esa-sceva/satcom-chunk-collection huggingface dataset:
# Various Generative Adversarial Networks Model for Synthetic Prohibitory Sign Image Generation
## 4 Discussion
LSGAN presents an SSIM value of 0.165 and an MSE value of  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Group**} & \multirow{3}{*}{**Total**} & \multirow{3}{*}{**Image**} & \multicolumn{6}{c}{**1000 EPOCH**} & \multicolumn{6}{c}{**2000 EPOCH**} \\ \cline{3-14}  & & & \multicolumn{2}{c}{**DCGAN**} & \multicolumn{2}{c}{**LSGAN**} & \multicolumn{2}{c}{**WGAN**} & \multicolumn{2}{c}{**DCGAN**} & \multicolumn{2}{c}{**LSGAN**} & \multicolumn{2}{c}{**WGAN**} \\ \cline{3-14}  & & & \multicolumn{2}{c}{**MSE**} & **SSIM** & **MSE** & **SSIM** & **MSE** & **SSIM** & **MSE** & **SSIM** & **MSE** & **SSIM** & **MSE** & **SSIM** \\ \hline \multicolumn{14}{c}{**TI**} \\ \hline
1 & 200 & 64 \(\times\) 64 & 7.906 & 0.525 & 8.219 & 0.459 & 8.124 & 0.48  
14.377 with 1000 epochs. Furthermore, WGAN reached an SSIM value of 0.336 and an MSE value of 11.603. DCGAN achieved SSIM values at 0.282 and MSE 11.943. All MSE scores were higher than 10, and the SSIM values were lower than the other groups. LSGAN exceeds other GANs, as LSGANs give certain advantages over standard GANs. LSGANs will first produce images of better quality than standard GANs. Secondly, LSGANs perform more stably during the learning process. For evaluating the image quality, we conducted qualitative and quantitative experiments, and the experimental results show that LSGANs can generate higher quality images than regular GANs. Moreover, LSGAN enhances the primary GAN loss function by substituting the original cross-entropy loss function with the least-squares loss function. This fixes the two major traditional GAN problems. LSGAN makes the image quality of the outcome stronger, the training process robust, and the speed of convergence faster. The synthetic image that LSGAN creates looks obvious, actual, and genuine. The Least Squares GAN (LSGAN) is planned to help generators become more valuable. Intuitively, LSGAN required the discriminator target label for the original image to be 1 and the resulting image to be 0. For the generator, we needed the target label for the resulting image to be 1. The LSGAN can be implemented with a minor change to the discriminator layer's output and the adoption of the least-squares, or L2, loss function. The output layer of the discriminator model must be a linear activation function.
## 5 Experimental Results
### Results
The model provided better results in terms of MAE, RMSE, and Precision metrics of 0.445, 0.743, and 92.07%, respectively, compared to recommendation models based on MLP, GRU, and SVD (Shown in Figure 5). Furthermore, our proposed transformer recommendation model provided better results when integrating five views than when integrating only two views containing user latent features and item latent features. Figure 4: The experimental results for the Precision metric with the Movielens dataset. When conducting experiments on the Amazon-Video and Games dataset, our proposed transformer recommendation model, which integrated five views, also gave the MAE, RMSE, and Precision metrics of 0.689, 1.471, and 83.31%, respectively. Compared to recommendation models based on MLP, GRU, and SVD, our proposed transformer recommendation model exhibited the best results in terms of MAE and Precision (shown in Figure 6). Moreover, our proposed transformer recommendation model provided better results when integrating five views than when integrating only two views containing user latent features and item latent features. We also conducted experiments on the Amazon-Electronics dataset, which resulted in MAE, RMSE, and Precision metrics of 0.554, 1.195, and 87.83%, respectively, demonstrating the effectiveness of our proposed transformer recommendation model that integrates five views. Compared to recommended models based on MLP, GRU, and SVD, our proposed transformer recommendation model exhibited the best results in terms of MAE and Precision (shown in Figure 7). Additionally, the proposed transformer recommendation model performed better when integrating five views rather than only two views containing user latent features and item latent features. Figure 5: The experimental results for the MAE, RMSE, and Precision with the Amazon-Toys and Games dataset. Figure 6: The experimental results for the MAE, Precision with the Amazon-Video and Games dataset. In addition to comparing our proposed model with SVD-based and DNN-based (MLP and GRU) recommendation models, we have conducted experiments using the graph-based recommendation model on the same train and test sets derived from the MovieLens and Amazon sub-datasets. Our experiments provide evidence that our proposed recommendation model surpasses the performance of the graph-based recommendation model. Specifically, the results from Table 5 illustrate that our proposed model achieves higher recommendation accuracy, with a Precision metric increase ranging from 11.14% to 108%, a MAE metric decrease ranging from 31.19% to 65.37%, and a RMSE metric decrease ranging from 15.56% to 59.24%.
