esa-sceva/satcom-chunk-collection huggingface dataset:
# Chemical and Enzymatic Treatment of Hemp Biomass for Bioethanol Production
## 3 Results and Discussion
### Chemical Pretreatment of Hemp Biomass
In addition, the band in the 3300-3400 cm\({}^{-1}\) region corresponds to bending vibrations of the O-H groups, and the 2900 cm\({}^{-1}\) band corresponds to the stretching vibrations of the C-H groups. The peak at 1610 cm\({}^{-1}\) is generated by the stretching vibrations of O-H bonds, derived from absorbed water or moisture in the sample, was reduced, which can be attributed to the loss of water due to drying the sample [24]. Absorption bands in the 1500-890 cm\({}^{-1}\) region show a reduction in intensity, indicating lower crystallinity and an increase in the amorphous form of cellulose as a result of alkaline treatment [25]. A slight decrease was observed at 1730 cm\({}^{-1}\), a characteristic band for carbonyl groups contained in hemicellulose. This demonstrates the effectiveness of alkaline treatment. Many researchers describe the problem that lignin can't be completely removed by the alkaline process (peak at 1510 cm\({}^{-1}\)). The process of degradation or fragmentation of lignin is complicated due to the presence of strong C-C bonds and other functional groups, such as aromatic groups [21]. The obtained results are consistent with the existing data found in literature describing studies of lignocellulosic biomass by infrared spectroscopy [26; 27]. The morphological changes of hemp biomass before and after the alkali pretreatment was investigated by using Scanning Electron Microscope, as illustrated in Figure 2.  
The untreated hemp biomass was observed to have a sedimentary layer on the surface area. The SEM images of hemp biomass after pretreatment showed that the surface area of the biomass was partially purified, was cleaner and smoother. The morphological changes were observed that indicated damage to the structure of biomass. It can be stated that 2% NaOH degrades the linkage between lignin and hemicellulose, increasing the surface area and making it more accessible to the cellulolytic enzymes [26; 28; 29].
# STFEformer: Spatial-Temporal Fusion Embedding Transformer for Traffic Flow Prediction
## 3 Experiments
### Baselines
STGODE [20]: The Spatial-Temporal Graph ODE Network (STGODE) proposes a novel ordinary differential equation based on tensor form to capture spatial-temporal dynamics. A semantical adjacency matrix was designed to comprehensively consider spatial correlations. Moreover, it also utilizes a temporal dilatated convolution structure to explore long-range temporal correlations. DSTAGNN [21]: The Dynamic Spatial-Temporal-Aware Graph Neural Network (DSTAGNN) constructs a graph that captures dynamic attributes related to nodes by mining historical observations. Based on multi-order Chebyshev polynomials in GCNs, a novel spatial-temporal attention module was designed to explore dynamic spatial dependencies, and an enhanced gated convolution module was designed to enhance the model's capacity to capture dynamic temporal correlations. ASTGCNs [43]: Adaptive Spatial-Temporal Graph Convolution Networks (ASTGCNs) utilize an adaptive graph convolution along with an attention mechanism to effectively address bias present in clearly defined graph structures and explore local spatial-temporal dependency in data. It also utilizes a temporal convolution network and an ordinary differential equation module to learn global spatial-temporal dependencies.
## 4 Query Processing Algorithm
### Detailed Steps  
The query processing algorithm consists of three steps. Here, we describe them as follows.  
* Step 1: starting from the grid cell monitored by the sensor s issuing the query, each grid cell is visited according to its distance to the sensor s. When a grid cell is visited, \(d(s,o)\) and \(D(s,o)\) of objects enclosed by this grid cell are computed. This step proceeds until \(d(s,s\,^{\prime})\) of the visited grid cell is greater than \(D(s,o)\) of an object. That is, all the unvisited cells are pruned.
* Step 2: sorting \(D(s,o)\) of the objects kept from Step 1 so as to derive a smallest \(D(s,o)\), defined as the _pruning distance_. For each object, if its \(d(s,o)\) is greater than the _pruning distance_, then it can be pruned. As such, in this step, \(d(s,o)\) of each object is compared against the _pruning distance_. Only the objects whose \(d(s,o)\) is less than or equal to the _pruning distance_ are kept for the next step.
