esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Distribution of Fatty Acids in Oils by GC-FID
Figure 2: Composition of total saturated, monounsaturated, and polyunsaturated fatty acids from the oil samples which was determined by GC-FID and percentage distribution of fatty acids (**a**) sunflower; (**b**) sesame; (**c**) hemp; (**d**) walnut; (**e**) linseed; (**f**) sea buckthorn. In the case of saturated fatty acids, it was observed that the concentration was similar in the samples of sunflower, hemp, walnut, and linseed oil, the average concentrations being around 10.08-12.71% in each type of oil. A slight increase in concentration was observed in sesame oil samples where the average concentration was 16.64%. Regarding monounsaturated fatty acids, there were significant differences, varying between the types of oil. The lowest concentration of monounsaturated fatty acids was observed in the case of hemp and walnut oil samples (14.55%, respectively 19.70%) increasing in the case of linseed and sunflower oil samples. In the samples of sesame oil, the average concentration was 41.08%, and in the case of sea buckthorn oil samples, the average concentration was 49.79%. The compound by which sea buckthorn oil could be identified compared to the other oil samples investigated was palmitoleic acid, which in the case of sea buckthorn oil samples was 35%, and in the rest of the oil samples, it was below 3%. In the case of polyunsaturated fatty acids, the average concentration was closer among the types of oil. In the sesame and hemp oil samples, the concentration varied between 42.28 and 74.29%, and in the case of the sunflower, walnut, and linseed oil samples, the average was between 62.71 and 70.22%. From the results obtained, it can be seen that linseed oil differed from other oil samples in terms of alpha-linolenic acid, compound which in the case of sunflower, sesame, and sea buckthorn oils, was found in a concentration below 2%; in the samples of walnut, 11%; hemp, 15%; and the highest concentration (50%) in linseed oil. Oleic acid concentrations ranged from 12 to 19% for hemp, walnut, and sea buckthorn oil samples, and from 20 to 40% for sunflower, sesame, and linseed oil samples. Stearic acid had close concentrations in all investigated oil samples. Linoleic acid was in almost all samples between 40 and 65%, except for the samples of linseed oil about 15% and sea buckthorn oil 6%. From the results obtained, the profile of fatty acids in oils varied with the type of oil sample studied.
## 1 Introduction  
Biochar is a carbon-rich substance that is a by-product of slow pyrolysis [1]. Typically, the biochar produced is alkaline, has a high surface area, and has a large cation exchange capacity [2, 3]. These properties have made it attractive for various end uses, including nitrogen management. Nitrogen fertilizers are essential to crop production, but application often results in a variety of losses. Production of nitrous oxide--a greenhouse gas--can occur under certain soil conditions, and nitrogen-rich wastewater can degrade surface and ground water leading to serious impacts on environmental and human health [4]. Improving soil nitrogen retention is an important strategy to mitigate these risks as well as increase crop yields. Nitrates however, can be particularly challenging to manage as they are highly mobile in soil systems [5]. Nitrates are known to cause health issues when consumed at certain concentrations, so limiting their movement to drinking water sources is especially important.  
Biochar has shown potential to accumulate nitrogen in many forms [6]. A number of studies have found reductions in nitrate leaching from biochar amended soils [7, 8, 9]. It has been hypothesized that certain conditions can lead to nitrate retention, however the mechanisms are currently unknown [10, 11, 12, 13]. In order to better assess and improve the performance of these systems, sound methodologies are needed to quantify nitrate uptake by biochar, which requires nitrate extraction for measurement. The most common method used to extract nitrate from biochar is derived from the soil KCl inorganic nitrogen extraction method [14]. This method involves mixing soil or biochar 1:4 (w/v) with a 2 M KCl solution and agitating for one hour at room temperature. However, it has been shown that this method results in incomplete nitrate recovery from biochar [12, 13, 15, 16]. Several studies have shown that repeated KCl extraction increased the mass of nitrate extracted from cocomposted biochar over the standard one-hour extraction, with higher temperatures and longer agitation further increasing extraction [12; 13; 15]. However, the initial amount of nitrate held by the biochar in these studies was unknown, so extraction efficiency cannot be determined. In this study, nitrate extraction methods are examined to determine the procedures that maximize extraction efficiency and quantify the mass fraction extracted.
