esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Methodology
### Data Preparation and Preprocessing
#### 4.3.3 Image Filtering  
Color filters play a crucial role in image processing, aiding in the differentiation of distinct regions based on their chromatic attributes. These filters enable the segmentation and classification of objects within an image by partitioning specific areas according to their color values. Various studies have highlighted the effectiveness of color filters in diverse applications, including quality control in the food industry, fruit and vegetable classification, and material identification in medical imaging. For instance, [PERSON] et al. [53] demonstrated the use of color filters to enhance accuracy in fruit classification, significantly reducing identification errors. Similarly, [PERSON] et al. [54] investigated the application of color filters in the quality inspection of meat products, showing that these filters could detect subtle differences in meat color associated with freshness and quality. In this study, color filters were employed to distinguish between different chicken parts, such as white and yellow wings, as well as white and yellow thighs. Figures 7 and 8 depict the original images and the images obtained after applying color filters, respectively, corresponding to the four classes: white wing, yellow wing, white thigh, and yellow thigh.  
Figure 6: Localization Process: The image illustrates the localization process for chicken meat in an image. Initially, an edge detection algorithm is applied to identify the boundaries of the chicken meat box. Subsequently, bounding rectangles are determined to precisely delineate the region of interest. Finally, the extracted region of interest is obtained for further processing.  
To identify these color differences, a straightforward color-level thresholding technique was employed. The color filters facilitated the initial separation of different chicken parts, reducing the dataset to images that met the specified color criteria. Initially, simple filtering methods effectively identified color differences without the need for artificial intelligence algorithms. Subsequently, convolutional neural network (CNN) classifiers were employed to differentiate between wings and thighs within each color category (white and yellow). This combination of color filtering and CNN classification optimized the analysis process, ensuring precise separation between wings and thighs of different colors.
# On the Secrecy Sum-Rate of Internet of Things Networks: Scheduling and Power Control +
## 5 Numerical Results  
In this section, we evaluate the performance of our proposed schemes in terms of the achievable secrecy sum-rate through simulations for two different eavesdropping scenarios: internal eavesdropping and external eavesdropping scenarios. To observe the effect of the power control mechanism on the secrecy sum-rate, we also consider the proposed scheme without adopting the power control mechanism. Our proposed schemes refer to _IHPC_ for the proposed scheme with the power control mechanism (IHPC: Information Hiding Power Control) and _TOS_ for the proposed scheme without the power control mechanism (TOS: Threshold-based Opportunistic Scheduling), respectively. Additionally, we consider three conventional user scheduling schemes as references: _MaxSNR_, _MinGI_, and _OS-MRC_. _MaxSNR_ indicates a user scheduling scheme that selects the transmitters having the maximum desired signal strength to each beam. Contrary to _MaxSNR_, _MinGI_ is a user scheduling scheme that selects the transmitters generating a minimum amount of interference to other beams. _OS-MRC_ represents a threshold-based opportunistic scheduling with a maximum ratio combining scheme instead of random beamforming at the receiver, as proposed in [21]. Note that although some recent studies in PLS [24] show a higher achievable secrecy rate than our proposed scheme, direct comparison with our scheme is difficult since those studies consider additional system elements such as RIS elements.  
**Remark 3**.: _Similarly to the proposed schemes, MaxSNR and MinGI select multiple transmitters by adopting random beamforming at receiver (i.e., \(S=M\)). However, OS-MRC selects only one user in one time slot (i.e., \(S=1\)). Additionally, metrics for scheduling and power control criteria (i.e., \(\eta_{I}^{\star}\) and \(\eta_{L}^{\star}\)) is optimized for the given system parameters \(M\), \(N\) and \(K\)._
# Soybean Germination Response to Algae Extract and a Static Magnetic Field Treatment
## 3 Results
### Germination Tests
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{Parameter} & \multicolumn{5}{c}{**(c)**} \\ \cline{2-7}  & \multicolumn{2}{c}{**E + SMF**} & \multicolumn{2}{c}{**E**} & \multicolumn{2}{c}{SMF} \\ \cline{2-7}  & 20\% + 3 min & 80\% + 3 min & 20\% & 80\% & 3 min & **250 mT** \\ \cline{2-7}  & 250 mT & 250 mT & & & & \\ \hline Hypocotyl length [cm] * & 12.0 \({}^{\text{th}}\) & 11.0 & 11.0\({}^{\text{1}}\) & 11.3\({}^{\text{1}}\) & 11.4 & 11.0\({}^{\text{k}}\) \\ \hline Epicotyl length [cm] * & 2.1 \({}^{\text{th}}\) & 1.5 \({}^{\text{th}}\) & 2.0 \({}^{\text{th}}\) & 1.7 \({}^{\text{th}}\) & 2.8 \({}^{\text{th}}\) & 2.0 \({}^{\text{th}}\) \\ \hline Plant length [cm] * & 14.6 \({}^{\text{th}}\) & 13.0 \({}^{\text{th}}\) & 13.4 \({}^{\text{th}}\) & 13.3 \({}^{\text{th}}\) & 14.5 \({}^{\text{th}}\) & 13.5 \\ \hline \hline \end{tabular} a, b, c,... --differences statistically significant for \(p<0.05\). * Kruskal–Wallis test (results presented as a median). ** Tukey multiple comparison test (results presented as a mean).  
