esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Determination of Dispersibility  
ZnO showed similar absorbance ratios of 0 to 96 h because both samples, without NOM (96 h, Ar = 0.92) and with NOM (96 h, Ar = 0.95), were fairly dispersed in DI water (Figure 1a). The specific gravity of ZnO was lower than that of water ranging from 0.3 to 0.8; therefore, precipitation did not occur, due to the specific gravity up to 96 h [41]. The addition of NOM caused a slight increase in the dispersion of nanomaterials.  
In the case of the ZnO dispersed in OECD algae growth media (72 h, Ar = 0), the media without NOM were precipitated owing to aggregation; however, the media with NOM (72 h, Ar = 0.98) were well-dispersed without aggregation (Figure 1b).  
In the case of the ZnO dispersed in ISO water flea test media, the media without NOM (48 h, Ar = 0.02) were aggregated only to be precipitated, while those with NOM (48 h, Ar = 0.96) were well-dispersed (Figure 1c). Both the samples with and without NOM were aggregated, but the NOM-added samples were well-dispersed.  
In the case of the ZnO dispersed in fish test medium, the media without NOM were aggregated, resulting in precipitation, but the media with NOM (96 h, Ar = 0.92) did not show precipitation and were well-dispersed (Figure 1d). The significant improvement in the stability of ZnO in the presence of NOM can be attributed to steric hindrance as reported by many previous studies [29; 30; 42].  
Figure 1: Sedimentation kinetics for zinc oxide nanoparticles (ZnO NPs) with/without natural organic matter (NOM) under deionized water (DI) water (**a**), Organization for Economic Co-operation and Development (OECD) medium (**b**), International Organization for Standardization (ISO) medium (**c**), andchlorinated tap water (**d**).
## 3 Results  
### OLE Characterization  
The TP content of OLE was estimated via the Folin-Ciocalteu method, resulting in 58.47 ug GAE/mg. Furthermore, HPLC analysis allowed to determine the main phenolic compounds present in OLE, which were oleuropein and luteolin-7-O-glucose, with a concentration of 32.64 \(\pm\) 3.06 mg/g of OLE and 6.97 \(\pm\) 0.24 mg/g of OLE, respectively. In addition, we detected apigenin-7-O-glucose with a concentration of 1.97 \(\pm\) 0.17 mg/g of OLE, rutin with a concentration of 3.37 \(\pm\) 0.33 mg/g of OLE and other compounds, such as hydroxytyrosol, caffeic acid and p-cumaric acid, with concentrations lower than 1 mg/g of OLE (Figure 1).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Gene** & **Primer Sequence** & **Conditions** & **Base Pairs** \\ \hline
**IL-1\(\alpha\)** & 5\({}^{\circ}\)-CATGTCAAATTTCACTGCTTCATC-3\({}^{\circ}\) & 5 s at 95 \({}^{\circ}\)C, 8 s at 55 \({}^{\circ}\)C, & \\
**IL-1\(\alpha\)** & 5\({}^{\circ}\)-GTCTCTGAATCGAAATCTTCTATC-3\({}^{\circ}\) & 1 s at 72 \({}^{\circ}\)C for 45 cycles & 421 \\
**IL-1\(\beta\)** & 5\({}^{\circ}\)-GCATCACCACTCAGATCTCTC-3\({}^{\circ}\) & 5 s at 95 \({}^{\circ}\)C, 14 s at 58 \({}^{\circ}\)C, & \\
**SF-CCAATCGACAGAGAGCTC-3\({}^{\circ}\)** & 28 s at 72 \({}^{\circ}\)C for 40 cycles & 708 \\
**TNF-\(\alpha\)** & 5\({}^{\circ}\)-CATGGGGAAAGCTCTCTAG-3\({}^{\circ}\) & 5 s at 95 \({}^{\circ}\)C, 6 s at 57 \({}^{\circ}\)C, & \\
**IL-6** & 5\({}^{\circ}\)-GAAAGAGCCTCCTCGATCGATCGATCG-3\({}^{\circ}\) & 13 s at 72 \({}^{\circ}\)C for 40 cycles & 324 \\
**IL-8** & 5\({}^{\circ}\)-TGAATCTTCTAGCCTCCTCTT
# EBR: Routing Protocol to Detect Blackhole Attacks in Mobile Ad Hoc Networks
## 3 Proposed Protocol  
### Problem Statement  
MANETs are very vulnerable to blackhole attacks. Whenever the source wants to send data packets and does not have a path towards the destination, it broadcasts a route request packet (RREQ). Perhaps a blackhole attacker node on receiving the RREQ packet will sends a route reply (RREP) packet immediately with false information of having the shortest path. The sender likely selects this unsafe path and forwards data packets though blackhole node(s), which in turn would drop these packets. In addition to the route security issue, energy consumption, throughput, and routing overhead are also affected by going through such attacker node(s).
