esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Temperature on Channel Compensation in Optical Camera Communication
## 1 Introduction
LED lamps located outside buildings directly exposed to solar radiation, vehicle headlights, or any LED-based source subject to critical heat conditions in industrial environments can suffer from the aforementioned effects. Large time-dependent temperature gradients can occur (in both directions), and the link quality must comply with the system's requirements regardless of the environmental conditions. This work  
Figure 1: Image sensor filter responses. These graphs illustrate the product between filter transmissivity and silicon sensor responsivity. (**a**) Sony IMX249. (**b**) Foveon X3. Dashed line, dotted line, and dash-dot line correspond to red, green, and blue, respectively. provides experimental evidence about the necessity of consistent channel compensation mechanisms in these scenarios. This paper is structured as follows. Section 2 performs an in-depth analysis of the related works available in the literature, Section 3 discusses the details of the OCC link, Section 4 describes the channel compensation procedure, and Section 5 introduces the methodology and the experimental setup implemented to obtain the results, which are presented in Section 6. Finally, these results are discussed in Section 7.
## 4 Simulation Experiments
### Issue 3: Uncertainty in Experimental Conditions  
Implementation of E3.a and E3.b is analogous to Issue 1. Inference is based on the posterior in (16) with \(\mathbf{z}_{i}=(I_{f,i}^{\text{true}},V_{f},\eta_{h})\) for E3.a and \(\mathbf{z}_{i}=(I_{f,i}=I_{f},V_{f},\eta_{h})\) for E3.b, with the difference being that the data have been generated with random \(I_{f,i}\), instead of \(I_{f}\). Pursuing production and application variability (Issue 1) in the presence of uncertainty in the experimental condition, a prior distribution for \(I_{f,i}\) is specified as in (7), where \(p(I_{f,i}|\mathbf{\theta}_{I_{f,i}})\sim\mathcal{N}(\mu_{I_{f}},\sigma_{I_{f}}^{2})\) with known hyper-parameters \(\mathbf{\theta}_{I_{f,i}}=\mathbf{\theta}_{I_{f}}=(\mu_{I_{f}},\sigma_{I_{f}})\). The parameter \(\sigma_{I_{f}}\) refers to the degree of uncertainty induced by the equipment used to adjust the current. We assume that information on \(\sigma_{I_{f}}\) is provided by the product datasheet of the power unit; cf. [66]. The joint (non-normalized) posterior in (16) is extended to  
\[p(\mathbf{R}_{\text{th}},\mathbf{\vartheta}_{R_{\text{th}}},I_{f}| \mathbf{y})\propto\prod_{i=1}^{n}p(y_{i}|R_{\text{th}_{i}},I_{f,i},\mathbf{z} _{i})\cdot\left(\prod_{i=1}^{n}p(I_{f,i}|\mu_{I_{f}},\sigma_{I_{f}})\right)\cdot \tag{18}\] \[\qquad\qquad\left(\prod_{i=1}^{n}p(R_{\text{th}_{i}}|\mu_{R_{ \text{th}}},\sigma_{R_{\text{th}}})\right)\cdot p(\mu_{R_{\text{th}}})\cdot p( \sigma_{R_{\text{th}}})\]  
with \(\mathbf{z}_{i}=(V_{f},\eta_{h})\) and corresponds to (12). The MCMC sampling scheme in Section 4.2 is extended by an additional block to sample the unknown parameters \(\mathbf{\Theta}_{3}=(\mathbf{R}_{\text{th}},\mathbf{\vartheta}_{R_{\text{th}}},\mathbf{I} _{f})\). Again, the thermal resistances and the currents are not of immediate interest and are considered nuisances. The primary focus is on the estimation of parameters \(\mu_{R_{\text{th}}}\) and \(\sigma_{R_{\text{th}}}\).
