esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions
This research was funded by the National Key Research and Development Program of China (Grant No. 2020 YFD1100703-04). Not applicable. The original contributions presented in this study are included in the article, and further inquiries can be directed to the corresponding authors. The authors would like to thank all peer reviewers and editors for their valuable contribution to this work. The authors declare no conflicts of interest. Using the displacement method to formulate the equilibrium equation for the structure shown in Figure A1, we obtain the following:  
\[\left\{ \begin{array}{l}4(i_{b}+i_{c})Z_{1}+2i_{b}Z_{2}-6i_{c}U_{Fi}/h=0\\ 2i_{b}Z_{1}+4(i_{b}+i_{c})Z_{2}-6i_{c}U_{Fi}/h=0\\ -6i_{c}Z_{1}/h-6i_{c}Z_{2}/h+24i_{c}U_{Fi}/h^{2}-F=0\end{array} \right. \tag{1}\]  
According to Equation (1), we obtain the following:  
\[\left\{ \begin{array}{l}Z_{1}=Z_{2}=h\rho/\left[4(6\rho+1)\right]\times F/i_{ b}\\ U_{Fi}=h^{2}\rho(3\rho+2)/\left[12(6\rho+1)\right]\times F/i_{b}\\ Z_{1}=3U_{Fi}/\left[(3\rho+2)h\right]\end{array} \right. \tag{2}\]  
The internal force diagram of the structure shown in Figure 1 can be obtained through using Equation (2). Then, the bending moment at the distance \(a\) from the left column end is as follows:  
\[M(x)=6i_{b}Z_{1}(1-2a/l) \tag{3}\]  
The rotation angle \(Z_{3}\) of the intermediate column node can be solved through using the integration method. Details are as follows:  
\[Z_{3}=-\frac{6Z_{1}}{l}\int\left(1-2a/l\right)da=6Z_{1}(\beta^{2}-\beta)+C \tag{4}\]  
By using displacement boundary condition \(Z_{3}(\beta=0)=Z_{1}\), we can obtain the following:  
\[Z_{3}=Z_{1}\Big{[}6\Big{(}\beta^{2}-\beta\Big{)}+1\Big{]} \tag{5}\]  
We assume the height of the intermediate column is half of the floor height \(h\), then the displacement \(u_{Fak}\) of damping viscous damper is as follows:  
\[u_{Fak}=U_{Fi}-Z_{3}h/2 \tag{6}\]  
Substituting Equations (2) and (5) into Equation (6), we obtain the following:  
\[u_{Fak}/U_{Fi}=1-3\Big{[}6(\beta^{2}-\beta)+1\Big{]}/[2(3\rho+2)] \tag{7}\]  
Since the lever's amplification factor is \(\chi\), the geometric amplification coefficient \(\gamma_{F}\) under the action of \(F\) is as follows:  
\[\gamma_{F}=u_{Fao}/U_{Fi}=\chi u_{Fak}/U_{Fi}=\chi-3\chi\Big{[}6(\beta^{2}- \beta)+1\Big{]}/[2(3\rho+2)] \tag{8}\]
## 8 Simulations on Social Cost and Closeness to Equilibrium
### Results  
From Figure 5 we can infer that prices in terms of both stretch and diameter are asymptotically increasing in case of MST while they are concentrated about small constants for DeltaHeur and LocalHeur. The stretch results of LocalHeur outperforms DeltaHeur almost always by an additive factor of 2. The cost of achieving this is minimal, considering that in all cases only at most 10 percent of the nodes deviate from their best-response (Table 1) and the deviation from best-response is only about 3 percent, which can be inferred by the constant shown in the average closeness to equilibrium plot (Figure 6). This is consistent with Theorem 7.1. For power \(p=2\), the performance becomes comparable to DeltaHeur, but still there are only 10 percent of nodes deviating, and the worst case deviation is a very slowly growing function consistent with the theoretical upper-bound from Theorem 7.2. An explanation for comparable stretch and diameter results upon powering the distance is that SPT tends to choose shorter edgesat higher powers [26] thus tending towards closer neighbor, while the choice of the next forwarding neighbor in the case of LocalHeur remains the same for all powers. For the random link models under both social cost functions, both DeltaHeur and MST prices appear growing, though for the stretch-based pricing (Figure 8), again MST appears growing asymptotically faster, and there is too much variance in the DeltaHeur plot to make further inferences. The primary consistent observation applying here is that the DeltaHeur is still an improvement over MST (whether by a constant or asymptotically growing factor), and that the md-prices are still quite small.  