## 2 Materials and Methods
### Plant and Soil Analysis  
After field collection, all samples were carefully washed (twice) with tap water and further with distilled water (twice) to remove soil particles. Subsequently, the samples were left to dry on a lab bench at room temperature and then dried for 48 h at 40 \({}^{\circ}\)C on a stove. After drying, leaves and flowers were picked from the main branches. Finally, leaves and flowers were manually triturated and individually placed into polyethylene bags until analysis.  
Plant sub-samples were mechanically homogenized in an agate planetary ball mill and manually sieved through a 0.2-mm polyethylene mesh. A microwave system (MARS-X 1200 W Microwave Accelerated Reaction System, CEM, Mathews, NC, USA) was used for sample digestion following the methodology described by [PERSON] et al. [25]. Briefly, 150 mg of real sample (a mixture of flowers and leaves and only leaves) and 300 mg of reference material were weighed and placed into Teflon digestion vessels. Afterwards, samples were pre-digested overnight with 4.5 mL HNO\({}_{3}\) and 1.5 mL HCL (9 mL HNO\({}_{3}\) and 3 mL HCL for reference material samples). Pre-digested samples were then filtered 2 or 3 times before digestion in a microwave at 160 \({}^{\circ}\)C (600 W, ramp time 15 min, and 15 min holding time). Then, samples were evaporated on a heater at 95 \({}^{\circ}\)C until near dryness. Additionally, 0.5 mL of concentrated HF was added to the real samples (1 mL for reference material samples) to remove silicates, and the samples were heated until they almost dried. After that, 1 mL of concentrated HNO\({}_{3}\) was added to the real samples (2 mL for reference material samples), and they were evaporated until near dryness. This last step was repeated twice. Finally, the real samples were diluted to a total volume of 10 mL HNO\({}_{3}\) (5% \(v/v\)), whereas reference material samples were brought up to a total volume of 50 mL.  
The digested samples were analyzed using ICP-MS equipment (PerkinElmer ELAN 9000). The instrumental settings were RF power 1000 W, plasma gas flow rate 1 L of argon min\({}^{-1}\), and 35 s washing time. Furthermore, 50 \(\mu\)L of a solution containing In (III) and Ge (II) (10 mg L\({}^{-1}\)) was used as an internal standard. Each sample was assayed in triplicate.  
Soil samples were assayed according to a previously described protocol based on acid digestion and ICP-MS measurement [10].
## 4 Experiment and Result
### GID Dataset  
The Gaofen Image Dataset (GID) [4] is a large dataset used for land use and land cover (LULC) classification. It contains 150 high-quality GF-2 images from more than 60 different cities in China, covering a geographic area of more than 50,000 square kilometers. GID images have high intra-class diversity and low inter-class separability. Some scholars [52; 53; 54] have started related research based on the GID dataset.  
This experiment used five categories of datasets (Large-scale Classification_5 classes), including built-up areas, cropland, forest land, grassland, and waters. As shown in Figure 8, this study extracted the cropland categories in the GID dataset separately, and assigned other categories to others.  
Figure 6: Two connection methods of the connected domain: (**a**) 8-connected mode and (**b**) 4-connected mode. The number “0” represents the central pixel, the other numbers are its adjacent pixels, and the arrow represents its azimuth relationship.  
Figure 7: Jilin-1 high-resolution remote sensing cropland dataset: (**a–d**) and (**i–l**) are true-color remote sensing images, and (**e–h**) and (**m–p**) are their corresponding labels.  
test set in a ratio of 5:3:2. The areas of the validation set of Jilin-1 and GID datasets were about 5530.7 and 6868.1 square kilometers, respectively. Table 1 introduces the cropland dataset in this study.
