esa-sceva/satcom-chunk-collection huggingface dataset:
# Overlapping Community Detection of Bipartite Networks Based on a Novel Community Density
## 2 Related Work
### Based on the Projecting of the Bipartite Network Community Detection Algorithm  
The methods of projecting bipartite networks to one-mode networks are mainly divided into unweighted one-mode projecting and weighted one-mode projecting. Unweighted one-mode projecting is simply projecting bipartite networks to one-mode networks. [PERSON] et al. [19] converted a bipartite network (scientific collaboration network) into two one-mode networks by using a single-mode right projection. [PERSON] et al. [20] proposed a statistical method that extends the bipartite network projection algorithm containing a single relationship and solves multiple types of relationship projection in large datasets. [PERSON] et al. [21] applied the traditional layered method to the projection process of bipartite network, proposes a multi-layered method based on projection. [PERSON] et al. [22] proposed a framework that could simultaneously detect the network structures of different communities and their communication, with advantages, such as strong adaptability and fast convergence. However, in the process of unweighted one-mode projection, network structure information, such as edge frequency, may be lost, leading to a significant decrease in the accuracy of community detection by this method. Due to the apparent defects of unweighted modular projecting, researchers turned their attention to weighted modular projecting.  
The weighted one-mode projecting converts the frequency of edges in a bipartite network to the weight of edges in a one-mode network during the projection process. [PERSON] et al. [23] presented a novel fast nonnegative matrix (F-NMTF) method for clustering two kinds of nodes in a bipartite network. [PERSON] et al. [12] used a structure that can describe the relationship between two kinds of nodes, the Weighted Symmetric Bipartite Matrix Factorization (WSBMF), to detect the overlapping communities in the bipartite network. [PERSON] et al. [13] presented a new way to find and extend the second part of the network's core community. Define two parameters to represent the relationship between nodes of the same type and heterogeneous nodes, respectively. In [PERSON] et al. [14], based on the Louvain algorithm, the Bi-Louvain algorithm suitable for the bipartite network is proposed, and a two-stage algorithm with partial modularization and quantification of partition strength is applied to bipartite network community detection. [PERSON] et al. [24] proposed an efficient algorithm, called Bilouvann, which implements a set of heuristics for fast and accurate community detection in bipartite networks. Compared with non-weighted modular projecting, the weighted modular projecting method can reduce structural information loss to a large extent. Still, its disadvantage is that the accuracy of the community detection results depends on the calculation of weights, which has substantial uncertainty, affecting the use of such methods.
# Charge Carrier Distribution in Low-Voltage Dual-Gate Organic Thin-Film Transistors
## 3 Results and Discussion
Channel-width-normalized channel resistance (\(R_{\text{ch}}W\)), contact resistance at source/channel (\(R_{\text{s}}W\)) and channel/drain (\(R_{\text{d}}W\)) were extracted from Figure 5a,b and plotted as a function of gate voltage in Figure 5c-e. At \(V_{\text{GS}}=-2\) V, \(R_{\text{ch}}W\) for the BG, TG, DG device was estimated to be \(1.8\times\) 10\({}^{3}\)\(\Omega\)m,  
Figure 4: (**a**) Device geometry in the simulation; free hole density as a function of \(y\)-axis at (**b**) \(V_{\text{GS}}=1\) V and (**c**) \(V_{\text{GS}}=-2\) V. Charge carrier distribution in (**d**) BG, (**e**) TG, (**f**) DG device at \(V_{\text{GS}}=1\) V (off-state), and (**g**) BG, (**h**) TG, (**i**) DG devices at \(V_{\text{GS}}=-2\) V (on-state). \(V_{\text{DS}}=-0.1\) V.  
