esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Differential Scanning Calorimetry  
The DSC melting and crystallization curves are shown in Figures 5 and 6, respectively. Table 5 summarizes the results obtained for melting temperature (T\({}_{\rm m}\)), crystallization temperature (T\({}_{\rm c}\)), heat of fusion (\(\Delta\)H), and crystallinity ratio (X\({}_{\rm c}\)). The PP/PE blend clearly displays two well-separated melting peaks for all composites, corresponding to the T\({}_{\rm m}\) of LDPE and PP components at low and high temperatures, respectively [17; 19]. The DSC data summarized in Table 5 indicate that the introduction of untreated fiber and cellulose does not significantly change the melting and crystallization behavior of both PE and PP. [PERSON] et al. [7] also reported minimal changes with variations in the blend compositions in their studies. The slight change to lower temperatures for the melting temperature of PP was correlated to a change in crystal structure. Similar observations were also reported by [PERSON] et al. [2] who investigated the recycled polymer-based date palm fiber reinforced composites. However, in our case, there is a delayed crystallization for blend composites. According to [PERSON] et al. [23], a small variation in the crystallization temperature of the PE influences the fiber to locate in PE, which leads to the crystallization route of the blend composites. The rationale is highly possible in the current study; however, it seems as though the SCB cellulose was dispersed almost homogeneously on both phases since its crystallization appeared to be more stable than that of the blend composites containing SW cellulose. Perhaps it is for that reason that it has shown exceptional crystallinity in XRD. As expected, the melting enthalpy (\(\Delta H^{\rm m}\)) of PP and PE decreased with the introduction of fiber and cellulose due to the decrease in polymer matrix quantity [25], signaling that lower crystallinity developed for the two components [8]. This decreasing trend of crystallinity was also reported in the XRD investigation. There is also a huge difference in the observed enthalpy (\(\Delta\)H\({}^{\rm obs}\)) values and calculated (\(\Delta\)H\({}^{\rm calc}\)) values of PE as compared with PP. This might indicate that the crystallization behavior of PE is probably influenced by the presence of fiber and celluloses in the blend composites.  
\begin{table}
\begin{tabular}{c c c} \hline
**Sample** & **Peak 1 (\({}^{\circ}\)C)** & **Peak 2 (\({}^{\circ}\)C)** \\ \hline PP/PE & 402.79 & 483.79 \\ PP/PE/SCB untreated 5\% & 358.54 & 466.88 \\ PP/PE/SCB cellulose 5\% & 347.89 & 456.86 \\ PP/PE/SW untreated 5\% & 367.68 & 466.76 \\ PP/PE/SW cellulose 5\% & 350.86 & 461.99 \\ \hline \end{tabular}
\end{table}
Table 4: DTG peaks temperatures of all the investigated samples.  
Figure 4: TGA and DTGA graphs of PP/PE/SCB and PP/PE/SW blend composites.
## 3 Results and Discussion
### Physicochemical Properties of SDF
#### 3.3.2 Effect of Three Extraction Methods on the Physicochemical Properties of SDF  
Different extraction methods may affect the functional and physicochemical properties of DF. As can be seen from Table 4, the WHC, OHC, solubility, and SC of SDF obtained using the enzymatic method were better than those extracted using acid and alkaline methods. [PERSON] et al. found that kiwifruit SDF, prepared using the enzymatic method, had a higher OHC and glucose adsorption capacity than those extracted using the acid method [28]. [PERSON] and [PERSON] found that enzymatic hydrolysis increased the SDF content of defatted coconut cake, thereby improving its WHC and SC [43].  
WHC, OHC, and SC are important indicators for evaluating the specific surface area and adsorption properties of DFs [44], and stronger adsorption properties may have better physiological functions. The human intake of DF with strong WHC, OHC, and SC can not only enhance the sense of satiety, delay gastric emptying, and reduce the accumulation of fat in the gastrointestinal tract, but also effectively promotes gastrointestinal peristalsis [45], increases the adsorption of gastrointestinal toxic substances, and promotes the elimination of toxic substances. Moreover, the SC of DF can make intestinal feces larger in size and softer in texture, which shortens the time taken for defecation [46]. The solubility reflects the molecular mass of SDF to some extent; the higher the solubility, the lower the molecular mass.  
