esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling Implicit Trust in Matrix Factorization-Based Collaborative Filtering
## 4 Experiment Details
### ML-100k  
Figure 2 shows the results for the ML-100k data set. All of the trust-based methods provide a better prediction accuracy than those of the baseline methods on the ML-100k data set. NGCF demonstrates the worst performance, whereas the M-Trust model demonstrated the best performance, with significant performance improvements of 9%, 8.82%, 32.58%, and 368.17% compared to SVD, SVD++, SocialMF, and NGCF, respectively. The performance of S-Trust was slightly better than that of W-Trust; it demonstrated a performance improvement of 0.38%, 0.21%, 22.1%, and 331.15%, against SVD, SVD++, SocialMF, and NGCF, respectively. The corresponding improvement values for the W-Trust were 0.34%, 0.17%, 22%, and 330.97%, respectively. I-Trust demonstrated the second-best performance, with performance improvements of 2.85%, 2.67%, 25.09%, and 341.72% compared to the baseline methods.  
To determine the effect of trust on the accuracy of the test methods, we plotted the prediction error against the approximated or initial trust, as shown in Figure 3.  
From all the trend lines of the graph in Figure 3a, it is clear that an increase in the estimated trust is associated with a low prediction error. However, in Figure 3b,c, the estimated trust, which is the same for S-Trust and W-Trust, consists of both positive and negative values. Since both the methods use the same trust, the two graphs seem almost identical. For these two methods, most of the data points are clustered around the center (the estimated trust value of zero). The trend line indicates that a slight increase in error is associated with increasing trust values. The I-Trust method also demonstrates the trend of increasing prediction error with increasing trust, as seen in Figure 3d. The slope of the trendlines indicates the strength of the association between trust values and errors. The M-Trust and I-Trust methods have a higher correlation than the S-Trust and W-Trust methods.  
Figure 2: Prediction accuracy obtained using different methods for the ML-100k data set. MAE: Mean absolute error; SVD: Singular value decomposition; NGCF: Neural graph collaborative filtering.
## 4 Case Study
### LSTM-Based Wind Prediction  
#### 4.2.1 Training Data and Prediction Results  
Historical data of wind power of nine years (2009-2017) are utilized as the training set of the LSTM model. Twenty-four hours of data of wind power randomly selected in 2018 is utilized as the validation set. Prediction step is one of the most significant parameters of LSTM prediction model. Aiming to improve the prediction accuracy, 24 different prediction steps were tested: from 15-min ahead to 6-h ahead. We take 1 h as interval to show the prediction result, and take 15 min as interval to further comparison in the following subsection. In Figure 9, the real data of wind power are depicted with the thick dashed line, and the predicted data of wind power are depicted with thin solid lines. Notice that the colors are gradually getting darker as the shortening of prediction step. Intuitively, the shortest prediction step can achieve the highest prediction accuracy.
# PPO-Based Joint Optimization for UAV-Assisted Edge Computing Networks
## 3 System Model and Problem Formulation
In the considered UAV-assisted edge computing network, the matching of UEs and ECSs has a crucial impact on the waiting delay and processing delay. Also, the choice of UAV hovering position determines the maximum sending rate and affects the sending delay. Therefore, we consider establishing the optimization problem for joint UE-ECS matching and deployment of three hovering positions of UAVs. Specifically, the problem is modeled as follows:  
\[\textbf{P}1: \min_{\textbf{q},\textbf{u}} \frac{1}{K}\sum_{k=1}^{K}Delay^{k}_{total}\] (14a) s.t. \[\textbf{q}\in\textbf{q}_{c}, \tag{14b}\] \[u_{k}\in\{1,2,\cdots,M\},k=1,2,\cdots,K\] (14c) \[|u_{k}|=1,k=1,2,\cdots,K \tag{14d}\]  
where the optimization variables are the three UAV deployment locations, **q**, and the matching of the UEs, **u**. The first constraint is that the UAV needs to hover within a reasonable range, \(\textbf{q}_{c}\); the second constraint is that the user needs to select the ECSs present in the network for matching, in which \(u_{k}\) represents the matching of the UEs, \(k\); and the last constraint is that each edge user can select only one ECS for matching.