* Step 3: for each object kept from Step 2, its probability of being the query result is computed (which will be discussed later).  
Figure 5: Illustration of parameters.  
**Algorithm 1: Query Processing Algorithm**  
**Input:** A sensor \(s\) issuing the query and a value of \(K\)  
**Output:** The \(K\)-nearest neighbors of \(s\)  
**foreach** grid cell monitored by sensor \(s\)' **do**  
compute \(d(s,s\)');  
**if**\(d(s,s\)')\(\leq\)\(D(s,o)\) of \(K\) objects **then**  
compute \(d(s,o)\) and \(D(s,o)\) of objects enclosed by the grid cell and insert the objects into set \(O\);  
sort objects in \(O\) in ascending order of \(D(s,o)\) and set _pruning distance_ to \(K\)-th smallest \(D(s,o)\);  
**foreach** object \(o\in O\)**do**  
**if**\(D(s,o)\) is greater than the _pruning distance_**then** remove \(o\) from \(O\);  
**foreach** object \(o\in O\)**do** compute its probability;  
return the \(K\) objects with highest probabilities.  
Let us use Figure 6 to illustrate how the query processing algorithm works. Assume that the sensor \(s_{5}\) issues the NN query. The network expansion starts from the grid cell monitored by sensor \(s_{5}\). As the object \(o_{3}\) is monitored by \(s_{5}\), its \(d(s_{5},o_{3})\) and \(D(s_{5},o_{3})\) are computed. Then, the grid cells monitored by the other eight sensors are visited because their \(d(s_{5},s\)') is less than \(D(s_{5},o_{3})\). When Step 1 is finished, objects \(o_{1}\) to \(o_{5}\) are kept with their distances \(d(s_{5},o)\) and \(D(s_{5},o)\). By comparing the distance \(D(s_{5},o)\) of objects \(o_{1}\) to \(o_{5}\), the _pruning distance_ is set to \(D(s_{5},o)\). As the distance \(d(s_{5},o)\) of each object is less than or equal to the _pruning distance_, it is possible to be the NN. Thus, objects \(o_{1}\) to \(o_{5}\) are kept for executing Step 3.  
Figure 6: Example of \(K\)-nearest neighbor (\(K\)NN) query.
# Improved Epidemic Dynamics Model and Its Prediction for COVID-19 in Italy
## 1 Introduction
They combined the multiple characteristics of the new coronavirus but ignored the death process and the difference in the infectiousness of the patients with different symptoms, which cannot be applied to some areas with a high mortality rate. [PERSON] et al. analyzed the impact of the resumption of work on the epidemic in each city by controlling the regeneration number [12]. Due to the uniqueness of COVID-19 and the diversity of outbreaks in different countries, different types of infected individuals require different prevention and control measures, which in turn lead to different fitness and predictions. In addition, most scholars model under the assumption that prevention and control measures are unchanged, but, in fact, different measures will affect the spread of the epidemic to varying degrees [13], so the simulation results will deviate from the actual situation. To meet the aforementioned issues and realistically reflect the transmission mechanism of COVID-19, we comprehensively consider the characteristics of COVID-19 (numerous asymptomatically infected individuals, different infectious carriers have different infectiousness) and government intervention measures based on the traditional SEIR model and propose a new epidemic model. The model divides the incubation period into quarantined and nonquarantined, adding asymptomatically infected individuals (IA). Meanwhile, we take the difference between asymptomatically and symptomatically infected individuals into account as well as the death process of infected individuals. We build a model called the SEIR_QJD model (SEIR with Quarantined, Dead, and Diagnosed) based on the Italian epidemic data and parameterize it with public data. Parameter analysis can reflect the effects of policy interventions and reveal the inherent laws of epidemic transmission. With these parameters, the modified model may renovate the development of the epidemic analysis and accurately predict the trend of the epidemic. Simultaneously, we simulate the time when the government takes measures and further explore the impact of the government response on the scale of the epidemic.
