esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Evaluation and Analysis  
### Simulation Model  
In this section, we simulate the Flooding and SFRRP routing protocols in MATLAB 7.10, to compare these protocols from the following points of view: data delivery ratio, data delivery delay, network life, total energy consumption of the nodes, number of live nodes, and percentage of filled node buffer. As there are variant factors [41] to evaluate the functionality of the connected wireless systems, we only  
Figure 8: Scheme of the SFRRP functionality to choose the selected node.  
used the above mentioned points to represent the performance of the SFRRP protocol compared to Flooding routing. Furthermore, we analyze the impacts of the experimental parameters on the mentioned protocols. The produced packets are transmitted by hop-to-hop delivery, because the base station is not in the range of nodes.  
We consider a region of 300 m \(\times\) 300 m \(\times\) 300 m, in which the sensor nodes are deployed in a static manner. The proposed network is considered as three-dimensional, because SFRRP uses three-dimensional distance between nodes. All sensor nodes are static immobile and have the same initial energy of 5 _Joule_. The sensor nodes update their neighborhood information by broadcasting the ND message and receiving the NA messages from the neighbors, as described in Section 4.1. There is one base station in the network to which all the nodes have to send their data packets to. The transmission range of the sensor nodes is 75 m. The buffer size of each node is 60 packets. This network is simulated for 4,000 rounds. The simulation parameters and their default values are represented in Table 4. It is essential to notice that we have used from cycle in some results; the reason is that showing the results of all rounds is not clear. Thus, we have applied the results of some rounds that are candidate rounds. These rounds are called the cycle. Further, it is essential to notice that searching a special path in the discovered paths in the Flooding routing, and fuzzy decision making in the proposed protocol, are both done by consuming a bit of energy at the sender note, called the _processing energy_. In the simulations, we have neglected the processing energy for both of the simulated protocols.  
Figure 9: Work flow of SFRRP.
# Research on Optimal Dispatching Strategy for Micro-Energy-Grid of Protected Agriculture
###### Abstract  
With the growth in demand for energy and the boom in energy internet (EI) technologies, comes the multi-energy complementary system. In this paper, we first model the components of the micro-energy-grid for a greenhouse, and then analyzed two types of protected agriculture load: time-shifting load and non-time-shifting load. Next, multi-scenario technology is directed against the uncertainty of photovoltaic (PV). Latin Hypercube Sampling (LHS) and the backward reduction algorithm are the two main methods we use to generate the representative scenarios and their probabilities, which are the basis for PV prediction in day-ahead scheduling. Third, besides the time of day (TOD) tariff, we present a model using real-time pricing of consumers' electricity load, which is proposed to compare consumers' demand response (DR). Finally, we establish a new optimization model of micro-energy-grid for greenhouses. By calculating the dispatch of electricity, heat, energy storage and time-shifting load under different conditions, the local consumption of PV and the comprehensive operational cost of micro-energy-grid can be analyzed. The results show that a storage device, time-shifting load and real-time pricing can bring more possibilities to the micro-energy-grid. By optimizing the time schedule of time-shifting load, the cost of the greenhouse is reduced.  