# A Novel Methodology for Planning Urban Road Safety Interventions
## 4 Validation of the Proposed Methodology via Case Study Application
### Cost-Benefit Analysis of Safety Interventions in Parma
#### 4.4.2 Critical Point B  
Two pedestrian crossings (B1 and B2) were analyzed for safety improvements. Two traffic calming proposals were evaluated: (1) installing refuge and anti-overtaking islands and (2) adding speed cushions. The iRAP model estimated the number of fatalities and serious injuries preventable by these measures, which could also be integrated for combined benefits (see Table 5).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Intervention Option (1)** & **Unitary Cost [€]** & **Unit** & **Quantiity** & **Cost [€]** \\ \hline Traffic light system & 16,253.20 & each & 1 & 16,253.20 \\ Excavation for laying the piles & 79.05 & m & 2 & 158.10 \\ Horizontal road markings & 6.93 & m\({}^{2}\) & 6 & 41.58 \\ Total & & & & 16,452.88 \\ Real cost, VAT included (\(\times\)1.35) & & & & 22,211.39 \\ \hline
**Intervention option (2)** & **Unitary Cost [€]** & **Unit** & **Quantiity** & **Cost [€]** \\ \hline Partial demolition of the pavement & 5.30 & m\({}^{2}\) & 90 & 477.00 \\ Removing the curbs & 4.25 & m & 60 & 255.00 \\ Excavation & 3.99 & m\({}^{2}\) & 90 & 359.10 \\ Subgrade preparation & 0.84 & m\({}^{2}\) & 90 & 75.60 \\ Paving & 23.80 & m\({}^{2}\) & 90 & 2142.00 \\ Curbs & 18.92 & m & 60 & 1135.20 \\ Horizontal road markings & 1.00 & m & 120 & 120.00 \\ Drainage channel & 36.48 & m & 60 & 2188.80 \\ Total & & & & 6752.70 \\ Real cost, VAT included (\(\times\)1.35) & & & & 9116.14 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Cost estimation of proposed interventions.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**[€/Year]** & \multicolumn{2}{c}{**SC\({}_{\text{F}}\)**} & \multicolumn{2}{c}{**SC\({}_{\text{SI}}\)**} & \multicolumn{2}{c}{**SC\({}_{\text{TOT}}\)**} & \multicolumn{2}{c}{_SC\({}_{\text{saved}}\)_} \\ \cline{2-9}  & **East** & **West** & **East** & **West** & **East** & **West** & **East** & **West** \\ \hline Opt.0 & 729,564.60 & 48,719.40 & 1,033,941.56 & 69,045.30 & 1,763,506.17 & 117,764.70 & - & - \\ Opt.1 & 131,470.79 & 28,715.37 & 186,320.87 & 40,695.53 & 317,791.66 & 69,410.90 & 1,445,714.50 & 48,353.80 \\ Opt.2 & 248,686.54 & 41,534.12 & 352,439.45 & 58,862.30 & 601,125.99 & 100,396.43 & 1,162,380.18 & 17,368.27 \\ \hline \hline \end{tabular}  
The intervention costs were estimated (see Table 4) using the latest regional price list for public works and market research for specific traffic-calming elements. For narrowing the carriageway from two lanes to one, the project includes a 1.5 m widening of the sidewalk over a 60 m stretch.  
\end{table}
Table 3: SC saved for the east and west directions.
## 3 Integration of Multi-Domain MEC Environments
### Business Implications
#### 4.2.4 Common Abstraction Models  
A common understanding of the resources (i.e., network, compute and storage) and the capabilities per domain is needed. Since the information will be necessarily abstracted, the same abstractions have to be handled by the different administrative domains in order to ensure consistency. Such abstractions at technical level imply the utilization of common information and data models for the resources to be configured and used. It can be foreseen the interchange of information by means, e.g., YANG models for what is supported per each domain. This is also applicable to the capabilities of monitoring and telemetry for the population of performance information across domains.  
2.5 Interfaces, Protocols and APIs for Remote Control and Management of Functions and Slices in Other Domains  
The possibility of deploying applications and services across administrative domains requires the design and specification of protocols or APIs which can allow the multi-domain components to not only exchange information for the provision of the service, but also expose interfaces for the application and service lifecycle management, as if they were implemented on a single domain. SLA enforcement mechanisms, as well, have to be integrated into the MEC orchestration framework. Relevant monitoring and maintenance information needs to be interchanged for managing any committed SLAs.
