esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
In general, oil slicks exhibit higher contrast values compared to look-alikes, making adaptive thresholding an effective approach for classifying the two objects based on differences in contour values. In this final step, oil objects identified by HSBA are overlapped with oil contours determined using the two non-linear filters and adaptive thresholding. Segments identified by HSBA that lack any intersecting contours delineated in the second-step procedure are discarded as look-alike objects. The introduced algorithm combines HSBA with oil contour determination, effectively addressing challenges in oil spill detection from satellite imagery. It leverages the strengthsof both approaches, which is especially valuable in discerning between oil slicks and look-alikes and in scenarios where oil-slick homogeneity is affected by strong surface winds. Moreover, HSBA excels in automatically detecting oil-spill or look-alike objects in an unsupervised manner, regardless of the satellite image extension and swath width. It is important to note that these last two aspects are significant. In many patch-wise detection algorithms, determining the patch size or identifying the appropriate area for applying the segmentation algorithm are non-trivial tasks. To study oil drifts, we collocate and superimpose oil slicks identified by the proposed method among different satellite datasets. Despite differences in spatial resolution, e.g., Sentinel-1/2 (10 m pixel-size) vs. Landsat-8/9 (30 m pixel-size), and oil features, e.g., Sentinel-1 SAR vs. Sentinel-2/Landsat-8/9 optics, observations of oil drifts in terms of changes in oil shape and location, as well as oil-drift direction and velocity, remain robust over short-term and long-term periods, allowing for evaluation of the effects of met-ocean conditions on oil drifts, using the CMEMS data for surface wind fields and current vectors.
## 1 Introduction
### Cytochrome C Oxidase  
CCO is a photo-sensitive enzyme that reacts with oxygen in the last step of the mitochondrial electron transport chain. In the electron transport chain, protons are moved across the mitochondrial inner membrane to produce ATP, the energy source used for cells to live and function. CCO exists in two states: oxidized CCO and reduced CCO. The sum of these two CCO states is a constant. The ratio of oxidized CCO to reduced CCO is termed the redox state and can be detected by its spectroscopic signal. (Note that when we refer to \"CCO measurement\", we mean that we are measuring the changes in the redox state of CCO.) Increased cellular activity uses more energy in the form of ATP. This increase in ATP utilization results in the conversion of oxidized CCO to reduced CCO, which equates to a reduction in the redox state of CCO and a decrease in the spectroscopic signal. It follows that a reduction in ATP synthesis, such as that which occurs when the substrates for ATP generation (oxygen and blood flow) are reduced from ischemic or hypoxic insults induced by illness or injury, would be reflected by changes in the CCO redox state.  
CCO is responsible for more than 95% of oxygen metabolism in the body and is essential for the efficient generation of ATP [30]. Thus, measuring CCO adds key precision in the understanding of brain and organ cellular function [31]. Simultaneous measurements of CCO and the hemoglobin redox states can provide complimentary information on the brain's or organ's metabolism and hemodynamics, which can improve the diagnosis and management of illness or injury. CCO concentration is also significantly higher in the brain than in extracerebral tissues. As such, there is minimal spectral interference from the scalp and skull when brain tissue is assessed noninvasively [32]. Therefore, noninvasive spectroscopic assessment of CCO and the hemoglobin redox states has the potential to significantly enhance many areas of clinical practice.  
Despite the attractiveness of CCO as a metabolic marker, measuring CCO is difficult for a number of reasons. First, the in vivo concentration of CCO is between 5% and 10% that of hemoglobin. Because CCO and hemoglobin have overlapping spectra, there can be significant cross-talk between CCO changes and the hemodynamic response. Second, CCO is an enzyme with four redox centers, one of which--copper A--has a broad absorption peak in the NIR spectrum between 700 and 900 nm that changes depending on its redox state. The relatively broad, featureless spectrum for the redox state of CCO makes it more difficult to distinguish from other interfering spectra. Thus, a high-brightness NIR light source can be a key enabling technology that permits the measurement of CCO in humans and animals and distinguishing the CCO from the hemodynamic response, skin interference, and motion artifacts. The SCL used in the experiments described in this paper provides the high brightness required for measuring CCO with an enhanced signal-to-noise ratio.