\end{table} 
Table 3: _Cont._Table 3c presents the effects of simultaneous treatment of soybean seeds with algal extract (20% and 80%) and a static magnetic field (3 min 250 mT) on plant growth and the pigment contents. The germination percentage was improved for all the tested groups as compared to C. Plant length was greater than in C only for two groups: E 20% with SMF (by 8.1%) and SMF (by 7.4%). Hypocotyl length was the greatest for the treatment with E 20% and SMF together (9.1% higher than for C) and epicotyl length for treatment with SMF alone (40% higher than for C). Root length was improved only for 2 groups--E 20% with SMF (3.8% higher than for C) and SMF (7.7% higher than for C). Plant weight was not statistically significantly affected by any treatment. Chlorophyll content measured by SPAD was lower for all tested groups than in the control. However, the differences were not statistically significant. Total chlorophyll measured with the spectrophotometric method was improved for all groups. The best results were found for E 80% (1.5 times higher than for C). Carotenoids content was improved for most groups (except E 80% with SMF). The most beneficial results were obtained for E 80% (3 times higher than for C).
# Exploiting Deep Learning for Wind Power Forecasting Based on Big Data Analytics
## 7 Results and Analysis
### Analysis of Proposed DSM Algorithm
The results confirm the effectiveness of proposed DSM algorithm as it achieves both the objectives: improving load factor and reducing consumption cost (as discussed in Section 6).
## 4 Methodology
### Architecture and Training
#### 4.3.3 Results
The most extreme case was the best performance for the highest compression ratio (32\(\times\) at a latent size of 256), requiring 4750 epochs, as shown in Table 4.  
Figure 7 further explores the performance of the best-performing network (1024 in the latent space compression). ROCs for the raw MSE (without MAF) are depicted, revealing a result of approximately 0.778 AUC. This indicates a skilled classifier, as an AUC of 0.50 represents random choice. Figure 7B illustrates the AUC of the network over the entire unseen test set while increasing the MAF window length for the MSE output. This figure demonstrates improved performance with MAF values, likely influenced by how the dataset is labelled. An MAF window length of 16 (approximately 4 s) performed the best, exhibiting a substantial increase in AUC from 0.778 raw to 0.876 with an MAF of 16. This ROC is visible in Figure 7C. The good detection versus false alarm rate is demonstrated in Figure 7D. This figure illustrates the event-based metric of the number of anomalies correctly identified compared to the total number of anomalies (as opposed to each window, it looks at each instance of an anomaly). A false alarm is calculated as a rate measured over one hour. As visible in Figure 7, this network can detect up to 50% of anomalies with no false detections, 70%of anomalies with fewer than ten false detections per hour, and 80% with approximately 50 false detections per hour. False detections per hour are crucial, as more false detections can disrupt the system's normal operation. The effect of such an MAF on the MSE can be seen in Figure 8. It is essential to note that each data point in this figure represents the MSE value for an entire window input to the network, to be explored further in this work. As seen in this figure, the MAF not only broadens the network response to anomalies--aiding in detecting more of the overall anomaly--but also helps in smoothing out and reducing the Bluetooth segment error, reducing the number of false positives where normal data have momentarily higher errors than the threshold (anomalous regions and normal Bluetooth regions are labelled in Figure 8). Figure 8: Effect of the MAF on the MSE. Figure 7: Performance of the Dragon_Slice_1024 CAE architecture in terms of raw AUC (**A**), best MAF size for MSE (**B**), AUC at best MAF size, (**C**) and GDR (**D**).