## 3 The Proposed Method
### Greedy NMS  
Greedy NMS, as the initial Non-Maximum Suppression algorithm, plays a critical role in removing duplicate proposals generated by pedestrian detectors. Each proposal is represented by a pair of confidence scores and a bounding box. The algorithm follows the steps outlined below:  
1. Sort all the bounding boxes in set \(B\) in descending order based on their confidence scores.
2. Calculate the intersection-over-union (\(IoU\)) of the first bounding box \(M\), which has the highest confidence score, and the sequenced bounding boxes \(b_{i}\). If \(IoU(M,b_{i})\) exceeds the rigid threshold \(N_{t}\), the confidence score of \(b_{i}\) will be set to zero.
3. Move the proposal \(m\), with bounding box \(M\), into the set \(F\), which is initialized with an empty set.
4. Repeat the above three steps for the remaining bounding boxes in \(B\) until complete traversal.  
The set \(F\) represents the final predictions of the pedestrian detector. The above process can be expressed as the following re-scoring function:  
\[s_{i}=\left\{\begin{array}{l}s_{i},IoU(M,b_{i})<N_{t},\\ 0,IoU(M,b_{i})\geq N_{t},\end{array}\right. \tag{1}\]  
where \(s_{i}\) and \(b_{i}\) represent the confidence score and bounding box of the \(i\)th proposal, and \(N_{t}\) is a constant rigid threshold that ranges between 0 and 1.  
Despite several studies [14; 15; 16] aiming to decrease the incorrect removal of occluded pedestrians by adjusting the single threshold or weakening the suppression operation in Greedy NMS, these approaches tend to generate more false positives while still mistakenly removing highly occluded pedestrians, as shown in Figure 1. Therefore, striking a balance between removing false positives and retaining occluded pedestrians in NMS remains a challenging task.
## 4 The Proposed Scheme: Architecture and Components
### The Communication Layer  
This layer is responsible for establishing the communication between the UAVs at the COVID-19 layer and the ground control stations at the governance layer. The following communication architecture employs 6G services that provide a vast spectrum and are well-suited for delay-sensitive applications. Various characteristics of 6G communication are as shown in Figure 4. The user-experienced data rate in 6G is \(\approx\)1 Gbps, which is almost ten times faster than 5G. The energy and spectrum efficiencies of 6G are 10-100 times and 5-10 times those of 5G, respectively [14]. Moreover, 6G allows the virtualization and slicing of networks through software-defined networking (SDN) and network function virtualization (NFV) of the physical and medium access control (MAC) sub-layer. To achieve the same, ML/DL techniques are employed to train the network for resource intelligence and form informed decisions on the trained network models. Thus, virtualization in 6G will prove to be the key enabler of a highly digitized society, where everything is autonomous and connected.  
Figure 3: UAV-based architectural solution to deal with the COVID-19 pandemic.  
[PERSON] et al. [40] suggested that the 6G communication network needs the support of new services beyond the 5G network as computation-oriented communications, contextually agile eMBB communications, and event-defined uRLLC. In similar directions, the 6G layer of the proposed scheme provides key services for various intelligent network orchestration for wired and wireless communication services. These services include (i) _operational intelligence_ that allocates the network resources (such as spectrum and power) proficiently to optimize the highly complex network operations, (ii) _environmental intelligence_ that helps to realize diverse application scenarios such as UAVs, autonomous vehicles, and auto-robots, and (iii) _service intelligence_ techniques such as ML/DL that assist many human-centric applications (i.e., e-healthcare, information searching, and learning and educational systems) intelligently to raise user satisfaction [41].  
6G networks in UAV communication play a vital role in controlling the UAVs efficiently and effectively. They also allow multiple UAVs to operate in a region because of their wide spectrum and high device connectivity density. The AI characteristic of 6G makes the proposed system precise, intelligent, and autonomous by monitoring real-time network performance parameters to maintain users' desired quality of experience (QoE) [42].