# Can OCT Angiography Be Made a Quantitative Blood Measurement Tool?
## 9 Conclusions  
Despite recent strides in OCTA imaging speed, field-of-view, and measurement of OCTA observables, OCTA remains a qualitative tool at present. The obstacles to quantification include the irregular shape of RBCs, the consequent orientation-dependence of RBC backscattering, and the high anisotropy of the RBC scattering phase function, which leads to multiple scattering in large vessels. Quantification of OCTA signals can be achieved only through a rigorous understanding of the relationship between hemodynamics, rheology, and light scattering of RBCs. Improvementsin theoretical models, validated in microvasculature in vivo against gold standard techniques and possibly in simulation, may help to improve this understanding. Finally, alternative measurements, based on absorption or exogenous contrast agents, may help to alleviate some of the confounds associated with RBC scattering and enhance the quantitative information provided by OCTA. More quantitative interpretation of OCTA would aid the application of this promising technique to study pathophysiology, and also potentially enhance the clinical impact of OCTA, making this endeavor well worth the effort.  
We acknowledge support from the National Institutes of Health (EB023591, AG010129, and NS094681) and the Glaucoma Research Foundation Catalyst for a Cure.  
[PERSON] and [PERSON] conceived the paper. [PERSON], [PERSON], and [PERSON] generated data for the figures. [PERSON] and [PERSON] processed the data. [PERSON], [PERSON], [PERSON], and [PERSON] made the figures for the paper. All authors wrote and reviewed the paper.  
[PERSON] receives royalties from Optovue, Inc.
## 1 Introduction
### Contributions of This Paper  
The main contributions of this study can be summarized as follows:Detailed EMF Exposure Assessment: Provides a comprehensive analysis of EMF exposure levels experienced by drivers and passengers in electric buses, considering various conditions such as different bus environments, times of the day, and passenger densities;
* Case Study in Samsun, Turkey: Provides a detailed assessment of B and RF-EMF exposure in electric buses in Samsun, Turkey. This case study highlights specific regional health risks and exposure characteristics, offering valuable insights into local public health and transportation safety;
* Comparative Analysis: Includes a comparative analysis of EMF exposure between electric and conventional diesel-powered buses, revealing potential differences in exposure levels and providing a clearer understanding of how different bus types affect EMF exposure;
* Impact of Passenger Density and Charging Process: Evaluates how passenger density influences EMF exposure and explores the effects of the charging process, particularly near critical components like the pantograph, on B;
* Statistical Analysis: To enhance the accuracy of our findings, we conducted Anderson-Darling goodness-of-fit tests to identify the best-fitting distribution models for the measured B and RF-EMF levels. This approach ensures a clearer and more precise understanding of EMF exposure in electric bus environments;
* Guidance for Safety and Policy: Identifies research gaps and informs strategies for mitigating EMF exposure, enhancing public transportation safety, and guiding future research and policymaking on EMF safety regulations. The findings contribute to refining existing EMF regulations and offer guidance for developing new policies;
* Future Research Directions: Highlights the need for future studies to explore the long-term health effects of EMF exposure and the potential for further optimizing safety measures in electric buses.
# Treatment Methods for Antibiotic Mycelial Residues: A Review
## 2 Common Properties of AMR  
AMR, a solid waste originating from antibiotic manufacturing, is distinguished by its elevated protein levels, considerable calorific value, and the presence of residual antibiotic compounds [4; 6]. Freshly produced AMR exhibits high viscosity and moisture content, with water predominantly existing in a bound state within the mycelial matrix, which significantly hinders the efficiency of mechanical dewatering processes [26]. Furthermore, residual antibiotic concentrations in AMR commonly surpass 1000 mg/kg; the material also contains metabolic intermediates, organic solvents, calcium and magnesium ions, and various trace elements [27; 28]. The principal characteristics of several prevalent types of AMR are detailed in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Type**} & **Antibiotic Concentration (mg/kg)** & **Moisture Content (\%)** & **C/N** & **References** \\ \hline \multirow{3}{*}{Beta-lactams} & Penicillin & 1000–2000 & 75–82 & 4.5–7.6 & [29; 30; 31; 32] \\  & Cephalosporin & 7860 & 83–89 & 5.6–5.9 & [33; 34] \\  & Cephalosporin C & 810–920 & 83–92 & 5.3–6.6 & [20; 35; 36; 37] \\ \hline \multirow{3}{*}{Macrolides} & Erythromycin & 185–2271 & 79–90 & 5.6 & [38; 39; 40; 41; 42] \\  & Spiramycin & 2110 & 65–90 & 4.9 & [43; 44] \\  & Avermectin & 1208–2653 & 68–87 & - & [26; 45] \\  & Tylosin & 15.3–8900 & 67–68 & 0.9–8.6 & [46; 47] \\ \hline \multirow{3}{*}{Aminoglycosoides} & Gentamicin & 4500 & 97 & 8.48 & [48; 49] \\  & Streptomycin & 5485 & 91 & - & [50] \\  & Neomycin & 4005–7500 & 12–50 & 8.8–9 & [51; 52] \\ \hline Tetracyclines & Oxytetracycline & 1012–7084 & 72–86 & 5.3 & [53; 54; 55; 56] \\ \hline \multirow{3}{*}{Others} & Sulfathiazole & 538 & 92 & 17.2 & [57] \\  & Lincomycin & 2157–14270 & 51–66 & 6.4 & [58; 59; 60] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main characteristics of common AMR types.
