esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 LPWAN Application Requirements
### Energy Efficient Operations and Low Power Sources  
In several applications of LPWANs, the environment and the constraints do not allow the recharging of batteries. The battery is expected to last over ten years without charging. Typically, AA or coin cell batteries are used. If the battery loses power, replacement of the battery may not be possible within short periods. The cost of battery needs to be low. The LPWAN should be operated with a strict and very low duty cycle limit so that node lifetime can be enhanced. Hence, ultra-low power operation is a crucial requirement for battery-powered IoT/M2M devices.
# An Accurate Forest Fire Recognition Method Based on Improved BPNN and IoT
## 2 Materials and Methods
### BPNNFire Algorithm  
We propose the improved BPNNFire algorithm. Figure 4 presents the steps of the BPNNFire algorithm. First, data were collected and preprocessed to remove noise. The model recognized the forest fire image after input, and the classifier distinguished between images with and without fire. An image with fire was classified locally for feature extraction  
Figure 3: Measurement equipment and processes in the experiment.  
Figure 2: Structure of a forest fire monitoring network.  
and transmitted to a remote server for storage. Figure 4 displays the framework of the forest fire recognition algorithm.  
The following steps are performed in the improved BPNNFire algorithm:  
* The checked window is divided into 16 \(\times\) 16 parts;
* Each pixel is compared to the neighboring pixel;
* If the value of the neighbor is smaller than the focal pixel value, the pixel value is set to 0; otherwise, it is 1, generating an 8-bit binary number;
* The frequency histogram generated by each member number is calculated in the entire cell;
* Histograms can be normalized according to the use-case authenticity, and the histogram of each cell is normalized to obtain the feature vector;  
When the DCNN model provides an early warning, the forest fire risk prediction model generates region of interest (ROI) particles in rectangles, constantly moving in various directions and changing positions. In this case, the size of the ROI is updated over time. Step 1 explains each point of ROI formation in detail, and the similarity of fire or smoke can be identified. The following are the model parameters: object weight \(Wi\), object height \(Hi\), central coordinates \(Ci(x,y)\), and rectangles of each coordinate described by the \(ROI\)\((RC_{1}(x,y),RC_{2}(x,y),RC_{3}(x,y),RC_{4}(x,y))\). The derivation process is as follows:  
\[C_{ROI}=C_{i-1}(x,y) \tag{1}\]  
\[W_{ROI}=W_{i-1}\times 1.2 \tag{2}\]  
\[H_{ROI}=H_{i-1}\times 1.2 \tag{3}\]  
\[RC_{1}(x,y)=C_{ROI}[x-(W_{ROI}\div 1.5),y-(H_{ROI}\div 1.5)] \tag{4}\]  
\[RC_{2}(x,y)=C_{ROI}[x+(W_{ROI}\div 1.5),y+(H_{ROI}\div 1.5)] \tag{5}\]  
\[RC_{3}(x,y)=C_{ROI}[x-(W_{ROI}\div 1.5),y+(H_{ROI}\div 1.5)] \tag{6}\]  
\[RC_{4}(x,y)=C_{ROI}[x+(W_{ROI}\div 1.5),y-(H_{ROI}\div 1.5)] \tag{7}\]  
\[Image=Corp(RC_{1},RC_{2},RC_{3},RC_{4}) \tag{8}\]  
Among them, \(H_{i-1}\), \(W_{i-1}\), and \(C_{i-1}(x,y)\) are inputs.  
Figure 4: Framework of the forest fire recognition algorithm.
# Solar Concentration for Wastewaters Remediation: A Review of Materials and Technologies
## 2 Solar Collector Systems Employed for Wastewater Treatment
### Concentrating Systems
#### 2.1.3 Parabolic Dish Concentrators (PDC)  
PDCs are widely studied and applied for converting solar energy into electrical or chemical energy, and are accepted as those of highest efficiency in terms of concentration factors. The work of [PERSON], dating back to the 1880s [49], is recognized as being among first examples of solar energy conversion and the coupling of a PDC to a Stirling engine. A PDC is a type of concentrator which reflects the sun's rays onto an absorber installed on its spot-like focus. It consists primarily of a support frame equipped with a sun-tracking system, a reflective concave parabolic dish, and an absorber [50]. In this case, tracking the sun is a necessity, since PDCs only work with direct radiation, and a two-axis tracker is commonly employed.  