micro-energy-grid; time-shifting load; multi-scenario technology; demand response +
Footnote †: journal: Applied Sciences
# A Survey on Unmanned Surface Vehicles for Disaster Robotics: Main Challenges and Directions
## 5 USV Applications for Preventive Maintenance and Disaster Response
### Search and Rescue
#### 5.3.2 Heterogeneous Teams of Unmanned Systems for SAR
They conclude that even though localization precision cannot be improved, motion planning can be the focus of future research involving the cooperation of UAV-USV for SAR. An approach combining the Pelagi USV, a 4.5 m Nacra catamaran, and the Vigil R6-WT UAV (a six-rotor vehicle capable of take-off and landing in water) was devised for SAR, to provide basic life-support kits and to provide shipwx survivor position information to the rescue team. The USV is equipped with a helipad, where the UAV can land and recharge its batteries. Experimental tests demonstrate the use of a 360\({}^{\circ}\) field of view and IR cameras to detect survivors using the USV [221], as well as the UAV landing in water and detecting survivors successfully. The Cognitive Autonomous Diving Buddy (CADDY) is an FP7 project ([[http://www.caddy-fp7.eu](http://www.caddy-fp7.eu)]([http://www.caddy-fp7.eu](http://www.caddy-fp7.eu)).) whose objective is to assist human divers using unmanned systems, namely USVs and UUVs, and other innovative technologies. The key idea behind the CADDY project is to use a UUV as a diver companion, taking photos and other tasks such as guiding the diver and bringing objects to the surface. One version of the system encompasses a MEDUSA USV [222] to determine its localization, the localization of a MEDUSA UUV (the diver buddy) and feedback it to the UUV and the diver [223]. The CADDY project also devised a way to reconstruct and track diver poses using 17 inertial sensors over the diver's body [224] fused with an analysis of stereo cameras from the UUV using a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) algorithm [225], to increase the understanding of diver behavior and to detect possible risks to the underwater diver. [PERSON] et al. [226] propose a way to track divers using a PlaDyPos USV, equipped with an acoustic positioning device, directly over the diver. Carefully planned experiments led to a mean error of 1.8 m due to factors such as air bubbles and diver motion uncertainties. The project trials were designed to detect information about a diver potentially in distress. Initial tests [227] included localization, tracking, and diver activity detection, while the final validation [228] presented the warning system for the diver in distress and showed that divers felt safe and comfortable using the system. Divers often are part of disaster scenarios as part of first-responders acting in extremely harsh environments such as the cave where the Thai boys were trapped in 2018, and one diver lost his life trying to save them. For this reason, we deem the CADDY project a fundamental stepping stone to assist SAR divers in the future.
# InfNRS-QC: Crowd-Sourced Creation of a Dataset and Machine Learning Model for fNIRS Quality Control
## 1 Introduction
### Aim of This Study  
This study aims at improving the quality control step of fNIRS studies by introducing an Artificial Intelligence framework that can support researchers in discriminating between usable and unusable fNIRS segments. Overall, this work brings three main contributions. First, an Open Source web interface that can be used to classify the quality of different signals using a crowd-science approach has been designed and developed. While here we employed it for the collection of fNIRS signals labels, a boilerplate of the platform that can be adapted for other signals or digital objects that require labeling has been made available, allowing other researchers to rapidly deploy citizen science platforms. The second contribution of this study is the creation of a reference dataset of fNIRS signals that can be used by researchers within the field to develop new tools for the preprocessing and analysis of fNIRS data and to train non-experts to discriminate between usable and unusable signals. Third, in this study, a proof-of-concept ML model that can support researchers by automatically assessing the quality of fNIRS signals is presented. The latter is of special interest considering the novelty of the field, the limited number of experts in the visual examination of the quality of fNIRS signals, and the increasing amount of young scholars with previous to no experience in fNIRS signal processing that may need support to evaluate the quality of recorded data. Overall, we believe that the created dataset and developed model favor a more objective and efficient approach to fNIRS quality control.
# Reducing the False Negative Rate in Deep Learning Based Network Intrusion Detection Systems
###### Abstract  
Network Intrusion Detection Systems (NIDS) represent a crucial component in the security of a system, and their role is to continuously monitor the network and alert the user of any suspicious activity or event. In recent years, the complexity of networks has been rapidly increasing and network intrusions have become more frequent and less detectable. The increase in complexity pushed researchers to boost NIDS effectiveness by introducing machine learning (ML) and deep learning (DL) techniques. However, even with the addition of ML and DL, some issues still need to be addressed: high false negative rates and low attack predictability for minority classes. Aim of the study was to address these problems that have not been adequately addressed in the literature. Firstly, we have built a deep learning model for network intrusion detection that would be able to perform both binary and multiclass classification of network traffic. The goal of this base model was to achieve at least the same, if not better, performance than the models observed in the state-of-the-art research. Then, we proposed an effective refinement strategy and generated several models for lowering the FNR and increasing the predictability for the minority classes. The obtained results proved that using the proper parameters is possible to achieve a satisfying trade-off between FNR, accuracy, and detection of the minority classes.  