## 4 Experimental Results  
The 50-km long fiber-optic frequency transfer experiment was carried out in our laboratory. In order to keep the environmental influence inside the experiment as consistent as possible with the outside, we opened the windows of the room and introduced wind flow and temperature drift. We estimated that a wide range of timing fluctuations will occur on a 50-km fiber link. Therefore, the electronic and optical joint phase compensator in this experiment could solve the problem of sole optical compensator, and improve the long-term stability of RF transfer.
# MobiRes-Net: A Hybrid Deep Learning Model for Detecting and Classifying Olive Leaf Diseases
## 4 Proposed Hybrid DL Model
### Data Preprocessing  
In this work, a camera was fixed on the drone, which could capture images from an altitude, and then send them to the user to identify the olive leaf diseases. Given the fact that the camera was able to capture leaf images from various occlusions, there was an urgent need to train the model with different images taken from various angles, directions, and altitudes [28]. Furthermore, the gathered images were preprocessed in order to resize the input image to fit the required pixels to train the dataset. The preprocessing methods were adopted for the sake of enhancing the robustness of the experimental dataset to various illumination conditions. They were also valuable for eliminating the undesirable  
Figure 4: Conceptual overview of the proposed work.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Class Name** & **Train Set** & **Test Set** & **Total** \\ \hline \hline Aculus Olearius & 1040 & 260 & 1300 \\ Olive scab & 800 & 200 & 1000 \\ Peacock spot & 1360 & 340 & 1700 \\ Healthy & 1120 & 280 & 1400 \\ \hline \end{tabular}
\end{table}
Table 2: Dataset description.  
noise which was present in the presented image. The data preprocessing consisted of two steps:  
* Step 1: All the images that were originally gathered were first checked to determine the minimum height and width. Once the minimum dimension was determined, the dataset images were resized to fit the required dimensions. Since the minimum dimension in our work was set to be 224 \(\times\) 224, all the dataset images were resized to 224 \(\times\) 224.
* Step 2: To acquire a better-enhanced image in the present work, two methods, i.e., contrast enhancement and image normalization, were applied to change the value of pixel intensity. By changing the pixel intensity, hidden information that existed within the low range could be revealed. The change of pixel intensity revealed the hidden information already existing within the low range.
# A Comprehensive Experimental Emulation For OTFS Waveform RF-Impairments
## 3 Channel Effects Furthermore, RF-Impairments  
The channel effect and the various RF-impairments degrade the system's performance. Thus, in this section, the critical RF-impairments are discussed, and their effects on OFDM and OTFS are shown and compared experimentally. In the experiment platform, the SDR device based on the Keysight EXG X-Series design is used to implement terminals. As depicted in Figures 3 and 4, the N5172B VSA with 9 kHz to 1, 3, or 6 GHz frequency range with an output power +27 dBm, 900-us switching speed. The receive antenna is connected to the N9010A EXA Keysight X-Series VSA. The setup parameters are summarized in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbol** & **Parameters** & **Value (OTFS)** & **Value (OFDM)** \\ \hline \(f_{c}\) & Carrier frequency & 2.4 GHz, 5 GHz & 2.4 GHz, 5 GHz \\ \(M\) & Number of & 432, 256 & 256, 1024 \\ \(N\) & Number of symbols & 432, 256 & 1 \\ \(T_{s}\) & Symbol duration & 10 us \\ \(M_{mod}\) & Modulation order & 4-QAM \\ \(\Delta f_{s}\) & Sub-carrier spacing & 100 KHz \\ \(f_{o}\) & Normalized & 0, 0.05, 0.1, 0.3 \\ \(\epsilon\) & I/Q gain imbalance & 0\%, 50\% \\ \(\Delta\phi\) & I/Q phase imbalance & 0\({}^{\circ}\), 10\({}^{\circ}\) degree \\ \(\gamma_{L}\gamma_{Q}\) & DC-offset & (0\%, 20\%, 50\%, 75\%)\(\sqrt{E_{s}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Setup parameters.  
