esa-sceva/satcom-chunk-collection huggingface dataset:
# Coarse-to-Fine Structure-Aware Artistic Style Transfer
## 3 Proposed Method
### Fine Network
#### 3.3.1 SSF Module  
The structural selective fusion (SSF) module is designed to fuse the reconstructed coarse stylized features from the coarse network to the reconstructed content features in the decoder of the fine network. Inspired by the attention mechanism [27; 28], we employ a weight matrix to select the key structural information of the reconstructed content features, which is learned by adopting the merged features. The merged features are obtained by concatenating reconstructed coarse stylized features and the reconstructed content features. The matrix can help the SSF module obtain the selective features that focus on meaningful structural information, and the selective feature is one part of the output of the SSF module. Another part of the output is the refined merged features, which include different scale information, such as some crucial local textures or global structures.  
The architecture of the SSF module is shown in Figure 4b. First, we concatenate the reconstructed coarse stylized features \(\overline{f}_{r}^{(i)}\) and the reconstructed content features \(f_{cs}\) as input \(f_{csr}\in\mathbb{R}^{(c_{cs}+c_{r})\times w_{r}\times h_{r}}\). The reconstructed content features \(f_{cs}\) are the output of the convolution layer in the decoder of the fine network (except that the first SSF module uses the content features \(f_{c}\) from the encoder of the fine network as \(f_{cs}\)). We adopt an average-pooling operation to aggregate the spatial information of \(f_{csr}\) to generate the input of the multilayer perceptron, which is adopted to produce an attention map \(M_{cs}\in\mathbb{R}^{c_{cs}\times 1\times 1}\) as the weight matrix. In summary, the attention map is calculated as follows:  
\[M_{cs}(f_{csr})=\sigma(MLP(AvgPool(f_{csr}))) \tag{1}\]  
where \(\sigma\) denotes the sigmoid function. Then the selective feature \(f_{cs}^{\prime}\) is calculated as follows:  
\[f_{cs}^{\prime}=M_{cs}(f_{csr})\otimes f_{cs} \tag{2}\]  
where \(\otimes\) denotes element-wise multiplication. Meanwhile, \(f_{csr}\) is fed into a convolutional layer to produce a refined merged feature \(f_{csr}^{\prime}\in\mathbb{R}^{c_{cs}\times w_{r}\times h_{r}}\). Eventually, the SSF module generates the final output \(f_{ssf}\in\mathbb{R}^{(c_{cs}+c_{r})\times w_{r}\times h_{r}}\) as the fused feature by directly concatenating \(f_{cs}^{\prime}\) and \(f_{csr}^{\prime}\).
## 3 Results and Discussion
### Nadir Points of the Moon-Based Observatory and Satellites on Earth
#### 3.2.1 Nadir Points Characteristics of Moon-Based Observatory  
Figure 6 shows the latitude and longitude of the sublunar points at 00:00:00 daily from 1 January 1980 to 1 January 2023.  
Figure 5: Simplified MS-BiSAR Earth observation geometrical model. \(O_{mbo}\) is the Moon-based observatory, \(O_{m}\) is the selenocenter, \(O_{E}\) is the geocenter, and \(O_{s}\) is the satellite. \(\theta_{im}\) and \(\theta_{is}\) are the threshold for the incidence angle of transmitter and receiver, respectively. \(S_{1}\) and \(S_{2}\) are the boundaries of the observable area of the satellite receiver. Meanwhile, \(M_{1}\) and \(M_{2}\) are the boundaries of the observable area of the Moon-based observatory. The red area is defined as the observable area of MS-BiSAR (not to scale for the sake of clarity).  
During this period, the longitude of the sublunar varies evenly between \(-180^{\circ}\) and \(180^{\circ}\). The maximum values of latitude fluctuate back and forth between \(18.5^{\circ}\) and \(28.7^{\circ}\) in the northern and southern hemispheres, respectively, and present a stable fluctuation period of 18.6 years, which corresponds to the variation period of the angle between the lunar orbital plane and the Earth's equatorial plane.  
Due to the regularity of lunar movement, we chose 1 January 2022 to 1 January 2023 as the research period and calculated the position of the sublunar point at 00:00 daily.  
As can be seen from Figure 7, the latitude of the sublunar point, centered on the equator, fluctuates sinusoidal between \(-27.4^{\circ}\) and \(27.4^{\circ}\) with a frequency of one sidereal month (27.3 days), resulting in a different coverage of the northern and southern hemispheres at different times.  