## 3 Stochastic FIR Filter with Adaptive Scaling
### Noise Analysis of Stochastic Bit Stream  
Consider a numerical value \(P\in[0,1]\) represented with a stochastic bit stream \(X_{i}\), \(i=1,2,...,N\). Each bit in the stochastic bit stream follows Bernoulli distribution, and the variance of each bit can be written as \(D(X_{i})=E[(X_{i}-P)^{2}]=P\cdot(1-P)\). When transformed back to a binary system, the estimated numerical value \(\hat{P}\) and the corresponding variance can be written as (3) and (4), respectively.  
\[\hat{P}=\frac{1}{N}\cdot\sum_{i=1}^{N}X_{i} \tag{3}\]  
\[D(\hat{P})=\frac{N\cdot D(X_{i})}{N^{2}}=\frac{P\cdot(1-P)}{N} \tag{4}\]  
For the stochastic bit stream representation, the representation noise power \(P_{noise}\) can be calculated as (5). It can be observed that the noise power \(P_{noise}\) decreases with the increasing bit stream length \(N\). In addition, the noise power \(P_{noise}\) also relies on the numerical value \(P\). A bit-by-bit stochastic FIR filter simulation is operated using Matlab software, and the theoretical and simulation results are shown as Figure 4a, which demonstrates that the noise power is much lower when the numerical value \(P\) approaches 0 or 1.  
\[P_{noise}=E[(P-\hat{P})^{2}]=D(\hat{P})=\frac{P\cdot(1-P)}{N} \tag{5}\]  
Furthermore, the signal-noise ratio (SNR) can be calculated as (6), where the signal power \(P_{s}=P^{2}\). It can be observed that the SNR would be affected by the numerical value \(P\). Thus, it would be possible to increase the SNR by scaling the numerical value. In other words, when \(P\) is scaled up, the SNR would be increased even with the same bit stream length.  
\[\text{SNR}=10\cdot\log_{10}\frac{P_{s}}{P_{noise}}=10\cdot\log_{10}\frac{N \cdot P}{1-P} \tag{6}\]
## 4 Experimental Results
### Experimental Results  
#### 4.2.1 Evaluation on Synthetic Datasets  
Our proposed network is evaluated on five synthetic datasets, i.e., Test100 [68], Rain100H [48], Rain100L [48], Test2800 [42], and Test1200 [50]. Both PSNR and SSIM values have been compared and tabulated in Table 3. It can be seen that our proposed model achieves remarkable improvement over SOTA deraining algorithms.  
Figure 8: The effect of performing rain streak removal and rain accumulation removal progressively.  
Figure 7: The effect of applying rain accumulation removal as a pre-processing step, and then rain streak removal.  
Qualitative results on different datasets, shown in Table 3, are shown in Figure 9. Our proposed model exhibits improved restoration performance especially on Test100, Rain100H, and Test1200 datasets, as these datasets include diverse raining conditions including rain streaks and rain streaks accumulation. Moreover, our proposed network showed equally improved performance on Rain100L and Test2800 datasets. Other networks tend to either blur the images by over-smoothing or leave some rain streaks visible in the images.  
are required to evaluate the performance of deraining (NIQE and SSEQ). Quantitative results are shown in Table 4.
## 3 Methodology
### Data Preprocessing  
The dataset utilized in this study was the CICIDS2017 dataset, which includes various features related to network traffic. To ensure the data's quality and consistency, the dataset underwent preprocessing. The following preprocessing steps were applied:  
* Handling missing values: The data consists of 2,830,743 rows and 79 columns. There were 308,381 duplicate values. We removed the duplicates using the Pandas drop_duplicates(inplace=True) function. We identified a total of 353 missing values using the Pandas isna().sum() function. The missing values were handled using mean imputation, where the mean value of the respective columns was used to fill in the missing entries. This technique was chosen because it is simple and effective for numerical features, and it minimizes the impact on the overall distribution of the data, which is important for maintaining the integrity of the models being trained.
* Removing infinity values: To identify rows with \"infinity\" values, we checked the dataset for entries that were either positive or negative infinity (inf or -inf). Any rows containing such values were removed from the dataset to avoid errors or distortions during model training. This was necessary because machine learning algorithms often cannot handle infinite values and could lead to model instability or incorrect learning.
* Consolidating labels: We modified the 'Label' column by consolidating similar attack types. For instance, all \"Web Attack\" labels were grouped under a single \"Web Attack\" label, and all \"DoS\" labels were consolidated under a single \"DoS\" label. Other attack types were left unchanged, and any remaining labels were categorized as \"Other\". This consolidation was performed to reduce the complexity of the classification task and ensure that each category had sufficient examples for the model to learn effectively.
