esa-sceva/satcom-chunk-collection huggingface dataset:
# Challenges and Recommendations for Improved Identification of Low ILUC-Risk Agricultural Biomass
## 3 Results
### National Yield Trends  
#### 3.1.1 Yield Growth  
The amount of low ILUC-risk feedstock that can be certified from a given farm depends on the difference between the farm's actual yields and the dynamic yield baseline, which is based on the global average. In this spirit, this section analyses national-level trends in yield growth compared with the global average. The examples in Figure 4 show the fluctuating annual yields in the period 2000-21 (points), overlaid with the best fit line. The global average, calculated from FAOstat data according to the RED II methodology set out in the Implementing Regulation [14], is indicated as a black dashed line for comparison.  
It is evident that some countries' slopes are greater than average (e.g., Bulgaria, which roughly doubles its yield over 20 years), and some are less (e.g., Germany, where yields are more static). There are many contributing factors to a country's yield growth, and it comes as no surprise that some countries out-perform others. Some of the factors are within the control of farmers themselves (possibly supported by national governments), such as improving mechanisation, adoption of appropriate crop varieties, targeting of chemical inputs, soil improvement measures, irrigation management, and protection against pests and diseases. Other factors are environmental and can't be fully controlled at farm level; these include long-term changes in rainfall and temperature patterns and disease prevalence.  
Figure 4: National annual yield for winter wheat for selected countries, with best fit slopes in the legend and the FAO global average slope for comparison.  
In Figure 5, the analysis in Figure 4 is extended to all countries offering sufficient data, with yield slopes presented over the period 2000-21 (note that throughout this paper, each country will be assigned to a consistent colour; for example, the dark blue for Bulgaria in Figure 4 is the same dark blue in Figure 5). The global average is indicated for comparison. Naturally, some countries' yield slopes are greater than the global average, and some are smaller.
## 5 Experimentation
### Directive Generation Frequency  
As we can see in Figure 20, for all the scenarios except for the Taxis scenario, we obtain the best performance by resending the last directive each 900 s, provided no contact has happened before. Nevertheless, for the Taxis scenario, we obtain the optimal performance with a directive frequency of 300 s. We can understand this slight difference, as the Taxis scenario is the sparsest scenario, which implies fewer contacts between nodes. Hence, a more frequent directive beckoning gives the nodes more chances to receive a directive, and consequently, we get better performance.  
Figure 18: Delivery ratio for different _decay thresholds._
# Comprehensive Effects of Atmosphere and Soil Drying on
## 4 Discussion
Less water supply from the soil to the root system induces the plant to reduce water loss and increase WUE through circulating abscisic acid (ABA) with reduced stomatal conductance [42]. Abscisic acid is a plant hormone that plays a crucial role in regulating various physiological processes, especially in response to environmental stress, such as water stress, cold, and drought. When plants experience water stress, ABA is synthesized in certain plant tissues and then transported to other plant organs, where it acts as a signaling molecule to trigger various adaptive responses, such as stomatal closure, regulating gene expression and metabolic processes, or regulating root growth, etc. [43; 44; 45]. Together, this generates a limitation through a decrease in the photosynthetic efficiency, GPP, and NPP of the plant. These adjustments and responses of physiological functions may result in decreased photosynthetic efficiency, total primary productivity, and net primary productivity of vegetation, thereby affecting water use efficiency of vegetation. As is shown in this study, it should be noted that different plant species may exhibit different physiological responses to water stress, thus WUE and CUE may be influenced differently by high vapor pressure deficit and low soil moisture content for different plant species.
## 3 Security Issues in the Cloud
### Cloud Threats  
Threats to the cloud's security include outside intruders, malicious insiders, and attacks like DoS and DDoS. Insiders are the entities that are internal to the organizations and have default permission to access the data. If the internal user is a malicious one with ill intentions, then this can affect the confidentiality, privacy, and integrity of the data [48]. Among these three, the loss of data integrity can result in a more serious issue than the other two. Whenever outside users obtain access to data in an unauthorized manner, then they are known as outside intruders. Through this scenario, the loss of data integrity can cause a critical problem [48]. When a malicious user sends a lot of fake requests to the service provider, making it busy and exhausting its capacity in responding these requests, this is known as a DoS attack. Due to this, the service provider becomes unavailable for its legitimate users, which leads to the loss of service availability [49]. Whenever fake service requests from multiple sources come to make the service provider unavailable, this kind of attack is named as DDoS [49]. Like DoS, a DDoS attack also leads to an availability problem [54].  
Apart from those discussed above, some of the other issues to be mentioned are resource location and multi-tenancy [38]. Data disputes do not occur because of the service provider's fault. One reason for data conflicts may be the geographical residence of a service provider [38]. Consequently, this will have an impact on data privacy. When several users or tenants of the same or separate organizations use the same resources, this is referred to as multi-tenancy [42]. In this case, the privacy and security of user's data may be the key problems.  