Figure 5: Plot of stretch and diameter for the MST, DeltaHeur and LocalHeur case when power = 1.  
Figure 6: Plot of average and worst-case closeness to equilibrium for LocalHeur when power = 1.  
Figure 8: Plot of stretch and diameter for the MST and DeltaHeur scheme with random link costs.  
Figure 7: Plot of stretch and diameter for the MST,DeltaHeur and LocalHeur case when power = 2.  
Figure 9: Plot of average closeness to equilibrium and average worst-case closeness to equilibrium for LocalHeur when power = 2.
# Assessment of the Technological Properties of Idebenone and
## 3 Results and Discussion  
Unloaded lipid nanoparticles showed mean sizes in the range 26-38 nm (Table 2). The comparison between the mean size of unloaded SLN and unloaded NLC pointed out that the inclusion in the nanoparticle lipid core of percentages of TA up to 3.5% \(w/w\) decreased the nanoparticle mean size, while TA percentages as high as 4% \(w/w\) led to an increase of nanoparticle mean sizes. However, no clear relationship between TA percentage and nanoparticle mean size could be outlined. PDI values for unloaded SLN and unloaded NLC containing TA percentages up to 3.5% \(w/w\) were similar and lower than 0.300, thus suggesting that monodisperse systems were obtained. On the contrary, unloaded NLC containing TA 4% \(w/w\) showed a much higher PDI value (0.55), owing to a non-homogenous dimensional distribution. All unloaded SLN and NLC had similar and slightly negative zeta potential values, regardless of the percentage of TA incorporated in the lipid core. As reported in literature [22], when emulsified systems are involved, the higher the PIT value, the greater the system stability. Therefore, formulation VIT 3 could be expected to show a greater stability than unloaded SLN and other unloaded NLC. The results of stability studies performed storing at room temperature unloaded SLN and NLC up to 12 months are depicted in Figure 1. Apart from formulation VIT 4, no significant alteration of nanoparticle mean size was observed. During storage, zeta potential values remained unchanged for all unloaded lipid nanoparticles, while PDI slightly increased only for formulation VIT 4 (data not shown). As all unloaded SLN and NLC showed similar zeta potential values, the lower stability of formulation VIT 4 could be attributed to its higher PDI value due to a non-homogenous size distribution that could more easily lead to nanoparticles aggregation during storage. These results suggest that the incorporation into the nanoparticle lipid core of TA as liquid lipid strongly affected the resulting technological properties such as mean size, PDI, and PIT, depending on the percentage of TA used. However, no significant morphological change was observed due to the incorporation of TA into the lipid nanoparticles, as shown in Figure 2. Regardless of the percentage of TA in the colloidal suspension, nanoparticles were approximately round-shaped with no sign of aggregation. As similar images were obtained for all lipid nanoparticles (unloaded and IDE-loaded SLN and NLC), in Figure 2, only two examples of unloaded lipid nanoparticles were illustrated. As shown in Table 2, IDE loading capacity of the nanoparticles under investigation depended on the percentage of TA incorporated into the lipid core. According to previous studies [14], the loading capacity was determined as the maximum amount of IDE that could be incorporated in the colloidal system without any sign of precipitation. [PERSON] et al. [23] claimed that when water-insoluble compounds are incorporated into lipid nanoparticles, if the resulting colloidal dispersion is clear, all drugs must be in the lipid
# Classroom Expression Recognition Based on Deep Learning
## 4 Experiment and Analysis
### Experimental Design and Analysis of MAF-ER Algorithm  
Experimental Setup  
1. Experimental environment: The experiment was conducted on a Linux system, using two NVIDIA GeForce GTX 1080 Ti graphics cards (NVIDIA Corporation, Santa Clara, CA, USA), and the framework was PyTorch 1.12.1.
2. Experimental parameters: The cross-entropy loss function was used, the optimizer was AdaBound, the initial learning rate was 0.01, and exponential decay was used. The parameter \(\delta\)1 between the high and low importance groups was set to 0.15 or a learnable parameter, and the relabeling parameter \(\delta\)2 was initially 0.2. The dataset was expanded using data augmentation, and the image size was adjusted to 128 \(\times\) 128. The dataset was divided into a test set and a validation set in a ratio of 7.3 and trained for 70 epochs.