# Data-Driven Analysis of Outdoor-to-Indoor Propagation for 5G Mid-Band Operational Networks
## 4 Statistical Analysis of Channel Propagation Characteristics
### Time-Related Characteristics  
To measure the multipath richness of the wireless channel, the parameters of the propagation channel, such as the number of paths, interarrival times, RMS delay spread, and mean excess delay, are inspected.
## 4 Results and Analysis
### Error Analysis  
The inversions of the FPARs from the FY-3C data and the MODIS, GEOV1, GLASS FPAR products had good consistency in time and space, but there remained a certain range of errors. The reasons for these errors are summarized as follows:  
* The difference in surface reflectance. The inversion directly used the top-of-reflectance from the FY-3C MERSI data. The MODIS-FPAR products were based on MOD09 surface reflectance products. The GEOV1-FPAR products were based on spot/vegetation surface reflectance products. In addition, the spectral ranges of their red, green, blue, and near red bands were not the same, and their reflectivity was also inconsistent, suggesting that the difference in reflectance data affected the inversion results of FPAR.
* Spatial misalignment. Each product had geometric accuracy and pixel location errors, where the geographic location of the FY-3C MERSI data had a non-uniform geometric error of 2-10 pixels (causing a mismatch), which could also contribute to the inconsistency among the data.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Data**} & \multirow{2}{*}{**Type**} & \multicolumn{3}{c}{**Correlation Coefficient (R)**} & \multicolumn{3}{c}{**Root Mean Square Error (RMSE)**} \\  & & \multicolumn{3}{c}{**(FY-FPAR and FPAR products)**} & \multicolumn{3}{c}{**(FY-FPAR and FPAR products)**} \\ \cline{3-8}  & & **MODIS** & **GEOV1** & **GLASS** & **MODIS** & **GEOV1** & **GLASS** \\ \hline Mean of & Grassland & 0.5549 & 0.6102 & 0.5827 & 0.1103 & 0.1157 & 0.1205 \\ the lush & (June–October) & & & & & & \\ period & Cropland & 0.6012 & 0.5913 & 0.495  & 0.1154 & 0.131 & 0.1377 \\  & (January, & & & & & \\  & February, April, & & & & & & \\  & July, November) & & & & & & \\  & Grassland & 0.4419 & 0.5101 & 0.5498 & 0.0868 & 0.0816 & 0.1137 \\ Annual & Cropland & 0.5452 & 0.4883 & 0.3940 & 0.1026 & 0.1146 & 0.1085 \\  & Shrub & 0.3444 & 0.3261 & 0.2665 & 0.0342 & 0.0633 & 0.0618 \\ average & Broadleaf Forest & 0.4544 & 0.2954 & 0.3073 & 0.1003 & 0.1034 & 0.0851 \\  & Needleleaf & 0.4735 & 0.4142 & 0.401 & 0.0836 & 0.0863 & 0.0886 \\  & Forest & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Correlation between FY-FPAR and MODIS/GEOV1/GLASS-FPAR products.  
* FY-3C image pre-processing was not perfect. De-clouding during data pre-processing was not very good for thin cloud removal and cloud-snow differentiation. Cloud-snow reflectance data brought into the model could cause large errors.  
Limitations of the physical model. Each product had a different model and different algorithms. Although there was strict physical theory support, these differences may have caused errors for the same area.
# Two-Sided Matching Decision Method of Electricity Sales Package Based on Disappointment Theory
## 3 Matching Method between Power User Demand and Power-Selling Package of Power-Selling Company  
### Overview of Two-Sided Matching Problem (TSMDM)  
The purpose of a two-sided matching problem is to find the best matching method between objects on both sides according to the preference information or evaluation results provided by matching objects, so as to maximize the interests of both sides. In this paper, the power user was set as a matrix \(P=\{P_{1},P_{2},\ldots,P_{n}\}\), where \(P_{i}\) represents the \(i\)-th user in the power user set, \(i\in\{1,2,\ldots,n\}=I\). The electricity sales package was set as a collection \(Q=\{Q_{1},Q_{2},\ldots,Q_{m}\}\) (\(n\leq m\)), where \(Q_{i}\) refers to the \(j\)-th electricity sales package in the electricity sales package set, \(j\in\{1,2,\ldots,m\}=J\). Two-sided matching is a one-to-one mapping \(\mu\) from set \(P\) to set \(Q\), where \(\mu(P_{i})=Q_{j}\) means that the power user \(P_{i}\) matches the electricity sales package \(Q_{i}\), and vice versa. It should be noted that there will be \(m\)-\(n\) packages that will not be selected.  