Acid extraction effectively retained the hydrophobic groups of SDF, while alkali extraction had relatively little effect on the hydrophobic groups of SDF, both of which showed weak oil adsorption capacity. The SDF prepared using the enzymatic method had strong oil adsorption capacity, probably because enzyme destroyed the intermolecular bond of SDF, resulting in a reduction in the particle size of fiber molecules and an increase in the specific surface area, which increased the side chains of carboxyl, hydroxyl, and amino groups in the chemical structure of SDF. Thus, it was easier for these side chains to attract and combine with the hydrogen atoms in the oil molecules [47], exhibiting high oil adsorption capacity.
# Applying ANN and TM to Build a Prediction Model for the Site Selection of a Convenience Store
## 3 Methodology
### BPN Network  
ANNs generally consist of input, output, and process layers (called hidden layers, as depicted in Figure 1). The basic settings used to construct a BPN are the process units: the number of hidden layers with the number of nodes at each one.  
Several indicators--usually root-mean-square error (RMSE) and error rate--are used to test the reliability of a network and the validity of its structure. Its learning process is used to adjust network-linking weights by applying the learning algorithm from the input data, as well as the error function, which is adopted to measure the learning effect of the BPN. When the BPN's leaning process finishes, the minimum error--which represents the maximum predicting accuracy--is expected. Its architecture is shown in Figure 1[31].  
\[Y_{j}=f(\sum_{i}W_{ij}\,X_{i}-\theta_{j})\]  
where:  
\(Y_{j}\): output data;  
\(f\): transfer function;  
\(W_{ji}\): weighting of connecting node \(i\) to node \(j\);  
\(X_{i}\): input data;  
Figure 1: Illustration of BPN.  
\(\theta_{j}\): threshold.  
The learning process of a neural network requires the collection of data in advance. Collected samples must be divided into two parts: samples for training and samples for testing. In the network learning phase, learning can be conducted repeatedly until it converges with the network training. In terms of the network test, some indicators may be used as a basis, such as the root-mean-square error (RMSE) and the error rate.  
A neural network consists of the above-mentioned input layer, a hidden layer, and an output layer. The learning process is used to adjust the weights of the network links using the learning algorithm from the learning samples. The error function is used to measure the network learning effect. The network learning process is also a process of minimizing the error function, as the smaller the error, the higher the prediction accuracy. Furthermore, the error is generally expected to be minimized as much as possible.  
In terms of the BPN, five important parameters must be set: neural network design, learning iterations, learning rate, momentum term, and transfer function. These parameters do not have a clear set of values. Only approximate guidance can be provided, based on the complexity of the issues and the level of noise. The parameters are generally set through trial and error and other data-mining methods. The setting of all parameter values causes a considerable impact on the performance of the network, which is described below.
# In-Motion Coarse Alignment Method Based on Position Loci and Optimal-REQUEST for SINS
## 2 Traditional Method of In-Motion Coarse Alignment and Related Work
### Traditional Construction Method Using Velocity Measurement  
The core issue for in-motion coarse alignment is to find the initial attitude matrix \(C_{b}^{n}\). According to the chain rule of the attitude matrix, the initial attitude matrix \(C_{b}^{n}\) can be divided into three matrices [11]:  
\[C_{b}^{n}(t)=C_{n(0)}^{n(t)}C_{b(0)}^{n(0)}C_{b(t)}^{b(0)} \tag{1}\]  
where \(n\)-frame denotes the navigation frame aligned with East-North-Up (ENU) geodetic axes, and \(b\)-frame denotes the body frame aligned with inertial measurement unit axes, namely right-forward-upper. \(C_{n(t)}^{n(0)}\) and \(C_{b(t)}^{b(0)}\) encode the attitude changes of the navigation frame and the body frame from the initial time to the current time, respectively. Using the attitude matrix differential equation, \(C_{n(t)}^{n(0)}\) and \(C_{b(t)}^{b(0)}\) can be calculated as follows:  
\[\dot{C}_{n(t)}^{n(0)}=C_{n(t)}^{n(0)}\omega_{in}^{n}\times \tag{2}\]  
\[\dot{C}_{b(t)}^{b(0)}=C_{b(t)}^{b(0)}\omega_{ib}^{b}\times \tag{3}\]  
where \(i\)-frame denotes the Earth-center-fixed orthogonal reference frame, which does not rotate with the alignment process, \(\omega_{in}^{n}\) denotes the rotation of \(n\)-frame relative to the \(i\)-frame, and \(\omega_{ib}^{b}\) denotes the rotation of the \(b\)-frame relative to the \(i\)-frame. After the above calculation, the determination of \(C_{b}^{n}\) is translated into the determination of \(C_{b(0)}^{n(0)}\), which is a constant matrix during the in-motion coarse alignment.