Then, the positions of sublunar points with time steps of 10 min were calculated from 1 January 2022 to 28 January 2022 (one sidereal month). As can be seen from Figure 8, in a sidereal month, the latitude of the sublunar points fluctuates once between the maximum and minimum values, and the longitude position of the sublunar point fluctuates between \(-180^{\circ}\) and \(180^{\circ}\), with a cycle of once per day. The latitude of the sublunar points has a significant sine wave period variation within one sidereal month, which also reflects the temporal coverage of Moon-based Earth observation in middle- and high-latitude regions. This is also a key factor that we focus on when analyzing the observational performance of Moon-based Earth observation.  
Figure 6: Position of sublunar points from 19802022: (**a**) latitude; (**b**) longitude.  
Figure 7: Latitude and longitude of sublunar points over 2022.
# Fault-Tolerant Topology of Agricultural Wireless Sensor Networks Based on a Double Price Function
## 2 Related Works
In the face of sensors sensing similar data in the same area, the network was divided into groups, and the redundant nodes were closed while the minimum number of alive nodes was kept, so as to maintain network connectivity. The clustering algorithm mainly divides the network into clusters based on residual energy, distance and node density, and selects the cluster head that can make the network performance better for data fusion. However, these algorithms rarely involve how to plan the topology between clusters after the network is divided into clusters. Clustering a network can reduce network energy consumption by using data fusion. Fault-tolerant topology improves data transmission reliability by providing multi-connected links. The above existing WSN topology control algorithms rarely cooperated to optimize the transmit power and clustering topology. This paper combines a clustering network with fault-tolerant topology. Firstly, a data fusion clustering method is used to reduce transmission energy consumption. Then the potential game is designed to initialize the topology between clusters, combined with the cut vertex detection to further reduce the redundant link on the basis of ensuring the 2-connected network.
## 3 Results  
A total of 227 publications were identified from the following databases: PubMed (48), Scopus (114), Web of Science (39) and ScienceDirect (26), which led to 171 articles after removing duplicates (56). Analysis of the title and abstract resulted in the exclusion of 136 articles. The writers successfully sought the remaining 35 papers for retrieval and evaluated their eligibility. The approach resulted in the exclusion of 18 articles for being off-topic. The evaluation includes the final 17 papers for qualitative analysis (Figure 1).  
Figure 1: PRISMA flowchart diagram of the inclusion process.  
The research shows that 13 articles evaluate patients who were administered inferior alveolar nerve block; in two articles, patients were administered local anesthesia; two other articles do not describe the type of anesthesia performed. It is inferred that less than half of the authors report the absence of symptomatology, one author does not evaluate the presence or absence of symptoms, and the remainder describe symptoms such as acute facial and neck pain, dysphagia, lockjaw, headache, tenderness, tinnitus, hearing loss, oral pain, difficulty opening the mouth, inflammation in the injection area, fever, vomiting, headache, nausea, photo-phono phobia, and nuchal rigidity. Most authors describe cases in which the fragment was eliminated; furthermore, none of the cases evaluated had complications.
## 4 Experiment and Results  
### Ablation Study  
Ablation experiments were performed on the PDED-ConvLSTM model to evaluate the effectiveness of the key components. Three variants of the PDED-ConvLSTM model were designed as follows:  
1. NO_PC: in this variant, the PC module for multi-scale extraction of spatial information is removed from PDED-ConvLSTM.
2. NO_ED: in this variant, the encoder-decoder structure for processing long sequences is removed.
3. NO_PD: this is a variant without the pyramidal dilated convolution, which is designed to increase the sensory field without increasing the number of parameters.  
Table 2 lists the performance differences between the three variants and PDED-ConvLSTM. Compared with NO_PC, the PDED-ConvLSTM model has smaller prediction errors and higher correlation coefficients. The results show that multi-scale extraction of spatial features using the PC module is effective in improving the prediction results. The substantial decrease in the predictive ability of NO_PD suggests that the pyramidal dilated convolution in PDED-ConvLSTM can improve the prediction ability substantially. Arctic sea ice concentration prediction is a long time-series prediction task, and NO_ED lacking the encoder-decoder structure reduces the ability to handle long time sea ice series. Therefore, the encoder-decoder structure is necessary.