* Data balancing: Upon assessing the dataset's class distribution, we found that the dataset was highly imbalanced, with some attack types having significantly fewer instances than others. To address this, we employed stratified sampling during the split of the dataset into training and testing sets, ensuring that each class was proportionally represented in both sets. Additionally, SMOTE (synthetic minority over-sampling technique) was applied to balance the dataset further by generating synthetic samples for underrepresented classes. This approach was chosen because it helps the model learn from a more diverse set of examples and improves its ability to generalize, especially for minority class detection.
## 6 Challenges and Opportunities
### Future Research Directions
#### 6.4.3 Interoperable Architectures and Open Standards  
The growing size and complexity of IoPVT systems increase their risk of fragmentation. Without common communication protocols or standardized data formats, achieving interoperability between diverse devices, networks, and administrative domains is becoming increasingly difficult [130]. Exploring open standards, potentially within existing IoT frameworks, could enhance interoperability, lower integration costs, and promote wider adoption of these technologies.  
Addressing large-scale environmental and disaster scenarios will likely require cooperation across multiple regions or even internationally. Standardized architectures and protocols could enable data sharing between different municipalities or countries. This alignment would improve collective safety response mechanisms and facilitate more comprehensive ecological data sets for comparative studies and the implementation of best practices worldwide.
###### Abstract  
The generation and propagation of internal waves in the ocean are a common phenomenon that plays a pivotal role in the transport of mass, momentum, and energy, as well as in global climate change. Internal waves serve as a critical component of oceanic processes, contributing to the redistribution of heat and nutrients in the ocean, which, in turn, has implications for global climate regulation. However, the automatic identification of internal waves in oceanic regions from remote sensing images has presented a significant challenge. In this research paper, we address this challenge by designing a data augmentation approach grounded in a modified deep convolutional generative adversarial network (DCGAN) to enrich MODIS remote sensing image data for the automated detection of internal waves in the ocean. Utilizing t-distributed stochastic neighbor embedding (t-SNE) technology, we demonstrate that the feature distribution of the images produced by the modified DCGAN closely resembles that of the original images. By using t-SNE dimensionality reduction technology to map high-dimensional remote sensing data into a two-dimensional space, we can better understand, visualize, and analyze the quality of data generated by the modified DCGAN. The images generated by the modified DCGAN not only expand the dataset's size but also exhibit diverse characteristics, enhancing the model's generalization performance. Furthermore, we have developed a deep neural network named \"WaveNet,\" which incorporates a channel-wise attention mechanism to effectively handle complex remote sensing images, resulting in high classification accuracy and robustness. It is important to note that this study has limitations, such as the reliance on specific remote sensing data sources and the need for further validation across various oceanic regions. These limitations are essential to consider in the broader context of oceanic research and remote sensing applications. We initially pre-train WaveNet using the EuroSAT remote sensing dataset and subsequently employ it to identify internal waves in MODIS remote sensing images. Experiments show the highest average recognition accuracy achieved is an impressive 98.625%. When compared to traditional data augmentation training sets, utilizing the training set generated by the modified DCGAN leads to a 5.437% enhancement in WaveNet's recognition rate.  
MODIS; internal waves; classification; DCGAN; transfer learning; deep neural network; attention +
Footnote †: journal: Applied Sciences
## 3 Results
### Effects of Different Treatments on the Content of Soil Active Organic Carbon
#### 3.4.1 Effects of Different Treatments on Soil Carbon Pool Management Index  
Soil Carbon Pool Management Index (CPMI) serves as a valuable tool for monitoring changes in soil carbon content, providing a comprehensive understanding of the soil carbon pool dynamics. Analysis of Table 5 reveals that the application of various materials led to a significant increase in soil CPMI compared to the T1 treatment, with varying degrees of improvement observed among the different materials. Specifically, both T2 and T3 treatments exhibited markedly higher CPMI values compared to T1 and T4 treatments. Additionally, T2 and T3 treatments demonstrated significantly higher carbon pool activity (A) and activity index (AI) compared to other treatments, while no significant differences were observed among the remaining treatments. Moreover, the CPMI of T2 and T3 treatments surpassed that of other treatments, with T4 treatment also showing a notably higher CPMI compared to T1 treatment. These findings highlight the effectiveness of organic manure or rancid agent application in enhancing soil CPMI, with livestock manure generally outperforming rancid agent. Notably, at 150 days, the soil CPMI of T2, T3, and T4 treatments exhibited a substantial increase of 70.22%, 69.75%, and 28.64%, respectively, compared to T1 treatment. Furthermore, T2 and T3 treatments demonstrated a 32.33% and 31.96% increase over T4 treatment.