## 3 Results and Discussion
### Performance Testing of Synthesized Membranes
Despite the commercial membrane having superior initial flux readings (more than double the flux of M8 samples), it experiences the most severe biofouling, as seen in the sharp decline in flux with incubation time. Interestingly, the sharpest drop in flux was in Day 1 incubation where the membrane's flux plummeted by 68%. Contrarily PES/GO membranes (M8) maintained an almost constant flux when comparing initial flux to final flux at Day 7 of the test period. The same experiment was set up where locally obtained real TSE was used in place of the bacterial suspensions. Once again, the results showed a similar trend where M8 outperformed both the neat membrane and the commercial Nadir membrane (see Figure S5). The results seen in Figure 13 showing the higher flux recovery for M8 samples and the robust biofouling resistance that the membrane displayed in Figures 14, 15, S4 and S5 can be explained  
Figure 14: Graph showing flux readings for the filtration of bacterial suspensions for selected fabricated membranes and the UF (ultrafiltration) commercial membrane (**a**) and the normalized flux for each of the three membranes calculated as the final reading at the end of filtration over the initial reading at the beginning of the filtration test (**b**). Figure 15: Membrane fluxes before and during incubation with _E. coli_ suspension at different time points. as discussed above, owing to the more hydrophilic nature and stronger negative charge of the M8 membrane surface. It is well known that activated sludge and other sewage treatment methods employ the use of microbial cells for the biodegradation of compounds that are found in the wastewater [61]. The results for testing the bacterial content in feed TSE water and the permeates that are collected from filtering TSE using M8 and the commercial TriSep UF membranes are shown in Figure 16. As can be seen from the panel, none of the cellulose discs showed any bacterial growth for all three bacterial tests (Total Bacterial Count, Total coliform bacteria and fecal coliforms) from the permeate of both M8 PES/GO membrane and the commercial membrane, indicating that the permeates were bacteria-free. The TSE feed, however, produced an abundance of bacterial growth for all three bacterial tests.
## 3 Control Target  
In this paper, an aircraft touch-down dynamic in landing is considered in order to assess landing gear performance. Accordingly, the process starts from the initial contact with the ground to the final equilibrium state. The key performance metric of the landing gear is the energy absorption ability of the damper through the entire period of impact absorption, which is called the total energy absorber efficiency \(\eta\)[19; 21; 22; 23]. This value is described as the ratio between the total amount of energy absorbed and the product of the maximum force value \(F_{\text{max}}\) and the maximum stroke reached \(s_{\text{max}}\), which is given by:  
\[\eta=\frac{\int\limits_{0}^{s_{final}}F_{d}ds}{s_{\text{max}}F_{\text{max}}} \tag{9}\]  
Figure 2 shows a shock absorber's load-stroke curve of a typical landing gear system. The kinetic energy of an aircraft in landing is absorbed during only the compression stroke. From Equation (6), the tire force approximately equals to the damping force due to small un-sprung mass and thus the load-stroke curve roughly represents the behavior of the tire force during touch down. Due to aircraft safety, the tire must contact the ground, which means the tire force should always be positive. The passive damping coefficient \(C\) in Equation (3) is designed so that the tire force in the extension stroke is kept positive. However, if additional MR damping force is applied during the extension stroke, which generates negative damping (see Equation (4)), the tire force can be negative, and results in rebound of the tire and the landing gear from the ground. Therefore, it is highly desirable to make the MR controller active during only the compression stroke. Then, the semi-active condition in Equation (4) is turned into:  
\[F_{MR}=u\text{ hardlim}(\hat{s}) \tag{10}\] \[\text{ hardlim (x)}= \left\{\begin{array}{ll}0&x\leq 0\\ 1&x>0\end{array}\right.\]  
The total energy absorber efficiency may change due to the variation of the energy absorption during touchdown in differing landing scenarios. The efficiency can be high in a certain landing scenario but low in another landing condition. Thus, the controller needs to be developed to maximize efficiency in differing landing conditions.  