## 3 Results and Discussion
### DSC Analysis  
To further explore the structure of the IPN, we used DSC analysis to study the changes of the glass-transition temperatures of sample slices along the gradient direction. The IPN samples are divided into 100 layers from bottom to top with a thickness of 21.5 um per layer. We cutting them layer by layer at the temperature of \(-50~{}^{\circ}\)C by utilizing a microtome (CM1950 kryotome, Lecia Biosystems,  
Figure 4: Water contact angles on the fracture surface of the gradient IPN. Regions are symmetrically selected along the diagonal line of the fracture surface and they are from 1/4 h, 2/4 h and 3/4 h, respectively. Here “h” represents the thickness of the IPN samples.  
wetzlar, Germany). The 90 th (rose), 45 th (black), 15 th (red) layers and a slice parallel to the gradient direction (blue) were selected for testing.  
The baseline shift in DSC curve usually happens in the glass transition region of polymer due to the changes of heat capacity. As shown in the Figure 5, the \(T_{\mathrm{g}}\) of the 15 th, 45 th and 90 th layers are \(-\)16.3, 14.1 and 51.8 \({}^{\circ}\)C, respectively. The glass-transition temperature increases along with the gradient direction of IPN from bottom to top. That can be explained by the content decrease of the PU phase in the IPN sample. Because of the relative faster curing rate of the polyurethane, PU gels earlier, and it will result in the IPN structure with PU as its main body (fewer EP content) near the bottom side. In contrast, the top side is the IPN structure with EP as its main body. The DSC curve of the whole slice appears as a multi-sidestep shape; that can be explained by the gradient distribution of the PU phase along the thickness direction in the IPN sample.
# Pan-Cancer Analyses Reveal Genomic Features of FOXM1 Overexpression in Cancer
## Materials and Methods
### Plasmid Constructs  
pCW57-FOXM1b (Addgene #68811) and pCW57-FOXM1c (Addgene #68810) were previously described [25]. pCW57-GFP-2A-MCS (Addgene #71783) and pCW57-MCS1-2A-MCS2 (Addgene #71782) were generated by converting pCW57.1 (Addgene #41393) from Gateway cloningto sticky end cloning by adding a GFP-P2A-multiple cloning site and a multiple cloning site, respectively. pCW57-GFP-P2A-E2F1 (RefSeq NM_005225) was generated by PCR subcloning E2F1 (Addgene #21667) into pCW57-GFP-P2A-MCS. pCW57-CCNE1 (RefSeq NM_001322262) was generated by PCR subcloning CCNE1 (Harvard PlasmID Repository, HSCD00326535, RefSeq NM_001322261) into pCW57-MCS1-2A-MCS2. TLCV2 (Addgene #87360) was generated from LentiCRISPR v2 by modifying it into an all-in-one inducible system; doxycycline induces Cas9-P2A-GFP expression. sgRNA and puromycin expression are driven by constitutive promoters. The following modifications were made to lenti CRISPR v2: the EF1a promoter was removed and replaced with a tight Tet Response Element (TRE) promoter; P2A-puromcycin was removed and replaced with T2A-eGFP; and a EF1a promoter-puromycin-P2A-rtTA cassette was added. TLCV2-RB1 (Addgene #87836) was generated by insertion of the previously validated _RB1_ guide RNA (sgRB1-GCTCTGGGTCCTCCTCAGGA, [50]) into TLCV2. The 6X-FOXM1 promoter was subcloned from PGL3-6X-FOXM1 [70] into PGL4 to generate PGL4-6X-FOXM1. The PGL3-FOXM1 promoter (\(-1.1\) kb) reporter construct was previously described [70]. The FOXM1 promoter (\(-2.4\) kb) was subcloned from pFlash-FOXM1 [53] into PGL3 to generate PGL3-FOXM1 (\(-2.4\) kb). pCMV6 plasmid (Origene, Rockville, MD, USA) containing FOXM1 isoform a, b, and c was generated with PCR, restriction enzyme digestion and ligation. pCMV6-FOXM1c DNA binding domain mutant (DBD MT) was generated by site-directed mutagenesis (QuikChange, Agilent Technologies, Santa Clara, CA, USA,) of the FOXM1c DNA binding domain at the following residues, R286A, H267A, and S290A, rendering FOXM1 deficient in DNA binding [64]. Primers were designed using QuikChange Primer Design software (Agilent) and are listed in Table S5. pCW57-FOXM1c DBD MT was generated by subcloning FOXM1c DBD MT from pCMV6 into pCW57-MCS1-2A-MCS2 (Addgene #71782). Lentiviral dox inducible shRNA constructs non-silencing (NS, RHS4743), shFOXM1 #1 (V3 LHS_396939), and shFOXM1 #2 (V3 LHS_396941) were purchased from Dharmacon. All cloning was sequence verified. Plasmids were transfected with Lipofectamine 2000 (Life Technologies), according to the manufacturer's instructions.