\(R_{\rm ch}W\) = 1.8 \(\times\) 10\({}^{3}\)\(\Omega\)m, 0.8 \(\times\) 10\({}^{3}\)\(\Omega\)m, respectively. There was negligible difference between \(R_{\rm ch}W\) for the BG device (\(R_{\rm ch}W^{\rm BG}\)) and \(R_{\rm ch}W\) for the TG device (\(R_{\rm ch}W^{\rm TG}\)), on the other hand, \(R_{\rm ch}W\) for the DG device (\(R_{\rm ch}W^{\rm DG}\)) was about 55% less than that of the single-gate devices at \(V_{\rm GS}\) = \(-\)2 V (\(R_{\rm ch}W^{\rm BG}\)\(\approx\)\(R_{\rm ch}W^{\rm TG}\)\(>\)\(R_{\rm ch}W^{\rm DG}\)), because the DG device produces the dual-channel as shown in Figure 4i. Contact resistances at \(V_{\rm GS}\) = \(-\)2 V for the DG device (\(R_{\rm s}W^{\rm DG}\): 1.5 \(\times\) 10\({}^{3}\)\(\Omega\)m, \(R_{\rm d}W^{\rm DG}\): 0.6\(\times\)10\({}^{3}\)\(\Omega\)m) were approximately equivalent values to those for the TG device (\(R_{\rm s}W^{\rm TG}\): 1.2 \(\times\) 10\({}^{3}\)\(\Omega\)m, \(R_{\rm d}W^{\rm TG}\); 0.6 \(\times\) 10\({}^{3}\)\(\Omega\)m). \(R_{\rm s}W\) and \(R_{\rm d}W\) (at \(V_{\rm GS}\) = \(-\)2 V) for the TG/DG device were 60\(-\)70% less than those for the BG device (\(R_{\rm s}W^{\rm BG}\): 4.1 \(\times\) 10\({}^{3}\)\(\Omega\)m, \(R_{\rm d}W^{\rm BG}\): 1.5 \(\times\) 10\({}^{3}\)\(\Omega\)m). In brief, \(R_{\rm s}W\), \(R_{\rm d}W\) for the TG, DG device were less than the BG device (\(R_{\rm s}W\) or \(R_{\rm d}W\): BG \(>\) TG \(\approx\) DG) [29]. This is the reason why mobility for the TG, DG devices was higher than the BG devices. Meanwhile, mobility for the DG devices was also higher than that for the TG devices. For a discussion about the reason for this, we focus on carrier density of the channels. Maximum carrier density of the dual-channel (3.8 \(\times\) 10\({}^{23}\) m\({}^{-3}\)) was 1.5 times more than that of single-channel (2.5 \(\times\) 10\({}^{23}\) m\({}^{-3}\)), as shown in Figure 4c. To achieve carrier density equivalent of the dual-channel, at least \(V_{\rm GS}\) \(<\) \(-\)2 V would be required in the TG devices. Since the mobility was associated with gate-voltage [30], we suggest that higher carrier density of the DG devices would contribute to obtaining the higher mobility than the TG devices.
## 3 Proposed Feed-Forward NN-Based DR Estimation Method  
### Feed-Forward NN Structure  
NNs may include a broad class of flexible nonlinear regression, discriminant and data reduction models, and nonlinear dynamical systems [51]. An NN consists of simple, highly interconnected computational units called artificial neurons. [PERSON] and [PERSON] [52] demonstrated that feed-forward NNs from the input to the output are the most popular NNs for use in function approximation. In addition, [PERSON] [37], [PERSON] et al. [36], [PERSON] [38], and [PERSON] et al. [53] demonstrated that a feed-forward NN including one hidden layer could approximate a continuous, arbitrary nonlinear, and multidimensional function. Both processes mean and standard deviation can be two output response characteristics of interest in the RD methodology. The proposed feed-forward NN-based method for estimating the functional relationship associated with input factors and output responses in the RD is depicted in Figure 2.  
