esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection of Forest Tree Losses in Cote d'Ivoire Using Drone Aerial Images
## 2 Materials and Methods
### Overall Methodology  
For the implementation of this study, a sampling protocol was set up that combines photogrammetry, stereoscopy, and change analysis techniques using very high spatial resolution images acquired with a drone (Figure 2).  
Figure 1: Geographic location of the Bossématé classified forest. Overview of study areas and 2016 land cover [12].
###### Abstract
At present, there are no suitable in vitro models which accurately reproduce a physiologically relevant environment for endovascular device testing. Enzymatic treatments represent a promising avenue to induce microstructural changes which mimic the increases in diameter and stiffness seen in AA. In a seminal study by [PERSON] et al., human iliac arteries treated with crude trypsin for elastin degradation demonstrated a greater diameter and significantly greater stiffness under low hydrostatic pressures [16]. Following on from this, [PERSON] et al. reported that elastin-depleted human iliac arteries exhibit decreased distensibility and only moderate dilation under physiological pressures; therefore, demonstrating the feasibility of this approach as a mechanical model for aneurysms [39]. In elastin-depleted strips of porcine aorta, [PERSON] et al. reported that a near aneurysmal increase in unloaded circumferential length can be achieved through application of a creep-loading protocol [40]. Furthermore, [PERSON] et al. reported that a mild elastase treatment produces gradual changes in the biaxial stress-strain behaviour of porcine aortic strips characterised by increasing stiffness with increasing elastase incubation time [41]. The viability of this approach is further supported by the successful use of elastase treatments to produce models of aneurysms in small vessels in an ex vivo setting [42; 43] and in vivo AA models in small and large animals [44; 45]. Researchers have been studying the effects of elastase on a wide range of in vitro and in vivo arterial tissues since the 1950s [16]. Despite this, a large scale ex vivo model of an aneurysm, suitable for endovascular device benchtop testing, has yet to be developed. We hypothesized that the localised application of elastase to porcine aortic segments would constitute a feasible methodology to recapitulate the critical features of the mechanical environment present within AA, specifically: increased diameter and stiffness under physiological pressures. To test this hypothesis, first the effect of elastase treatment on aortic geometry and tensile behaviour was studied in rings of porcine aorta to demonstrate proof-of-principle. Subsequently, locally elastin-depleted whole aorta were produced using a specially designed degradation rig and the changes in pressure-diameter behaviour were measured via inflation extension testing. Together this work aimed to show the viability of a localised degradation approach to produce porcine aortae with aneurysmal geometry and compliance; therefore, making it a viable preclinical ex vivo model for endovascular device testing.
## 2 Conserved Genes of the _exo-xis_ Region  
By virtue of their location in the phage genome, _exo-xis_ genes are among the earliest transcribed during infection from the _pL_ promoter. Figure 1 presents an overview of the open reading frames (ORFs) comprising the _exo-xis_ regions of bacteriophage \(\lambda\) and two model Stx\({}^{+}\) phages, \(\Phi\)24\({}_{\text{B}}\) and 933W. In each phage genome, five genes consisting of _orf60a_, _orf63_, _orf61_, _orf73_ and _ea22_ form a conserved block followed by a variable set of downstream genes both in terms of number and kind. Originally predicted by bioinformatics, these ORFs have since been confirmed by ribosome profiling methods of bacteriophage \(\lambda\) as _bona fide_ expressed genes [23]. In \(\lambda\), ribosomal profiling has also identified two additional genes (_orf2311_ and _orf2313_) in the same conserved region that encode peptides of 7 and 31 amino acids, respectively.  