Keywords:NIDS; deep learning; false negative rate; machine learning; artificial neural network +
Footnote †: journal: 2022, 15, 258. [[https://doi.org/10.3390/a15080258](https://doi.org/10.3390/a15080258)]([https://doi.org/10.3390/a15080258](https://doi.org/10.3390/a15080258))
###### Abstract  
The objective of this work was to improve the thermal stability, flame resistance, and surface properties of black poplar (_Populus nigra_ L.) wood via different impregnation methods. The impregnation methods were employed through two distinct modalities: vacuum impregnation and immersion impregnation. Here, poplar wood was impregnated with calcium oxide solutions (1%, 3% and 5%). Fourier-transform infrared spectroscopic analysis revealed a shift in the typical peaks of cellulose, hemicellulose, and lignin depending on the impregnation method and solution ratio. Thermogravimetric analysis and the limiting oxygen index indicated that the samples impregnated with lime solutions exhibited higher thermal stability than the unimpregnated wood. Both impregnation methods caused a decrease in water absorption and thickness swelling of the sample groups. Using a scanning electron microscope, the effect of the impregnation process on the structure of the wood was examined. In terms of surface properties, it was determined that the surface roughness value increased. On the contrary, it was observed that the contact angle value also increased. A significant difference emerged between the applied methods. In conclusion, the applied lime minerals are suitable substances to increase the flame resistance and thermal stability of black poplar wood.  
black poplar; impregnation; surface properties; thermal stability; wood protection Article
# Hybrid Deep Learning Approach for Automated Sleep Cycle Analysis
###### Abstract  
Health and well-being, both mental and physical, depend largely on adequate sleep. Many conditions arise from a disrupted sleep cycle, significantly deteriorating the quality of life of those affected. The analysis of the sleep cycle provide valuable information about sleep stages, which are employed in sleep medicine for the diagnosis of numerous diseases. The clinical standard for sleep data recording is polysomnography (PSG), which records electroencephalogram (EEG), electrocoulogram (EOG), electromyogram (EMG), and other signals during sleep activity. Recently, machine learning approaches have exhibited high accuracy in applications such as the classification and prediction of biomedical signals. This study presents a hybrid neural network architecture composed of convolutional neural network (CNN) layers, bidirectional long short-term memory (BiLSTM) layers, and attention mechanism layers in order to process large volumes of EEG data in PSG files. The objective is to design a framework for automated feature extraction. To address class imbalance, an epoch-level random undersampling (E-LRUS) method is proposed, discarding full epochs from majority classes while preserving the temporal structure, unlike traditional methods that remove individual samples. This method has been tested on EEG recordings acquired from the public Sleep EDF Expanded database, achieving an overall accuracy rate of 78.67% along with an F1-score of 72.10%. The findings show that this method proves to be effective for sleep stage classification in patients.  
