esa-sceva/satcom-chunk-collection huggingface dataset:
# Land-Cover Classification Using Deep Learning with High-Resolution Remote-Sensing Imagery
## 3 Experimental Results and Discussion
### Evaluation Parameters  
The evaluation parameters used in this study are _F1-score_, _accuracy_, _precision_, and _recall_. In comparison to all observations, the ratio of correctly predicted observations is known as accuracy. Accuracy is assessed using Equation (2), wherein the terms false positive (_FP_), false negative (_FN_), true positive (_TP_), and true negative (_TN_) are utilized.  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{2}\]  
_Precision_ is the ratio of correctly predicted positive observations to all predicted positive observations, as shown in Equation (3).  
\[Precision=\frac{TP}{TP+TN} \tag{3}\]  
The ratio of all actual class observations to all correctly predicted positive observations is known as recall, as shown in Equation (4).  
\[Recall=\frac{TF}{TP+FN} \tag{4}\]  
_F1-score_ is the precision and recall weighted average. It is computed using Equation (5).  
\[F1-score=2\cdot\frac{(Precision\times Recall)}{Precision+Recall} \tag{5}\]  
The hyper-parameters configuration is used to fine-tune both CNN networks. All the models are trained with a 0.0001 learning rate and 32-batch size using the Adam optimizer. Using the UCMerced_LandUse dataset, we fine-tune the inception-V3, DenseNet121, and ResNet-50 models by freezing all top layers and adding new layers according to the number of classes in the dataset classes. Each model is trained by using Python 3.8, TensorFlow 2.13.0, sklearn 1.0, matplotlib 3.7.3, and NumPy 1.24.3 libraries and an Intel(R) Core (TM) i7-8700 CPU with RAM of 16 GB, Seoul, South Korea.
# An Optimal Online Resource Allocation Algorithm for Energy Harvesting Body Area Networks
## 7 Conclusions  
In this paper, we developed an online resource allocation algorithm for BANs that maximizes user utility subject to energy limitation. This algorithm is an online algorithm and does not require any a priori knowledge of the the energy harvesting process and data collection process. We consider that the energy harvesting technique in BANs and energy sources come from renewable energy, such as body heat and kinetic energy. To save the energy consumption, we use the sleeping mode. We use Lyapunov optimization approach to decompose the user utility optimization problem into three sub-problems, i.e., battery management, collecting rate control, and transmission power allocation. For the optimality gap and the bounds of energy queues, data queues have been derived. Simulation results test and verify the performance of BAN system, as well as the proposed algorithm.  
For our future work, we plan to investigate channel allocation problem based on Game Theory of the BANs. We consider the interference between sensors. Moreover, the communication of health related information between sensors in BANs and over the internet to servers is strictly private and confidential, so we plan to investigate security and privacy.  
This work was partially supported by The Fund of Graduate Student Independent Innovation Project (2017 zzts624). It was also supported by the National Natural Science Foundation of China (61672540).  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] contributed reagents/materials/analysis tools; [PERSON] wrote the paper.  
The authors declare no conflicts of interest.
## 4 Discussion
### Comparison of the Performances of the Different Classification Algorithms
used Landsat 8 image data and remote sensing classification algorithms, including the k-nearest neighbor (KNN), RF, SVM, and ANN, to carry out land cover classification research in the Dengkou Oasis in China. They found that the ANN had the highest classification accuracy, followed by the RF, SVM, and KNN [58]. The basic reasons for the difference in results were the different study area, topographic characteristics, and inconsistencies in the land cover types. Secondly, different satellite data sources were used in the different studies, and even the same data source with different time phases can also cause changes in the classification results. Therefore, the differences in the study areas and remote sensing data sources participating in the classification had a greater influence on the effects of the different classifiers, and the classification effect of the same remote sensing classification algorithm varied from one region to other.
## 3 Results and Discussion
### NO\({}_{x}\) Emissions
However, the higher CN levels in the DSO biodiesel assessed in this study also contributed to the shortening of the premixed combustion phase and therefore alleviated the pressure and temperature variations within the cylinder, ultimately resulting in reductions in the NO\({}_{\rm x}\) emissions. Several previous studies experimentally demonstrated the predominance of this latter effect [63; 64]. The peak (worst) increments in NO\({}_{\rm x}\) emission of 2.8, 3.3, 5.4, and 7.4% for blends B5, B10, B15, and B20, respectively, were all seen at loadings below 50%. These findings agree with those of a significant number of studies carried out on different biodiesel types and blends under various engine operating conditions [48; 65; 66]. [PERSON] et al. [67] also reported similar findings and interpretations of NO\({}_{\rm x}\) trends with the engine load and speed.
