esa-sceva/satcom-chunk-collection huggingface dataset:
# The Diffusion Role in Adsorption of Hexavalent Chromium on Solid Olive Mill Waste
## 1 Introduction  
The majority of consumable vegetable oils must be refined in order to eliminate undesirable substances affecting the quality of the oil. On the contrary, the quality of olive oil in terms of free fatty acids makes its refining unnecessary and the oil can be consumed as \"virgin olive oil\" [1]. Virgin olive oil is produced by the mechanical pressing of olive fruits and the olive oil is released from the oil bodies of the fruits, and together with many other ingredients, forms the bulk olive oil [2]. The malaxation step aims to destroy the oil bodies (inner structure of the olives acting as tiny reservoirs) and liberate the oil. After the first pressing of the olives remains a mixture of the pulp and stones of olives, which constitute the solid olive mill waste. Many reports indicate that such residue is a combustion material for heating, due to its content of cellulose, hemicellulose and lignin [3, 4, 5, 6]. It was also reported that olive mill waste has good adsorbing capacity of heavy metals [7, 8, 9].  
It is well known that the uncontrolled disposal of olive mill waste produces severe environmental problems. In order to prevent serious damage to the environment, various alternatives can be adopted. With this in mind, in recent years we investigated the ability of solid olive mill waste to adsorb heavy metals and in particular Cr(VI). The adsorption process is affected by the chemical composition of biomass as well as its surface characteristics. On the other hand, several experimental investigations have established good potentiality for the composites of solid olive mill waste clays to be used as adsorbent for the removal of pollutants from wastewater [10, 11, 12]. Furthermore, various types of nanoclayssuch as kaolinite nanosheets and halloysite nanotubes have become important targets for applications in bioremediation [13].  
In this study, the adsorption behavior of solid olive mill waste treated with laponite was investigated. The research had a dual purpose: on the one hand, to analyze the adsorption of the treated biomass; and on the other hand, how a highly plastic synthetic clay [14] modifies the surface of organic residue. In this study, the ability of solid olive mill waste treated with laponite (LAP) to adsorb chromium (VI) from aqueous solutions was investigated. The first objective was addressed by directly comparing the amount of Cr(VI) adsorbed on equal quantities of treated and untreated biomass. The second was approached by analyzing the role of intraparticle diffusion in the adsorption process. The experimental results were interpreted on the basis of a simple binding-diffusion model to justify the evidence of a reinforced adsorption capacity towards Cr(VI).
## 3 Results and Discussion
### Effects of Domestic Cooking on Changes in the Contents of VOCs of Faba Leaves
#### 3.4.3 Alkenes/Alkenes/Aromatic Hydrocarbons  
In the fresh faba leaves, the alkanes, alkenes, and aromatic hydrocarbons were three groups present, but only in small amounts of 0.37%, 0.15%, and 0.03%, respectively (Figure 5, Table S2). The content of each group was increased similarly by domestic cooking with a different pattern according to the method. For the alkanes, faba leaves treated by microwaving exhibited 10.03% of the total peak area of fresh faba leaves, followed by 6.95% for roasting, 0.79% for boiling, and 0.71% for steaming. Of these alkanes, the significant increase in the contents of decane, dodecane, and 3,3,5-trimethyl-heptane were responsible for the increase of the total alkane content (Figure 4, Table S2). Similar results were observed by [PERSON] et al. [38] who reported that decane and dodecane increased during the drying of the edible mushroom, _Flammulina velitopes_, but were not detected in the fresh fungus. Interestingly, unlike the alkanes, the alkene content was increased by the roasting (1.16%), steaming (0.91%), and boiling (0.99%) treatments, but not by the microwaving (0.16%) treatment. This might be explained by the difference in the heating mechanism between the microwave treatment and traditional heat transfer methods, resulting in different modes of transforming VOCs during processing [23]. It is important to note the effect of microwaving on changing the contents of aromatic hydrocarbons (3.56%): it increases the content of \(p\)-xylene (1.61%) by 54 times, as well as newly synthesized naphthalene derivatives (1.94%) (Figure 4, Table S2). \(p\)-Xylene is an important aromatic hydrocarbon found in faba beans and sprouts [35]; however, unlike its relatively non-toxic isomers (\(o\)-xylene and \(m\)-xylene), exposure to \(p\)-xylene vapor at over one hundred parts per million has been suggested to cause toxic effects such as irritation to the eyes and throat, possible chest tightening, and an abnormal gait [39]. Although [PERSON] et al. [19] have reported that both boiling and oven cooking increased the \(m\)-xylene content of two different cultivars of mushroom, the reason why \(p\)-xylene was specifically boosted by microwaving in the present study is still unclear. Microwaves are electromagnetic radiations, which lie between radio waves and infrared radiations [40; 41; 42]. With the difference of conventional heating methods, several specific microwave effects caused by the dielectric heating mechanism have been reported, such as superheating, selective heating, exciting water molecules by a particular frequency of microwaves, breaking covalent bonds of polymerized compounds, and so on [23; 40; 43]; therefore, future studies could investigate how factors of time and power during microwave treatment affect the synthesis of the relatively harmful \(p\)-xylene, not only in faba leaves, but also in other food matrices. In addition, future studies could also investigate whether the increased \(p\)-xylene content is harmful to human health.