Keywords:polysomnography; sleep stages; electroencephalogram; convolutional neural networks; long short-term memory networks; attention mechanisms +
Footnote †: journal: Applied sciences
## 2 Automatic Geometric Corrections
### Automatic GCP Extraction
#### 2.1.2 Modifications of GCP Extraction Module for VHR Panchromatic Images
* Brightness: Road segments have low variations in grey levels--the lower the standard deviation, the higher the possibility to represent a road [20]. Road segments are also mostly darker than other objects [13]. All of the described properties are set after the segmentation in an adaptive way and their values are dependent on the image metadata. The two geometric properties rely on the image spatial resolution, while the brightness property is based on the radiometric range of the image. Finally, the selected segments are stored in a binary mask (Figure 4). The final mask is far from perfect: the roads are missing due to occlusions (by buildings, trees, cars, etc.) and imperfect segment detection; other objects are present, because of their similarity to road patches (e.g., roofs). Image matching is possible despite numerous imperfections. Figure 5 shows three GCP extraction examples. The first column presents the reference crossroad data chips; the second shows the same crossroads as detected from the satellite image; the third demonstrates the coarse matching results between the corresponding data; the fourth exhibits the fine positioning results; and, the last presents the final matching position after super-fine positioning (here, the detected roads are superimposed to the orthophoto chip of a smaller size than the reference crossroad chips). Example (a) presents the ideal case, where the detection is perfect and its result is easily matched to the reference crossroad. Example (b) has two starting problems: the reference data is outdated (a new road is missing in the lower-right part) and the detection is incomplete. Nevertheless, the matching is still successful and it resulted in a GCP. Example (c) is taken from a highly urbanised area. In this case, the detection is inexact: two side roads are missing and the main road is only partially extracted, mainly due to occlusions (e.g., cars, trees). Despite all of these imperfections, the matching still produced an accurate GCP. Figure 4: Subset of a panchromatic Pleiades image of the city centre of Ljubljana (left) and the binary image of the road mask extracted from the same image (right).
# Effect of Fuel Mass Flow at the End of Injection on Cavitation and Gas Ingestion in the Nozzle
## 1 Introduction
At present, there is not much research on gas ingestion and cavitation at the EOI. The current research on cavitation mainly focuses on nozzle structure and shape [31; 32], injection pressure [33], back pressure [34], wall roughness [35], aspect ratio of orifice [36], and fuel type [22; 37; 38]. Interestingly, one of the findings is that cavitation possesses a significant cycle-to-cycle variation under the conditions of multiple injections [17; 39], which will affect fuel atomization. However, the mechanism of gas ingestion is not clear enough. In this paper, a two-phase three-component numerical method for nozzle studies is presented, combined with a visualization experiment to further study the cavitation and gas ingestion at the EOI and at different fuel mass fluxes. These results are helpful for future researchers to further study cavitation in the nozzle to improve the quality of fuel atomization.
## 3 Results
### As-Cast Micrographs  
As expected, both LOM and SEM micrographs of the as-cast structure show a completely different microstructure compared to the as-built microstructure. Micrographs of an unetched as-cast sample in Figure 6a,b show a random distribution of coarse particles of varying sizes and shapes in the matrix. The size of these particles ranges from as small as 5 \(\upmu\)m to as huge as 20 \(\upmu\)m. SEM/EDS mapping and quantitative analyses are reported in both Figures 6c and 7. EBSD phase analyses confirm these particles as niobium carbide particles (NbC).  
While some authors [30; 31] reported the formation of NbC particles in conventionally fabricated Monel 400 Ni-Cu alloys, others [32; 33; 34] found these particles in Selective Laser Melting (SLM) or Laser Metal Deposition (LMD) built IN718 and IN625. To the best of our knowledge, no work is currently reported concerning their formation in the G-NiCu30 Nb Ni-Cu alloy processed via LPBF additive manufacturing technique. Since the various magnifications employed in the characterization of the as-built microstructures could not confirm NbC in the as-built condition, further work will be carried out in the next phase of this study not only to ascertain expected NbC particles formation but also the nature of their distribution in the as-built condition.