Figure 3: Laboratory equipment setup connection for multi-path emulation using reverberation chamber.
# Smart Sensors and Smart Data for Precision Agriculture: A Review
###### Abstract
The journal _Sensors_ has the highest record count, accounting for 8% of the total, indicating a significant focus on sensor-related research. Following closely are _Computers and Electronics in Agriculture_ with 5% and _Remote Sensing_ with 4%. Other prominent titles include _Precision Agriculture_, _Agronomy-Basel_, and _Agriculture-Basel_, as shown in Figure 4.  
Figure 4: Annual publications of papers indexed in Web of Science by publication title during the period 2018–2023. Figure 3: Annual distributions of papers indexed in Web of Science by country during the period 2018–2023. The data can also provide an overview of the distribution of records across different Sustainable Development Goals (SDGs), of which \"13 Climate Action\" stands out as the most prevalent goal, representing 28% of the total records, followed by \"02 Zero Hunger\" with 23%, and \"15 Life on Land\" with 22%. These high percentages indicate a significant focus on environmental and climate-related themes in the dataset. \"11 Sustainable Cities and Communities\" also has a notable representation at 17%. Other SDGs such as \"03 Good Health and Well Being\", \"06 Clean Water and Sanitation\", and \"07 Affordable and Clean Energy\" contribute to the dataset, albeit to a lesser extent. It is noteworthy that 20% of the records do not contain data on the analyzed SDG field (Figure 5). This article aims to provide an overview of current smart sensors and smart data applications in PA, focusing on the monitoring and control of indoor microclimatic conditions in smart greenhouses. The article is structured as follows: Section 2 introduces various types of smart sensors crucial in PA. Subsequent sections delve into control and monitoring techniques specific to different applications. Section 3 details data collection methods and optimization techniques for monitoring and control in smart greenhouses. Section 4 explores the application of smart sensors in PA for indoor parameters such as soil moisture, temperature, air humidity, CO\({}_{2}\), and light intensity. In addition, it provides case studies illustrating precision irrigation, fertilizer management, and pest and disease monitoring. Section 5 discusses the challenges and describes future directions for smart sensors in PA.
## 5 Conclusions  
In this work, an efficient wireless power transmission system for drone battery charging was developed. A charging station with multiple transmission coils was used to transfer power to the receiving side (drone) to charge the battery. This study aimed to solve the problem caused by uneven drone landing on a charging station which leads to inefficient wireless power transmission due to the poor alignment between the transmitting and receiving coils. To solve this problem, a control mechanism based on the hill climbing algorithm was proposed. The control mechanism was used to mitigate the uneven landing effect of the drone on a charging station by carefully moving the charging station to a point where wireless power transmission was maximum. A practical test bench was developed to test the system feasibility. The power transmission efficiency was 85% and the test accuracy of the system was 98.8%. It can be observed from the results that the proposed system performed well compared to previously used techniques, without any need of a physical sensor such as a position sensor. Also, the use of the hill climbing algorithm gives the system a much faster response compared to any image-based target detection scheme. It also eliminates the possibility of getting affected by environmental conditions. The drone simply needs to land on the charging station, and, within 2 s, wireless power transmission starts at its maximum designed efficiency without any misalignment.
## 4 Numerical Results  
The LDCP-MILP model is evaluated by means of numerical experiments performed in three networks of different size. The network scenario assumptions discussed in Section 2 are applied. The following topologies of the packet-switched transport network are considered: a 10-node ring network (RING-10), a 16-node double-ring network (DRING-16), and a 20-node mesh network (MESH-20), presented in Figure 3. The topologies have been selected based on the assumptions presented in the literature. Namely, ring networks are considered for fronthaul/midhaul [6; 7], where the number of switches does not exceed 10, as mentioned in [7]. Mesh networks are also foreseen for NGFI [6]. Eventually, reference networks DRING-16 and MESH-20 were used in C-RAN optimization studies in [16; 35], respectively.  
