision vibration signals. Simultaneously, by incorporating a support vector machine (SVM) as a classifier, the model is capable of accurately estimating the specific region in which the collision occurs. The experimental results demonstrate that the proposed collision localization method maintains a collision localization accuracy of 91.27% and a collision localization RMSE of 1.46 mm, despite a 48.15% reduction in data scale.  
automatic charging; data-driven collision localization; sparse modular point matrix; convolutional neural network; echo state network; support vector machine +
Footnote †: journal: Applied  
doi:10.3390/app14052131
## 2 Motivation and Methods
### Spectral-Domain Optical Coherence Tomography for In Vivo Brain Imaging  
Since digital focusing utilizes a complex signal of OCT images, the stable phase of the spectral-domain OCT (SD-OCT) is advantageous for refocusing, and was hence selected for the experiment. As a light source, a superluminescent diode (SLD, DenseLight Semiconductors Incorporated, Singapore) centered at 1310 nm with a full spectral-width of 150 nm was used to illuminate the sample, and an interferometric signal was detected by an InGaAs line-scan camera (Sensors Unlimited Corporation, Princeton, NJ, USA) with 2048 pixels, 12-bit resolution, and a maximum line rate of 76 kHz. The spectrometer operates in k-space, and does not need a calibration for the wavelength dispersion. The axial resolution of the system was measured to be 8 \(\upmu\)m. The effective imaging range for a 10 dB sensitivity drop was 3 mm. More details of the system can be found in [30].
## 3 The Adaptive Droop Control of the ILC
### Adaptive Bidirectional Droop Control Strategy
\tag{11}\]  
The control effect of Equation (11) is shown in Table 1. In the limit state \(f_{\text{pu0}}\gg U_{\text{pu0}}\), only the power support effect of the AC sub-microgrid on the DC sub-microgrid is shown. When \(f_{\text{pu0}}\ll U_{\text{pu0}}\), only the power support of the DC sub-microgrid to the AC sub-microgrid is exhibited. Therefore, Equation (10) can satisfy Equation (7) to correct the droop coefficient to change the microgrid priority and improve the support capacity of the microgrid on the light load side to the microgrid on the heavy load side. Figure 7: The diagram of \(\lambda\) varies with the value of \(n\). The adaptive bidirectional droop control equation represented by Equation (11) and the conventional bidirectional droop control equation represented by Equation (5) respond in real time to the reference power according to the magnitude of AC frequency and DC deviation, which are both binary functions. Their function images are the surfaces shown in Figure 8.  
From Figure 8, it can be seen that when \(f_{\text{pu0}}<U_{\text{pu0}}\), the transmission power under both controls is greater than 0. At this time, the AC sub-microgrid power is lower than the DC sub-microgrid power, the DC grid provides power support to the AC sub-microgrid, and the ILC is in the inverter state. The transmission power \(P_{\text{ILC}}^{*}\) under adaptive droop control is larger than that under conventional droop control. At this point, if \(U_{\text{pu0}}\) is constant, the smaller \(f_{\text{pu0}}\) is, the more \(P_{\text{ILC}}^{*}\) increases under both controls, and the larger \(P_{\text{ILC}}^{*}\) is for adaptive droop control compared to conventional droop control. Conversely, when \(f_{\text{pu0}}>U_{\text{pu0}}\), the transmission power under both controls is less than 0. At this time, the AC sub-microgrid power is higher than the DC sub-microgrid power, the AC sub-microgrid provides power support to the DC sub-microgrid, the ILC is in a rectified state, and the transmission power \(P_{\text{ILC}}^{*}\) under adaptive droop control is greater than that under conventional droop control. At this point, if \(f_{\text{pu0}}\) is constant, the smaller \(U_{\text{pu0}}\) is, the more \(P_{\text{ILC}}^{*}\) increases under both controls, and the larger \(P_{\text{ILC}}^{*}\) is for adaptive droop control compared to conventional droop control. When \(U_{\text{pu0}}\) and \(f_{\text{pu0}}\) gradually approach equal values, the transmission power \(P_{\text{ILC}}^{*}\) under both controls tends to be 0. Therefore, it can be seen that the adaptive bidirectional droop control can effectively improve the transmitted power at the output stage of the ILC during both rectification and inversion processes, and the two power regulating capabilities tend to be the same when the hybrid AC-DC microgrid power deficit is close to the same level.
## 2 The Proposed Long-Distance, Real-Time LED-DCC System
### LED-DCC Point Clustering Scheme  
In the previous work, the maximum achievable distance at which the VLC signal broadcast by an LED display point could be received and recovered was only 15 cm under the 7% FEC limit. The data transmission distance is limited by the quality of the received VLC signal. Increasing the light-emitting area or the brightness of the LED-DCC point can help improve the visible signal quality captured by the image sensor. Increasing the brightness of LED display points requires the support of hardware, e.g., the driving circuit and the LED display element. Furthermore, increasing the brightness of LED display points may not only change the brightness of the display screen, but also the other display parameters, such as contrast and/or sharpness, as well as the viewing experience.  
Figure 1: (**a**) The overall block diagram of the proposed long-distance, real-time LED-DCC system. (**b**) LED displays for displaying and communication. (**c**) Smartphone’s CMOS camera for receiving VLC signals.  
Therefore, to solve the problem of the short transmission distance in the LED-DCC systems, the LED-DCC point clustering scheme is proposed to increase the light-emitting area by making use of a large number of pixels in the LED display, improving data transmission distance and system reliability.  