PDCs offer the possibility of being installed in hybrid operation plants in combination with distinct types of solar concentrators [51]. The solar dish system can achieve, if temperature is of interest, high values due to high concentration factors. Figure 3 presents a schematic drawing and an image of a parabolic module in use in our laboratories. This collector is based on an innovative process to manufacture parabolic mirrors, resulting in potentially very low production costs [52; 53].  
Figure 2: Schematic drawing and picture of a compound parabolic collector [27; 45].  
In solar wastewater purification, the selection of a PDC is motivated by specific needs. Being the most effective concentrators--reaching concentration ratios higher than 2000 suns--they can provide an advantage in cases where a very high photon density and/or thermal processes enhance pollutant degradation, for example, when the rapid abatement of a high content of pollutant is needed [54].
###### Abstract  
Accurate estimation of soil temperature (T\({}_{\mathrm{s}}\)) at a national scale under different climatic conditions is important for soil-plant-atmosphere interactions. This study estimated daily T\({}_{\mathrm{s}}\) at the 0 cm depth for 689 meteorological stations in seven different climate zones of China for the period 1966-2015 with the MSP model tree (MSP), random forests (RF), and the extreme gradient boosting (XGBoost). The results showed that the XGBoost model (averaged coefficient of determination (R\({}^{2}\)) = 0.964 and root mean square error (RMSE) = 2.066 \({}^{\circ}\)C) overall performed better than the RF (averaged R\({}^{2}\) = 0.959 and RMSE = 2.130 \({}^{\circ}\)C) and M5P (averaged R\({}^{2}\) = 0.954 and RMSE = 2.280 \({}^{\circ}\)C) models for estimating T\({}_{\mathrm{s}}\) with higher computational efficiency. With the combination of mean air temperature (T\({}_{\mathrm{mean}}\)) and global solar radiation (R\({}_{\mathrm{s}}\)) as inputs, the estimating accuracy of the models was considerably high (averaged R\({}^{2}\) = 0.96-0.97 and RMSE = 1.73-1.99 \({}^{\circ}\)C). On the basis of T\({}_{\mathrm{mean}}\), adding R\({}_{\mathrm{s}}\) to the model input had a greater degree of influence on model estimating accuracy than adding other climatic factors to the input. Principal component analysis indicated that soil organic matter, soil water content, T\({}_{\mathrm{mean}}\), relative humidity (RH), R\({}_{\mathrm{s}}\), and wind speed (U\({}_{\mathrm{2}}\)) are the main factors that cause errors in estimating T\({}_{\mathrm{s}}\), and the total error interpretation rate was 97.9%. Overall, XGBoost would be a suitable algorithm for estimating T\({}_{\mathrm{s}}\) in different climate zones of China, and the combination of T\({}_{\mathrm{mean}}\) and R\({}_{\mathrm{s}}\) as model inputs would be more practical than other input combinations.  
soil temperature; machine learning models; climatic zones; extreme gradient boosting; principal components analysis +
Footnote †: journal: Applied Sciences
# Traffic-Aware Optimization of Task Offloading and Content
## 4 Joint Optimization for Offloading and Content Caching with Traffic Stream Prediction
### MOEA/D-Based Joint Optimization
#### 4.2.2 Chromosome Coding and Genetic Operators  
In this paper, the chromosome encoding for the population evolution strategy uses Real Integer Encoding (RI), where each bit on the chromosome represents the true value of the decision variable. For selection, etour was used. For recombination, we used Simulated Binary Crossover (SBX) to simulate single point crossover based on binary strings. For mutation, we generated a new population chromosome matrix by polynomially varying each decision variable in the real integer-encoding population chromosome matrix according to the mutation rate.