# Electrospun Nanofibers for Integrated Sensing, Storage, and Computing Applications
## 3 Electrospun Nanofibers in Transistors: Computation and Multifunctional Electronics
### Typical Applications of Nanofiber-Based Transistors
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Year** & **Nanofiber Material** & \
\begin{tabular}{c} **Mobility** \\ **(\(\mu\), cm\({}^{2}\) V\({}^{-1}\) s\({}^{-1}\))** \\ \end{tabular} &
\begin{tabular}{c} **Ion/In\({}_{\text{off}}\)** \\ \end{tabular} & **Characteristics** & **Applications** & **References** \\ \hline
2003 &
\begin{tabular}{c} \\ acid-doped PEO \\ \end{tabular} & \(1.43\times 10^{-4}\) & & & — & [61] \\ \hline
2005 & RRP3 HT & 0.03 & \(10^{3}\) & — & — & [84] \\ \hline
2005 & P3 HT & \(4\times 10^{-4}\) & 7 & & [64] \\ \hline
2006 & CuO(p-type) & — & — & Conductivity-\(3\times 10^{-3}\) S/cm & — & [67] \\ \hline
2009 & P3 HT & 0.017 & \(10^{2}\) & & [62] \\ \hline
2010 & P3 HT & 2 & \(10^{5}\) &
\begin{tabular}{c} Flexible, high frequency \\ operation \\ \end{tabular} & & [63] \\ \hline
2010 & MEH-PPV & \(10^{-3}\) & 780 & Single nanofiber &
\begin{tabular}{c} Photoluminescence/ \\ Light-emitting \\ \end{tabular} & [82] \\ \hline
2011 & P4 TDPP/PMMA & 0.305 & \(1.3\times 10^{5}\) & Crystalline-induced procedure & [85] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Electrical Properties and Applications of FETs with Different Kinds of Nanofibers. Figure 14: (**a**) The normalized current of A-CMCNFs FET sensors generated with varying oxidation durations in real-time. (**b**) Calibration curves of FET sensors generated with different acid treatment times as a function of BPA concentration where S denotes the normalized current after 10 s of exposure to the BPA solution. Reprinted with permission from Reference [83]. Copyright 2016 American Chemical Society. (**c,d**) ZnSnO fiber-based FET and its demonstration of a resistor-loaded inverter: (**c**) VTC curves of the inverter based on Zn\({}_{0.3}\)Sn\({}_{0.2}\)O NFNs/ ZrO\({}_{\times}\) FETs with forward and reverse sweep. (**d**) Signal gains of the inverter as a function of V\({}_{\text{DD}}\). Reproduced from Ref. [13] with permission from the Royal Society of Chemistry.
