esa-sceva/satcom-chunk-collection huggingface dataset:
# Correction Compensation and Adaptive Cost Aggregation for Deep Laparoscopic Stereo Matching
## 4 Experiments and Results
### Ablation Study
#### 4.3.2 Adaptive Cost Aggregation Module  
The adaptive cost aggregation module aims to guide the network to aggregate more cost information of high value to improve the registration performance in areas with specular highlights and textureless regions. The corresponding experimental results are presented in Table 3. From the first two rows of Table 3, we observed that the EPE metric decreased from 1.951 to 1.877 if an ACAO was added to the network. By observing the loss curve shown in Figure 6, we could find that it accelerates the convergence of the model, suggesting that adaptively aggregating cost volumes based on the value differences of their elements is effective. Meanwhile, the second and third rows of Table 3 indicate that the DCS also contributed to a decrease in the EPE metric from 1.877 to 1.848. In particular, we found that the metrics became worse when only the IGCS was integrated into the network. This was because it brought about a decline in the number of learnable parameters. The IGCS was replaced with CGS to prove its effectiveness. Conclusive evidence supporting its efficacy was derived from the results obtained in the last two groups of experiments. In the end, the adaptive cost aggregation module reduced the number of network parameters by 18% and the number of floating-point operations by 26%. Additionally, it led to a slight improvement in the model performance.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**dim** & \(\mathbf{L}_{\text{mag}}\) & \(\mathbf{L}_{\text{scale}}\) & \(\mathbf{L}_{\text{lr}}\) & **EPE\(\downarrow\)** & **D1\(\downarrow\)** & **D3\(\downarrow\)** & **MAE\(\downarrow\)** \\ \hline  & & & & & 2.616 & 20.104 & 17.407 & 2.767 \\ \(x,y\) & & & & & 71.431 & 90.505 & 90.116 & 26.557 \\ \(x,y\) & ✓ & & & & 2.641 & 20.690 & 17.887 & 2.765 \\ \(x,y\) & ✓ & ✓ & & & 2.244 & 14.338 & 11.986 & 2.475 \\ \(x,y\) & ✓ & ✓ & ✓ & **1.951** & **12.073** & **9.557** & **1.897** \\ \(y\) & ✓ & ✓ & ✓ & 2.163 & 14.879 & 12.271 & 2.142 \\ \hline \hline \end{tabular} \(\downarrow\) indicates that the smaller the value, the better the model performs.  
\end{table}
Table 2: Ablation results for the correction compensation module. This module and its accompanying losses brought over 25% improvement in both EPE and MAE metrics, as well as over 39% improvement in D1 and D3 metrics.
# Research on Dynamic Path Planning of Multi-AGVs Based on Reinforcement Learning
## 5 Experimental Results and Discussion
### Experiment 5: Experiments Based on GA-KL Path Planning Method for Real Environments
#### 5.6.1 Experiments on Frontal Avoidance of Dynamic Obstacles by AGVs Based on the GA-KL Method  
First, the AGV with serial number A receives a task from node 8 to node 1. As shown in Figure 15, the AGV can complete the task either through the _Path_ (8-7-6-5-1) or through the _Path_ (8-7-2-1). In terms of distance, _dis_ (8-7-2-1) \(<\)_dis_ (8-7-6-5-1), but the staff moves back and forth from node 2 to node 3, increasing the node busy factor of node 2, resulting in a larger \(f\)-value according to Equation (13). According to the roulette selection method, the T-GA algorithm chooses _Path_ (8-7-6-5-1) with a longer distance but relatively small \(f\) value. When the AGV with sequence number A arrives at node 7, the AGV with sequence number B departs from node 5 to node 7. At this time, the detection radar of the AGV with serial number A has already detected the AGV with serial number B, so it immediately uses the K-L algorithm to plan the local path and successfully achieves avoidance.  
Figure 14: The scene map.  
Figure 13: Experimental scene.  