3. Algorithm Analysis  
The confusion matrix is used to evaluate the algorithm, showing the classification performance of the model on different categories and analyzing its performance through accuracy and error rate:  
Figure 10: Schematic diagram of the MAF-ER structure.  
* Accuracy: The proportion of samples correctly classified by the model; the formula is \[\text{Acc}=\frac{\text{TP}+\text{TN}}{\text{Total}}\] (2)  
Among them, True Positive (TP) means that the true category of the sample is positive, and the result of model recognition is also positive; True Negative (TN): the true category of the sample is negative, and the model predicts it as a negative.  
* Error Rate: The proportion of samples misclassified by the model; the formula is \[\text{ER}=\frac{\text{FP}+\text{FN}}{\text{Total}}\] (3)  
Among them, False Positive (FP): the true category of the sample is a negative example, but the model predicts it as a positive example; False Negative (FN): the true category of the sample is a positive example, but the model predicts it as a negative example.  
As illustrated in Figures 11 and 12, the proposed algorithm has led to improvements in the accuracy of each type of expression. The figures demonstrate a noticeable reduction in misclassification phenomena. For instance, the misclassification rate for the \"Resist\" class has decreased to 10% compared to 11% for the IXception algorithm, while the accuracy of the \"Neutral\" class has increased to 96%. This indicates that the algorithm has effectively reduced misclassification across most categories. Overall, the accuracy of the MAF-ER algorithm surpasses that of the IXception algorithm in most categories [29]. Notably, MAF-ER excels in minimizing misclassification and enhancing the model's ability to recognize complex features.  
Figure 11: Confusion matrix of the IXception algorithm.
## 2 Results  
Synthesis and Characterization of Triblock Copolymer Poly(lactide-co-glycolide)-Poly(ethylene glycol)-Poly(lactide-co-glycolide) (PLGA-PEG-PLGA)  
Ring-opening copolymerization between D,I-lactide (LA) and glycolide (GA) were achieved in the existence of initiator poly(ethylene glycol) (PEG) and catalyst Sn(Oct)\({}_{2}\) at 150 \({}^{\circ}\)C, yielding the triblock copolymers designated as PLGA-PEG-PLGA. Purified copolymers were then characterized by nuclear magnetic resonance (\({}^{1}\)H NMR) in CDCl\({}_{3}\). In the spectroscopy shown in Figure 1A, peaks at 5.19 ppm and 1.56 ppm were respectively attributed to the protons (-CH-) and methyl protons (-CH\({}_{3}\)) of the PLA segment. The peak at 4.82 ppm was assigned to the methylene protons (-CH\({}_{2}\)-) of PGA segment, and 3.65 ppm peak represented the methylene protons (-CH\({}_{2}\)-) of PEG segment [25; 26]. The curve of the gel permeation chromatography (GPC) shown in Figure 1B presented a unimodal rather than a multimodal pattern, indicating the presence of successfully prepared PLGA-PEG-PLGA copolymers, not a simple physical mixture of PLA, PGA, and PEG. In addition, the molecular weight of the copolymers was determined to be 27630, which further confirmed the success of the copolymerization processes. Besides, PLGA-PEG-PLGA output was found to be about 89.5% by calculating the ratio of product weight to the theoretical value.  
Preparation and Characterization of Deferoxamine (DFO)-Loaded PLGA-PEG-PLGA Nanoparticles (DFO-Loaded NPs)  
By applying a solvent extraction/evaporation method, we successfully prepared the DFO-loaded PLGA-PEG-PLGA NPs (DFO-loaded NPs). Surface morphology of the DFO-loaded NPs was investigated with a field emission scanning electron microscope (FESEM), and the representative image is shown in Figure 2A. It was obvious that the DFO-loaded NPs had a spherical shape and were about 100 nm in diameter.  
By using dynamic light scattering (DLS), the size of the DFO-loaded NPs was determined to be about (116.3 \(\pm\) 6.5) nm in diameter, consistent with that obtained from FESEM images. Size distribution of the DFO-loaded NPs was shown in Figure 2B, displaying a narrow distribution profile. Moreover, the polydispersity index (PDI) of the NPs was less than 0.15 (mean value 0.142), further confirming  
Figure 1: Nuclear magnetic resonance (\({}^{1}\)H-NMR) spectra (**A**) and gel permeation chromatography (GPC) curve (**B**) of PLGA-PEG-PLGA triblock copolymers.  
narrow size distributions of the NPs, which is a remarkable advantage for them to be efficient drug delivery vehicles. Additionally, the calculated loading content (LC) and encapsulation efficiency (EE) of DFO in the NPs were 9.8% and 98.5%, respectively. It is notable that the LC value of approximately 10% indicated a satisfactory drug loading efficiency for this system [21].