# Intelligent Fault Diagnosis of Rotating Machinery Using Hierarchical Lempel-Ziv Complexity
## 2 Proposed Hierarchical Lempel-Ziv Complexity  
### Lempel-Ziv Complexity  
LZC, as a nonlinear method, has been proven to be an efficient tool to measure the complexity for a given time series. LZC consists of two basic operations: copy and insert [20]. The LZC algorithm can be detailed as  
(1) Cover the finite sequence \(x(t)\) into 0-1 sequence by comparing with the median value \(T_{d}\) using Equation (1). Then, we can obtain the symbol series \(S_{N}=\left\{s_{1}s_{2}\ldots s_{N}\right\}\).  
\[s_{i}=\left\{\begin{array}{ll}0,&\text{if }x(i)<T_{d}\\ 1,&\text{otherwise}\end{array}\right. \tag{1}\]  
(2) Set the initial value \(S_{v,0}=\left\{\right\}\), \(Q_{0}=\left\{\right\}\), \(C_{N}(0)=0,\text{ and }i=1\). Note that \(S_{v}\) and \(Q\) represent the substrings of the symbol series \(S_{N}\), and \(C_{N}\) represents complexity counter.  
(3) Let \(Q_{i}=\left\{Q_{i-1}s_{i}\right\}\) and judge whether \(Q_{i}\) belongs to \(S_{v,i-1}=\left\{S_{v,i-2}s_{i-1}\right\}\). If so, set \(C_{N}(i)=C_{N}(i-1)\) and \(i=i+1\). Otherwise, set \(Q_{i}=\left\{\right\}\), \(C_{N}(i)=C_{N}(i-1)+1\), and update \(i=i+1\).  
(4) Repeat Step (3) until the end of symbol series \(S_{N}=\left\{s_{1}s_{2}\ldots s_{N}\right\}\), and then the \(C_{N}(N)\) can be obtained. \(C_{N}(N)\) is the last complexity counter, which reflects upon the number of all different subsequences contained in the original data sequence.  
(5) Normalize the \(C_{N}(N)\) to obtain relatively independent indicator \(C_{n,N}\) using Equations (2) and (3).  
\[C_{n,N}=\frac{C_{N}(N)}{C_{UL}} \tag{2}\]  
\[C_{UL}=\lim_{N\rightarrow\infty}C_{N}(N)\approx\frac{N}{\log_{2}N} \tag{3}\]
## 3 Results and Discussion
### Characterization of the N-TiO\({}_{2}\) Powder
#### 3.1.2 Raman Analysis  
Figure 2 illustrates a representative Raman spectrum of the as-prepared powder.  
The peaks observed corresponding to the Raman fundamental modes of pure anatase crystal phase are located at 142 (E\({}_{\text{g1}}\)), 196 (E\({}_{\text{g2}}\)), 396 (B\({}_{\text{1g}}\)), 516 (A\({}_{\text{1g}}\)), and 638 (E\({}_{\text{g3}}\)) cm\({}^{-1}\), while other peaks located at 142 (B\({}_{\text{1g}}\)), 448 (E\({}_{\text{g}}\)), and 608 (A\({}_{\text{1g}}\)) cm\({}^{-1}\), are characteristic of the rutile phase. A small blue-shift compared to pure TiO\({}_{2}\) anatase (142 (E\({}_{\text{g1}}\)), 196 (E\({}_{\text{g2}}\)), 398 (B\({}_{\text{1g}}\)), 517 (A\({}_{\text{1g}}\)), and 638 (E\({}_{\text{g3}}\)) cm\({}^{-1}\)) is observed at some of the recorded peaks [42; 43]. This is due to the presence of the N dopant and the doping effect, which leads to an alteration of the TiO\({}_{2}\) lattice. Shifts are generally a result of changes in crystallinity or grain size, presence of defects, or changes of surface oxygen deficiencies [42; 44]. No other crystalline form of TiO\({}_{2}\) was detected. These findings are in agreement with the XRD results of this study, as well as with the relevant literature for Raman analysis of anatase and rutile TiO\({}_{2}\)[42; 45].
