esa-sceva/satcom-chunk-collection huggingface dataset:
# Mapping Subtidal Marine Forests in the Mediterranean Sea Using Copernicus Contributing Mission
###### Abstract
species are protected under the Barcelona Convention (Annex II, COM/2009/0585 FIN) and the Bern Convention (Annex I), and they are listed as vulnerable by the IUCN, RAC/SPA, and MedPAN. Despite reforestation and protection efforts, these forests continue to decline [19; 20; 21]. Marine Protected Areas (MPAs) aim to safeguard critical ecosystems but their effectiveness for _Cystoseira_ s.l. remains understudied. Several restoration initiatives, such as the ROC-POP-LIFE [22] and REEForest [23] projects, have focused on the restoration of _Cystoseira_ s.l. forests but challenges remain due to the slow growth and low reproductive rates of _Cystoseira_ s.l. species along with persisting pressures. For instance, deep-water _Cystoseira zosteroides_ forests demonstrate slow population dynamics akin to long-lived terrestrial perennials, thus rendering them highly susceptible to environmental disturbances [24; 25; 26]. It is imperative to implement continuous monitoring and adaptive management strategies to halt the decline of _Cystoseira_ s.l. forests. Effective conservation and restoration strategies are urgently needed to preserve these fundamental species and the ecosystem services they provide. Accurate mapping of _Cystoseira_ s.l. forests is a cornerstone of monitoring efforts and necessary for assessing their status and trends. In the EU, this is essential for developing programs of measures for achieving Good Environmental Status (GES) under the Marine Strategy Framework Directive (MSFD) and restoring algal canopy forests under the Nature Restoration Regulation (NRR). By mid-2026, EU member states must establish habitat baselines to guide National Restoration Plans. Habitat extent is a key metric, yet large-scale mapping of _Cystoseira_ s.l. forests is lacking. Satellite remote sensing has been widely used to map a variety of marine habitats, such as _Seagrass_[27; 28; 29; 30] and surface canopy-forming algae such as kelp and sargassum [31; 32; 33]. However, no study to date has employed these methods to map _Cystoseira_ s.l. forests. In this study, we leverage high-resolution PlanetLabs imagery (a Copernicus Contributing Mission) to map _Cystoseira_ s.l. forests in the eastern Mediterranean Sea. The methodology is refined through a case study on Skyros Island [34], a unique site in the Aegean Sea where extensive _Cystoseira_ forests persist in geometric settings favorable for satellite mapping.
## 3 System Model and Parameterization  
SCNs have some peculiar characteristics for traffic requirements [29]. We proposed a simplified graph-theoretic approach to model the SCNs of SGs. Particularly, due to generic considerations, the proposed analysis can be extended to communication networks for other applications. First, the preliminaries of SCN modelling are presented, followed by the resiliency estimation metric.
# Sesame Plant Disease Classification Using Deep Convolution Neural Networks
## 5 Conclusions  
Several kinds of sesame plant diseases are major reasons for the reduction in product quality. The early detection of diseases is important for taking action to prevent the spread of disease to other crops. In this study, we proposed a deep conventional neural network-based classification model for sesame leaf disease, i.e., bacterial blight and phyllody, using images. We evaluated the performance of the proposed model trained with the original images, trained with preprocessed and segmented images, and trained after image augmentation. The model achieved 90%, 94.44%, and 96.67% testing accuracy, respectively. The proposed model was compared with previous models, InceptionV3 and Xception. According to the results, the Xception model achieved 93.3% testing accuracy, and InceptionV3 achieved 90% when trained with the augmented images. Therefore, as one can see from the results, this study achieves good results for the classification of sesame leaf diseases.  
Figure 11: Sesame disease classification.  
**Author Contributions:** Conceptualization and methodology were led by E.A.N. and A.M.M., who also performed the state-of-the-art analysis. Software implementation was conducted by E.A.N., Resources, and formal analysis, data curation, and validation were carried out by K.K. and R.M.-B., Writing--original draft preparation was undertaken by E.A.N., A.M.M. and A.M.A., while writing--review & editing and administration--interoperability information and relevant use cases were managed by R.M.-B. and the A.M.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Ethical review and approval were waived for this study due to the nature of the data collection, which involved only plant images.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** The datasets generated and/or analyzed during the current study are not publicly available but are available from the corresponding author upon reasonable request.  
