esa-sceva/satcom-chunk-collection huggingface dataset:
# The Effect of Hydrolysis on the Antioxidant Activity of Olive Mill Waste
## 3 Results
### Phenolic Content
#### 3.1.2 Total Flavonoid Content  
Figure 3 shows that methanol macerated extracts had the highest TFC. In fact, the highest concentration was found in methanolic-derived extracts obtained from the cultivar of Leccino (155.97 \(\pm\) 2.44), and the lowest was found in Picholine (22.42 \(\pm\) 1.48), whereas for alkaline and acid hydrolysis, the highest concentration was found in the Maltese cultivar 'Malti' (alkaline: 95.92 \(\pm\) 9.56 and acid: 83.62 \(\pm\) 10.75). The lowest concentration in alkaline hydrolysis was found in Picholine (13.36 \(\pm\) 0.41), whilst the lowest concentration for acid hydrolysis was found in the cultivar of 'Bajda' (26.70 \(\pm\) 1.32). All concentrations were expressed in mg QE/g (Quercetin equivalent).  
Figure 2: Mean values for total phenolic content (TPC) of olive mill waste extracts using three different solvent extraction methods—methanol, acid and alkaline (black, red and green bars, respectively) with error bars showing \(\pm\) 1 SD for three independent trails.
## 4 Experiments and Results  
### Datasets and Evaluation Metrics  
The experiment was conducted using three public datasets: REFUGE Challenge [35], DRISHTI-GS [36] and RIM-ONE v3 [37]. All images were cropped to a size of 576 \(\times\) 576 pixels according to the approach proposed by [6]. REFUGE dataset : The REFUGE dataset contains 1200 images in total, which is divided into three parts. There are 400 images that can be used for training, 400 images for validation and 400 images for testing. DRISHTI-GS dataset : The DRISHTI-GS dataset contains 101 images: 51 images are randomly selected for training and 50 images for testing. RIM-ONE v3 dataset: There are 159 images in the data set. In this paper, we randomly selected 99 images for training and 60 images for testing.
## 4 Problem Definition and Modeling
### Profit Function  
For a station \(s\) with \(m\) charger types, let \(c_{i}\) denote the available charging power of the charger \(t_{ij}\) and \(C(s)\) is the whole capacity of the charging station \(s\). \(C(s)\) is computed as: \(C(s)=\sum_{i=1}^{m}t_{i}c_{i}\).  
Intuitively, a charging station with a larger capacity should serve more electric vehicles and have a wider range of influence. Hence, the influential radius \(r(s)\) is defined to indicate the distance within which the charging station attracts electric vehicles. The decay of influence with increasing distance is described using a Gaussian function:  
\[r(s)=r_{max}e^{-C(s)/2} \tag{12}\]  
where \(r_{m}ax\) is the maximal influential radius of the charging station \(s\).  
The service scope for CS \(s\)\(scope(s)\) and the coverage for the vertex \(v\)\(cov(v)\) are defined as follows:  
\[scope(s)=|\{vs.\in V|d(v,s)\leq r(s)\}| \tag{13}\]  
\[cov(v)=|\{s\in S|d(v,s)\leq r(s)\}| \tag{14}\]  
where \(d(v,s)\) represents the Euclidean distance between charging station \(s\) and vertex \(v\). The value of \(scope(s)\) for charging station \(s\) corresponds to the number of vertices within the scope of station \(s\). Similarly, the coverage \(cov(v)\) for vertex \(v\) indicates the number of charging stations within the influential radius of vertex \(v\).  
When the number of charging stations is not infinite, we anticipate that the limited number of charging stations can provide services to a greater number of electric vehicles. Additionally, for a single vertex \(v\), having more choices is considered advantageous. Consequently, the profit function is defined as:  
\[profit(p)=\Big{(}\frac{1}{|V|}\sum_{v\in V}cov(v)+\frac{1}{|S|}\sum_{s\in S}scope (s)\Big{)} \tag{15}\]
# Optimization of Virtual Loudspeakers for Spatial Room Acoustics Reproduction with Headphones
## 2 Virtual loudspeaker Position Optimization  
The sound reproduction system used in this paper is similar to one presented by [PERSON] et al. [10]. The system performs SDM analysis and NLS reproduction as described in Section 1, resulting in a sparse impulse response (IR) for each reproduction loudspeaker. Finally, those sparse IRs are convolved with the audio signals to create the spatial sound. The used version of the SDM also post-equalizes the loudspeaker IRs as the rapid channel changes of the IR cause whitening of the signal. It should be noted that the optimization of the reproduction loudspeaker positions is done for SDM data before the convolution step. In other words, the optimization requires a spatial sound reproduction technique that has information on the spatial IR for each sound source, and thus cannot be applied to an arbitrary spatial sound reproduction technique.  