## 6 Energy-Efficient, Priority-Based Forwarding (EPF)  
To cope with the broadcast storms and communication delays in the network, and to improve overall energy consumption, an Energy efficient Priority based Forwarding strategy has been proposed in this paper. Two main architectures are presented in our system model: 1. Node side architecture and 2. Controller side architecture.
# Oil Spill Monitoring of Shipborne Radar Image Features Using SVM and Local Adaptive Threshold
## 4 Conclusions  
In this study, we proposed an oil spill classification method for shipborne radar gray images, including image preprocessing, wave classification, effective monitoring area selection, and oil spill extraction. The images collected during the cruise of the 7.16 oil spill in Dalian Port were used to illustrate the detailed process of the methodology. The feasibility of oil spill monitoring by shipborne radar was validated by real-time night shipborne infrared images. Compared with the global threshold method, the local adaptive threshold method is more suitable for the oil film segmentation of shipborne radar gray images because of the very uneven gray distribution of waves. The selection of the local adaptive threshold method is also very important. In comparison, the improved [PERSON]'s method yielded optimal results. The successful application of this method will provide a safety guarantee for offshore oil production and transportation, as well as technical support for cleaning an oil spill after an accident.  
The images used in our experiment were obtained on the same cruise and sea conditions. In future work, we will continue to collect shipborne radar images with oil films under different sea conditions on ship laneways, oil terminals, and oil platforms, thus perfecting the sample library of wave artificial intelligence classification. In addition, we will continue to investigate improving other local adaptive methods to enhance the effectiveness of oil film identification.  
[PERSON] conceived, performed the experiments; [PERSON] and [PERSON] collected data and helped perform the analysis with constructive discussions; [PERSON] revised the manuscript; [PERSON] contributed to revisions and approved the final version. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China [grant number 51709031], the Fundamental Research Funds for the Central Universities [grant number 3132019138] and the Innovation Support Project of Dalian [grant number 2018 RQ22].  
The authors of this research would like to thank all the field management staff at the teaching-training ship Yukun for their support of our research.  
The authors declare no conflict of interest.  
Figure 19: Comparison with other oil film classifications of Figure 1b showing (**a**) [PERSON]’s method, (**b**) [PERSON]’s method (\(T_{\text{gray}}=55\)), and (**c**) [PERSON]’s method (\(T_{\text{gray}}=100\), \(Tarea=30\)).
## 1 Introduction  
According to previous surveys and trends for the first quarter of 2019, Fused deposition modelling (FDM) technology still accounts for most additive manufacturing technologies, up to 46%. Despite the development of new technologies and designs, FDM technology increased compared to 2018. Excessive production of plastic waste is currently a global problem. There are now 12 different basic types of additive devices on the market (Figure 1). As a result, this technology's demand, despite its now common occurrence, is growing in schools or households and various industries [1]. For this reason, the technology can also be considered as a significant producer of plastic waste, to which its \"hobby\" use also makes a significant contribution, which makes it difficult to monitor in this respect [2].  
This article closes the imaginary circle of reducing the impact of the environmental load produced by FDM technology, addressed in the publication Polylactic Acid and Its Cellulose Based Composite as a Significant Tool for the Production of Optimized Models Modified for Additive Manufacturing [3]. This publication presented the possibilities of using topological optimisation of models for FDM production and composite biodegradable plastics. Another phase aimed at improving the state of sustainable development in FDM technology is the modification of the process characteristic of this type of model production and the recycling of plastic waste associated with production [4; 5]. We consider creating an alternative type of construction with the possibility of integrating a combined print head to be the main benefit of the ongoing research. As presented in the publication, FDM technology works with 3D objects divided into planes or layers. These then serve as perimeters for creating 3D objects. This research aims to create a prototype device to finish FDM technology items by applying non-planar layers. Due to the use, recycling of filaments and failures from FDM production, we intend that this device allows the use of recycled plastic in various forms. The integration of the printhead, which, by its very nature, can process such material, has a significant impact on the recycling process itself in the industry.  
