esa-sceva/satcom-chunk-collection huggingface dataset:
# Flocculation Harvesting Techniques for Microalgae: A Review
## 6 Electrochemical Flocculation
### Electro-Fotation  
If the ECF-based harvesting process is properly operated either with sacrificial or non-sacrificial electrodes, well-distributed and dense gas bubbles of micro-scale size can be generated from water electrolysis in the aqueous solution. The bubbles can attach efficiently to the suspended algal cells, thus inducing the flotation of cells to the liquid surface [138; 139; 140; 141]. It should be noted that, in the cases of conventional air-pressure flotation processes, the gas bubbles generated are of comparatively large size and lack good distribution [137]. [PERSON] et al. [126] developed an integrated photovoltaic electro-flotation system utilizing various electrode materials such as aluminum, iron, zinc, copper, and carbon for harvesting of _C. pyrenoidosa_. They reported that the aluminum electrode exhibited highest harvesting efficiency of over 95%, compared with the other types of electrode (efficiency order: Zn \(>\) 93%, Cu \(>\) 83%, C = 79%, Fe \(>\) 70%). The electrical power for 1 kg algal biomass harvesting were estimated to be 0.28 and 0.34 kWh for aluminum and carbon electrodes, respectively.  
Electrochemical algae harvesting processes are applicable to almost all types of algal species, and also offer technical advantages such as high efficiency, continuous operation, and relatively short-processing time. In addition, effective recycling of spent media (e.g., from the non-reactive electrode case) can reduce overall algal biomass production cost [142]. However, the complex electrochemical system, power consumption, and/or biomass contamination (e.g., by sacrificial electrode use) must be carefully considered for practical, large-scale algae biofuel applications [143].
## 4 Evaluation
### Performance-Based Evaluation
#### 4.2.2 Error Rate Reduction  
Reducing the number of incorrect submissions is a critical indicator of improved learning effectiveness. The system's adaptive hints and difficulty adjustments were expected to help students minimize errors by providing timely support without making the exercises too easy, as seen in Table 3 and Figure 4.  
The largest improvement was observed among slow learners, with a 30.9% reduction in errors, reinforcing the idea that adaptive UI elements significantly benefited struggling students.
## 3 Results
### Comparison with State of the Art
While both VGG16 and VGG19 utilize resources in a similar range to the works in [8; 21; 22], the superior accuracy and significantly lower inference latency achieved by our models highlight effective resource management in this design. This efficiency ensures scalability and adaptability for deploying complex models on constrained hardware platforms. Moreover, the resource utilization reported in this work, as shown in [25], demonstrates the ability to handle images with the same resolution as those in [8], further emphasizing the superiority of our design. Notably, our approach outperforms [21; 22], which employs the same dataset as ours, in terms of balancing resource usage and performance. This integration of efficient resource utilization, high performance, and scalability underscores the robustness of our approach for edge AI applications. Our implementation's key advantage lies in its ability to provide high accuracy and low latency while maintaining efficient resource utilization. Unlike lightweight models such as SqueezeNet [8] and LeNet [22], which prioritize speed at the cost of precision, or complex architectures like ResNet, which achieve higher accuracy at the expense of resource demands, our VGG16 and VGG19 implementations strike a unique balance. This makes them highly suitable for real-world applications, including IoT, autonomous systems, and edge AI, where both performance and efficiency are critical. In conclusion, our FPGA-based approach highlights the potential of deploying advanced CNNs on FPGAs, achieving a compelling trade-off between accuracy, latency, and resource usage. By leveraging the Xilinx Vitis-AI workflow effectively, our work positions itself as a scalable and efficient solution for real-time DL in resource-constrained environments, standing as a competitive alternative to existing state-of-the-art designs.