The classification of the above-gathered security issues in terms of the five security requirements comprising confidentiality, integrity, availability, authenticity, and privacy is shown in Figure 3.
## 4 Discussion
### Strengths/Limitations  
This review is the first to scope the literature on dancers' energy status and diets to assess overall adequacy of their intake to meet energy demands. It employed a comprehensive approach to database selection, including performing arts databases and grey literature, with minimal restrictions. To understand the complexity of nutrition for dancers, there was extensive data extraction for energy, nutrients, and beverages, as well as other important contextual factors, including eating patterns, nutrition knowledge, and supplement use. All but one paper included in this review reported on ballet dancers, further highlighting gaps in the literature and the need for further research into different genres such as musical theatre and opera, the complexities that define them, and the unique requirements presented in each of these styles of dance.  
However, there are also notable limitations to consider. As a scoping review was selected to scope and synthesize the evidence, we are unable to draw implications or recommendations for practice [55]. Only twelve papers were eligible due to the strict definition for pre-professional dancers and limited research into the artform. With ballet as the predominant genre included in this review, the inferences and recommendations made are primarily representative of trends and interventions found in this specific genre and may not be generalizable to dance as a whole sport. Samples were also predominately female, with male subjects underrepresented or excluded from the studies as there were \"few\" [23]. Five out of twelve included studies were published before the year 2000, and thus may not be reflective of how the dance industry has evolved over time, the growing influence of social media on body image and food knowledge [56], and other sociocultural changes. Data heterogeneity posed a challenge when comparing datasets and referring to sports nutrition recommendations, with only two studies reporting macronutrients by grams per kilogram of bodyweight per day. The validity of the data may have been undermined by recall bias or measurement error. Most of the dietary assessment methods reported rely on memory and self-report and no studies reported the use of the Goldberg method, or any other, to exclude misreporting. Although dual energy x-ray absorptiometry is considered the criterion standard, it is not always accessible or feasible [57]. The field methods of skinsfolds are the least affected by day-to-day variation and restrictive and/or compensatory behaviors [57; 58]. In fact, the measurement of body composition has potential to cause harm in athletes and should only be done if justified [59].
## 2 Materials and Methods
### Data Analysis and Multivariate Modelling  
The Hypertools open-source interface for hyperspectral image analysis was used for data mining and analysis. To remove the noisy background around the bacterial cell, an image masking was created on the basis of k-means clustering with k = 6. The 4 k-means classes related to the background were removed as belonging to the mask, and an image composed by the pixels belonging to the bacterium only was generated.  
Spectra preprocessing for PCA consisted in baseline correction (least squares fitting with a 2 nd-order polynomial) and smoothing (2 nd-order Savitsky-Golay method with 41 points window), the range between 666 cm\({}^{-1}\) and 3120 cm\({}^{-1}\) was considered. The spectra were mean-centered before PCA analysis.  
Since PCA revealed two relevant PCs, the MCR-ALS method was set to resolve 2 components based on the recovery of a bilinear model D = CS\({}^{\rm T}\) + E, where D is the raw measurement, C is the matrix of concentration profiles, and S\({}^{\rm T}\) the matrix of related pure spectra. E contains the experimental error of raw measurement. Because the Raman image dataset is structured as a data cube (3D), where two dimensions (x and y) are the pixel coordinates and the third is the spectral dimension, the data cube needs to be unfolded into a data matrix D, where the rows are the pixel spectra and the columns are related to the frequency (Raman shift) or, in general, spectral channels. The matrix D contains the raw pixel spectra, which will be decomposed into the concentration profiles (C matrix) and pure spectra (S\({}^{\rm T}\) matrix) of the constituents present in the hyperspectral image. Every row of S\({}^{\rm T}\) represents the pure spectrum of one of the image constituents. By taking a row of the C matrix, information on the chemical composition of the related pixel can be obtained, whereas by taking a column, the pixel-to-pixel variation of the concentration of a particular image constituent can be seen. Then, by folding back each C column to recover the original two-dimensional (2D) or 3D image configuration, the distribution map of each particular image constituent can be obtained [19]. The number of image constituents was determined by PCA on the whole image. The alternating optimization started by using the original preprocessed measurement, D, and an initial guess of either the C or the S\({}^{\rm T}\) matrices. Non-negativity constraint was imposed for the two model components (MCR1 and MCR2).