###### Abstract  
The loose integration system of high-rate GNSS and strong-motion records based on Kalman filtering technology is currently a research focus for capturing broadband co-seismic displacements. To address the problem of time-varying system noise variance in the standard Kalman filter (SKF), a variance compensation adaptive Kalman filter (VC-AKF) was adopted in this study to obtain more accurate high-precision broadband co-seismic displacement and provide reliable data support for seismic scientific research and practical applications. The algorithm continuously updates the system noise variance and calculates the state vector by collecting prediction residuals in real time. To verify the effectiveness and superiority of this method, a numerical simulation and a seismic experiment from the 2017 Ms 7.0 Jiauzhaigou earthquake were carried out for comparative analysis. Based on the simulation results, the precision of the proposed algorithm was 46% higher than that of the SKF. The seismic experiment results indicate that the proposed VC-AKF approach can eliminate the baseline shift of accelerometers and weaken the influence of time-varying system noise variance towards more robust displacement information.  
high-rate GNSS; strong-motion records; broadband co-seismic displacements; Kalman filter +
Footnote †: journal: Applied sciences
# Which, When, and How: Hierarchical Clustering with Human-Machine Cooperation
## 5 Algorithm Overview
### Time Complexity Analysis  
This section discusses the time complexity of Algorithm 3. First, we clarify the time complexity of Algorithm 1. In Algorithm 1, finding the closest two clusters at each clustering step takes \(O(N^{2})\), since there are at most \(O(N^{2})\) pairs of clusters. Meanwhile, we have at most \(O(N)\) clustering steps (i.e., the number of iterations). Thus, the time complexity of Algorithm 1 is \(O(N^{3})\). However, this time complexity can be reduced, if each cluster keeps a sorted list (or heap) on its distances to all the other clusters: then, finding the closest two clusters at each clustering step is reduced to \(O(N)\). Considering that maintaining the sorted list (or heap) needs an additional time complexity of \(O(\log N)\), the time complexity of Algorithm 1 should be \(O(N^{2}\log N)\).  
Then, let us focus on the time complexity of Algorithm 3. First, note that the additional time complexity is brought by lines 5 to 14. Then, to calculate the certainty, lines 5 and 6 take \(O(kN)\) for each clustering step, since each cluster can maintain and update the certainties brought by pairs of data points. Exhaustive derivation of transitive relations in line 7 takes in total \(O(Q^{2})\). As previously mentioned, the tracing-back scheme (lines 10 and 11) at most doubles the time complexity. The local adjustments take \(O(N^{2})\) for each time, up to \(O(Q^{2}N^{2})\) in total. Saving the current clustering state (line 13) takes \(O(N^{2})\) for each time, also up to \(O(Q^{2}N^{2})\) in total. Therefore, the overall time complexity is \(O(N^{2}\log N+kN^{2}+Q^{2}+Q^{2}N^{2})\). Both \(k\) and \(Q\) are relatively small numbers, when compared to \(N\). Note that \(k\) is used to define the neighborhood of one data point. \(Q\) is also limited, due to relatively costly human computations. Therefore, the time complexity of Algorithm 3 is \(O(N^{2}\log N)\), which is asymptotically the same as that of Algorithm 1. We assume that the human answers a question instantaneously. In other words, we consider that the computational time for the machine's task is much longer than the question response time.
## 2 Materials and Methods
### Measurement of Expert Consensus  
Consensus is defined as an opinion or position reached by a group of people as a general agreement [54].  