The NLS requires information on the reproduction loudspeaker positions in order to be able to distribute the samples properly. As mentioned in Section 1, the positions are usually static and not changed according to the acoustics of the space being reproduced. The method presented in this section replaces these static loudspeaker positions with optimal ones that are calculated from the RIR of the room being reproduced. The system used in reproduction is therefore the same as described above except for the way the loudspeaker setup is determined.  
Figure 1 outlines the position optimization process which is done in two steps in its most basic form. First, SDM samples including RIR pressure and directional metadata (azimuth and elevation) are weighted according to their energy as well as their spatiotemporal properties. Then, loudspeaker positions are initialized based on the calculated weights and the final positions are obtained by clustering weighted DOAs iteratively until convergence. The clustering is computationally heavy by default due to a large number of RIR samples. To accelerate this process, the weighting data are reduced to a discrete number of equidistantly spaced points on the surface of a unit sphere, creating a downsampled spatial map of weights. Here, this operation is called spatial downsampling.  
These steps are described in detail in the following subsections. First, the weighting and clustering operations are described, followed by the downsampling step.
## 2 Materials and Methods  
### Study Site and Crop Condition  
The data for this study were collected in 2017 at the Optimizing Production Inputs for Economic and Environmental Enhancement (OPE3) site at the Beltsville Agricultural Research Center in Beltsville (BARC), MD, USA (lat/lon 39.030686/\(-\)76.84546, 50 m asl), operated by the US Department of Agriculture (USDA) Agricultural Research Service (ARS). At the OPE3 site, corn (_Zea mays_ L.) is planted annually in predominately loamy sand soils, which are rain fed and augmented with optimal nitrogen fertilizer rates. The site is instrumented with a 10 m eddy covariance tower. The local climate is warm and temperate, with hot, humid summers, and typically mild winters with occasional freezes.  
Corn has distinct growth stages, divided into vegetative and reproductive. The first and last vegetative stages are designated as emergence and tussling, while the reproductive stages start with the development of the corn silk and are based on the development of the kernels, which accumulate plant resources, leading to a decline in leaf photosynthesis and the onset of canopy senescence. In 2017 the crop emerged around June 20-22 (DOY 171-173) based on a site visit, Pheno-cam records [53], and the operational flux tower measurements (Figure 1) [54]. A 3 m tall portable tower supporting the optical fibers of the fluorescence box (FLoX) instrument [55] and a set of monitoring-PAM (MoniPAM) probes were installed on July 6/7, when the corn was sufficiently developed. The collection of measurements started the following week on July 11 (DOY 192, Figure 1 bright green). The FLoX and MoniPAM collections occurred within the footprint of the flux tower, separated by approximately 120 m.
# Halophyte Plants and Their Residues as Feedstock for Biogas Production--Chances and Challenges
## 6 Conversion of Halophytes into Biogas  
### The AD Process of Halophytes  
Anaerobic digestion of organic matter involves a sequence of conversion phases that are mediated by consortia of microorganisms that work in synergy in the absence of oxygen to degrade organic macromolecules, to produce methane and carbon dioxide, along with trace quantities of other gases such as hydrogen sulfide. In the initial hydrolysis phase, insoluble complex organic macromolecules such as carbohydrates, protein and fats are hydrolyzed into soluble monomers (sugars, fatty acids and amino acids) by extracellular enzymes excreted by hydrolytic bacteria. In the following acidogenesis stage, the soluble monomers are further broken by acidogenic bacteria into volatile fatty acids (VFA), with acetic acid being the predominant intermediate, as well as carbon dioxide and hydrogen. Other fatty acids are eventually also converted into acetic acid, hydrogen and carbon dioxide. These compounds are finally converted into methane and carbon dioxide by acetoclastic and hydrogen-utilizing methanogens, respectively ([PERSON] et al., 2018; [PERSON] et al., 2018). However, the conversion of halophytic biomass into biogas depends on its composition. Therefore, it is essential to establish the chemical composition and mineral concentration of the biomass to be used in AD, as it may have an inhibitory effect on due to variation in lignin content and concentration of metals such as Na, K and Mg, which can also inhibit microbial activity [69; 79]. Evidently, the AD of halophyte biomass for biogas production is deemed a profitable process from an economical and environmental perspective. There are copious amounts of benefits using halophyte biomass to produce biogas in arid saline-affected lands; for instance, no competition for land or fresh water uses in these regions and low resource costs. Although halophytes can be converted to safe energy by AD, there exist some unfavorable factors which can affect biogas production.