# Robust Hybrid Beamforming Scheme for Millimeter-Wave Massive-MIMO 5G Wireless Networks
## 3 Proposed Algorithm
### Energy Efficiency Analysis  
To evaluate the energy efficiency aspect of the proposed scheme, Figure 7 compares the energy efficiency of the proposed scheme with other state-of-the-art schemes against the number of RF chains. As can be seen from Figure 7, the energy efficiency of the proposed and all other schemes decreases with an increasing number of RF chains, but as indicated by its curves, the proposed Alt-MMSE schemes had better energy efficiency than that of the other algorithms [12; 13; 14; 18]. Moreover, the proposed schemes had similar energy-efficiency performance with optimal digital precoding, making it more suitable for low-energy requirements. This also gives us important information about the impact of energy consumption on system complexity. As the proposed algorithm has less power consumption, its corresponding hardware is less complex, and faster.  
Figure 6: Comparison of spectral efficiency against the number of user antennas with 8 RF chains and SNR = 0 dB.  
Figure 7: Comparison of energy efficiency against number of RF chains.
# An Overview of Material Extrusion Troubleshooting
## 3 Troubleshooting of ME Parts
### Deposition-Associated Issues
#### 3.2.9 Skipped Layers  
Some layers are shown to be missing and visible gaps appear on the printed object. This could be due to under-extrusion because of an inconsistent diameter of the filament, extruder issues caused by the feeder wheel, misaligned or shifted rod, a worn bearing, and/or a semi-clogged nozzle. Solutions include the following:  
Figure 10: Shifted layers.  
1. Using a caliper to reconfirm the consistency of the filament diameter. The diameter difference should not exceed \(\pm\) 0.05 mm (Refer to 3.2.1: (3) Poor quality filament with diameter fluctuations).
2. Run a mechanical check and inspect all moving parts. Oil the joints using a suitable lubricant.
3. Ensure that all rods are properly seated into the bearings or clips. The rods should not pop out, shift or bend. Switch off the power and move the print head along the axis. If there is resistance, further inspection is required.
4. worn bearing will create an audible din and uneven motion of the print head. Switch off the printer and move the print head along the axis to locate the broken bearing.
5. Remove any foreign debris in the extruder (refer to 3.1.4: Clogged nozzle).
## 4 Xanthohumol Derivatives  
The prenylflavonoids in fresh hops are primarily composed of chalcone xanthohumol. Desmethylxanthohumol is also found in trace amounts in the lupulin glands. These two compounds are naturally present in hops and are precursors of the isomeric flavanones [102]. Most of these prenylchalcones will be converted into isoxanthohumol and prenylnaringenins during the brewing process, respectively [103]. Xanthohumol can only convert to isoxanthohumol by a cyclization reaction; desmethylxanthohumol will produce a mixture of 6-phenylaringenin and (\(\pm\)) 8-prenylnaringenin [104; 105]. Compared to 6-prenylnaringenin, isoxanthohumol, and xanthohumol, 8-prenylnaringenin was regarded as the effective phytoestrogen [106]. Phytoestrogens were proven to help prevent cardiovascular diseases, urogenital menopause symptoms, and cancer [107; 108; 109; 105]. A xanthohumol- or 8-prenylnaringenin-enriched diet was recently proposed to regulate glucose and lipid pathways so as to ameliorate diabetic-associated metabolic disturbances [9].  
In the biosynthesis pathway of xanthohumol (Figure 1), a sequential action catalyzed by phenylalanine ammonialyase (PAL), C4H, and HICCL1 produced \(\rho\)-coumaroyl-CoA. Subsequently, chalcone synthase (CHS; EC 2.3.1.74) catalyzes the condensation of \(\rho\)-coumaroyl-CoA with malonyl-CoA to produce naringenin chalcone. CHS_H1, a CHS gene specifically in trichome, has been identified from hops [110]. Naringenin chalcone is then prenylated by HIPT1L, and further methylated by an O-methyltransferasease in _Humulus lupulus_ (HIOMT1) to form xanthohumol [47; 59; 111]. C4H, HICCL1 (described in 2.3), CHS_H1, and OMT1 are the most commonly reported enzymes participating in the pathways. Some chalcone isomerase (CHI)-like proteins (CHIL) have recently been shown to aid in the production of desmethylxanthohumol.