# Influence of a Helium-Nitrogen RF Plasma Jet on Onion Seed Germination
## 4 Conclusions  
With the growth of environmental awareness, special emphasis is placed on limiting the role of pesticides, fertilizers, and other agents required for plant growth, which can accumulate in the soil. On the other hand, harmful residue-free, high-quality crops are demanded.  
The germination energy and capacity were increased by cold plasma treatment. The 240 s cold plasma treatment, when the distance of the reactor nozzle from the sample was 50 mm, produced the highest stimulatory effect among the different tested treatment conditions.  
In summary, cold plasma treatments have the potential to enhance the germination of onion and could be used in future production. More studies are needed to elucidate the mechanisms that promote the effects of cold plasma treatment on onion seed germination.  
Conceptualization: A.S.-W., J.P., M.K., and P.T.; methodology: A.S., J.P., and P.K.; investigation: A.S.-W., J.P., M.K., and P.T.; data curation: A.S.-W., J.P., and A.S.; writing--original draft preparation: A.S.-W. and J.P.; writing--review and editing: A.S.-W., A.S., P.T., J.P.; visualization: J.P., M.K., A.S.-W., and A.S.; supervision: [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This study was supported by Polish-Slovak Bilateral Cooperation Programme (PlasmaBioAgro) PPN/BIL/2018/1/00065+SK-PL-18-0090, LUT research found, COST Action PIAgri CA19110 and CEEPUS CIII-AT-0063.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline
**Time of Stimulation [s]; Distance between** & **Germination Energy \(G_{EN}\) [\%]** & **Germination Capacity \(G_{C}\) [\%]** \\
**Nozzle’s Outlet and Tested Material [mm]** & **Control—71.0 \(\pm\) 0.82 ** & **Control—91.25 \(\pm\) 0.96 ** \\ \hline
2; 20 & 72.5 \(\pm\) 1.7  & 86.3 \(\pm\) 2.6  \\
5; 20 & 78.3 \(\pm\) 2.2  & 87.5 \(\pm\) 2.7  \\
10; 20 & 71.8 \(\pm\) 2.5  & 85.5 \(\pm\) 3.0  \\
15; 20 & 67.5 \(\pm\) 2.1  & 85.3 \(\pm\) 3.9  \\
60; 50 & 80.0 \(\pm\) 1.4  & 90.3 \(\pm\) 0.9  \\
120; 50 & 83.8 \(\pm\) 3.1  & 92.5 \(\pm\) 0.5  \\
240; 50 & 93.3 \(\pm\) 1.8  & 99.3 \(\pm\) 0.8  \\
480; 50 & 87.8 \(\pm\) 1.1  \\ \hline \end{tabular}
\end{table}
Table 2: The results on germination energy and germination capacity parameters of Wolska onion seeds after pre-sowing treatment with cold plasma at different exposure times and distance between electrode and tested material.
# Can Blockchain Strengthen the Energy Internet?
## 4 Ultimate Reliability and Stability
### Role of Blockchain  
Blockchain and smart contracts can be utilized to invoke certification for seamless grid integration of renewable energy generation upon fulfillment of the prerequisites. This would decrease the latency and improve grid stability [59; 60]. Lightweight blockchain platforms would further provide means of increasing the transaction throughput of the energy grid. Efficient correction of supply-demand mismatch through precision decisions arrived upon predictive analysis would facilitate reliable future grids. Blockchains would be the means for secure storage and broadcasting of energy bids and demand requests, enabling the predictive analysis on aggregated EI data. The integrity of data sets used for the application of AI and ML technologies could be ensured through the cryptographic encryption methods incorporated with blockchain [29]. The Spanish renewable initiative Iberdrola is utilizing blockchain for tracking of wind power generation and is expected to contribute in seamless grid integration of DER by issuing origin certification [13]. The decentralized solution eliminates the need for a third party as a middle man.  