The existing definitions are as follows: \(u_{ij}^{P}\) indicates the user \(P_{i}\)'s satisfaction with the package \(Q_{j}\), and \(u_{ij}^{Q}\) indicates the adaptation degree of the package \(Q_{j}\) to the user \(P_{i}\). In the following two situations, the two-sided match is unstable; otherwise, it is a stable two-sided match:  
1. \(\exists P_{i},\ P_{i}\in P,\ Q_{j},\ Q_{k}\in Q,\ \mu(P_{i})=Q_{k},\ \mu(P_{i})=Q_{j}\), making \(u_{ij}^{P}>u_{ik}^{P}\) and \(u_{ij}^{Q}>u_{ij}^{Q}\);
2. \(\exists P_{i}\in P,\ Q_{j},\ Q_{k}\in Q,\ \mu(P_{i})=Q_{k},\ \mu(Q_{j})=Q_{j}\), making \(u_{ij}^{P}>u_{ik}^{P}\).  
Next, we introduce a binary variable \(x_{ij}\) to indicate whether \(P_{i}\) and \(Q_{j}\) is matched. The conditions for stable two-sided matching can be defined as follows:  
\[x_{ij}+\sum_{u_{ik}^{P}>u_{ij}^{P}}x_{ik}+\sum_{u_{ij}^{Q}>u_{ij}^{Q}}x_{kj} \geq 1,\ i\in I,\ j\in J \tag{12}\]  
\[x_{ij}=\left\{\begin{array}{ll}1,\ \mu(P_{i})=Q_{j}\\ 0,\ \mu(P_{i})\
eq Q_{j}\end{array}\right. \tag{13}\]
## 4 Handover and Mobility Process in 5G HetNet
### Challenges in Mobility Management for 5G HetNet  
The smaller size of cells in 5G HetNet may increase unnecessary mobility requests, resulting in an excessive number of HOs. Despite the numerous advantages of mobility management in 5G HetNet, significant challenges can degrade the system's performance. These challenges include managing multi-RATs (mmWave and Sub-6 GHz), load balancing, FHO, signaling overhead, power and energy consumption, low latency and high reliability, security, scalability, network optimization, and location management. The reasons, consequences, and significant challenges of HO and mobility management in 5G HetNet are further discussed in Section 6.
# Self-Improving Generative Artificial Neural Network for Pseudorehearsal Incremental Class Learning
## 4 Experiments
### Experiment 3: How Long Could the Method Last?  
As a final experiment, we measured the number of incremental training cycles our network can perform when learning a new set of data identified by the detector. We trained the model with ten classes, representing numbers, and incrementally trained with a new random class containing samples obtained from the detection stage of our model, and measured the accuracy loss in each training step until the number of correctly identified new samples was less than 5% of the total from that class.  
When combining both our network and novelty detector, we start to see a slow decline in accuracy in each evaluation step, mostly due to an increase in noise in the network's generated samples from earlier tasks, this is an effect of using generated samples of already generated data. Figure 5 shows that for at least three training iterations, our model can adequately retain previous information while learning a new task, without any effects caused by generator noise. Afterwards, the network starts to gradually lose information, lowering its accuracy at a rate of 7% per training step.  
This information loss can be explained by the way information is encoded in a limited space, defined on \(\hat{z}\), as shown in Figure 6.  