# Social-Aware Peer Selection for Device-to-Device Communications in Dense Small-Cell Networks
## 3 System Description
### Parameters' Definition
#### 3.2.1 SPL Parameters  
Social Friendship Index  
The _SFI_ describes the strength of the social relationships between the users based on their interaction history on various social networks. The higher the social network interaction, the closer friendship and consequently a higher _SFI_ value. We categorized _SFI_ into four classes: close friend,  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol/Acronym** & **Description** \\ \hline \(h_{(T_{X},RX)}\) & Rayleigh coefficient \\ \hline \(\beta\) & Path loss exponent \\ \hline \(\Omega_{\textit{UIE}}\) & Set of UEs \\ \hline \(G_{\textit{SPL},\textit{PPL}}\) & Weighted directional graph for social/physical proximity layer \\ \hline \(V_{\textit{SPL},\textit{PPL}}\) & Vertex set for social/physical proximity layer \\ \hline \(\varepsilon_{\textit{SPL}/\textit{PPL}}\) & Edge set for social/physical proximity layer \\ \hline _SFI_ & Social friendship index \\ \hline _SCI_ & Social closeness index \\ \hline \(\Phi_{T_{X}/RX}\) & Friend list of \(T_{X}/Rx\) \\ \hline _ISI_ & Interest similarity index \\ \hline \(\Psi_{T_{X}/Rx}\) & Interest list of \(T_{X}/Rx\) (individual) \\ \hline \(\Psi_{T_{X}Rx}\) & Mutual interest list of \(Tx\) and \(Rx\) \\ \hline \(T_{(Tx,Rx)}\) & Encounter duration between \(Tx\) and \(Rx\) \\ \hline \(e\) & Number of encounters \\ \hline \(t_{(Tx,Rx)}\) & Time of \(i\)th encounter between \(Tx\) and \(Rx\) \\ \hline \(\Gamma\) & Total sample time \\ \hline \(X_{T_{X}/Rx}\), \(\theta_{T_{X}/Rx}\) & Polar coordinates of \(Tx\) and \(Rx\) \\ \hline \(\gamma\) & Signal-to-noise ratio \\ \hline \(P_{(Tx)}\) & Transmission power of \(Tx\) \\ \hline \(\Delta_{(T_{X},Rx)}\) & Cumulative closeness coefficient between \(Tx\) and \(Rx\) \\ \hline \(\sigma\) & Variance \\ \hline \(R_{(Tx,Rx)}\) & Data rate for D2D communications between \(Tx\) and \(Rx\) \\ \hline \(BW\) & Available bandwidth \\ \hline \(\Im\) (\(Tx,Rx)\) & Throughput of D2D communication between \(Tx\) and \(Rx\) \\ \hline \(\text{T}_{Tx}\) & Tx transmission time \\ \hline \(n\) & Number of UEs probed \\ \hline \(\tau\) & D2D probing time \\ \hline \(\varepsilon\varepsilon_{(Tx,Rx)}\) & Energy efficiency of D2D communication between \(Tx\) and \(Rx\) \\ \hline \(\varepsilon\varepsilon_{\textit{Init}(Tx,Rx)}\) & Initial \(\varepsilon\varepsilon\) of D2D communication between \(Tx\) and \(Rx\) \\ \hline \(\rho(Tx,Rx)\) & Power consumption co-efficient between \(Tx\) and \(Rx\) \\ \hline \(P_{(\textit{dst})}\) & Power dissipated in the circuit \\ \hline \(N\) & Upper limit for \(\Phi\), \(\overline{\Psi}\), and \(t_{(i(Tx,Rx))}\) \\ \hline \(\mathbb{N}\) & Set of natural numbers \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._colleague, acquaintance, and stranger as given in Table 2. In the real system, the SCBS will assign _SFI_ values based on the interaction history of users on various social network applications. However, for our simulations, we assume random values for _SFI_ between the users as discussed in Section 5.
## 5 Results and Discussion
### Intra-Cluster Communication
#### 5.1.4 Network Lifetime Achieved by Intra-Cluster Communication  
This experiment explores the efficient utilisation of heterogeneous node resources in small-scale networks, where multi-hop routing may not be necessary for communication between cluster heads and the base station. Figure 17 highlights the performance of the proposed method in extending the lifetime of each node within a cluster compared to the fixed cluster-head and traditional cluster-head rotation schemes.  
Without any rotation, the difference between the death of the first and last node is the most noticeable, with significant variations between nodes. However, this difference is reduced with the traditional rotation method and further minimised with the proposed rotation scheme. For \(n=3\), the difference between the first and last node's death decreases from 417 cycles to 165 cycles when using traditional rotation, while the proposed method achieves a near-equal node lifespan. For \(n=5\), the difference in node death reduces from 415 cycles to 294 cycles with traditional rotation, and the proposed method narrows this further, with only 8 cycles difference between the first and last node's death.  
Figure 18 summarises the overall cluster lifetime, comparing the first node death (FND) and last node death (LND) across the different schemes. The proposed cluster-head rotation method notably enhances the cluster's lifetime on the FND scale while also maintaining stable performance. This makes it particularly suitable for scenarios requiring consistent and reliable operation, ensuring the cluster operates efficiently even as nodes approach their energy depletion.  