**Acknowledgments:** This study forms part of the AGROALNEXT program and was supported by MCIN with funding from the European Union NextGenerationEU (PRTR-C17.I1) and by Fundacion Seneca with funding from Comunidad Autonoma Region de Murcia (CARM).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# PMT Fluorescence Signal Denoising Processing Based on Wavelet Transform and BP Neural Network
## 2 Detection System Section
### Circuit Section  
The acquisition circuit is composed of the STM32 platform and the FPGA platform [22,23]. The STM32 part selects STM32F407 ZGT6 (STMicroelectronics, Plan-les-Ouates, Switzerland) as the main control chip, responsible for communicating with the FPGA platform and upper computer. The FPGA platform chooses EP4 CE6E22 CSN (Intel, Santa Clara, CA, USA) as the main control chip, which can drive high-performance ADCs with its bandwidth and slew rate. It is also responsible for joint communication with the STM32 main control chip. LTC2387-18 FA (Analog Devices, Wilmington, MA, USA) is chosen as the analog-to-digital converter, which is a high-performance ADC with 18-bit depth and 10 M speed.  
Figure 1: The optical path and assembly diagram of the detection device.  
Due to the fact that the original signal output by PMT is a reverse signal, the signal first enters an inverted operational amplifier circuit and, then, outputs a signal with adjustable size and positive direction. Then, the subtractor circuit is entered. This process is shown in Figure 2, where the red part represents the fluorescence signal of sulfur dioxide. After passing through this part of the circuit, the proportion of sulfur dioxide fluorescence signal significantly increases, which helps to improve the sensitivity and resolution of the detection device.  
Then, the signal enters the differential amplification circuit, which divides the signal into two paths with different DC biases, opposite directions, and no phase deviation. The difference between the two signals is used as an analog signal to enter the ADC for analog-to-digital conversion. The two signals are transmitted by two signal lines with extremely short physical distances, and it can be considered that they are subjected to the same external interference, which can effectively reduce the impact of external interference and common mode noise on the electrical signal [24]. In addition, a four-layer circuit board is used in the experiment, with the power supply and grounding parts located in separate board layers. Compared to common double-layer circuit boards, it has better electrical performance. The physical image of the collection circuit is shown in Figure 3.
# Blyproducts as a Sustainable Source of Cosmetic Ingredients
## 5 Successful Cases  
When considering successful cases of byproducts' incorporation into cosmetics, one key indicator that can be considered are patents; these will be the most reliable source of information on any given product composition.  
Using Espacenet and Google Patents (accessed on 1 September 2024) and filtering by the classification family A6 IQ (\"specific use of cosmetics or similar toilet preparations\" according to the International Patent Classification and Cooperative Patent Classification), 2815 patents have been filled on this topic since 2022. Of these, 2439 regard the application of plant byproducts in cosmetics, with L'Oreal (11.8%) and Unilever Global Ip Limited (1.7%) being the main assignees, and 376 were related to animal byproducts-based cosmetic applications, with the Korean Institute of Oriental Medicine being the primary assignee (9.3%) followed by L'Oreal (5.5%). Applications range from redness-correcting skin care formulations (US20240285509A1), composition for hair or scalp treatment (KR20230155841A), or a plant fat composition for cosmetic preparation (US2018110719A1). Examples of byproducts used are Vinasse (CN117547498A), a byproduct of biomass distillation that can be used as a skincare toner, ginger byproducts (KR20230113481A) such as leaf and root, that can be used in deodorants, or even tobacco seed (US10639269B2) used in eye creams and makeup products.  
When considering already-existing applications, several examples of byproducts' usage for cosmetics production can already be found in the marketplace. Some examples are L'Oreal Age Perfect Cream, which is produced with grape seed extract from byproducts of the wine industry, Estee Lauder Revitalizing Supreme+, which is produced with collagen-rich extracts from fish byproducts, Nicaraguan Coffee Intense Awakening Mask from the Body Shop, which contains extracts from coffee grounds, Dove Nourishing Secrets, which is produced with rice milk, a byproduct of rice milling, or Lush Fresh Face Masks which use various fruit and vegetable scraps to produce the face masks and their packaging.
# A Hybrid Neural Network Model for Predicting Bottomhole Pressure in Managed Pressure Drilling
###### Abstract  
Managed pressure drilling (MPD) is an essential technology for safe and efficient drilling in deep high-temperature and high-pressure formations with narrow safety pressure windows. However, the complex conditions in deep wells make the mechanism of multiphase flow in drilling annulus complicated and increase the difficulty for accurate prediction of bottomhole pressure (BHP). Recently, an increasing volume of research shows that intelligent technology is an efficient means of accurately predicting BHP. However, few studies have focused on the temporal properties and variation mechanism of BHP. In this paper, hybrid neural network prediction models based on the multi-branch parallel are established by combining the different advantages of back propagation (BP), long short-term memory (LSTM), and a one-dimensional convolutional neural network (1 DCNN) model. The results show that the relative error of the best model is about 70% lower than the optimal single intelligent model. Preliminary experimental results reveal that the hybrid models combine the advantages of different single models, which is more accurate and robust for extracting the temporal features of MWD. Finally, based on the trend analysis, the validity of the hybrid model is further verified. This study provides a reference for solving the problem of optimizing temporal characteristics and guidance for fine pressure control in complex formations.  