# AWMC: Abnormal-Weather Monitoring and Curation Service Based on Dynamic Graph Embedding
## 4 AWMC Description
### Implementation
#### 4.2.2 Data Source  
The source of weather data is from the Korean Meteorological Administration. The dataset included weather data for each day from 2018 to 2021 for 18 cities in Korea: Seoul, Busan, Incheon, Wonju, Chungju, Seosan, Boryeong, Daejeon, Jeonju, Gwangju, Mokpo, Yeosu, Daegu, Gumi, Andong, Yeongdeok, Ulsan, and Jeju. In the dataset, weather data were of seven types: temperature, humidity, vapor pressure, dew-point temperature, local air pressure, sea-surface pressure, and ground temperature. In addition, the weather data for each hour from 0:00 to 23:00 were included in the data for each day. This means that the dataset recorded every hour of weather data from 2018 to 2021.
## 3 Results
### RQ1: What Are the Existing Recognition-Based Methods?
#### 3.2.3 Publication Sources  
Figure 5 displays the quantity of chosen studies, categorized by their respective publishing channels. According to the data presented in the figure, it can be observed that IEEE has the biggest quantity of papers, namely 12, while Springer follows with a count of 7 studies. Additionally, MDPI is revealed to have a total of three papers. Elsevier, ACM, academia, Hindawi, arXiv, and ResearchGate each have one publication.
# Sequence-to-Sequence Video Prediction by Learning Hierarchical Representations
## 4 Experiments
### KTH Action Dataset  
The KTH dataset consists of 600 videos of 25 humans performing 6 types of actions (boxing, hand-clapping, hand-waving, jogging, running, and walking) in 4 scenarios. Each video has the duration of 4 seconds on average.  
**Implementation.** We use the same settings of MCnet [16] as follows. We use person 1-16 for training and 17-25 for testing. We gather 1525 video clips of various length for training and 2819 video clips (30 frames) for testing.  
Then each frame is resized to a resolution of \(120\times 120\) where the pixel value is re-scaled to the range of \([-1,1]\). We train our network and baselines by feeding the first 10 frames as input, and making the models to predict the next 10 frames. The batch size is set to 8 where the training is done for \(2\times 10^{5}\) iterations with Adam optimizer with constant learning rate of 0.001. The rate of Scheduled Sampling is gradually increased to 1 during the first \(10^{5}\) iterations. At testing time, the prediction horizon is extended to 20 future frames. For fair comparison, we have trained MCnet, PredRNN++ and HRPAE under the same experimental settings.  
**Results.** In this section, we present quantitative results comparing HRPAE with baselines. Table 4 shows the overall prediction results compared with PredRNN++ and MCnet in terms of the PSNR and SSIM. We observe that 4-level HRPAE outperforms PredRNN++ and MCnet. Figure 6 shows the frame-wise losses over 20 prediction frames, which shows our model performs consistently better than the baseline models at all the time steps. Figure 7 presents qualitative prediction examples, which shows that our model is able to make accurate predictions about the human motion trajectories, and generates sharp video frames.
# TrustShare: Secure and Trusted Blockchain Framework for Threat Intelligence Sharing
## 3 Materials and Methods
### Methodology
#### 3.2.5 Integration and Security Measures  
The integration of **Hyperledger Fabric** and the **IPFS** facilitates the seamless exchange of threat intelligence data. The blockchain ensures data integrity and authentication through cryptographic proofs, while the IPFS allows for efficient off-chain storage. By utilizing encryption techniques such as **CP-ABE** (Ciphertext-Policy Attribute-Based Encryption), we ensure the confidentiality and integrity of the threat data.  
This architecture introduces a novel decentralized approach to threat intelligence sharing, emphasizing security, scalability, and interoperability. The collaboration of Hyperledger Fabric and IPFS enables lightweight and efficient operations, reducing the computational load on the blockchain while maintaining robust security.