A vector \(\mathbf{x}\) comprising \(k\) control factors \(x_{1},\ldots,x_{i},\ldots,x_{k}\), named as the input layer, is the input to the hidden neuron in the hidden layer. The summation of \(k\) inputs of neurons with identified weights (i.e., coefficients) and bias \(a\) is transformed using the transfer function \(f\) to generate the associated output of the h  
Figure 2: Proposed feed-forward NN-based RD estimation method.  
the hidden neuron are used as inputs to the neuron in the output layer. Assume that there are \(h\) hidden nodes \(1,\ \ldots,\ j,\ \ldots,h,w_{ij},\ v_{j},\ a_{j}\), and \(b\) denotes the weight connecting input factor \(x_{i}\) to hidden node \(j\), the weight connecting hidden node \(j\) to the final output, the bias at the hidden node \(j\), and the bias at the output, respectively. Then, the general form of the response function obtained using the proposed NN-based estimation method is as follows:  
\[\hat{y}_{NN}=g\Bigg{\{}\sum_{j=1}^{h}v_{j}y_{j}^{hid}+b_{out}\Bigg{\}}=g \Bigg{\{}\sum_{j=1}^{h}v_{j}\Bigg{[}f\Bigg{(}\sum_{i=1}^{k}w_{ij}x_{i}+a_{j} \Bigg{)}\Bigg{]}+b_{out}\Bigg{\}}. \tag{5}\]
## 2 Materials and Methods
### NCS Production  
Sugarcane stalks of the \"Nourin No. 15\" cultivar were harvested at the mature stage in December 2019 from a farm at the Okinawa Prefectural Agricultural Research Center, Okinawa, Japan. The sugarcane stalks were crushed using a roller-type crusher, and the squashed juice (Brix 23.9%, pH 5.17) was purified using cold liming through calcium hydroxide addition. The mixture was centrifuged at 6000 rpm for 20 min, and the clarified juice (supernatant) was heated to prepare sugarcane syrup (50% Brix). The NCS was produced from 800 g of syrup using tabletop-type manufacturing equipment (Nishikawa Keisoku, Tokyo, Japan) [19]. The syrup was processed in two thermal evaporation stages: final heating and solidification (Table 1). Each stage comprised four heating segments. The incremental temperatures of the final heating stage were tested in three patterns, in which the syrup temperature at heating segment No. 4 was set at 120, 130, or 140 \({}^{\circ}\)C. Changes in the syrup temperature during production were recorded, as shown in Figure 1a. The rotation of the mixing blade was set to 100 rpm and 200 rpm in the final heating and solidification stages, respectively. The final torque of the rotor during the solidification evaporation was set to 2.08 N-m. Solidified NCS (approximately 300 g) was collected from the manufacturing equipment after finishing the thermal process of heating segment 8, and the solidified product was immediately crushed into a powder (Figure 1b). All experiments were performed in triplicate. The powdered NCS was stored at \(-\)30 \({}^{\circ}\)C prior to analysis.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Parameter**} & \multicolumn{3}{c}{**Final Heating**} & \multicolumn{3}{c}{**Solidification**} \\  & & **Temperature (\({}^{\circ}\)C)** & & & **Temperature (\({}^{\circ}\)C)** & \\ \hline Heating segment No. & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 \\ Heater temperature & 250 & 220 & 220 & 220 & 140 & 140 & 140 & 140 \\ Syrup temperature of & 105 & 110 & 115 & 120 & 115 & 110 & 105 & 80 \\ pattern 130 \({}^{\circ}\)C \({}^{1}\) & 115 & 120 & 125 & 130 & 115 & 110 & 105 & 80 \\ Syrup temperature of & 140 \({}^{\circ}\)C \({}^{1}\) & 125 & 130 & 135 & 140 & 115 & 110 & 105 & 80 \\ \hline \hline \end{tabular}  
* Set temperature on syrup; the maximum temperatures of syrup at heating segment No. 4 were set at 120, 130, and 140 \({}^{\circ}\)C. To achieve the maximum temperatures, different stepwise set temperature increments at the previous heating segments (segments No. 1-3) were applied.  
\end{table}
Table 1: Evaporation (final heating and solidification) temperature settings in non-centrifugal case sugar (NCS) production.
## 4 Results and Discussion
### Experimental Results  
In Section 4.1, we verified the performance of our proposed PS scheme on the simulation platform. However, the channel of the experimental platform is much more complicated than that of the simulation platform. Therefore, it is necessary to verify the performance of the proposed PS scheme on the VLC experimental setup. Since we need to adjust the code rate according to the measured SNR in scenario 3, which is impossible to achieve in the actual system, we only tested the performance under the two thresholds of 0.86 and 0.92 on the experimental setup. To ensure the system meets the NGMI threshold, we choose to subtract the achieved SE by 0.1 bpQs.  