# Proposal of an Adaptive Fault Tolerance Mechanism to Tolerate Intermittent Faults in RAM
## 3 Design of an Adaptive Fault Tolerance Mechanism
### Methodology
\end{array} \tag{1}\]  
where \(\alpha\) is the value of the counter, and \(J\) is a system binary signal that represents that an error has been detected. When a counter surpasses a predetermined threshold (\(\alpha_{\text{TH}}\), that can be configured in the model), it overflows. This means that a change in the error conditions has been detected. Then, the AFT mechanism (i.e., the adaptive memory controller) should be reconfigured. This is carried out in four steps (see Figures 1 and 2):  
1. The microprocessor is stopped (by setting the aforementioned \"MEMORY BUSY\" signal), and the \"ECC Encoder\" module is changed to the corresponding to the new ECC to be used;
2. The whole RAM is scrubbed by reading all memory addresses sequentially using the old \"ECC Decoder\" to get the data and re-encoding them with the new \"ECC Encoder\". This operation is similar to the faulty read shown in Figure 3e, and hence it takes two clock cycles per memory address;
3. The \"ECC Decoder\" is changed to the corresponding to the new ECC to be used;
4. The microprocessor is resumed (unsetting \"MEMORY BUSY\"), continuing its operation from the same point.
# Simple K-Medoids Partitioning Algorithm for Mixed Variable Data
## 2 Related Works and the Proposed Method  
### K-Medoids Algorithms  
A well-known choice of a medoid-based algorithm is the PAM algorithm [2]. The PAM algorithm consists of three steps: initial medoid selection, medoid swapping, and object labeling. The initial medoids are chosen randomly. Then, medoids are swapped with randomly-selected \(n-k\) possible medoids, where \(n\) is the number of objects and \(k\) is the number of clusters.  
Besides PAM, the k-medoids (KM) [15] and simple and fast k-medoids (SFKM) [16] algorithms have been developed. Both algorithms have restricted the medoid swapping step. It works within clusters only, i.e., similar to the centroid updating in k-means. However, they both differ in the initial medoid selection where the KM algorithm uses randomly-selected initial medoids. Meanwhile, SFKM selects the initial medoids using a set of ordered \(v_{j}\). The value of \(v_{j}\) is defined as a standardized row sum or standardized column sum of the distance matrix that is calculated by:  
\[v_{j}=\sum_{i=1}^{n}\frac{d_{ij}}{\sum_{l=1}^{n}d_{il}},\quad j=1,2,3,\ldots,n,\]  
where \(d_{ij}\) is the distance between object \(i\) and object \(j\). When the pre-determined number of clusters is \(k\), the first \(k\) smallest set is selected from the ordered \(v_{j}\) as the initial medoids.  
Due to the similarity to the k-means centroid updating, the KM and SFKM algorithms suffer from possible local optima [17] and empty cluster [18] problems. The result of the partition in the k-means greatly depends on the initialization step. The work in [17] listed and compared 12 initialization strategies in order to obtain a suitable final partition. For general practice, the procedure of multiple random initial centroids produces a very good partition compared to more complicated methods. In addition, when two or more initial centroids are equal, an empty cluster occurs. It can be solved by preserving the label of one of the initial centroids [18].  
For the k-medoids algorithm, on the other hand, a ranked k-medoids [19] has been introduced as a way out for the local optima. Instead of using the original distance between objects, it applies a new asymmetric distance based on ranks. The asymmetric rank distance can be calculated by either a row or column direction. Despite its way out of the local optima problem, this algorithm transforms the original distance into an asymmetric rank distance such that a tie rank of two objects having an equal distance can arise. However, it does not discuss how a tie rank is treated, whereas it can be a source of an empty cluster. Thus, we propose a simple k-medoids algorithm.