As shown in Figure 2, each data bit in the codeword encoded by the ABRC scheme is synthesized with N adjacent LED display points. Therefore, the value of the data bit is carried by N LED display points. Since the N LED display points are adjacent, they can be processed as a clustered point group.  
\[\text{LED}_{i}(x,\,y)\ =\text{b}t\ \times\text{p}_{i}(x,\,y),\ i\in\ [1, \text{N}] \tag{1}\]  
where \(\text{p}_{i}(x,\,y)\) is the value of the \(i\)-th pixel located at the coordinates (\(x,\,y\)) in the light-emitting area of N adjacent pixels in each video frame (frame), N is the total number of pixels in the clustered group, and \(\text{b}_{i}\) is the spread-spectrum code chip that encodes each original data bit based on the ABRC scheme. \(\text{b}_{i}\) is combined with \(\text{p}_{i}(x,\,y),\,i\in[1,\,\text{N}]\) in each frame\({}_{j}\) of the video. Finally, the hidden data is broadcasted by the LED-DCC clustering points (LED(\(x,\,y\))), \(i\in[1,\,\text{N}]\)) located at the coordinates (\(x,\,y\)) in a 2D point array at the same band rate as the refresh rate of the LED display while avoiding flicker perceived by the human eye.
_Article_  
**Nondestructive Ultrasonic Inspection of Composite Materials: A Comparative Advantage of Phased Array Ultrasonic**  
**[PERSON] 1,*\({}^{\bullet}\) and [PERSON] 2,3**  
1 Department of Manufacturing Engineering, Georgia Southern University, Statesboro, GA 30460, USA  
2 Manufacturing Demonstration Facility (MDF), Oak Ridge National Laboratory (ORNL), Knoxville, TN 37932, USA; [PERSON]ADDRESS]  
3 Department of Mechanical, Aerospace and Biomedical Engineering, University of Tennessee, Estabrook Rd, Knoxville, TN 37916, USA  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +1-912-478-7463 check for updates  
Received: 17 March 2019; Accepted: 16 April 2019; Published: 19 April 2019  
**Featured Application: The featured application of the proposed study is to develop the application and describe the advantages of phased array ultrasonic technique for the inspection of composite materials. The proposed method not only enhances the probability of detection of the defects in composite materials, but also increases the distance over which the defects are detectable with a single inspection location.**  
**Abstract: Carbon- and glass fiber-reinforced polymer (CFRP and GFRP) composite materials have been used in many industries such as aerospace and automobile because of their outstanding strength-to-weight ratio and corrosion resistance. The quality of these materials is important for safe operation. Nondestructive testing (NDT) techniques are an effective way to inspect these composites. While ultrasonic NDT has previously been used for inspection of composites, conventional ultrasonic NDT, using single element transducers, has limitations such as high attenuation and low signal-to-noise ratio (SNR). Using phased array ultrasonic testing (PAUT) techniques, signals can be generated at desired distances and angles. These capabilities provide promising results for composites where the anisotropic structure makes signal evaluation challenging. Defect detection in composites based on bulk and guided waves are studied. The capability of the PAUT and its sensitivity to flaws were evaluated by comparing the signal characteristics to the conventional method. The results show that flaw sizes as small as 0.8 mm with penetration depth up to 25 mm can be detected using PAUT, and the result signals have better characteristics than the conventional ultrasonic technique. In addition, it has been shown that guided wave generated by PAUT also has outstanding capability of flaw detection in composite materials.**  
**Keywords: phased array ultrasonic; composites; signal sensitivity; defect detection; nondestructive testing (NDT)**
# Towards Hyper-Relevance in Marketing: Development of a Hybrid Cold-Start Recommender System
## 4 Methodology
### Evaluation
#### 4.6.1 Baseline Recommender System  
The RS previously applied to the business case is presented in detail in Ref. [22], consisting of a cascade-hybrid recommender system customisable for each particular retailer. This will be considered the baseline RS, providing guideline values for the evaluation metrics. A diagram summarizing the cascade-hybrid approach is presented in Figure 6. The recommender's first level, culminating in the content-based approach, consists of filtering and ranking functions, providing an initial filtration of items. The second level further ranks  
Figure 5: Diagram of the hybrid recommender system approach.  
the items using alternative collaborative filtering approaches for high and low interaction users, either model-based or memory-based, resulting in the final recommendation list.  
Further detailing the adopted methodology, the content-based approach employs Natural Language Processing (NLP), analysing item descriptions. The pre-computing process includes three phases. Firstly, a vectorised keyword representation is formulated to extract and represent features. Secondly, a user-specific model analyses previous transactions, associating each user with a series of items; similarity between items is calculated based on the cosine distance metric. Finally, the previously extracted information is used to provide item recommendations for specific users.  
With a focus on the cold-start user segment, a memory-based system was developed comprising item classification using the K-nearest neighbours algorithm--a supervised learning classifier. This algorithm adopts the inverse logic of the model-based approach, identifying the similarity between items based on transactional data, categorised as item-based collaborative filtering. Relying only on identified relations between items previously purchased by a particular user, a low number of interactions causes a low impact in the RS's performance, proving itself suitable for cold-start users.  
Additionally, the model includes a time-decay approach for the computation of implicit ratings. This rating contemplates the number of units purchased and the price of the product, introducing the time-decay element that rewards recent events. The consideration of the number of purchased units increases the rating for items that were purchased multiple times, while the product's price contemplates the user's willingness to purchased higher priced products as an indicator of a stronger preference. The time-decay element adjusts the ratings according to the decay of the relevance of ratings, dependant on each specific market.