esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Method
### CH Selection Stage
#### 4.1.2 Fitness Function of SO  
The fitness function used by the SO algorithm to determine the ideal set of CHs is made up of the following variables and formulas.  
1. Energy consumption of CH: This is the first parameter of the fitness function. Our goal is to prioritize nodes with low energy consumption as CH. Let \(E_{c}(CH_{i})\) represent the \(CH_{i}\)'s energy consumption, and assume that \(1\leq i\leq K\), \(K\) is the number of CHs. Consequently, minimizing the objective function \(f_{1}\) is our aim: \[f_{1}=\frac{1}{K}\sum_{i=1}^{K}E_{c}(CH_{i})\] (24)
2. Distance between node and BS: This is the second parameter of the fitness function. Our goal is to prioritize the node with the shortest distance to the common node and BS as the CH. Let \(D\left(s_{j},CH_{i}\right)\) be the distance from intra-cluster node \(s_{j}\) to cluster head node \(CH_{i}\). Let \(D(CH_{i},BS)D(CH_{i},BS)\) be the distance from \(CH_{i}\) to BS where \(1\leq i\leq K\), \(K\) represents the quantity of CHs and \(1\leq j\leq M\), \(M\) represents the quantity of cluster members. Consequently, minimizing the objective function \(f_{2}\) is our aim: \[f_{2}=\sum_{i=1}^{K}\frac{1}{M}\left(\sum_{j=1}^{M}D\big{(}s_{j},CH_{i}\big{)}+D (CH_{i},BS)\right)\] (25)
3. Node Degree of CH: This is the third parameter of the fitness function. High-degree nodes are given preference to become CH in the ESO-GJO CH selection process. Consequently, our goal is to maximize the objective function \(f_{3}\) as follows \[f_{3}=N_{deg}(CH_{i})\] (26)  
Our suggested SO algorithm's fitness function is to minimize the aforementioned \(f_{1}\), \(f_{2}\), and \(f_{3}\) functions. As a result, the following is the fitness function for our algorithm:  
\[f=\delta_{1}\times f_{1}+\delta_{2}\times f_{2}+\delta_{3}\times f_{3} \tag{27}\]  
where the weights for the goal function are provided by the constants \(\delta_{1}\), \(\delta_{2}\), and \(\delta_{3}\). The total of these constants ought to equal one. Minimizing the cumulative objective function is our aim.
## 2 Problem Definition and Model  
In this section, we discuss in more detail the problem under consideration and the system model. Limitations of underwater communications bring challenges when deploying a practical UASN. Unfortunately, those limitations have more significant effects on the network performance when a large scale multi-hop underwater sensor network needs to be deployed. More specifically, packets have a higher possibility of being impaired when they are relayed in a multi-hop fashion due to accumulated effects of the communication limitations. In addition, the signal interference becomes a significant  
Figure 1: Underwater Acoustic Sensor Network with multiple AUVs.  
challenge due to the fact that several copies of packets need to be transmitted by a low bandwidth channel. As a result, the network capacity decreases significantly, and many applications (e.g., multimedia data such as snapshots, low rate video, _etc._) requiring a relatively high data rate transmission cannot be used.  
In this work, we aim at tackling those limitations by designing an underwater routing scheme that can scale with a large number of nodes and can reliably deliver a large amount of data. More specifically, our major design objectives include the maximization of the data delivery ratio and the minimization of energy consumed by U-sensor nodes.  
In the UASN under consideration, there are a large number of U-sensors that are deployed on the underwater surface. In the UASN under consideration, U-sensors can be stationary or mobile. Stationary U-sensors are anchored with strings to keep the position despite the underwater current. The collected sensed data by U-sensors are sent in a multi-hop fashion to the underwater sink that communicates with the surface unit via a long distance acoustic channel to transmit and receive aggregated data and control signal, respectively. Optionally, the underwater sink can be connected with the surface unit via a fiber optic and power cable in case very high data rate transmission is required. Multiple _AUVs_ (Autonomous Underwater Vehicles) are also used to route sensing data from the U-sensors to the sink.  