Aircraft mass \(m_{1}\) and sink speed \(v\) are the two main factors that significantly affect potential and kinematic energy during touchdown. In this paper, twelve combinations from the two factors are  
Figure 2: Load–stroke curve.  
chosen to train and test the neural network, as shown in Table 2. The control target is to maximize the total energy absorber efficiency for every single landing case and also minimize the difference between them. Thus, the control target is the maximization of the cost function, which is given by:  
\[J=E(\eta)+w\Delta\eta \tag{11}\]  
where  
\[\left\{\begin{array}{c}E(\eta)=\frac{1}{12}\sum\limits_{i=1}^{12}\eta_{i}\\ \Delta\eta=\frac{1}{12}\sum\limits_{i=1}^{12}\left|\eta_{i}-E(\eta)\right|\\ \end{array}\right. \tag{12}\]  
and \(w=-1\) is the scalar value.
## 3 Measurement Setup  
### Hardware Setup  
The designed measurement system mainly consists of two wireless sensor nodes, one as the transmitter and the other as the receiver. A laptop, connected to the receiving node, calculates the path loss by extracting the RSSI from received data packets. We adopted Silicon Labs Si4432 as the radio frequency (RF) chip, and MSP430F5438 as the Microprogramed Control Unit (MCU) chip (Texas Instruments, Dallas, TX, USA). The sensor nodes were custom-built in-house, with the transmitter and receiver nodes working at the 470 MHz band, the data transfer rate set at 9.6 kbps, the transmitting power of +20 dBm, and the antenna gain of 0 dBi.
# Open-Set Recognition of Pansori Rhythm Patterns Based on Audio Segmentation
###### Abstract  
Pansori, a traditional Korean form of musical storytelling, is characterized by performances involving a vocalist and a drummer. It is well-known for the singer's expressive narrative (aniri) and delicate gesture with fan in hand. The classical Pansori repertoires mostly tell love, satire, and humor, as well as some social lessons. These performances, which can extend from three to five hours, necessitate that the vocalist adheres to precise rhythmic structures. The distinctive rhythms of Pansori are crucial for conveying both the narrative and musical expression effectively. This paper explores the challenge of open-set recognition, aiming to efficiently identify unknown Pansori rhythm patterns while applying the methodology to diverse acoustic datasets, such as sound events and genres. We propose a lightweight deep learning-based encoder-decoder segmentation model, which employs a 2-D log-Mel spectrogram as input for the encoder and produces a frame-based 1-D decision along the temporal axis. This segmentation approach, processing 2-D inputs to classify frame-wise rhythm patterns, proves effective in detecting unknown patterns within time-varying sound streams encountered in daily life. Throughout the training phase, both center and supervised contrastive losses, along with cross-entropy loss, are minimized. This strategy aimed to create a compact cluster structure within the feature space for known classes, thereby facilitating the recognition of unknown rhythm patterns by allocating ample space for their placement within the embedded feature space. Comprehensive experiments utilizing various datasets--including Pansori rhythm patterns (91.8%), synthetic datasets of instrument sounds (95.1%), music genres (76.9%), and sound datasets from DCASE challenges (73.0%)--demonstrate the efficacy of our proposed method to detect unknown events, as evidenced by the AUROC metrics.  
open-set recognition; Pansori rhythm patterns; audio segmentation +
Footnote †: journal: Applied Sciences
## 5 Testing and Analysis of the Obtained Results  
In the final stage of our research, it was necessary to test the developed decision-making system. To achieve this, we utilized a dataset containing 2634 various objects. This dataset served as a collection of objects that had been identified and classified based on the prediction results of the CNN model, which was evaluated on a previously prepared dataset designed for testing purposes.  
The results generated by our decision-making system categorized 1645 objects as requiring urgent repainting. Examples of such road signs can be observed in Figure 7. The remaining 989 elements were designated as objects that remain in a satisfactory visibility condition and do not require immediate intervention. Figure 8 showcases examples of road signs that still maintain an adequately visible state and do not necessitate repainting.  
Upon meticulous analysis of both groups of objects, we can confirm that the individual horizontal road signs were correctly assigned to their respective categories. This represents a crucial step in the process of managing and maintaining road signs.  