# Removal of Methylene Blue by Metal Oxides Supported by Oily Sludge Pyrolysis Residues
## 3 Results and Discussion
### Removal Parameters Optimization  
Table 3 shows that the regression model had a good fit and high experimental accuracy. Therefore, the results of the quadratic polynomial regression equation (Equations (10)-(12)) were credible. Based on the optimization results in the equations, it can be inferred that: (1) when the dosage was 4.20 g/L, the contact time was 106.48 min and the concentration was 5 mg/L, the removal effect of AC-CuO on methylene blue was 95.28%, and the corresponding removal capacity was 4.76 mg/g; (2) when the dosage was 4.25 g/L, the contact time was 80.78 min and the concentration was 5 mg/L, the removal effect of AC-ZnO on methylene blue was 94.95%, and the corresponding removal capacity was 4.75 mg/g; and (3) when the dosage was 4.50 g/L, the time was 84.95 min and the concentration was 5 mg/L, the removal effect of AC-TiO\({}_{2}\) on methylene blue was 96.96%, and the corresponding removal capacity was 4.85 mg/g.  
In addition, the effect of the response surfaces of AC-CuO, AC-ZnO, and AC-TiO\({}_{2}\) under different conditions on the removal effect of methylene blue are shown in Figures 12-14. It can be seen that when the concentration was constant, the contact time had a greater impact on the removal effect of methylene blue than its dosage; when the contact time was constant, the dosage had a greater impact than its concentration; and when the dosage was constant, the contact time had a greater effect than its concentration. As a matter of fact, the factors of AC-CuO, AC-ZnO, and AC-TiO\({}_{2}\) exhibited the similar trend of methylene blue removal. Therefore, the strength of the effect of AC-CuO, AC-ZnO, and AC-TiO\({}_{2}\) on the removal of methylene blue with different dosage, contact time, and concentration can be given in the order contact time \(>\) dosage \(>\) concentration.  
\[\text{R}_{\text{AC-CuO}}=79.71+12.12\text{A}+14.52\text{B}-3.69\text{C}-0.56 \text{AB}+0.15\text{AC}+0.52\text{BC}-7.92\text{A}^{2}-4.56\text{B}^{2}+1.10 \text{C}^{2} \tag{10}\]  
\[\text{R}_{\text{AC-ZnO}}=85.03+9.93\text{A}+17.60\text{B}-3.19\text{C}+0.055 \text{AB}+0.33\text{AC}-0.87\text{BC}-6.51\text{A}^{2}-12.73\text{B}^{2}+0.3 1\text{C}^{2} \tag{11}\]  
\[\text{R}_{\text{AC-TiO2}}=83.47+4.37\text{A}+25.01\text{B}-3.92\text{C}-0.12 \text{AB}+0.055\text{AC}+0.083\text{BC}-1.22\text{A}^{2}-14.97\text{B}^{2}+0.2 2\text{C}^{2} \tag{12}\]
# A Location-Aware Waypoint-Based Routing Protocol for Airborne DTNs in Search and Rescue Scenarios
## 4 Protocol Design
### Contact Movement Path Checking  
Once node \(c\) is approaching to check a contact \(u\) for a message \(m\), the protocol works as follows. As said in Section 4.1, in addition to the neighbor current position, also the planned movement route is communicated, under the form of a set of waypoints [\(w_{k}\),..., \(w_{n}\)]. \(w_{k}\) is the waypoint from which the node \(u\) is currently moving, \(w_{k+1}\) is the waypoint \(u\) is currently reaching, and \(w_{n}\) is the last mission waypoint. Each waypoint is represented as coordinates \((x,y)\).  