# Adaptive Modulation Scheme for Soft-Switching Hybrid FSO/RF Links Based on Machine Learning
## 3 Threshold Estimation and Data Set Generation by Machine Learning  
According to the atmospheric characterization parameters given in the CCSDS141.1-R-1 red book, we gain the original data set \(S_{1}\) from the website of [[https://rp5.ru/](https://rp5.ru/)]([https://rp5.ru/](https://rp5.ru/))(accessed on 1 September 2020 to 1 December 2022), which involves rainfall rate, visibility, temperature, humidity, etc. The LB is used to plan the resource allocation for each modulation and to determine the working mode for hybrid FSO/RF links. The ML model is constructed to learn and predict from the data on rainfall and visibility on rainy and foggy days.
# Detection of Logos of Moving Vehicles under Complex Lighting Conditions
## 1 Introduction
Following the above methodological ideas, this study firstly proposes an algorithm for adaptive image enhancement in the principle of gamma transform, and this algorithm can enhance the quality of images under complex light; then, in the process of vehicle logo detection, a darknet algorithm is designed for multi-feature recognition network of different images; finally, an optimization method is proposed corresponding to such features as small display areas of vehicle logos in data as well as high data redundancy.
## 4 Analysis and Results
### Performance Results
#### 4.2.3 Communication Overhead  
The communication overhead is measured by the number of packets (total size in bits) transferred between two entities. The size of bits transferred in our authentication scheme is shown in Table 6. In the communication between C (which indicates to the publishers, subscribers, and brokers that act as clients) and B (which includes the AM), C transmits the parameters C1, C2, C3, AIDC, and Hash2. Assume C3, which is a result of an XOR operation between ID\({}_{\text{C}}\) and ID\({}_{\text{B}}\), is 16 bits. The total size of these parameters is 4 \(\times\) 160 + 16 = 656 bits. However, in the communication between C and AM, C sends the parameters C10, Hash5, and Hash7 with a size of 3 \(\times\) 160 = 480 bits. Therefore, the total bit size of the client cost is 656 + 480 = 1136 bits.  
On the other hand, the communication cost of B, including the AM, is less overhead than C. B must send 320 bits to C, which is the total size of C9 (160 bits) and Hash6 (160 bits). On the other hand, its AM sends Verifier and Hash3 to C, which cost 160 bits each. Therefore, the total size of bits is 4 \(\times\) 160 = 640 bits. In our scheme, no communication overhead occurs between B and AM because both entities are deployed in the same server. Since FB (LB and AB) can act as a traditional broker and as a client, it bears the communication overhead of C and B combined; thus, the total bit size cost is 1136 + 640 = 1776 bits.
# Collective Communication Performance Evaluation for Distributed Deep Learning Training
## 7 Summary of Experimental Results
### PyTorch  
#### 7.2.1 Collective Communication Simulation Experiments  
In Figure 23, the results are consolidated for using four GPUs in each subroutine. In MPI broadcast, the latency gradually increases in the order of bare metal, singularity, and single docker. Conversely, for the rest of the gather and all-reduce subroutines, lower latency is observed in the single-container setups compared to bare metal. For example, in the gather subroutine, bare metal recorded 3.53 s, while singularity achieved 3.20 s. In single docker, it took 3.18 s. In MPI all-reduce, bare metal took 2.8 s, whereas singularity recorded 2.38 s, and single docker took 2.39 s, resulting in latency being 17% lower. In GLOO gather, cross container decreased by 36.6% compared to single container, and overall, compared to other communication backends, the gap between single container and cross container is relatively small. NCCL showed excellent overall performance in the bare metal and single-container setups. However, latency increased rapidly in the cross-docker environment, which involves a single GPU per container. When comparing bare metal to cross docker, broadcast was 89% higher, allgather was 54% higher, and all-reduce recorded 131% higher latency.  
Table 9 summarizes the scenarios with the lowest latency, while Table 10 presents the scenarios with the highest latency. Across all scenarios, multi-GPU per container setups consistently performed exceptionally well, whereas single GPU per container environments exhibited the opposite trend, showcasing higher latency.  
