esa-sceva/satcom-chunk-collection huggingface dataset:
# Research and Application of Edge Computing and Deep Learning in a Recommender System
## 4 Experimental Process and Experimental Result Analysis
### Experimental Process  
As illustrated in Table 1 below, the experimental dataset comprises four categories of data: ticket purchase data, user information, train information, and real-time interaction.  
(1) Training Stage  
The most advanced algorithms at present, DeepFM, DCN, and xDeepFM, were used as teacher models. Random seeds were varied for five experiments to ensure statistical significance. In addition, cross entropy served as the loss function to train all teacher models. Subsequently, based on ensemble knowledge distillation (EKD), the teacher model was initially trained to recognize the target dataset and extract key attributes from samples. Afterward, the ticket purchase dataset was selected for \(Softmax\)-based computing based on an outcome achieved by dividing the output of logits in the teacher model by the T-parameter, thus producing the values of soft labels. The dataset was then input into the student model, repeating operations of the teacher model and acquiring an output of logits. Afterwards, calculations were performed in two steps: First, the output of logits was divided by the same T-parameter as that of the teacher model, followed by \(Softmax\)-based computation, generating soft predictions that were further used as outputs to be compared  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Instances** & **Fields** & **Positive Ratio** \\ \hline Ticket purchase data & 240 M & 38 & 86\% \\ User info & 25 M & 40 & 92\% \\ Train info & 3000 & 20 & 95\% \\ Real-time interaction & 125 M & 18 & 74\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental datasets.  
with soft labels. Second, after \(Softmax\)-based computing, predicted values were obtained and then compared with Ture labels. The total loss function, \(KDLoss\), was acquired by adding the loss functions of the above two steps together. After determining the loss function, the gradient decreased, and the corresponding parameters needed to be updated. Finally, the availability of the trained model was analyzed in comparison with FM, FFM, Wide&Deep, DeepFM, and the DCN using the same dataset.  
Real-Time Computing Stage  
Real-time computing is designed to assess the concurrency and timeliness of the deployed recommendation system. Following the segmentation of the student model, these segments were deployed on both the cloud and terminal devices. A recommendation list was generated through collaborative computing based on passengers' demands. Simultaneously, other models were deployed on the cloud to conduct concurrency testing. The same cloud resources and terminal devices used during training were employed to analyze their concurencies and computational efficiency.
## 3 System Model and Parameterization
### Operation Cycle of SCN
#### 3.3.2 Partially Failed State  
In a partially failed state, the SCN fails partially. Thus, the \(PDR\) of the corresponding PMUs is affected. Therefore, for the PMUs with partially failed SCNs, the \(PDR\) is always less than 1. As an example, for the \(PDR\) at node \(B\) corresponding to partially failed node \(A\), we have \(\sum\limits_{t=t_{B0}}^{t_{B0}}\alpha_{t}^{B}<\sum\limits_{t=t_{A0}}^{t_{B0}} \beta_{t}^{A}\) and thus \(PDR_{B-A}<1\).
## 7 Miscellaneous
### Code Size of Different Components in MIROS  
Currently, two kinds of network stacks, the Atmel IEEE802.15.4 [42] and the Atmel lightweight mesh stack [43], have been ported to the MIROS. In Figure 16, the code size of different components in the MIROS is shown. If the SFL allocator is applied, the size of the MIROS kernel is 2896 bytes. If the Atmel mesh stack is used, the size of the software firmware is 24,590 bytes. If the Atmel IEEE802.15.4 stack is used, the size of the software firmware is 53,234 bytes. These results prove that  
Figure 15: New debugging approach by using the multi-core technology.  
the MIROS system is suitable to be used on many popular WSN platforms (BTnode, IMote, SenseNode, TelosB and T-Mote Sky, _etc._) to provide the basic sensing data services.  