## 6 Results and Discussion
### Comprehensive Evaluation of ML Models  
In addition to the classic model performance evaluation indices, such as \(R^{2}\), MSE, MAE, new indices, such as VAE, PI, and \(A_{10-index}\), are proposed to assess the efficiency of the developed models by [PERSON] et al. [84]. It was noted that the smaller RMSE, MAE, PI indicate more trustable statistical impressions [84]. PI and \(A_{10-index}\) are represented by Equations (1) and (2) [84].  
\[PI=\frac{1}{|\overline{t}|}\frac{RMSE}{\sqrt{R^{2}}+1} \tag{1}\]  
\[A_{10-index}=\frac{m_{
###### Abstract
Moreover, the problems and future development prospects of bionic camouflage materials that imitate the hyperspectral reflection characteristics of plants are discussed. The progress and direction of research identified in this issue address the following areas:  
1. The main factors affecting the spectral reflectance characteristics of plant leaves include pigment-visible light, structure-near infrared, and moisture-mid infrared, as well as leaf age, fertilizer (nutrient) depletion, and diseases. The PROSPECT model is a classic model that describes the radiation process of plant leaves in the solar spectral band and is widely used in the field of RS detection;
2. Hyperspectral RS technology is widely used in fields such as vegetation monitoring [18], plant functional trait mapping [19], biodiversity estimation [20; 21], and pest and disease control. Meanwhile, due to the strong ability of hyperspectral RS images to identify camouflage, they can distinguish real targets from decoy targets against the background of green vegetation and quickly detect small tactical targets against a desert background. The current military applications of hyperspectral RS mainly cover three aspects: detailed battlefield reconnaissance, identification of camouflaged targets, and detection and calculation of the target's true temperature and emissivity;
3. Plant-mimicking camouflage materials mainly include bionic camouflage materials that can only imitate the color of vegetation and cannot resist hyperspectral RS detection, bionic camouflage materials based on inorganic pigments that can achieve \"same color\" but do not have the ability to completely achieve \"same spectrum\" with green vegetation; bionic materials with hyperspectral similarity containing chlorophyll; bionic materials with hyperspectral similarity of leaves at different growth stages, seasons, and species; and bionic materials with solar spectrum reflection characteristics and low infrared emissivity similar to natural leaves.
# Iterative MLFMA-MADBT Technique for Analysis of Antenna Mounted on Large Platforms
## 3 Formulation
### Iterative MLFMA-MADBT Method
#### 3.4.3 Iterative Process  
An iterative process will be implemented from (7) to (9) to consider the coupling interaction between the MLFMA region and BT region. When the equivalent surface currents of the MLFMA region reach stable status, the field generated by the whole structure become stable, too. Thus, the current continuity at the boundary between the MLFMA region and BT regions can be guaranteed. In addition, the iteration stop criteria is defined as  
\[\frac{\parallel\textbf{I}_{i}^{MLFMA}\parallel-\parallel\textbf{I}_{i-1}^{ MLFMA}\parallel}{\parallel\textbf{I}_{i-1}^{MLFMA}\parallel}<\varepsilon,i=1,2, \ldots,I_{Iter} \tag{10}\]  
where the norm is defined in the 2-norm form, \(i\) stands for the \(i\)th iterative process and \(\varepsilon\) is the threshold value.
# An Active Path-Associated Cache Scheme for Mobile Scenes
###### Abstract
* Different from other active caching methods that directly push content to the target AP, our idea is to make full use of the forwarding path from the source AP to the destination AP and cache content with high freshness and popularity in high-value nodes. This idea ensures the cache hit rate while using the limited network equipment space and reduces the network delay. * We use real network topology to evaluate the performance of the proposed cache co-location scheme. Through experiments, the active caching strategy and other caching schemes are compared, and the results show that our scheme has the best performance in different network topologies. The rest of this article is organized as follows. Section 2 reviews the related research on the ICN mobility mechanism and caching scheme. Section 3 introduces our proposed caching scheme, including the neighbor message propagation mechanism and the cache co-location strategy. In the fourth part, we conducted simulation experiments and analyzed the results and performance of collaborative cache placement. Finally, we summarized and looked forward to future research.