5.6.2. Experiments on Side Avoidance of Dynamic Obstacles by AGVs Based on the GA-KL Method  
As shown in Figure 16, the AGV with sequence number A receives a task from node 1 to node 8, and its global path is _Path_ (1-2-7-8). When the AGV with sequence number A reaches node 2, the AGV with sequence number B departs from node 8 to node 1, and its global path is _Path_ (8-7-6-5-1). At this time, the detection radar of the AGV with sequence number B has detected the AGV with sequence number A, so it immediately uses the K-L algorithm to plan the local path. At this point, if the K-L algorithm selects the AGV upward avoidance, which is blocked by the staff at node 3, the algorithm selects downward avoidance and successfully returns to node 6.
## 5 Methods for Estimation of HFR Video Characteristics
### Hurst Index
Specifically, the method relies on calculating the periodogram:  
\[I_{L}(\omega_{k})=\frac{1}{L}\left|\sum_{i=1}^{L}x(t)e^{-2\pi i(t-1)\omega_{k}} \right|^{2}, \tag{3}\]  
and it is based on the discrete Fourier transform applied for a set of samples \(\{x_{t}\): \(t=1,\ldots L\}\), where \(\omega_{k}=k/L\), \(k=1,\ldots,\)\([L/2]\) are corresponding frequencies. When plotting the periodogram in a log-log domain, the index \(H\) can be found according to the slope of regression line as \(H=(1-slope)/2\). One of the best-known procedures for calculating the LRD is the R/S statistics or the R/S method [70; 73; 74]. Firstly, a times series can be divided into \(d\) subseries or blocks of length \(n\), where each subseries \(m=1\), \(\ldots\), \(d\) is normalized in order to generate cumulative time series \(y_{i,m}\) for \(I=1\), \(\ldots\), \(n\). The range is calculated for each block as \(R_{m}=\max\{y_{i,m}\): \(I=1,\ldots n\}-\min\{y_{i,m}\): \(I=1,\ldots n\}\) and rescaled. The mean value of all subseries of length \(n\) can be found as  
\[\left(R/S\right)_{n}=\frac{1}{d}\sum_{m=1}^{d}R_{m}/S_{m}, \tag{4}\]  
where the ratio R/S follows the rule \((R/S)_{n}\sim\ n^{H}\), enabling estimation of the \(H\) index as the slope of linear regression line in a log(\(R\)/S) versus \(\log(n)\) plot. One should have in mind that each of the abovementioned methods for Hurst index estimation may give different \(H\) values. Here, they are used for LRD estimation of HFR data. Hurst indices for self-similar series are in the range of 0.5-1, where it can be considered that for higher \(H\) value that is closer to \(1\), the degree of self-similarity increases. The difference between the Hurst indices of a recovered/modified and original sequence is calculated as  
\[H_{diff}=\frac{H_{rec}-H_{original}}{H_{original}}, \tag{5}\]  
where \(H_{rec}\) denotes index of a recovered sequence, and \(H_{orig}\) is the index of a corresponding sequence where no frame rate change is made.
## 3 Testing and Analysis
### Surface Morphology  
The ZnO films prepared at different pH values were tested using a Scanning electron microscope (JEOL Ltd., SEM-JSM-IT200, Tokyo, Japan). The SEM pattern is shown in the figure.  
Figure 2 depicts that: as the pH increases, the deposition rate increases. The nanorods of the sample of pH 8.5 were thicker and shorter, whereas the nanorods of the sample of pH 9.5 were thin and long. In comparison, the homogeneity of the sample at pH 9.5 is also the best. On the graph, the zinc oxide nanowires could be seen. From the top view of zinc oxide, the film is uniform. The nanostructures were shown in the figures. Based on the HCD work we did before, we obtained adaptable ZnO nanostructures. They are uniform and have island structures, which allow us to obtain a good structure for the deposition of silver and hence make a good silver structure.  
Table 3 were drawn from the data obtained from the SEM figures. With the increase in pH value, the thickness of the prepared ZnO nanofilm gradually increased, and the diameter of the ZnO nanopillar also gradually increased. With the increase in pH, the film thickness changes almost linearly.  