Figure 16: Code size of the different MIROS components on the iLive platform.  
\begin{table}
\begin{tabular}{|c|c|c|c|c|c|c|} \hline \multirow{3}{*}{**WSN OSes**} & \multicolumn{5}{c|}{**Titles**} \\ \cline{2-7}  & \multirow{2}{*}{**Scheduling Model**} & \multirow{2}{*}{**Real-Time Supported**} & \multirow{2}{*}{**Memory Allocation**} & \multirow{2}{*}{**Deep coupling of APP from System**} & \multirow{2}{*}{**Network Protocols**} & \multirow{2}{*}{**Energy Conservation**} \\ \cline{5-6}  & & & & & & \\ \hline TinyOS & Event-driven & No & & No & TYMO, 6 LoWPAN, \\ \hline TinyOS with TOSThread & Hybrid & Not well, event-driven scheduling in native layer & Static & Yes, by dynamic linking & Active message, TDMA, IEEE 802.15.4, _etc._ & Sleep/Wakeup \\ \hline Contiki & Event-driven & No & & Yes, by & uIP, Rime, ContikiRPL, & Sleep/Wakeup, controlled from the 6 LoWPAN, _etc._ & Sleep/Wakeup program \\ \hline SOS & Event-driven & No & Dynamic SFL & Yes, by & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\ \hline MantisOS & Multi-threaded & Not well, the RR scheduling is used & Dynamic SF & No & Comm & Sleep/Wakeup \\ \hline openWSN & Event-driven & No & Static & No & CoAP, HTTP, UDP, TCP, RPL, 6 LoWPAN, IEEE 802.15.4e \\ \hline simpleRTJ & Multi-threaded & Not well, the RR scheduling is used & Static & Yes, by virtual machine & N/A & Sleep/Wakeup \\ \hline uCOS & Multi-threaded & Yes, preemptive real-time RMS scheduling & Dynamic SFL & N/A & μC/TCP-IP & Sleep/Wakeup \\ \hline FreeRTOS & Multi-threaded & Yes, supports both preemptive and cooperative scheduling & Four approaches & N/A & Thread-aware UDP & tickless mode for low power applications \\ \hline MIROS & Hybrid & Yes, preemptive real-time RMS scheduling & Improved SFL and SF & Yes, by pre-linking EMIDE & Atmel IEEE lightweight mesh stack & Sleep/Wakeup and Multi-core context aware mechanism \\ \hline \end{tabular}
\end{table}
Table 12: Feature comparison of the different embedded OSes.
# Vectored-Bloom Filter for IP Address Lookup: Algorithm and Hardware Architectures
###### Abstract  
The Internet Protocol (IP) address lookup is one of the most challenging tasks for Internet routers, since it requires to perform packet forwarding at wire-speed for tens of millions of incoming packets per second. Efficient IP address lookup algorithms have been widely studied to satisfy this requirement. Among them, Bloom filter-based approach is attractive in providing high performance. This paper proposes a high-speed and flexible architecture based on a vectored-Bloom filter (VBF), which is a space-efficient data structure that can be stored in a fast on-chip memory. An off-chip hash table is infrequently accessed, only when the VBF fails to provide address lookup results. The proposed architecture has been evaluated through both a behavior simulation with C language and a timing simulation with Verilog. The hardware implementation result shows that the proposed architecture can achieve the throughput of 5 million packets per second in a field programmable gate array (FPGA) operated at 100 MHz.  
Bloom filter; IP address lookup; vectored-bloom filter; FPGA; hardware accelerator +
Footnote †: journal: Applied Physics Letters  
_Article_
# Mental Workload Assessment Using Machine Learning Techniques Based on EEG and Eye Tracking Data
## 3 Materials and Methods
### Brief Overview of Machine Learning Algorithms
#### 3.5.2 Random Forests  
The random forest (RF) technique, introduced by Breiman [49] in 2001, offers a wide range of applications, including regression, classification, and variable selection, as highlighted by Genuer et al. [50]. By employing ensemble learning, RF combines multiple trees into a single algorithm, as explained by Cun et al. [51]. The prediction for a new observation is then determined by aggregating the predicted values from each individual tree in the forest. The key parameters for RF algorithms are the number of trees, the minimum number of observations in the terminal node, and the number of suitable features for splitting. In the existing literature, comprehensive mathematical explanations for RFs can be found [49].