Figure 16: Performance comparison in terms of throughput to BS against the network lifetime; (**a**) Number of nodes \(n=3\); (**b**) Number of nodes \(n=5\).  
Figure 17: Comparison of lifetime and stability; (**a**) Number of nodes \(n=3\); (**b**) Number of nodes \(n=5\).
## 2 Related Work
### Sensors and Ranging Technology
#### 2.5.1 RGB Cameras  
There are two types of cameras: visible VIS or infrared cameras, where the imaging sensor captures the image, usually with \"either a charge-coupled device (CCD) or complementary metal oxide semiconductor (CMOS)\" as identified by [61].  
High precision and low noise are two of the unique characteristics of CCD image sensors, which are manufactured through a complex manufacturing process at a high cost. Additionally, CMOS devices consume less power than CCDs. For companies today, using video cameras as a sensor to detect the environment around self-driving forklifts is one of the most promising options. Compared to radar or LiDAR, they are more affordable, easy to use and highly efficient at classifying textures.
## 3 Results and discussion
### Independent Validation of the Proposed Models
This study found that the WUE estimates from MODIS GPP and ET products performed poorly in capturing the seasonal dynamics of tower-based observations, which were mainly attributed to the uncertainties in the GPP estimates. A number of studies have indicated this deficiency [65,66]. Various efforts are ongoing to improve the GPP performance including the algorithm and data inputs. [PERSON] _et al._[67,68] developed the satellite-based VPM model using MODIS EVI, land surface water index (LSWI) and climate data, and the predicted GPP values agreed reasonably well with observed GPP for both deciduous broadleaf forest and evergreen needleleaf forest. [PERSON] _et al._[69] calibrated the GR model for estimating monthly GPP at a wide range of ecosystems and provided better GPP estimates than the TG model and the standard MODIS GPP product. [PERSON] _et al._[70] also developed a simple model using a chlorophyll-related vegetation index to estimate crop GPP and accurately detected GPP variations in both irrigated and rainfed croplands in three Nebraska AmeriFlux sites. Based on these studies and our own work, there remains a need to improve the current MODIS GPP product and then re-evaluate our ability to estimate WUE. Figure 7: Seasonal dynamics in ecosystem WUE from flux tower measurements (WUE\({}_{\text{EC}}\)), predicted from MODIS products (WUEs), and estimates based on MODIS EVI data (WUE\({}_{\text{GR}}\)) at the flux tower site US-Ro1 during 2005 (corn) and 2006 (soybean).
# Evaluation of a New Bulk Packaging Container for the Ripening of Feta Cheese
## 4 Conclusions  
In general, wooden barrels and stainless-steel tanks performed better than the tin can containers in relation to Feta cheese's quality characteristics. During ripening, pH, moisture, and lactose decreased, while fat, protein, and salt increased in the cheese. The proteolysis indices of % TN, % WSN, 12% TCA, and 5% PTA showed the highest values for cheeses packaged in wooden barrels, followed by those in stainless-steel tanks and tin can containers, and the values increased with ripening time. The most abundant volatiles were free fatty acids, alcohols, and esters. The amount of volatile compounds on day 60 followed the order: stainless-steel tank > wooden barrel > tin can container. Cheeses packaged in wooden barrels and stainless-steel tanks (day 60) showed higher (\(p\) < 0.05) hardness and fracturability values compared to those in tin can containers. Finally, the sensory acceptability of the cheeses followed the order: stainless-steel tank > wooden barrels > tin can containers. In conclusion, the experimental stainless-steel container proved to be the most suitable bulk packaging material for the ripening of Feta cheese, performing better than the conventional tin cans and as well as the wooden barrel, while providing convenience and savings in the use of packaging materials.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/foods12112176/s1](https://www.mdpi.com/article/10.3390/foods12112176/s1)]([https://www.mdpi.com/article/10.3390/foods12112176/s1](https://www.mdpi.com/article/10.3390/foods12112176/s1)). Figure S1: (a) wooden barrel, (b) tin can, (c) stainless-steel tank and (d) stainless-steel tank lined with wood for the ripening/bulk packaging of Feta cheese.  
**Author Contributions:** Conceptualization, M.G.K., A.V.B., P.T. and K.N.; methodology, M.G.K. and A.V.B. software, I.S.K.; validation, M.G.K. and A.V.B.; formal analysis, P.T.; investigation, P.T.; resources, M.G.K., K.N. and A.V.B.; writing--original draft preparation, M.G.K. and A.V.B. writing--review and editing, M.G.K., A.V.B. and I.S.K.; visualization, M.G.K. and A.V.B. supervision, A.V.B. and M.G.K.; project administration, M.G.K. and A.V.B.; funding acquisition, N/A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** Data is contained within the article or Supplementary Materials.  