Figure 2: \(Cont.\)  
Figure 2: (**a**). SEM images of ZnO thin films prepared by hydrothermal evaporation on silicon substrates at pH 8.0. (**b**). SEM images of ZnO thin films prepared by hydrothermal evaporation on silicon substrates at pH 9.0. (**c**). SEM images of ZnO thin films prepared by hydrothermal evaporation on silicon substrates at pH 9.5. (**d**). SEM images of ZnO thin films prepared by hydrothermal evaporation on silicon substrates at pH 10.0.
## 2 Materials and Methods
### Biological Activities of Endophytic Bacteria
#### 2.4.3 Cytotoxic Activity  
To screen the anticancer activity of the secondary metabolites of crude ethyl acetate extracts of endophytic bacterial isolates, the MTT (3-(4,5-dimethylthiazol))-diphenyl tetradzolium bromide) assay method was performed as described by [PERSON] et al. [19]. Firstly, we prepared the samples using 50 mg ethyl acetate extracts of bacteria dissolved in 1 mL of DMSO. In vitro anticancer screening used MCF-7 (breast cancer), HeLa (cervical cancer), and HT-29 (colon cancer) cells. MCF-7 and HeLa cells were purchased from the Shanghai Cell Bank (Shanghai, China), and HT-29 cells were obtained from Procell Life Science and Technology Co., Ltd. (Wuhan, China). The significant anticancer drug doxorubicin (DOX) was used as a positive control and purchased from BBI Inc. (Shanghai, China). Three human cancer MCF-7, HeLa, and HT-29 cell lines were particularly seeded in aliquots of 200 \(\upmu\)L in 96-well plates at a density of 3-10\({}^{3}\) cells per well. After incubation at 37 \({}^{\circ}\)C, 95% moisture, and 5% CO\({}_{2}\) for 24 h, cells were treated with 30 \(\upmu\)M of the crude extracts of endophytes and kept for 48 h. After that, into each well was placed 20 \(\upmu\)L of 5 mg/mL MTT and incubated at 37 \({}^{\circ}\)C for 4 h. Then, the supernatant was removed, and 200 \(\upmu\)L of DMSO was added to each well. Finally, the multiwall plates were shaken for 10 min to completely dissolve the test samples. Absorbance was read at 540 nm using an enzyme-linked immunosorbent assay reader (SpectraMax M5, Molecular Devices, USA). The IC\({}_{50}\) values were measured using the inhibition rate.  
\[IR=\frac{(C-S)}{(C-B)}\times 100\]  
Here, C is the OD of the control group, S is the OD of the experimental group, and B is the OD of the blank.
## 2 Results
### Analysis of Essential Oil Compositions
They provide a critical regulatory site in the terpene biosynthesis pathway in the cytoplasm [22]. HMGR is encoded by the gene CcPMK [23]. As an example, the high content of terpenoids in Cinnamomum camphora is correlated with the high expression levels of the HMGR and PMK genes, as the upstream terpenoid synthesis pathway. MK triggers the transfer of phosphate groups to produce mexulonate-5-phosphate (MVAP) [24]. The IPP/DMAPP synthase-related gene TmHDR has shown expression in all plant tissues [25]. Figure 3: MEP and MVA pathway of terpenoids biosynthesis in plants (DXP, deoxyxylulose-5-phosphate; CDP-ME, 4-CDP-2-C-methyl-D-erythnitol; CDP-MEP, CDP-ME 2-phosphate; MEcPP, 3-methyl-1,2,3,4-tetrahydrooxybutane-1,3-cyclic bisphosphate; HMBPP, (E)-4-Hydroxy-3-methylbut-2-enyl diphosphate; HMG-CoA, 3-hydroxy-3-methylglutaryl CoA; MVP, 5-phosphomevalonate; MVPP, 5-diphosphomevalonate) (Adapted with permission from Ref. [21]. 2022, [PERSON]; [PERSON]). As for the MEP pathway, 1-deoxy-D-xylulose-5-phosphate synthase (DXS) and 1-deoxy-D-xylulose-5-phosphate reduction of the isomeras (DXR) are the major rate-limiting enzymes [21]. In this work, analysis of leaf and stem oils demonstrated a similar total terpenoid content, accounting for 16.2% and 17.0%, respectively. However, the monoterepenes-rich stem volatiles may have resulted from the highly expressed genes of the rate-limiting enzymes HMGR, PMK, and MK in the MVA pathway, or DXS and DXR in the MEP pathway. This could directly relate to their stronger antioxidative and antibacterial capacities, but more research is needed to verify this speculation in _F. japonicum_.