# Structural-Missing Tensor Completion for Robust DOA Estimation with Sensor Failure +
## 1 Introduction
In this case, by incorporating the proposed algorithm in the co-array domain, the virtual array aperture can be further expanded, contributing to enhanced DOA estimation performance. The preliminary results of this work were presented in the conference paper [55]. In this journal paper, we present more details of the tensor signal model and the spatio-temporal dimension augmentation method. Moreover, to ensure more effective tensor signal recovery, the dimensional-augmented Hankel tensor is completed with a low-rank regularization in this paper instead of Tucker approximation as in the conference paper [55]. We demonstrate the advantage of using the low-rank regularization both analytically and numerically. We elaborate the procedures of Hankel tensor completion and completed covariance tensor decomposition. Furthermore, to validate the effectiveness of the proposed algorithm, we present new simulation results, including comparison of estimation accuracy, comparison of angular resolution, performance of source identifiability, comparison with other completion methods, and comparison of computational time. The rest of the paper is organized as follows. In Section 2, we formulate the incomplete tensor signal model under sensor failure. In Section 3, we design the structural-missing tensor completion method, which includes the procedures of spatio-temporal dimension augmentation and Hankel tensor completion. In Section 4, we propose the robust DOA estimation method based on the completed Hankel tensor. We present numerous simulation results in Section 5 and provide our conclusions in Section 6.
# Journey to the Market: The Evolution of Biodegradable Drug Delivery Systems
## 2 The Evolution of Biodegradable Polymeric Drug Delivery Systems
### Phosphorus-Containing Polymers  
In contrast to other biodegradable polymers, phosphorous-containing polymers are distinct due to their degradation kinetics determined by the structural modification of the side-chain rather than the polymer backbone [44]. Use of phosphorus-based materials (polyphosphates, polyphosphonates, polyphosphazenes, polyphosphoesters, phosphonated poly(meth)acrylates, etc.) in biomedical applications is increasing due to their good biodegradability and biocompatibility [45; 46]. [PERSON] et al. reported that control of the degradation rate may be achieved by the selection of various amine groups in the side chain [47]. As the degradation products such as phosphate, ammonia, and ethanol are non-toxic, phosphorous-containing polymers are employed as the carrier for controlled delivery of multiple drugs and peptides [48]. For example, a polyphosphazene-based polymeric formulation loaded with naproxen produced sustained release over a 4-week period [49].
## 5 Conclusions  
In this paper, the effect of the rock/coal strength on uniaxial compressive strength, failure behavior, and energy evolution of CRC specimens were studied by uniaxial compression, uniaxial cyclic loading, and unloading experiments. The following conclusions were obtained:  
(1) The stress-strain curve of the uniaxial compression test of the CRC specimen is located between the pure coal specimen and the pure rock specimen, and the strength of the CRC specimen is greater than the strength of the pure coal specimen, which is smaller than that of the pure rock specimen. The Young's modulus, uniaxial compressive strength, and axial strain of the CRC specimen tend to increase with an increase in the coal strength instead of the rock strength.  
(2) The pure coal and pure rock specimens exhibited typical axial splitting failure. For the CRC specimen under uniaxial compression, when the strength of the rock is small, the shear failure in the coal section extends to the rock section. When the strength of the rock specimen is large, the coal section exhibits partial splitting damage, and the rock section does not substantially break. This finding also indicates that the stability of the CRC specimen depends mainly on the strength of the coal.  
(3) According to the failure mode of CRC specimens, the final destruction of CRC specimens with coal section was dominated by axial splitting. In addition, lateral tensile ruptured easily in the coal section, as such, it is suggested that lateral support of coal pillar should be strengthened in the mining field.  
(4) The elastic release energy \(U_{e}\), the dissipative energy \(U_{\mathrm{d}}\), and the kinetic energy \(U_{r}\) increase with an increase in the rock/coal strength. Additionally, for the CRC specimen with the same coal section, as the strength of the rock increases, the more severe the damage of the CRC specimen is, the greater the kinetic energy is. In addition, more strain energy could be stored in the coal pillar located in the hard roof. As such, it is suggested that some active prevention techniques should be used to weaken the energy accumulation.  
All authors have contributed to this work. [PERSON] and [PERSON] conceived the experimental plan and wrote the paper, [PERSON] revised the paper, [PERSON] conducted the data analysis.  