To ensure the reliability of our research, it is essential to note that during the third stage of our work (the decision-making process), only objects correctly classified in the earlier stages were considered. Objects not identified during the image-segmentation process were naturally excluded from the assessment of correct visibility. It is crucial to emphasize that this exclusion constituted only a negligible percentage of the total number of objects, directly reflecting the accuracy of the CNN model.  
The performance of the YOLOv4-Tiny classifier is outstanding. This model proficiently detects objects on the image surface and accurately classifies them, even when dealing with road signs of various sizes. Some of these signs are exceptionally small; nonetheless, the CNN model exhibits remarkable precision in their classification. Although we acknowledge the availability of alternative CNN models for segmentation and classification, including newer versions of the YOLO model, we firmly maintain that the YOLOv4-Tiny model stands as the optimal choice from our perspective.  
The developed decision-making system represents a wholly original concept, devised based on the intricacies of digital image analysis and processing. While an alternative approach could involve the creation of another network model, the extensive work required  
Figure 8: Examples of images containing correctly visible objects.  
Figure 7: Examples of images containing poorly visible objects.  
for its development and the necessity of preparing a suitably representative dataset led us to our chosen solution. We firmly believe that our system yields excellent results and stands as an effective solution to the identified problem.
## 2 Materials and Methods
### Phylogenetic Analysis and Genomic Characterization  
The 16S rRNA gene sequence was amplified by PCR with the universal primers 27F and 1492R [24]. The purified PCR product was then cloned into a pMD18-T vector (Takara) based on the TA cloning method, followed by the transformation of the ligation product into _Escherichia coli_ DH5\(\alpha\) cells [19]. The positive clone was selected from the antibiotic-containing plate and sent to sequence by BGI (Qingdao, China). The assembled 16S rRNA gene sequence of strain OS4\({}^{\mathrm{T}}\) was submitted to the National Centre for Biotechnology Information (NCBI) GenBank database to search for similar sequences using the blast algorithm and confirmed by using the EzBioCloud database [25]. Multiple sequences were aligned using the CLUSTAL_X program and the phylogenetic position was determined by phylogenetic trees which were constructed using the neighbor-joining (NJ), maximum-likelihood (ML), and maximum-parsimony (MP) algorithms in MEGA version 7.0 [26; 27]. The reliability of the relationships was ensured by performing bootstrap analyses based on 1000 replications.  
Genomic DNA was isolated and purified by using a genomic DNA extraction kit (Takara, Beijing, China). NanoDrop 2000 (Thermo Fisher Scientific, Wilmington, DE, USA) was employed to assess DNA purity and Qubit 3.0 (Thermo Fisher Scientific, Wilmington, DE, USA) was used to validate the genome DNA content. Library preparations were prepared with the NEBNext Ultra DNA Library Prep kit for Illumina and were sequencedusing an Illumina HiSeq 2500 (Beijing Novogene Bioinformatics Technology, Beijing, China). Illumina reads were trimmed and assembled using trimmomatic (v.0.38) and SPAdes (v.3.15.3) [28; 29]. The genome sequence was deposited in the GenBank database. The G + C content of the chromosomal DNA was calculated using a genome sequence. Average nucleotide identity (ANI) and in silico DNA-DNA hybridization (DDH) were calculated by FastANI (v.1.33) and GGDC (v.3.0) for those strains with the highest similarities [30]. The detection of the antibiotic-resistance gene (ARG) was conducted by using RGI (v.5.1.0); the results were compared and confirmed with the experimental results from an antibiotic susceptibility test [31].
## 3 Advanced Techniques in Remote Sensing of EBC
### Data Fusion  
Each kind of imagery has its own benefits and drawbacks, which provide great potential to fully exploit increasingly sophisticated multisource data through data fusion. For example, MODIS imagery has significant advantage in temporal resolution (one day) but is very poor in spatial resolution (250, 500 or 1,000 m) for certain applications, whereas Landsat TM imagery performed very well in spatial resolution (30 m) but with 16-day revisit. Therefore, [PERSON] _et al._ developed Spatial Temporal Adaptive Algorithm for Mapping Reflectance Change (STAARCH) model to fuse high spatial- (Landsat) and temporal-resolution (MODIS) for mapping of forest disturbance [91]. A general definition of remotely sensed data (image) fusion is given as 'the combination of two or more different images to form a new image by using a certain algorithm' [92]. Since the late 1980s when data fusion emerged as a new topic [93], several comprehensive review papers have been published to review the data fusion techniques, such as [92-95]. In general, the fusion techniques can be categorized into two classes [92]: (1) colour-related techniques, such as colour composites (RGB), intensity-hue-saturation (IHS); (2) Statistical or numerical methods, such as principal component analysis (PCA), band combinations using arithmetic operators and others. Besides typical techniques, wavelet transform, SVM (support vector machine) and ANN (artificial neural network) represent the heart of new data fusion methods (e.g., [96-98]).  