## 2 Materials and Methods
### Decision Trees  
A decision tree is an analytical method that categorizes a group of interests into several subgroups or performs predictions by charting decision rules. Because the analytical process is represented by a tree structure, researchers can easily understand and explain the analytical process compared to other methods, such as discriminant analysis, regression analysis, and neural networks [20]. In general, a decision tree analysis involves the following steps: Through the formation of the first decision tree, a decision tree is obtained by specifying an appropriate split criterion and a stopping rule according to the purpose of the analysis and data structure. In the second step, i.e., pruning, branches with a high risk of large classification errors or inappropriate rules are removed. In the third validity evaluation, the decision tree is evaluated using cross-validation with benefit charts, risk charts, or test data. Finally, a decision tree is analyzed, and a classification and prediction model is established [21]. A decision tree is a data-mining technique that expresses the decision-making process in the form of a tree, divides the target group of the problem domain into subgroups, and performs classification and prediction. Decision trees are appropriate for solving classification problems in data mining. The decision-tree modeling process proceeds in such a manner that a tree structure is created by identifying important attributes according to the degree of correlation between each attribute among the analyzed attributes of the data. Hence, efficient decision-making can be achieved.  
In this study, a classification model was developed by setting the algal alarm rating as a categorical variable using a decision tree. RF is an ensemble model of decision trees, which can be relatively difficult to interpret in terms of model configuration. A decision tree is an analytical method that categorizes a group of interests into several subgroups or performs predictions by charting decision rules. In this study, a variation of the decision tree algorithm, classification and regression tree (CART), was used. The criteria for issuance based on the algae warning system were set as the categorical target variables, and the conditions for the occurrence of blue-green algae were analyzed according to the major influencing factors. In addition, the model accuracy was evaluated using the validation data.
# Preprocessing for Multi-Dimensional Enhancement and Reconstruction in Neural Video Compression
## 2 Materials and Methods
### Dense Feature-Enhanced Video Compression (DFVC)  
In the FVC framework, ResBlocks play a central role in managing the transition between pixel space and feature space through the feature extraction module and frame reconstruction module. ResBlocks preserve detail and texture in video frames, ensuring natural appearance and high quality in compressed video playback. They also prioritize computational efficiency, in order to reduce resource requirements while maintaining high-quality reconstruction.  
During encoding, ResBlocks are crucial in the deformable motion compression and residual compression modules. Additionally, their residual connections alleviate gradient issues, so as to optimize deep network learning. Despite their effectiveness in enhancing video processing efficiency and compression quality, ResBlocks do not fully exploit video data redundancy. Their design prioritizes facilitating information flow and learning residual representations over directly leveraging spatial and temporal redundancies in video data.  
To further maximize the use of redundant information, we incorporate Dense Blocks. The original ResBlock structure is replaced with a Dense Block configuration, which enhances and reconstructs important features in the video data. The feature extraction and frame reconstruction modules are shown in Figure Figure 4a and Figure 4b, respectively, with Figure 4c illustrating the specific design of the Dense Block module. The Dense Block consists of four consecutive convolutional (Conv) layers, each followed by ReLU activation functions, which are arranged in a densely connected manner. The Conv+ReLU units in the Dense Block incrementally refine and augment feature information. The convolutional layers also perform feature downscaling and tuning, which are utilized to ensure accurate and robust feature representation for subsequent processing stages. The dense connections between feature maps enable each convolutional layer to utilize features from previous layers, which preserves information richness and enhances the network's understanding of image context.