As shown in Equation (2), consensus is a measure of attraction to a mean value:  
\[\text{Cns}(X)=1+\sum_{i=1}^{n}\text{p}_{i}\cdot\log_{2}\biggl{(}1-\frac{|X_{i}- \mu_{x}|}{\text{d}_{x}}\biggr{)} \tag{2}\]  
where:  
* X= list of categories (\"1--Insignificant (I)\"...\"5--The Most Significant (TMS)\");
* p1 = probability of each X;
* dx = Xmax -Xmin;
* Xi = particular element of X;
* \(\mu_{x}\) = mean or expected value;  
It is, therefore, a measure of dispersion for ordinal data in the interval [0, 1] and which, on a Likert scale with gradation between responses, can be transformed into the form of percentage agreement [55], as shown in Table 8.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Expert Group** & **Number of Projects** & **Mean** & **Standard Deviation** \\ \hline
1 & 40 & 4.0900 & 0.53340 \\
2 & 41 & 4.1244 & 0.54067 \\
3 & 40 & 4.0023 & 0.51783 \\
4 & 33 & 4.0752 & 0.43769 \\
5 & 32 & 3.9850 & 0.47944 \\ \hline \hline \end{tabular} Note. Own elaboration.  
\end{table}
Table 7: Basic statistical parameters of project distributions among the groups of experts.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Interval** & **Consensus Classification** \\ \hline \(Cns(X)\geq 90\%\) & Very strong consensus \\
80\% \(\leq Cns(X)<90\%\) & Strong consensus \\
60\% \(\leq Cns(X)<80\%\) & Moderate consensus \\
40\% \(\leq Cns(X)<60\%\) & Balance \\
20\% \(\leq Cns(X)<40\%\) & Moderate dissent \\
10\% \(\leq Cns(X)<20\%\) & Strong dissent \\ \(Cns(X)<10\%\) & Very strong dissent \\ \hline \hline \end{tabular} Note. Adapted from [56].  
\end{table}
Table 8: Expert consensus interpretation.
# A Survey of LoRaWAN for IoT: From Technology to Application
## 2 Introduction to LoRa and LoRaWAN
### LoRaWAN MAC Layer
#### 2.2.1 Adaptive and Data Rate Mechanism  
An Adaptive Data Rate (ADR) mechanism is built into LoRaWAN for dynamically managing an end node's link parameters in order to increase the packet delivery ratio. The ADR mechanism manages the data rate and transmit power of end devices. If the device wants to allow network server to manage its transmit parameters, it will set the uplink ADR bit in its uplink communication packets. Otherwise, the end device has the possibility to manage its transmit parameters itself by making use of the ADR mechanism that resides at the end device side. Thus, both parts of the ADR mechanism run asynchronously at the network server and at the end node.  
According to the latest release of the LoRaWAN standard (v1.1), the ADR algorithm at the end node is simple. It includes two parameters ADR_ACK_LIMIT and ADR_ACK_DELAY that are specified according to lor [10] and that have been set to be 64 and 32, respectively. The end device will increment ADR_ACK_CNT for every uplink packet that is sent. Once the ADR_ACK_CNT will become higher than ADR_ACK_LIMIT, the end device will set the ADRACKReq bit and will wait for an acknowledgment from the network for the next ADR_ACK_DELAY uplink packets. If there is no  
Figure 3: LoRaWAN network topology.  
acknowledgment before ADR_ACK_DELAY uplink packets, the end device will decrease the data rate trying to regain connectivity to the network [9]. According to the newest standard, the end device first tries to gain connectivity by increasing its transmit power. If this is not sufficient, it will continue to decrease the data rate as part of the next step [11]. In Figure 4, the ADR mechanism implemented in the end node is shown.  
The ADR mechanism in the network server determines the transmission parameters (SF and transmit power) of the end node based on the estimation of the link budget in the uplink and the threshold SNR for decoding the packet correctly at the current data rate.
# A Study of MANET Routing Protocols in Heterogeneous Networks: A Review and Performance Comparison
## 4 Results and Discussion
### Throughput Performance  
In this section, we present the throughput results for all four MANET routing protocols that we considered in this study. The impact of various node types (PDAs, laptops, and mobile phones) on the system performance is also discussed and the results are presented.