# A Laser-Based Method for the Detection of Honey Adulteration
## 4 Conclusions  
In the present work, LIBS was used to assess the feasibility of detection of adulteration of honey with some glucose syrup. In that view, LIBS spectra of honey samples, adulterated honey samples, and glucose syrup were acquired and were compared. The amount of added glucose syrup in the honey samples ranged from 10% to 90% (\(w/w\)). The assessment of the discrimination procedure was conducted following two different approaches of samples split into train and test sets, each one exhibiting a different level of difficulty concerning the algorithmic procedure. The obtained accuracies were quite high, exceeding 90%, for both approaches. Furthermore, it was found that some of the observed spectral lines, in particular those of some inorganic ingredients of honey (i.e., calcium, sodium, and potassium), were strongly varying in the degree of adulteration, exhibiting a clearly  
Figure 5: Feature importance plot obtained using the ERT algorithm together with a honey LIBS spectrum in the (**a**) 200–1000 nm, (**b**) 240–500 nm, and (**c**) the 560–780 nm spectral regions.  
decreasing trend as the degree of adulteration increased. Based on this observation, the spectral lines of these three elements were selected as key parameters for the detection of honey adulteration, using LDA for classification, resulting in quite high accuracies. This result is very useful, since, instead of the whole LIBS spectrum, only three spectral lines are used for the construction of a successful predictive model for honey adulteration. Finally, the application of ERT confirmed the importance of these features for the detection of honey adulteration, and identified spectral features that are not clearly observed in the spectra (e.g., they are very weak, or overlap with nearby stronger features, etc.), such as those of Mg (II) at 279.6 and 280.3 nm and Ca (I) at 422.7 nm. The present findings suggest the great potential of LIBS aided by machine learning for the detection of honey adulteration.  
D.S., N.G., P.K., and E.N.: data curation; S.C.: funding acquisition; D.S., N.G., P.K., E.N., and V.K.: investigation; C.B. and S.C.: methodology; S.C.: project administration; S.C.: resources; D.S., N.G., E.N., and V.K.: software; C.B. and S.C.: supervision; D.S., N.G., P.K., E.N., and V.K.: writing--original draft; S.C. and C.B.: review and editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Support from Greek national funds through the Public Investments Program (PIP) of General Secretariat for Research and Technology (GSRT), under the Emblematic Action \"The bee routes\" (project code: 2018 ZE01300000) is acknowledged. We also thank the network of the Emblematic Action \"The Bee Routes\" for making available the honey samples. D.S. acknowledges support from the <<Andreas Mentzelopoulos Foundation>>.  
The authors declare no conflict of interest.