When trained on the initial classes (Figure 6a), each character clusters within the distribution space, allowing the generation of different characters as the encoding vector \(\hat{z}\) is defined within it. As the number of new classes increases, information regarding shape, style, and class is clustered within a reduced space, thus reducing the variability of the generated samples upon decoding. This effect is shown in Figure 6b,c, where boundaries between clusters are muddled, thus reducing differences between outputs from different classes. In addition to this, as previous information is continuously retrained without the original content, this reduction in variability creates less-defined outputs, impacting how the network can retain long-term information about old data.  
Figure 5: Accuracy of incremental training in each training step with random character images.  
Figure 6: t-SNE visualization of distribution space \(\hat{z}\) with generated samples for the starting classes (**a**), after 4 steps (**b**) and after 8 training steps (**c**). Each color represents a class.
# Efficient Implementation of Polymer Microwave Fiber Links Employing Non-Galvanic Coupling Mechanism
## 2 PMF Overview
### PMF Propagation Characteristics
#### 2.2.2 Dispersion  
In PMFs, three types of dispersion are usually considered [13]: waveguide dispersion, material dispersion and modal dispersion. Waveguide dispersion occurs when a nonlinear change in the phase constant of the propagating wave is present. This is observed for every propagating mode of the PMF near its cutoff frequency. To avoid this nonlinearity in the dominant mode of propagation, the PMF must operate at a frequency band that is far from the cutoff frequency of the dominant mode. To further reduce waveguide dispersion, more complex methods can be implemented. Material dispersion is an intrinsic property of the dielectric, where the dielectric constant of the material varies over the frequency. For dielectric materials such as PTFE, polyethylene or ceramics, the dielectric characteristics--the permittivity and the dielectric loss tangent--are relatively constant over a large band of frequencies [25; 26]. Finally, modal dispersion occurs when multiple modes exist in the waveguide. This kind of dispersion is very important as it introduces a tradeoff between multi-mode and single-mode operation. On one hand, by operating the PMF in a single mode, we limit the effective bandwidth of the structure, and the system is more prone to waveguide dispersion. On the other hand, by allowing multiple modes to propagate in the PMF, we achieve a higher bandwidth and lower waveguide dispersion for the dominant mode, but we introduce modal dispersion due to the different group delays of each mode.
# A Simple Neural-Network-Based Decoder for Short Binary Linear Block Codes
## 5 Dataset Generation and Neural Network Training
### Generation of Received Signal by BPSK over AWGN Channel  
Algorithm 1 shows the data generation method for the BPSK-modulated signal over the AWGN channel as the signal-to-noise ratio (SNR) is given. The SNR is defined as  
\[\gamma\equiv 10\log_{10}\frac{E_{b}}{N_{0}} \tag{14}\]  
where \(E_{b}\) is the energy per bit. When generating the dataset at the various SNR \(\gamma\), one could set \(E_{b}\) as 1 and change the noise variance \(N_{0}\). That is,  
\[N_{0}=\sqrt{\frac{10^{\frac{\gamma}{10}}}{2}} \tag{15}\]  
How to generate the dataset is described in Algorithm 1.  
```
Input: codeword c, SNR \(\gamma\)  Output: received signal sequence \(y\)
1: Compute \(N_{0}\) by (14)
2:for\(i=0,1,\ldots,n-1\) do
3: Generate Gaussian noise \(e_{i}\)\(N(0,N0/2)\)
4:if\(c_{i}=1\) then
5:\(y_{i}=-1+e_{i}\)
6:else
7:\(y_{i}=1+e_{i}\)
8:endif
9:end for
10:return\(y\)
```  
**Algorithm 1 Generative Algorithm of Additive White Gaussian Noise**
# Distributed Attitude Synchronization for Spacecraft Formation Flying via Event-Triggered Control
## 4 Numerical Simulation  
In this section, numerical simulations are come out to illustrate the superiority of the developed distributed event-triggered attitude coordinated controller. A SFF system including four spacecraft with the information flow presented in Figure 1 is considered.  