## 4 Results
### Post-Test Analysis of Public Art and Environmental Literacy Issues
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{} \end{tabular} } & \multicolumn{4}{c}{**Paired Sample \(t\)-Test Results**} \\ \cline{2-5}  & **MD** & \
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} &
\begin{tabular}{c} **Standard Error** \\ **of the Mean** \\ \end{tabular} & **t-Value** \\ \hline Public Art Knowledge & 1.01563 & 2.38708 & 0.29839 & 3.404 ** \\ \hline Public Art Skills & 1.4063 & 2.03516 & 0.25439 & 5.528 *** \\ \hline Attitude toward Public Art & 1.6797 & 2.401 & 0.30013 & 5.597 *** \\ \hline Overall Effectiveness of Public Art & 3.6719 & 4.77611 & 0.59701 & 6.150 *** \\ \hline Environmental Literacy Knowledge & 1.7578 & 3.69147 & 0.46143 & 3.809 *** \\ \hline Environmental Literacy Skills & 1.59375 & 2.87004 & 0.35875 & 4.442 *** \\ \hline Attitude toward Environmental Literacy & 2.9297 & 3.79418 & 0.47427 & 6.177 *** \\ \hline Overall Effectiveness of Environmental Literacy & 5.66406 & 7.57557 & 0.94695 & 5.981 *** \\ \hline Knowledge of Space Performance in Public Art Creation & 2.4609 & 4.72439 & 0.59055 & 4.167 *** \\ \hline \multicolumn{5}{c}{\({}^{***}p<0.001\); \({}^{**}p<0.01\).} \\ \end{tabular}
\end{table} 
Table 27: Pre-test and post-test analysis summary for public art and environmental literacy issues. 9. Public Art Creation--Spatial Knowledge:  
Finally, in the area of spatial performance knowledge, the post-test score (M = 2.46094, SD = 4.72439) was significantly higher than the pre-test score, with a t-value of 4.167, degrees of freedom of 63, and a \(p\)-value of 0.000. This suggests that the intervention was effective in improving participants' knowledge of spatial performance.
## 3 Our Model
### User Feature Representation
3:for each epoch in \(EPOCHS\)do
4:for each \(k\) in \(R\)do
5:\(\tilde{A}=A+1\)
6:\(\tilde{D}\leftarrow\operatorname*{diag}(\tilde{A}\triangle)\)
7:\(\mathrm{H}^{0}\gets X\)
8:\(\mathrm{H}^{1}_{k}\leftarrow\operatorname*{Re}LU\big{(}\tilde{D}^{-\frac{1}{ 2}}\tilde{A}\tilde{D}^{-\frac{1}{2}}H^{0}_{k}W^{1}_{k}\big{)}\)
9:\(\mathrm{H}^{2}_{k}\leftarrow\mathrm{ReLU}\left(\tilde{D}^{-\frac{1}{2}}\tilde {A}\tilde{D}^{-\frac{1}{2}}H^{1}_{k}W^{2}_{k}\right)\)
10:\(\mathrm{e}_{k}\gets H^{2}_{k}\)
11: Add \(\mathrm{e}_{k}\) into \(E\)
12:endfor
13:for each \(\mathrm{e}_{i}\) in \(E\)do
14:\(\mathrm{e}_{k}\leftarrow\frac{\exp(\mathrm{e}(\mathrm{e}_{i}))}{\sum_{k\in E }\exp(\mathrm{e}(\mathrm{e}_{i}\alpha_{k}))}\)
15:endfor
16:\(\mathrm{e}\leftarrow\sum_{k=1}^{R}\mathrm{e}_{k}\mathrm{e}_{k}\)
17:\(D_{all}\leftarrow[]\)
18:for each \(D_{i}\) in \(D\)do
19:\(\mathrm{Z}_{all}\leftarrow D_{\mathrm{Z}all}+D_{i}\)
20:endfor
21:\(\mathrm{X}_{n_{i}+d_{d}}\leftarrow\sin\!\left(\frac{d_{i}}{10000\frac{2d_{i}} {D}}\right)\)
22:\(\mathrm{X}_{n_{i}+2d_{d}}\leftarrow\cos\!\left(\frac{d_{i}}{10000\frac{2d_{i}} {D}}\right)\)
23:\(Q\gets XW^{Q}\)
24:\(K\gets XW^{K}\)
25:\(\mathrm{Y}\gets XW^{V}\)
26:\(Z_{\mathrm{Z}nd}\leftarrow\mathrm{I}^{\mathrm{head}}_{\mathrm{Z}nd}\) Attention \((Q_{k},K_{k},V_{k})\)
27:\(e_{T}\leftarrow\sigma(Z_{\mathrm{Z}nd}W_{1}+b_{1})W_{2}+b_{2}\)
28:\(e_{all}\leftarrow[](e_{e},e_{T},X_{n})\)
29:for each \(i\) in \(L\)do
20:\(\mathrm{u}_{i}\leftarrow\text{Maxpooling}\left(\mathrm{L}\mathrm{L}_{i-1} \mathit{e}_{all_{i}}\right)\)
21:\(\mathrm{emb}_{u_{i}e_{j}}\leftarrow\mathrm{L}_{i-1}^{\mathrm{I}}\mathrm{I}_{j -1}^{\mathrm{I}}\mathrm{I}_{j-1}^{\mathrm{I}}\mathit{Linear}\left(u_{i} \mathit{e}_{all_{j}}\right)\)