Some U-sensors are equipped with two network interfaces: one is for mid-range communications with other U-sensors, and the other is for short-range and high data rate communications with AUVs. Those sensors with two network interfaces can be _GWs_ (gateways), and they communicate with AUVs when AUVs are close enough for the high data rate transmission. The GWs can be pre-determined before deployment or selected by AUVs during the initialization phase. AUVs also have more than one interfaces: one is to communicate with GWs/sink and the other is to communicate with the surface unit to receive motion control signals.
###### Abstract  
This work presents a novel design model of a magnetorheological (MR) fluid-based shock absorber (MR shock absorber in short) that can be applied to an aircraft landing gear system. When an external force acts on an MR shock absorber, pressure loss occurs at the flow path while resisting the fluid flow. During the flow motion, two pressure losses occur: the major loss, which is proportional to the flow rate, and the minor loss, which is proportional to the square of the flow rate. In general, when an MR shock absorber is designed for low stroke velocity systems such as an automotive suspension system, the consideration of the major loss only for the design model is well satisfied by experimental results. However, when an MR shock absorber is applied to dynamic systems that require high stroke velocity, such as aircraft landing gear systems, the minor loss effect becomes significant to the pressure drop. In this work, a new design model for an MR shock absorber, considering both the major and minor pressure losses, is proposed. After formulating a mathematical design model, a prototype of an MR shock absorber is manufactured based on the design parameters of a lightweight aircraft landing gear system. After establishing a drop test for the MR shock absorber, the results of the pressure drop versus stroke/stroke velocity are investigated at different impact energies. It is shown from comparative evaluation that the proposed design model agrees with the experiment much better than the model that considers only the major pressure loss.  
magnetorheological (MR) fluid; MR shock absorber; aircraft landing gear; valve path; major and minor pressure losses; impact energy +
Footnote †: journal: Applied Sciences
## 1 Introduction  
Environmental pollution is currently a cause of serious concern worldwide. Anthropogenic activities produce increasing amounts of processing waste and effluents that contain a diversity of pollutants, including harmful heavy metals [1]. Among these, chromium is well known for its mutagenic and carcinogenic effects [2]. Chromium naturally occurs in the earth's crust and water bodies but its level in the environment tends to increase in association with anthropogenic activities such as metallurgy, wood processing, and production of inorganic chemicals, printed circuit boards and dyes [3,4].  
Chromium predominantly occurs in the +3 (Cr(III)) and +6 (Cr(VI)) oxidation states. Cr(VI) is highly soluble in aqueous systems and exhibits higher soil mobility and higher toxicity than Cr(III) [5]. In fact, the hexavalent form of chromium is highly toxic and carcinogenic, while the trivalent form is innocuous and may even be used as a micronutrient in certain organisms [6]. Cr(VI) has a high diffusion and dissolution capacity in the tissues [7]. Its compounds can be absorbed by humans through the skin and respiratory system and rapidly diffuse in the body due to their ability to enter the erythrocytes and bind to hemoglobin. Documented harmful effects of Cr(VI) in humans include lung cancer, respiratory problems, renal failure, weakening of the immune system, skin lesions, genetic damage and infertility [8]. Cr(VI) species enter the cells across non-specific anionic channels, thanks to structural analogy with sulphate and phosphate ions. Cr\({}^{5+}\) complexes, produced by reduction of Cr(VI) within the cells, may react with endogenous H\({}_{2}\)O\({}_{2}\) to generate potentially mutagenic OH radicals [9].  
Recognizing the multi-faceted harmful effects of Cr(VI) on living organisms, the US Environmental Protection Agency (US-EPA) has established a maximum level in drinking and surface water of 0.015 and 0.1 mg L\({}^{-1}\), respectively [10].  
The removal of Cr(VI) from water is an issue that is drawing increasing attention within the scientific community. Among a diversity of methods tested so far, sorption is the most popular because it is fast, relatively inexpensive and easy to use. Not surprisingly, therefore, a considerable effort has been done for the development of novel sorbents for Cr(VI) removal since the last century [11; 12; 13; 14; 15; 16]. Here we present a critical review of the experimental work performed on Cr(VI) sorption in the last decade, with emphasis on proposed mechanisms and thermodynamic and kinetic aspects.