bottomhole pressure; temporal properties; hybrid neural networks +
Footnote †: journal: Applied Sciences
## 2 Hybrid Edge-Cloud Approach
### Motivation for the Edge-Cloud Approach
#### 2.1.2 IEC 61850-90-5 Evaluation--Use Case  
An urban-pedestrian LTE network scenario was chosen for the use case as it resembles the environment of DNs the closest. The scenario was already predefined by the LTE testbed provider and was further divided into four sub-scenarios that emulated different conditions in an urban environment. The duration of each sub-scenario was set to 30 s since a steady state of the LTE network was emulated and the network stabilized within a few 100 ms after every sub-scenario switch. However, to eliminate any potential sub-scenario's switching dependency, each of the sub-scenarios was run five times, which resulted in a total test duration of 600 s. The scenario was emulated with a DEKRA data stream of 80 kbit/s and 50 packets/s, which represents a typical PMU configuration with a reporting rate of 50 Hz.  
The use case represents the PMU in a synchrophasor mode, where raw measurements of grid voltage and current are used for the calculation of synchrophasors. Those are then wrapped in IEEE C37.118-2018 packets and sent to a concentrator. Since either the transmission control protocol (TCP) or the user datagram protocol (UDP) can be used for sending the results, both protocols were tested with the aforementioned urban-pedestrian network scenario. The main difference between the protocols is that the TCP requires the authentication of the received packets, whereas the UDP does not. Therefore, some packet loss is expected for the UDP protocol, however, the upper limit is not prescribed by the standard. In contrast, no packet loss is expected for the TCP protocol as it inherently handles the loss of packets. However, each lost packet on the underlying network layers will result in increased data transmission latency.  
Each of the protocols was tested in terms of packet loss and one-way delay. The C37.244-2013 standard states that the required measurements have to be provided to the time-critical applications within a short delay (between 30 ms and 40 ms). In this paper, we decided to use the lower of the two thresholds in order to give a general assessment of whether the latency requirement for real-time applications is fulfilled or not. However, for a more detailed specification of the various applications' requirements, we refer the reader to the IEC 61850-90-5 standard.
# Near Real-Time Freeze Detection over Agricultural Plots Using Sentinel-1 Data
###### Abstract  
Short-term freeze/thaw cycles, which mostly occur in the northern hemisphere across the majority of land surfaces, are reported to cause severe economic losses over broad areas of Europe and North America. Therefore, in order to assess the extent of frost damage in the agricultural sector, the objective of this study is to build an operational approach capable of detecting frozen plots at the plot scale in a near real-time scenario using Sentinel-1 (S1) data. C-band synthetic aperture radar (SAR) data show high potential for the detection of freeze/thaw surface states due to the significant alterations to the dielectric properties of the soil, which are distinctly observable in the backscattered signal. In this study, we propose an approach that relies on change detection in the high-resolution Sentinel-1 C-band SAR backscattered coefficients, to determine surface states at the plot scale as either frozen or unfrozen. A threshold analysis is first performed in order to determine the best thresholds for three distinct land cover classes, and for each polarization mode (VH, and VV). S-1 SAR data are then used to detect a plot's surface state as either unfrozen, mild-to-moderately frozen or severely frozen. A temperature-based filter has also been applied at the end of the detection chain to eliminate false detections in the freezing detection algorithm due mainly to rainfall, irrigation, tillage, or signal noise. Our approach has been tested over two study sites in France, and the output results, using either VH or VV, compared qualitatively well with both in situ air temperature data and soil temperature data provided by ERA5-Land. Overall, our algorithm was able to detect all freezing episodes over the analyzed S-1 SAR time series, and with no false detections. Moreover, given the high-resolution aspect of S-1 SAR data, our algorithm is capable of mapping the local variation of freezing episodes at plot scale. This is in contrast with previous products that only offer coarser results across larger areas (low spatial resolution).  
frozen/unfrozen; Sentinel-1; radar; real-time; agricultural areas; plot scale 19 May 2020; Accepted: 17 June 2020; Published: 19 June 2020
# Developing Data-Conscious Deep Learning Models for Product Classification
## 1 Introduction  
Recently, internet commerce has become more active as internet distribution networks have grown. Because of the resulting increase in volume and variety of products, classification methods have become important. Traditional electronic commerce (e-commerce) is based on business-to-consumer (B2C) transactions. Herein, classification tasks are convenient because companies have all the information on the products they sell to customers. However, the recently emerged used-product trading is consumer-to-consumer (C2C) trading. Herein, any entity can input and register product information. Furthermore, a private seller can arbitrarily input the product name, detailed description, price, and product category of a product they are selling. However, unlike companies, private sellers do not have the correct information on the products sold, and the product information entered may be inaccurate.  