# A Survey of Handover Management in Mobile HetNets: Current Challenges and Future Directions
## 3 Background  
Mobile communication technologies have evolved into an essential aspect of modern life. The growing number of communication devices, services, diverse applications and user behaviors necessitates fast data speeds, large capacity, and continuous connectivity. As the most recent mobile communication systems are capable of providing high data rates and lower latency with higher bandwidths, the increase in the number of connections necessarily requires an increase in network capacity, which can be substantially increased up to 100 times by using small cells and HetNets along with other benefits like offloading of congested macro cells, extend cell coverages, and better-received signal quality at the edges of macro cells [31; 32].  
HetNets are wireless networks that make use of multiple types of access networks. A broadband network may offer wireless services in places with various traffic densities, such as outdoor areas, within buildings, residences, and even underground sites such as tunnels, by employing macro cells, micro cells, and femtocells. These types of deployments always need better network capability in terms of network coverage, capacity, and data rates. High data rates, spectrum efficiency, energy efficiency, and lower congestion are among the benefits of HetNets. However, mobile communication systems provide a more complex scenario. As a result of dense deployments of access points and base stations, mobility and HO issues, as well as higher path losses, smaller coverage regions, and rapid signal fading, will occur, resulting in more errors and a drop in QoE. To maximize overall network performance, HO and mobility problems must be addressed effectively [33].  
This section's goal is to provide an overview of the handover mechanism and its numerous related functions.
## 3 Results  
In the open surgical experiments, the parameters measured in the experiment did not show statistically significant differences between the two devices (Table 2). The success rate of the ligation of the rabbit aorta was 100% in QMR energy generators and 66.67% in LigaSure(tm). The LigaSure(tm) demonstrated a mean temperature of the surrounding tissue which was about five degrees lower (73.68 \({}^{\circ}\)C) compared to the QMR energy generator (79.30 \({}^{\circ}\)C). In comparing the estimated sealing stability using burst pressure, the QMR energy generator showed a higher burst pressure (963 mmHg) than LigaSure(tm) (802 mmHg). The mean sealing width of the ligated vessels using the LigaSure(tm) and QMR energy generator was 1.92 mm and 1.98 mm, respectively. In microscopic analysis, less collagen denaturation and wall layer cleavage was noted when using a QMR energy generator  
\begin{table}
\begin{tabular}{l l} \hline
**1. Amount of surgical smoke formation** & \\ Grade & Evaluation criteria \\
0 & No smoke formation \\
1 & Mild smoke formation but no visual interference \\
2 & Moderate smoke formation, must be removed to increase visibility \\
3 & Severe smoke formation, difficult to proceed with surgery \\
**2. Tissue carbonization** & \\ Grade & Evaluation criteria \\
0 & No carbonization \\
1 & Mild carbonization, brown-colored tissue observed \\
2 & Moderate carbonization, blackened tissue observed \\
3 & Severe carbonization, all tissue is black \\
**3. Stickiness** & \\ Grade & Evaluation criteria \\
1 & No stickiness \\
2 & Stick a little but falls off by itself \\
3 & Must be removed by holding the tissue with the opposite hand \\  & but involves no tissue damage \\
4 & Sticking caused damage tissue but no bleeding \\
5 & Sticking is severe and produces tissue damage and bleeding when \\  & removing the instrument \\ \hline \end{tabular}
\end{table}
Table 1: Criteria in the surgeon’s experience during laparoscopic surgery.  
compared to LigaSureTM (1.36 mm vs. 1.60 mm, 20.0% vs. 30.0%, respectively). Additionally, less wall injury was identified when using a QMR energy generator compared to LigaSureTM (54.98% vs. 73.87%).  
In the laparoscopic surgical experiments, a total of 26 video clips that contain the sealing moments of the peritoneum, vessel (renal vein), and ureter was obtained. Regarding the formation of surgical smoke (Table 3), the surgeons reported significantly less surgical smoke production when using the QMR energy generator to the peritoneum and vessels compared to LigaSureTM (\(p\) = 0.025 and \(p\) = 0.013, respectively). In the tissue carbonization parameter, the QMR energy generator showed a significantly favorable outcome when dissecting the peritoneum (\(p\) = 0.014). The use of the QMR energy generator to seal and cut the peritoneum and ureter showed that it was significantly less likely to stick to the instrument compared to LigaSureTM (\(p\) = 0.036 and \(p\) = 0.044, respectively).