# Measurement of the Impact Loads to Reduce Injuries in
## 4 Discussion
On the other hand, a significant correlation was observed between the maximum load and the gymnast's weight (\(p=0.001\)). During landing, the ground impact applies forces and moments of force that accelerate hip, knee, and ankle flexion (dorsiflexion), giving rise to kinetic energy to be absorbed by the body [32]. Analyzing together Figure 12 and Table 4, it can be observed that landing on the forefoot induces ratio values of less than 13 times body weight. Regarding the landing on the midfoot, for all gymnasts except for gymnast number 10, this value was greater than 13 times body weight. These values suggest a lower maximum load is exerted when performing a forefoot landing. Landing must be made by the forefoot to allow the kinetic energy to be absorbed gradually up to the maximum flexion of the knee joints. When the landing is on the midfoot, the greatest absorption of kinetic energy occurs in the foot, which can lead to an imbalance in the gymnast, increasing the tension in the bone structures of the foot. According to [PERSON] et al. [37], forefoot landings are better for load absorption but are associated with smaller knee flex angles at initial contact, which can cause injuries, namely in the foot, ankle, and/or knee. Repeated landings and the loads absorbed during these landings contribute to the serious injuries suffered by many gymnasts; it is therefore important to assess these loads so that coaches are aware of the high loads their gymnasts are exposed to during landings, in order to promote training strategies in terms of landing technique to reduce these loads [7]. Thus, it is important to develop a platform, perfectly adapted to acrobatic gymnastics, which can give coaches and athletes the maximum load values reached during landings. [PERSON] et al. [38] carried out a study to evaluate the relationship between the energy absorption of the lower limbs and biomechanical factors, having concluded that injuries are influenced both by the magnitude and the moment of energy absorption by the lower limbs during landings. This platform is designed to replicate training equipment, enabling gymnasts to analyze jumps and improve their performance while minimizing the risk of injuries. The development of a platform that represents the same landing conditions improves the performance of athletes. As this is a prototype platform, it can be improved in terms of acquisition frequency, coupled with software that analyzes the jumps made during training.
## 2 Materials and Methods
### Catalysts Characterization  
X-ray diffraction (XRD) analyses were performed with a Miniflex-600 horizontal-scan powder diffractometer (Rigaku, Tokyo, Japan) using Cu-K\(\alpha\) radiation with a graphite monochromator on the diffracted beam. The crystallite size was calculated according to the Scherrer equation, Equation (1).  
\[\mathrm{D}=(\mathrm{K}\,\lambda)/(\beta\ \cos\theta) \tag{1}\]  
where D is the crystal size, \(\lambda\) is the X-ray wavelength (0.154 nm with Cu K\(\alpha\) generator), K is the shape factor (0.9), \(\beta\) is the width at half maximum of the peak (i.e., FWHM), and \(\theta\) is the Bragg angle.  
Diffuse reflectance (DR) UV-Vis spectra of samples were recorded on a UV-3600 Plus (Shimadzu, Kyoto, Japan) in the range of 200-800 nm, using an integrating sphere and BaSO\({}_{4}\) as the reference standard. The results were processed according to the Kubelka-Munk elaboration and with Equation (2), using the reflectance spectra as input data [21].  
\[\mathrm{F}(\mathrm{R}_{\infty})=(1-\mathrm{R}_{\infty})^{2}/2\mathrm{R}_{\infty} \tag{2}\]  
The calculated (\(\mathrm{F}(\mathrm{R})\mathrm{h}\mathrm{v}\))\({}^{1/\mathrm{r}}\) (with r = 2 or \(\frac{1}{2}\) for direct and indirect band gaps, respectively) was plotted versus hv to obtain the band gap of each sample according to the Tauc Equation (3) [22].  
\[(\mathrm{F}(\mathrm{R})\mathrm{h}\mathrm{v})^{\frac{1}{n}}=\mathrm{B}\big{(} \mathrm{h}\mathrm{v}-\mathrm{E}_{\mathrm{g}}\big{)} \tag{3}\]  
All the samples were diluted one-to-one or one-to-two with BaSO\({}_{4}\), the reflectance standard that prevents saturation when mixed with samples that absorb too much. Therefore, the shape of the curves was not affected by the blending, but the absolute value of reflectance cannot be compared.  