**Acknowledgments:** The authors would like to thank the Dodoni S.A. plant in Thebes, Greece, for the preparation of the experimental cheeses.  
**Conflicts of Interest:** The authors declare that this study received no funding from Dodoni S.A. The only involvement of the company Dodoni S.A. in the present study was the preparation of the Feta cheese samples. The company Dodoni S.A. was not involved in the study design, the collection, analysis, or interpretation of data, the writing of the article, or the decision to submit it for publication.
## 5 The Modular Segment LLL Reduction with Modular Arithmetic
### The Modular Segment LLL using Fast Matrix Multiplication  
The complexity of Step 2 of the **ModSegmentLL** algorithm is bounded by the following theorem when using fast matrix multiplication.  
**Theorem 2**: _If \(d=O(n)\), \(k=\lceil n^{\frac{1}{5-\theta}}\rceil\), then using fast matrix multiplications Step 2 of the **ModSegmentLL** algorithm can be performed in \(O(n^{1+\frac{1}{5-\theta}}(\log_{1/\delta}M_{sc}))\) operations using integers of bit length \(O(\log_{2}M_{sc})\)._  
**Proof:** As discussed above, there are at most \(n(\log_{1/\delta}M_{sc})\) LLL-exchanges, each requiring \(O(k)\) arithmetic operations for a local \(\delta\)-reduction. According to [20, Theorem 3], there are \(decr\leq 2\frac{n}{k^{3}}\log_{1/\delta}M_{sc}\) calls of the **ModLocSegmentLL** algorithm. Each call requires \(O(nk^{\theta-1}+nk+k^{2}(\ln k))\) arithmetic operations for updating matrices \(A\) and \(T\). The complexity of Step 2 of the **ModSegmentLLL** algorithm is bounded by  
\[O(nk(\log_{1/\delta}M_{sc}))+O(2\frac{n}{k^{3}}(\log_{1/\delta}M _{sc})(nk^{\theta-1}+nk+k^{2}(\log_{2}k)))\] \[\leq O(nk(\log_{1/\delta}M_{sc}))+O(\frac{n^{2}}{k^{4-\theta}}( \log_{1/\delta}M_{sc}))\] \[=O(n^{1+\frac{1}{5-\theta}}(\log_{1/\delta}M_{sc}))\]  
when \(k=\lceil n^{\frac{1}{(5-\theta)}}\rceil\). \(\Box\)  
[PERSON] [14] showed that the fraction free Gaussian elimination and Step 3 of the algorithm can be performed in \(O(n^{\theta}\log n)\) arithmetic operations for \(\theta=2.376\) with integers of bit length \(O(\ln M_{sc})\). The bound in Theorem 2 is \(O(n^{1.382}(\log_{1/\delta}M_{sc}))\) where \(M_{sc}>2^{n}\). Hence Step 2 of Algorithm **ModSegmentLLL** dominates the overall effort giving the following corollary.  
**Corollary 2**: _For \(d=O(n)\), and \(k=\lceil n^{\frac{1}{(5-\theta)}}\rceil\) the running time of Algorithm **ModSegmentLLL** is bounded by \(O(n^{1.382}\log_{1/\delta}M_{sc})\) operations using integers of bit length \(O(\ln M_{sc})\) when using fast matrix multiplication._
# Synergistic Optimization of Coal Power and Renewable Energy Based on Generalized Adequacy
## 4 Results--Case Study
### Impact of Wind-Solar Ratio on Installed Capacity  
In practical engineering applications, different ratios of wind and solar power generation can influence the installation of other types of power sources. Figure 5 compares various types of power installations under different wind-solar installation ratios. The optimal economic scenario does not restrict the wind-solar ratio to explore the system's preferred ratio. The other three scenarios adjust the wind-solar ratio with economic goals in mind to compare the impacts on various power installations.  
From an installation impact perspective, the region tends to favor a wind-solar installation ratio of approximately 3:2 due to wind power's higher capacity value. Simulations reveal that as this ratio decreases, the system's coal installation capacity significantly increases, which hinders the gradual retirement of coal units. This leads to a greater need for carbon capture devices, resulting in a higher proportion of such units. Additionally, a lower wind-solar ratio significantly reduces the system's energy storage demand, highlighting that wind power necessitates more storage batteries for effective energy transfer and output smoothing.