## 3 Results and Discussion  
### XRD, FTIR and Morphology Analysis of LDHs  
The XRD patterns of S-NiMgAl-Mn, S-NiMgAl-Co, S-NiMgAl-Cu and S-NiMgAl-Zn are shown in Figure 1a-d, respectively. All patterns exhibit the (003), (006), (012), (015), (018), (110) and (113) characteristic reflections of the LDHs structure [26,27]. From the intensity of the main reflections (003) and (006), one can find that under the same reaction conditions, the Mn- and Zn-doped LDHs have higher crystallinity than those containing Co and Cu [28,29]. The basal spacing (\(d_{\rm basal}\)) of S-NiMgAl-Mn, S-NiMgAl-Co, S-NiMgAl-Cu and S-NiMgAl-Zn were calculated from the (003) reflection, are 7.76, 7.81, 7.82 and 7.73 A, respectively. These values confirm that the interlayers of the four LDHs exist with CO3\({}^{2-}\), but do not coexist with NO3\({}^{-}\) or C18H35O2\({}^{-}\)[30,31]. Furthermore, FTIR spectra of LDHs are shown in Figure 2A. The bands at 1369 cm\({}^{-1}\) also confirm that the interlayer anions of all LDHs are CO3\({}^{2-}\) without the coexistence of NO3\({}^{-}\) (at 1384 cm\({}^{-1}\)) [32, 33, 34]. The bands at 867 and 665 cm\({}^{-1}\) are characteristic of carbonate for the \(\
u_{2}\) (out-of-plane deformation) and the \(\
u_{4}\) (in-plane bending) vibrations, respectively [35]. Meanwhile, the bands at 1484 and 1426 cm\({}^{-1}\) are assigned to the bridged bidentate complexation of interlayer CO3\({}^{2-}\)-CO3\({}^{2-}\) in the LDHs [36]. Note that the bands at 1484 and 1426 cm\({}^{-1}\) disappear in S-NiMgAl-Mn due to the existence of Mn\({}^{2+}\).  
Figure 1: XRD (X-Ray diffraction) patterns of the synthesized layered double hydroxides (LDHs). (**a**) S-NiMgAl-Mn; (**b**) S-NiMgAl-Co; (**c**) S-NiMgAl-Cu and (**d**) S-NiMgAl–Zn.  
The broad and intense FTIR absorption band at 3460 cm\({}^{-1}\) is attributed to the O-H stretching vibrations of layered hydroxyl groups and interlayer water molecules [37]. The bands at 1634 cm\({}^{-1}\) can be assigned to the bending vibration of interlayer water molecules [38]. The bands at 786 and 427 cm\({}^{-1}\) are ascribed to [AlO6]\({}^{3-}\) condensed groups and the deformation of M-OH in the brucite-like layers, respectively [39; 40]. Furthermore, all LDHs also display bands at 2956, 2922 and 2852 cm\({}^{-1}\), which are associated with the C-H stretching vibrations of stearate. In addition, the antisymmetric stretching vibration of the carbonyl group from 1560 cm\({}^{-1}\) of pure stearate is shifted to 1545 cm\({}^{-1}\) due to the layer hydroxyl groups hydrogen-bonded to carbonyl group of stearate anions [30; 41; 42]. Therefore, the combined analysis of FTIR and XRD confirms that the stearate is successfully grafted on the surface of LDHs. Figure 2B shows the schematic illustration of stearate surface modified LDHs. Moreover, it is worth mentioning that the weak shoulder at 1545 cm\({}^{-1}\) is not detected for S-NiMgAl-Zn. This fact indicates that the grafted amount of stearate was least for S-NiMgAl-Zn. In contrast, the grafted amount of stearate was the most for S-NiMgAl-Mn. The grafted amount of stearate can be further confirmed by morphological studies of LDHs.
## 3 Methodology
### Data Preparation
#### 3.3.2 Moving Averages  
This feature calculates the moving average of throughput in time, which can provide insight into the application's traffic patterns. The moving averages of throughput in uplink and downlink are denoted by \(MA_{Sent}\) and \(MA_{Recived}\), respectively, and computed using Equations (2) and (3):  
\[MA_{Sent}[i]=\frac{1}{k}\sum_{j=i-k+1}^{i}N_{\text{sent}}[j], \tag{2}\]  
\[MA_{\text{received}}[i]=\frac{1}{k}\sum_{j=i-k+1}^{i}N_{\text{received}}[j], \tag{3}\]  
where \(k\) represents the moving average period (window size), \(i\) denotes the current time index, and \(N_{\text{sent}}[j]\) represents the throughput at that time \(j\).  
The moving average of throughput allows the model to consider the dynamic behavior of network traffic over time. By incorporating this feature, the model can detect variations in data flow rates and adapt its classification decisions based on temporal patterns, which is crucial for identifying intermittent low-latency conditions. In Figure 8, the MA of YouTube traffic is demonstrated.  