One of the current trends and at the same time a way to reduce the time required for the production of the model is the so-called non-planar method of layering the material. We currently encounter this method especially in its application to robotic arms. These are difficult to compare with conventional commercial FDMs. However, their high accuracy, speed, rigidity of the entire system and other properties that make them at the forefront in many fields, are also directly proportional to their price. The article presents the process of design and presentation of partial results achieved by the created prototype of the device, the main benefit of which is to make this method of model production available to a wider group of users.
# Assessment of Carbon Flux and Soil Moisture in Wetlands Applying Sentinel-1 Data
## 4 Discussion
### NEE Modeling  
The studies on carbon exchange in grassland areas applying in-situ and satellite MODIS (Moderate-Resolution Imaging Spectroradiometer) and TerraSAR-X StripMap VV data have been presented by authors in [68]. The results showed significant correlations between NEE and Normalised Difference Vegetation Index - NDVI (R\({}^{2}\) = 0.56), and between surface temperature (Ts) and RESP (R\({}^{2}\) = 0.54). Also, a significant relationship has been found between NEE and \(\sigma^{\circ}\) calculated from TSX data (R\({}^{2}\) = 0.53)--the higher CO\({}_{2}\) absorption corresponded to higher NDVI and \(\sigma^{\circ}\) values. The proposed model for Net Ecosystem Exchange is based on a simple relationship with Sentinel-1 VV and VH data, which are actually delivered with high temporal resolution. This gives the possibility of frequent monitoring of the wetlands ecosystem, especially important under high soil-vegetation dynamics. However, these preliminary results have to be validated and corrected to improve statistical correlation. The carbon flux depends mainly on vegetation and humidity conditions. The study explained why the microwave data of Sentinel-1 VH and VV give promising results to the NEE estimates. Changes in soil moisture and vegetation cover (cuts or grazing) of the wetlands vegetation causes the carbon balance disturbance. Since wetland ecosystems are very important in carbon cycling, they have to be monitored regularly, which is possible by applying Sentinel-1 data.
## 7 Experiments and Results
### Environmental and Experimental Setups  
The experiments were executed using a laptop with 16 GB of RAM and eight cores of 2.3-GHz CPUs. We used Evolopy-NN [48] to implement the ANN, which was powered by a PSO or a CSO as the optimization technique with the cost sensitivity fitness function. Evolopy-NN is an open-source nature-inspired optimization framework for training neural networks using evolutionary and metaheuristic algorithms, which was built with Python 3.7. Both datasets were split into a training dataset (66%) and a testing dataset (34%) [49,50]. We used stratified sampling to maintain the ratio between the minor and major classes in the resulting datasets. So, after the sampling, the minor classes formed 2% of the training and testing datasets for the Spanish companies. Similarly, the minor classes formed 3% and 2% of the training and testing datasets for the Taiwanese companies and the Polish companies, respectively.  
Each experiment was executed 10 different times for 100 iterations, in which the population size was set to 50. During the ensemble learning, we used five weak learners and majority voting to generate the final predictions.  
As described in Section 5, we proposed the use of two optimization algorithms, a PSO and CSO, and three fitness functions, MSE, accuracy, and cost sensitivity. In this experiment, we constructed six variations of the MHOANN, as follows:  
1. The ANN with a PSO and MSE as the fitness function;
2. The ANN with a PSO and accuracy as the fitness function;
3. The ANN with a PSO and cost sensitivity as the fitness function (ENS_PSONN\({}_{\text{cost}}\));
4. The ANN with a CSO and MSE as the fitness function;
5. The ANN with a CSO and accuracy as the fitness function;
6. The ANN with a CSO and cost sensitivity as the fitness function (ENS_CSONN\({}_{\text{cost}}\)).