Inaccurate product classification is disadvantageous for both seller and buyer. The appropriate product category can be identified if text data extracted from past data of used-product transactions are refined and used to train a deep learning model. This is a technical solution to the problem of inaccurate classification. Deep learning models have been developed mainly by two approaches. The first approach is to apply a well-known typical deep model as-is by tuning hyperparameters. This approach sets the weights of some particular layers appropriately and applies it in typical models such as a convolutional neural network (CNN) [1, 2]. The second approach is to develop a technically new model [3, 4].  
This study found that deep learning model development and hyperparameter tuning should be integrated for the resulting model to be conscious of the product informationdata [5]. Specifically, we considered the text data of product information appropriately and propose an architecture that integrates the data selection, transformation, and filtering processes with the training of CNN [6; 7; 8] and bidirectional long short-term memory (Bi-LSTM) [8; 9; 10] models. We adopted a methodology that performs the required partial transformation and adds layers to fit the data in these typical deep learning models. These models are established to be suitable for the product classification of product information text data. We performed a comparative analysis using actual data to determine the proposed deep learning model that is more suitable for the automatic classification of products. In addition, we conducted a case study in which stopwords [11; 12] were filtered out using heuristics during the text data filtering process to investigate its effect on the model performance.  
The remainder of this paper is organized as follows: In Section 2, we briefly review the related works. Section 3 presents our proposed method. Section 4 presents the experimental results and a case study. Finally, Section 5 concludes the paper.
## 2 Materials and Methods  
### Compost Preparation and Analysis  
The preparation of the composts used for the experiment was carried out at the CREACI experimental site of Caserta. Three types of compost, named C1, C2, and C3, were made using the following raw materials mixed in different volumes: shredded, dried cardoon biomass, fresh fruit waste (plums and peaches), and lignocellulosic waste from pruning (Table 1). Three static piles of 2 m\({}^{3}\) were prepared and composted for 132 days. The size of particles of cardoon and pruning wastes from fruit trees and garden plants were predominantly in the range of 5-15 cm in length while whole, unmarketable fruits of peach and plum were mixed as such. Composting piles were built manually by arranging in alternate layers different wastes, but taking care that shredded lignocellulosic or cardoon particles were the first layer at the bottom of the pile to drain water and leachate from the pile. Oxygenation was carried out with a flow of air blown into the base of the pile by PVC pipes; three manual turnings to ensure new substrates for the active microbial component and to achieve homogeneity of the composition were carried out. The humidity of the piles was measured weekly and maintained above 50% through watering. Temperature monitoring was carried out using thermoresistance sensors (Datalogger Watchdog series 1000, Spectrum Technologies Inc., 3600 Thayer Court, Aurora, IL, USA) placed in different areas within each pile. Twenty days of thermophilic phase were followed by a mesophilic and slow maturation phase of 112 days. In the thermophilic phase, the temperature was in the range of 50-65 \({}^{\circ}\)C for more than 5 days, and in conditions allowing for sanitization [49].  
Homogenous samples were collected from each compost and prepared to perform the following physico-chemical analyses. Total carbon (C) and nitrogen (N) determinations were performed by an Elementar Analyzer CN802 (VELP, 20865 Usmate Velate, Italy). Humic and fulvic acids (HA + HF) were measured according to ANPA 3/2001 [50]. pH and electrical conductivity (EC) were determined on water extracts (1:10, \(w/v\)) of the composts by a conductivity/pH meter Crison, (HACH LANGE, Barcellona, Spain). The ash content is determined according to the gravimetric procedure suggested in the ANPA 3/2001 [50]. All analyses were performed in triplicate. TC, N, and HA + HF were expressed as % of dry matter (d.m.).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Compost** & & **C1** & **C2** & **C3** \\ \hline \multirow{3}{*}{Feedstocks (\% \(v\):\(v\))} & cardoon biomass & 50.0 & 68.0 & 33.3 \\  & fresh fruit waste & 25.0 & 32.0 & 33.3 \\  & waste from pruning & 25.0 & 0 & 33.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition of piles for composite in percentage (\(v\):\(v\)).