# Logistics Service Provider Lifecycle Model in Industry 4.0: A Review
## 4 Concept of Logistic Service Provider Lifecycle Model (LSLM)
### The Beginning of Life (BOL) Phase
#### Sub-Criteria of Increasing Customer Satisfaction by Using Service Information of Existing Services  
Identifying customer requirements is a connecting step from creating innovation to checking and confirming the idea or service innovation that has an opportunity to produce a new product or service [50; 56; 63; 64] and increase value to create customer loyalty to a brand [65]. Customer requirement data are simulated customer information data that can enable companies to support customer needs, such as demand forecasting, marketing trends, marketing information, and cost-effectiveness [62; 66; 67]. Historical data, maintenance data, and complaint data can be used to classify a client's needs [68]. Idea evaluation and forecasting demand critical thinking to predict marketing demand or trends in supporting the project manager or R&D team to authorize new services [69; 70].
## 3 Results and Discussion
### Application of reFAE2 in SSP Solution  
CGA is easily oxidized to form green quinones in the protein extraction process, which binds to protein and affects the sensory quality of SSP. The green color of SSP is profoundly  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Substrate**} & \multicolumn{2}{c}{**Specific Activity (U/g)**} \\ \cline{2-3}  & **reFAE1** & **reFAE2** \\ \hline MFA & 8.90 \(\pm\) 0.01  & 12.86 \(\pm\) 0.28  \\ MCA & 433.35 \(\pm\) 0.68  & 583.66 \(\pm\) 1.03  \\ MSA & 14.21 \(\pm\) 0.08  & 7.13 \(\pm\) 0.05  \\ MpCA & 258.97 \(\pm\) 0.16  & 294.58 \(\pm\) 0.82  \\ CGA & 246.37 \(\pm\) 0.02  & 340.95 \(\pm\)0.04  \\ \hline \hline \end{tabular} Different letters (a, b) in the same column indicate significant differences at \(p<\) 0.05.  
\end{table}
Table 2: Results of purification of esterases.  
correlated with the high content of CGA [3]. After the reFAE2 treatment, the CGA in the sunflower seeds was completely hydrolyzed to produce caffe acid, and the protein became milk-white, which greatly eliminated the green color of SSP (Table 3).  
Color change was measured with a colorimeter according to the principle of CIE color space. The instrument had been calibrated with a standard color plate (white). Parameters L*, a*, and b* indicate the intensity of lightness, redness and yellowness of the sample, respectively. After the enzyme treatment, the a* of the SSP solution became positive, as compared to the negative a* without enzyme treatment. Meanwhile, the b* of the protein solution was significantly higher than that without enzyme treatment. The L* of the SSP solution (87.75) after enzyme treatment was much higher than that before the enzyme treatment (53.61), illustrating that CGA hydrolysis significantly increased the brightness of the SSP solution. The change in color from green to milk-white increased the acceptability and nutritional value of SSP. Therefore, chances for SSP as an important protein source as well as general food ingredients were increased.
## 4 Experimental Analysis
### Parameter Settings  
In this paper, assign 0.05 to the iteration threshold \(\delta\) of the Bayesian estimation, and assign 0.2 to the similarity adjustment parameter \(\lambda\). However, assign 0.5 to the similarity adjustment parameter \(\lambda\) in Equation 2 and Equation 3, and refer to the literature [11], assign 0.5 to the parameter of conditional probability similarity in the LDP item-base CF algorithm. To observe the algorithm performance under different privacy budgets, the range of the privacy budget is assigned to [0.1, 1] with the step size of 0.1. To observe the effect of the number of neighbors on the algorithm, the number N of neighbors of items is assigned to [20; 100] with the step size of 20.  
### Parameter Settings  
#### 4.3.1 Effect of \(N\) on Experimental Results  
Assign 1 to the privacy budget \(\varepsilon\), and Table 2 shows the performance of each algorithm with different numbers of neighbors, i.e., \(N\).  