## 4 Discussion
There is some ambiguity in what is and is not considered part of a cell, especially at different levels of focus. A computer vision model will learn to detect cells in the same way that cells are segmented in the ground-truth. Therefore, comparisons conducted with ground-truth segmentations created using different guidelines for what constitutes part of a cell, will lead to poor reported performance, even if performance appears qualitatively very high. To train a robust model, more labeled data is required. This will enable the model to generalize better to new types of datasets, including data from: different imaging modalities, lighting conditions, resolution, and magnification levels. Analysis of time-lapse fluorescence microscopy usually involves cell segmentation followed by cell tracking. In this work, we used a simple linear sum assignment solution to the problem of cell tracking since our focus was to develop and train a network for yeast-cell segmentation. With this simple solution, YeastNet still achieves very high tracking performance. An adaptation of a recent algorithm for cell tracking such as the algorithm used in [21], or a new deep-learning approach, would lead to even higher tracking performance. Since the completion of this work, we have identified several possibilities for future improvements of YeastNet. A large new dataset has been published by [PERSON] et al. in support of their yeast segmentation platform YeaZ [34]. Additional training data, especially from other sources is very useful for training accurate and general models and we will use the increasing public data to improve YeastNet. We are also working on making architectural improvements to YeastNet with a focus on leveraging the recent success of adapting attention-based neural networks such as Transformers to computer vision tasks [48]. Furthermore, there is an opportunity to experiment with newly developed loss functions such as the soft Dice and soft Jaccard loss functions. Analyses comparing these loss functions to the cross-entropy and class-weighted cross-entropy loss functions has been done recently [49] and shown that model performance is higher when using soft Dice and soft Jaccard. Although this analysis does not investigate the spatially weighted cross-entropy introduced in [25] that we use in this work, determining how it fits into the landscape of biomedical imaging semantic segmentation is an important question that we will investigate to further improve YeastNet performance.
## 1 Introduction
with whole-rock analysis to understand the physical and chemical processes of skarn formation, using a simple skarn profile. With the rapid development of in-situ analysis techniques in recent years, the testing accuracy has been greatly improved, especially Laser Ablation Inductively Coupled Plasma Mass Spectrometer (LA-ICP-MS), which can achieve ppm-level element content and spatial distribution analysis of minerals at the micrometer scale [18; 19; 20]. Compared with whole-rock geochemical analysis, LA-ICP-MS also has lower costs and faster speeds. Garnet is the most common mineral in skarns, forming through contact metamorphism or hydrothermal alteration of silicate minerals [1]. The chemical formula of garnet is A\({}_{3}\)B\({}_{2}\)[SiO\({}_{4}\)]\({}_{3}\), where A is a divalent cation, mainly including Mg\({}^{2+}\), Fe\({}^{2+}\), Mn\({}^{2+}\), Ca\({}^{2+}\) and B is a trivalent cation, mainly including Al\({}^{3+}\), Fe\({}^{3+}\), Cr\({}^{3+}\)[21]. This structure enables certain elements to enter garnet through some mechanisms such as substitution and lattice defects [22]. It is often used as an indicator to trace the evolution of hydrothermal fluids in skarn-type mineral deposits [14; 16; 17]. [PERSON] et al. (1994) discovered that the garnet in the Oslo region showed significant differences in the contents of F and some other trace elements from the andradatite core to the grossularite edge [23]. This change indicates that fluid convection and fluid dynamic mechanisms affected the distribution of elements in garnets during brittle deformation. Some garnet crystals show obvious zonation, which can reflect the process of water-rock reactions, indicating the source of ore-forming fluids, fluid mixing, changes of pH value and of \(f\)O\({}_{2}\) and other physicochemical properties were changed during the garnet forming [24; 25; 26]. We discovered a typical skarn profile in the Tongshan Cu-Mo deposit of the Ningzhen mining district, located in the Lower Yangtze River Metallogenic Belt (LYRMB). This skarn profile closely conforms to the classic skarn zonation model, which includes a fresh intrusive rock, a weakly altered zone, a skarn zone (comprising inner and outer skarn), a mineralized zone (mainly characterized by Cu mineralization) and the surrounding marble over a span of approximately 4 m. The profile remains exceptionally fresh and has not undergone any significant alteration from late-stage low-temperature or supergene weathering. Furthermore, garnet and diopside are widely prevalent within this skarn profile. In this study, we compared the distribution of whole-rock and garnet geochemistry between the inner and outer skarn zones, delved into the migration behavior of elements during skarnization (particularly trace element geochemistry) and aimed to characterize the formation process of the skarn. Our findings demonstrate that when it comes to distinguishing the boundary between the rock body and carbonate, combining both whole-rock and mineral geochemistry methods proves more effective than relying solely on either approach.