Firstly, we adjusted the signal peak to peak voltage (V\({}_{\text{PP}}\)) from 0.19 V to 0.35 V, and the measured SNR of uniform 64 QAM is between 14.5 dB to 21 dB and shown as the blue bars in Figure 8a. The source entropies at different operation points are achieved by the measured SNR and shown as the green bars in Figure 8a. Figure 8b demonstrates the NGMI and AIR performance from 0.19 V to 0.35 V. The NGMI fluctuates between 0.860 and 0.865. Therefore, the experimental results prove the validity of the simulation results. At the top of Figure 8b, the received PS constellations of each V\({}_{\text{PP}}\) are shown.  
Finally, we measured the system performance at the NGMI threshold 0.92. Similarly, the SNR of different operation points is adjusted between 14.5 dB and 21 dB, and the source entropies are achieved according to the measured SNR in Figure 3a, which is shown in Figure 9a. The NGMI, GMI, and  
Figure 8: Experimental results at the NGMI threshold 0.86.  
Figure 7: The NGMI and AIR simulation performance of scenario 1 to 3.  
received PS constellations are shown in Figure 9b. The measured NGMI of PS constellations is all above the 0.92 NGMI threshold, and the maximum NGMI is 0.924. The NGMI fluctuates between the \(\pm\)0.005 of the preset NGMI threshold. Therefore, the simulation results match well with the experimental results, which proves that the PS mapping scheme is feasible.
## 5 Experimental Results and Discussions
### Performance Evaluation
The overhead value increases for each number of nodes. The royal blue graph line represents the proposed method. With the increasing number of nodes, the overhead increases. The proposed FCSEO method achieves less routing overhead than other compared methods, achieving 5500 for 50 nodes, 5700 for 100 nodes, 5900 for 150 nodes, and 10,150 for 200 nodes. Figure 9 depicts the comparative analysis of the structural similarity index measure (SSM) of the proposed method. The proposed method is compared with various existing methods, such as ER-SR, FLB-EOR, EGR, and HRDSS. A higher structural similarity index measure was achieved by the proposed method. For 6 bits per pixels, the SSIM value obtained is 0.87, and for 12 bits per pixel, the SSIM value achieved is 0.96 by the proposed FCSEO method. The comparative analysis of the packet delivery ratio is depicted in Table 8. Here, \(x\)-axis specifies the nodal number, and the \(y\)-axis specifies the packet delivery ratio. The proposed method is compared with various existing methods, such as ER-SR, FLB-EOR, EGR and HRDSS. The proposed method achieves a high packet delivery ratio than other methods. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Total Number of Nodes**} & \multicolumn{5}{c}{**Methods**} \\ \cline{2-6}  & **Proposed** & **FLB-EOR** & **EGR** & **HRDSS** & **ER-SR** \\ \hline
0 & 5000 & 5000 & 5000 & 5000 & 5000 \\ \hline
50 & 5500 & 18,000 & 18,000 & 17,000 & 8000 \\ \hline
100 & 5700 & 21,000 & 20,000 & 18,000 & 8200 \\ \hline
150 & 5900 & 35,000 & 31,000 & 29,000 & 10,100 \\ \hline
200 & 10,150 & 58,000 & 56,000 & 54,000 & 22,000 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparative results based on routing overhead. Figure 8: Comparative analysis of routing overhead. Table 9 represents the determination of end-to-end delay, where the \(x\)-axis specifies the nodes and the \(y\)-axis specifies the end-to-end delay in seconds. The efficiency of the proposed approach was evaluated by comparing with existing methods, such as ER-SR, FLB-EOR, EGR and HRDSS. The end-to-end delay of the proposed technique is very low compared to other methods.