###### Abstract  
Fungal endophytes colonizing plant tissues are considered a reservoir of secondary metabolites that exhibit diverse bioactivities applicable in biomedicine. This study aims to reveal for the first time endophytic fungi associated with _Keteleeria evelyiana_ as a potential source of bioactive compounds. A total of 24 fungal endophytes were successfully isolated from _K. evelyiana_ and classified into 10 genera: _Apergillus_, _Penicillium_, _Fusarium_, _Xylaria_, _Talamomyces_, _Nodulsiporium_, _Apiospora_, _Neepestalioopsis_, _Diaporthe_, and _Hyproxylon_. Screening for antimicrobial activity revealed that 8 out of 24 ethyl acetate extracts inhibited antimicrobial activity against at least one tested pathogen. Among them, _Xylaria_ sp. KET18 showed the most potent antimicrobial activity with inhibition diameters ranging from 16.5 to 21.5 mm. In addition, the KET18 extract showed the most significant cytotoxic effects against A549 (IC\({}_{50}\) = 18.8 \(\pm\) 3.1 ug/mL) and MCFF cell lines (IC\({}_{50}\) = 24.1 \(\pm\) 2.5 ug/mL). The KET18 extract showed moderate antioxidant activity against hydroxyl and 2,2-diphenyl-1-picrylhydraz (DPPP) free radicals. The chemical characterization and structural elucidation indicated the presence of four bioactive compounds that have not been found in fungi; including methyl pyroglutamate, prunetin, macrolactin A, and macrolactin F. These findings demonstrated that _K. evelyiana_ is a host of endophytic fungi with antimicrobial, anticancer, and antioxidant potential.  
anticancer; antimicrobial; antioxidant; endophytic fungi; _Keteleeria evelyiana_; _Xylaria_
# A Novel Self-Adaptive VM Consolidation Strategy Using Dynamic Multi-Thresholds in IaaS Clouds
## 3 Novel Self-Adaptive VM Consolidation Strategy
### Dynamic Multi-Thresholds (DMT)
#### 3.1.2 DMT Adjustment Mechanism  
To efficiently balance the consumption of energy cost and SLA violation, the rules of dynamically adjusting parameter \(t_{c}\) and \(T_{c}\) within \(T_{l}\) and \(T_{u}\) are restricted to SLA violation threshold \(T_{ SLA}\). We divided the problem into the following two situations, where the \(Current_{ SLA}\) can be calculated by statistic Interquartile range of SLA violation on all hosts.  
* Situation 1: \(Current_{ SLA}<T_{ SLA}\)  
Since the \(Current_{ SLA}\) is below the threshold of SLA violation (Table 2), our purpose of this situation can mainly focus on energy saving. When the predicted utilization \(\widehat{p_{c}}\) of all hosts is greater than the current threshold \(T_{c}\) in the case of the parameter of CPU, this means that there will be many CPU requests by VMs in the future. In such a case, we choose to replace \(T_{c}\) by \(\widehat{p_{c}}\), since the higher the upper threshold, the less VM migration will take place and the more the energy will be saved. At the same time, when \(\widehat{p_{c}}\) is below \(T_{c}\), keep the previous value of \(T_{c}\) instead of replacing. If \(\widehat{p_{c}}\) is below the current lower threshold \(T_{l}\) in CPU parameter \(t_{c}\), this means that there will be fewer requests of CPU resources in the future. In such a case, we choose to replace \(t_{c}\) by \(\widehat{p_{c}}\), since the lower the \(T_{l}\), the more the hosts will be switched to sleep mode to eliminate the idle power consumption, and all VMs that are on low resource utilization host will be migrated to other suitable hosts. Otherwise, keep \(t_{c}\) as its previous value.  
precious lower threshold will help prevent low load hosts from migrating all VMs to other hosts which will help reduce SLA violation. Similarly, when \(\widehat{p_{c}}\) is higher than \(t_{c}\), replacing \(t_{c}\) of \(\widehat{p_{c}}\) could drop the SLA violation and thus help improve the QoS.  