Blockchain, however, cannot be identified as the ultimate solution for ensuring stability and reliability of futuristic decentralized grid architecture with no central authority. Advanced control strategies need to be proposed in this aspect, with blockchain facilitating them from the rear end by providing secure data transmission and storage. Deployment of smart contracts would enable autonomous execution of the control strategies for securing grid stability.
## 4 Experiment Setup
### Baseline Methods  
In this work, we perform several widely used pattern recognition techniques as baseline methods, including naive Bayes, Support Vector Machines (SVM), Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), fully convolutional networks (FCN), FCN-LSTM, and residual networks (ResNet).  
Naive Bayes is a probabilistic algorithm based on [PERSON] theorem, which assumes that features are conditionally independent given the class label. SVM is a powerful algorithm that constructs a hyperplane to separate different classes in the feature space, often using a kernel trick to implicitly map the data to a high-dimensional space. LSTM is a type of recurrent neural network (RNN) that utilizes a memory cell to selectively forget or remember information over time, allowing it to handle long-term dependencies in sequential data. Bi-LSTM is an extension of LSTM that includes a backward pass through the sequence, enabling the network to capture information from both past and future inputs. FCN is a neural network architecture that consists entirely of convolutional layers. FCN-LSTM is a hybrid model that combines the strengths of FCN and LSTM and has been shown to effectively model spatio-temporal data. Finally, ResNet is a deep neural network architecture that utilizes residual connections to enable the training of very deep networks and has achieved promising performance on a wide range of computer vision tasks and audio pattern recognition tasks.
## 3 Results and Discussion
### Rheological Measurements
#### 3.2.2 Young's Modulus of Elasticity and Elongational Viscosity  
Young's modulus of elasticity is a textural parameter that can be used to assess the firmness of the product. Figure 1 depicts the Young's modulus values for wheat flour dough (control, 0% hemp protein) and dough containing hemp protein (5, 10, or 15%). As shown in Figure 1, the fortified hemp protein doughs had significantly higher values of Young's modulus compared to the control doughs. This is probably related to the disruption of the starch-protein matrix in the dough, resulting in reduced elasticity [52]. The absence of gluten in the matrix could reduce the ability of the dough to retain air [59] resulting in the formation of a firm texture. A partial substitution of wheat flour with hemp protein could possibly affect the firmness of the dough. [PERSON] et al. [58] reported that the replacement of wheat protein with soy protein reduced dough elasticity. Thus, the observed differences in [PERSON]'s modulus of elasticity values could be due to the nature of the samples. Based on the above, it can be inferred that the hemp protein is the dominant component that affects the firmness of the dough. The typical force-distance curves and elongational viscosity curves as a function of the biaxial strain rates of the dough are presented in Figures 2 and 3. It is noteworthy that very few researchers [38] have investigated dough's rheological properties using lubricated squeeze flow experiments. As shown in Figure 2, the doughs containing hemp protein exhibited higher force values compared to the control samples (0% hemp protein). These results are in agreement with the findings obtained from [PERSON]'s modulus (Figure 1). Similarly, the highest elongational viscosity values were recorded for the fortified bread with 15% hemp protein, followed by the doughs with 10% and 5%. The lowest values were measured for the control dough (Figures 3 and 4). It should be noted that the maximum elongational viscosity values measured for the plateau section of the flow curves, were characterized by stable flow and constant elongational viscosity (Figure 4). These differences could be attributed to the presence of the hemp protein in the dough matrix along with the higher-water content of the dough, which resulted in the enhanced consistency of the dough's structure and in the formation of a more compact dough. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Samples**} & **Water** & **Development** & **Stability** & **Degree of** \\  & **Absorption** & **Time (DDT)** & **(min)** & **(min)** & **Softening** \\ \hline Control dough (0\%) & 56.2 & 2.7 & 6.2 & 96 \\ Dough enriched with 5\% hemp powder & 56.8 & 1.8 & 6.6 & 104 \\ Dough enriched with 10\% hemp powder & 57.6 & 6.2 & 8 & 92 \\ Dough enriched with 15\% hemp powder & 58.4 & 5.7 & 7.6 & 92 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Farinograph parameters (water absorption, development time, stability, degree of softening) of wheat flour dough (control, 0% hemp protein) and doughs enriched with 5, 10, and 15% hemp protein.