Regarding library performance, NCCL demonstrated the best performance in the broadcast and all-reduce subroutines, while MPI exhibited the lowest latency in the gather subroutine. Conversely, when it came to the lowest-performing scenario, NCCL's allgather subroutine recorded a latency of 5.45 s. This represented a 21% difference compared to MPI and a substantial 61% difference compared to GLOO when compared to other libraries. MPI exhibited lower performance in broadcasting and all-reduce subroutines, and it is noteworthy that the subroutine with good performance in NCCL recorded the lowest latency. Overall, GLOO consistently showed intermediate results across all subroutines.
# Monitoring Technologies for Truck Drivers: A Systematic Review of Safety and Driving Behavior
## 4 Discussion
### Policy Implications  
The findings of this review have significant implications for policymakers and industry stakeholders. Addressing barriers such as privacy concerns and usability challenges is critical to enhancing the adoption and effectiveness of monitoring technologies. Regulatory frameworks should prioritize the development of adaptive systems that align with individual driver behaviors and preferences, as these are essential for fostering trust and compliance among drivers.  
Policies promoting data anonymization and the adoption of non-invasive monitoring solutions are crucial for mitigating privacy concerns and driver resistance. Additionally, the establishment of standardized evaluation frameworks would enable consistent and robust assessments of monitoring technologies, facilitating evidence-based decision-making. Policymakers should also consider financial incentives to encourage the adoption of advanced monitoring systems, particularly in high-risk sectors, where safety improvements can have significant societal and economic benefits. Collaboration between technology developers, fleet operators, and regulatory agencies will be essential to ensure that innovations address practical needs and industry goals.
## 3 Methodology
### Building Detection
#### 3.3.1 Seed-Block Generation  
A seed-block is a block that functions like a seed, a part of a building that could provide information about location, brightness, and even shape for building detection. The process of seed-block generation is shown in Figure 6.  
In Figure 6a, the blue line has a direction opposite to the shadow direction, and the orange sector region is blue-line-centered and \(5^{\circ}\) on both sides. There is a one-to-one correspondence sector region for the points on the shadow edges. The sector regions are examined to detect the borderlines. If the entire sector region is out of the shadow region, the corresponding shadow edge point is regarded as the borderline between shadows and buildings. If the sector region partially falls into the shadow region, the corresponding edge point is regarded as a normal edge point and is left out. In Figure 6b, the points on the borderline between a shadow and a building are shifted slightly in the direction opposite of the shadow. The seed-block is composed of that area that is circled by the shifted line and the original borderline. It is located along the edge between the shadow and the building.  
Objects other than buildings that generate shadows may be mistaken for buildings. Errors are mainly the result of trees. Trees can complicate building detection in urban areas. Trees have different side effects on building detection. In the growing season, trees create a large amount of shadows, which could also generate seed-blocks in building detection. To solve this problem, we detect vegetation areas and then delete the seed-blocks that fall into the vegetation areas; color vegetation indices [39] were used for vegetation detection. In the fall foliage season, trees without leaves have a limited effect on building detection using their shadows. However, vegetation could still have an effect stemming from their color. In some remote sensing images, vegetation might exhibit low brightness, so some trees are recognized as shadows and are hard to detect. In this case, we check the brightness for every seed-block and delete the seed-blocks with low brightness.  
Figure 6: Diagram of seed-blocks. (**a**) Detection of borderlines between shadows and buildings. The blue line has a direction opposite to the shadow direction, and the orange sector region is blue-line-centered and \(5^{\circ}\)on both sides. (**b**) Seed-block generation by shifting the borderline.