23:\(\mathrm{score}_{u_{i}e_{j}}\leftarrow\mathrm{sigmoid}\Big{(}emb_{u_{i}e_{j}} \Big{)}\)
24:endfor
25: calculated loss \(\leftarrow\) CrossEntropy \((\) score \(\mathit{u}_{i}e_{j}-\text{target}_{u_{i}e_{j}})\)
26:update parameters
27:if loss not decline then
28: break
29:endif
30:endfor
31:Return \(W,b\)
32:end
```  
**Algorithm 1** Sequential recommendation algorithm based on graph neural network and improved transformer.
# Cervical Cell Segmentation Method Based on Global Dependency and Local Attention
## 2 Related Work  
### Segmentation of Cervical Cells  
Accurate segmentation of cervical cancer cells, especially the nuclei, is important for the quantitative analysis of cervical cancer [25]. Traditional image representation-based cervical segmentation methods have been widely used [26; 27; 28; 29; 2; 4]. However, due to the small public dataset of cervical cells and the small amount of data, deep learning has been less studied in this task than in other medical image processing tasks [25; 30; 31]. For instance, [PERSON]. [4] proposed a learning-based method with a robust shape before segmenting individual cells in a Pap smear image, also incorporating high-level shape information to guide the segmentation. Ref. [29] improved the existing fuzzy c-mean clustering algorithm for nuclear segmentation by finding the optimal number of clusters to segment cell nuclei. Ref. [2] proposed a new superpixel-based Markov random field (MRF) segmentation framework to acquire cell images' nuclei, cytoplasm, and image background. A graph search-based method for improving the segmentation of abnormal cervical nuclei is proposed in [3]. The nuclear shape constraint is embedded in the construction of the segmentation graph. The nuclei of cervical cells were localized and segmented using Mask-RCNN to obtain coarse segmentation results in [32], followed by refining the segmentation using a local fully connected conditional random field. [PERSON] et al. [31] segmented the whole cervical cell image via transfer learning, which was effective but treated the nuclei and cytoplasm as the same class. A deformable multi-path integration-based model is proposed in [7], which used Dense-Unet as the backbone network and used deformable convolution for nuclei of different shapes and sizes. Ref. [8] proposed a progressive step-by-step training method with small to large image resolutions and shallow to deep networks. A two-stage framework based on Mask-RCNN to automatically segment overlapping cells is proposed in [5]. In the first stage, candidate cytoplasmic bounding boxes are proposed. In the second stage, pixel-to-pixel alignment is used to refine the boundaries. [PERSON] et al. [33] generate preliminary predictions for each nucleus and boundary in the image, and then use the predictions of the nucleus and cell boundary as input to the watershed algorithm to create a final instance segmentation mask for each cell and nucleus in the image. In this study, an end-to-end network training model is implemented using a modified U-Net network. It involves capturing global dependency and refining the features to enhance feature representation in the encoder part, and then connecting refined feature maps to the decoder network via skip connection. It can steadily improve network performance and enhance segmentation without additional prior knowledge and post-processing.