## 1 Introduction
### Objective  
The issue of energy conservation is of vital importance in WWPM. However, most WWPM studies focus on leak localization while neglecting the energy consumption of their proposed WWPM solutions. As such, there are very few studies in the literature that concern themselves with the evaluation of the energy consumption of their proposed WWPM solution and means on how to conserve energy in order to prolong the lifespan of the WWPM system. Additionally, most of the studies that have treated energy consumption with their proposed WWPM solutions have been based on simulations, and very few studies go to the extent of performing physical experiments on a laboratory testbed. Thus, to the best of our knowledge, no existing study has experimentally evaluated the combined effected duty cycling, hierarchical sensing, and distributed computing on the energy consumption and lifespan of WWPM systems. In this paper, the aim is to experimentally investigate the impact of a hybrid technique, incorporating distributed computing, hierarchical sensing, and duty cycling on the energy consumption of a sensor node in prolonging the lifespan of a WWPM system. From the existing literature, it is evident that a limited number of studies are experimental, and only a scant few employ an approach that integrates both simulation and physical deployment. The novelty of this study is two folds. Firstly, it evaluates the energy consumption of three variants of Distributed Kalman Filter (DKF) applied in the context of a distributed computing solution for WWPM via simulations and laboratory experiments. Secondly, it implements a hybrid energy reduction technique involving duty cycling, hierarchical sensing, and distributed computing and then compares the energy reduction achieved from this hybrid technique with the lone implementation of distributed computing. From results obtained from experiments, it shows that this hybrid technique achieves an energy reduction that is 8 times lower compared to the lone implementation of distributed computing.
# MuIm: Analyzing Music-Image Correlations from an
## 4 MuIm Dataset
### Music-Visual Pairing Methodology
#### 4.2.2 Realistic Segment Pairing  
The realistic data segment consists of data obtained from web sources, containing multiple user-defined labels. We adopted an automated pipeline to filter and refine these labels for both emotional and semantic information. After processing through this pipeline, we obtained textual information for semantics, allowing for similarity scoring similar to the artistic segment. However, unlike the artistic segment, this segment lacked predefined emotional vectors with intensities to measure the emotional similarity. Therefore, we employed a unique approach to derive emotion vectors, as follows:  
* Sentiment Analysis: Sentiment analysis was performed on the tags to identify words that express positive or negative sentiments using the VADER sentiment analysis model [60].
* Clustering Emotions: A large language model (LLM) [54] was utilized to group and classify the identified sentiments using the 8-emotion model proposed by [61].
* Emotional Vector Creation: Emotionally similar word sentiments were averaged to generate an 8-dimensional affective vector. This strategy aimed to capture nuanced emotional undertones, potentially spanning multiple emotions represented in the media tags.  
We then used the Mahalanobis distance as \(d\) to find the similarity measure between the music and image emotional data, while the semantic similarity measure was computed similarly to for the artistic segment. The final similarity measure is expressed as  
\[\begin{split} S_{\text{final}}=w_{s}\cdot\text{CosineSim}\left(S_{ \text{semantic}}^{I_{i}},S_{\text{semantic}}^{M_{j}}\right)+w_{e}\cdot S\left(S_ {\text{emotional}^{I_{i}}}^{I_{i}},S_{\text{emotional}}^{M_{j}}\right)\\ \text{where }i\in\{1,2,\dots,n\},\;j\in\{1,2,\dots,m\}\end{split} \tag{5}\]  
Figure 6 illustrates the music-image pairing process, incorporating dual-dimension information for comprehensive matching.  
Figure 6: Detailed summary of the adapted pipeline for pairing image-music data using dual-dimension information.
## 3 Results and Discussion of CNN Models Classification  
In this section, we disclose the findings from our study on Convolutional Neural Network (CNN) models applied to classify drilled holes in melamine-faced chipboard. We assessed the efficacy of each model by examining their accuracy, significant error frequencies, and confusion matrices over various folds. Detailed examinations of the outcomes for the VGG16, VGG19, and ResNet101 models are elaborated in the subsequent sections.  
The evaluation metrics used in this study include:  
* Accuracy--refers to the ratio of correctly identified instances to the overall number of instances.
* Critical errors--instances where the model misclassifies a \"Green\" hole as \"Red\" or vice versa, which are particularly significant due to their potential impact in practical applications.
* Confusion matrix--a table that provides a summary of the model's performance by displaying the count of accurate and inaccurate forecasts for every category.  