## 3 Results  
### Datasets: CT Examinations Included and Technical Parameters  
Center 1 retrospectively included 200 chest CT studies including the aortic arch and supra-aortic vessels belonging to 200 individual patients, 100 with CILCA (60 type 1 CILCA and 40 type 2 CILCA) (positive class), and 100 without CILCA (negative class). All CT examinations were acquired on a 64-row CT scanner (Somatom Definition, Siemens Healthineers,Erlangen, Germany) but with different acquisition, reconstruction, and contrast medium conditions. Acquisition collimation ranged from 0.6 to 1.2 mm, slice thickness reconstruction from 1.0 to 5.0 mm, tube voltage between 100 and 140 kVp. Contrast-enhanced scans the protocol included the intravenous injection of iopamidol (Iopamiro, Bracco Imaging S.p.A., Milan, Italy) at 1 mL/kg followed by 30-70 mL of saline solution with a flow rate of 3.0-5.0 mL/s. Among the 200 patients, the median age was 76 years (interquartile range 69-84 years), and 97 were males (49%).  
Patients' demographics and CT technical parameters for the CILCA and non-CILCA groups are reported in Table 1.  
Center 2 retrospectively included 102 chest CT datasets, including the aortic arch and supra-aortic vessels belonging to 102 individual patients, 23 with CILCA (13 type 1 and 10 type 2) (positive class), and 79 without CILCA (negative class). All CT examinations were acquired on a 64-slice scanner (SOMATOM, Siemens, Erlangen, Germania). Collimation ranged from 3 mm, slice thickness reconstruction at 0.6-1.2 mm, tube voltage was set between 100 and 140 kVp. When contrast-enhanced scans were available, the protocol included the intravenous injection of iopamidol (Iopamiro, Bracco Imaging S.p.A., Milan, Italy) ranged from 0.8 to 1.2 mL/kg followed by 30-70 mL of saline solution with a flow rate of 3.0 mL/s.  
Among the 102 patients, the median age was 65 years (interquartile range 55-73 years), and 66 were males (65%). Data for the CILCA and non-CILCA groups at Center 2 are reported in Table 2.
# Impact of Direction Parameter in Performance of Modified AODV in VANET
## 1 Introduction
In our method, the carry and forward methods are applied so that the nearest vehicle to the destination carries the information as long as the source can send the packets to those vehicles going in the same direction, thereby making the route more stable. Filtering is done in two stages to remove the unwanted nodes moving in the opposite direction. In the above-mentioned studies, the packets were sent to the closest vehicles in all directions; but in our approach, we used a priority-based routing where the packets were routed to the nodes that were closest and were moving in the same direction. This new method uses two filtering steps and, therefore, increases the throughput to more than 10%, compared to other state-of-the-art techniques, reduced the packet loss to 2%, and improved the E2E delays as compared to other state-of-the-art methods.
## 2 Methodology
### Voronoi Vertex-Based Search Model Based on Weight-Based Heuristic
The heuristic weight value \(h_{p}\) of each Voronoi vertex \(v_{p}\) in the computational graph is calculated, and verification is carried out to ensure that there is no obstruction in the line connecting the vertices and the current planning starting point. The weight formula is defined as follows:  
\[ \begin{split} h_{p}=w_{1}\times\frac{ds_{p}}{\text{dmax}}+w_{2} \times\text{exp}\bigg{(}-1\times\bigg{(}\frac{\text{dg}_{p}}{\text{dmax}} \bigg{)}\bigg{)}+w_{3}\times\text{exp}\Big{(}-1\times\bigg{(}\frac{\text{ theta}_{p}}{\text{pl}}\bigg{)}\bigg{)}+\\ w_{4}\times\frac{h_{\text{pero}}}{H_{max}}\end{split}  \tag{12}\]  
If there is any obstruction, then \(h_{p}=0\). Among them, \(ds_{p}\) represents the distance from the current vertex to the planned starting point, dmax represents the distance between the start and end points, \(\text{dg}_{p}\) represents the distance from the vertex to the end point, and theta\({}_{p}\) represents the angle between the vector from the current vertex to the starting point and the vector from the current vertex to the ending point. pi is a constant, h_vec\({}_{p}\) represents the height from the vertex to the ground, and \(H_{max}\) represents the maximum flying height of the drone. The weighting parameters are defined as follows:  
\(w_{1}\): Path proximity-to-origin weight. Increasing this value prioritizes waypoints closer to the starting point, preventing premature deviations from the initial heading. \(w_{2}\): Path proximity-to-goal attenuation weight. Higher values emphasize feature points near the destination, accelerating the terminal approach. \(w_{3}\): Path directional consistency weight. Enhanced weighting selects waypoints that maintain heading alignments, reducing course oscillations. \(w_{4}\): Flight altitude safety margin weight. Increased weighting favors waypoints with greater vertical clearance from terrain/obstacles. The sum of the four weights is designed to equal 1. In this algorithm, \(w_{1}\) is 0.1, \(w_{2}\) is 0.3, \(w_{3}\) is 0.5, and \(w_{4}\) is 0.1. The Voronoi vertex \(h_{\text{max}}\) corresponding to the largest value in the sequence is selected, and \(h_{\text{p}}\) is the next waypoint connected to the current starting point. The Euclidean distance between this vertex and the planned endpoint is recorded as \(D=\)\(|v_{\text{max}}-end|\). Then, this vertex is updated as the new search starting point, the original starting point is removed from the search space, the weights are recalculated, and the vertex with the decreased \(D\) value is selected; this process is then repeated until the planned endpoint is found. The schematic of the rapid path search method using Voronoi vertices is shown in Figure 2. By iteratively selecting \(start\), \(v_{4}\), \(v_{3}\), and \(end\) as waypoints, a quick generation of the UAV's trajectory within the flight plane is achieved.