## 2 Materials and Methods
### Measurement of Environmental Variables  
In this study, a comprehensive set of meteorological data was collected from an automated meteorological station. The variables measured included photosynthetic activity radiation (_PAR_, expressed in W m\({}^{-2}\)), net radiation (\(R_{\rm n}\), W m\({}^{-2}\)), relative humidity (\(RH\), %), _VPD_ (kPa) calculated from measured air temperature (\(T_{\rm a}\), \({}^{\circ}\)C) and RH, wind speed (\(u\), m s\({}^{-1}\)), rainfall (\(R_{\rm f}\), mm), soil heat fluxes (\(G\), W m\({}^{-2}\)), soil volumetric water content (\(SWC\), %), and soil surface temperature (\(S_{\rm t}\), \({}^{\circ}\)C). Radiation sensors were positioned at 3.5 m above ground level (AGL). SWC sensors were deployed in two groups, each containing four sensors buried at 20, 40, 60, and 80 cm deep below the surface. Soil surface temperature sensors were positioned at 5, 10, 15, and 20 cm below the surface. Data collection was performed at 5-s intervals, and data were averaged or aggregated (in the case of precipitation) at 30-min intervals for analysis.
# Computational Modeling of the Thermal Behavior of a Greenhouse
## 3 Results
### Case Studies
#### 3.2.6 Geographical Location  
The final group of case studies consists in simulating the reference greenhouse in various locations of the globe so as to assess its performance and limitations in different climates. Case studies 19 to 21 have the reference greenhouse set in Lisbon, Moscow, Ostersund, and Riyadh, respectively. Figures 23 and 24 show the results of these case studies for the day of 15 January, and Figures 25 and 26 contain the results for the day of 15 July.  
As shown in Figure 24, the relative humidity values for 15 January demonstrate that Moscow and Riyadh have a more constant relative humidity throughout the day, although reaching relatively high and low values, respectively. Ostersund and Lisbon have a wider range of values, with Lisbon having bigger oscillations but not surpassing the highest and lowest values of all case studies.  
The temperature results in January show that the greenhouse mostly requires heat extraction during the day when located in Lisbon and Riyadh in order to achieve an adequate temperature for operation. When located in Moscow and Ostersund, constant auxiliary heating is necessary to achieve an appropriate temperature in the greenhouse. Thus, this greenhouse is better suited for warmer climates if winter operation is required.  
The air temperature results for 15 July shown in Figure 25 allow us to conclude that the greenhouse reaches very high temperatures in Lisbon and, especially, Riyadh, meaning that constant heat extraction will be necessary to achieve adequate temperature levels for any kind of crop development. Moscow's and Ostersund's temperature values show that the greenhouse is very suitable for these locations, as it able to maintain a much more constant and adequate temperature throughout the day.  
As shown in Figure 26, the relative humidity values for case studies 19 to 21 show the greenhouse, when located in Riyadh, having a very constant and low relative humidity level, followed by Ostersund, where a larger range but average relative humidity values are observed, and, lastly, with Lisbon and Moscow showing the largest oscillation as the day progresses. This set of case studies shows that the reference greenhouse's capabilities depend not only on the location but also on the season. The reference greenhouse's low  
Figure 26: Relative air humidity values for case studies 19–21 on 15 July.  
Figure 25: Air temperature values for case studies 19–21 on 15 July.  
insulation mean that additional energy sources will be necessary to provide more adequate ambient conditions, particularly if the greenhouse is to be operated during the entire year.
# Improved Video Action Recognition Based on Pyramid Pooling and Dual-Stream C3D Networks
## 2 Methodology
### GRU-Based Video Character Classification
#### 2.3.2 Video Classification Process for GRU Networks  
GRU is a more efficient variant of LSTM that is able to handle time-series problems efficiently, and hence GRU has become a more popular choice for solving such problems. Since GRU is an optimisation of the LSTM structure, it can effectively solve the gradient vanishing and gradient explosion problems in RNNs (recurrent neural networks). Figure 4 shows the structure of the GRU unit.  