Most of the effects of the _exo-xis_ region genes to date have been described by the Wegrzyn laboratory (Univ. Gdansk). Throughout their studies, they have used four functional assays to assign the behavior of a given _exo-xis_ gene as either pro-lysogenic or pro-lytic [24; 25; 26; 27]. Two of the assays examine the time it takes for the lytic cycle to begin either from the onset of infection or the induction of lysogens by stressors such as hydrogen peroxide, antibiotics, and ultraviolet light. The remaining two assays quantify the relative number of survivors that escape infection or continue as lysogens. Collectively the assays suggest _orf/3_ and _ea22_ as pro-lysogenic genes while _orf60a, orf63_ and _orf61_ tend to be pro-lytic. Since all _exo-xis_ genes are actively transcribed during early viral development, the interplay among viral proteins and host proteins could contribute considerable complexity to the lysogenic-lytic decision. To resolve this complexity, a systems biology approach incorporating contemporary genomics and proteomics approaches may offer a way forward.
## 4 Current Off-Training Wellbeing/Wellness Profiling in Elite Sports
### Performance/Training Readiness  
Athlete self-reported measures (ASRM) stand out as the most popular tool to assess the outcomes of interventions on athlete's health and well-being. These measures are questionnaire-based and typically cover subjective aspects such as mood disturbance, perceived stress and fatigue, muscle soreness, energy levels, and readiness to perform [92; 93]. ASRMs are also claimed to reflect acute and chronic training responses in athletes' well-being [94]. However, the literature has raised concerns regarding the interpretation of these measures, citing a lack of conceptual and practical validity [95; 96; 23].  
The ability of ASRM to predict performance or readiness to perform has been recently explored using different approaches. For instance, [PERSON] et al. [97] conducted a study on female football and found no significant relationship between wellness-related measures and match performance outcomes, using a linear regression model derived from a performance analysis company. However, when the focus is on predicting the ability to perform in matches or training sessions, wellness variables appear to play a significant role. [PERSON] et al. [98] and [PERSON] et al. [99] achieved positive results when applying machine learning methods to predict readiness to play in footballers. Both studies used subjective self-reported wellness parameters from mobile ASRM, including readiness, mood, stress, sleep quality, fatigue, and soreness. The models were trained using team and individual data to predict the readiness of individual players. In both studies, training the model on team data but predicting for a specific target player achieved scores above 0.90, with higher accuracy for teams that had a greater volume of data collected. Daily predictions outperformed long-term predictions, such as weekly assessments.  
Monitoring wellness and training load in elite athletes is a complex task, and current athlete self-reported measures (ASRM) methodologies still have room for improvement to enhance efficacy and usefulness. While there are examples of validated multi-item questionnaires developed for athlete assessment [100; 101], their use might be tedious and fatiguing for athletes, affecting usability. The practical implementation of ASRM in sports organizations often involves custom-made assessments, [102], and surprisingly, over 45% of them are single-itened. Most of these assessments have not been validated, and modifications from the originally non-validated items are common in practice [96]. With the development of mobile applications for ASRM, there is a certain excitement to strengthen its use as a monitoring option for elite sport due to its low financial investment and staffing expertise [103]. However, the overall quality of current ASRMs is debatable and should be carefully considered before implementation. This highlights the need for careful consideration and validation of ASRMs used in sports settings to ensure their reliability and effectiveness.