The first action made by \(c\) is the analysis of [\(w_{k}\),..., \(w_{n}\)] to check if any segment [\(w_{i}\), \(w_{i+1}\)], \(i=k,...,n-1\), crosses the transmission range of \(d_{m}\); if none of the segments satisfies the condition, \(c\) does not transmit \(m\) to \(u\), and waits for another event (new message or contact). If a segment satisfies the condition, this means that \(u\) will reach \(d_{m}\) in a finite time \(TTA_{u}\). In this case, node \(u\) is called _Final Node_ (FN); in our work, we assume the ideal case in which an FN will deliver a message to the destination with very high probability, avoiding delay errors, transmission failures, etc. At this point, we can define two types of protocol variants, according to two strategy aspects to pursue.
# Fault Diagnosis System of Power Grid Based on Multi-Data Sources
## 2 System Framework and Operation Process  
In this article, the fault diagnosis system is divided into a real-time processing system, quasi-real-time processing system and batch processing system from the timeliness of information transmission. The system framework is shown in Figure 1.
# Investigation of Segmented Halbach Array Design for Passive Magnetorheological Brake System
## 4 Simulation and Analysis
### Magnetic Flux Intensity at Different Lengths of the Magnets  
The relative magnetic flux density across various configurations of adjustable magnet lengths and gap widths, highlighting the impact of these design parameters on the magnetic flux distribution, are shown in Figure 7. The smaller magnets' lengths are in increments of 5 (25%) mm, 10 (50%) mm, 15 (75%) mm, and 20 (100%) mm, with the percentage indicating increasing length to larger magnets. This directly influences the array magnetic strength as larger magnets provide a broader and more intense magnetic field. The gap width (\(W\)), adjustable between 1 and 10 mm, affects the continuity of the magnetic field; smaller gaps increase field concentration and reduce leakage, while wider gaps allow for some field dispersion, which can benefit specific applications. The width between the magnets is represented in millimeters ranging from 1.0 mm to 10.0 mm, while the plot area shows four different lengths of smaller magnets: 20 mm, 15 mm, 10 mm, and 5 mm. The position of the  
Figure 6: Flux distribution through permanent magnets. (**a**) All the PMs in the array are of the same size. (**b**) A combination of stationary and adjustable permanent magnets form an array.  
magnets is fixed at the center point. As the gap distance between the magnets decreases, the magnetic flux density increases across all lengths of magnets. In the proposed design of a cylindrical configuration, reducing the size of the magnets in both segments results in a uniform decrease in magnetic flux density. This weakens the Halbach array's ability to focus the field, reducing the system's performance. On the other hand, reducing the size of the magnets in the adjustable segment of the Halbach array allows for more selective optimization. Since the adjustable segment primarily shapes the magnetic field rather than generates it, reducing its size minimizes material usage.
## Appendix A
### Dynamic Programming Pseudo Code  
This algorithm outlines the standard, computationally intractable backward induction method for solving the full DP formulation of the problem, based on foundational methods described in the literature [24].  