Let \(N\) denote the number of switching nodes. We have \(N=10\), \(N=16\), and \(N=20\), respectively, for RING-10, DRING-16, and MESH-20. We assume that there is one PP node connected to each switching node; hence, the total number of PPs is \(N\) in the networks considered. We assume that there are \(R\) RUs, where different values of \(R\) are considered in the evaluation, and the RUs are connected to the switching nodes randomly. We assume that each RU is connected to one switch, and all the RUs connected to a given switch constitute a cluster. In Table 3, we show the values of links lengths and capacities. In particular, the length of a link is a random number generated within the limits given in Table 3. The capacities of links are in accordance to the assumptions concerning NGFI scenarios discussed in [7].  
Figure 3: Network topologies: RING-10, DRING-16, and MESH-20.  
According to [23], the total baseband processing demand of one RU of the radio system considered in Section 2.4 is about 1800 giga operations per second (GOPS). Based on the estimations presented in [23; 36], in the analysis we assume that the processing loads of DU and CU are \(\rho^{D}=5\) and \(\rho^{C}=1\) processing units (PUs), respectively, where one PU represents about 300 GOPS. Due to clustering constraints, the processing capacity of a PP node (\(\rho\)) should be enough to support the total DU processing load of all RUs belonging to a cluster. Therefore, we assume that each PP node has capacity  
\[\rho=\rho^{D}\times R^{max}\times 2\times C, \tag{29}\]  
where \(R^{max}\) denotes the size of the largest cluster of RUs in given network scenario, factor 2 is due to the DU processing of (two) associated demands (i.e., uplink and downlink) in the same PP node, and \(C\) is a PP capacity multiplier used in the analysis to scale the processing capacity of the PP node.  
The routing paths between network nodes over the packet transport network have been generated using the Dijkstra shortest path algorithm. All the numerical experiments are performed on a 3.7 GHz 32-core Ryzen Threadripper-class machine with 64 GB RAM. To solve the LDCP-MILP model, we use CPLEX v.12.9 [11], which is run in a parallel mode and with default settings.
# Near Real-Time Extracting Wildfire Spread Rate from Himawari-8 Satellite Data
## 3 Methodology
### Comparison of Results with the CSIRO GFS Model  
The FSR computed from the CSIRO GFS model [27] was used as a benchmark to assess the performance of H8-FSR. The CSIRO GFS model was calibrated using 121 experimental fires and 20 major wildfires in Australia [65]. This model is broadly used by the Australian fire authorities [65]. The inputs of the model include meteorological information (10 m open wind speed, temperature, relative humidity) as well as a Grassland curing index (GCI). In this study, the meteorological data was obtained from three stations as described in Section 2.3. GCI, defined as the percentage of dead materials over the grass sward, has a large influence on grassland fire propagation [66]. To date, there are two GCI satellite-based products for Australia, with a time and spatial resolution of eight days and 500 m respectively. The GCI product developed by [PERSON] et al. [67] is more accurate than the curing product developed by [PERSON] et al. [68]. The algorithm developed by [PERSON] et al. [67] retrieves GCI using the Normalized Difference Vegetation Index (NDVI) and the Global Vegetation Monitoring Index (GVMI) computed from three window bands of MODIS (MOD09A1) satellite data, centered at 0.64 \(\upmu\)m (band 1), 0.86 \(\upmu\)m (band 2) and 1.64 \(\upmu\)m (band 6). The algorithm was trained on curing visual estimates obtained by following the Australia Country Fire Authority (CFA) guide [67]. Therefore, GCI was eventually obtained by applying this algorithm to the Himawari-8 data acquired from the Esperance bushfire that broke out at AWST time 08:30.  
FSR derived from the CSIRO GFS model and the H8-FSR were compared using the coefficient of determination (R\({}^{2}\)), the mean bias error (MBE) (Equation (9)), the mean absolute percent error (MAPE) (Equation (10)) and the root mean square error (RMSE) (Equation (11)) [69].  
\[MBE =\frac{\sum(V_{H-8}-V_{M})}{n} \tag{9}\] \[MAPE =\frac{\sum\left(\frac{V_{H-8}-V_{M}}{V_{M}}\right)}{n}*100\] (10) \[RMSE =\sqrt{\frac{\sum(V_{H-8}-V_{M})^{2}}{n}} \tag{11}\]  
where \(V_{H-8}\) is the FSR extracted from THE Himawari-8 data, \(V_{M}\) is the FSR estimated from the CSIRO GFS model and \(n\) is the number of observations.