###### Abstract
In the article, the authors optimize anchors using K-mean++ technology to reduce classification errors, and focus on small- and medium-sized wildfire smoke areas with a spatial pyramid structure. The results of the paper show that the improved Yolov5 recognition rate with the dataset is 73.6%. The experiment verifies that Yolov5 can be successfully used for the detection of straw-burning smoke. However, the modification does not account for whether the speed and accuracy of the model can be further optimized, and more research is needed. The current research does not consider the large-scale monitoring of straw-burning smoke. Firstly, when observing from a distance, the area of smoke is relatively small, and targeted optimization of the algorithm is needed. At the same time, the morphology of smoke observed at different distances is different, so it is necessary to collect smoke data from different distances. Secondly, the background color of the same location varies in different seasons, such as green fields in spring and yellow fields in autumn. Therefore, it is necessary to collect smoke data from straw burning across different seasons. Finally, existing research has not considered optimizing the speed and accuracy of algorithms. When the hardware requirements of the algorithm are too high, and the algorithm is applied in the wild, it is not conducive to deployment. This article will research these issues. Based on Yolov5s [16], this study proposes a target detection algorithm with a small model size and a fast detection speed [17]. A lightweight network is very suitable for mobile and embedded devices [18]. Based on the Yolov5s network, the network's backbone is improved, and the depthwise separable convolution [19; 20] and ECA (Efficient Channel Attention) mechanism [21] are introduced. We call the improved network Yolo-Light. Datasets are extremely important for machine learning. Currently, there are no public datasets of straw-burning smoke, so datasets of straw-burning smoke in different seasons, locations, and crops were collected in this study. Yolo-Light was trained using the straw-burning smoke target dataset and deployed on the Jetson Xavier NX embedded platform. The straw-burning smoke target dataset was used to train Yolo-Light and Yolov5s, and the model parameters and performance were compared. Moreover, the actual detection ability of Yolo-Light on the embedded Jetson Xavier NX device was verified. Compared with Yolov5s, Yolo-Light has fewer parameters and calculations, and can achieve faster detection speeds on embedded devices.
## 4 Experiments and Discussion
### Performance of Variable Circular Correlation Time
The green circles indicate the difference of acquisition results without/with the resampling strategy; the cyan circles indicate the incomplete acquisition results with too short circular correlation time.
# Adjustments to SIF Aid the Interpretation of Drought Responses at the Caatinga of Northeast Brazil
## 3 Results  
### Caatinga Ecoregion Land Cover Analysis  
Data from the ESA GlobCover 2009 product shows that the following land cover classes are the most representative among all ecoregions of the Caatinga: shrubland (averaging 27.66% relative cover among all ecoregions), mosaic of vegetation and cropland (averaging 23.93%), mosaic of cropland and vegetation (averaging 20.4%) and rainfed crops (averaging 14%). Human influence on the region can be seen in the high proportion of the crop mosaic classes (either with crop or vegetation dominance) and Rain-fed Crops observed, constituting the majority at all ecoregions but Campo Maior and Aarripe-Ibiapaba Complex (Figure 3). Barren and Urban areas were relatively rare and covered less than 0.5% of any ecoregion.  
Tables of relative cover and complete nomenclature of observed classes are included in the appendices for precise reference (Tables A1 and A2).
# WAMs Based Eigenvalue Space Model for High Impedance Fault Detection
## 3 HIF Detection in Eigenvalue Space  
Merely a decade ago, proposing a solution based on advanced metering at multiple ends of a line, with the end goal of estimating eigenvalues, would have been met with significant scepticism regarding the feasibility of the solution's implementation. However, due to increased integration of synchrophasor technology into the grid, solutions based on Wide Area Measurements (WAMS) made possible by Phasor Measurement Units (PMUs) are gaining momentum. Even at the distribution level, smaller and cheaper units called Micro-PMUs are being added to the smart grid [24].  
This work envisions PMUs, as the metering devices collecting information for the eigenvalue estimation step. This is due to their high sampling rates (120 Hz), and wide area measuring capabilities. These two characteristics are critical as higher sampling rates increase the robustness of the estimates, while the ability to operate over a wide area means that this solution could be implemented on lines of varying length. Figure 3 illustrates the configuration of the system proposed in this work.  
Figure 2: Out of Step Relay characteristics [26].  
One of the key features that distinguishes this work from other solutions is that this method doesn't attempt to identify HIFs or their characteristics, but instead focuses on identifying deviations in the Eigenvalue Space (ES). Identifying HIFs by focusing on the characteristics of the fault presents an immense engineering challenge, but this is a challenge that can be avoided altogether.  