Table 2 shows that with the increasing value of \(N\), both \(MAE\) and \(RMSE\) of each algorithm continue to decrease, indicating that the increase in the number of neighbors will improve the accuracy of the recommendation system. PNCF performed poorly because it did not have the data reconstruction module. Among these differential privacy protection-based algorithms, PPPCE performed best in terms of \(MAE\) and \(RMSE\), which were closest to the baseline algorithm IBCF-DS. Meanwhile, comparing with Truncated PPPCE, the average \(MAE\) of PPPCE is reduced by 8.2% (0.07) and the average \(RMSE\) of PPPCE is reduced by 8.3% (0.09) on the dataset of MovieLens 1M; the average \(MAE\) of PPPCE is reduced by 8.2% (0.07) and the average \(RMSE\) of PPPCE is reduced by 8.3% (0.09) on the dataset of Yahoo Music, indicating that the Bayesian estimation module has a good reconstruction effect on the perturbated data and can effectively improve the performance of the recommendation algorithm. Additionally, seen from Table 2, PPPCE outperforms DPLCF and LDP item-base CF on the two datasets, indicating that the Bayesian estimation method and the data sensitivity division method adopted in PPPCE perform better than the cardinality estimation method and frequency estimation method used in those two algorithms in terms of the data reconstruction.
## 4 Results
### Hydrological Assessment
Monthly applications of the GPM products will be more useful and widely applicable when a longer GPM measurement period (currently only 4 years) is available. For example, the TRMM monthly precipitation products are recently widely applied in a drought assessment [16; 37] that requires long term monthly precipitation information. A comparison of the cumulative streamflow as a function of time simulated by the three GPM products at the Jambatan Guillermard was conducted to understand how the streamflow cumulative errors propagate with time. As shown in Figure 7, cumulative streamflow increased dramatically in December 2014 due to the big yellow flood event. The three GPM IMERG products and precipitation gauges overestimated the observed accumulative streamflow from March to December  
Figure 5: Hydrological reliability assessment of the GPM IMERG products in simulating daily streamflow at the Jambatan Guillermard using the SWAT model from 12 March 2014 to 31 December 2016. Figure 6: Statistical analysis of the GPM IMERG products in SWAT-simulated monthly at the Jambatan Guillermard from March 2014 to December 2016. 2014. Meanwhile, a significant underestimation of accumulative streamflow was simulated by the IMERG_E and IMERG_L from January 2015 to December 2016. The IMERG_F had a lower streamflow cumulative error compared to the near real-time products because it captured the peak flows better (Figure 5). This finding, while preliminary, suggests that the gauge-adjustment scheme within IMERG_F improves the peak flows simulations and cumulative streamflow measurements in KRB.
# Design of Miniaturized Dual-Band Microstrip Antenna for WLAN Application
## 1 Introduction  
In recent years, with the rapid development of wireless communication technology, the WLAN communication system has also flourished, and the applied range in the market is increasingly wide [1, 2]. WLAN communication systems generally require two-way sending and receiving data in a fast, high-efficiency and reliable way, which is reflected in the antenna subsystem. The antenna is an important part of the wireless communication system [3, 4]. Modern society has entered into the information age, and people present higher requirements for the antenna, that is, the antenna not only has a wider frequency band, smaller size and is easier to install, but also has a high radiation efficiency and anti-interference performance, and other characteristics [5]. Therefore, the study of multiband and miniaturized antennas becomes an important issue in the field of antennas [6]. Compared with the traditional microwave antenna, the microstrip antennas are low profile, small size, low cost and light weight, which can meet the demands of miniaturization. However, microstrip antennas inherently have narrow bandwidth; hence, the study of dual-band microstrip antennas is necessary.  
At present, extensive studies of dual-band microstrip antennas applied in WLAN have been carried out, and a lot antenna types which work in a dual-band have been put forward, such as Dipole antennas [7], Planar Inverted-F antennas [8], Planar Monopole antennas [9] and Quasi-Yagi antennas [10; 11]. These antennas are simple in structure and low in production cost, which are suitable for the use of WLAN devices. The research of microstrip antennas is mainly focused on small scale, broadband, multi polarization, multi band and high gain [12; 13; 14], etc. For example, [PERSON] et al. [15] have designed a microstrip antenna for dual band operation, as shown in Figure 1.  
This paper mainly studies the dual frequency characteristics of microstrip antennas. Two dual frequency microstrip antennas designed with HFSS, that is manufactured by Ansoft of the United States, can be applied to WLAN. The simulation results indicate that the antenna has a satisfactory performance.  
The rest of the paper is organized as follows. In Section 2, the paper introduces the related principle and method of microstrip antenna design. A broadband dual band printed antenna for WLAN is designed in Section 3. In Section 4, a dual frequency dual band microstrip antenna is designed, which is used to realize the dual band operation on the radiation side. Section 5 is the summary of the paper.