The research described in this paper was financially supported by National Key R&D Program of China (No. 2018 YFC0604703); National Natural Science Foundation of China (No. 5157414); Natural Science Foundation of Shandong Province, China (No. ZR2019 QEE002); Key R&D Programs of Shandong Province (No. 2018 GSF16003); SDUST Graduate Student Technology Innovation Project (No. SDKDYC190116).  
The authors declare no conflict of interest.  
Figure 13: Relationship between the kinetic energy and rock/coal strength in CRC specimens.
## 1 Introduction
In actual irrigation areas, it is difficult to accurately distinguish the various flow patterns of water flow, and corresponding flow calculation formulas cannot be selected in a timely manner, which will result in significant calculation errors. The existing research has shown that neural networks can effectively solve the above problems [10; 11; 12]. [PERSON] [13] investigated the application of different artificial neural networks and their optimization models for flow prediction in channel systems. The research shows that the optimization model of the BP neural network is not affected by the change in flow pattern, and has high flow prediction accuracy and convergence speed, which can be used to calculate the flow of the main channels in the Ganfu Plain irrigation area. [PERSON] et al. [14] established a prediction model of diversion capacity for Weishan sluice based on the GA-RBF neural network. They concluded that the GA-RBF model has the characteristics of high prediction accuracy and strong adaptability. The data samples chosen in the above studies for the training and testing of neural network models are limited, and the generalization ability of the prediction model needs to be verified. Therefore, it is necessary to verify the generalization and prediction accuracy of neural network models through a large amount of experimental data. In this paper, taking the widely used box culvert-type integrated measurement and control gate in Ningxia Yellow Diversion Irrigation Area as an example, a large number of overflow tests with different gate openings and flow discharges were conducted at Ningxia Tianxianglin Water Conservancy Measurement Facility Inspection Co., Ltd. (located in Wuzhong, China). The gate discharge was predicted using the traditional gate overflow formula and the neural network prediction model. The predictive performance of traditional flow calculation methods and neural network prediction methods, as well as the predictive performance of three different neural network models, were compared. The reasons for errors in different neural network models were analyzed. This study can provide an efficient and fast method for precise control of the integrated measurement and control gate and the prediction of gate flow.
###### Abstract  
The objective of this study was to assess the mineralization and microhardness of bovine dental enamel surfaces treated with fluoride, tri-calcium phosphate, and infrared (IR) 810 laser irradiation. The study used 210 bovine incisors, which were divided into six groups (\(n=35\) in each): Group A: Untreated (control), Group B: Fluoride (Durapath-Colgate), Group C: Fluoride+Tri-calcium phosphate (Clin-Pro White-3 M), Group D: Laser IR 810 (Quantum), Group E: Fluoride+laser, and Group F: Fluoride+tri-calcium phosphate+laser). Mineralization was measured via UV-Vis spectroscopy for phosphorus and via atomic absorption spectroscopy for calcium upon demineralization and remineralization with proven agents. Microhardness (SMH) was measured after enamel remineralization. Mineral loss data showed differences between the groups before and after the mineralizing agents were placed (\(p<0.05\)). Fluoride presented the highest remineralization tendency for both calcium and phosphate, with a Vickers microhardness of 329.8 HV0.1/11 (\(p<0.05\)). It was observed that, if remineralization solution contained fewer minerals, the microhardness surface values were higher (\(r=-0.268\) and \(-0.208\); \(p<0.05\)). This study shows that fluoride has a remineralizing effect compared with calcium triphosphate and laser IR810. This in vitro study imitated the application of different remineralizing agents and showed which one was the most efficient for treating non-cavitated injuries. This can prevent the progression of lesions in patients with white spot lesions.  