As mentioned in the previous section, each powerline has a unique set of eigenvalues determined by the parameters illustrated in Figure 4, and these eigenvalues are sensitive to changes in impedance, such as the deviations seen during a fault.  
Therefore, instead of trying to predict the magnitude and order of the harmonics generated by an electrical arc, or how a highly resistive surface will change the angle of an apparent impedance, this works presents a method that establishes a baseline of acceptable values in the ES and makes decisions when eigenvalues drift outside of these zones. Focusing on deviations from a baseline allows this solution to:  
1. Utilize alternative means of monitoring, in this case eigenvalues.
2. Create a significantly higher degree of generalization as the solution can applied to just about any system with virtually zero human interaction (other than the installation of the metering infrastructure).
## 1 Introduction
By classifying the groups according to the importance of the encoded bits, based on the decoding process, the proposed algorithm improves the performance in the same overall interference ratio compared to that of the conventional FTN method. Lastly, we applied the proposed UEP-FTN method to an orthogonal frequency division multiplexing (OFDM) system in multipath channels. Through simulation, the performance of the conventional FTN method and FTN based on the UEP transmission method were compared. Additionally, the performance of the OFDM-FTN transmission method applying UEP was analyzed in a multipath channel environment.
# Measurement of Bridge Vibration by UAVs Combined with CNN and KLT Optical-Flow Method
## 2 Methods  
Firstly, the video of a static structure taken by a UAV is converted into a set of continuous digital image sequences stored in the frame form. The coordinates of reference points and target points are obtained by DIC and KLT optical-flow method to train the CNN. Then, the structure is excited and the image sequences are captured by a fixed camera and UAV. The target points are tracked with DIC technology and KLT optical-flow method, respectively, to obtain the displacement time-history curves (the one obtained by the UAV needs to be corrected by a CNN). The natural frequency of the structure is then extracted by OMA. The technical flowchart is shown in Figure 1.
# Development of a Digital System for Monitoring Emergency Conditions in 20 kV Distribution Networks
## 1 Introduction
The scientific novelty of the research results lies in the classification of wireless transmission technologies and the modulation methods used in these technologies based on their resistance to intense electromagnetic interference typical of areas near power transmission lines. The objective of the research is to identify practical opportunities for automating the monitoring of ground faults in various sections of power lines based on the digitalization of zero-sequence voltage measurement functions, signal conversion, and information transmission to a centralized dispatcher server. This study explores the option of deploying measurement nodes along the power lines, which would enable the automation of fault monitoring processes, eliminating the need for personnel to travel to measurement sites. This approach would provide the necessary information about the presence or absence of emergency-level zero-sequence voltage and its parameters. Consequently, the research process must determine the methods, technologies, and equipment required for:  
* Generating initial electrical analog signals,
* Converting these analog signals into digital form,
* Transmitting digital signals via wireless communication channels,
* Receiving signals and performing automated centralized processing in the dispatch service for further use as intended. For such measurement nodes, it is crucial to meet the requirements of energy efficiency, ultralow power consumption, and power supply from built-in batteries with a long lifespan of several years. It is evident that to select wireless signal transmission technologies, it is necessary to model the practical transmission environment under interference conditions and identify the technology that meets the requirements for protection against the disruptive effects of intense interference present on power lines. Based on the results of the modeling, the appropriate signal transmission technology must be selected, and the equipment and devices available for practical use, preferably produced in CIS countries, must be determined. Taking into account the selected equipment and technologies, the next step is to design the structure of the information network and develop its schematic. It should be noted that during the development of the project concept, the option of using standard SCADA-based automation systems was considered. However, it was concluded that such a project would be highly complex and costly due to the need to install bulky and expensive power supply equipment for energy-intensive automation devices in field conditions.