For a brief description, we only take the threshold of CPU utilization (\(t_{c},~{}T_{c}\)) of all hosts as an example, showing how it dynamically adjusts its current thresholds by comparing its current value with the predicted one. Similarly, the procedure of adjusting the other two parameters of RAM (\(t_{r},~{}T_{r}\)) and Bandwidth (\(t_{b},~{}T_{b}\)) can also following these steps. Many details for selecting source PM based on this DMT are shown below.
# PowerBridge: Covert Air-Gap Exfiltration/Infiltration via Smart Plug
## 4 Power Line Communication and Smart Plug
### Power Line Communication  
Power line communication (PLC) pertains to the utilization of power lines and their transmission and distribution networks as a medium for communication technology and system applications [33], which has witnessed an escalating deployment in recent times.  
Due to its utilization of existing power grids for communication, it offers a solution without the need for additional wiring in network environments such as hotels and duplex residences that lack wired networks or face routing difficulties (as depicted in Figure 8). Manufacturers of PLC devices report shipping millions of such devices annually with expectations for continued growth in the future [34]; the common topology of the power cat is shown in Figure 9, and the electrical schematic is shown in Figure 10. PLC not only supplements wireless network solutions but also emerges as a formidable competitor; hence, extensive research is being conducted in this field. Images of common electric cats are shown in Figure 11.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Air-Gap Channels** & **Communication Medium** & **Sender** & **Receiver** & **Method Name** \\ \hline Electromagnetic & Electromagnetic & Memory, hard disk & Smartphone, SDR & BitJabber, AIR-FI, RAMBO \\ \hline Sound & Ultrasound, noise, vibration & Loudspeaker, Fan, Power adapter & Mobile phone accelerometer, microphone, gyroscope & AIR-ViBeR, InKiltration \\ \hline Optical & Laser, Visible light, infrared light & Laser, Hard disk lamp, CD lamp, Network card light, Camera infrared light & Camera & LaserShark, ETHERLED, CD-Blink \\ \hline Thermal & Radiation of heat & air conditioner & Infrared sensor & HVACKer \\ \hline Power sector & Power line & Power adapter & Current transformer, smartphone & PowerHammer, POWER-SUPPLAY \\ \hline \end{tabular}
\end{table}
Table 1: Common air-gap channels.
## 4 Conclusions  
In the present study, we fabricated Si NCs in Si\({}_{3}\)N\({}_{4}\) matrix by high temperature annealing of SiN\({}_{\rm x}\)/Si\({}_{3}\)N\({}_{4}\) multilayered films deposited by low frequency discharge PECVD. Formation of Si NCs is confirmed by HRTEM, EFTEM, XRD and XPS measurements. It is shown that superlattice structure remains stable in general after 1150 \({}^{\circ}\)C annealing with some point distortions such as coalescent nanocrystals. Size of these distinct ellipsoidal-shaped nanocrystals exceeds more than twice the period of multilayered structure. In turn, an increase of annealing temperature up to 1250 \({}^{\circ}\)C led to superlattice breakdown and Si NC mean size growth, in full analogy with the previous observations of the similar effect in SiO\({}_{\rm x}\)N\({}_{\rm y}\)/SiO\({}_{2}\) multilayered films annealed under the same conditions. Fabricated samples are characterized by a photoluminescence signal with the peak in near IR range (1.3-1.4 eV), while time-resolved PL measurements allowed us to ascribe this PL to a recombination of quantum confined excitons in Si NCs. In our opinion, the main reason for such low-energy luminescence, which is not typical for SiN\({}_{\rm x}\)-embedded Si NCs, is a negligible contribution from radiative defects in matrix due to small enough thickness of Si\({}_{3}\)N\({}_{4}\) barrier layers. Our results are found to agree well with the predictions of quantum confinement theory, which states a redshift of confined energy upon reduction of potential barrier height by changing a wide band gap SiO\({}_{2}\) matrix to a lower band gap Si\({}_{3}\)N\({}_{4}\).  