## 1 Introduction  
Coal is an extremely complex and heterogeneous material. It is a challenging task to characterize the structure of coal due to the complex and heterogeneous nature. Understanding the origin, chemical structure, and carbon skeleton are important for coal exploitation and further utilization [1]. Comprehensive utilization of coal is of critical importance for the development of the economy. The production of high valued chemicals from coal is a tempting method for coal utilization, which is also a fundamental direction of coal conversion technologies, highly efficient, and of rational utilization. It has been demonstrated that coal extraction is a good method for achieving the above goals since many aromatic compounds are obtainable from the extraction process. Such aromatic compounds can be used as raw and starting materials for the synthesis of aromatic polymers and engineering plastics [2; 3]. On the other hand, solvent extraction is an effective approach to study the compositions and structures of coal and to assist coal liquidation and faction [4; 5; 6]. In recent years, scientists have made in-depth studies on the solvent extraction yield and extraction efficiency of coal [7; 8; 9; 10; 11; 12; 13]. [PERSON] et al. used acetone as a solvent to extract lignite by microwave-assisted heating, and obtained some correlations between extraction yield and power of microwave [14]. [PERSON] et al. used carbon disulfide and petroleum ether to extract lignite through a Soxhlet extraction (SE) for studying the extracted components of coal [15]. The solubilization of small molecules in coal by solvent extraction and its effect on pore distribution of low-rank coal were studied. [PERSON] et al. used high-performance liquid chromatography-electrospray ionization time-of-flight mass spectrometry to separate and identify several kinds of organic nitrogen isomers in Dongming lignite, and analyzed their structural information [16].  
In this work, three different extraction methods, i.e., Soxhlet extraction (SE), ultrasonic-assisted extractions (UAE), and microwave-assisted extractions (MAE) were employed to study the extraction process of Daxing coal (Daxing Coal Mine, Zaozhuang, China). The extraction yields, the extracted components, and the residues of the extracted coal were also investigated. We herein report our results.
## 2 Study Area and Datasets
### Satellite Products  
The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) is a satellite sensor operated by the National Aeronautics and Space Administration (NASA) for Earth environmental monitoring. The MODIS captures data in 36 spectral bands ranging in wavelength from 400 to 1440 nm, at varying spatial resolutions (two bands for red and near infrared rays at 250 m resolution, five bands for blue, green, and shortwave infrared rays at 500 m resolution, and a further 29 bands for ultraviolet, visible, and infrared rays at 1 km resolution) [23]. It produces 44 products for the atmosphere, land,  
Figure 1: Study area.  
and oceans. We obtained the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), leaf area index (LAI), fraction of photosynthetically active radiation (FPAR), and gross primary production (GPP) as indicators of vegetation vitality, photosynthesis, and biomass [24].  
The NDVI, which represents the vitality of vegetation and photosynthetic activity, is the most commonly used vegetation index, and is calculated using the reflectance of near-infrared and red bands. The EVI is calculated by including the reflectance of the blue band to solve the signal saturation problem of the NDVI. Because the EVI is more sensitive to vegetation vitality than the NDVI in highly vegetated areas, it has been widely used for cropland monitoring [25; 26].  
\[\text{NDVI}=\frac{\rho NIR-\rho Red}{\rho NIR+\rho Red}, \tag{1}\]  
\[\text{EVI}=2.5\times\frac{\rho NIR-\rho Red}{\rho NIR+6\times\rho Red-7.5 \times\rho Blue+1}. \tag{2}\]  
The LAI is defined as the total leaf area per unit of ground surface area and is conventionally used as a proxy for the biomass of leaves [27]. It can be derived using maximum primary production and a crop-specific growth coefficient. The FPAR is defined as the fraction of incident photosynthetically active radiation (400-700 nm) absorbed by the green elements of a vegetation canopy [27]. GPP represents the vegetation biomass resulting from photosynthetic activity, in terms of chemical energy (kg C m\({}^{-2}\)) [28].  
We used the products for NDVI and EVI, which were provided in the form of 250 m spatial resolution and 16 day temporal resolution. Also, we obtained the products for LAI, FPAR, and GPP which have 500 m spatial resolution and eight-day temporal resolution.