# Single-Task Joint Learning Model for an Online Multi-Object Tracking Framework
## 3 Experiments
### Evaluation of STJL Performance
In contrast, the models trained using cosine metric learning exhibited significantly better performance. Specifically, they outperformed the former by 52.13% for CUHK03, 33.23% for Market-1501, and 33.87% for DukeMTMC-reID. These experiments confirmed that cosine metric learning is more effective than the conventional softmax classifier in learning discriminative features across different environments, ensuring that models can extract universal and representative features. Subsequently, the proposed method was juxtaposed with state-of-the-art techniques. Methods included in this comparison encompassed S2S [42], BaseIDE [43], MARS (IDE+XQDA) [44], CML, LOMO+XQDA, and TAUDL [45]. Except for LOMO+XQDA, all of these methods are deep-learning-based approaches. The compared datasets included Market-1501, CUHK03, and DukeMTMC-reID, with CMC-Rank 1 accuracy as the evaluation criterion. Figure 6: Rank 1 accuracy comparison: (**a**) multi-dataset models, (**b**) different learning algorithms. The accuracy results for the three datasets are shown in Figure 7. Our model achieved the highest Rank 1 accuracy on CUHK03, surpassing the latest methods with a 17.1% improvement over S2S and a 36% improvement over TAUDL (Figure 7a). Additionally, as shown in Figure 7b, our model outperformed other state-of-the-art methods in Rank 1 accuracy on Market-1501, with a 15.52% lead over TAUDL, 13.08% over S2S, and 1% over CML. In Figure 7c, it is evident that our model also achieved the best results in Rank 1 accuracy for Duke-MTMC-reID. Compared with traditional methods like LOMO+XQDA, our model achieved a remarkable 36.38% improvement. Among other deep learning methods, it outperformed TAUDL by 4.46% and BaseIDE by 0.94%. Overall, our model demonstrated superior Rank 1 accuracy across all datasets compared with other state-of-the-art methods.
## 8 Experiments
### Results
#### 8.3.2 LSTM Models  
In the second set of experiments, we evaluated neural network architectures. Table 10 shows the results obtained with LSTM models and characters as basic units. As the information source, word 3-grams were used instead of segments as in experiments with statistical models. According to all metrics, the results of character-based LSTM are better than those of statistical models for all experimental datasets. As presented in Table 11, the results of experiments performed on subwords as basic units led to opposite conclusions. The subword-based LSTM models did not improve the baseline in terms of WER; however, because they can model unknown word forms, they showed better results in terms of CER. The subword-based LSTM models did not outperform the subword-based SMT models. From the results in Tables 10 and 11, we can also conclude that, in the case of LSTM models, characters are a more suitable modeling unit than subwords.
## 5 Conclusions  
In this study, the effects of various operating parameters on the performance of the piezoelectric jetting dispenser were experimentally tested, and the most crucial parameters that affect the jetting performances were statistically analyzed and identified. First, the structural configuration and operating principles of the piezoelectric jetting dispenser driven by dual piezostack actuators were described. An experimental apparatus was prepared with a precision scale after manufacturing the piezoelectric jetting dispenser. The selection of four primary parameters with two levels for each parameter was followed by constructing sixteen experimental sets based on the design of the experiments. The weight of a single dispensed dot was measured 100 times for each experimental set. Additionally, the average weight and standard deviation were also calculated for each experimental set. Subsequently, the experimental results were statistically analyzed by using a commercial software package. The optimal operating parameters for the minimum average weight and the minimum variation of the weight of a single dispensed dot were determined. It was confirmed that the reduction of the open time is an important parameter that reduces the average weight and the variation of the weight in the dispensed amount. However, it is difficult to increase the opening time so that it is much faster than in the presented condition. The durability of the piezoelectric actuator and the efficiency of the packaging process are considered, and the operating frequency is approximately 100 Hz. Subsequently, the operating cycle of the piezoelectric actuator that is composed of rising, open, falling, and closing is approximately 10 ms. The increment in the open time may reduce the operating frequency and result in an increment of the packing time and a decrease in the efficiency.  
The results presented in this work indicated that the needle stroke and the open time are important parameters and, hence, the reduction of the needle stroke and of the open time is the most effective method to obtain a low average weight and a low variation in the weight of a single jetted dot. In other words, it has been confirmed that, as the needle stroke and the opening time are reduced, the discharged amount decreases. Therefore, a new design of the jetting dispenser considering these two most significant parameters can be accomplished in a practical environment, especially for the LED-packaging process industry. It is finally remarked that in the next phase of this study, an experimental investigation will be carried out considering other design parameters, such as the diameter of the nozzle and the operating frequency, besides the needle stroke and the open time considered in this study.  
[PERSON] analyzed the experimental results and wrote the manuscript; [PERSON] conceived and designed the research and supervised the projects; all authors provided input for the manuscript and reviewed and approved its final version.  
The authors declare no conflict of interest.