Data fusion has matured into a widely used application of EBC. Pan-sharpening technique, which is to integrate a panchromatic (Pan) image with high spatial resolution and a multispectral (MS) image with high spectral resolution [94] to produce a high spatial resolution MS image, is likely to be the first data fusion method to make installing to the commercial remote sensing software such as PANSHARP module in PCI Geomatica software. For example, [PERSON] _et al._ pan-sharpened SPOT-5 imagery to classify stand age of western red cedar in British Columbia (Canada) [99]. Due to the complementary nature of optical and radar imagery, their both fusion is always at the leading edge of remotely sensed data fusion [44]. [PERSON] _et al._ estimated the quantity and quality of coarse woody debris in Yellowstone post-fire forest ecosystem from fusion of SAR and optical data [100]. Optical (Landsat-5 TM) and SAR (RADARSAT-1 Wide 1) images were fused through the combination of PCA and IHS transforms to map geomorphological and environmental sensitivity index in the Amazonian Mangrove Coast (Brazil) [101].
# Comparative Analysis of Current Control Techniques to Support Virtual Inertia Applications +
## 3 Design of Current Controllers
### Model Predictive Controller Design  
For the design of the MPC, the parameters from Table 1 were used. The current prediction model described in (5) was used for predicting the one time step-ahead currents. The sampling time, \(T_{s}\), used in the simulation was 2 \(\upmu\)s. Similarly, the grid voltage required in (5) is estimated using (6). The predicted currents for each of the possible switching combinations is compared against the reference currents provided within the cost function minimization block. The cost function used in the design is the same as the one described in (7). Both the current prediction model and the cost function are implemented using the \(m-\) function block within MATLAB/Simulink.
# Research of Calibration Method for Industrial Robot Based on Error Model of Position
## 4 Parameter Error Identification and Compensation
### Parameter Identification Algorithm  
The differential error model of position and posture of robot DCP is determined as follows:  
\[\epsilon=M\Delta\mathbf{q}. \tag{29}\]  
Assuming that \(\Delta\mathbf{q}\) removes redundant parameters, the number of identifiable parameters is \(N\), then \(M_{p}\) and \(M_{\delta}\) are both the coefficient matrices of \(3\times N\). To solve \(N\) parameter errors, at least \(N\) equations are needed, that is, the position of \(N/3\) sampling points must be measured before the solution to the equations can be obtained. In actual measurement, there may be unavoidable error factors that interfere with the accuracy of the solution. Therefore, a large amount of data needs to be used to fit the model. Assuming that the group \(K\) (\(K>N/3\)) position data of robot can be measured, the equations can be obtained:  
\[\left\{\begin{array}{l}M_{11}\Delta q_{1}+M_{12}\Delta q_{2}+\cdots+M_{1N} \Delta q_{N}=\Delta\text{P}_{1x}\\ M_{21}\Delta q_{1}+M_{22}\Delta q_{2}+\cdots+M_{2N}\Delta q_{N}=\Delta\text{P} _{1y}\\ M_{31}\Delta q_{1}+M_{32}\Delta q_{2}+\cdots+M_{3N}\Delta q_{N}=\Delta\text{P} _{1z}\\ \vdots\\ M_{11}\Delta q_{1}+M_{12}\Delta q_{2}+\cdots+M_{1N}\Delta q_{N}=\Delta\text{P} _{Kx}\\ M_{21}\Delta q_{1}+M_{22}\Delta q_{2}+\cdots+M_{2N}\Delta q_{N}=\Delta\text{P} _{Ky}\\ M_{31}\Delta q_{1}+M_{32}\Delta q_{2}+\cdots+M_{3N}\Delta q_{N}=\Delta\text{P} _{Kz}\\ \end{array}\right.. \tag{30}\]  
Equation (30) is an overdetermined equation set, which can be solved by least square method:  
\[\Delta\mathbf{q}=M^{+}\mathbf{\epsilon}. \tag{31}\]  
The \(M^{+}\) in the equation represents the generalized inverse of the coefficient matrix \(M\).