Conceptualization, D.M.Z. and I.A.K.; investigation, Y.M.C., A.M.L, I.A.S. and O.A.S.; resources, A.A.P.; writing--original draft preparation, D.M.Z.; writing--review and editing, A.L.V. and A.M.L.; supervision, D.M.Z., S.N.Y. and A.L.V.  
This work was financially supported by the Ministry of Science and Higher Education of the Russian Federation, project No. RFMEFI58117X0026. Silicon nitride film deposition has been performed under the State Program 0066-2019-0003. D.M.Z. gratefully acknowledges the support from the Russian Foundation for Basic Research (grant No. 18-32-20217). We are grateful to [PERSON] at the ESRF for providing assistance in using beamline BM25B. XRR and electron microscopy measurements have been carried out using the equipment of Resource Centers of the X-ray techniques and Probe and Electron Microscopy of NRC \"Kurchatov Institute\". The authors declare no conflict of interest.
_Appl. Sci._**2015**, \(5\), 1016-1032; doi:10.3390/app5041016  
_applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Article_  
**The Effects of Cu-doped TiO\({}_{\bf 2}\) Thin Films on Hyperplasia, Inflammation and Bacteria Infection**  
**[PERSON], [PERSON], [PERSON], [PERSON], [PERSON] *, [PERSON], [PERSON] * and [PERSON]**  
Key Laboratory for Advanced Technologies of Materials, Ministry of Education, School of Material Science and Engineering, Southwest Jiaotong University, Chengdu 610031, China;  
E-Mails: [PERSON]DRESS] (L.L.); xying1226(@163.com (Y.X.); [PERSON]IL_ADDRESS]m (Z.Z.);  
[EMAIL_ADDRESS] (J.C.); [EMAIL_ADDRESS] (Y.Y.); [EMAIL_ADDRESS] (N.H.)  
* Authors to whom correspondence should be addressed; E-Mails: [EMAIL_ADDRESS] (P.Y.);  
[EMAIL_ADDRESS] (J.L.); Tel.: +86-28-8763-4148-802 (P.Y.);  
Fax: +86-28-8760-0625 (P.Y.).  
Academic Editor: [PERSON]  
_Received: 10 September 2015 / Accepted: 21 October 2015 / Published: 3 November 2015_  
**Abstract:** In the present work, different concentrations of Cu ion (1, 2, 5 and 10 wt %) were doped in the TiO\({}_{2}\) film by a sol-gel method and dip coating process. The morphology of the Cu-doped TiO\({}_{2}\) films were characterized by scanning electron microscopy (SEM) and the results showed that the doped Cu made no change to the TiO\({}_{2}\) films. The nitric oxide (NO) release experiment showed that these Cu-doped surfaces showed the ability of catalytic decomposition of exogenous donor \(S\)-nitroso-\(N\)-acetyl-penicillamine (SNAP) to generate (NO). Based on fluorescence analysis and CCK-8 quantitative results, such films had the ability to inhibit smooth muscle cells adhesion, proliferation and migration with SNAP _in vitro_. The macrophage adhesion assay and anti-bacterial test proved that such Cu-doped TiO\({}_{2}\) films also possessed anti-inflammatory and anti-bacterial abilities. All the abilities above showed positive correlation with the amounts of the doped Cu. This study suggested that the Cu-doped TiO\({}_{2}\) films were capable of generating physiological levels of NO in the presence of endogenous donor \(S\)-nitrosothols (RSNO), endowing the TiO\({}_{2}\) films with anti-hyperplasia, anti-inflammatory and anti-bacterial abilities.  
**Keywords:** Cu-doped; TiO\({}_{2}\) thin film; nitric oxide; biocompatibility