###### Abstract  
Laboratory bioassays were conducted to evaluate the insecticidal efficacy of a diatomaceous earth deposit from Greece, for a wide range of stored product insects. In this context, populations of five different insect species, _Tribolium confusum_ [PERSON], the confused flour beetle; _Sitophilus oryzae_ ([PERSON]), the rice weevil; _Rhyzopertha dominica_ (F), the lesser grain borer; _Oryzaophilus surinamensis_ ([PERSON]), the sawtoothed grain beetle; _Cryptolestes ferrugineus_ ([PERSON]), the rusty grain beetle, which cover a major spectrum of insects species of stored products worldwide, were used in the bioassays. The different treatment of diatomaceous earth (DE) rocks (grinding, diatomaceous enrichment, powder granulometry) led to the creation of five types of formulations (namely DE1, DE2, DE3, DE5 and DE6) that exhibited significant fluctuations in their insecticidal efficacy when applied on wheat. In general, some of the modified formulations were found to be very effective against species such as _R. dominica_ and _T. confusum_ that may be difficult to control at the current labeled doses of commercial DE formulations. Overall, our data clearly indicate that this specific Greek deposit has considerable insecticidal properties, which can be further utilized in designing commercial formulations for insect control at the postharvest stages of durable agricultural commodities, provided that the deposit will be modified at specific enrichment and granulometry levels.  
Keywords: diatomaceous earth; inert dusts; natural insecticides; stored product insects; granulometry +
Footnote †: journal: Applied Sciences
## 2 Data Generation and Ingestion Pipeline
### Data Sources
#### 2.1.1 QTLs  
QTL studies have widely been published in scientific articles, particularly in tables or supplementary materials. However, there is no established repository where experimental data on plant-specific QTL studies can be submitted. Therefore, QTL information was extracted from XML-based scientific literature and processed into RDF graphs [35] using the QTLTableMiner\({}^{++}\) (QTM, v1.1.0) [28; 36] and the OpenRefine software [29]. QTM extracted 237 QTLs from a total of 21 _Solanaceae_-specific full-text articles in the Europe PMC repository. 147 of these QTLs (i.e., 85 in tomato and 62 in potato) were associated with exact chromosomal locations based on peak/flanking markers while 108 of the QTLs (i.e., 77 in tomato and 31 in potato) were found to encompass one or more candidate genes.
## 2 Methods  
### Overall Algorithm Design  
The flowchart depicting the proposed image-segmentation-based method for identifying liquid aluminum leakage is provided in Figure 2. The process is divided into two main parts:  
Part 1: Offline modeling of casting mold liquid surface image segmentation:  
* Historical image acquisition: To augment the variety of data samples, movies documenting the casting process in diverse settings were recorded at different intervals. Subsequently, these recordings were divided into individual frames in order to acquire the initial collection of images.
* Establishment of casting mold liquid surface dataset: In light of the lack of study in this field, creating a thorough dataset on the liquid surface of casting molds was crucial. The construction procedure encompassed the extraction of regions, denoising of images, augmentation of data, and annotation of images.
* Enhancement of the DeepLabV3+ casting mold liquid surface segmentation model: To overcome issues such as long training times, large data requirements, and insufficient learning of casting mold liquid surface features in typical DeepLabV3+ models, the model was enhanced by replacing the original feature extraction network with the ResNet-50 network. In addition, the CBAM attention mechanism and transfer learning approaches were introduced.  
Part 2: Online identification of liquid aluminum leakage:  
A. Online image acquisition: The system triggers the start casting signal; then, it uses predetermined parameters to identify the camera detection region and capture real-time RGB photos.  
B. The upgraded DeepLabV3+ model is used to segment the images of the casting mold liquid surface.  
C. Online identification of liquid aluminum leakage: The proposed technique, utilizing OpenCV, is employed to compute the surface area of the casting mold liquid in pixels. The proposed method in this work calculates the change in the indicator of the liquid surface area of the casting mold to determine if there is any leakage of liquid aluminum. This is performed by comparing it to a threshold value.  
Figure 2: Overall algorithm design.