In contrast to LSTM, GRU contains only update gates and reset gates. In Figure 4, zt represents the update gate, and rt represents the reset gate. The input and forget gates in the LSTM are equivalent to the update gate zt, which determines the discarding and adding of information, whereas the extent to which the previous data-are erased is determined by the reset gate rt [19]:  
\[z_{t}=\sigma(W_{z}x_{t}+U_{z}h_{t-1}) \tag{11}\]  
\[r_{t}=\sigma(W_{r}x_{t}+U_{r}h_{t-1}) \tag{12}\]  
where xt denotes the input, \(\sigma\) at the current moment denotes the sigmoid function, and W and U denote the weight parameter matrices that the GRU unit needs to be trained in advance. ht denotes the candidate value of the hidden layer hiding condition as shown in Equation (13):  
\[\hat{h}=\tanh(W_{h}x_{t}+r_{t}U_{h}h_{t-1}) \tag{13}\]  
Figure 4: GRU unit structure.  
Figure 3: A model for classifying video characters based on GRU.  
\[h_{t=}(1-z_{t})\hat{h}+z_{t}h_{t-1} \tag{14}\]  
The current state of the hidden layer can be found by combining \(\mathrm{h_{t}}\) in Equation (14):  
When the reset gate rt tends to 0, then most of the state information of the previous hidden layer will be ignored, and the inputs of the currently fused dynamic and static features will take up a larger proportion of the inputs whereas tz is used to determine the ratio of the state of the buried layer at the previous moment to the current-state candidate of the current hidden layer, and thus to determine the hidden layer that is currently in the state of use.  
The fused static and dynamic features trained by the video character classification model in GRU are able to ensure that the video is characterised over the time-series, which can improve the recognition accuracy [20].
# Biosignals Monitoring of First Responders for Cognitive Load Estimation in Real-Time Operation
## 3 Materials and Methods
### Sensors
#### 3.2.3 HoloLens  
To extract the eye gaze position and derived features (movement and speed), the Microsoft HoloLens 2 ([[https://www.microsoft.com/en-us/hololens](https://www.microsoft.com/en-us/hololens)]([https://www.microsoft.com/en-us/hololens](https://www.microsoft.com/en-us/hololens)), accessed on 12 June 2023) has been selected. The glasses are already included in the RESCUER framework to provide AR capabilities to FRs [68]. Nonetheless, it also includes user's eye gaze and dwell information for interaction purposes. Eye gaze is extracted at a 30 Hz frame rate, stored in the HoloLens temporarily and relayed to the embedded computer every 10 s to avoid sending the information continuously and prevent bandwidth limitations (see Table 1).
## 8 Debriefing & Analysis of Results
### Classifying Field Notes  
As previously explained, the field notes were taken down by different research teams based in each city. One of the challenges encountered in this process was ensuring that the information being gathered by the researchers during observation sessions was standardized [69]. Once again, the Reframer tool was used in order to achieve consistency in data gathering. The process used was as follows:  
Defining tagsReframer allows users to define tags that serve as metadata for grouping and classifying field notes. The following tags were established for each of the urban interfaces being assessed: one tag of each guideline, and one tag for each interaction touchpoint identified in the observation guidelines. Figure 14 shows an example of this definition for the urban interface of the automated parking machines in Cali. The 14 tags in blue contain the names of the 14 guidelines, and the tags in green contain the four touchpoints that a user will have to interact with on the interface. This activity was performed for each of the urban interfaces evaluated.  
Debriefing: field notes and classification based on tagsResearchers held debriefing meetings once observation sessions had been concluded in order to review the field notes collected. The objective of these meetings was to avoid the duplication of similar notes, and at the same time, to revise the frequency with which commonplace issues arose at each touchpoint. Each field note was logged once using Reframer, together with a short description of what had been observed. If the observation occurred more than once, researchers noted the frequency with which it occurred in brackets. Finally, tags were assigned: A minimum of one tag was used per guideline and per touchpoint. By way of example, Figure 15 shows a field note that has been logged in Reframer. This particular log pertains to the automated parking machines in Cali.  