## 5 Food and Feed Contaminations of TAs and PAs
Many plants in the Boraginaceae family are also appreciated for the quality of the honey they produce, although this can pose a risk if the honey becomes contaminated with PAs from these plants. Indeed, honey was the first food product to raise concerns about its safety regarding the contamination of PAs, both because it is a widely used product and is mostly consumed by vulnerable subjects, such as children [68]. To date, 25 PAs and 20 PANOs have been researched in commercially available honeys; among the most investigated PAs are echimidine, lycopsamine, retrorsine, seneiconine, seneicophyllline, heliotrine, senkirkine, intermediate, lasiocarpine, and monocrotaline, while among the PANOs, seneiconine N-oxide, retorsine N-oxide, monocrotaline N-oxide, and seneicophyllline N-oxide are the most investigated ones. According to studies in the literature, the percentage of contamination of honey samples varies from 17% to 91%, and the PAs responsible for the contamination are echimidine- and lycopsamine-like compounds [69]. [PERSON] et al. analyzed 76 honey samples, and 96% of samples were contaminated by one or more PAs. Echimidine was the most abundant PA (83% honey samples), followed by intermediate and lycopsamine detected in 69% of the analyzed samples [70; 71]. Other food categories at risk of contamination by PAs are botanical preparations, such as teas, herbal infusions, and plant-based dietary supplements. A recent study of [PERSON] and co-workers analyzed 168 samples of teas and herbal infusions regarding the presence of 28 PAs and found that 91% of them were contaminated by one or more PAs. Senecionine, retorsine, and their N-oxides were the most detected PAs in high concentrations, together with lycopsamine and heliotrine-like compounds [72]. Regarding plant-based dietary supplements, a study conducted by EFSA in 2016 revealed the presence of a number between 9 and 28 PAs in 278 samples of dietary supplements. The analyzed samples contained extracts of different PA-producing plants (_Borago officinalisis_, _Eupatorium cannabinoid_, _Symphytum officinal_, and _Tussilago farfura_); among them, lycopsamine, intermediate, and their respective N-oxides represented the PAs detected in the highest concentrations while senkirkine was found in 80-90% of the samples containing extracts of _Tussilago farfara_[73]. The European Commission selected 28 PAs as relevant in food samples [73], based on the EFSA 2011 reports [48; 74; 75; 73; 76; 77] and considering the available analytical standards. Subsequently, the European Commission published the Regulation (EU) 2020/2040 concerning the maximum residue levels of pyrrolidine alkaloids in certain foodstuffs [76]. The regulation sets maximum levels (ug/kg) of 35 PAs (21 PAs and 14 coeluting isomers) for 11 foodstuff categories [76]. The International Agency for Research on Cancer (IARC) evaluated several PAs and PA-containing plants and classified riddelline, lasiocarpine, and monocotaline in group 2B (possibly carcinogenic to humans), and retrovirus, retrovirus N-oxide, jacobine, senkirkine, and seneichylline in group 3 (not classifiable) [77; 78].
## 7 Modification Strategies for Sludge-Derived Biochar
### Heteroatom Doping  
Sludge itself contains N and S from proteins and biogenic matter. Thus, SDBs naturally have N-doped carbon domains (pyridinic, pyrrolic, graphitic N) and some sulfur functionalities. These heteroatoms can modulate the electronic structure of carbon, creating basic sites or catalytic centers. Recently, heteroatom doping has been widely employed by researchers to impart unique properties to SDB, allowing it to be customized for specific wastewater treatment challenges. N, S, and P were introduced into the matrix of SDB during or after pyrolysis. In terms of nitrogen doping, by mixing sludge with urea, melamine, or ammonia treatment prior to pyrolysis, the biochar's N content can increase, yielding more amine, amide, or graphitic-N sites [102]. Nitrogen doping introduces pyridinic-N sites, lowering the work function from 4.8 to 4.3 eV to enhance electron density [103]. In terms of sulfur doping, by impregnating sludge with sulfate salts, S-doped SDB can have an affinity for certain metals, which is attributed to the strong affinity between sulfur atoms and metals [104]. In terms of phosphorus doping, a phosphoric acid or phosphate addition produces P-functionalized SDB, which exhibits strong metal-chelating properties (e.g., with Cd and Pb) [105]. In addition, the electrical conductivity of SDB can be improved through phosphorus doping, which is beneficial for applications in electrochemical systems such as microbial fuel cells and capacitive deionization. Overall, heteroatom doping tailors the surface chemistry of sludge biochar to target contaminants. As for the concern of heteroatom leaching, it is also possible to immobilize the active element through biochar-mineral composites to provide a safer alternative.