## 3 Results
### Verification Studies
#### 3.1.1 Static Analysis of an Isotropic Beam  
The first verification case consists of studying a cantilever beam [40], made of an isotropic material with a modulus of elasticity of 200 GPa, with a length of 3 m, and a cross-section with a second moment of area \(I_{y}=29\times 10^{-6}\)\(m^{4}\). In the present study, the cross-section of the beam was considered to be square, with the second moment of the area referenced by [40]. The beam is subjected to a point load of 60 kN at the free end and a uniformly distributed load of 24 kN/m over its entire length. The beam and the actuation direction of these loads are schematically shown in Figure 2.  
Reddy [40] presents the exact value and the one obtained by the finite element method using two elements, for the relation M/EI, where M stands for the bending moment and EI the bending stiffness. This corresponds for the moment, respectively, the values of 288,260 N.m and 283,620 N.m. Table 1 presents for a set of discretizations, the moments, and the relative deviations, in comparison to the values presented by [40].  
Figure 2: Cantilever beam with transverse uniformly distributed and tip loads.  
The discretizations considered were aimed to approximate the bending moment exact value, although for the lower discretization, only two elements, the relative deviation was already very small. These deviations were calculated according to the expression  
\[Deviation(\%)=\left\lceil\frac{Calculated-Exact}{Exact}\right\rceil\times 100 \tag{4}\]  
considering as reference the exact solution [38].  
Table 2 shows the transverse displacement and rotation results for the beam along its length, obtained using the finite element method and the reference solution, derived from the expressions defined by the direct integration method. In this case, as it is a beam with a high length-to-thickness ratio, the results from the present model are very close to both the reference solutions [40], based on the Euler-Bernoulli theory, using the exact solution obtained from direct integration of the equilibrium differential equation that rules the problem and the finite element implementation.
# A Review: Image Processing Techniques' Roles towards Energy-Efficient and Secure IoT
## 5 Analysis
### Distribution of Papers According to the Type of IoT Application  
In this type of analysis, the papers are classified into groups based on the type of IoT applications in which image-processing techniques are exploited. According to the reviewed literature, there are five types of IoT applications. In Figure 8, the distribution of papers according to the IoT application type is shown.  
Figure 7: IoT healthcare applications based on image processing: a graphical representation of medical data communication proposed by [64].  
It is shown in Figure 8 that the most frequently applied image processing techniques can occur with the IoT security applications with a percentage of about 28% amongst other IoT applications, including IoT safety applications that ranked second and have 25% of the frequency.
# Influence of CeO\({}_{2}\) Addition to Ni-Cu/HZSM-5 Catalysts on Hydrodeoxygenation of Bio-Oil
## 3 Results and Discussion
### Characterization of Catalysts
#### 3.2.3 N\({}_{2}\) adsorption-desorption  
The surface area and pore volume measured by nitrogen adsorption-desorption for the different catalysts before and after the 1 h HDO reaction at 270 \({}^{\circ}\)C are shown in Table 5. As can be seen from Figure 5, with the increase of the CeO\({}_{2}\) content, the dispersion of Ni increased; furthermore, nickel became smaller particles attached to the catalyst support with a decreased specific surface area (Table 5). Clearly, the specific surface area and pore volume of the catalyst were reduced with the addition of CeO\({}_{2}\), but the activity of the catalyst was improved (see yield in Figure 1). After the HDO reaction, carbon was deposited on the catalyst, resulting in the decreased surface area and pore volume of the catalyst. For Ni-Cu/HZSM-5, 5% CeO\({}_{2}\)-Ni-Cu/HZSM-5, 15% CeO\({}_{2}\)-Ni-Cu/HZSM-5, 20% CeO\({}_{2}\)-Ni-Cu/HZSM-5, the differences in the surface area before and after the reaction were 188, 113, 100, and 105 m\({}^{2}\)/g, respectively. The advantage of using CeO\({}_{2}\) is reflected by the reduction of the specific surface area, e.g., a 54% reduction after HDO with Ni-Cu/HZSM-5, whereas only a 31% reduction is observed in 15% CeO\({}_{2}\)-Ni-Cu/HZSM-5.  