Keywords:dental mineralization; enamel microhardness; low-level laser (LLL); fluoride +
Footnote †: journal:
## 3 Factors Influencing PSMs Production in Plants
### Effects of Drought Stress on PSMs  
Climate change is expected to alter precipitation patterns and results in drought stress (water deficit) in some plants. Drought stress is considered major abiotic stress that impedes metabolism [212; 213] and leads to changes in plants at the morphological, physiological, biochemical, metabolic, and transcriptional levels. ROS formation is one drought stress effect, which damages cellular components, including proteins, lipids, and nucleic acids [214; 215]. Accumulation of flavonoids such as flavonoids and anthocyanins is essential in protecting against abiotic stresses, including drought stress, but the mechanism of action is poorly understood [216]. For example, concentrations of antioxidant flavonoids epigallocatechin gallate, epicatechin, and epicatechin gallate increase in the leaves of _Cistus clusii_ under drought stress, reaching a maximum after 30 days of exposure [120; 217]. However, the efficacy of photosystem II (PSII) and lipid peroxidation remained unchanged. Under drought stress, PSII in the cotton (_Gossypium hirsutum_) also remained unaffected [218]. [PERSON] et al. [216; 219] also obtained a similar result (increasing flavonoids and anthocyanins) under drought stress in the aerial parts of _Anbidopsis thaliana_ (wild type, Col-0) and confirmed that overaccumulation of flavonoids is key to drought tolerance. There was also a drastic increase in the concentrations of glycosides of kaempfer, quercetin, andcyanidin along with drought stress marker metabolites (proline, raffinose, and galactinol). Excessive accumulation of anthocyanins protects plants against drought stress [219], and anthocyanins are thought to be more robust antioxidants due to their higher level of hydroxylation [220]. A few other studies [221; 222] have reported similar observations, i.e., increased accumulation of anthocyanins in plants under drought. Drought stress in _Ama-ranthus tricolor_ genotype VA3 increased concentrations of at least 16 phenolic compounds, including six hydroxybenzoic acids, seven hydroxycinnamic acids, three flavonoids, and a new phenolic acid, trans-cinnamic acid (Table 1) [112]. In tea plants, fulvic acid is the primary driver of tolerance against drought stress by enhancing ascorbate and glutathione metabolism and promoting flavonoids biosynthesis [223]. More examples and patterns of biochemical changes induced by drought stress in plants are given in Table 1.
## 2 Results and Discussion  
### Adsorption  
In order to select the most effective adsorbents a comparative batch adsorption experiment was performed. The total phenolics and the ABTS [2,2'-azinobis (3-ethylbenzothazoline-6-sulfonate)] radical scavengers adsorbed were measured by \(q\), as mg of gallic acid equivalent (GAE)/g resin and as mg Trolox equivalents (TE)/g resin, respectively. The kinetic data corresponding to the time course of the adsorption of phenolics and ABTS radical scavengers onto the evaluated resins are shown in Figure 1a,b. The adsorption yields ranged from 67 to 90%, with increasing values for Diaion HP2 MG (68.7) and HP20 (75.2), Amberlite XAD2 (69.2), XAD7 HP (70.4), XAD1180 (75.3), XAD761 (79.2), XAD16 HP (80.2), XAD4 (81.8), and Sepabeads SP70 (83.6), SP850 (84.1), SP825 (85.6), SP207 (88.1) and SP700 (89.5). The time to reach the steady state was relatively short, and for further experiments was fixed at 0.5 h; Amberlite XAD2 and XAD4 required 3 h. These values were shorter than those needed to retain phenolics from winery wastes onto activated carbon [3]. The adsorption of ABTS radical scavengers was accomplished in shorted times than the adsorption of phenolics. In 2 min 91% of the  
Figure 1: Adsorption kinetics of (**a**) phenolic compounds (expressed as gallic acid equivalents, GAE) and (**b**) ABTS radical scavengers (expressed as Trolox equivalents, TE) from winery wastes. The symbols correspond to experimental data and the lines to the calculated trend according to the selected models. active compounds were adsorbed onto SP207, SP70, SP825 and SP850, between 64 and 78% onto HP20, HP2 MG, XAD 7 HP, XAD 16 HP, XAD761 and XAD1180, whereas 20 min were required to achieve these yields onto XAD2. Operating with XAD4 a different behaviour was observed, reaching 75% adsorption yield after 20 min. The experimental data in Figure 1 were fitted to the pseudo first order model and the pseudo second order model. For all the tested resins, the \(R^{2}\) values (0.8544-0.9855) observed with pseudo first order models were lower than those (0.9929-0.9999) found for the pseudo second order model. According to these values, summarized in Table 1, the sorption kinetics followed a pseudo second order model, also reported for other phenolics and resins [10,13,14].