## 4 Fault-Localization Method Using Memory-Updated Patterns
### Fault-Localization Process  
An ECU that has passed a unit test performs an integration test under various conditions. Therefore, when a fault occurs in the integration test, it means that the function that operates normally in the unit test is integrated and operates abnormally. In this paper, we use the memory-updated feature and the memory-updated patterns of the ECU that are operated normally in the unit test to find the faulty pattern of the memory-updated information of the faulty integrated test and present it as the fault occurred point. Addresses to be updated at that time are presented as fault candidates.  
Figure 7 summarizes the process of localizing the location and occurrence of a fault with the memory-updated patterns prepared in Section 3 and the image of each frame. The process of the proposed fault-localization method is divided into a unit test phase and an integrated test phase. First, in the unit test phase, the updated feature classification model is learned by using the normal updated pattern prepared in the data preparation and the labeled image file. Next, in the integrated test phase, the updated feature classifier is used to classify the image files prepared previously according to the memory-updated feature. The classified frame is compared to the memory-updated pattern of normal operation to determine the sequence of the suspected fault that is not matched. This sequence frame and the previous sequence are specified as fault-occurrence points, and the addresses to be updated in the corresponding sequence are presented as fault candidates.  
Figure 7: Process of fault localization using memory-updated patterns.
## 3 Results and Discussion
### Changes in the Thickness of S. aureus Biofilms  
Two fluorescent dyes were applied to assess the damages caused by PAH to _S. aureus_ biofilms. The green fluorescent SYTO 9 stains only live bacteria within biofilms, while the red fluorescent PI stains only nonviable bacteria [27]. The LSCM images of _S. aureus_ biofilms after PAH treatment exhibited in Figure 3A further confirmed the inactivation of bacterial cells in biofilms. The image of biofilms without PAH treatment was mainly green. The intensity of the red color increased upon increasing incubation time. The merged image of biofilms after the 30 min incubation was almost yellow, suggesting that the majority of live _S. aureus_ cells in biofilms were non-viable after PAH treatments. The initial thickness of _S. aureus_ biofilms (Time 0) was around 10.7 \(\upmu\)m. Meanwhile, DIW and H\({}_{2}\)O\({}_{2}\) solution had no significant (\(p>0.05\)) effect on the thickness of the biofilm. After PAH treatment, the thickness of the biofilm decreased significantly (\(p<0.05\)) to 8.1 \(\upmu\)m when the incubation time was prolonged to 30 min (Figure 3B). The thickness of dead biofilm cells increased from 0.4 to 6.2 \(\upmu\)m and the thickness of live cells reduced from about 10.3 to less than 1.9 \(\upmu\)m. Results suggested that PAH treatment might destruct the _S. aureus_ (ATCC 6538) biofilms by killing live cells. With the reduced thickness of biofilms, the protective effect of the biofilm matrix was gradually lost so that more _S. aureus_ cells were directly in contact with PAH.  
According to the results of the thickness of _S. aureus_ biofilm, the _Z_-axis LSCM images of 1-4 layers (0-8 \(\upmu\)m) biofilms were selected, as shown in Figure 4A. With the increase in the PAH treatment time, the red dots of the first layer (0-2 \(\upmu\)m) gradually increased, indicating that the cell membranes in these biofilm layers were gradually destroyed. With the extension of biofilm depth, especially in the fourth layer of biofilm (6-8 \(\upmu\)m), the red dots gradually decreased, indicating that the depth of action on _S. aureus_ biofilm was 6-8 \(\upmu\)m after 30 min of PAH treatment.  
The percentage of intact cell membrane structure in biofilms after PAH treatment was shown in Figure 4B. Before PAH treatment, about 98% of _S. aureus_ had a complete cell membrane structure. With the extension of treatment time, the percentage of _S. aureus_ with complete cell membrane structure showed a significant (\(p<0.05\)) downward trend, and after 30 min treatment, the percentage decreased to 28.27%. This result was higher than the number of bacteria with reproductive ability in the biofilm as exhibited in Figure 1B. This phenomenon might be attributed to some bacteria cells still had membrane structure after PAH treatment, but intracellular contents had been destroyed and the ability to reproduce was lost.