## 2 Materials and Methods
### Analysis of Trace Metals  
Sample pretreatment and analysis were followed by NIEA standard method (NIEA M353.01C). Porewater samples were filtered through cellulose acetate filters (pore diameter, 0.45 um). The filtrate was acidified with 1% trace-metal grade nitric acid as a pre-treatment. The extracted portion was detected using an inductively coupled plasma optical emission spectrometer (ICP-OES, Optima 2100 Perkin-Elmer, Waltham, WA, USA) for Cr, Ni, Cu, Zn, Pb, and Cd analysis.  
Calculations of Porewater Toxicity by Interstitial Water Benchmark Units (IWBUs)/Interstitial Water Toxic Units (IVTUI)  
The pollutant distribution between the sediment and the porewater is expected to cause different toxic effects. It is not proper to restrict toxicity evaluation to either result. Therefore, interstitial water benchmark units (IWBUs) can be used to predict the toxic effects of various metals on aquatic organisms. These IWBU values are calculated based on the porewater concentration and the final chronic value (_FCV_) as proposed by [PERSON] et al. in 2005 (Equation (2), Table 1).  
\[\sum_{i}\frac{M_{i}}{FCV_{i}}=\frac{M_{Cu}}{FCV_{Cu}}+\frac{M_{Pb}}{FCV_{Pb}}+ \frac{M_{Ni}}{FCV_{Ni}}+\frac{M_{Zn}}{FCV_{Zn}}+\frac{M_{Cd}}{FCV_{Cd}} \tag{2}\]  
For PAHs, the dissolved phase concentrations (\(C_{d}\)) of each nonionic organic contaminant are divided by its corresponding _FCV_ to derive interstitial water toxic units (_IVTUs_) (Equation (3)). Table 2 shows \(C_{d,\ PAH_{i},\ FCV_{i}}\) values for the 16 compounds.  
\[\sum_{i}{IWTU_{FCV,16}}=\sum_{i}\frac{C_{d}}{C_{d,\ PAH_{i},\ FCV_{i}}} \tag{3}\]  
In sediments in which IWBUs or IWTU is >1.0, benthic organisms are not protected and adverse effects may occur. Conversely, if the IWBUs/IWTU is \(\leq\)1.0, sediment toxicity due to mixing of contaminants is unlikely.
# Deep Convolutional Neural Networks Object Detector for Real-Time Waste Identification
## 3 Results
### Hyper-Parameters Tuning  
At this phase, transfer learning and fine-tuning are used for all the models taken into account. At first, the networks are fine-tuned without freezing bottleneck layers. In this manner, the models pre-trained on different datasets work as a warm-up for our fine-tuned parameters, allowing the gradient to propagate through the network. After completing this task, transfer learning will be used by freezing the bottleneck layers where high-level representations are learned. Thus, the gradient is able to back-propagate through these convolutional layers but with a very small learning rate in order to enable small adjustments to the weights. The fine-tuning of the hyper-parameters is introduced hereunder. CNN Object Detectors training depends on multiple hyper-parameters, e.g., feature extractor depth multiplier, feature extractor minimum depth, weight of the l2 regularizer, x/y and height/width scale of the box coder, scales and aspect rations of the SSD anchor generators, etc., but our research focused on fine-tuning the most important of them e.g., batch size, learning rate, decay, decay steps, optimizer, dropout, etc.  
For SSD architecture, the hyper-parameters tuning is:  
* Batch size = 24 images;
* Learning rate using momentum RMSprop optimizer;
* Initial learning rate: \(1\times 10^{-2}\);
* Decay: 0.95;
* Decay steps: 10,000;
* Momentum optimizer: 0.9;
* Final learning rate: \(1\times 10^{-5}\).  
For the Faster R-CNN architecture detector, the hyper-parameters tuning is:  
* Batch size = 3 images;
* Learning rate using momentum RMSprop optimizer;
* Initial learning rate: \(1\times 10^{-2}\);
* Decay: 0.95;
* Decay steps: 10,000;* Momentum optimizer: 0.9;
* Final learning rate: \(1\times 10^{-5}\).  
Faster R-CNN parameters for RPN:
* _IoU_ = 0.7;
* Proposed maps: 100;
* Crop size: 17.