Every model underwent training and assessment through a 5-fold cross-validation method, in which the dataset was segmented into five parts. Each part served as a test set on one occasion, with the rest of the parts merged to create the training set. Such a strategy guarantees a thorough appraisal of the model's effectiveness.  
The subsequent subsections present comprehensive findings for each model, encompassing their precision, significant error frequencies, and confusion matrices. These findings are then examined in detail to pinpoint the advantages and disadvantages of each model and to identify the most appropriate model for the classification task.
## 2 Materials and Methods  
To synthesize M-type Sr-hexaferrites with the cationic composition ratios of Sr\({}_{0.8}\)Ca\({}_{0.2}\)Fe\({}_{11-3x}\)Mn\({}_{x}\)Al\({}_{x}\)Si\({}_{x}\), Sr\({}_{0.8}\)Ca\({}_{0.2}\)Fe\({}_{10.7-x-y}\)Mn\({}_{0.1+x}\)Al\({}_{0.1+y}\)Si\({}_{0.1}\), Sr\({}_{0.7+x}\)Ca\({}_{0.3-z}\)Fe\({}_{10.2}\) Mn\({}_{0.1}\)Al\({}_{0.2}\)Si\({}_{0.1}\), and Sr\({}_{0.7+z}\)Ca\({}_{0.3-z}\)Fe\({}_{9.7}\)Mn\({}_{0.1}\)Al\({}_{0.2}\)Si\({}_{0.1}\) (\(0\leq x,y,z\leq 0.2\)), a solid-state reaction method was employed. Setting the atomic ratio of [Sr-site]/[Fe-site] in the chemical formula to 11 instead of 12 was based on the experimental results from previous research [5; 6], aiming to suppress the formation of the secondary phase of Fe\({}_{2}\)O\({}_{3}\). Powders of Fe\({}_{2}\)O\({}_{3}\) (99%, industrial use, Pohang, South Korea), SrCO\({}_{3}\) (99.9%, Kojundo Chemical, Sakado, Japan), CaCO\({}_{3}\) (98%, Showa chemical, Minato, Japan), Co\({}_{3}\)O\({}_{4}\) (98%, Top new energy tech. Co., Xiamen, China), MnCO\({}_{3}\) (99.9%, Kojundo Chemical, Sakado, Japan), La\({}_{2}\)O\({}_{3}\) (Sigma-Aldrich, 99.9%, St. Louis, MO, USA), and SiO\({}_{2}\) (99.5%, JK Silica Co., Shaxian, China) were weighed according to the cationic composition ratios. The weighed powders were mixed with distilled water in sample containers (Nalgene) and subjected to wet ball milling at 120 rpm for 20 h using zirconia balls. The fully dried powders were placed in alumina crucibles and calcined at 1100 \({}^{\circ}\)C for 4 h in an atmospheric electric furnace. After calcination, the powders were crushed, followed by a second 24 h wet milling. The milled powders were then dried and sieved through a 200 mesh sieve. A total of 5 g of the sieved powder was placed into a disk-shaped mold (20 mm diameter) and molded under a pressure of 0.3 tons. The molded samples were then sintered in an electric furnace under atmospheric conditions at 1210 \({}^{\circ}\)C for 2 h. During the ramp-up and ramp-down phases of the heating process, the furnace temperature was controlled at a rate of 5 \({}^{\circ}\)C/min, while natural cooling (furnace cooling) was employed during the cooling phase.  
The density of the samples was calculated from the external dimensions and mass of the sintered disk-shaped samples. For phase analysis, the sintered samples were powdered, and X-ray diffraction (XRD) analysis was performed using an X-ray diffractometer (D8 Advance, Bruker, Karlsruhe, Germany) with Cu K\(\alpha\) radiation (\(\lambda\) = 0.154056 nm). For microstructure observation, the fractured surface of the sintered samples were examined using a field emission scanning electron microscope (FE-SEM, JSM-7610F, JEOL, Tokyo, Japan). Magnetic properties were characterized by measuring the M-H curves of the sintered samples using a B-H loop tracer (BH-5501, Denshijki Industry, Tokyo, Japan). The demagnetization curves in the second quadrant, measured by applying a maximum magnetic field of 25 kOe and then gradually decreasing the magnetic field, are presented.