Figure 14: Screenshot of Reframer showing the definition and configuration of tags used for the urban interface of automated parking machines in Cali.
# Clinical Applications of In Vivo and Ex Vivo Confocal Microscopy
## 3 Ex Vivo Confocal Laser Scanning Microscopy
In particular, cellular nuclei stained with acridine orange appear white, therefore enabling a better visualization of keratinocytes, hair follicle epithelium, fibroblasts, sebaceous and ecrine glands and tumor cells. Consequently, the brightness of images is directly related to the amount of nuclei in the tissue, with bright images obtained in lymph nodes or in tumors, and a dark background in hypocellular samples such as in fibrous or in adipose tissue showing a low-to-absent fluorescent signal. Despite the limitation due to the low fluorescent signal, ex vivo CLSM enables the study of muscles and adipose tissue, with the combination of both reflectance and fluorescence modes [7,88]. [PERSON] et al. reported the digital purple and pink stain of confocal mosaics in order to simulate pathology images appearance, in order to improve the recognition of structures, adapting FCM images to those of hematoxylin and eosin (H&E) stain [89]. When nuclei are stained with acridine orange, they are imaged in exogenous fluorescent mode while cell cytoplasm and dermis are visualized through reflectance contrast. After that, fluorescence mosaics can be digitally stained purple (mimicking hematoxylin), whereas reflectance mosaics can be digitally stained pink (mimicking eosin). The final result is a mosaic similar to histology [86,89,90]. Overall, different image visualizations are available including separate or combined reflectance/fluorescence and H&E-like modes. The most employed application of FCM is the intraoperative assessment of tumor margins during Mohs surgery for the removal of BCC [91,92,93]. However, FCM use has been extended to other skin tumors, such as SCC and melanoma [82], healthy skin [94], nails and hair diseases [95,96], diagnosis of skin infections (viral and superficial/deep fungal infections) [97], mucosa diseases [95,98], inflammatory disease [99] and aesthetic medicine [100, 101, 102]. Main applications of ex vivo CLSM are summarized in Table 1.
# Intelligent Glioma Grading Based on Deep Transfer Learning of MRI Radomic Features
###### Abstract  
According to a classification of central nervous system tumors by the World Health Organization, diffuse gliomas are classified into grade 2, 3, and 4 gliomas in accordance with their aggressiveness. To quantitatively evaluate a tumor's malignancy from brain magnetic resonance imaging, this study proposed a computer-aided diagnosis (CAD) system based on a deep convolutional neural network (DCNN). Gliomas from a multi-center database (The Cancer Imaging Archive) composed of a total of 30 grade 2, 43 grade 3, and 57 grade 4 gliomas were used for the training and evaluation of the proposed CAD. Using transfer learning to fine-tune AlexNet, a DCNN, its internal layers, and parameters trained from a million images were transferred to learn how to differentiate the acquired gliomas. Data augmentation was also implemented to increase possible spatial and geometric variations for a better training model. The transferred DCNN achieved an accuracy of 97.9% with a standard deviation of \(\pm\)1% and an area under the receiver operation characteristics curve (Az) of 0.9991 \(\pm\) 0, which were superior to handcrafted image features, the DCNN without pretrained features, which only achieved a mean accuracy of 61.42% with a standard deviation of \(\pm\)7% and a mean Az of 0.8222 \(\pm\) 0.07, and the DCNN without data augmentation, which was the worst with a mean accuracy of 59.85% with a standard deviation \(\pm\)16% and a mean Az of 0.7896 \(\pm\) 0.18. The DCNN with pretrained features and data augmentation can accurately and efficiently classify grade 2, 3, and 4 gliomas. The high accuracy is promising in providing diagnostic suggestions to radiologists in the clinic.  
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