# Fundus Image Registration Technique Based on Local Feature of Retinal Vessels
## 4 Experimental Setup  
The CURVE-SIFT was implemented in MATLAB R2016b running on a virtual machine from Google Cloud Engine with specifications of Intel Xeon(r) E5 2.6 GHz (24 vCPUs) and 40 GB of RAM. Toolboxes employed were Image Processing, Computer Vision, Signal Processing and VLFeat [46].  
The evaluation was divided into two parts. First, CURVE was evaluated in extracting feature points on retinal vessels. The performance of CURVE was compared with five feature extraction methods from the existing feature-based RIR techniques, namely, Harris corner [26], SIFT [15], SURF [27; 28], Ghassabi's [8] and D-Saddle [14]. Then, CURVE-SIFT was evaluated in registering image pairs from three retinal image registration applications and compared with five existing feature-based RIR techniques; GDB-ICP [13], Harris-PIIFD [10], Ghassabi's-SIFT [8], H-M 16 [16], H-M 17 [9] and D-Saddle-HOG [14]. In the experiment, these five existing feature-based RIR techniques were utilized exactly as they are. H-M 16, H-M 17 and D-Saddle-HOG were originally developed using FIRE dataset for super-resolution, image mosaicking and longitudinal study applications, while Ghassabi's-SIFT, GDB-ICP and Harris-PIIFD were developed for image mosaicking and low-quality image using other datasets.
# Design and Implementation of a Smart Traffic Signal Control System for Smart City Applications
## 3 Smart Traffic Control System
### STSC State Transition  
In order to integrate multi-modal transportation applications in an RSU controller, a five-status state transition diagram for the RSU controller is designed as illustrated in Figure 3, where the state transition condition and control flow of the five states (S0, S1, S2, S3, S4) are described.  
The RSU controller switches from shutdown state (S0) to traffic signal pre-timed control state (S1) when the system is started up, the traffic signal control follows the predefined time\(-\)based signal plan optimized by historical data which is assigned from the cloud center. It switches to ATSC mode (S2) when the RSU has enough real-time traffic information collected from external data sources  
Figure 3: Smart traffic signal controller’s state transition diagram.  
Figure 2: Software architecture of the RSU controller.  
(such as loop detector, smart AVI), a special designed adaptive traffic signal control algorithm will be responsible for the traffic flow control optimization in this mode.  
EVSP (S3) state has the absolute priority since emergency vehicles has the non-stop authority over the traffic signal control when the EVs are on duty. When RSU detects the OBU packets from EV, it switches to EVSP state (S3) as soon as possible, despite which mode it is currently on. Comparing to absolute priority strategy applied in EV, the public transportation has the relative priority, so that an independent TSP mode (S4) is designed for public transit signal priority because the signal control algorithm of TSP is very different from the one in EVSP mode.  
Usually the RSU operation is mainly on S1 or S2 mode depending on whether the real-time traffic information is enough or not. When the RSU controller receives the messages from an emergency vehicle or a transit signal priority vehicle (such as BRT, bus), the state transition control algorithm will first determine whether it is the host RSU (i.e., the responsible intersection) by comparing the location, direction, speed message collected from the vehicle and the location of itself. It then switches to S3 and begins the EVSP process or S4 for the TSP process if it is the host RSU. In these two modes (S3 or S4), the RSU controller will monitor the status of the vehicle from the vehicle-to-RSU (V2R) packets until it already passes through the intersection; it then begins the signal compensation process and switches to the original operation mode (S1 or S2).