## 2 Materials and Methods
### Consumer Sensory Evaluation  
The consumer sensory evaluation was carried out for CSRT and WSRT samples. The evaluations were performed in the sensory evaluation laboratory of the University of Eastern Finland [18]. Sensory evaluation sessions were designed, and data collected with EyeQuestion software (Elst, The Netherlands, version 5.0.8.3).  
Sensory evaluation was conducted following the ethical principles of the University of Eastern Finland. All participants provided their informed consent for inclusion. The non-trained consumers were recruited from the Kuopio area (Finland) by distributing a research call in different social media platforms. The exclusion criteria were pregnancy, breastfeeding, daily smoking, and fish allergy. All participants had to consume fish at least once every two weeks. A total of 53 consumers (37 females, 16 males) aged 18-65 years participated in the study (Appendix A).  
Prior to the evaluation, samples were cut in pieces (15 g) and stored at 6 \({}^{\circ}\)C in transparent plastic plates covered with a transparent lid. Samples were taken at room temperature 10 min prior to evaluation and the lid removed. All the samples were coded with random three-digit-numbers and presented to the panellists in a randomized order. The HPP treated (200, 400 or 600 MPa) and untreated control samples were evaluated in separate sets for CSRT and HSRT. The participants were asked to rinse their mouth with filtered tap water and eat a piece of gluten-free crisppread prior to each sample.  
The Rate-All-That-Apply (RATA) method [19] was applied to profile the sensory properties of rainbow trout samples treated with HPP. In addition, participants were asked to evaluate the appeal of the samples prior to tasting using a 9-point scale (1 = not appealing at all, 9 = very appealing) and overall liking after tasting the samples (1 = I do not like it at all, 9 = I like it very much). The list of RATA terms was chosen based on the literature [20; 21; 22] and modified based on the consensus of the research team. Altogether, 18 RATA terms were grouped into three categories most likely to be affected by the HPP treatment: texture (11 terms), appearance (four terms), and overall impression (three terms). The order of the RATA terms in each category was randomized. Consumers were asked to choose the RATA terms they perceived applicable to describe the samples and then rate their intensity with a 5-point scale (1 = slightly applicable, 5 = very applicable). In the end, participants were able to provide voluntary written comments regarding the evaluated samples. The sensory evaluation protocol was validated with a trained in-house panel (n = 3) prior to consumer sensory evaluation.
## 4 Results and Discussion
### Results of the Active Control for White Noise
On the other hand, the distance between the disturbance source, the error microphones, and the control loudspeakers has been preserved; in this way, the attenuation provided by the ANC system is measured starting from a similar level of disturbance and can be compared for the cases with and without cabin. Since the reference microphone is directly exposed to the noise coming from the disturbance source, the step-size parameters of the \"external\" case have been used. The results are reported in Table 9 for one pure tone (500 Hz), pseudo-random noise and white noise and should be compared with the results of Table 3, Table 5 and Table 7 respectively. Comparing the tables, it can be observed that all the algorithms are able to reach a higher level of attenuation in the free field case with respect to the case where the cabin is present, highlighting that the ANC system inside the small enclosure loses some of its performance due to the reflections of the walls. For the case of pure tone, the algorithms FF_FXLMS, FB_FXLMS, and FF_FURLMS show a higher speed of convergence with respect to the case with the cabin, while FF_FXNLMS seems to be slower on the left side. However, this last consideration is valid only for the first second of convergence, where the speed is evaluated, since successively, the speed for the case with the cabin reduces faster than the free field case. Comparing the different algorithms, similar conclusions as in the case with the cabin can be drawn: FF_FXLMS, FF_FXNLMS, and FF_FURLMS have similar performance, while the FB_FXLMS algorithm exhibits the worst behavior both in terms of attenuation and speed of convergence due to its necessity of internally modeling the reference signal, making the algorithm slower. For the analysis with the pseudo-random signal, the speed of convergence remains similar to the one evaluated in the case with the cabin for the FF_FXLMS, FF_FXNLMS, and FF_FURLMS algorithms. On the other hand, the speed increases for FB_FXLMS, becoming greater than that of the FF_FXNLMS algorithm. Finally, for the white noise signal, the attenuation maintains the same order of magnitude for all the algorithms, smaller than the one calculated with the pseudo-random signal, with a little improvement for the FF_FXLMS, FB_FXLMS, and FF_FURLMS algorithms.