## 5 Experiments
### Evaluated Self-Collision Pairs  
The types of self-collision can be significantly reduced by setting the joint limits properly. In addition, collisions among the first three links and the last three links can be taken care of by maximizing the arm manipulability, because the 6-DOF manipulator has poor manipulability if the arm is retracted to the point where the wrist is close to the base of the arm. Therefore, only the self-collision between the arm and the mobile platform needs consideration. Such a type of collision takes place when the elbow of the manipulator collides with the top of the platform, or when the wrist collides with the front of the platform. The distances associated with these potential self-collisions are depicted in Figure 5. Given that the platform is fixed with respect to the arm, setting frame \(X_{p}Y_{p}Z_{p}\) (located on the center of the wheels' axle, as shown in Figure 4) as the reference frame allows to use (22), where \(\mathbf{p_{H}}\) is a point in the arm's elbow or wrist, correspondingly, and \(\mathbf{p_{12}}\) is a fixed point with respect to \(X_{p}Y_{p}Z_{p}\):As illustrated in Figure 5, to prevent the elbow collision, a collision pair between the \(z\) component of \(\mathbf{p_{etbow}}\) with respect to frame \(X_{p}Y_{p}Z_{p}\) and a point located at 0.5 (m) from the origin of frame \(X_{p}Y_{p}Z_{p}\) in the \(Z_{p}\) direction is selected. The distance for this pair is named \(d_{etbow}\). To prevent wrist collision, a collision pair between the \(x\) component of \(\mathbf{p_{wrist}}\) with respect to frame \(X_{p}Y_{p}Z_{p}\) and a point located at 0.37 (m) from the origin of frame \(X_{p}Y_{p}Z_{p}\) in the \(X_{p}\) direction is selected. The distance for this pair is named \(d_{wrist}\). The wrist collision is only evaluated if the wrist height \(h_{wrist}\), the \(z\) component of \(\mathbf{p_{wrist}}\) with respect to frame \(X_{p}Y_{p}Z_{p}\), is lower than 0.5 (m), otherwise, its associated weighting collision matrix is assigned to identity. This was done to avoid restricting the movement of the joints that reduce \(d_{wrist}\) when the wrist is above the top of the mobile platform. All these parameters were chosen based on the physical dimensions of the NMM, and the second point in each of the collision pairs was selected so that when \(d_{elbow}\) and \(d_{wrist}\) are zero, a gap still exists between the potentially colliding links.
# State Estimation for DC Microgrids using Modified Long Short-Term Memory Networks
## 4 DC Microgrid SE and System Configuration
### DC Microgrid System Configuration  
In this paper, a 9-bus DC microgrid test system is designed and used for the simulation. As illustrated in Figure 4, the DC microgrid is connected to an AC utility grid by a bi-directional interlink converter (IC) at bus 8. The test system is mainly composed of loads, a battery energy storage system (BESS), and a photovoltaic (PV) system. The BESS is modeled using the electrical equivalent circuit model of a lithium-ion battery and the PV is modeled after the monocrystalline solar cell. In the simulation, the DC loads are rated at 20 kW, and are located on bus 1, bus 2, and bus 3. The BESSs are located on bus 5, bus 6, and bus 8, all with a rating of 20 kW. The PV systems are also located on bus 5, bus 6, and bus 8, with a rating of 20 kW.  
In normal operation, the power consumed by the load and the power generated from generation sources inside the microgrid are balanced. Meanwhile, for fluctuating DC loads, the BESS control should solve this problem by instructing BESS to charge or discharge power. Additionally, with the addition of BESSs and PV systems, if the grid operation mode changes from grid-connected mode to islanded mode, the microgrid will be able to maintain the system stability, as it can maintain the balance between load and generation within the grid.