From Figure 1, it is easy to obtain that matrices \(A\) and \(L\) are as follows:  
\[A=\left[\begin{array}{cccc}0&0&1&0\\ 1&0&1&0\\ 1&1&0&0\\ 1&0&0&0\end{array}\right],\;L=\left[\begin{array}{cccc}1&0&-1&0\\ -1&2&-1&0\\ -1&-1&2&0\\ -1  
Figure 3: Case 1: The angular velocity errors of four spacecraft.  
Figure 2: Case 1: The attitude errors of four spacecraft.  
Figure 4: Case 1: Control signals of four spacecraft.  
Figure 5: Case 1: Triggering intervals of spacecraft 1.  
Figure 6: Case 1: Triggering intervals of spacecraft 2.  
Figure 7: Case 1: Triggering intervals of spacecraft 3.  
Figure 11: Case 2: Control signals of four spacecraft.  
Figure 10: Case 2: The angular velocity errors of four spacecraft.  
Figure 12: Case 2: Triggering intervals of spacecraft 1.  
Figure 13: Case 2: Triggering intervals of spacecraft 2.  
From Figures 2, 3, 9 and 10, one obtains that the system state errors are able to converge to the reference neighborhood around the origin and the attitude consensus can be achieved under the proposed controller. To present the advantages of the event-driven control scheme, the developed cooperative controller is executed in a time-driven manner  
Figure 14: Case 2: Triggering intervals of spacecraft 3.  
Figure 15: Case 2: Triggering intervals of spacecraft 4.
## Appendix A  
\begin{tabular}{l l c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Hyperparameter**} & \multicolumn{6}{c}{**Window Size**} \\ \cline{3-8}  & & **1** & **2** & **3** & **5** & **7** & **14** \\ \hline \multirow{3}{*}{LSTM} & Input size & 128 & 112 & 96 & 128 & 64 & 32 \\  & Dropout rate & 0.1 & 0.1 & 0.4 & 0.3 & 0.2 & 0.3 \\  & Dense activation function & Sigmoid & Sigmoid & ReLU & ReLU & Sigmoid & Sigmoid \\  & Learning rate & 0.01 & 0.01 & 0.01 & 0.01 & 0.01 & 0.01 \\ \hline \multirow{3}{*}{CNN} & Filter size & 64 & 32 & 128 & 48 & 32 & 64 \\  & Kernel size & 2 & 5 & 3 & 2 & 3 & 5 \\  & Number of hidden layer inputs & 64 & 96 & 80 & 48 & 32 & 48 \\  & Learning rate & 0.01 & 0.001 & 0.001 & 0.01 & 0.001 & 0.001 \\  & Dense activation function & Sigmoid & Sigmoid & ReLU & Sigmoid & Sigmoid & Sigmoid \\ \hline \multirow{3}{*}{XGBoost} & Maximum depth & 7 & 2 & 2 & 7 & 2 & 7 \\  & Learning rate & 0.1 & 0.01 & 0.01 & 0.1 & 0.01 & 0.1 \\  & Number of estimators & 1000 & 1000 & 1000 & 1000 & 1000 & 1000 \\  & Colsample byte & 0.6 & 1.0 & 1.0 & 1.0 & 1.0 & 1.0 \\  & Subsample & 0.5 & 0.5 & 0.5 & 0.7 & 0.5 & 0.5 \\  & Gamma & 0.1 & 2.0 & 1.5 & 0.5 & 2.0 & 1.0 \\  & Alpha & 0.5 & 0.0 & 0.5 & 1.0 & 0.0 & 1.0 \\  & Lambda & 4.5 & 2.0 & 3.0 & 4.5 & 4.5 & 1.0 \\  & Minimum child weight & 1 & 1 & 3 & 1 & 1 & 3 \\ \hline \multirow{3}{*}{RF} & Maximum depth & 110 & 110 & 100 & 100 & 100 & 90 \\  & Minimum number of sample leaf & 5 & 5 & 4 & 5 & 5 & 5 \\ \cline{1-1}  & Minimum number of sample split & 8 & 10 & 8 & 8 & 10 & 10 \\ \cline{1-1}  & Number of estimators & 100 & 100 & 100 & 100 & 500 & 100 \\ \hline \hline \end{tabular}  
Best hyperparameter values for selected sliding window sizes.