Scanning electron microscopy (SEM) images and energy dispersive X-ray analysis (EDX) spectra were obtained using a JSM-7900F Schottky field emission scanning electron microscope (JEOL, Tokyo, Japan) operating at an accelerating potential of 20 kV.
# Survey on Ranging Sensors and Cooperative Techniques for Relative Positioning of Vehicles
###### Abstract  
Future driver assistance systems will rely on accurate, reliable and continuous knowledge on the position of other road participants, including pedestrians, bicycles and other vehicles. The usual approach to tackle this requirement is to use on-board ranging sensors inside the vehicle. Radar, laser scanners or vision-based systems are able to detect objects in their line-of-sight. In contrast to these non-cooperative ranging sensors, cooperative approaches follow a strategy in which other road participants actively support the estimation of the relative position. The limitations of on-board ranging sensors regarding their detection range and angle of view and the facility of blockage can be approached by using a cooperative approach based on vehicle-to-vehicle communication. The fusion of both, cooperative and non-cooperative strategies, seems to offer the largest benefits regarding accuracy, availability and robustness. This survey offers the reader a comprehensive review on different techniques for vehicle relative positioning. The reader will learn the important performance indicators when it comes to relative positioning of vehicles, the different technologies that are both commercially available and currently under research, their expected performance and their intrinsic limitations. Moreover, the latest research in the area of vision-based systems for vehicle detection, as well as the latest work on GNSS-based vehicle localization and vehicular communication for relative positioning of vehicles, are reviewed. The survey also includes the research work on the fusion of cooperative and non-cooperative approaches to increase the reliability and the availability.  
vehicle sensors; laser scanner; GNSS; cooperative; vehicle-to-vehicle; relative positioning; localization +
Footnote †: journal: Sensors
# Hybrid Ring- and Tree-Topology RoF Transmission System with Disconnection Protection
## 2 Experimental Setup
The signals injected into the Add/Drop_2 of the SBOADM will be routed to the I/O_P2 of the SBOADM and moved forward to the CO along with the residual downlink light signals. Uplink RoF signal energy will continue to be consumed in the process, along with the residual downlink RoF signal energy. In parallel, the RoF signal injected into the Add/Drop_1 of the SBOADM will be routed to the I/O_P1 of the SBOADM and routed back to the CO along the reversed transmission pathway of the downlink signals. The uplink optical signal in the CO end is converted back to the electrical domain and verified by instruments.
## 3 Results
### Results for Refinement Factors \(r\), \(r_{mod}\), and \(r_{\text{exp}}\)
#### 3.2.2 An Attempt with Exponential-Forgetting Filters  
An alternative idea is to reduce the integral gain in (5) from 1 to, say, \(2/3\), i.e., change the exponent from \(1/p\) to \(2/(3p)\) by constructing another modified refinement factor. As this corresponds to an exponential-forgetting filter, we denote this new refinement factor by \(r_{\text{exp}}\). However, this does not result in a smoother step-size sequence, although it is well known to have such an effect in the sequential case. Again, this appears to be due to the ceiling function. The corresponding figures can be found in the GitHub repository [29].  
Figure 8: The experiment shown in Figure 5 was re-simulated using the modified \(r_{mod}\) refinement factor with a compensating constant \(c=0.4\).  
Figure 7: The experiment shown in Figure 4 was re-simulated using the modified \(r_{mod}\) refinement factor with a compensating constant \(c=0.4\).  
Figure 9: The experiment shown in Figure 6 was re-simulated using the modified \(r_{mod}\) refinement factor with a compensating constant \(c=0.4\).  
For sharp tolerances, the results are effectively identical. In contrast, the exponential-forgetting alternative \(r_{\text{exp}}\) provides less benefit.  
Based on Figures 4-12, it is qualitatively clear that \(r_{mod}\) is the best among the three discussed refinement factors. Naturally, one may ask whether the suppression of zigzag patterns is also beneficial in a quantitative sense. To investigate this, we compare the \(r_{mod}\) refinement factor with the less zigzag-prone \(r\) refinement factor rather than with the more erratic \(r_{\text{exp}}\). As shown in Table 1, \(r_{mod}\) makes the step-size sequences smoother, improves tolerance proportionality, and significantly reduces computational cost.