## 4 Results and Discussion
### Critical SM and ET Regimes during 2014  
Figure 12 shows the monthly actual evapotranspiration (\(ET_{a}\)) from HidroMORE, the potential evapotranspiration (\(ET_{0}\)) and the in situ SM from the REMEDHUS network along year 2014. It can be observed that \(ET_{0}\) and \(ET_{a}\) are very similar in some months of the year (November-December-January-February, NDJF) and the SM is larger than the estimated critical SM in Table 1. This result reveals an energy-limited ET regime. In this regime, the temperature regulates the ET, the \(ET_{0}\) is very low since there is not enough energy and the \(ET_{a}\) is almost able to reach its maximum possible value (i.e., \(ET_{0}\)), but there is enough SM. Thus, the ET process is independent of the SM content and has an important dependence on the sensible heat flux, which impacts on the air temperature and the LST. By contrast, \(ET_{0}\) and \(ET_{a}\) have a different behavior in the other months (March-April-May-June-July-August-September-October, MAMJJJASO) and the SM is lower than the critical SM, indicating a water-limited ET regime. In this case, the \(ET_{0}\) increases from March to July and decreases from July to September, according to the temperature variation in this area. The \(ET_{a}\) is always lower than the \(ET_{0}\) because the SM controls the ET process. Note that \(ET_{a}\) minimum is in August, corresponding to the minimum SM, and the driest month in semi-arid climates. The energy-limited and the water-limited periods found for 2014 approximately agree with the periods between transitions in Figure 11 (top).  
Table 2 summarizes the correlation between daily in situ SM from the REMEDHUS network and \(ET_{a}\) from HidroMORE, and the number of observations (N) for the energy-limited (NDJF) and water-limited (MAMJJASO) periods along 2014. As the persistence of SM is an important component in land-atmosphere interactions [21], the correlation has been computed considering the delayed SM with respect to the \(ET_{a}\) day (with a delay between 0 to 5 days, 0 days corresponds to the same day). In the energy-limited period, most correlation values are very weak (\(\mid\)R\(\mid\)\(<\) 0.2) and others are non-significant. This result supports the argument that there is not a clear relationship between the SM and \(ET_{a}\) under energy-limited conditions due to SM-LST decoupling. Instead, correlations obtained in the water-limited period (R \(\approx\) 0.54 to 0.62) reveal an important relation of SM and \(ET_{a}\) due to SM-LST coupling. Since the SM of a particular day is related to the amount of previously lost water, the correlation increases considering the SM of one or two days after the \(ET_{a}\) day. This result indicates that there is a temporal lag between the ET behavior and its impact on the SM.  
Figure 12: Monthly actual evapotranspiration (\(ET_{a}\), blue line) from HidroMORE, potential evapotranspiration (\(ET_{0}\), red line), and SM (green line) from the REMEDHUS network along 2014.
## 6 Conclusions  
A new terrain matching algorithm, F-WAPSO, which combines ABC and PSO, able to reduce the cumulative error of inertial navigation, is proposed in this paper. F-WAPSO first improves the acceleration factor and inertia weight of the traditional PSO, respectively, to improve the convergence speed and global optimization ability of PSO. Secondly, ABC is introduced into the improved PSO to further reduce the occurrence of local optimization of the improved PSO. F-WAPSO was applied to terrain matching in inertial navigation. The results show that the matching accuracy of F-WAPSO is better than that of TERCOM, and the matching time is the shortest regardless of the initial errors. Even if the initial error is large, F-WAPSO improves the matching accuracy and reduces the matching time. F-WAPSO greatly improves the effect of terrain matching, effectively solves the problem of cumulative errors in INS, and fulfills the requirements of autonomous and accurate navigation of AUVs. The matching results of this new algorithm have a certain significance for potentially contributing to the successful correction of inertial navigation positions.  
Conceptualization, D.W. and L.L.; methodology, D.W. and P.D.; software, D.W., P.D. and J.W.; validation, D.W., L.L. and Y.B.; formal analysis, D.W. and Y.B.; investigation, J.W. and Y.B.; resources, Y.B.; data curation, D.W., Y.B. and J.W.; writing--original draft preparation, D.W.; writing--review and editing, L.L. and Y.B.; visualization, D.W.; supervision, Y.B.; project administration, Y.B.; funding acquisition, L.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National key research and development program of China (grant number 2021 YFC2801300) and the National key research and development program of China (grant number 2020 YFE0202600).  
Not applicable.  
The authors declare no conflict of interest.