Step 0: Initialization  
0.1 Load historical demand data from the database  
0.2 Define state variables, decision variables, and planning horizon T  
0.3 Initialize value function \(V_{t}(S_{t})=0\) for final stage T  
Step 1: Backward Induction  
For t = T - 1, T - 2,..., 1:  
For each state \(S_{t}\) :  
\(V_{t}(S_{t})=\infty\)  
For each action \(a_{t}=\left(Q_{it},QN_{it},X_{it}{}^{W},D_{it}^{S},D_{it}^{W},D_{it}^{P},C_{it}^ {D}\right)\):  
Compute immediate cost:  
\(cost_{t}=min_{a_{t}}\left\{\sum\limits_{i=1}^{N}\left(Q_{it}c^{\prime}+C_{it}^ {D}c^{d}+\left(WAR_{it}-DW_{it}+WBR_{it}\right)c^{w}+X_{it}{}^{W}c^{wx}\right)\right\}\)  
Compute the next state \(S_{t+1}\) according to the given constraints:  
\(XAR_{it}=XBR_{it}+QN_{it}C_{i}-X_{it}{}^{W}\)  
\(XBR_{it+1}=XAR_{it}-D_{it}^{S}\)  
\(WAR_{it}=X_{it}{}^{W}+WBR_{it}\)  
\(WBR_{it+1}=WAR_{it}-D_{it}^{W}+C_{it}^{D}C_{i}-D_{it}^{P}\)  
Evaluate the next value:  
Next Value = \(V_{t+1}(S_{t+1})\)  
Update value function:  
\(V_{t}(S_{t})\)\(=\)\(\min(V_{t}(S_{t})\,,\,cost_{t}\)+ Next Value)  
Step 2: Forward Simulation  
Initialize starting state \(S_{0}\)  
For t = 1, 2,..., T:  
Determine optimal action \(a_{t}^{*}\) based on \(V_{t}(S_{t})\)  
Apply optimal action \(a_{t}^{*}\) :  
Execute optimal replenishment decisions \(Q_{it}^{*},QN_{it}^{*},X_{it}{}^{W*},D_{it}^{S^{*}},D_{it}^{W*},D_{it}^{P*},C_{it} ^{D*}\)  
Update state \(S_{t+1}\)  
Record actual demand and update historical data  
Update initial states for the next iteration  
End
# Detecting Change at Archaeological Sites in North Africa Using Open-Source Satellite Imagery
## 3 Results
### Aswan and Kom Ombo
#### 3.1.2 False Positives  
The user's accuracy was relatively low at 61%; a manual examination of the imagery suggested that 28 of the 'changed' sites were, in fact, false positives, some located adjacent to cliffs casting a shadow which varied depending on the conditions under which the imagery was taken (Figure 13). Others may have been caused by natural movements of sand in areas where dunes were present (difficult to quantify in imagery or on the ground). Some discrepancies in image registration in Sentinel-2 data may also have caused errors.
# A Comprehensive Worst Case Bounds Analysis of IEEE 802.15.7
## 3 Technical Overview  
The IEEE 802.15.7 defines the physical layer (PHY) and Medium Access Control (MAC) sub-layer for short-range optical wireless communications (OWC), which operates at a range of 10,000 nm to 190 nm using visible light. Due to its capability to support high data rates, OWC can even facilitate multimedia services such as video transmission. From a PHY perspective, this standard explores the compatibility with various light infrastructures, and tries to mitigate impairments due to noise and interference from other light sources, by relying upon different PHY configurations.
With this method, incorrect judgments can be identified and even eliminated from the estimation. The above-mentioned experiments are executed and the results are introduced through the case study of an operating Hungarian waste separation technology. This system satisfies the conditions above, namely the parameters are not known, measurements are rarely available in this type of processes, and the knowledge of experts has a distribution due to subjectivity. Moreover, experts cannot give their assessments of the parameters and outputsas exact values, only as intervals. With these features, this system is applicable to achieve the above-mentioned research goals as an example. However, the proposed method can be used in any kind of system, wherein there is a lack of measurements but expert knowledge is available even in an imprecise form. The contributions of this paper are listed below:  
* An industrial example is shown on how uncertain expert knowledge can be applied to the parameter estimation, modelling, and analysis of the system if measurement data are missing or incomplete. * Monte Carlo (MC) method and importance sampling (IS) technique are integrated to handle uncertain expert knowledge. MC helps calculating outputs from uncertain inputs/parameters, and IS gives a feedback opportunity to estimate parameters by validating according to the outputs. Thereby, a consistency analysis is performed in this (inner) iteration circle. * Expert judgments are evaluated and weighted according to the results of the estimation using the Jaccard index as a metric of similarity to create goodness values. * Incorrect expert judgments are eliminated from the estimation in an outer iteration circle by estimating the weights of expert judgments iteratively. Regarding the roadmap of the article, in Section 2, the methodological background of the research is introduced, containing some ideas about the representation possibilities of uncertain expert knowledge in Section 2.1, describing the proposed parameter estimation technique based on IS in Section 2.2, introducing the application of the results for the validation of expert judgments and providing a method for the elimination of incorrect expert assessments in Section 2.3. The experimental results are provided in Section 3. The investigated waste separation system is introduced in Section 3.1, the available expert knowledge about the system in Section 3.2, the experiments and results of parameter estimation are presented in detail in Sections 3.3 and 3.4, the evaluation of the expert judgments in Section 3.5, and the investigations on the outer iteration circle in Section 3.6. Finally, in Section 4, the main conclusions, limitations of the method, and future research directions are revealed.