Figure 7: Throughput of YouTube traffic.
# A Deep Analysis of Brain Tumor Detection from MR Images Using Deep Learning Networks
## 2 Related Works
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Dataset** & **Models** & **Performance** & **Limitations** \\ \hline \multirow{4}{*}{[17], 2022} & \multirow{4}{*}{10,000 MR images} & Deep educational model & \multirow{4}{*}{Deep educational model: accuracy 98\%} & Need to apply image augmentation methods \\  & & & Inception V3 & \\ \hline \multirow{4}{*}{[34], 2022} & \multirow{4}{*}{3394 MR images} & Deep convolutional neural & \multirow{4}{*}{DCNN: accuracy 97.72\%} & Should consider more \\  & & network (DCNN) & & & datasets and various types \\  & & (proposed), VGG16, VGG19, CNN-SVM & & & of images \\ \hline \multirow{4}{*}{[35], 2021} & \multirow{4}{*}{3064, CT brain scan images} & Correlation learning & \multirow{4}{*}{CLM model: accuracy 96\%, precision and recall 95\%} & Should consider more \\  & & mechanism (CLM) with CNN & & & datasets to obtain more \\ \hline \multirow{4}{*}{[36], 2021} & \multirow{4}{*}{2556 brain tumor images} & Hybrid ensemble classifier & \multirow{4}{*}{36, (KNN-RF-DT): accuracy 97.305\%, precision 97.73\%, specificity 97.60\%, sensitivity 97.04\%, and F1-score} \\  & & & KNN, and decision tree & 97.41\%, and F1-score \\ \hline \multirow{4}{*}{[37], 2022} & \multirow{4}{*}{3260 MRI brain images} & Dense EfficientNet & Dense EfficientNet: accuracy 98.78\%, \multirow{4}{*}{F1-score 98\%} & Higher number of \\  & & (proposed), ResNet 50, MobileNet, MobileNetV2 & 93.21\% & \\ \cline{1-1} \cline{2-5}  & & & MobileNet, MobileNetV2 & 93.21\% & \\ \hline \multirow{4}{*}{[38], 2022} & BRATS 2015 dataset, 2000 & \multirow{4}{*}{Residual network} & \multirow{4}{*}{Residual network: accuracy of 97.05\%} & Lack of performance \\  & & MR images & & accuracy of 97.05\% & metrics evaluation \\ \hline \multirow{4}{*}{[39], 2020} & BRATS 2017, & Modified two-step & \multirow{4}{*}{Accuracy: 98.20\%, recall: 95.13\%, and precision: 95.13\%, and precision: 93.21\%} & Data processing and \\  & & & & over-fitting \\ \hline \multirow{4}{*}{[40], 2019} & BRATS 2013 MRI & \multirow{4}{*}{Hybrid CNN models} & Dice score of 86\%, sensitivity of 86\%, and specificity of 91\% & Only one tumor per slice \\  & & & & was evaluated \\ \cline{1-1} \cline{2-5}  & & & & \\ \cline{1-1} \cline{2-5}  & & & & \\ \cline{1-1} \cline{2-5}  & & & & \\ \cline{1-1} \cline{2-5}  & & & & \\ \hline \multirow{4}{*}{[15], 2019} & \multirow{4}{*}{227 MR images} & KNN classifier (proposed), RF, NB, and RBF & Accuracy of 95.6\% and AUC of 99\% & Lack of MR images \\ \cline{1-1} \cline{2-5}  & & & & \\ \hline \multirow{4}{*}{Our work} & \multirow{4}{*}{3264 MR images} & CNN (proposed), ResNet-50, VGG16, and Inception V3 & Accuracy of 93.30\%, AUC of 98.43\%, recall of 91.13\%, and loss of 0.25 & — \\ \cline{1-1} \cline{2-5}  & & & & \\ \cline{1-1} \cline{2-5}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Literature Review.