Figure 5: TEM images of different catalysts: (**a**) Ni–Cu/HZSM-5, (**b**) 5% CeO\({}_{2}\)–Ni–Cu/HZSM-5, (**c**) 15% CeO\({}_{2}\)–Ni–Cu/HZSM-5, and (**d**) 20% CeO\({}_{2}\)–Ni–Cu/HZSM-5.
## 2 Experiments and Methods  
### Laser Parameters  
Here, the preparation of bulk Mo samples with a purity of 97.96% was carried out by a fiber laser to produce nano/micro-structures surface at room temperature, as shown in Figure 1. Specifically, a K20-CS nanosecond pulsed fiber laser with a laser wavelength of 1064 nm was utilized for processing Mo samples with a dimension of 20.0 mm \(\times\) 9.0 mm \(\times\) 0.5 mm in an atmospheric environment. Mo samples were purchased from ELETM Company (Qingdao, China). Before SEY measurements, Mo samples were ultrasonically cleaned in acetone and absolute ethyl alcohol for 15 minutes, respectively.  
As listed in Table 1, low SEY laser processed Mo samples were fabricated by adjusting the laser average power, pitch spacing and scanning speed with cross and line hatched patterns. The 1/\(e^{2}\) diameter of spot size was about 15 \(\upmu\)m. The focused length was \(233\pm 0.1\) mm. The pulse duration was 10 ns, with a repetition rate of 20 kHz, an average power of 10-13.3 W and a fluence per dose of 0.5 mJ at average power of 10 W. A scanner head was adopted to scan over the Mo samples which were fixed on the sample stage.  
Figure 1: Schematic of the strategy for obtaining laser structured surfaces with a low secondary electron yield (SEY) property. (**a**) Original bulk Mo sample. (**b**) Integrated flocculent arrays formed after laser processing.
## 4 Steady-State Performance Index and Optimal Gains
### Comparison of Performance Indices with Those of Conventional Filters  
Table 2 summarizes the derived performance indices of the \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) filter and those of the conventional \(\alpha\)-\(\beta\) and \(\alpha\)-\(\beta\)-\(\gamma\) filters. As seen, the tracking performance of the \(n\)-th-order filter depends on the inverse of the gain of the \(n\)-th motion parameter. This indicates that the degree of the bias error due to the maneuvering motion of the target increases when a small gain is set for the highest-order motion parameter (velocity of the \(\alpha\)-\(\beta\) filter, acceleration of the \(\alpha\)-\(\beta\)-\(\gamma\) filter, and jerk of the \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) filter).  
Furthermore, the smoothing performance indices of the \(\alpha\)-\(\beta\) and \(\alpha\)-\(\beta\)-\(\gamma\) filters are special cases of that of the \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) filter. From (45)-(47), we obtain:  
\[\sigma^{2}_{\text{p},\alpha\beta\gamma\delta}(\alpha,\beta,0,0) =\frac{2\alpha^{2}+2\beta+\alpha\beta}{\alpha(4-2\alpha-\beta)}B_ {\text{x}}, \tag{48}\] \[\sigma^{2}_{\text{p},\alpha\beta\gamma\delta}(\alpha,\beta,\gamma,0) =\frac{8\beta^{2}+\alpha(2\alpha\beta-\gamma(2-\alpha))(4-2\alpha -\beta)}{(2-\alpha)(2\alpha\beta-\gamma(2-\alpha))(4-2\alpha-\beta)}B_{\text{ x}}. \tag{49}\]  
These are equal to the smoothing performance indices of the conventional filters shown in Table 2.  
including the jerk by setting large gains, the errors caused by the measurements are not sufficiently suppressed compared with those in the conventional filters. Moreover, the smoothing performance deterioration of the \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) filter compared with that of the \(\alpha\)-\(\beta\)-\(\gamma\) filter is larger than that of the \(\alpha\)-\(\beta\)-\(\gamma\) filter compared with that of the \(\alpha\)-\(\beta\) filter. These results show that there will be a further decrease in the smoothing performance with the increasing order of the tracking filter. That is, it is not effective to increase the order of the tracker when the target motion is relatively simple such as targets moving with nearly constant velocity and acceleration. Figure 2 demonstrates the quantitative difference among the filters' smoothing performance against related bias errors.